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COMPUTATIONAL
ANDSTRUCTURAL
BIOTECHNOLOGY
JOURNAL
journal homepage: www.elsevier.com/locate/csbj
Identification of SHCBP1 as a potential biomarker involving diagnosis, prognosis, and tumor immune microenvironment across multiple cancers
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Ning Wang ª, Lingye Zhu ª, Liangxing Wangª,*, Zhifa Shenb,*, Xiaoying Huanga,*
ª Division of Pulmonary Medicine, The First Affiliated Hospital of Wenzhou Medical University, Key Laboratory of Heart and Lung, Wenzhou, Zhejiang 325000, PR China b Key Laboratory of Laboratory Medicine, Ministry of Education of China, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, Zhejiang 325035, PR China
ARTICLE INFO
Article history: Received 27 February 2022 Received in revised form 29 May 2022 Accepted 15 June 2022 Available online 18 June 2022
Keywords: SHCBP1 Immuno-oncology Biomarker Diagnosis Prognosis Pan-cancer
ABSTRACT
Shc SH2-domain binding protein 1 (SHCBP1), a protein specific binding to SH2 domain of Src homolog and collagen homolog (Shc), takes part in the regulation of various signal transduction pathways, which has been reported to be associated with tumorigenesis and progression. However, the pathological mech- anisms are not completely investigated. Thus, this study aimed to comprehensively elucidate the poten- tial functions of SHCBP1 in multiple cancer types. The comprehensive analyses for SHCBP1 in various tumors, including gene expression, diagnosis, prognosis, immune-related features, genetic alteration, and function enrichment, were conducted based on multiple databases and analysis tools. SHCBP1 was upregulated in most types of cancers. The results of qRT-PCR had confirmed that SHCBP1 mRNA was sig- nificantly upregulated in lung adenocarcinoma (LUAD) and liver hepatocellular carcinoma (LIHC) cell lines. Based on the receiver operating characteristic (ROC) and survival analysis, SHCBP1 was considered as a potential diagnostic and prognostic biomarker. Furthermore, SHCBP1 expression was linked with tumor immunity and immunosuppressive microenvironment according to the correlation analysis of SHCBP1 expression with immune cells infiltration, immune checkpoint genes, and immune-related genes (MHC genes, chemokines, and chemokines receptors). Moreover, SHCBP1 expression correlated with tumor mutational burden (TMB), microsatellite instability (MSI), and neoantigens. The feature of SHCBP1 mutational landscape in pan-cancer was identified. Finally, we focused on investigating the clin- ical significance and the potential biological role of SHCBP1 in LUAD. Our study comprehensively uncov- ered that SHCBP1 could be identified as an immune-related biomarker for cancer diagnosis and prognosis, and a potential therapeutic target for tumor immunotherapy.
@ 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creative- commons.org/licenses/by-nc-nd/4.0/).
Abbreviations: AUC, area under the curve; CCLE, cancer cell line encyclopedia; HR, hazard ratio; ROC, receiver operating characteristic; SHCBP1, Shc SH2-domain binding protein 1; THPA, the human protein atlas; TMB, tumor mutational burden; TIME, tumor immune microenvironment; 3D, three-dimensional; MSI, microsatellite instability; TAM, tumor-associated macrophages; MDSC, myeloid-derived suppressor cells; CAF, cancer-associated fibroblasts; DEGs, differentially expressed genes; ER, endoplasmic reticulum; GO, Gene Ontology; KEGG, Kyoto encyclopedia of genes and genomes; GSEA, Gene Set Enrichment Analysis; GTEx, genotype-tissue expression; ICIs, immune checkpoint inhibitors; OS, overall survival; DSS, disease specific survival; DFS, disease free survival; PFS, progression free survival; PPI, protein-protein interaction; TCGA, the cancer genome atlas; TIMER 2.0, tumor immune estimation resource, version 2; 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, brain 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 tumors; THCA, thyroid carcinoma; THYM, thymoma; UCEC, uterine corpus endometrial carcinoma; UCS, uterine carcinosarcoma; UVM, uveal melanoma.
* Corresponding authors.
E-mail addresses: wzyxywlx@163.com (L. Wang), shenzhifa@wmu.edu.cn (Z. Shen), zjwzhxy@126.com (X. Huang).
https://doi.org/10.1016/j.csbj.2022.06.039
1. Introduction
Shc SH2-domain binding protein 1 (SHCBP1), a protein specific binding to SH2 domain of Src homolog and collagen homolog (Shc), takes part in the regulation of various signal transduction path- ways exerting a vital role in signal transduction [1]. Recent study evidence has confirmed that SHCBP1 nuclear translocation induced by abnormal EGFR signaling pathway activation can promote the activity of ß-catenin contributing to lung cancer progression [2]. The activation of AKT-GSK3x/B signaling mediated by the interac- tion between FGF13 and SHCBP1 can enhance the growth of tumor cells [3]. SHCBP1-related Wnt pathway activation contributes to the apoptosis resistance resulting in cisplatin resistance in the treatment of lung cancer [4]. The upregulation of SHCBP1 heralds a poor clinical response to trastuzumab therapy in the patients with gastric cancer owing to activation of HER2-SHCBP1-PLK1 axis [5]. SHCBP1 has been considered as an oncogenic gene correlating with the tumorigenesis and progression [6-8]. Owing to the lack of sufficient relevant studies on the pathogenic mechanisms of SHCBP1 across multiple tumors and its influence on tumor immune microenvironment, we conducted a comprehensive anal- ysis for investigating the potential roles of SHCBP1 in TCGA cancers via RNA-sequencing data.
In the current study, we identified SHCBP1 expression charac- teristics, diagnostic and prognostic value across multiple cancer types, and analyzed the relevance between SHCBP1 expression and immune cells infiltration, immune checkpoint genes, and other immune-related markers, including tumor mutational burden (TMB), microsatellite instability (MSI), chemokines, chemokine receptors, MHC genes, and neoantigen. We further conducted the genetic alteration features analyses. The results indicated that SHCBP1 exerted a predictive role in diagnosis and prognosis in a variety of tumor types. Particularly, SHCBP1 expression was corre- lated with immunosuppressive tumor microenvironment. In sum- mary, this study first comprehensively elucidates that SHCBP1 may function as an emerging onco-immunological biomarker involving diagnosis and prognosis and is also expected to become an encour- aging therapeutic target for tumor immunotherapy.
2. Materials and methods
2.1. Gene expression analysis of SHCBP1
RNA-sequencing data and corresponding clinical data among 33 cancer and corresponding normal tissues was acquired from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and Genotype-Tissue Expression (GTEx) databases (https://www.gtex- portal.org/home/index.html). The gene expression data involved tumor cell line was collected from Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/ccle). After log2 transformation, statistical analysis was conducted using R software, and visualized via the “ggplot2” package. Furthermore, the distribution and subcellular localization of SHCBP1 protein expression were determined by immunofluorescence staining based on the Human Protein Atlas (THPA) (https://www.proteinat- las.org/). SHCBP1 RNA expression in different cell cycles was also obtained from THPA database.
2.2. Cell culture and qRT-PCR
Human bronchial epithelial cells (BEAS-2B), human LUAD cell lines (H1975), human normal hepatocytes (LO2), and human LIHC cell lines (MHCC-97H) were purchased from the American Type Culture Collection (ATCC, United States). All cells were cultured in high glucose Dulbecco’s Modified Eagle’s media (DMEM) sup-
plemented by 10% fetal bovine serum (FBS), and 1% Penicillin/ Streptomycin at 37 ℃ in a humidified atmosphere with 5% CO2. Total RNA was isolated from cells using the RNA fast200 Extrac- tion kit (Fastagen Biotech, China), and then RNA concentration was detected using NanoDrop 2000 spectrophotometer (Thermo, United States). After reverse transcription via PrimeScript RT Mas- ter Mix (Takara, Japan), qRT-PCR was conducted using Taq Pro Universal SYBR qPCR Master Mix (Vazyme, China) via the CFX96 Real-Time System (Bio-Rad, United States). GAPDH was used for normalization. Primers were purchased from Sangon Biotech (Shanghai, China) and the sequences were shown in Supplemen- tary data (Table S1).
2.3. Clinical correlation analysis of SHCBP1
The receiver operating characteristic (ROC) analysis method was utilized to estimate the diagnostic value of SHCBP1 in various cancers via the “pROC” package. Then, the value of area under the curve (AUC) (0.5 to 1.0) was calculated. The higher the AUC value, the better the diagnostic value. Generally, AUC value (0.5-0.7, 0.7- 0.9, 0.9-1.0) indicates a low, middle, and high predicted effects, respectively. The relevance between SHCBP1 expression and patients’ survival outcomes such as overall survival (OS), disease specific survival (DSS), disease free survival (DFS), and progression free survival (PFS) were investigated and exhibited via forest plots and Kaplan-Meier curves utilizing the “survival” package. That hazard ratio (HR) was over 1 (HR > 1) indicated that it served as a risk factor for patients’ survival. On the contrary, “HR < 1” indi- cated that it had the protective effect on patients. The connection between SHCBP1 expression and the clinicopathologic characteris- tics in LUAD were exhibited via Sankey diagram accomplished by the “ggalluvial” package. The univariate and multivariate Cox regression analyses in LUAD were performed via the “forestplot” package.
2.4. Immune-related characteristics analysis of SHCBP1
We first evaluated the correlation of SHCBP1 expression with Immune Score, Stromal Score and ESTIMATE Score across multiple cancer types by Spearman’s correlation analysis using the “esti- mate” package. Furthermore, the relevance between SHCBP1 expression and the infiltration level of immune cells, such as CD8 + T cells, M2 subtype of tumor-associated macrophages (M2-TAM), myeloid-derived suppressor cells (MDSC), cancer- associated fibroblasts (CAF), and regulatory T (Treg) cells in various cancers was conducted via tumor immune estimation resource (TIMER 2.0) web server (http://timer.cistrome.org/). Furthermore, the correlation between SHCBP1 expression and immune check- point genes such as CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, SIGLEC15, and TIGIT was observed by Spearman’s cor- relation analysis.
Previously published studies demonstrate that TMB and MSI have been considered as emerging predictive biomarkers closely associated with the response to the treatment of immune check- point inhibitors (ICIs) [9,10]. TMB reflects the presence of somatic mutation sites in tumor genome, which contributes to producing neoantigen and immunogenicity leading to T cell responses [11]. TMB was calculated according to the somatic data acquired from the TCGA database, and MSI score of each cancer was analyzed. Then, the relevance between SHCBP1 expression and TMB or MSI was investigated via the Spearman’s correlation analysis. We also investigated the association of SHCBP1 expression with the count of immune neoantigens and other immune-related genes, including MHC genes, chemokines, and their receptors in pan-cancer using Spearman’s correlation analysis method.
2.5. Genetic alteration analysis of SHCBP1
SHCBP1 mutation features across multiple cancers were ana- lyzed utilizing the cBioPortal platform (https://www.cbioportal. org/) containing multidimensional cancer genomics information [12,13]. “SHCBP1” was input into the “quick selection” module for the exploration of genetic alteration. The alteration frequency of SHCBP1 was observed via the “Cancer Types Summary” module. The mutation sites and three-dimensional (3D) structure could be acquired using “Mutations” module. Waterfall plot displaying genetic alterations of SHCBP1 was obtained via “OncoPrint” mod- ule. In addition, somatic mutation landscape based on SHCBP1 expression in LUAD was constructed utilizing the “maftools” package.
2.6. Construction of gene-gene and protein-protein interaction network
GeneMANIA (https://genemania.org/) is a web interface for establishing gene-gene interaction network [14]. STRING website (https://string-db.org/) is utilized to construct SHCBP1 protein- protein interaction (PPI) network containing 50 related proteins. The main parameters were as follows: active interaction sources (“experiments”), minimum required interaction score [“Low confi- dence (0.150)”], and max number of interactors to show (“no more than 50 interactors”). Subsequently, the visualization of PPI net- work was achieved utilizing Cytoscape (version 3.8.0). The degree score of each node was calculated by “CytoHubba” plug-in.
2.7. Enrichment analysis
To further investigate the functions of these tightly connected proteins and genes in PPI and gene-gene interaction network, Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) analyses were performed via the “ClusterProfiler” package. Gene Set Enrichment Analysis (GSEA) was applied to further inves- tigate the significant pathways between the SHCBP1high and SHCBP1low expression samples, the most obvious signaling path- ways in KEGG and HALLMARK were visualized in plots with certain criteria (p < 0.05) identified to be enrichment significant. In addi- tion, the differentially expressed genes (DEGs) were explored by comparing the gene expression between SHCBP1high and SHCBP1low groups in LUAD samples with setting a threshold of adjusted p < 0.05 and fold change > 2 using the “Limma” package.
2.8. Statistical analysis
The Kruskal-Wallis test was utilized to compare gene expres- sion across different tissues and cancer cell lines, and the Wilcoxon rank sum test was applied to assess gene expression status between tumor tissues and normal tissues. All R packages men- tioned above were operated under R software version v4.0.3, and statistical significance was acknowledged in case of p < 0.05.
3. Results
3.1. Gene expression analysis of SHCBP1 in pan-cancer
We first analyzed the expression level of SHCBP1 in 31 types of normal tissues from the GTEx database. SHCBP1 expression varied across different types of normal tissues, and was generally at a low level according to the value of gene expression (log2(TPM + 1)) (Fig. 1A). In contrast, SHCBP1 was highly expressed in most tumor cell lines based on the value of gene expression (Fig. 1B). Further- more, we evaluated SHCBP1 mRNA expression levels in TCGA
tumors and adjacent normal tissues, which indicated SHCBP1 expression was significantly higher in 17 cancer types, including bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adeno- carcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarci- noma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), LIHC, LUAD, lung squamous cell carcinoma (LUSC), pheochromocytoma and paraganglioma (PCPG), prostate adeno- carcinoma (PRAD), rectum adenocarcinoma (READ), stomach ade- nocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC), while it was significantly lower in kidney chromophobe (KICH), kidney renal clear cell carci- noma (KIRC), and kidney renal papillary cell carcinoma (KIRP) (Fig. 1C). Finally, based on TCGA and GTEx databases, SHCBP1 expression was significantly upregulated in 26 cancer types, involving in adrenocortical carcinoma (ACC), BLCA, BRCA, CESC, CHOL, COAD, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), ESCA, GBM, HNSC, brain lower grade glioma (LGG), LIHC, LUAD, LUSC, ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), PCPG, PRAD, READ, skin cutaneous mela- noma (SKCM), STAD, testicular germ cell tumors (TGCT), THCA, thymoma (THYM), UCEC, and uterine carcinosarcoma (UCS), while it was downregulated in KICH, KIRC, KIRP, and acute myeloid leu- kemia (LAML) (Fig. 1D).
3.2. SHCBP1 intracellular localization, expression, and qRT-PCR validation.
To identify the intracellular localization of SHCBP1, we evalu- ated subcellular distribution of SHCBP1 in A-431 (human epider- moid carcinoma cells), PC-3 (human prostate cancer cells), and U- 2 osteosarcoma (OS) cells through immunofluorescent staining of microtubules, endoplasmic reticulum (ER), and nucleus based on THPA database. As shown in Fig. 2A, SHCBP1 was characterized as - co-localization with DAPI labelling nuclear in A-431, PC-3, and U-2 OS cells, which indicated the subcellular localization of SHCBP1 was in nuclei. Additionally, the immunofluorescent of SHCBP1was also present in the cytoplasm in PC-3 and U-2 OS cells. Further- more, single cell variation analysis revealed that SHCBP1 RNA expression was correlated with the cell cycle (Fig. 2B). In addition, we confirmed that SHCBP1 mRNA was remarkably upregulated in LUAD cell lines (H1975) and LIHC cell lines (MHCC-97H) with the normal cell lines as control by qRT-PCR (Fig. 2C).
3.3. Identification of the diagnostic and prognostic value of SHCBP1 in pan-cancer
AUC is characterized with sensitivity and specificity, which is generally utilized to indicate the intrinsic effectiveness of diagnos- tic tests [15]. The results suggested that SHCBP1 had a high accu- racy (AUC > 0.8) in predicting the diagnosis according to AUC value in ROC curve in 20 cancer types, such as ACC (AUC = 0.833), BLCA (AUC = 0.896), BRCA (AUC = 0.948), CESC (AUC = 0.995), CHOL (AUC = 1.000), COAD (AUC = 0.984), DLBC (AUC = 0.822), ESCA (AUC = 0.950), GBM (AUC = 0.964), HNSC (AUC = 0.962), KICH (AUC = 0.817), LAML (AUC = 0.883), LIHC (AUC = 0.894), LUAD (AUC = 0.850), LUSC (AUC = 0.960), OV (AUC = 0.972), PAAD (AUC = 0.972), READ (AUC = 0.974), STAD (AUC = 0.956), and UCEC (AUC = 0.981) (Fig. 3).
We further investigated the prognostic impact of SHCBP1 on the patients with cancer encompassing OS, DSS, DFS, and PFS analyses via Cox proportional hazards model. As shown in the forest plots (Fig. 4A), high SHCBP1 expression was significantly predictive of worse OS in the patients with ACC, KIRC, KIRP, LGG, LIHC, LUAD, and MESO. The results of DSS analysis indicated that SHCBP1
N. Wang, L. Zhu, L. Wang et al.
A
The expression of SHCBP1 Log2 (TPM+1)
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GTEx
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Kruskal-Wallis test p=0
0
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Adipose Tissue(N=515)
Adrenal Gland(N=128)
Bladder(N=9)
The expression of SHCBP1 Log2 (TPM+1)
Blood Vessel(N=606)
Blood(N=444)
11
Bone Marrow(N=70)
CCLE
Brain(N=1152)
9
Breast(N=179)
Cervix Uteri(N=10)
Computational and Structural Biotechnology Journal 20 (2022) 3106-3119
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5.
Kruskal-Wallis test p=2.2e-25
Colon(N=308)
Esophagus(N=653)
Fallopian Tube(N=5)
Heart(N=377)
biliary_tract(N=7)
Kidney(N=28)
C
bone(N=29)
Liver(N=110)
Lung(N=288)
breast(N=60)
Muscle(N=396)
The expression of SHCBP1 Log2 (TPM+1)
central_nervous_system(N=103)
Nerve(N=278)
Ovary(N=88)
6
TCGA
haematopoietic_and_lymphoid(N=146)
Pancreas(N=167)
5
intestine(N=61)
Pituitary(N=107)
4
kidney(N=36)
Prostate(N=100)
3
Salivary Gland(N=55)
Skin(N=812)
2
liver(N=108)
Small Intestine(N=92)
7
lung(N=107)
Spleen(N=100)
D
C
oesophagus(N=26)
Stomach(N=174)
Testis(N=165)
ACC
ovary(N=52)
Thyroid(N=279)
The expression of SHCBP1 Log2 (TPM+1)
BLCA
pancreas(N=52)
Uterus(N=78)
BRCA
Vagina(N=85)
TCGA+GTEx
CESC
pleura(N=11)
5
CHOL
salivary gland(N=2)
4
COAD
DLBC
skin(N=62)
3
ESCA
soft_tissue(N=21)
2
GBM
stomach(N=38)
normal tissues based on integrated database of TCGA and GTEx. SHCBP1 expression levels are assessed using log2 (TPM + 1). TPM, Transcript per million. (*p < 0.05, ** p < 0.01,
1
HNSC
0
KICH
p < 0.001, and ns, no significance).
Fig. 1. The expression characteristics of SHCBP1. (A) The expression of SHCBP1 in normal tissues based on GTEx database. (B) The expression of SHCBP1 in tumor cell lines based on CCLE database. (C) Comparison of the expression of SHCBP1 in TCGA tumors and adjacent normal tissues. (D) Comparison of the expression of SHCBP1 in tumor and
thyroid(N=12)
KIRC
ns
KIRP
upper_aerodigestive_tract(N=32)
ACC
BLCA
LAML
A
urinary_tract(N=27)
BRCA
LGG
CESC
LIHC
uterus(N=27)
CHOL
LUAD
COAD
LUSC
DLBC
MESO
ESCA
OV
GBM
PAAD
HNSC
PCPG
KICH
PRAD
KIRC
READ
KIRP
SARC
LAML
SKCM
LGG
STAD
LIHC
TGCT
LUAD
THCA
Tumor
Normal
LUSC
THYM
MESO
UCEC
OV
UCS UVM
PAAD
PCPG
3109
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PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
Tumor
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UCEC
UCS
UVM
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SHCBP1/ Microtubules
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Microtubules
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PC-3
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RNA expression across cell cycle
LUAD
LIHC
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SHCBP1 expression
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exerted the hazardous effects on the patients with ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, and SARC (Fig. 4B). The results of DFS anal- ysis confirmed the protective effects of SHCBP1 expression in the patients with ESCA, and risky effects on the patients with KIRP, LIHC, PAAD, PRAD, SARC, THCA, and UCEC (Fig. 4C). In terms of PFS analysis, SHCBP1 served as a risk factor for the patients with ACC, BLCA, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PRAD, SARC, and UVM (Fig. 4D). Therefore, the results demonstrated that higher SHCBP1 expression closely predicted a worse prognosis in several cancers, including KIRP, LIHC, LUAD, etc. Taken together, the results of ROC and prognosis analysis suggested that SHCBP1 had high diagnostic and prognostic value across multiple cancer types.
3.4. Immune-related characteristics of SHCBP1 in pan-cancer
Tumor immune microenvironment (TIME) was largely affected by the infiltrating tumor components, which was one of the key determinants for the outcome of tumor immunotherapy [16]. We first identified the relationship between SHCBP1 expression and immune infiltration via Immune Score (Fig. 5), Stromal Score (Fig. S1) and ESTIMATE Score (Fig. S2) analyses. The top three can- cers with the most significant correlation of SHCBP1 expression with immune infiltration were THCA, KIRC, and LGG. Furthermore, we analyzed the level of immune cells infiltration via several algo- rithms such as TIMER, EPIC, MCPCOUNTER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, and XCELL using TIMER 2.0 database. As shown in Fig. 6A, a significantly positive association was observed between SHCBP1 expression and CD8 + T cells infiltration
in COAD, KIRC, TGCT, THYM, and UVM, while there appeared neg- ative correlation or no significant correlation in many cancer types.
In addition, immunosuppressive microenvironment is consid- ered as a critical factor contributing to uncontrollable tumor growth and even the progression and metastasis resulting in poor immunotherapy response. Thus, we assessed the correlation of SHCBP1 expression with the infiltration levels of M2-TAM, MDSC, CAF, and Treg using TIMER 2.0 database. Interestingly, SHCBP1 was significantly positively linked with the infiltration of M2- TAM, MDSC, CAF, and Treg across most cancers (Fig. 6B). The cur- rent findings revealed that high SHCBP1 expression might be asso- ciated with immunosuppressive microenvironment offering a potential new therapeutic target for tumor immunotherapy.
Next, we identified the potential relevance between SHCBP1 expression and immune checkpoint genes in pan-cancer. Signifi- cantly, SHCBP1 expression was positively related with these immune checkpoint genes, including CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, SIGLEC15, and TIGIT in multiple cancers such as LUAD, KIRC, LIHC, etc. (Fig. 6C). Meanwhile, we conducted the correlation analysis of SHCBP1 expression with TMB and MSI. As shown in Fig. 6D, the expression of SHCBP1 appeared signifi- cantly positively associated with TMB in LUAD, LUSC, STAD, BRCA, UCEC, UCS, etc. In contrast, there was a negative correlation with TMB in THYM and ESCA. For MSI, SHCBP1 expression was posi- tively related with MSI in COAD, LIHC, LUSC, SARC, STAD, and UCEC, and negatively related with MSI in DLBC and TGCT (Fig. 6E). Gene co-expression analyses revealed positive correla- tions of SHCBP1 expression with most chemokines (Fig. 7A), chemokine receptors (Fig. 7B), and MHC genes (Fig. 7C) in
ACC
BLCA
BRCA
CESC
CHOL
1.0
1.0
1.0
1.0
1.0
0.8
Sensitivity (TPR)
Sensitivity (TPR)
0.8
0.8
0.8-
Sensitivity (TPR)
Sensitivity (TPR)
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
SHCBP1
AUC: 0.833
0.2
SHCBP1
AUC: 0.896
0.2
SHCBP1
AUC: 0.948
0.2-
SHCBP1
AUC: 0.995
0.2
SHCBP1
0.0
CI: 0.766-0.899
AUC: 1.000
0.0 0.2 0.4 0.6 0.8 1.0
0.0
CI: 0.820-0.973
CI: 0.935-0.961
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
CI: 0.984-1.000
CI: 1.000-1.000
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
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
COAD
DLBC
ESCA
GBM
HNSC
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
SHCBP1
AUC: 0.984
0.2
SHCBP1
AUC: 0.822
0.2-
SHCBP1
AUC: 0.950
0.2
SHCBP1
0.2
SHCBP1
0.0
CI: 0.977-0.991
0.0
CI: 0.784-0.860
CI: 0.935-0.965
AUC: 0.964
CI: 0.951-0.978
AUC: 0.962
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
0.0
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
CI: 0.945-0.978
0.0
0.2
.2 0.4 0.6 0.8
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1.0
1-Specificity (FPR)
1-Specificity (FPR)
KICH
LAML
LIHC
LUAD
LUSC
1.0
1.0
1.0
1.0
1.0
Sensitivity (TPR)
0.8
0.8
Sensitivity (TPR)
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
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
SHCBP1
AUC: 0.817
0.2.
SHCBP1
AUC: 0.883
0.2
SHCBP1
AUC: 0.894
0.2.
SHCBP1
AUC: 0.850
0.2
SHCBP1
CI: 0.732-0.903
CI: 0.840-0.927
CI: 0.865-0.922
CI: 0.824-0.877
AUC: 0.960
0.0
0.0
CI: 0.945-0.975
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-
0.0
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
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
OV
PAAD
READ
STAD
UCEC
1.0
1.0
1.0
1.0.
1.0
0.8.
0.8-
0.8.
0.8-
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
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.
SHCBP1
AUC: 0.972
0.2.
SHCBP1
AUC: 0.972
0.2-
SHCBP1
0.2.
SHCBP1
AUC: 0.956
0.2-
SHCBP1
0.0
CI: 0.958-0.985
0.0
CI: 0.954-0.990
AUC: 0.974
CI: 0.958-0.989
0.0
CI: 0.941-0.971
AUC: 0.981
CI: 0.965-0.996
0.0
0.2
0.4
0.6
0.8
1.0
1.0
0.0
0.0
0.2
0.4
0.6
0.8
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
0.0
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)
pan-cancer. Meanwhile, we confirmed that SHCBP1 expression was significantly positively associated with the count of immune neoantigens in LUAD, OV, BRCA, UCEC, STAD, PRAD, and LGG (Fig. S3). All these results above demonstrated that high SHCBP1 expression is significantly relevant to tumor immunity.
3.5. Genetic alteration analysis of SHCBP1
SHCBP1 mutation features in diverse types of tumor samples were explored based on the cBioPortal database. As shown in Fig. S4A, the highest alteration frequency of SHCBP1 emerged in the UCEC patients, of which “Mutation” was the major type. And the highest “amplification” type alteration occurred in the PRAD patients with an alteration frequency of ~ 3%. Moreover, the genetic alteration frequency of LUAD cases (containing “Deep Dele- tion” type) was higher than LUSC. The types, sites and correspond- ing domain of the SHCBP1 mutations were observed in Fig. S4B.
The 3D structure of SHCBP1 protein with the mutation sites (G446, T473, A487, and X489) mapped was presented in Fig. S4C. Similarly, the frequency and pattern of SHCBP1 alteration, includ- ing mutation, amplification, deep deletion, and structural variant were displayed via waterfall plot (Fig. S4D).
3.6. Construction of PPI and genes interaction network, and enrichment analysis
PPI network involved in 50 SHCBP1-interacting proteins was established utilizing STRING and visualized by Cytoscape (Fig. 8A). Each node was present as a different depth of color reflecting the extent of interaction, which revealed the interaction strength between the proteins. The gene-gene interaction network was achieved via Genemania, which exhibited top 20 most fre- quently altered genes linking with SHCBP1 (Fig. 8B). Subsequently, we performed the functional enrichment analysis (GO and KEGG)
A
| Cancer | Pvalue | Overall survival Hazard Ratio(95% CI) | |
|---|---|---|---|
| ACC | 2e-04 | 5.24826(2.21423,12.43964) | |
| BLCA | 0.8281 | 1.03316(0.76965,1.3869) | |
| BRCA | 0.1448 | 1.26709(0.92175,1.74182) | |
| CESC | 0.2868 | 1.28883(0.80805,2.05569) | |
| CHOL | 0.9904 | 1.0059(0.38514,2.62716) | |
| COAD | 0.1354 | 0.74268(0.50258,1.09749) | |
| DLBC | 0.1895 | 3.07298(0.57439,16.44041) | |
| ESCA | 0.9083 | 0.97139(0.59263,1.59223) | |
| GBM | 0.5459 | 1.11914(0.77667,1.61261) | |
| HNSC | 0.1112 | 1.24273(0.95114,1.62372) | |
| KICH | 0.0947 | 3.82248(0.7933,18.41845) | |
| KIRC | 0.0099 | 1.49041(1.1007,2.0181) | |
| KIRP | 0.0011 | 3.01841(1.55287,5.86706) | |
| LAML | 0.7267 | 0.92757(0.60851,1.41393) | |
| LGG | <0.0001 | 3.14126(2.1066,4.68409) | |
| LIHC | 0.0178 | 1.52253(1.07532,2.15574) | |
| LUAD | 2e-04 | 1.7635(1.30887,2.37605) | |
| LUSC | 0.1817 | 0.83136(0.63398,1.09019) | |
| MESO | <0.0001 | 4.71656(2.80552,7.92934) | |
| OV | 0.6802 | 0.9467(0.72962,1.22837) | |
| PAAD | 0.1097 | 1.39862(0.92714,2.10986) | |
| PCPG | 0.0978 | 5.99318(0.71959,49.91468) | |
| PRAD | 0.1296 | 2.94713(0.72846,11.92318) | |
| READ | 0.2656 | 0.63639(0.28718,1.41021) | |
| SARC | 0.0516 | 1.48787(0.99721,2.21994) | |
| SKCM | 0.157 | 1.21493(0.92783,1.59088) | |
| STAD | 0.3983 | 0.86778(0.62447,1.2059) | |
| TGCT | 0.2118 | 4.36985(0.43172,44.23152) | |
| THCA | 0.4092 | 1.53242(0.55612,4.22263) | |
| THYM | 0.0542 | 0.2085(0.04226,1.02869) | |
| UCEC | 0.8018 | 1.05408(0.69863,1.59038) | |
| UCS | 0.7784 | 1.10358(0.55552,2.19231) | |
| UVM | 0.3253 | 1.52095(0.65958,3.50721) | |
0.04226 10 15 20 25 30 35 40 45 50 Hazard Ratio
C
| Disease free survival Cancer Pvalue Hazard Ratio(95% CI) | |||
|---|---|---|---|
| ACC | 0.4593 | 1.57347(0.4737,5.2266) | |
| BLCA | 0.9705 | 0.98672(0.48579,2.00421) | |
| BRCA | 0.0885 | 1.4605(0.94452,2.25834) | |
| CESC | 0.2606 | 1.57419(0.71404,3.47046) | |
| CHOL | 0.6464 | 0.74657(0.2142,2.60207) | |
| COAD | 0.9081 | 1.04958(0.46136,2.38775) | |
| ESCA | 0.0351 | 0.36081(0.1398,0.9312) | |
| HNSC | 0.8896 | 0.94829(0.44811,2.00677) | |
| KICH | 0.4871 | 0.42615(0.03844,4.72393) | |
| KIRC | 0.6140 | 0.76678(0.27325,2.15168) | |
| KIRP | 0.0017 | 4.81(1.80413,12.824) | |
| LGG | 0.3874 | 1.48382(0.60639,3.63087) | |
| LIHC | 0.0094 | 1.54932(1.1132,2.15629) | |
| LUAD | 0.0740 | 1.46903(0.96337,2.2401) | |
| LUSC | 0.6983 | 0.9054(0.54775,1.49657) | |
| MESO | 0.1626 | 3.48807(0.60403,20.14245) | |
| OV | 0.0886 | 0.73443(0.51481,1.04773) | |
| PAAD | 0.0038 | 4.00553(1.56515,10.25095) | |
| PCPG | 0.2428 | 3.88229(0.39865,37.80821) | |
| PRAD | 0.0424 | 2.18179(1.02725,4.63395) | |
| READ | 0.4984 | 1.7986(0.32877,9.83962) | |
| SARC | 0.0008 | 2.38398(1.43111,3.97131) | |
| STAD | 0.3661 | 0.74056(0.38608,1.42051) | |
| TGCT | 0.4643 | 0.74139(0.33268,1.65224) | |
| THCA | 0.0056 | 3.60415(1.45459,8.93029) | |
| UCEC | 0.0257 | 1.85758(1.07806,3.20073) | |
| UCS | 0.3012 | 0.47798(0.11794,1.93708) | |
0.03844 7.5 12.5 20 25 30 35 38 Hazard Ratio
B
| Disease specific survival | ||
|---|---|---|
| Cancer | Pvalue | Hazard Ratio(95% CI) |
| ACC | 5e-04 | 4.80337(1.99863,11.54412) |
| BLCA | 0.3984 | 1.16718(0.81528,1.67097) |
| BRCA | 0.0532 | 1.53446(0.99408,2.36857) |
| CESC | 0.5666 | 1.16805(0.68667,1.98687) |
| CHOL | 0.9764 | 1.01556(0.36492,2.82627) |
| COAD | 0.109 | 0.66591(0.40503,1.0948) |
| DLBC | 0.2125 | 4.39695(0.42867,45.10074) |
| ESCA | 0.9862 | 0.99488(0.55566,1.78127) |
| GBM | 0.4978 | 1.14498(0.77412,1.69351) |
| HNSC | 0.0659 | 1.38682(0.97874,1.96504) |
| KICH | 0.0794 | 6.6554(0.8004,55.34009) |
| KIRC | 1e-04 | 2.24229(1.49376,3.36589) |
| KIRP | 9e-04 | 29.44479(4.00029,216.73314) |
| LGG | <0.0001 | 3.14519(2.06744,4.78475) |
| LIHC | 7e-04 | 2.24135(1.40695,3.5706) |
| LUAD | <0.0001 | 2.29449(1.54501,3.40753) |
| LUSC | 0.9611 | 1.01053(0.66316,1.53987) |
| MESO | <0.0001 | 4.69281(2.40629,9.15204) |
| OV | 0.4826 | 0.90444(0.6833,1.19715) |
| PAAD | 0.0642 | 1.55476(0.97423,2.48122) |
| PRAD | 0.1917 | 4.3363(0.47943,39.22057) |
| READ | 0.6983 | 0.81193(0.28313,2.32839) |
| SARC | 0.0196 | 1.69911(1.08853,2.65218) |
| SKCM | 0.079 | 1.29538(0.9705,1.72902) |
| STAD | 0.2896 | 0.79719(0.52406,1.21269) |
| TGCT | 0.5315 | 2.15235(0.19507,23.74781) |
| THCA | 0.6644 | 0.71786(0.16056,3.20954) |
| THYM | 0.183 | 0.20772(0.02056,2.09909) |
| UCEC | 0.6202 | 1.13684(0.68453,1.88799) |
| UCS | 0.8405 | 1.07649(0.52508,2.20698) |
| UVM | 0.1889 | 1.81108(0.74673,4.3925) |
0.02056
50 75 100
150 200
Hazard Ratio
D
| Progression free survival Cancer Pvalue Hazard Ratio(95% CI) | |||
|---|---|---|---|
| ACC | 3e-04 | 3.385(1.75446,6.53091) | |
| BLCA | 0.0177 | 1.44329(1.06574,1.95459) | |
| BRCA | 0.082 | 1.33587(0.96389,1.8514) | |
| CESC | 0.297 | 1.28072(0.80444,2.039) | |
| CHOL | 0.7192 | 1.17586(0.48619,2.84387) | |
| COAD | 0.1946 | 0.79005(0.55333,1.12803) | |
| DLBC | 0.6249 | 1.39572(0.36675,5.3116) | |
| ESCA | 0.6575 | 0.90423(0.57943,1.4111) | |
| GBM | 0.9708 | 1.00676(0.70176,1.44432) | |
| HNSC | 0.0724 | 1.29721(0.97659,1.7231) | |
| KICH | 0.0973 | 3.07436(0.8151,11.5957) | |
| KIRC | 4e-04 | 1.79188(1.29876,2.47223) | |
| KIRP | <0.0001 | 3.9909(2.14266,7.4334) | |
| LGG | <0.0001 | 2.00957(1.49862,2.69472) | |
| LIHC | 4e-04 | 1.7064(1.26884,2.29486) | |
| LUAD | 3e-04 | 1.66083(1.25796,2.19273) | |
| LUSC | 0.8888 | 0.97707(0.70586,1.35249) | |
| MESO | 4e-04 | 2.75149(1.57303,4.8128) | |
| OV | 0.2138 | 0.85925(0.67649,1.09138) | |
| PAAD | 0.0093 | 1.68292(1.13696,2.49104) | |
| PCPG | 0.1218 | 2.03505(0.82744,5.00508) | |
| PRAD | 4e-04 | 2.17635(1.41983,3.33598) | |
| READ | 0.9822 | 0.99258(0.51534,1.91177) | |
| SARC | 0.0057 | 1.60848(1.14799,2.25369) | |
| SKCM | 0.2906 | 1.12835(0.90193,1.41161) | |
| STAD | 0.31 | 0.83248(0.58434,1.186) | |
| TGCT | 0.4817 | 0.78185(0.3939,1.55188) | |
| THCA | 0.0711 | 1.65505(0.95758,2.86056) | |
| THYM | 0.2466 | 0.58582(0.23708,1.44759) | |
| UCEC | 0.2954 | 1.20791(0.84796,1.72066) | |
| UCS | 0.6798 | 0.87016(0.4496,1.68412) | |
| UVM | 0.0131 | 2.86267(1.24668,6.57338) | |
W
0.23708
2
3
4
5
6
2 7
8
9
1011 112
Hazard Ratio
Fig. 4. The forest plots of univariate Cox regression analyses in (A) overall survival, (B) disease specific survival, (C) disease free survival, and (D) progression free survival. The red mark demonstrates that SHCBP1 expression was significantly associated with patients’ prognosis. That hazard ratio (HR) is over 1 (HR > 1) indicates that it serves as a risk factor for patients’ survival. HR < 1 indicates that it has the protective effect on patients. CI, confidence interval. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
in PPI and genes interaction network, respectively. The results revealed that the proteins in PPI network were mainly enriched in the events such as mitosis, tubulin binding, microtubule binding, and ATPase activity (Fig. 8C). The genes in the network were mainly enriched in mitosis process, and participated in several
pathways such as microRNAs in cancer, ErbB signaling pathway, and EGFR tyrosine kinase inhibitor resistance (Fig. 8D).
GSEA analysis was conducted to investigate the potential sig- naling pathways in KEGG and HALLMARK from high and low SHCBP1 expression samples (Fig. 8E-H). Results of the functional
ACC
BLCA
BRCA
CESC
CHOL
COAD
8
Spearman’s rho:
R =- 0.303
Spearman’s rho:
Spearman’s rho:
Spearman’s rho:
R=0.111
Spearman’s rho:
Spearman’s rho:
p=0.00677
R=0.161
R =- 0.08
R=0.015
R=0.039
6
p=0.000232
p=0.00112
p=0.164
p=0.933
p=0.4
..
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ESCA
GBM
HNSC
KICH
KIRC
8
Spearman’s rho:
Spearman’s rho:
R =- 0.142 p=0.0722
Spearman’s rho:
Spearman’s rho: R =- 0.127
Spearman’s rho:
Spearman’s rho: R=0.373 p=6.36e-19
R =- 0.131
R =- 0.356
R =- 0.059
6
p=0.372
p=2.81e-06
p=0.00454
p=0.642
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KIRP
LGG
LIHC
LUAD
LUSC
8
Spearman’s rho:
R =- 0.009
Spearman’s rho: R=0.258
Spearman’s rho:
Spearman’s rho:
R=0.111 p=0.0323
R=0.013 p=0.775
Spearman’s rho:
R =- 0.232
6
p=0.879
p=2.23e-09
p=1.42e=07
4
log2(SHCBP1 TPM+1)
2
0
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PAAD
PCPG
PRAD
READ
8
Spearman’s rho:
Spearman’s rho: R=0.183 p=0.000337
Spearman’s rho: R=0.101
Spearman’s rho: R =- 0.027 p=0.712
Spearman’s rho:
R=0.026
R=0.19
Spearman’s rho: R =- 0.145 p=0.0609
6
p=0.811
p=0.179
p=2.18e-05
4 .
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SKCM
STAD
TGCT
THCA
THYM
8
Spearman’s rho:
R =- 0.083
Spearman’s rho: R =- 0.041
Spearman’s rho: R =- 0.163 p=0.00156
Spearman’s rho: R=0.188
Spearman’s rho: R=0.481
Spearman’s rho: R=0.145 p=0.115
6
p=0:182
p=0.378
p=0.0185
p=0
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UCS
UVM
8
Spearman’s rho: R =- 0.126
Spearman’s rho:
R=0.011
Spearman’s rho:
6
p=0.0033
p=0.937
R=0.097
p=0.389
4
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Immune Score
| LAML | ||||
|---|---|---|---|---|
| Spearman's rho: | ||||
| R=0.005 p=0.951 | 88 | |||
Fig. 5. Spearman’s correlation analysis showing the association between SHCBP1 expression levels (log2(TPM + 1)) and Immune Score across 33 different TCGA cancer types. TPM, Transcript per million.
enrichment of KEGG terms revealed that high SHCBP1 expression was linked with P53 signaling pathway, regulation of actin cytoskeleton, and cell cycle. HALLMARK terms demonstrated that high SHCBP1 expression was mainly involving in complement, inflammatory response, and G2M checkpoint.
3.7. Clinical characteristics of SHCBP1 expression in LUAD
In addition, we focused on investigating the clinical characteris- tics of SHCBP1 in LUAD. We first confirmed that SHCBP1 was remarkably upregulated in tumor tissues compared to the normal tissues via TCGA and GTEx database (Fig. 9A). The SHCBP1 expres- sion was dramatically associated with different pathological stages of LUAD patients (Fig. 9B). The distribution tendency between SHCBP1 expression and the clinicopathological characteristics
involving in ages, stages, and survival state in LUAD patients was exhibited via Sankey diagram (Fig. 9C). High SHCBP1 expression was identified to be related to poor clinical outcomes in LUAD patients according to Kaplan-Meier survival curves of OS, DSS, DFS, and PFS (Fig. 9D-G). Subsequently, SHCBP1 expression was identified as an independent prognostic factor for LUAD patients’ OS through the univariate and multivariate Cox regression analy- ses (Fig. 9H and I).
3.8. Identification of the potential biological function of SHCBP1 expression in LUAD
To begin with, the landscape of somatic mutations in LUAD cohort revealed that the samples with high SHCBP1 expression had a high frequency of gene mutations such as TP53, TTN,
A
B
C
-T cell CD8+_MCPCOUNTER
* *
T cell CD8+_TIMER -T cell CD8+_EPIC
-T cell CD8+_CIBERSORT
-T cell CD8+_CIBERSORT-ABS
UVM
-T cell CD8+_QUANTISEQ
-T cell CD8+ central memory_XCELL
T cell CD8+ effector memory_XCELL
UCS
*
T cell CD8+_XCELL
-T cell CD8+ naive_XCELL
X p > 0.05
[X] p > 0.05
UCEC
**
**
**
**
p ≤ 0.05
p ≤ 0.05
THYM
**
**
**
**
**
*
Partial_Cor
Partial_Cor
M2-TAM
THCA
p < 0.05
**
** **
**
**
**
**
1
1
MDSC
** p < 0.01
0
0
CAF
Treg
TGCT
**
**
** **
*
**
-1
-1
Correlation
STAD
**
0.50
ACC (n=79)
ACC (n=79)
SKCM
0.25
BLCA (n=408)-
* *
*
BLCA (n=408)
0.00
BRCA (n=1100)
BRCA (n=1100)
SARC
-0.25
**
BRCA-Basal (n=191)
BRCA-Basal (n=191)
-0.50
BRCA-Her2 (n=82)
BRCA-Her2 (n=82)
READ
** *
BRCA-LumA (n=568)
BRCA-LumA (n=568)
PRAD
**
**
*
**
**
**
BRCA-LumB (n=219)
BRCA-LumB (n=219)
CESC (n=306)-
PCPG
CESC (n=306)
CHOL (n=36)-
PAAD
**
*
*
*
*
**
*
CHOL (n=36)
COAD (n=458)
COAD (n=458)
OV
**
** **
**
**
**
**
DLBC (n=48)
DLBC (n=48)-
ESCA (n=185)
ESCA (n=185)
MESO
*
**
*
**
*
GBM (n=153)
GBM (n=153)-
LUSC *
*
HNSC (n=522)
HNSC (n=522)
LUAD
**
**
**
**
** **
** **
HNSC-HPV- (n=422)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
LIHC
**
**
**
**
**
**
**
HNSC-HPV+ (n=98)
KICH (n=66)
KICH (n=66)-
LGG
** **
**
**
**
*
KIRC (n=533)
KIRC (n=533)
KIRP (n=290)-
LAML
**
**
KIRP (n=290)
LGG (n=516)
LGG (n=516)
KIRP
**
LIHC (n=371)
LIHC (n=371)
KIRC
**
**
**
* *
**
** **
LUAD (n=515)
LUAD (n=515)
*
LUSC (n=501)
LUSC (n=501)
KICH
MESO (n=87)
MESO (n=87)
HNSC
**
*
OV (n=303)
OV (n=303)
GBM
* **
*
*
PAAD (n=179)
PAAD (n=179)-
PCPG (n=181)
PCPG (n=181)
ESCA
PRAD (n=498)
PRAD (n=498)
*
READ (n=166)
READ (n=166)
DLBC
COAD
**
**
*
**
**
*
**
SARC (n=260)
SARC (n=260)
SKCM (n=471)
SKCM (n=471)
CHOL
SKCM-Metastasis (n=368)
SKCM-Metastasis (n=368)
SKCM-Primary (n=103)
CESC
*
SKCM-Primary (n=103)
STAD (n=415)
STAD (n=415)
BRCA ** **
**
**
**
**
**
**
TGCT (n=150)
TGCT (n=150)
THCA (n=509)
BLCA **
**
**
**
*
**
**
**
THCA (n=509)
ACC
*
**
*
*
THYM (n=120)
THYM (n=120)
UCEC (n=545)
UCEC (n=545)
CD274
CTLA4
HAVCR2
LAG3
PDCD1
PDCD1LG2
SIGLEC15
TIGIT
UCS (n=57)
UCS (n=57)
UVM (n=80)
UVM (n=80)
D
E
Tumor mutational burden
Microsatellite instability
UVM,P=0.88
BLCA,P=0.00014
UVM,P=0.088
BLCA,P=0.16
BRCA,P=0.59
UCS,P=0.014
BRCA,P=4.4e-31
0.5
CESC,P=0.45
UCS,P=0.22
UCEC,P=3.1e-08
0.29
CESC,P=0.22
UCEC,P=6e-09
CHOL,P=0.17
CHOL,P=0.46
THYM,P=9.3e-09
COAD,P=6.1e-07
THYM,P=0.86
COAD,P=4.9e-08
THCA,P=0.13
DLBC,P=0.053
THCA,P=0.58
DLBC,P=0.044
0
TGCT,P=0.26
ESCA,P=0.031
TGCT,P=0.041
ESCA,P=0.2
STAD,P=1.4e-15
GBM,P=0.21
STAD,P=9.7e-08
GBM,P=0.4
5
29
SKCM,P=0.0016
HNSC,P=0.37
SKCM,P=0.058
HNSC,P=0.43
SARC,P=6.9e-10
KICH,P=0.06
SARC,P=0.00013
KICH,P=0.37
READ,P=0.13
KIRC,P=0.012
READ,P=0.059
KIRC,P=0.077
PRAD,P=2.9e-13
KIRP,P=0.61
PRAD,P=0.98
KIRP,P=0.11
PCPG,P=0.14
LAML,P=0.66
PCPG,P=0.42
LAML,P=0.94
PAAD,P=0.00035
LGG,P=1.1e-17
PAAD,P=0.32
LGG,P=0.58
OV,P=0.066 MESO,P=0.4
LIHC,P=0.54
OV,P=0.5
LIHC,P=0.04
LUAD,P=1.9e-15
MESO,P=0.15
LUAD,P=0.99
LUSC,P=0.0018
LUSC,P=0.00017
A
B
CCL14
**
*
CXCR5
CCL16
CCR9
* CCL19
CCR8
CCL21
CXCR4
CCL17
* CCR4
*CXCL14
CCR7
XCLI
XCRI
CCL5
CCR1
XCL2
*CXCL16
CXCR3
CCR2
CCL2
CCR5
CCL23
CXCR6
CCL13
*CCR6
CCLII
CCL22
CX3CR1
CCR10
CCL18
CXCR1
CCL24
CCL1
CCR3
CXCL13
CCL3 CCL4 CXCL9
THCA KIRC
LGG
UVM
OV
PAAD
LIHC
PRAD
THYM
TOCT
STAD
ESCA
LUSC
ACC
GBM
MESO
LUAD
BLCA
BRCA
DLBC
KICH CHOL
READ
COAD
SKCM
UCS
UCEC
SARC
CESC
HNSC
KIRP
PCPG
CXCR2
LAMI
CXCL10
correlation coefficient
p Value
CXCL11
CCL26
CCL20
CCL7
1.0-0.5
0.0
0.5
1.0
0.0
0.5
1.0
CCL8
CXCL2
C
CXCL3
CXCL5
CXCL8
CXCLI
TAPI
CXCL6
TAP2
CCL27
HLA-DMB
CCL15
HLA-DMA
CXCL17
* HLA-DPB1
CX3CL1
HLA-DQAI
CCL28
HLA-DOA
CCL25
HLA-DRB1
HLA-DPAI
THCA
KIRC
LGG
UVM
OV
PAAD
LIHC
PRAD
THYM
TGCT
STAD
ESCA
LUSC
ACC
GBM MESO
LUAD
BLCA
BRCA
DLBC
KICH CHOL
CXCL12
READ
COAD
LAML
SKCM
UCS
UCEC
SARC
CESC
HNSC
KIRP
PCPG
* HLA-DRA
HLA-DOBI
HLA-DOB
HLA-DQA2
correlation coefficient
p Value
TAPBP
B2M
HLA-C
0.0
0.5
1.0
0.0
0.5
1.0
HLA-E
-1.0-0.5
HLA-F
HLA-G
HLA-A
HLA-B
THCA
KIRC
LGG
UVM
OV
PAAD
LIHC
PRAD
THYM
TGCT
STAD
ESCA
LUSC
ACC
GBM
MESO
LUAD
BLCA
BRCA
DLBC
KICH
CHOL
READ
COAD
LAML
SKCM
UCS
UCEC
SARC
CESC
HNSC
KIRP
PCPG
correlation coefficient
pValue
-1.0-0.5 0.0 0.5 1.0
0.0
0.5
1.0
MUC16, etc. (Fig. 10A). The immune checkpoint genes were all upregulated in LUAD patients with high SHCBP1 expression (Fig. 10B). The results of DEGs analysis between high and low SHCBP1 expression in LUAD identified 199 genes upregulated and 90 genes downregulated (Fig. 10C and D). The detailed genes infor- mation can be inquired in the Supplementary data (Table S2). Then, enrichment analysis results suggested that upregulated DEGs were primarily connected with organelle fission, nuclear division, chro- mosome segregation, and mitotic nuclear division via GO analysis (Fig. 10E) and cell cycle via KEGG analysis (Fig. 10F).
4. Discussion
It is well established that SHC1 is considered as a key protein exerting a vital role in participating in many signal transduction pathways. SHCBP1, present in the cytoplasm, can bind specifically to SH2 domain of SHC1 participating in intracellular signal trans- duction and the downstream biological events. Accumulated evi- dences have demonstrated that SHCBP1 is closely associated with cell division, cell differentiation and proliferation. According to research findings, SHCBP1 is significantly related with the development and progression of multiple diseases including can- cers. Previous studies have found that SHCBP1 plays significant roles in various cancer types [17-20]. The potential diagnostic and prognostic value of SHCBP1 and the molecular mechanisms underlying in tumor immune microenvironment remain to be explored. Thus, the comprehensive pan-cancer analysis is crucial and meaningful for determining the potential biological function of SHCBP1 among different cancers. Our present study identified the molecular expression, clinical characteristics, genetic alter- ations of SHCBP1 and investigated the correlation of SHCBP1 with immune cells infiltration, immune checkpoint genes expression,
TMB, MSI, immune-related genes, and neoantigen across TCGA cancer types.
Different cancer types appear distinct genetic heterogeneity owing to the genetic and epigenetic alterations during tumor evo- lution [21]. Gene expression profiling analysis can be applied to explore relevant biomarkers for diagnosis and prognosis in cancer [22]. In this study, we determined SHCBP1 expression features, and discovered that SHCBP1 was upregulated in almost all TCGA can- cers, which indicated that SHCBP1 might serve as an oncogene for tumor development and progression. SHCBP1 had a high diag- nostic value according to the AUC value in ROC curve across most cancer types, which suggested that SHCBP1 could have high sensi- tivity and specificity in distinguishing tumor patients from healthy individuals. Furthermore, clinical prognosis analysis suggested that SHCBP1 functioned as a risk factor predicting worse prognosis in the patients with ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, and SARC based on DSS analysis. These results implied SHCBP1 could be considered as a significant diagnostic and prognostic biomarker in several cancer types.
Recent years have witnessed noteworthy progresses and break- throughs in cancer immunotherapy and there has achieved greater improvement in the clinical prognosis of the patients with multiple types of cancer. TME, particularly TIME, is becoming the fo- cus of cancer immunity research. Immune cells infiltration per- forms a vital role in anti-tumor immunotherapy. However, the immunosuppressive status of TME could hinder the antitumor immunity leading to poor prognosis and therapy resistance. The cells infiltration including CAFs, Tregs, TAMs, and MDSCs con- tribute to the formation of immunosuppressive TME. Increasing evidence has demonstrated that TAM, particularly M2-TAM, serve as key drivers participating in the establishment of an immune- suppressive TME through hampering the differentiation and func- tion of T cells, and promoting the recruitment of MDSCs and Treg
A
PPI
B
Genemania
ACOT9
GOPC
CNEP1R1
SHCBP1L
KLHDC4
ZNF3
C8orf76
PCLAF
GPC5
ATAD5
FERD3L
CAPN13
THOC5
PDGFRL
TRAM1
PDIA4
MTUS2
CCAR2
KIFC1
HEL
LIN54
RRC46
MNTD
PVRL4
PRKCB
DDX49
KIF23
MORC2 FAM83
CENP
ANLN
JUP88 WDR75
SHC1
SHCBP
C16orf78
RACGAP1
SHCBP1
SHC1
WDR76 MKI67
CDCA
KIF14
CGAP
SERTAD4
N4BP
KIF4A
COL28A1
ATAD3A
PKHD1
PHF7
NCENF
HAUS5
ECT2
SS18
TRIM7
FBXO10
FBXO11
PKHD1L1
MTFR2
NSL1
FAM64A
EID2B
CUL9
KIF23
SRSF11
TRIM71
ZUP1
ZUFSP
PKD2L 1
ZADH2
FBXO10
C
D
PPI
Gene network
organelle fission
cytokinesis
chromosome segregation
00
cytoskeleton-dependent cytokinesis ·
BP
0
mitotic nuclear division
p.adjust
p.adjust
0.006
spindle midzone assembly
0.04
0.004
0.03
0.02
spindle
0.002
spindle
0.01
microtubule
CC
Counts
mitotic spindle
CC
Counts
6
anchored component of plasma membrane
2.0
8
2.5
midbody
10
3.0
12
MicroRNAs in cancer
3.5
tubulin binding
4.0
microtubule binding
KEGG
MF
ErbB signaling pathway
ATPase activity
EGFR tyrosine kinase inhibitor resistance
0.1 0.2
0. .3
30.
.4
0
0.15
15 0.20 0.25 0.30
5 0.6
GeneRatio
GeneRatio
E
F
Enrichment score
Enrichment plot KEGG terms
Enrichment score
Enrichment plot KEGG terms
0.0
0.50
Term
-0.2
Term
ARACHIDONIC_ACID_METABOLISM
P53_SIGNALING_PATHWAY
0.25
ES=0.43,NES=1.6,P=0.02,FDR=0.43 FATTY_ACID_METABOLISM
-0.4
-0.6
ES =- 0.61,NES =- 2.3,P=0,FDR=0.0025
0.00
REGULATION_OF_ACTIN_CYTOSKELETON
ES=0.52,NES=1.6,P=0.039,FDR=0.32
ES =- 0.53,NES =- 2.1,P=0,FDR=0.013
TAURINE_AND_HYPOTAURINE_METABOLISM
ES=0.68,NES=1.7,P=0.02,FDR=0.32
Rank
0.50
High_exp
CELL_CYCLE
ES =- 0.68,NES =- 2.1,P=0,FDR=0.0084
Rank
0.50
0.00
High_exp
ALPHA_LINOLENIC_ACID_METABOLISM
0.00
Low_exp
02-0.75
Low_exp
ES=0.58,NES=1.8,P=0.012,FDR=0.37
-0.75
0
10000
20000
0
10000
20000
Rank in ordered dataset
Rank in ordered dataset
G
H
Enrichment score
Enrichment plot HALLMARK terms
Enrichment score
Enrichment plot HALLMARK terms
0.0
-0.2
Term
0.2
Term
COMPLEMENT
0.1
PANCREAS_BETA_CELLS
-0.4
-0.6
ES =- 0.56,NES =- 2.1,P=0,FDR=0.0047
0.0
ES=0.25,NES=0.8,P=0.75,FDR=0.63
INFLAMMATORY_RESPONSE
.1
-0.2
KRAS_SIGNALING_DN
ES =- 0.59,NES =- 2.1,P=0,FDR=0.0023
ES=0.21,NES=0.93,P=0.6,FDR=0.6
G2M_CHECKPOINT
FATTY_ACID_METABOLISM
Rank
0.50
High_exp
ES =- 0.73,NES =- 2.1,P=0,FDR=0.0025
Rank
0.50
ES=0.26,NES=0.96,P=0.48,FDR=0.83
0.00
BILE_ACID_METABOLISM
-0.75
Low_exp
0.00
High_exp
0
10000
20000
-0.75
Low_exp
ES=0.27,NES=1.1,P=0.33,FDR=1
0
10000
20000
Rank in ordered dataset
Rank in ordered dataset
cells. Furthermore, previous studies have revealed that Treg cells also exert a crucial role in immunosuppressive microenvironments in tumors [23]. Immunosuppression modulated by TAM and MDSC is considered as an underlying mechanism of the treatment resis- tance to ICIs [24,25]. CAF, a critical component of the tumor
microenvironment, participates in the tumor development and progression, metastasis, and immune evasion [26,27].
On the one hand, our findings confirmed that there were nega- tive correlation or no significant correlation between SHCBP1 expression and CD8 + T cells infiltration in various cancer types.
A
B
C
ns
ns
⇐ 60
T1
SHCBP1 expression
6
SHCBP1 expression
7.5
ns *
I
High
exp
Alive
**
**
4
5.0
>60
T2
11
2
2.5
Low
III
exp
Dead
0
T3
Normal
Tumor
Stage | Stage II Stage III Stage IV
14
Age
pT_stage
pTNM_stage
SHCBP1
Status
D
E
F
G
OS
DSS
DFS
PFS
Overall survival probability
1.00
Disease specific survival probability
High exp
1.00
+ High exp
1.00
+ High exp
Progression free survival probability
1.00
Low exp
+ Low exp
Disease free survival probability
+ Low exp
+ High exp
0.75
Log-rank P = 0.0002
0.75
Log-rank P = 3.86e-05
HR(High groups)=2.294
0.75
Log-rank P = 0.074
+ Low exp
HR(High groups)=1.764
95%CI(1.309, 2.376)
95%CI(1.545, 3.408)
HR(High groups)=1.469
Log-rank P = 0.000345
95%CI(0.963, 2.24)
0.75
HR(High groups)=1.661
95%CI(1.258, 2.193)
0.50
0.50
0.50
0.50
0.25
0.25
0.25
0.25
0.00
High
0.00
0.00
0.00
252
22
3
1
0
Low
6
0
High
234
18
2
1
0
High
150
18
1
1
252
0
30
2
High
252
17
1
1
Low
235
29
6
2
0
Low
150
18
6
2
0
Low
252
20
4
2
0
5
10
15
20
0
5
10
15
20
0
5
10
15
20
0
5
10
15
Time (years)
Time (years)
Time (years)
Time (years)
H
Uni_cox
P value
Hazard Ratio(95% CI)
Mult_cox
p.value
Hazard Ratio(95% CI)
SHCBP1
3e-05
1.35771(1.17565,1.56795)
SHCBP1
0.00508
1.25977(1.07185,1.48064)
Age
0.26432
1.00869(0.99348,1.02414)
Age
0.23814
1.0109(0.99286,1.02927)
pT_stage
<0.0001
1.54925(1.28882,1.86231)
pT_stage
0.06829
1.22825(0.98468,1.53206)
pN_stage
<0.0001
1.71018(1.44292,2.02695)
pN_stage
0.41155
1.15474(0.81912,1.62786)
pM_stage
0.00651 2.11088(1.23233,3.61577)
pM_stage
0.69452
0.81952(0.30351,2.21281)
pTNM_stage
<0.0001
1.67006(1.45744,1.9137)
pTNM_stage
0.06776
1.4214(0.97465,2.07294)
U
U
1
1.5
2
2.5
3
1
1.5
2
Hazard Ratio
3.5
0.5
Hazard Ratio
0
0
20
While CD8 + T cell tumor infiltration was one of key characteristics of effective cancer immunotherapy. Therefore, overexpression of SHCBP1 in several tumors might limit T cell-infiltration resulting in unsatisfied tumor-killing effects. On the other hand, SHCBP1 expression was positively correlated with the infiltration levels of M2-TAM, MDSC, CAF, and Treg, which contributed to the formation of an immunosuppressive TME. The immunosuppressive status in TME is a major barrier to successful immunotherapy. In addition, there was also a positive correlation between SHCBP1 expression and immune checkpoint genes in some cancers. Overexpression of SHCBP1 was accompanied by the upregulation of immune checkpoint molecules, which promoted these tumor cells to evade the immune surveillance. Furthermore, T cells killing effect could be blunted in tumors owing to up-regulation of immune check- points. In summary, these results provided supportive evidence that SHCBP1 expression was linked with immunosuppressive microenvironment in cancers.
We also found that SHCBP1 expression was highly associated with TMB, MSI, chemokines, chemokine receptors, and MHC genes in various cancer types. These results implied that SHCBP1 might exert a significant role in immune response of
tumor cells to the treatment of immunotherapy. Consequently, SHCBP1 could be further explored as a promising therapeutic target for tumor immunotherapy. Finally, we focused on inves- tigating the clinical features and the potential biological role of SHCBP1 in LUAD. Through systematic analysis, this study reveals the important role of SHCBP1 in diagnosis, prognosis and tumor immune microenvironment across multiple cancer types. The prediction of the interacting genes may also provide new targets for cancer treatment. On the basis of our research, many studies revolving around SHCBP1 will be conducted in other cancers.
In conclusion, our study indicated that SHCBP1 had high diag- nostic value, oncogenic characteristics predicting poor prognosis and a significant association with immunosuppressive TME, which suggested that it could be identified as an immune-related biomar- ker for cancer diagnosis and prognosis, and a potential therapeutic target for tumor immunotherapy.
Authors’ contributions
Huang XY, Wang LX, and Shen ZF contributed to the conception of this work. Wang N and Zhu LY performed the experiments, data analysis and figure generation. Wang N wrote a draft and Huang
A
B
1518-
High SHCBP1 exp
Low SHCBP1 exp
0.
0
241
**
TP53
43%
Immune checkpoint
TTN
40%
7.5
MUC16
35%
CSMD3
33%
5.0
RYR2
32%
LRP1B
29%
USH2A
28%
2.5
ZFHX4
27%
KRAS
25%
XIRP2
22%
0.0
SHCBP1
1%
CD274
CTLA4
HAVCR2
LAG3
PDCD1
PDCD1LG2
TIGIT
SIGLEC15
Groups
Missense_Mutation
. Splice_Site
. In_Frame_Ins
. In_Frame_Del
Groups
· Frame_Shift_Del
Translation_Start_Site
· High SHCBP1 exp
Low SHCBP1 exp
· Nonsense_Mutation
. Frame_Shift_Ins
· Multi_Hit
C
D
Up-regulation: 199 genes
Down-regulation: 90 genes
group
· Down-regulation
3
None
group
·
Up-regulation
2
High SHCBP1 exp
90
KIF4A
1
Low SHCBP1 exp
-Log10 (p.adjust)
0
TPX2
TOPŁAC
0
RM2
ANIN
60
MELK
ONMI
0
MYBL3
-1
UBEXC
0
-2
-3
30
SCTHISAZP
SL
SCOBRA
0
SFTP
AĢPS
NAPS.A
MSLN
0
SCGRIAD
-1
0
1
Log2 (fold change)
E
F
GO
KEGG
spindle organization
p53 signaling pathway
sister chromatid segregation
Viral carcinogenesis
regulation of nuclear division
Pyrimidine metabolism
regulation of mitotic nuclear division
Prostate cancer
-log10(p.adjust)
regulation of mitotic cell cycle phase transition
-log10(p.adjust)
Progesterone-mediated
16
regulation of chromosome segregation
oocyte maturation
Platinum drug resistance
regulation of cell cycle phase transition
Pancreatic cancer
12
positive regulation of cell cycle process
16
Oocyte meiosis
8
organelle fission
MicroRNAs in cancer
4
nuclear division
nuclear chromosome segregation
Human immunodeficiency virus 1 infection
mitotic spindle organization
Count
Human T-cell leukemia virus 1 infection
Homologous recombination
Count
mitotic sister chromatid segregation
30
mitotic nuclear division
40
Hepatitis B
5
FoxO signaling pathway
10
mitotic cell cycle checkpoint
50
Fanconi anemia pathway
15
microtubule cytoskeleton organization
60
20
involved in mitosis
chromosome separation
DNA replication
25
chromosome segregation
Cellular senescence
cell cycle checkpoint
Cell cycle
DNA replication
Apoptosis - multiple species
Antifolate resistance
0.10 0.15 0.20 0.25 0.30 0.35
Enrichment Ratio
0.1
0.2
0.3
Enrichment Ratio
XY revised the manuscript. All authors approved the submitted version of the manuscript.
Declaration of Competing Interest
The authors declare that they have no known competing finan- cial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.org/10.1016/j.csbj.2022.06.039.
References
[1] Zhang GY, Ma ZJ, Wang L, et al. The Role of Shcbp1 in Signaling and Disease. Curr Cancer Drug Targets 2019;19(11):854-62.
N. Wang, L. Zhu, L. Wang et al.
[2] Liu L, Yang Y, Liu S, et al. EGF-induced nuclear localization of SHCBP1 activates B-catenin signaling and promotes cancer progression. Oncogene 2019;38 (5):747-64.
[3] Lu H, Yin M, Wang L, et al. FGF13 interaction with SHCBP1 activates AKT- GSK3a/B signaling and promotes the proliferation of A549 cells. Cancer Biol Ther 2020;21(11):1014-24.
[4] Zou A, Wu A, Luo M, Zhou C, Lu Y, Yu X. SHCBP1 promotes cisplatin induced apoptosis resistance, migration and invasion through activating Wnt pathway. Life Sci 2019;235:116798.
[5] Shi W, Zhang G, Ma Z, et al. Hyperactivation of HER2-SHCBP1-PLK1 axis promotes tumor cell mitosis and impairs trastuzumab sensitivity to gastric cancer. Nat Commun 2021;12(1):2812.
[6] Yang C, Hu JF, Zhan Q, et al. SHCBP1 interacting with EOGT enhances O- GlcNAcylation of NOTCH1 and promotes the development of pancreatic cancer. Genomics 2021;113(2):827-42.
[7] Geng H, Guo M, Xu W, et al. SHCBP1 Promotes Papillary Thyroid Carcinoma Carcinogenesis and Progression Through Promoting Formation of Integrin and Collagen and Maintaining Cell Stemness. Front Endocrinol (Lausanne) 2020;11:613879.
[8] Zhou Y, Tan Z, Chen K, et al. Overexpression of SHCBP1 promotes migration and invasion in gliomas by activating the NF-KB signaling pathway. Mol Carcinog 2018;57(9):1181-90.
[9] Ballhausen A, Przybilla MJ, Jendrusch M, et al. The shared frameshift mutation landscape of microsatellite-unstable cancers suggests immunoediting during tumor evolution. Nat Commun 2020;11(1):4740.
[10] Georgiadis A, Durham JN, Keefer LA, et al. Noninvasive Detection of Microsatellite Instability and High Tumor Mutation Burden in Cancer Patients Treated with PD-1 Blockade. Clin Cancer Res 2019;25(23):7024-34.
[11] Reuben A, Zhang J, Chiou SH, et al. Comprehensive T cell repertoire characterization of non-small cell lung cancer. Nat Commun 2020;11(1):603.
[12] Cerami E, Gao J, Dogrusoz U, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012;2(5):401-4.
[13] Gao J, Aksoy BA, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013;6(269):pl1.
[14] Warde-Farley D, Donaldson SL, Comes O, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010. 38(Web Server issue): W214-20.
[15] Kumar R, Indrayan A. Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr 2011;48(4):277-87.
[16] Fridman WH, Zitvogel L, Sautes-Fridman C, Kroemer G. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol 2017;14 (12):717-34.
[17] Gao W, Qi CQ, Feng MG, Yang P, Liu L, Sun SH. SOX2-induced upregulation of IncRNA LINC01561 promotes non-small-cell lung carcinoma progression by sponging miR-760 to modulate SHCBP1 expression. J Cell Physiol 2020;235 (10):6684-96.
[18] Mo M, Tong S, Yin H, Jin Z, Zu X, Hu X. SHCBP1 regulates STAT3/c-Myc signaling activation to promote tumor progression in penile cancer. Am J Cancer Res 2020; 10(10):3138-56.
[19] Huang H, Cai H, Zhang L, Hua Z, Shi J, Wei Y. Oroxylin A inhibits carcinogen- induced skin tumorigenesis through inhibition of inflammation by regulating SHCBP1 in mice. Int Immunopharmacol 2020;80:106123.
[20] Xu N, Wu YP, Yin HB, et al. SHCBP1 promotes tumor cell proliferation, migration, and invasion, and is associated with poor prostate cancer prognosis. J Cancer Res Clin Oncol 2020; 146(8): 1953-69.
[21] Yang D, Denny SK, Greenside PG, et al. Intertumoral Heterogeneity in SCLC Is Influenced by the Cell Type of Origin. Cancer Discov 2018;8(10):1316-31.
[22] Wang E, Lu SX, Pastore A, et al. Targeting an RNA-Binding Protein Network in Acute Myeloid Leukemia. Cancer Cell 2019;35(3):369-384.e7.
[23] Hsu TS, Lin YL, Wang YA, et al. HIF-2x is indispensable for regulatory T cell function. Nat Commun 2020;11(1):5005.
[24] Takenaka MC, Gabriely G, Rothhammer V, et al. Control of tumor-associated macrophages and T cells in glioblastoma via AHR and CD39. Nat Neurosci 2019;22(5):729-40.
[25] Zhang Y, Bush X, Yan B, Chen JA. Gemcitabine nanoparticles promote antitumor immunity against melanoma. Biomaterials 2019;189:48-59.
[26] Duperret EK, Trautz A, Ammons D, et al. Alteration of the Tumor Stroma Using a Consensus DNA Vaccine Targeting Fibroblast Activation Protein (FAP) Synergizes with Antitumor Vaccine Therapy in Mice. Clin Cancer Res 2018;24(5):1190-201.
[27] Katarkar A, Bottoni G, Clocchiatti A, et al. NOTCH1 gene amplification promotes expansion of Cancer Associated Fibroblast populations in human skin. Nat Commun 2020;11(1):5126.