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Comprehensive analysis of the effects of the cuprotosis- associated gene SLC31A1 on patient prognosis and tumor microenvironment in human cancer
Guiqian Zhang1,2,3,4*, Ning Wang1*, Shixun Ma2,3,4#, Pengxian Tao2,3,4, Hui Cai2,3,4
1The First Clinical Medical College of Gansu University of Chinese Medicine (Gansu Provincial Hospital), Lanzhou, China; 2General Surgery, Clinical Medical Center, Gansu Provincial Hospital, Lanzhou, China; ‘Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou, China; 4NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumors, Gansu Provincial Hospital, Lanzhou, China
Contributions: (I) Conception and design: G Zhang, N Wang, H Cai; (II) Administrative support: P Tao, H Cai; (III) Provision of study materials or patients: S Ma; (IV) Collection and assembly of data: G Zhang, N Wang, S Ma; (V) Data analysis and interpretation: G Zhang, P Tao; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.
*These authors contributed equally to this work as co-first authors.
Correspondence to: Pengxian Tao, MD; Hui Cai, MD. General Surgery, Clinical Medical Center, Gansu Provincial Hospital, 204 Donggang West Road, Chengguan District, Lanzhou 730000, China; Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, Gansu Provincial Hospital, Lanzhou, China; NHC Key Laboratory of Diagnosis and Therapy of Gastrointestinal Tumors, Gansu Provincial Hospital, Lanzhou, China. Email: taopx2017@163.com; caialonteam@163.com.
Background: Solute carrier family 31 (copper transporter), member 1 (SLC31A1) is a key factor in maintaining intracellular copper concentration and an important factor affecting cancer energy metabolism. Therefore, exploring the potential biological function and value of SLC31A1 could provide a new direction for the targeted therapy of tumors.
Methods: This study assessed gene expression levels, survival, clinicopathology, gene mutations, methylation levels, the tumor mutational burden (TMB), microsatellite instability (MSI), and the immune cell infiltration of SLC31A1 in pan-cancer using the Tumor Immune Estimation Resource 2.0 (TIMER2.0), Gene Expression Profiling Interactive Analysis (GEPIA), University of Alabama at Birmingham CANcer (UALCAN) data analysis portal, and cBioPortal databases. To further understand the potential biological mechanisms of SLC31A1 in different cancers, single-cell level sequencing and a Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment analysis of SLC31A1 were also performed. Finally, real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting (WB) were used to validate the expression of SLC31A1 in cancers, such as gastric cancer.
Results: SLC31A1 was expressed in most cancer tissues. In kidney renal clear cell carcinoma (KIRC), the high expression of SLC31A1 was associated with good overall survival (OS), while in adrenocortical carcinoma (ACC), breast invasive carcinoma (BRCA), lower grade glioma (LGG), mesothelioma (MESO), and skin cutaneous melanoma (SKCM), the low expression of SLC31A1 was associated with good OS. The highest frequency of SLC31A1 amplification was observed in ACC. In addition, missense mutations accounted for a major portion of the mutation types. The truncation mutation S105Y may be a putative cancer driver. SLC31A1 affected methylation levels in cancer and was associated with the TMB, MSI, and the level of infiltration of various immune cells. Additionally, the single-cell sequencing results showed that SLC31A1 was associated with multiple biological functions in cancer. Finally, the SLC31A1 enrichment analysis revealed that the SLC31A1-related genes were mainly enriched in the mitochondrial matrix and envelope vesicles. The RT-qPCR and WB results were consistent with the predicted results.
Conclusions: SLC31A1 may be a potential target related to cancer energy metabolism and may have prognostic value.
Keywords: Cuprotosis; solute carrier family 31 (copper transporter), member 1 (SLC31A1); human cancer; prognosis; tumor microenvironment (TME)
Submitted Jul 25, 2023. Accepted for publication Nov 21, 2023. Published online Feb 28, 2024.
doi: 10.21037/tcr-23-1308
View this article at: https://dx.doi.org/10.21037/tcr-23-1308
Introduction
Cancer is the leading cause of death worldwide and is a major public health issue (1,2). The incidence of cancer and cancer-related mortality rates are increasing rapidly worldwide due to aging and growing populations (3). Cancer is described by the histopathological, genomic, and transcriptomic heterogeneity of the tumor, and its tissue microenvironment. Cancer heterogeneity results in changes in patient outcomes (4). Histopathology biomarkers can be used to diagnose cancer; however, most histopathology biomarkers are based solely on the morphology and location of tumor cells, and a fine-grained understanding of how the spatial organization of stromal, tumor, and immune cells in the tumor microenvironment (TME) contributes to patient risk is lacking (5-7).
In studying the process of cell death carrying copper ions, Golub’s team identified a new mode of cell death involving copper ions in cells that depends on and is regulated by copper ions, called cuprotosis (8). The mechanism of copper death involves copper ions binding directly to the lipid acylated components of the tricarboxylic acid cycle, which leads to the abnormal aggregation of fatty acylated proteins and the loss of iron-sulfur cluster proteins, which in turn leads to cell death mediated by a proteotoxic stress response (9).
Highlight box
Key findings
· Cuprotosis-associated gene solute carrier family 31 (copper transporter), member 1 (SLC31A1) affects patient prognosis and the immune microenvironment in human cancer.
What is known, and what is new?
· SLC31A1 is responsible for copper ion transport in cuprotosis and is a key molecule in the development of cuprotosis.
· SLC31A1 is involved in regulating the prognosis of human cancer and is associated with tumor immunity.
What is the implication, and what should change now?
· SLC31A1 may be a potential tumor-associated biomarker.
Interestingly, solute carrier family 31 (copper transporter), member 1 (SLC31A1) is also a cuprotosis- associated gene. The human body contains the following two copper transporter (CTR) family proteins: SLC31A1 (CTR1) and SLC31A2 (CTR2) (10). SLC31A1 is a key residue in the highly conserved C-terminal HCH190 triplet for cell membrane cystine 189 (Cys189) and copper uptake, as in Methionine 154 (Met-154) (11-13). The main role of SLC31A1 is to transport cytosolic copper (14). SLC31A1 is engaged in the cuprum (Cu) access-dependent activation of mitogen-activated protein kinase signaling (15), which is induced by growth factors, such as the fibroblast growth factor and insulin, and the activation of Cu enzymes, including lysine oxidase (16-18). SLC31A2 is predominantly located intracellularly (19) and unlike SLC31A1, the expression level of human SLC31A1 does not result in any significant changes in cellular copper metabolism (20). SLC31A1P1 has been identified as a processing gene highly homologous to SLC31A1 (21).
In this study, we investigated the regulatory function of SLC31A1 in various cancers through a series of bioinformatics online databases. We also compared the differential expression of SLC31A1 in tumor tissues and their paracancerous tissues. In addition, patient survival and methylation levels, and their role in immune regulation were also evaluated in this study. The results suggest that SLC31A1 is closely related to tumor pathogenesis and the immune response. Finally, the results were further validated by real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting (WB). The objective of this study was to identify potential targets for cancer therapy by analyzing the expression prognosis and immunity of SLC31A1 in pan-cancer, building upon previous research. The findings may provide novel insights into the molecular mechanisms of cancer and facilitate personalized treatments for pan-cancer patients. The entire study flow is shown in Figure 1. We present this article in accordance with the MDAR reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr- 23-1308/rc).
Cuprotosis-associated gene SLC31A1
Differential expression analysis
Enrichment analysis
Clinicopathological correlation analysis
Pan-cancer analysis
Genetic alterations
Prognostic analysis
tumor mutational burden, Tumor microenvironment,
microsatellite instability
and drug sensitivity
Immune infiltration analysis
Single-cell sequencing
Experimental validation
RT-qPCR
WB
Methods
Cell culture
Cells for experiments were obtained from the Cell Resource Center of the Chinese Academy of Sciences (Shanghai, China). The cells were cultured in Roswell Park Memorial Institute-1640 (RPMI-1640), containing 10% fetal bovine serum, 100 units/mL of penicillin, and 100 µg/mL of streptomycin, and placed in a 37 ℃, 5% carbon dioxide incubator.
RT-qPCR
Total RNA was extracted separately from the cells using the Trizol method, and the purified RNA was reverse-transcribed into a complementary DNA template using a reverse transcription kit, after which RT-qPCR was performed on the target genes using PCR optics, and finally the relative expression of SLC31A1 was analyzed using the 2-44Ct method and normalized with reference to glyceraldehyde- 3-phosphate dehydrogenase (GAPDH). The SLC31A1
| Abbreviations | Full names |
|---|---|
| ACC | Adrenocortical carcinoma |
| BLCA | Bladder urothelial carcinoma |
| BRCA | Breast invasive carcinoma |
| CESC | Cervical squamous cell carcinoma and endocervical adenocarcinoma |
| CHOL | Cholangiocarcinoma |
| COAD | Colon adenocarcinoma |
| DLBC | Lymphoid neoplasm diffuse large B-cell lymphoma |
| ESCA | Esophageal carcinoma |
| GBM | Glioblastoma multiforme |
| HNSC | Head and neck squamous cell carcinoma |
| KICH | Kidney chromophobe |
| KIRC | Kidney renal clear cell carcinoma |
| KIRP | Kidney renal papillary cell carcinoma |
| LAML | Acute myeloid leukemia |
| LGG | Lower grade glioma |
| LIHC | Liver hepatocellular carcinoma |
| LUAD | Lung adenocarcinoma |
| LUSC | Lung squamous cell carcinoma |
| MESO | Mesothelioma |
| OV | Ovarian serous cystadenocarcinoma |
| PAAD | Pancreatic adenocarcinoma |
| PCPG | Pheochromocytoma and paraganglioma |
| PRAD | Prostate adenocarcinoma |
| READ | Rectum adenocarcinoma |
| SARC | Sarcoma |
| SKCM | Skin cutaneous melanoma |
| STAD | Stomach adenocarcinoma |
| TGCT | Testicular germ cell tumor |
| THCA | Thyroid carcinoma |
| THYM | Thymoma |
| UCEC | Uterine corpus endometrial carcinoma |
| UCS | Uterine carcinosarcoma |
| UVM | Uveal melanoma |
TCGA, The Cancer Genome Atlas.
primers were designed and synthesized by Bioengineering (Shanghai, China). The forward primer for SLC31A1 was: 5’-GTAAGTCACAAGTCAGCATTCG-3’; the reverse primer was: 5’-CAACAGTTTTGTGTGTCTCCAT-3’; and the GAPDH forward primer was: 5’-GGAAGCTTG TCATCAATGGAAATC-3’, reverse primer 5’-TGAT GACCCTTGGCTCCC-3’.
WB
The liver cancer cells, other cancer cells, and normal cells underwent protein extraction. The separation process involved the use of reduced sodium dodecyl sulfate- polyacrylamide gel electrophoresis, followed by the transfer of the separated components onto polyvinylidene fluoride membranes. Subsequently, the immunoblot technique was employed for the analysis. To detect the target proteins, rabbit-horseradish peroxidase (HRP) and mouse-HRP were employed as secondary antibodies. The gray values of the protein bands were quantitatively assessed using Image J software (*, P<0.05; ** , P<0.01; *** , P<0.001; **** , P<0.0001).
Differential expression analysis
The Tumor Immune Estimation Resource 2.0 (TIMER2.0) (http://timer.cistrome.org/) database was used to analyze the differential expression of SLC31A1 in different cancer tissues and normal tissues. Additionally, the Gene Expression Profiling Interactive Analysis (GEPIA) (http:// gepia.cancer-pku.cn) and GEPIA2.0 (http://gepia2. cancer-pku.cn/#index) databases were used to assess the differential expression of SLC31A1 in 33 cancers with corresponding pan-cancer tissues, and pan-cancer correlation with SLC31A1. The University of Alabama at Birmingham CANcer (UALCAN) (http://ualcan. path.uab.edu/index.html) database was used to assess the methylation levels and protein expression levels of SLC31A1 in pan-cancer based on The Cancer Genome Atlas (TCGA) database samples. The Human Protein Atlas (HPA) (https://www.proteinatlas.org) database was used to present staining visualization to reflect the protein levels of SLC31A1 in normal tissues and corresponding cancer tissues (the screening criteria were moderate or high staining intensity, and a cell count ≥25-75%). The full names and abbreviations of the pan-cancers are shown in Table 1.
Clinicopathological correlation analysis
The R packages “limma” and “Nagpur” were used for the clinicopathological related studies. TCGA and the genotype-tissue expression (GTEx) RNA-sequencing data were analyzed and visualized using the R packages “PROC” and “ggplot2”. Xiantao Academic (https://www. xiantao.love/) was used to obtain the area under the receiver operating characteristic (ROC) curve (AUC) to determine diagnosis and prognosis.
Genetic alterations and prognostic analysis
We assessed the genetic alterations of the SLC31A1 gene, including missense mutations, deletions, and splicing, in different cancers using the cBioPortal (https://www. cbioportal.org/) database. Survival information data for each sample were retrieved and downloaded from the TCGA database. The OS, disease-specific survival (DSS), disease- free survival (DFS), and progression-free survival (PFS) of the cancer patients were analyzed using a Cox regression analysis. In addition, the GEPIA2.0 database was used to analyze the prognosis of SLC31A1 gene expression in different cancers, including OS and recurrence-free survival (RFS).
TME, tumor mutational burden (TMB), microsatellite instability (MSI), and drug sensitivity
The R packages “ggplot2”, “ggpubr”, and “ggExtra” were used to analyze the correlation between SLC31A1 expression and the TME (P<0.001 was set as the cut- off value). Correlations between the TMB and MSI and SLC31A1 expression were calculated using the Spearman method. The R package “fmsb” was used for image visualization. NCI-60 compound activity data and RNA- sequencing expression profiles were downloaded from CellMiner™ to analyze the drug sensitivity of SLC31A1 in pan-cancer (https://ngdc.cncb.ac.cn/databasecommons/ database/id/5025). Food and drug administration (FDA)- approved drugs or drugs in clinical trials were selected for the analysis. The visualization was performed using the R packages “impute”, “limma”, “ggplot2”, and “ggpubr” (*, P<0.05; ** , P<0.01; *** , P<0.001).
Immune infiltration analysis
We used the TIMER2.0 database to explore the relationship between SLC31A1 gene expression and the immune
infiltrating cells.
Single-cell sequencing data analysis
The different biological functions of cancer cells in multiple cancers were analyzed at the single-cell level using the single-cell sequencing platform CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/) database. Data on the correlation between SLC31A1 expression and different tumor functional states were downloaded from the CancerSEA database, and correlation heat maps were created using the Xiantao Academic Online website. t-distributed stochastic neighbor embedding (t-SNE) plots from the CancerSEA database were used to identify SLC31A1 expression in individual cancers.
Enrichment analysis
The SLC31A1 protein co-expression network was analyzed using the BioGRID database (https://thebiogrid.org/). The top 100 SLC31A1-related genes in pan-cancer were obtained using the GEPIA2.0 database. The association heat map between SLC31A1 and its related genes in pan- cancer was generated using the TIMER2.0 database. In addition, a Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of SLC31A1-related genes was conducted using Xiantao Academic.
Statistical analysis
The differences between SLC31A1 expression and prognosis (OS and RFS) in different cancer patients were obtained from the GEPIA 2.0 database. In addition, a t-test, Cox regression analysis, and linear regression analysis were used to compare the differences between different groups, and the data are expressed as the mean ± standard deviation. A P value <0.05 was considered statistically significant.
Results
The expression levels of SLC31A1 in pan-cancer
The expression profile of SLC31A1 was explored using the TIMER2.0, GEPIA2.0, and UALCAN platforms. First, we used the TIMER2.0 platform to evaluate the expression profile of SLC31A1 in tumor tissues and normal tissues. We found that SLC31A1 expression was up-regulated in a portion of cancers, such as bladder urothelial carcinoma (BLCA), BRCA, cervical squamous
cell carcinoma and endocervical adenocarcinoma (CESC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), pheochromocytoma and paraganglioma (PCPG), stomach adenocarcinoma (STAD), and uterine corpus endometrial carcinoma (UCEC). Conversely, SLC31A1 expression was down-regulated in another fraction of cancers, such as cholangiocarcinoma (CHOL), KIRC, kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), and thyroid carcinoma (THCA) (Figure 2A).
We also analyzed the protein expression of SLC31A1 using the UALCAN platform. The results showed that the SLC31A1 protein was highly expressed in GBM and lowly expressed in LIHC (Figure 2B). Since the TIMER2.0 database does not contain the paraneoplastic tissues data of several cancers, we also used GEPIA2.0 to explore the SLC31A1 expression levels between these tumors and the corresponding normal tissues. The results showed that SLC31A1 was highly expressed in colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), GBM, LGG, pancreatic adenocarcinoma (PAAD), PCPG, rectum adenocarcinoma (READ), STAD, and UCEC, and lowly expressed only in CHOL and acute myeloid leukemia (LAML) (Figure 2C).
Additionally, we analyzed the correlation between SLC31A1 expression and pathological stages using the GEPIA 2.0 database. The results showed that SLC31A1 expression was closely correlated with the stage of patients with ACC, KIRC, ovarian serous cystadenocarcinoma (OV), and THCA. In ACC, SLC31A1 had the highest expression in stage IV and the lowest expression in stage I. In KIRC, SLC31A1 had the highest expression in stage I and the lowest expression in stage IV. In OV and THCA, SLC31A1 had the highest expression in stage II and the lowest expression in stage IV (Figure 2D).
We then selected cancer types with differential expression of SLC31A1 in the UALCAN database and visualized their protein expression levels using the HPA database. The results showed that SLC31A1 expression was up-regulated in BRCA, STAD, and UCEC, and down-regulated in LIHC. SLC31A1 showed moderate or high staining in BRCA, STAD, and UCEC tumor tissues with ≥25-75% stained cells, while its corresponding normal tissues were moderately stained or unstained. Conversely, the LIHC tumor tissues were moderately stained or unstained, while its corresponding normal tissues were moderately or highly
stained. The differential expression and protein levels differential expression of the above results were consistent (Figure 3). The differential expression results for the other cancers with opposite protein-level expression are shown in Figure S1.
The survival analysis of SLC31A1 in cancer
The GEPIA2.0 database was used to study the prognostic value of SLC31A1 expression in cancer. We divided the patients into a high expression group and a low expression group. In KIRC, high SLC31A1 expression was associated with good OS, and in ACC, BRCA, LGG, MESO, and SKCM, low SLC31A1 expression was associated with good OS (Figure 4A). Further, we found that high expression levels of SLC31A1 were associated with good DFS in KIRC and STAD, and low expression levels of SLC31A1 were associated with good DFS in ACC, LGG and MESO (Figure 4B).
Correlation of SLC31A1 expression with clinicopathology
As Figure 5A shows, SLC31A1 was highly expressed in ACC stage III-IV patients and lowly expressed in stage I- II patients. SLC31A1 was highly expressed in testicular germ cell tumors (TGCTs) stage II-III patients and lowly expressed in stage I patients. Conversely, SLC31A1 was highly expressed in KIRC and THCA stage I-II patients and lowly expressed in stage III-IV patients. In addition, SLC31A1 expression was strongly correlated with the age of ESCA, OV, sarcoma (SARC), STAD, and UCEC patients. Of these, SLC31A1 was highly expressed in OV and UCEC patients up to and including 65 years of age, while it was highly expressed in ESCA, SARC and STAD patients over 65 years of age. Finally, we found that SLC31A1 was highly expressed in female patients with ACC, BRCA, kidney chromophobe (KICH), KIRC, and KIRP. Next, ROC curves were used to verify the diagnostic value of SLC31A1 for different cancers. As Figure 5B shows, SLC31A1 had more than moderate diagnostic accuracy (AUCs above 0.69 and even 0.8) for a variety of tumors including BRCA, CESC, and CHOL. In conclusion, the ROC curve analysis showed that SLC31A1 is a valuable diagnostic biomarker.
Genetic alterations of SLC31A1 in pan-cancer
We used the cBioPortal tool to study SLC31A1 gene alterations in pan-cancer. As Figure 6A shows, SLC31A1
A
SLC31A1 expression level (log2 TPM)
B
Protein expression of SLC31A1 in glioblastoma multiforme
*
**
*
**
*
8
3.
*
2
6
Z-value
1.
0-
-1
4
-2
-3
Normal (n=10)
Primary tumor (n=99)
2
CPTAC samples
ACC.Tumor (n=79)
BLCA. Tumor (n=408)
BLCA.Normal (n=19)
BRCA.Tumor (n=1,093)
BRCA.Normal (n=112)
BRCA-Basal. Tumor (n=190)
BRCA-Her2.Tumor (n=82)
BRCA-LumA. Tumor (n=564)
BRCA-LumB. Tumor (n=217)
CESC.Tumor (n=304)
CESC.Normal (n=3)
CHOL.Tumor (n=36)
CHOL. Normal (n=9)
COAD.Tumor (n=457)
COAD.Normal (n=41)
DLBC.Tumor (n=48)
ESCA. Tumor (n=184)
ESCA.Normal (n=11)
GBM.Tumor (n=153)
GBM.Normal (n=5)
HNSC.Tumor (n=520)
HNSC.Normal (n=44)
HNSC-HPV+.Tumor (n=97)
HNSC-HPV -. Tumor (n=421)
KICH.Tumor (n=66)
KICH.Normal (n=25)
KIRC. Tumor (n=533)
KIRC.Normal (n=72)
KIRP. Tumor (n=290)
KIRP.Normal (n=32)
LAML. Tumor (n=173)
LGG. Tumor (n=516)
LIHC. Tumor (n=371)
LIHC.Normal (n=50)
LUAD. Tumor (n=515)
LUAD.Normal (n=59)
Lusc. Tumor (n=501)
LUSC.Normal (n=51)
MESO.Tumor (n=87)
OV.Tumor (n=303)
PAAD. Tumor (n=178)
PAAD. Normal (n=4)
PCPG. Tumor (n=179)
PCPG.Normal (n=3)
PRAD. Tumor (n=497)
PRAD.Normal (n=52)
READ.Tumor (n=166)
READ.Normal (n=10)
SARC.Tumor (n=259)
SKCM.Tumor (n=103)
SKCM.Metastasis (n=368)
STAD. Tumor (n=415)
STAD.Normal (n=35)
TGCT.Tumor (n=150)
THCA. Tumor (n=501)
THCA.Normal (n=59)
THYM. Tumor (n=120)
UCEC.Tumor (n=545)
UCEC.Normal (n=35)
UCS.Tumor (n=57)
UVM.Tumor (n=80)
Protein expression of SLC31A1 in liver hepatocellular carcinoma
4
3
2
Z-value
1
0
-1
-2
-3
Normal (n=165)
Primary tumor (n=165)
CPTAC samples
C
Expression-log2 (TPM+1)
8
6
4
2
:
0
CHOL [num (T)=36; num (N)=9]
COAD [num (T)=275; num (N)=349]
DLBC [num (T)=47; num (N)=337]
GBM [num (T)=163; num (N)=207]
LAML [num (T)=173; num (N)=70]
LAML [num (T)=518; num (N)=207]
PAAD [num (T)=179; num (N)=171]
PCPG [num (T)=182; num (N)=3]
READ [num (T)=92; num (N)=318]
STAD [num (T)=408; num (N)=211]
UCEC [num (T)=174; num (N)=91]
D
6
ACC
6
KIRC
F value=5.92 Pr (>F)=0.00Q562
6
OV
THCA
Expression-log2 (TPM+1)
F value=3.72 Pr (>F)=0.0152
Expression-log2 (TPM+1)
Expression-log2 (TPM+1)
F value=3.23 Pr (>F)=0.0405
5
Expression-log2 (TPM+1)
5
F value=3.93 Pr (>F)=0.00861
5
5
4
4
4
4
3
3
3
3
2
2
2
2
1
1
1
0
1
Stage I Stage II Stage III Stage IV
Stage I Stage II Stage III Stage IV
Stage II Stage III Stage IV
Stage I Stage II Stage III Stage IV
Expression of SLC31A1 in BRCA based on sample types
70
60
Transcript per million
50
40
30
20
10
0
Normal (n=114)
Primary tumor (n=1,097)
Normal breast
BRCA
TCGA samples
Expression of SLC31A1 in LIHC based on sample types
80
Transcript per million
60
40
20
0
Normal (n=50)
Primary tumor (n=371)
Normal liver
LIHC
TCGA samples
Expression of SLC31A1 in STAD based on sample types
80
*
Transcript per million
60
40
20
0
Normal (n=34)
Primary tumor (n=415)
Normal stomach
STAD
TCGA samples
Expression of SLC31A1 in UCEC based on sample types
50
Transcript per million
40
30
20
10
0
Normal (n=35)
Primary tumor (n=546)
Normal endometrioid
UCEC
TCGA samples
Log10 (HR)
A
1.0
ENSG00000136868.10
0.5
0.0
(SLC31A1)
-0.5
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
-1.0
1.0
Low SLC31A1 Group
High
1.0
Low SLC31A1 Group
High
1.0
Low SLC31A1 Group
Logrankp=0.0012
High Group
0.8-
HR(high)=3,9
Logrank P=0.0027
0.8
HR(high)=1.6
Logrank p=3.5e-05
Overall survival
P(HR)=0.0025
Overall survival
P(HR)=0.0031
HR(high)=0.52
n(high)=38
n(high)=535
Overall survival
0.8
P(HR)=4.8e-05
n(high)=258
0.6
Alap
0.6
n(low) :533
0.6
n[low)=257
0.4-
0.4
0.4
0.2
0.2
0.2
0.0
ACC
0.0
BRCA
0.0
KIRC
0
50
100
150
0
50
100
150
200
250
0
50
100
150
Months
Months
Months
1.0
Low SLC31A1 Group
1.0
Low SLC31A1 Group
1.0
High SLC31A1 Group
Low SLC31A1 Group
Logrank P=0.00012 HR(high)=2.1
High SLC31A1 Group
Logrank P=1,Be-05
High SLC31A1 Group
HR(high)=3
Logrank P=0.027
HR(high)=1.4
Overall survival
0.8
P(HR)=0.00017
Overall survival
0.8
P(HR)=3.50-05
Overall survival
0.8
n(high)=41
P(HR)=0.027
n(high)=257
n(high)=229
0.6
n(low)=257
0.6
n[low)=41
0.6
n(low)=229
0.4
0.4
0.4
0.2
0.2
0.2
0.0
LGG
0.0
MESO
0.0
SKCM
0
50
100
150
200
0
20
40
60
80
0
100
200
300
Months
Months
Months
B
Log10 (HR)
0.50
ENSG00000136868.10
0.25
0.00
(SLC31A1)
-0.25
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
-0.50
1.0
Low
1.0
1.0
High SLC31A1 Group
Low SLC31A1 Group
High Group
Low Group
High SLC31A1 Group
Disease-free survival
Logrank P=7e-04
HR(high)=3.3
Disease-free survival
Logrank P=6.7e-06
HR(high)=0.42
Disease-free survival
Logrank P=0.032
0.8
P(HR)=0.0013
0.8
-P(R)=1.3e-05
0.8
HR(high)=1.4
n(high)=38
Mhigh),258
P(HR)=0.032
n(low)=38
n[high)=257
0.6
0.6
n[low)
257
0.6
n(low)=257
0.4
0.4
0.4
0.2
0.2
0.2
0.0
ACC
0.0
KIRC
0.0
LGG
0
50
100
150
0
20
40
60
80
100
120
140
0
50
100
150
Months
Months
Months
1.0
Low SLC31A1 Group
1.0
High
Low SLC31A1 Group
Logrank P=0.044
High
Disease-free survival
0.8
HR(high)=1.8
Logrank P=0.02
P(HR)=0.041
Disease-free survival
0.8
HR(high)=0.64
n(high)=41
P(HR)=0.021
n[low)=41
n(high)=192
0.6
0.6
Dlow)=192
0.4
0.4
0.2
0.2
0.0
MESO
0.0
STAD
0
20
40
60
80
0
20
40
60
80
100
120
Months
Months
A
Stage Stage I Stage II Stage III Stage IV
Age, years số5 ]>65
Sex FEMALE MALE
-
-
*
*
*
*
*
*
*
*
*
6
SLC31A1 expression
SLC31A1 expression
6
SLC31A1 expression
6
4
4
4
2
2
2
ACC
BLCA
BRCA
CHOI
COAD
ESCA
HNSC
KICK
KIRO
KIRA
CHO
LUAD
LUSC
MESO
PAAD
REAN
SKCM
STAD
GCT
THCA
UVM
8,00
0,10
ESPC
LANE
MESO
AHO
PSG
SEM
710
20 am
400 UVA
ACC
B/C
BACA
1990 0/10
440
HESA
ALL
3540
ICH
TAPA
UVM
B
BRCA
CESC
CHOL
COAD
COADREAD
1.0
1.0
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
SLC31A1
0.2
SLC31A1
0.2
SLC31A1
0.2
95% CL: 0.774-0.829
95% CL: 0.773-0.920
AUIC: 0.926
SLC31A1
0.2
ALIC: 0.847
SLC31A1
95% CL: 0.849-1.000
ALIC: 0.904
95% CI: 0.881-0.926
AUC: 0.886
0.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
95% CI: 0.862-0.910
0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1.0
DLBC
1.0
ESAD
1.0
ESCA
1.0
GBM
1.0
GBMLGG
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
SLC31A1
0.2
0.2
0.2
0.2
AUC: 0.693
SLC31A1
AUC: 0.780
SLC31A1
AUC: 0.93H
SLC31A1
SLC31A1
95% Cl: 0.619-0.767
95% CI: 0.605-0.955
96% CI: 0.917-0.959
ALIC: 0.984
95% CI: 0.971-0.997
ALIC: 0.968
0.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
95% CI: 0.960-0.977
0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1.0
LAML
1.0
LGG
1.0
OV
1.0
PAAD
1.0
READ
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.4
0.2
SLC31A1
0.2
0.2
AUC: 0.989
SLC31A1
SLC31A1
0.2
SLC31A1
0.2
AUC: 0.964
AUC: 0.769
AUC. 0.963
SLC31A1
0.0
95% Ci: 0.973-1.000
95% CI: 0.964-0.973
95% CL: 0.728-0.810
AUC: 0.911
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
95% CI: 0.928-0.977
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0
95% CI: 0.874-0.949
0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
STAD
TGCT
UCEC
UCS
1.0
1.0
1.0
1.0
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
Sensitivity (TPR)
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
SLC31A1
0.2
SLC31A1
0.2
SLC31A1
0.2
AUC: 0.854
AUC: 0.836
ALIC: 0.837
SLC31A1
ALIC: 0.786
0.0
95% CL: 0.824-0.885
0.0
95% CL: 0.788-0.884
0.0
95% CI: 0.791-0.884
0.0
95% CI: 0.708-0.865
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
A
2.5
· Mutation . Amplification
Alteration frequency, %
· Deep deletion . Multiple alterations
2.0
1.5
1.0
0.5
Structural variant data Mutation data + +
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
CNA data
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)
Adrenocortical Carcinoma (TCGA, PanCancer Atlas)
Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)
Prostate Adenocarcinoma (TCGA, PanCancer Atlas)
Sarcoma (TCGA, PanCancer Atlas)
Kidney Renal Papillary cell Carcinoma (TCGA, PanCancer Atlas)
Skin Cutaneous Melanoma (TCGA, PanCancer Atlas)
Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Thyroid Carcinoma (TCGA, PanCancer Atlas)
Head and Neck Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Breast Invasiva Carcinoma (TCGA, PanCancer Atlas)
Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas)
Stomach Adenocarcinoma (TCGA, PanCancer Atlas)
Glioblastoma Multiforme (TCGA, PanCancer Atlas)
Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)
Esophageal Adenocarcinoma (TCGA, PanCancer Atlas)
Liver Hepatocellular Carcinoma (TCGA, PanCancer Atlas)
Lung Adenocarcinoma (TCGA, PanCancer Atlas)
Brain Lower Grade Glioma (TCGA, PanCancer Atlas)
Pheochromocytoma and Paraganglioma (TCGA, PanCancer Atlas)
Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)
Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Mesothelioma (TCGA, PanCancer Atlas)
Acute Myeloid Leukemia (TCGA, PanCancer Atlas)
Kidney Renal Clear Cell Carcinoma (TCGA,PanCancer Atlas)
Uveal Melanoma(TCGA, PanCancer Atlas)
Thymoma (TCGA, PanCancer Atlas)
Cholangiocarcinoma (TCGA, PanCancer Atlas) Uterine Carcinosarcoma (TCGA, PanCancer Atlas) Kidney Chromophobe (TCGA, PanCancer Atlas)
Testicular Germ Ce Tunors (TCGA, PanCancer Atias)
Dilfruse Large B-Cell Lyimphorma (TCGA, PanCancer Atias)
B
C
O
Missense
23
Missense
Truncating
4
Truncating
SLC31A1 mutations
5
0
Inframe
0
Inframe
0
Splice
1
Splice
0
SV/Fusion
1
SV/Fusion
S105Y
..
0
CTR
0
100
190aa
Protein change
had the highest amplification frequency in ACC, the highest mutation frequency in UCEC, the highest deep deletion frequency in THCA, and the highest multiple alteration frequency in BRCA. In addition, we explored the mutation types and mutation sites in the SLC31A1 sequence. The mutation types of SLC31A1 mainly included missense mutations, truncation mutations, splice mutations, and synaptic vesicle (SV)/fusion. In addition, missense mutations accounted for a portion of the mutation types. The truncating mutation, S105Y, may be a putative cancer driver (Figure 6B). We further derived the three-dimensional (3D) structure of S105Y (Figure 6C).
Next, we used forest plots to show the association
between SLC31A1 genetic alterations and patient prognosis. SLC31A1 genetic alterations were associated with OS in patients with ACC, BLCA, BRCA, KIRC, LGG, MESO, and SKCM. Among them, SLC31A1 genetic alterations were positively correlated with OS in ACC, BLCA, BRCA, LGG, MESO, and SKCM patients, and SLC31A1 genetic alterations were negatively correlated with OS in KIRC patients (Figure 7A). SLC31A1 genetic alterations were associated with PFS in ACC, BLCA, BRCA, CESC, KIRC, LGG, MESO, READ, and uveal melanoma (UVM) patients. Among them, SLC31A1 genetic alterations were positively correlated with PFS in ACC, BLCA, BRCA, CESC, LGG, MESO, and UVM patients, and SLC31A1
A
| Overall survival Cancer P value Hazard Ratio(95% CI) | |||
|---|---|---|---|
| ACC | 0.0005 | 4.77585(1.99281.11.44552) | |
| BLCA | 0.0199 | 1.42309(1.05731.1.91543) | |
| BRCA | 0.0024 | 1.6528(1.19422.2.28749) | |
| CESC | 0.2862 | 1.28874(0.80854.2.05414) | |
| CHOL | 0.4891 | 0.71547(0.27706.1.84758) | |
| COAD | 0.4519 | 0.86106(0.58314.1.27143) | |
| DLBC | 0.7393 | 0.79001(0.19707,3.16707) | |
| ESCA | 0.3309 | 0.78374(0,47955.1.28088) | |
| GBM | 0.8286 | 1.04159(0.72032.1.50614) | |
| HNSC | 0.4648 | 1.10463(0.84596.1.44238) | |
| KICH | 0.3684 | 1.89175(0.47165,7.58772) | |
| KIRC | 0.0001 | 0.53366(0.39134,0.72774) | |
| KIRP | 0.2095 | 0.67357(0.36337.1.24855) | |
| LAML | 0.1046 | 1.41967(0,92985.2.16752) | |
| LGG | 0.0008 | 1.93279(1.31737.2.83571) | |
| LIHC | 0.0675 | 0.72399(0.51214.1.02347) | |
| LUAD | 0.6499 | 0.93494(0,69924.1.25008) | |
| LUSC | 0.8976 | 1.01794(0.77632,1.33478) | |
| MESO | 0,0001 | 2.73985(1.68173,4.46371) | |
| OV | 0.3067 | 1.14456(0.88351,1.48274) | |
| PAAD | 0.4057 | 1.19245(0.78753.1.80556) | |
| PCPG | 0.2621 | 2.56316(0.49465,13.28159) | |
| FRAD | 0.7733 | 0.82671(0.22644,3.01818) | |
| READ | 0.3530 | 0.6842(0.30715.1.52409) | |
| SARC | 0.2373 | 1.26927(0.85471,1.8849) | |
| SKCM | 0.0054 | 1.47407(1.12176.1.93702) | |
| STAD | 0.2219 | 0.81497(0.5869,1.13167) | |
| THCA | 0.3329 | 1.64991(0.59884.4.54576) | |
| THYM | 0.0522 | 7.85815(0.98067.62.96793) | |
| UCEC | 0.5513 | 0.88091(0.58045,1.33688) | |
| UCS | 0.1178 | 1.7203(0.87173.3.3949) | |
| UVM | 0.5113 | 1.32008(0.57635,3.02352) | |
| 0.197 | 12.5 20 30 40 5 Hazard Ratio | ||
B Progression-free survival
IT
50 60
0.20871
2
3
1.
5
8 9
Hazard Ratio
C
Disease-specific survival
D
Disease-free survival
Cancer
P value
Hazard Ratio(95% CI)
ACC
0.2621
2.02098(0.59093,6.91172)
BLCA
0.6483
1.17915(0.58087,2 39366)
BRCA
0.7707
1.06572(0.69464,1.63501)
CESC
0.5709
1.25108(0.57656.2.71473)
CHOL
0.8770
0.90467(0.25444,3.21661)
COAD
0.2308
0.59776(0.25762,1.38699)
DLBC
0.5536
0.48251(0.04328,5.37917)
ESCA
0.4596
0.72312(0.30626,1.70736)
HNSC
0.7419
0.88249(0.41938,1.85701)
KIRC
0.4150
0.65053(0.23136,1.82911)
KIRP
0.1343
1.80738(0.83286.3.92219)
LGG
0.1802
0.54609(0.22545.1.32277)
LIHC
0.8574
1.0306(0.74188.1.43169)
LUAD
0.8993
1.0272(0.67783,1.55664)
LUSC
0.7060
1.10218(0.66488,1.82711)
MESO
0.1467
3.63768(0.63598,20.80692)
OV
0.0798
0.7309(0.51467,1.03799)
PAAD
0.3788
1.46262(0.62716,3.411)
PCPG
0.9362
1.08431(0.1494,7.86952)
PRAD
0.4142
0.74437(0.36649,1.51186)
READ
0.8867
1.12422(0.22456,5.62811)
SARC
0.9326
0.97932(0.60325.1.58985)
STAD
0.0755
0.54925(0.28367,1.06345)
TGCT
0.7265
1.14635(0.53323,2.46445)
THCA
0.0812
0.49063(0.22038,1.09227)
UCEC
0.3997
0.79744(0.47088,1.35047)
UCS
0.8468
0.88244(0.24815.3.13808)
1
0.04328
5 7.5 10 12.5 15 17.5 20
Hazard Ratio
0.11203 5 7.510
15
20
25
Hazard Ratio
| Cancer | P value | Hazard Ratio(95% CI) | |
|---|---|---|---|
| ACC | <0.0001 | 4.35661(2.1808,8.70328) | |
| BLCA | 0.0155 | 1.45179(1.07352,1.96334) | |
| BRCA | 0.0362 | 1.41885(1.02268.1.96849) | |
| CESC | 0.0447 | 1.62543(1.01155.2.61187) | |
| CHOL | 0.3787 | 0.66883(0.27314.1.63777) | |
| COAD | 0.2045 | 0.7934(0.55493.1.13436) | |
| DLBC | 0.534 | 0.68562(0.20871,2.25232) | |
| ESCA | 0.4297 | 0.83764(0.53961.1.30028) | |
| GBM | 0.9768 | 0.9946(0.69091,1.43179) | |
| HNSC | 0.9194 | 1.01469(0.76489.1.34608) | |
| KICH | 0.3417 | 1.81669(0.53067,6.21924) | |
| KIRC | <0.0001 | 0.4281(0.30638.0.59817) | |
| KIRP | 0.5392 | 1.17969(0.69608,1.99928) | |
| LGG | 0.0162 | 1.42806(1.06817,1.9092) | |
| LIHC | 0.6793 | 1.06379(0.7935.1.42615) | |
| LUAD | 0.4551 | 0.90095(0.68524.1.18457) | |
| LUSC | 0.9159 | 1.01768(0.73517,1.40874) | |
| MESO | 0.0084 | 2.06416(1.20427.3.53803) | |
| OV | 0.1843 | 0.85073(0.67012,1.08001) | |
| PAAD | 0.2388 | 1.26293(0.85643.1.86239) | |
| PCPG | 0.8699 | 0.93014(0.39093.2.2131) | |
| PRAD | 0.267 | 0.79409(0.52854.1.19306) | |
| READ | 0.0352 | 0.47472(0.237280.94975) | |
| SARC | 0.5449 | 1.10842(0.79432.1.54673) | |
| SKCM | 0.595 | 1.06276(0.84915.1.33012) | |
| STAD | 0.0599 | 0.71173(0.49945.1.01425) | |
| TGCT | 0.3156 | 1.41677(0.71743.2.79784) | |
| THCA | 0.2527 | 0.72981(0.42543.1.25195) | |
| ΤΗΥΜ | 0.4714 | 1.37441(0.57843.3.26573) | |
| UCEC | 0.1978 | 0.79111(0.55383.1.13007) | |
| UCS | 0.2296 | 1.48744(0.7783,2.8427) | |
| UVM | 0.0346 | 2.38828(1.06512.5.35518) |
| Cancer | P value | Hazard Ratio(95% CI) | |
|---|---|---|---|
| ACC | Sc-04 | 5.21193(2.04958.13.25359) | |
| BLCA | 0.0695 | 1.39672(0.97368.2.00357) | |
| BRCA | 0.0673 | 1.49677(0.97167.2.30564) | |
| CESC | 0.2765 | 1.34532(0.78851.2.29531) | |
| CHOL | 0.3801 | 0.63509(0.23042.1.75042) | |
| COAD | 0.2968 | 0.76865(0.46888.1.26008) | |
| DLBC | 0.8203 | 0.7966(0.11203.5.66426) | |
| ESCA | 0.2282 | 0.69938(0.39096.1.25109) | |
| GBM | 0.4261 | 1.17492(0.78999,1.7474) | |
| HNSC | 0.9159 | 1.01878(0.72124.1.43908) | |
| KICH | 0.2853 | 2.44891(0.47362,12.66245) | |
| KIRC | <0,0001 | 0.41907(0.27842,0.63077) | |
| KIRP | 0.5905 | 0.81416(0.38501.1.72166) | |
| LGG | 6c-04 | 2.03484(1.35869.3.04749) | |
| LIHC | 0.3439 | 0.80761(0.51887.1.25703) | |
| LUAD | 0.5574 | 0.89449(0.61632.1.29819) | |
| LUSC | 0.9859 | 0.99622(0.65367.1.51827) | |
| MESO | 0.0028 | 2.61205(1.39086,4.90549) | |
| OV | 0.6357 | 1.06976(0.80931.1.41403) | |
| PAAD | 0.0688 | 1.5531(0.96659.2.49547) | |
| PCPG | 0.6118 | 1.59231(0.26411,9.59991) | |
| PRAD | 0.526 | 1.81243(0.28842,11.38942) | |
| READ | 0.9779 | 0.98528(0.34523.2.81202) | |
| SARC | 0.441 | 1.18602(0.76845,1.83051) | |
| SKCM | 0.0026 | 1.57068(1.17082.2.10709) | |
| STAD | 0.3419 | 0.81667(0.53787.1.23998) | |
| THCA | 0.7342 | 1.29656(0.28949.5.80702) | |
| THYM | 0.3785 | 2.77791(0 28584.26.99715) | |
| UCEC | 0.3077 | 0.76358(0.45477.1.28211) | |
| UCS | 0.5349 | 1.2502(0.61752.2.53107) | |
| UVM | 0.2919 | 1.60915(0.6644,3.8973) |
Figure 7 SLC31A1 and prognosis of pan-cancer. (A) OS. (B) PFS. (C) DSS. (D) DFS. CI, confidence interval; ACC, adrenocortical carcinoma; BLCA, bladder urothelial carcinoma; BRCA, breast invasive carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; DLBC, lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe; KIRC, kidney renal clear cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LAML, acute myeloid leukemia; LGG, lower grade glioma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; MESO, mesothelioma; OV, ovarian serous cystadenocarcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma and paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; TGCT, testicular germ cell tumor; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma; SLC31A1, solute carrier family 31 (copper transporter), member 1; OS, overall survival; PFS, progression-free survival; DSS, disease-specific survival; DFS, disease-free survival.
genetic alterations were negatively correlated with PFS in KIRC and READ patients (Figure 7B). SLC31A1 genetic alterations were associated with DSS in ACC, KIRC, LGG, MESO, and SKCM patients. Among them, SLC31A1 genetic alterations were positively correlated with DSS in ACC, LGG, MESO, and SKCM patients, and SLC31A1 genetic alterations was negatively correlated with DSS in KIRC patients (Figure 7C). Figure 7D shows the relationship between cancer and DFS. Taken together, these results suggest that genetic alterations in SLC31A1 are closely associated with the prognosis of patients with the above- mentioned cancers.
TME, TMB, MSI, and drug sensitivity
Our study showed that SLC31A1 has a prognostic role in pan-cancer; thus, we further explored the expression of TME and SLC31A1 in different tumors. The results showed that the expression of SLC31A1 was positively correlated with the scores of stromal cells and immune cells in DLBC, LAML, LUAD, MESO, and SARC (Figure 8A), which suggest that the content of stromal cells or immune cells increases as the SLC31A1 expression level increases. However, the opposite results were found in ACC and thymoma (THYM). TMB refers to the rate of the base mutation in 1 million bases. MSI refers to the phenomenon of the emergence of new microsatellite alleles at the microsatellite site of a tumor due to gene insertion or deletion compared to normal tissue. This study found a clear association between SLC31A1 and TMB in ACC, BLCA, BRCA, COAD, LGG, LUAD, OV, READ, SARC, STAD, THYM, UCEC, and uterine carcinosarcoma (UCS) (Figure 8B). This study also found a clear association between SLC31A1 and MSI in CESC, COAD, DLBC, HNSC, LUSC, PRAD, SARC, STAD, THCA, and UCEC (Figure 8C). Finally, we used the CellMiner™M database to explore the potential correlation between drug sensitivity and SLC31A1 expression. The results showed that SLC31A1 expression was negatively correlated with the drug sensitivity of denileukin diftitox, entinostat, and alectinib, but positively correlated with (+)-BET bromodomain inhibitor (JQ1) (Figure 8D). In summary, SLC31A1 may be associated with chemotherapy resistance to certain chemotherapy drugs.
Methylation levels of SLC31A1 in pan-cancer
We explored the methylation levels of SLC31A1 in various
cancers using the UALCAN database. The results showed that SLC31A1 methylation levels were highly expressed in LUSC and READ. Conversely, SLC31A1 methylation levels were lowly expressed in HNSC, KIRP, LIHC, PRAD, and UCEC (Figure 9).
Correlation between SLC31A1 expression and the immune response
We used the TIMER2.0 database to explore the correlation between immune infiltrating cells and SLC31A1 expression using TCGA database. We found that SLC31A1 expression was positively correlated with the immune infiltration of B cells in PAAD and PCPG. Conversely, it was negatively correlated with the immune infiltration of B cells in BRCA-Basal, DLBC, MESO, and TGCT (Figure 10A). Meanwhile, SLC31A1 expression was positively correlated with the immune infiltration of cluster of differentiation (CD)4+ T cells in COAD and DLBC. Conversely, it was negatively correlated with the immune infiltration of CD4+ T cells in CHOL and GBM (Figure 10B). Further, SLC31A1 expression was positively correlated with the immune infiltration of CD8+ T cells in DLBC, LGG, PAAD, and UVM. Conversely, it was negatively correlated with the immune infiltration of CD8+ T cells in ACC and ESCA (Figure 10C). Further, SLC31A1 expression was positively correlated with the immune infiltration of natural killer (NK) cell in BLCA, COAD, DLBC, LIHC, and TGCT. Conversely, it was negatively correlated with the immune infiltration of NK cells in KIRC, KIRP, LGG, MESO, SKCM, and THCA (Figure 10D). In addition, SLC31A1 expression was positively correlated with the immune infiltration of dendritic cells (DCs) in BRCA, COAD, DLBC, HNSC-human papillomavirus (HPV)*, KIRC, KIRP, LGG, LUAD, PAAD, PRAD, SKCM, and TGCT. Conversely, it negatively correlated with the immune infiltration of DCs in ESCA and LIHC (Figure 10E). Finally, SLC31A1 expression was positively correlated with the immune infiltration of regulatory T cells (Tregs) in BLCA, CESC, ESCA, LGG, LUSC, PAAD, SKCM, TGCT, and UVM. Conversely, it was negatively correlated with the immune infiltration of Tregs cells in ACC, DLBC, KIRC, and PCPG (Figure 10F). Further, Figure S2 sets out the correlations between SLC31A1 expression and neutrophils, monocytes, macrophages, mast cells, cancer-associated fibroblasts, common lymphoid progenitor cells, endothelial cells, common myeloid progenitor cells, the immune infiltration of eosinophils,
A
Cancer: ACC
Cancer: DLBC
Cancer: LAML
Cancer: LUAD
Cancer: MESO
Cancer: SARC
Cancer: THYM
5
Au-0.40, P.5.18-06
5
A-0.5, P.D.00007
R=0.57, P.2.00-16
6
R-0.24, Pc5.40-00
A=0.24, P=0.029
Re-0.27, Pu0.0004
SLC31A1
4
SLC31A1
SLC31A1
3.0
4
SLC31A1
4
5
4
SLC31A1
SLC31A1
4
SLC31A1
3
2.5
3
3
4
2
3
3
2.0
2
2
3
1
2
:
1
1.5
.
2
2
-1000
0
1000
2000
2400 2800 3200 3600 ImmuneScore
1500 2000 2600 3000 3500 4000 ImmuneScore
-1000 0 1000 2000 3000 ImmuneScore
0
1000 2000 3000 ImmuneScore
-1000 0 1000 2000 3000 ImmuneScore
0
1000 2000 3000
ImmuneScore
ImmuneScore
Cancer: ACC
Cancer: DLBC
Cancer: LAML
Cancer: LUAD
Cancer: MESO
Cancer: SARC
Cancer: THYM
5
Rx-0.36, PuQ.001.3
5
R=0.55, Pad.le-05
A.0.60, Pc2:00-16
6
R.0.21, P=2.48-06 *
R.0.42, P=5.le-05
R.0.20, P.0.0023
SLC31A1
4
SLC31A1
4
SLC31A1
4
SLC31A1
5
SLC31A1
4
SLC31A1
4
SLC31A1
3.0
3
3
2.5
3
4
3
3
2
2.0
2
3
1
2
1.5
1
-
2
2
2
F
-1000
0
1000
0
500
1000
StromalScore
StromalScore
-1500 -1000 -500 0 StromalScore
-1000 0 1000 2000 StromalScore
-500
0
500 1000 1500 2000
-1000
0
1000 2000
-1500 -1000 -500 0 500 1000 StromalScore
StromalScore
StromalScore
B
Tumor mutation burden
C
Microsatellite instability
BRCA **
BLCA ** ACC ***
UVM
UCS*
BRCA
BLCA ACC
UVM
UCS
CESC
0.5
UCEC ***
CESC*
0.4
UCEC ***
CHOL
0.25
THYM ***
CHOL
0.2
THYM
COAD ***
THCA
COAD ***
THCA ***
0
0
DLBC
TGCT
DLBC*
TGCT
-0.25
-0.2
ESCA
ESCA
-0.5
STAD ***
STAD ***
-0.4
GBM
SKCM
GBM
SKCM
HNSC
SARC ***
HNSC ***
SARC*
KICH
READ*
KICH
READ
KIRC
PRAD
KIRC
PRAD ***
KIRP
PCPG
KIRP
PCPG
LAML
PAAD
LAML
PAAD
LGG ***
LIHC LUAD* LUSC
OV **
LGG
OV
MESO
LIHC
LUAD LUSC
MESO
D
SLC31A1, Denileukin diftitox Cor =- 0.467, P<0.001
SLC31A1, (+)-JQ1 Cor=0.359, P=0.006
SLC31A1, Entinostat Cor =- 0.356, P=0.006
SLC31A1, Alectinib Cor =- 0.262, P=0.047
6
·
1
2
4
4
IC50
1
IC50
0
IC50
IC50
2
0
2
-1
-1
0
..
*
0
-2
-2
·
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
Expression
Expression
Expression
Expression
Promoter methylation level of SLC31A1 in HNSC
Promoter methylation level of SLC31A1 in KIRP
Promoter methylation level of SLC31A1 in LIHC
Promoter methylation level of SLC31A1 in LUSC
0.08
0.07
0.07
0.07
**
0.07
**
**
0.06
0.06
0.06
Beta value
0.06
Beta value
Beta value
0.05
0.05
Beta value
0.05
0.05
0.04
0.04
0.04
0.04
0.03
0.03
0.02
0.03
0.03
0.02
0.01
Normal (n=50)
Primary tumor (n=528)
0.02
Normal (n=45)
Primary tumor (n=275)
0.02
Normal (n=50)
Primary tumor (n=377)
0.01
Normal (n=42)
Primary tumor (n=370)
TCGA samples
TCGA samples
TCGA samples
TCGA samples
Promoter methylation level of SLC31A1 in PRAD
Promoter methylation level of SLC31A1 in READ
Promoter methylation level of SLC31A1 in UCEC **
0.07
0.07
0.07
*
0.06
0.06
Beta value
Beta value
0.06
0.05
Beta value
0.05
0.05
0.04
0.04
0.03
0.04
0.03
0.02
Primary tumor (n=502)
0.03
0.02
Normal (n=50)
Normal (n=7)
Primary tumor (n=98)
Normal (n=46)
Primary tumor (n=438)
TCGA samples
TCGA samples
TCGA samples
granulocyte-monocyte progenitor cells, hematopoietic stem cells, follicular helper T cells, gamma delta T cells, NK T cells, and myeloid-derived suppressor cells. The above study indicates the potential significance of SLC31A1 in the immune infiltration of the TME.
Expression of SLC31A1 at the single-cell level
The CancerSEA database was used to study the expression levels of SLC31A1 at the single-cell level to further explore the role of SLC31A1 in bio-functional states. SLC31A1 expression in UVM was negatively correlated with DNA damage, DNA repair, and apoptosis. The expression level of SLC31A1 in retinoblastoma (RB) was positively correlated with angiogenesis and differentiation. The expression of SLC31A1 in OV was positively correlated with quiescence (Figure 11A). Further, Figure 11B shows the correlations between SLC31A1 expression and DNA repair in UVM, angiogenesis in RB, and quiescence in OV. Finally, Figure 11C shows the distribution of SLC31A1 expression at the single-cell level in UVM, RB, and OV. Taken together, it appears that SLC31A1 could potentially be critical in the regulation of biological functions in cancer.
Enrichment analysis of SLC31A1-related genes
We used bioGRID to investigate the SLC31A1-interacting
biomarkers (Figure 12A). Meanwhile, the top 100 genes associated with SLC31A1 were downloaded from the GEPIA 2.0 database (Table S1). The expression levels of SLC31A1 were correlated with CHGB, CYB561, DBH, EML5, PHOX2B, and TBX20 in pan-cancer (Figure 12B). Further, as the heat map in Figure 12C shows, SLC31A1 was positively correlated with the above genes in most cancers. Finally, the Gene Ontology (GO) and KEGG enrichment analysis indicated that SLC31A1-related genes were mainly enriched in the mitochondrial matrix and coated vesicles (Figure 12D).
Validation of SLC31A1 expression
To verify the expression of SLC31A1 in pan-cancer, we explored it at the messenger RNA (mRNA) and protein levels in liver cancer cells (L-O2, SMMC-7721, HUh7, H-97, and HepG2), gastric cancer cells (GES-1, HGC- 27, AGS, and MKN-54,) and colon cancer cells (NCM460, RKO, LoVo, and DLD-1) using both RT-qPCR and WB. We found that at the mRNA level, the liver, gastric, and colon cancer cells were lowly, highly, and lowly expressed, respectively, compared with their corresponding normal cells (Figure 13A-13C). Next, our results showed that hepatocellular carcinoma and gastric carcinoma cells were highly expressed at the protein level (except SMMC- 7721) compared with their corresponding normal cells.
A
B
C
B cell_QUANTISEQ
B cell memory_CIBERSORT
B call memory_CIBERSORT-ABS
B oall naive_CIBERSORTABS
B cell plasma_CIBERSORT-ABS
Class-switched memory B cell_XCELL
T cell CD4+ (non-regulatory)_QUANTISEQ T cell CD4+ (non-regulatory)_XCELL
Toall CD4+ naive_CIBERSORT-ABS
T call CD4+ central memory_XCELL
T cell CD4+ effector memory_XCELL
T call CD4+ memory activated_CIBERSORT
cal Coat memory activated_CIBERSORT-ABS Toall CD4+ memory mesting_CIBERSORT
T cell CD4+ memory resting_CIBERSORT-ABS Toall CD4+ Th1_XCELL
T cell CD8+ MCPCOUNTER T cell CD8+_CIBERSORT
T cell CD8+_CIBERSORT-ABS
T cell CD8+_QUANTISEQ
T cell CD8+ naive_XCELL
T cell CD8+ central memory_XCELL
T cell CD8+ eflector memory_XCELL
B cell_TMER B call_EPIC
B cell_XCELL
B cell_MCPCOUNTER
B call memory_XCELL
B cell naive_CIBERSORT
B call naive_XCELL
B cell plasma_CIBERSORT
B cell plasma_XCELL
T cell CD4+_EPIC
T cell CD4+_TIMER
T call CD4+ naive_CIBERSORT
T call CD4+ naive_XCELL
T call CD4+ memory_XCELL
T call CD4+ Th2_XCELL
T cell CD8+_TIMER T cell CD8+_EPIC
T cell CD8+_XCELL
ACC (n=79)
XXX
ACC (n=79)
BLCA (n=408)
BLCA (n=408)
BRCA (n=1,100)
BRCA (n=1,100)
BRCA-Basal (n=191)
ACC (n=79)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
BLCA (n=408)
BRCA-Her2 (n=82)
BRGA-LumA (n=568)
BRCA (n=1,100)
BRGA-LumA (n=568)
BRCA-LumB (n=219)
BRCA-Basal (n=191)
BRCA-LumB (n=219)
CESC (n=306)
BRCA-Her2 (n=82)
X
CESC (n=306)
CHOL (n=36)
BRGA-LumA (n=588)
CHOL (n=36)
XXX
XXIX
COAD (n=458)
BRCA-LumB (n=219)
COAD (n=458)
DLBC (n=48)
X
CESC (n=306)
ESCA (n=185)
CHOL (n=36)
IXIX
X
XIX
X
DLBC (n=48)
ESCA (n=185)
GBM (n=153)
COAD (n=458)
GBM (n=153)
HNSC (n=522)
DLBC (n=48)
XIX
X
HNSC-HPV- (n=422)
ESCA (n=185)
HNSC (n=522)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
XIXIIX
GBM (n=153)
X
HNSC (n=522)
HNSC-HPV+ (n=98)
KICH (n=66)
X
XXIX
X
P>0.05
HNSC-HPV- (n=422)
KICH (n=66)
[X
KIRC (n=533)
X P>0.05
KIRC (n=533)
HNSC-HPV+ (n=98)
KIRP (n=290)
.
P=0.05
KICH (n=66)
XIX
KIRC (n=533)
8
KIRP (n=290)
LGG (n=516)
P>0.05
M
Ps0.05
LGG (n=516)
LIHC (n=371)
X
X
KIRP (n=290)
. P=0.05
LIHC (n=371)
LUAD (n=515)
Partial_Cor
LGG (n=516)
LUAD (n=515)
LIHC (n=371)
LUSC (n=501)
Partial_Cor 1
LUSC (n=501)
X
MESO (n=87)
X
X
X
1
LUAD (n=515)
MESO (n=87)
0
LUSC (n=501)
Partial_Cor
0
OV (n=303)
PAAD (n=179)
X
-1
MESO (n=87)
XIX
XIXIXIXI
1
OV (n=303)
-1
OV (n=303)
0
PAAD (n=179)
PCPG (n=181)
XXIX
XII
PAAD (n=179)
XIXIXD
-1
PCPG (n=181)
PRAD (n=498)
PRAD (n=498)
READ (n=166)
XXXXXX
PCPG (n=181)
XI
PRAD (n=498)
READ (n=166)
SARC (n=260)
XIIX
READ (n=166)
XIX
SARC (n=260)
XIX
SARC (n=260)
SKCM (n=471)
X
XIX
SKCM (n=471)
SKCM-Metastasis (n=368)
SKCM (n=471)
SKCM-Metastasis (n=368)
SKCM-Primary (n=103)
XX
X
SKCM-Metastasis (n=388)
SKCM-Primary (n=103)
STAD (n=415)
SKCM-Primary (n=103)
XXX
X
XIX
STAD (n=415)
TGCT (n=150)
XIXIX
X
STAD (n=415)
TGCT (n=150)
TGCT (n=150)
XIX
THCA (n=509)
THYM (n=120)
X
THCA (n=509)
X
X
THCA (n=509)
THYM (n=120)
UCEC (n=545)
THYM (n=120)
UCEC (n=545)
UCEC (n=545)
UCS (n=57)
UCS (n=57)
X
UVM (n=80)
X
UCS (n=57)
UVM (n=80)
UVM (n=80)
B cell
T cell CD4+
T cell CD8+
D
NIK call_MCPCOUNTER
NIK call_QUANTISEO NK cell_XCELL
NK cell activated_CIBERSORT
NK cell activated_CIBERSORT-ABS
NIK call resting_CIBERSORT
NIK call resting_CIBERSORT-ABS
E
F
Myeloid dendritic cell_TIMER Myeloid dendritic cell_XCELL
Myeloid dendritic cell_MCPCOUNTER
Myeloid dendritic cell_QUANTISEQ
Myeloid dendritic cell activated_CIBERSORT
Myelad dendritic cell activated_CIBERSORT-ABS
Myeloid cell activated_XCELL
Myeloid dendritic cell resting_CIBERSORT
Myeloid dendritic cell resting_CIBERSORT-ABS Plasmacyloid dendritic cell_XCELL
T cell regulatory (Tregs)_CIBERSORT
T cell regulatory (Tregs)_CIBERSORT-ABS T call regulatory ( Tregs)_QUANTISEQ
NK cell_EPIC
T cell regulatory (Tregs)_XCELL
ACC (n=79)
BLCA (n=408)
BRCA (n=1,100)
ACC (n=79)
BRCA-Basal (n=191)
ACC (n=79)
BLCA (n=408)
BRCA-Her2 (n=82)
XXX
BLCA (n=408)
BRCA (n=1,100)
BRGA-LumA (n=568)
XIX
BRCA (n=1,100)
BRCA-Basal (n=191)
BRCA-LumB (n=219)
BRCA-Her2 (n=82)
CESC (n=306)
BRCA-Basal (n=191)
BRGA-LumA (n=568)
CHOL (n=36)
XXX
XXIXIX
BRCA-Her2 (n=82)
BRGA-LumA (n=568)
BRCA-LumB (n=219)
COAD (n=458)
BRCA-LumB (n=219)
CESC (n=306)
DLBC (n=48)
XXX
CESC (n=306)
CHOL (n=36)
XXX
ESCA (n=185)
XIX
CHOL (n=36)
XIXIXIXIX
COAD (n=458)
GBM (n=153)
COAD (n=458)
DLBC (n=48)
HNSC (n=522)
DLBC (n=48)
ESCA (n=185)
HNSC-HPV- (n=422)
ESCA (n=185)
GBM (n=153)
HNSC-HPV+ (n=98)
X
GBM (n=153)
HNSC (n=522)
KICH (n=66)
XI
XIX
HNSC (n=522)
HNSC-HPV-(n=422)
HNSC-HPV+ (n=98)
KIRC (n=533)
8
P>0.05
HNSC-HPV- (n=422)
KIRP (n=290)
M
HNSC-HPV+ (n=98)
KICH (n=66)
Ps0.05
KICH (n=66)
KIRC (n=533)
8
P>0.05
P>0.05
LGG (n=516)
KIRC (n=533)
KIRP (n=290)
. Ps0.05
LIHC (n=371)
KIRP (n=290)
M
LGG (n=516)
Ps0.05
LGG (n=516)
LUAD (n=515)
Partial_Cor
LIHC (n=371)
LUSC (n=501)
LIHC (n=371)
LUAD (n=515)
MESO (n=87)
X
X
1
LUAD (n=515)
Partial_Cor
0
Partial_Cor
LUSC (n=501)
OV (n=303)
X
LUSC (n=501)
1
MESO (n=87)
1
PAAD (n=179)
XIX
-1
MESO (n=87)
XX
OV (n=303)
0
PCPG (n=181)
OV (n=303)
0
PAAD (n=179)
-1
PRAD (n=496)
PAAD (n=179)
PCPG (n=181)
-1
PCPG (n=181)
READ (n=166)
XX
PRAD (n=498)
PRAD (n=498)
SARC (n=260)
SKCM (n=471)
READ (n=166)
XIX
X
READ (n=166)
SARC (n=260)
x
SARC (n=260)
SKCM-Metastasis (n=368)
SKCM (n=471)
SKCM (n=471)
SKCM-Primary (n=103)
X
SKCM-Metastasis (n=368)
SKCM-Metastasis (n=368)
STAD (n=415)
SKCM-Primary (n=103)
SKCM-Primary (n=103)
TGCT (n=150)
STAD (n=415)
STAD (n=415)
THCA (n=509)
TGCT (n=150)
XIXIX
X
TGCT (n=150)
THYM (n=120)
THCA (n=509)
UCEC (n=545)
THYM (n=120)
THCA (n=509)
THYM (n=120)
X
UCS (n=57)
UCEC (n=545)
UCS (n=57) UVM (n=80)
X
UCEC (n=545)
UVM (n=80)
X
XIXIX
UCS (n=57)
×
UVM (n=80)
NK cell
DC
Tregs
A
B
ALL
*
**
*
*
AML
CML *
geneExp
CRC
*P<0.05
*
BRCA
UVM
**
*
*
Correlation
P value
AST
** P<0.01
*
**
**
**
**
-0.50
GBM
Correlation
DNArepair
Glioma
* *
* *
**
**
**
**
**
**
**
1.0
HGG
*
**
**
**
**
*
**
ODG
**
0.5
HNSCC
*
**
**
**
RCC
0.0
*
LUAD
**
**
-0.5
RB
geneExp
**
*
**
*
NSCLC
**
*
*
**
*
**
Correlation
OV
P value
*
**
*
**
-1.0
MEL
Angiogenesis
0.57
*
**
*
**
**
*
**
**
*
RB
**
**
**
**
**
**
**
**
**
*
** **
UVM
**
**
**
**
**
**
**
**
**
**
**
**
**
**
Angiogenesis
Apoptosis
CellCycle
Differentiation
DNAdamage
DNArepair
EMT
Hypoxia
Inflammation
Invasion
Metastasis
Proliferation
Quiescence
Stemness
geneExp
OV
Correlation
Quiescence
P value **
0.50
C
Expression distribution with t-SNE plot
Expression distribution with t-SNE plot
Expression distribution with t-SNE plot
60
75
15
40
50
10
tSNE2
20
3.1
3.1
5
2.6
tSNE2
25
3.1
2.6
tSNE2
26
2.1
0
2.
1.6
0
2.1
1.6
1.0
0
1.6
1.0
0.5
1.0
-25
0.0
0.5
-20
0.0
0.5
Expression
Expression
-5
0.0
Expression
-40
-50
-10
-60
-75
-15
-60
-40
-20
0
20
40
60
-75
-50
-25
0
25
50
-15
-10
-5
0
5
10
15
tSNE1
tSNE1
tSNE1
UVM
RB
OV
Conversely, the colon cancer cells were lowly expressed at the protein level (Figure 13D-13F). Figure 13G-13I provides a quantitative graph of protein expression.
Discussion
In this study, we explored SLC31A1 expression, prognosis, mutation, methylation, and the immune response, and conducted a single-cell assessment, and an enrichment analysis in cancer using a series of bioinformatics online database approaches. Finally, RT-qPCR and WB were used to validate the differentially expressed significant hepatocellular carcinomas, gastric cancer, and colon cancer. The results showed that as a CTR, SLC31A1 has an
important effect on the human body.
Copper is an essential trace element for life. All living things require copper to function properly and to maintain homeostasis; thus, maintaining the proper level of copper is crucial. Copper deficiency impairs the activity of copper-binding enzymes, and copper buildup causes cell death (22). Tsvetkov et al. recently demonstrated that copper alone, as opposed to copper ion clusters, is harmful to cells (8). Unlike other known types of death, such as apoptosis, ferroptosis, and necroptosis, cuproptosis is a completely new type of cell death (8). Instead of making adenosine triphosphate (ATP), it depends on mitochondrial respiration ATP (8). One of the key elements in maintaining intracellular copper concentration is the copper importer
A
APBA3
FMN2
LGR4
NSUN3
FAR2
USP32
NMD3
ADCK1
ARRB2
FAM208A
HBS1L
SLC31A2
ENDOG
DGKZ
CERK
CEP57
TMEM200A
PMS1
NUS1
ESCO2
CD320
MARC2
PDE2A
POGZ
DPH1
RAB13
HSDL1
KLHDC10
ACVRZA
POLDI
SAR1B
AHBDF2
USP30
TRAPPC6B
ZNF598
PUSLI
ZNF268
DDX58
CSNK1G3
MZB1
KIAA1804
NCR3LG1
CDC42BPB
NSDHL
OSMR
C2ORF72
MARK4
M
NEDD4L
MTMR1
RABIA
ACVR2B
ELP3
SBF1
ARL13B
IKBKAP
IL1RAP
DTWD1
WASF2
AGBLS
PEAK1
PIK3CA
TLDC1
ST3GAL2
P14K2B
CCS
SEC23IP
SNX9
SLC12A7
SLC25A31
AGAP3
SHB
PIK3R1
SBF2
PHGDH
AMMECR1
PIK3R3
STX7
NEIL3
FBXL 17
TMEM55A
B3GAT3
ABCBB
CISD2
KRAS
DLG1
ZNF692
SLC1244
AXL
CTU1
DIP2A
ARHGEF39
CYB5R1
TAFEL
SLC31A1
LIMK2
ARL17A
RETSAT
SNAP47
S
SLC30A1
LAMTORS
BRIP1
PTPAD
FOXMI
LIN54
ZRANB3
FBXL 19
PIK3R2
ERB83
XRCC3
TTC31
AURKA
EGFR
CDC42BPA
SLC5AB
DHODH
DTL
TMPRSS11B
ALDH3B1
GUCD1
MCM10
NMT2
RELT
MPP7
LGALSB
TRIM25
FTSJ2
ATP2C1
DCAF15
ST3GAL4
PLEKHH3
NIT1
MTOR
LIN7C
GRK6
UBE3D
EXD2
DENNDBA
PRIM1
RITI
WNTSA
SMGB
DDR1
NISCH
KIAA0391
NXF1
ARF5
WDR76
ADCY6
MOV10
GAK
WDR44
ACP2
THAP11
DNAH14
DSTYK
DIP28
C4ORF29
MYO9A
DABZIP
SNAK
ACSLA
P14K2A
INPP4B
CYP51A1
TRAPPC9
APBA2
CEP295
CD70
PLEKHG4
HNRNPL
BMPR1A
CC2D1A
RTEL1
HELZ2
PLXDC2
TRAPPC10
ACAD10
TM4SF5
TMEM151A
POPK1
KLHDC3
DOX11
PRIM2
L2HGDH
PLD1
B
15
P value=0
12
P value=0
14
P value=0
R=0.54
log2 (CYB561 TPM)
10
R=0.55
12
R=0.54
log2 (CHGB TPM)
log2 (DBH TPM)
10
8
10
8
6
6
5
4
4
2
2
0
0
0
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
log2 (SLC31A1 TPM)
log2 (SLC31A1 TPM)
log2 (SLC31A1 TPM)
6
P value=0
8
P value=0
P value=0
R=0.54
R=0.52
6
R=0.53
log2 (EML5 TPM)
5
log2 (PHOX2B TPM)
6
log2 (TBX20 TPM)
5
4
4
3
4
3
2
2
2
1
1
0
0
0
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
log2 (SLC31A1 TPM)
log2 (SLC31A1 TPM)
log2 (SLC31A1 TPM)
C
CHGB CYB561
EML5
TBX20
D
-DBH
-PHOX2B
ACC (n=79)
Citrate cycle (TCA cycle)
BLCA (n=408)
BRCA (n=1,100)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
Phosphatidylinositol binding
BRGA-LumA (n=568)
BRCA-LumB (n=219)
CESC (n=306)
Phosphatidylinositol phosphate binding
p.adjust
CHOL (n=36)
COAD (n=458)
0.06
DLBC (n=48)
ESCA (n=185)
Poly-purine tract binding
0.04
GBM (n=153)
HNSC (n=522)
HNSC-HPV- (n=422)
0.02
HNSC-HPV+ (n=98)
Mitochondrial matrix
KICH (n=66)
KIRC (n=533)
P>0.05
KIRP (n=290)
Counts
P≤0.05
LGG (n=516)
Coated vesicle
4
LIHC (n=371)
LUAD (n=515)
Spearman_Cor
6
LUSC (n=501)
1
MESO (n=87)
0
Ribonucleoprotein granule
☐ 8
OV (n=303)
-1
PAAD (n=179)
☐
10
PCPG (n=181)-
PRAD (n=498)
Cellular respiration
READ (n=166)
SARC (n=260)
SKCM (n=471)-
SKCM-Metastasis (n=368)
Oxidative phosphorylation
SKCM-Primary (n=103)
STAD (n=415)
TGCT (n=150)
Aerobic respiration
THCA (n=509)
THYM (n=120)
UCEC (n=545)
UCS (n=57)
0.04
0.06
0.08
0.10
0.12
UVM (n=80)
GeneRatio
(SCL31A1). SCL31A1 encodes CTR1, which is essential for the uptake of high-affinity copper (23).
Our study showed that SLC31A1 is expressed in most cancers. Among them, SLC31A1 was highly expressed in BLCA, BRCA, CESC, COAD, DLBC, ESCA, GBM, LGG, HNSC, PAAD, PCPG, READ, STAD, and UCEC.
Conversely, SLC31A1 was lowly expressed in CHOL, KIRC, KIRP, LAML, LIHC, LUAD, LUSC, PRAD, and THCA. Barresi et al. reported high expressions of SLC31A1 in COAD (24). Li et al. showed that SLC31A1 was highly expressed in BRCA, which validates our findings (25). Jiang et al. showed that SLC31A1 was highly
A
B
C
2.0
Relative expression Level
Relative expression Level
4
**
Relative expression Level
2.0
*
1.5
*
**
3
1.5
**
1.0
2
1.0
0.5
1
0.5
0.0
L-O2
SMMC-7721
Huh7
H-97
HepG2
0
0.0
GES1
HGC-27
AGS
MKN-45
NCM460
RKO
LoVo
DLD-1
D
E
F
SLC31A1
32 KD
SLC31A1
32 KD
SLC31A1
32 KD
Tubulin
55 KD
Tubulin
55 KD
L-O2
HepG2
Huh7
SMMC-7721
H-97
GES1
HGC-27
MKN-45
AGS
Tubulin
55 KD
NCM460
RKO
LoVo
DLD-1
G
Relative expression of protein
H
4
8
I
Relative expression of protein
*
Relative expression of protein
2.0
3
6
1.5
**
2
4
1.0
1
2
0.5
0
L-O2
HepG2
Huh7
SMMC-7721
H-97
0
0.0
GES1
HGC-27
MKN-45
AGS
NCM460
RKO
LoVo
DLD-1
Liver cancer
Gastric cancer
Colon cancer
expressed in HNSC epithelial cells, which also validates our findings (26). Song et al. showed that the suppression of the SLC31A1 gene, which is responsible for encoding the primary transmembrane CTR1, effectively diminishes the malignancy of PAAD through the down-regulation of intracellular copper levels. These findings align with our own research outcomes (27). In summary, SLC31A1 is expressed in most cancers.
Our study found that high SLC31A1 expression was associated with good OS in KIRC, while the opposite was true in ACC, BRCA, LGG, MESO, and SKCM. High expression levels of SLC31A1 were associated with good DFS in KIRC and STAD, but the opposite was true in
ACC, LGG, and MESO. Lv et al. showed that SLC31A1 up-regulation has value in predicting the prognosis of SKCM patients. This is consistent with our findings (28). Li et al. found that SLC31A1 may be a promising diagnostic/ prognostic biomarker and predictor of the drug response in breast cancer patients (25).
Further, we evaluated the genetic alterations of SLC31A1 in pan-cancer. The results showed that the amplification frequency of SLC31A1 was the highest in ACC, and its mutation types mainly included missense mutations, truncating mutations, splicing mutations, and fusion. The truncating mutation, S105Y, has the potential to be a putative cancer driver. We also presented the 3D structure
of S105Y.
DNA methylation is known to be abnormal in all forms of cancer (29). Normal cells may be transformed by the onset of driver mutations and then consequently undergo de novo and demethylation processes, thereby initiating a series of programmed changes in gene expression. Alternatively, a subpopulation of normal cells that may have undergone methylation changes, possibly due to senescence, may be preferred targets for oncogenic transformation (30,31). Our study found that the methylation level of SLC31A1 was highly expressed in LUSC and READ; however, it was lowly expressed in HNSC, KIRP, LIHC, PRAD, and UCEC.
Adaptive immune responses may be triggered by innate immune cells, and research into and the development of immunotherapy will be aided by knowledge of the internal workings of cancer (32,33). According to Schalper et al. and Bremnes et al. (34,35), type I immune responses are typically associated with CD8+ T cells and T cells triggered by CD4+ type 1 T helper cells, and they are associated with a positive prognosis for lung cancer patients. According to Marshall et al. (36), type 2 T helper cells, type 17 T helper cells, and Foxp3+ Tregs are frequently linked to poor tumor growth and prognosis. Sautès-Fridman et al. (37) showed that tumor-infiltrating B lymphocytes elicit a strong and advantageous immune response in the majority of solid tumors. According to Petitprez et al. (38), the B-cell count is the best indicator of long-term survival in soft tissue sarcomata. Our study found that SLC31A1 expression was positively correlated with the immune infiltration of B cells in PAAD and PCPG. However, the immune infiltration of B cells was negatively correlated with BRCA-basal, DLBC, MESO, and TGCT. SLC31A1 expression was positively correlated with the CD4+ immune infiltration of T cells in COAD and DLBC. However, it was negatively correlated with the immune infiltration of CD4+ in T cells in CHOL and GBM. SLC31A1 expression was positively correlated with the CD8+ immune infiltration of T cells in DLBC, LGG, PAAD, and UVM. However, it was negatively correlated with the T cell CD8+ immune infiltration in ACC and ESCA. SLC31A1 expression was positively correlated with the immune infiltration of NK cells in BLCA, COAD, DLBC, LIHC, and TGCT. Conversely, it was negatively correlated with the immune infiltration of NK cells in KIRC, KIRP, LGG, MESO, SKCM, and THCA. SLC31A1 expression was positively correlated with the immune infiltration of DCs in BRCA, COAD, DLBC, HNSC-HPV*, KIRC, KIRP, LGG, LUAD, PAAD,
PRAD, SKCM, and TGCT. Conversely, it was negatively correlated with the immune infiltration of DCs in ESCA and LIHC. Finally, SLC31A1 expression was positively correlated with the immune infiltration of Tregs in BLCA, CESC, ESCA, LGG, LUSC, PAAD, SKCM, TGCT, and UVM. Conversely, it was negatively correlated with the immune infiltration of Tregs cells in ACC, DLBC, KIRC, and PCPG. In summary, our study further elucidated the TME of pan-cancer.
Further, we investigated the single-cell level expression of SLC31A1 and conducted an enrichment analysis of the SLC31A1-related genes. We found that SLC31A1 plays an important role in UVM, RB, and OV, etc., which helps us explore further the role of SLC31A1 in tumors. The SLC31A1 enrichment analysis revealed that the SLC31A1- related genes were mainly enriched in the mitochondrial matrix and coated vesicles, which gives us a better understanding of its mechanism. It has been demonstrated that SLC31A1 affects intracellular (Cuprum) Cu2+ levels by acting as a copper importer. High cell membrane trace elements and a higher threshold level of mitochondrial membrane potential (44m) are required for trace element entrance into the mitochondrial matrix. Due to the use of all ATP, the increase of trace elements in the mitochondrial matrix causes a decrease in ATP levels. Ca2+ inflow into the mitochondrial matrix is mostly regulated by the (Natrium) Na+/Ca2+ exchanger (NCX) and the mitochondrial calcium monomer (MCU).
Finally, we used RT-qPCR and WB to verify the expression of SLC31A1 in the pan-cancer. The RT-qPCR results showed that the expression of SLC31A1 in liver and gastric cancers was consistent with our predicted results, but in colon cancer the expression was opposite to our predicted results. This could be caused by the heterogeneity between cells, as our prediction results came from tissue samples in a largely open database, and could also be caused by differences in tissue sample volume or tissue and cellular levels. Based on the above issues, we further validated the protein expression levels in liver, gastric, and colon cancer cells using the WB technique. Contrary to our RT-qPCR results and bioinformatics predictions, the results showed high SLC31A1 expression in liver cancer cells (except SMMC-7721), which may be due to cell-to-cell differences; thus, further validation of its expression in tissues will be necessary in future studies.
The present study observed contrasting outcomes pertaining to SLC31A1 expression at both the mRNA and protein levels in hepatocellular carcinoma cells, which
might be due to the following factors: (I) given that this study serves as a fundamental validation of a bioinformatics analysis, it is plausible that incongruity exists in the expression of this gene at both the transcript and protein levels; (II) intriguingly, this discrepancy could potentially be attributed to a multitude of transcriptome or protein-level modifications, which will subsequently be explored as the next avenue of investigation in our research; (III) in light of the absence of evidence regarding the expression of the SLC31A1 gene in hepatocellular carcinoma in the previous relevant literature, we intend to delve deeper into this finding by conducting further investigations; (IV) despite the low mRNA level, the protein level adequately reflects the clinical significance of SLC31A1, and its elevated expression is strongly associated with clinical prognosis. The expression of gastric cancer cells at the protein level was in full agreement with our RT-qPCR results and bioinformatics predictions, which further improved the reliability of our study. The expression of colon cancer cells at the protein level was consistent with our RT-qPCR results, but contrary to the bioinformatics predictions. This may be because our predictions were based on tissue samples from the database, which may have some differences among the cells. Therefore, we should have further investigated the expression of SLC31A1 at the tissue level, but we lacked the conditions to validate it in tissues, which is the biggest limitation of our study.
Conclusions
In summary, this study analyzed the expression level, methylation level, gene mutation, patient survival prognosis, and immune cell infiltration of the cuprotosis-related gene SLC31A1 in pan-cancer. We also analyzed SLC31A1 at the single-cell transcriptional sequencing level and explored its different biological functions. SLC31A1 may mediate the prognosis of tumor patients by regulating tumor energy metabolism processes, coating vesicles, and affecting the immune microenvironment, and may be a potential genetic, immune, and energy-metabolic-dependent predictive target.
Acknowledgments
We would like to thank the staff at the Key Laboratory of Molecular Diagnostics and Precision Medicine for Surgical Oncology in Gansu Province, and the General Surgery, Clinical Medical Center of Gansu Provincial Hospital for their contributions.
Funding: This work was supported by grants from the National Natural Science Foundation of China (82360498), Gansu Joint Scientific Research Fund Major Project under Grant (23JRRA1537), Gansu Provincial People’s Hospital Key Research Fund (20GSSY1-11), 2021 Central- Guided Local Science and Technology Development Fund (ZYYDDFFZZJ-1), Key Talent Project of Gansu Province of the Organization Department of Gansu Provincial Party Committee (2020RCXM076), Gansu Provincial Youth Science and Technology Fund Program (21JR7RA642), Non-Profit Central Research Institute Fund of Chinese Academy of Medical Sciences (21GSSYC-2), Gansu Key Laboratory of Molecular Diagnosis and Precision Treatment of Surgical Tumors (18JR2RA033), Gansu Provincial People’s Hospital Excellent Master/PHD Student Incubation Program Project Fund (22GSSYD-19), Natural Science Foundation of Gansu Province (21JR11RA186), National Health Care Commission Key Laboratory of Gastrointestinal Tumor Diagnosis and Treatment Open Fund (NHCDP2022022), and Key Project of Science and Technology Innovation Platform Fund of Gansu Provincial People’s Hospital (21gssya-4).
Footnote
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References
1. The global burden of adolescent and young adult cancer in 2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Oncol 2022;23:27-52.
2. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49.
3. Gersten O, Barbieri M. Evaluation of the Cancer Transition Theory in the US, Select European Nations, and Japan by Investigating Mortality of Infectious- and Noninfectious-Related Cancers, 1950-2018. JAMA Netw Open 2021;4:e215322.
4. Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer 2012;12:323-34.
5. Kather JN, Suarez-Carmona M, Charoentong P, et al. Topography of cancer-associated immune cells in human solid tumors. Elife 2018;7:e36967.
6. Tarantino P, Mazzarella L, Marra A, et al. The evolving paradigm of biomarker actionability: Histology- agnosticism as a spectrum, rather than a binary quality. Cancer Treat Rev 2021;94:102169.
7. Ahmed R, Augustine R, Valera E, et al. Spatial mapping of cancer tissues by OMICS technologies. Biochim Biophys Acta Rev Cancer 2022;1877:188663.
8. Tsvetkov P, Coy S, Petrova B, et al. Copper induces cell death by targeting lipoylated TCA cycle proteins. Science 2022;375:1254-61.
9. Jiang Y, Huo Z, Qi X, et al. Copper-induced tumor cell death mechanisms and antitumor theragnostic applications of copper complexes. Nanomedicine (Lond) 2022;17:303-24.
10. Kim H, Wu X, Lee J. SLC31 (CTR) family of copper transporters in health and disease. Mol Aspects Med 2013;34:561-70.
11. Eisses JF, Kaplan JH. The mechanism of copper uptake mediated by human CTR1: a mutational analysis. J Biol Chem 2005;280:37159-68.
12. Maryon EB, Molloy SA, Ivy K, et al. Rate and regulation of copper transport by human copper transporter 1 (hCTR1). J Biol Chem 2013;288:18035-46.
13. Aupič J, Lapenta F, Janoš P, et al. Intrinsically
disordered ectodomain modulates ion permeation through a metal transporter. Proc Natl Acad Sci U S A 2022;119:e2214602119.
14. Wei J, Wang S, Zhu H, et al. Hepatic depletion of nucleolar protein mDEF causes excessive mitochondrial copper accumulation associated with p53 and NRF1 activation. iScience 2023;26:107220.
15. Das A, Ash D, Fouda AY, et al. Cysteine oxidation of copper transporter CTR1 drives VEGFR2 signalling and angiogenesis. Nat Cell Biol 2022;24:35-50.
16. Brady DC, Crowe MS, Turski ML, et al. Copper is required for oncogenic BRAF signalling and tumorigenesis. Nature 2014;509:492-6.
17. Chen GF, Sudhahar V, Youn SW, et al. Copper Transport Protein Antioxidant-1 Promotes Inflammatory Neovascularization via Chaperone and Transcription Factor Function. Sci Rep 2015;5:14780.
18. Grasso M, Bond GJ, Kim YJ, et al. The copper chaperone CCS facilitates copper binding to MEK1/2 to promote kinase activation. J Biol Chem 2021;297:101314.
19. Logeman BL, Wood LK, Lee J, et al. Gene duplication and neo-functionalization in the evolutionary and functional divergence of the metazoan copper transporters Ctr1 and Ctr2. J Biol Chem 2017;292:11531-46.
20. Wezynfeld NE, Vileno B, Faller P. Cu(II) Binding to the N-Terminal Model Peptide of the Human Ctr2 Transporter at Lysosomal and Extracellular pH. Inorg Chem 2019;58:7488-98.
21. Møller LB, Petersen C, Lund C, et al. Characterization of the hCTR1 gene: genomic organization, functional expression, and identification of a highly homologous processed gene. Gene 2000;257:13-22.
22. Kahlson MA, Dixon SJ. Copper-induced cell death. Science 2022;375:1231-2.
23. Lutsenko S. Human copper homeostasis: a network of interconnected pathways. Curr Opin Chem Biol 2010;14:211-7.
24. Barresi V, Trovato-Salinaro A, Spampinato G, et al. Transcriptome analysis of copper homeostasis genes reveals coordinated upregulation of SLC31A1,SCO1, and COX11 in colorectal cancer. FEBS Open Bio 2016;6:794-806.
25. Li X, Ma Z, Mei L. Cuproptosis-related gene SLC31A1 is a potential predictor for diagnosis, prognosis and therapeutic response of breast cancer. Am J Cancer Res 2022;12:3561-80.
26. Jiang X, Ke J, Jia L, et al. A novel cuproptosis-related gene signature of prognosis and immune microenvironment in
head and neck squamous cell carcinoma cancer. J Cancer Res Clin Oncol 2023;149:203-18.
27. Song G, Dong H, Ma D, et al. Tetrahedral Framework Nucleic Acid Delivered RNA Therapeutics Significantly Attenuate Pancreatic Cancer Progression via Inhibition of CTR1-Dependent Copper Absorption. ACS Appl Mater Interfaces 2021;13:46334-42.
28. Lv H, Liu X, Zeng X, et al. Comprehensive Analysis of Cuproptosis-Related Genes in Immune Infiltration and Prognosis in Melanoma. Front Pharmacol 2022;13:930041.
29. Klutstein M, Nejman D, Greenfield R, et al. DNA Methylation in Cancer and Aging. Cancer Res 2016;76:3446-50.
30. Issa JP. Aging and epigenetic drift: a vicious cycle. J Clin Invest 2014;124:24-9.
31. Easwaran H, Tsai HC, Baylin SB. Cancer epigenetics: tumor heterogeneity, plasticity of stem-like states, and drug resistance. Mol Cell 2014;54:716-27.
32. Kishi M, Asgarova A, Desterke C, et al. Evidence of Antitumor and Antimetastatic Potential of Induced Pluripotent Stem Cell-Based Vaccines in Cancer Immunotherapy. Front Med (Lausanne) 2021;8:729018.
33. Shen P, Deng X, Hu Z, et al. Rheumatic Manifestations and Diseases From Immune Checkpoint Inhibitors in Cancer Immunotherapy. Front Med (Lausanne) 2021;8:762247.
34. Schalper KA, Brown J, Carvajal-Hausdorf D, et al. Objective measurement and clinical significance of TILs in non-small cell lung cancer. J Natl Cancer Inst 2015;107:dju435.
35. Bremnes RM, Busund LT, Kilvær TL, et al. The Role of Tumor-Infiltrating Lymphocytes in Development, Progression, and Prognosis of Non-Small Cell Lung Cancer. J Thorac Oncol 2016;11:789-800.
36. Marshall EA, Ng KW, Kung SH, et al. Emerging roles of T helper 17 and regulatory T cells in lung cancer progression and metastasis. Mol Cancer 2016;15:67.
37. Sautès-Fridman C, Lawand M, Giraldo NA, et al. Tertiary Lymphoid Structures in Cancers: Prognostic Value, Regulation, and Manipulation for Therapeutic Intervention. Front Immunol 2016;7:407.
38. Petitprez F, de Reyniès A, Keung EZ, et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature 2020;577:556-60.
Cite this article as: Zhang G, Wang N, Ma S, Tao P, Cai H. Comprehensive analysis of the effects of the cuprotosis- associated gene SLC31A1 on patient prognosis and tumor microenvironment in human cancer. Transl Cancer Res 2024;13(2):714-737. doi: 10.21037/tcr-23-1308