Medicine
OPEN
Comprehensive analyses of cuproptosis-related gene CDKN2A on prognosis and immunologic therapy in human tumors
Di Zhang, MMa,b (D, Tao Wang, MDa,b, Yi Zhou, MDa,b, Xipeng Zhang, MDa,b,c,*[D
Abstract
Recent studies have identified a novel programmed cell death based on copper, named cuproptosis. However, as an anti- cuproptosis gene, the functional roles, definite mechanisms and prognostic value of CDKN2A in pan-cancer are largely unclear. The GEPIA2, cancer genome atlas (TCGA), the tumor immune estimation resource 2.0 and CPTAC databases were performed to validate the differential expression of CDKN2A in 33 tumors. The clinical features and survival prognosis analysis were conducted by GEPIA2 and UALCAN web tool. Genetic alteration analysis of CDKN2A in pan-cancer was also evaluated. Furthermore, the functional roles of CDKN2A were explored via DNA methylation analysis, tumor microenvironment, infiltration of immune cells, enrichment analysis and gene co-expression associated with cuproptosis and immune regulation. The CDKN2A expression, both at the transcriptional and translational level, was obviously upregulated in most cancer patients, which might lead to poor survival in certain cancer types. CDKN2A expression was significantly associated with tumor pathological stages in some cancer types. In adrenocortical carcinoma (ACC) and kidney renal clear cell carcinoma (KIRC), DNA methylation of CDKN2A was explored to induce poor clinical outcomes. Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis indicated that CDKN2A expression was closely related to several cancer-associated signaling pathways, such as the p53 signaling pathway, Cellular senescence, DNA replication and Cell cycle signaling pathways. Gene set enrichment analysis (GSEA) analysis suggested that aberrantly expressed CDKN2A took part in the cell cycle regulation, immune regulation and mitochondrial signaling pathways in certain cancer patients. In addition, aberrant CDKN2A expression was closely correlated to immune infiltration and the levels of immune-regulatory genes. The study deeply defined the concrete roles of cuproptosis-related gene CDKN2A in tumorigenesis. The results provided new insights and pieces of evidence for treatment.
Abbreviations: ACC = adrenocortical carcinoma, BLCA = bladder urothelial carcinoma, BRCA = breast invasive carcinoma, CAF = cancer-associated fibroblasts, CC = cellular component, CDKN2A = cyclin dependent kinase inhibitor 2A, CESC = cervical squamous cell carcinoma and endocervical adenocarcinoma, COAD = colon adenocarcinoma, DFS = disease-free survival, GBM = glioblastoma multiforme, GEPIA2 = gene expression profiling interactive analysis, GO = gene ontology, GSEA = gene set enrichment analysis, HNSC = head and neck squamous cell carcinoma, KEGG = Kyoto encyclopedia of genes and genomes, KICH = kidney chromophobe, KIRC = kidney renal clear cell carcinoma, KIRP = kidney renal papillary cell carcinoma, LIHC = liver hepatocellular carcinoma, LUAD = lung adenocarcinoma, OS = overall survival, OV = ovarian serous cystadenocarcinoma, PAAD = pancreatic adenocarcinoma, SKCM = skin cutaneous melanoma, TGCT = testicular germ cell tumors, THCA = thyroid carcinoma, THYM = thymoma, UCEC = uterine corpus endometrial carcinoma.
Keywords: cuproptosis, CDKN2A, pan-cancer, immunotherapy, prognosis
1. Introduction
Due to its rapidly increased incidence and mortality, cancer is a major public health problem and a barrier to prolonging life expectancy.[1,2] The highly intricate process of tumorigene- sis and poor prognosis pose a great threat to cancer therapy.[1]
Therefore, it is urgent to explore in-depth the definite mecha- nisms and elucidate the expression of relevant genes related to tumor occurrence so that the results provided new directions for diagnosis and treatment.
Programmed cell death is vital to the homeostasis of body tis- sues.[3] At present, more types of cell death, such as necroptosis,
The authors have no funding and conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are publicly available.
The study protocol complied with the Declaration of Helsinki. The work described has not been submitted elsewhere for publication, in whole or in part, and all authors listed have approved the manuscript that in enclosed.
Supplemental Digital Content is available for this article.
a Department of Colorectal Surgery, Tianjin Union Medical Center, Tianjin, China,
b Tianjin Institute of Coloproctology, Tianjin, China ” The Institute of Translational Medicine, Tianjin Union Medical Center of Nankai University, Tianjin, China.
*Correspondence: Xipeng Zhang, Department of Colorectal Surgery, Tianjin Union Medical Center, 300121, Tianjin, China (e-mail: zhxp0813@163.com).
Copyright @ 2023 the Author(s). Published by Wolters Kluwer Health, Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.
How to cite this article: Zhang D, Wang T, Zhou Y, Zhang X. Comprehensive analyses of cuproptosis-related gene CDKN2A on prognosis and immunologic therapy in human tumors. Medicine 2023;102:14(e33468).
Received: 19 December 2022 / Received in final form: 10 March 2023 / Accepted: 16 March 2023
http://dx.doi.org/10.1097/MD.0000000000033468
pyroptosis and ferroptosis, have been found and identified.[4-6] It is reported that cell death is tightly involved in tumorigen- esis and prognosis.[7] Therefore, striking cell death might be a latent strategy for tumor therapy. Cuproptosis, arising from copper-mediated mitochondrial proteotoxic stress, is a newly identified form of regulatory cell death.[8] The process was veri- fied that the lipoyl moiety of increased lipoylated TCA enzyme bound to copper, which induced proteotoxic stress character- ized by lipoylated protein aggregation, decreased Fe-S cluster- containing proteins and increased HSP70.[8] Several studies have recognized the crucial roles of copper and cuproptosis modu- lated by copper in living things, such as the liver,[9] lung,[10] and other tissues.[8,11] A total of 12 genes were verified to take part in the cuproptosis pathway, consisting of 7 pro-cuproptosis genes (ferredoxin 1, dihydrolipoamide dehydrogenase, dihy- drolipoamide S-Acetyltransferase, lipoic acid synthetase, homo sapiens lipoyltransferase 1, pyruvate dehydrogenase E1 sub- unit Alpha 1 and pyruvate dehydrogenase E1 subunit beta), 3 anti-cuproptosis genes (CDKN2A, glutaminase and metal regu- latory transcription factor 1), and 2 transporters for copper-sol- ute carrier family 31 member 1 and ATPase copper transporting beta.[12]
Cyclin dependent kinase inhibitor 2A (CDKN2A), serving as a tumor suppressor and cell cycle regulator, has been eluci- dated in some cancers, such as glioblastoma (GBM),[13] follic- ular lymphoma,[14] and colorectal cancer (CRC).[15] Disruption of CDKN2A (deletion or methylation) has been reported to be a frequent event in tumorigenesis, which affects the clinical characteristics and patient outcomes.[14-16] A meta-analysis from Xing showed that CDKN2A hypermethylation was significantly associated with unfavorable prognosis in CRC patients.[15] Alhejaily and his colleagues revealed that silence of CDKN2A by deletion or methylation was correlated with worse clinical outcome in follicular lymphoma.[14] Similar results were also found in pancreatic ductal adenocarcinoma (PDAC), thymic car- cinoma, head and neck squamous cell carcinoma (HNSC) and muscle invasive bladder cancer (MIBC).[16-19] Although the tum- origenic effects of disruption of CDKN2A were well confirmed, unexpectedly high CDKN2A indicated a poor clinical outcome in some cancer, including Colon adenocarcinoma (COAD), Bladder Urothelial Carcinoma (BLCA) and Liver hepatocellular carcinoma (LIHC).[17,20-22] The mechanism by which CDKN2A serves as a tumor suppressor but results in unfavorable prog- nosis is speculated that CDKN2A may involve in cuproptosis activity.[23-25] Nevertheless, as an anti-cuproptosis gene, the roles or signatures and the regulatory mechanisms of CDKN2A in pan-cancer have not yet been explored in depth.
In this study, a comprehensive analysis of CDKN2A expres- sion in 33 cancer types was performed. In detail, aberrantly expressed genes and protein analysis, survival rate, methylation analysis and enrichment analysis were carried out. The correla- tion between CDKN2A expression and immune infiltration was followed explored. Lastly, the association between CDKN2A expression and gene related to immune regulation or cupro- ptosis was studied. The results explored the predictive value of CDKN2A and provided new insight into cancer therapy.
2. Methods
2.1. CDKN2A differential expression analysis
The Tumor Immune Estimation Resource (https://cistrome.shin- yapps.io/timer/)[26] and Gene Expression Profiling Interactive Analysis (GEPIA2, http://gepia2.cancer-pku.cn/#analysis)[27] were applied to compare the CDKN2A expression in pan-cancer and their corresponding paracancerous tissue samples with the threshold of P value < . 05 and ILog2FCI > 1. The total protein level of CDKN2A between normal tissues and ten cancer sam- ples (kidney renal clear cell carcinoma [KIRC], uterine corpus endometrial carcinoma [UCEC], lung adenocarcinoma [LUAD],
HNSC, LIHC, breast cancer, ovarian cancer, COAD and pan- creatic adenocarcinoma [PAAD]) was analyzed by the “CPTAC analysis” module in the UALCAN database (http://ualcan.path. uab.edu)[28] and immunohistochemical (IHC) staining down- loaded from “The Human Protein Atlas” database. The GEPIA2 database was performed to investigate the clinic correlation of CDKN2A expression.
2.2. Survival analysis in pan-cancer
The prognostic value of CDKN2A, including overall survival (OS), disease-free survival (DFS), disease-specific survival and progress-free interval, were determined by GEPIA and Xiantao bioinformatics toolbox (https://www.xiantao.love) tool accord- ing to the Kaplan-Meier analysis between high-expression and low-expression groups. We performed GEPIA2 to examine the relationship between CDKN2A expression and OS and DFS. The heatmap data and survival curve were displayed. Xiantao toolbox was carried out to analyze TCGA data of pan-cancer. Forest plots and Kaplan-Meier curves were drawn to explore the effect of CDKN2A expression on disease progression. Furthermore, the frequency, type, and site information related to mutation of CDKN2A in pan-cancer were analyzed by cBio- Portal (https://www.cbioportal.org/).[29]
2.3. Genetic alteration analysis of CDKN2A in pan-cancer
The genetic alteration, including mutation type, alteration fre- quency and the copy number alteration (CNA), of CDKN2A was performed by cBioPortal tool. The mutation site with highest change frequency was visualized in the 3D structure of CDKN2A protein.
2.4. Immune infiltration analysis
The association between CDKN2A expression and immune infiltration in pan-cancer was explored by using the “Immune” module of TIMER (http://timer.cistrome.org/).[26] We applied 7 algorithms, exactly, MCPCOUNTER, CIBERSORT, CIBERSORT-ABS, EPIC, QUANTISEQ and XCELL, to analyze the infiltration of tumor-infiltration immune cells, such as mac- rophages (including M0, M1 and M2) and cancer-associated fibroblasts (CAF). Heat maps and scatter plots were displayed according to the TCGA data.
2.5. Methylation analysis
The effect of DNA methylation on CDKN2A expression in some cancers was explored based on TCGA databases by the “Methylation” module of the xiantao bioinformatics toolbox. The “MethSurv” web tool (https://bit.cs.ut.ee/methsurv/)[30] was carried out to further excavate the correlation between CDKN2A methylation and patients’ survival. Kaplan-Meier plots were drawn for visualization.
2.6. Enrichment analysis of CDKN2A-related genes
The Protein-Protein Network Interaction network was con- structed based on the STRING website, and the top 20 experimental identified CDKN2A-binding molecules were ana- lyzed by Cytoscape. Gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) enrichment analysis about selected genes were performed by the xiantao bioin- formatics toolbox. GO [including biological process, cellular component (CC) and molecular function], and KEGG analy- sis were visualized by the xiantao bioinformatics toolbox and bioinformatics website (http://www.bioinformatics.com.cn/). GEPIA2 tool was carried out to explore similar genes related
to CDKN2A. TCGA data in pan-cancer were downloaded. Gene Set Enrichment Analysis (GSEA) and correlation analy- sis was performed via the xiantao bioinformatics toolbox to uncover relevant pathways affected by CDKN2A expression in pan-cancer. INESI >1, adjust P value < . 05 and FDR < 0.25 were considered as obviously enriched.
2.7. Statistical analysis
In TIMER 2.0, the statistical significance calculated by the Wilcoxon test is annotated by the number of stars. The ANOVA method was carried out to compare the tumor and all normal samples. The Kaplan-Meier method was utilized to assess the relationship between prognosis versus CDKN2A expression, mutation or methylation levels. The Pearson rank correlation coefficient was used to explore the association between the 2 groups. A P value <. 05 was considered statisti- cally significant.
3. Results
3.1. Aberrant expression of CDKN2A in pan-cancers
The CDKN2A expression in pan-cancer and paracancerous tis- sue samples was analyzed by the TIMER database. As displayed in Figure 1A, CDKN2A was dramatically higher expressed in cancer tissues than in paracancerous tissue samples, such as BLCA, Breast invasive carcinoma (BRCA), Cholangiocarcinoma, COAD, HNSC, Kidney Chromophobe (KICH), KIRC, Kidney renal papillary cell carcinoma (KIRP), LIHC, LUAD, Lung squa- mous cell carcinoma, Prostate adenocarcinoma (PRAD), Rectum adenocarcinoma, Stomach adenocarcinoma, Thyroid carcinoma (THCA) and UCEC. Furthermore, the TCGA and GTEx data from GEPIA2 were integrated to analyze CDKN2A expression
in some cancers lacking paracancerous normal tissues. The results indicated that the CDKN2A expression in cancer sam- ples, respectively, Adrenocortical carcinoma (ACC), Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), Lymphoid Neoplasm Diffuse Large B-cell Lymphoma, Acute Myeloid Leukemia, Brain Lower Grade Glioma, Ovarian serous cystadenocarcinoma (OV), PAAD, Pheochromocytoma and Paraganglioma (PCPG), Sarcoma, Thymoma (THYM) and Uterine Carcinosarcoma was obviously upregulated (Fig. 1B). Instead, CDKN2A was significantly downregulated in Testicular Germ Cell Tumors (TGCT) tissues (Fig. 1B). We further accessed the DCKN2A protein levels and IHC staining in various cancers. The “CPTAC analysis” results showed that the total expres- sion of CDKN2A was dramatically elevated in KIRC (Fig. 2A), UCEC (Fig. 2C) and LUAD (Fig. 2E), and decreased in HNSC (Fig. 2G) and LIHC (Fig. 2I). There was no significant change in Breast cancer (Fig. 2K), Ovarian cancer (Fig. 2M), Colon can- cer (Fig. 20) and PAAD samples (Fig. 2Q). Consistent with the “CPTAC analysis” results, stronger IHC staining was discov- ered in UCEC (Fig. 2D) and LUAD (Fig. 2F). Contrary to above protein expression results, IHC staining showed that CDKN2A expression was increased in LIHC tissues (Fig. 2J), with no statis- tical significance in KIRC (Fig. 2B), HNSC (Fig. 2H) and PAAD (Fig. 2R) tissues. Besides, IHC staining results also indicated the increased expression in breast cancer (Fig. 2L), Ovarian cancer (Fig. 2N) and Colon cancer (Fig. 2P).
3.2. Correlation between CDKN2A expression and tumor pathological stage in pan-cancer
The GEPIA2 tool was used to elucidate the relationship between CDKN2A and clinical information. Figure 1C indi- cated that 9 cancers had a stage-specific change of CDKN2A,
A
C
F value = 3.08
CDKNZA Expression Level (log2 TFM)
F value = 4.95 Pr(>F) = 0.00234
F value = 6.76
100
…
…
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ACC
Pr(MF) = 0.0329
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COAD
KICH
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Pr(F) = 0.000509
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75
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25
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Stage I
Stage II
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Stage I
Stage II
Stage IN
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BLCA Normal
BRCANOTTE
BRCA-Hez. Tumor
BRCA-Luminal. Tumor
CESC.Tumor CHOL Tung
DLBIC. Tumor
ESCA. Tumor
ESCA Normal
GEM Tunge
HNSC Normal
HNSC-HPVpos. Tumor
HNSC-HPVneg. Tumor
KIDH. Temor
KICH Normal
KIRC Tumar
KIRP.Tumor
KRP Normal
LAML. Tumor
LGG.Tumor
LIHC Tumor
MESO. Tumor
PRAD Normal
READ. Tumor
READ Normal
SARC. Tumor
STAD Normal
TGCT.Tumor
THICA.Tumor
THCA Normal
vat Tumor
UČST UCS. Tumor
10
UCEC
F value = 3.24 Pr[>F) = 0.0234
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KIRC
F value = 3.74 Pr[>F) = 0.0112
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Protein expression of CDKNZA in Lung adenocarcinoma
LUAD
Median: - 4.013
15-
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Median: - 4.108
%Area
10
5
P=1.197E-02
0
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H
Normal
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Protrin expression of CDKNŽA in Head and neck squamous
HNSC
Medianc -4.835
20
p=0.3430
Median: D.45
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10
5-
P=1.292E-02
=
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0
CPEAC sangies
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Tumor
Potrin expersion of CDANZA in Hepatocellular carcinoma
LIHC
Median: 4.013
Median: 0.001
2.5
2.0
-
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1.0
0.5
P=1.922E-02
=
0.0
K
COPIAĆ tungtes
L
Normal
Tumor
Protein expression of ČEKZa in Breast cancer
breast cancer
Median: 4.826
4-
Median: 4.056
Q
3
%Area
2
1
P=0.5997
0
Normal
Tumor
M
=
OPTIC cangini
N
Protein expression of CORNZA In Ovarian cancer
Ovarian cancer
Median: 4.002
15-
Median: 4.375
%Area
10
5-
P=0.1337
=
0
O
OPTIC sengin
P
Normal
Tumor
Protein expression of CDKNIA in Colon cancer
Colon cancer
Median: 0.005
25-
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Median: 4.351
20-
%Area
15
10
5-
P=0.0930
0
Q
=
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Normal
Tumor
OFTAC Langles
Protein expressions of CDKNZA in Funcritic adenocarcinoma
Pancreatic adenocarcinoma
Median: 6.874
Median :- 4.044
4-
p=0.8890
:
-
3-
%Area
N
P=0.4324
1
=
0
CPTAC sangles
Normal
Tumor
including ACC, COAD, KICH, UCEC, BRCA, KIRC, KIRP, LIHC, and THCA. In other tumor types, there was no obvi- ous correlation between CDKN2A and pathological stage (see Figure S1, Supplemental Digital Content 1, http://links.lww. com/MD/I754, Supplemental Content, which demonstrates the CDKN2A expression through different pathological stages in some cancers).
3.3. Prognostic value of CDKN2A in cancer patients
The potential role of CDKN2A in prognosis was assessed by the GEPIA2 tool. As displayed in Figure 3A, the results indi- cated that CDKN2A expression was negatively related to OS of ACC (P = . 011), COAD (P = . 013) and LIHC (P = . 0049). Moreover, high expression of CDKN2A predicted poor DFS in ACC (P = . 03, Fig. 3B), COAD (P = . 0066, Fig. 3B), KIRC (P = . 026, Fig. 3B), LIHC (P = . 003, Fig. 3B), PRAD (P = .0049, Fig. 3B), skin cutaneous melanoma (SKCM) (P = . 028, Fig. 3B), THCA (P = . 012, Fig. 3B) and UCEC (P = . 018, Fig. 3B), while high CDKN2A predicted better DFS in GBM (P = . 014, Fig. 3B).
The results of Cox regression model indicated that CDKN2A expression may play adverse roles in the OS of ACC (P < . 001, Fig. 4A), COAD (P = . 002, Fig. 4A), KICH (P = . 019, Fig. 4A), LIHC (P = . 003, Fig. 4A), THCA (P = .007, Fig. 4A) and UCEC (P < . 001, Fig. 4A), but protective roles in HNSC (P = . 004, Fig. 4A) and ESCC (Esophageal Squamous Cell Carcinoma, P = . 022, Fig. 4A). Consistent with the Cox regression results, Kaplan-Meier curve showed that CDKN2A expression was negatively related to OS in ACC (P = . 006, Fig. 4A), COAD (P = . 007, Fig. 4A), LIHC (P = . 001, Fig. 4A), and UCEC (P <. 001, Fig. 4A). There was no clear correlation between CDKN2A levels and the outcomes of KICH, THCA, HNSC, and ESCC patients (see Figure S2A, Supplemental Digital Content 2, http://links.lww.com/ MD/I755, Supplemental Content, which indicated the effect of CDKN2A on disease outcomes). For disease-specific sur- vival, in accord with Cox regression, Kaplan-Meier analysis showed that high level of CDKN2A predicted poor outcome in ACC (P = . 01, Fig. 4B), COAD (P = . 001, Fig. 4B), KIRC (P = . 015, Fig. 4B), LIHC (P = . 02, Fig. 4B) and UCEC (P < .001, Fig. 4B). For HNSC and KICH patients, the survival
A
0.5
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESAD
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
OV
PAAD
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
0
Overall Survival
Overall Survival
Overall Survival
=
L
Low CDKN2A Group
=
Low COKN2A Group
1.0
High CURTOA Group Logrank pao.co.re
High CORNZA Group
Low COKN2A Group
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Logrank pa0.011
High CORNZA GROUD
0.8
Hola
HR(high)=1.9
0.8
HR(high)=1.7
Percent survival
P(HR)=0.011 nghigh)=38
Percent survival
P(HRI0.013
nonight#1.35 m(lo=)=135
Percent survival
P(HR)-0.0049
06
0.6
nhg12 njom)=182
0.5
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100
150
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100
150
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20
40
60
80
100
120
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Months
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Disease Free Survival
Disease Free Survival
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Disease Free Survival
Disease Free Survival
Disease Free Survival
2
Low COINZA Group
Low COKNZA Group
Low CORNZA Group
:
Ow COKNDA Group
=
LOW COKINZA Group CORNZA GROUP
Logrank pr0.027
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Logrank pm0 016
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HR(high)=1.3.
P(HR)=0 028
0.8
HR(high)=2.3
Percent survival
n(high)=38 now)=38
Percent survival
miow)=81
Percent survival
Percent survival
n(high):228
Percent survival
p(HR)-0 018 n(high)=86
0.6
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ngow)=229
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100
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300
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20
40
60
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0.6
0.3
ACC
BLCA
BRCA
CESC
CHOL
COAD
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KIRC
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LAML
LGG
LIHC
LUAD
LUSC
OV
PAAD
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
0
Disease Free Survival
Disease Free Survival
Disease Free Survival
Disease Free Survival
10
Low CONTA CTOUP
10
LOW CONTA OrOUP
1.0
Low CDKONZA Group
1.0
Low COKNDA Group
Logrank p=0 0057
LograrA PHO 023
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0.8
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Da
D.8
Percent survival
DOHR)-0 004 nhighi=135 mow)= 135
Percent survival
nghigh=258
Percent survival
p(HR)=0.0049 n(high)-246
0.8
POURHO 615
0.6
0.6
)-246
Percent survival
0.6
0.8
now)-255
0.4
0.4
0.4
0.4
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0.2
0.2
0.2
00
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0.0
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50
100
150
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20
40
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100
120
140
Months
Months
Q
50
100
150
0
50
100
150
Months
Months
| Characteristics | N (%) | HR (95% CI) | P value | |
|---|---|---|---|---|
| ACC | 79 | 1.844 (1,312-2.593) | <0.001 | |
| BLCA | 413 | 0.986 (0.923-1.054) | 0.683 | |
| BRCA | 1082 | 0.925 (0.809-1.056) | 0.249 | |
| CESC | 306 | 0.964 (0.800-1.162) | 0.701 | |
| CHOL | 36 | 1.001 (0.607-1.652) | 0.996 | |
| COAD | 477 | 1.288 (1.094-1.516) | 0.002 | |
| DLBC | 48 | 0.875 (0.478-1.602) | 0.666 | |
| ESAD | 80 | 1.032 (0.825-1.291) | 0.784 | |
| ESCC | 82 | 0.758 (0.598-0.960) | 0.022 | |
| GBM | 168 | 0.959 (0.882-1.042) | 0.322 | |
| HNSC | 501 | 0.906 (0.846-0.969) | 0.004 | |
| KICH | 64 | 1.958 (1.117-3.433) | 0.019 | |
| KIRC | 539 | 1.280 (0.948-1.729) | 0.107 | |
| KIRP | 288 | 1.425 (0.952-2.134) | 0.086 | |
| LAML | 140 | 1.103 (0.847-1.435) | 0.458 | |
| LGG | 527 | 0.914 (0.763-1.094) | 0.320 | |
| LIHC | 373 | 1.251 (1.081-1.447) | 0.003 | |
| LUAD | 526 | 1.081 (0.988-1.184) | 0.091 | |
| LUSC | 496 | 0.995 (0.924-1.072) | 0.901 | |
| OV | 377 | 0.953 (0.892-1.017) | 0.147 | |
| PAAD | 178 | 0.986 (0.850-1.145) | 0.855 | |
| PRAD | 499 | 2.152 (0.871-5.313) | 0.097 | |
| READ | 166 | 1.285 (0.882-1.871) | 0.192 | |
| SARC | 263 | 0.982 (0.882-1.093) | 0.741 | |
| SKCM | 456 | 0.978 (0.904-1.059) | 0.588 | |
| STAD | 370 | 2.075 (0.363-11.876) | 0.412 | |
| TGCT | 139 | 3.290 (0.634-17.001) | 0.156 | |
| THCA | 510 | 1.893 (1.193-3.005) | 0.007 | |
| THYM | 118 | 1.279 (0.597-2.738) | 0.527 | |
| UCEC | 551 | 1.284 (1.152-1.432) | <0.001 | |
| UCS | 56 | 0.981 (0.746-1.291) | 0.894 | |
A
Cancer: ACC
Cancer: COAD
1.0 -
CDINZA
1.0 -
CDKNIA
Low
Low
High
High
0.8
0.8
Survival probability
Survival probability
0.6
0.6
0.4
0.4
0.2
Overall Survival HR -3.19(1.40-7.26)
0.2
Overall Survival
HR-1.72 (1.16-2.56)
0.0
P=0.006
0.0
P = 0:007
.
50
100
150
0
50
100
150
Time (months)
Time (months)
Cancer: LIHC
Cancer: UCEC
1.0-
CDKNIA
1.0 -
CDKNIA
Layw
Low
High
High
0.8
0.8
Survival probability
Survival probability
0.6
0.6
0.4
0.4
0.2
Overall Survival
0.2
1EX-1.78 (1:25-2 52)
Ovenil Survival
HER - 2 30(1.50-3.53)
2
3
0.0
P = 0.001
0.0
P < 0.001
0
30
60
90
120
0
50
100
150
200
B
Time (months)
Time (months)
Cancer: ACC
Cancer: COAD
Cancer: LIHC
1.0
CDKNZA
1.0-
CDKNIA
10-
CDKNZA
Low
Low
Low
High
High
High
0,8
0.8
0.8
Survival probability
Survival probability
Survival probability
0,6
0.6
0.6
0.4
0.4
0.4
0.2
Disease Specifie Survival HR - 2:98 41.29-6.89)
0.2
Discase Specific Survival HR - 2.39(1.41-4.04)
0.2
Disease Specific Survival
HE-170(1.88-268)
0.0
P-001
0.0
P=0.001
0.0
P =0.02
0
50
100
150
0
50
100
150
0
30
60
90
120
Time (months)
Timc (months)
Time (months)
Cancer: KIRC
Cancer: UCEC
1.0 -
FGL2
1.0 -
CDKNZA
Low
+ Low
High
10gh
0.8
Survival probability
0.8
Survival probability
0.6
0.6
0.4
0.4
0.2
Disease Specific Survival HR - 0.62 (0 43-0.91)
0.2
Disease Specific Survival
3
HR - 2.98 (1,72-5.15)
1
2
0.0
P-0015
0.0
P < 0:001
0
50
100
150
0
50
100
150
200
C
Time (months)
Time (months)
Cancer: ACC
Cancer: COAD
Cancer: UCEC
1.0
J
CDKNZA
1.0 -
CDKNIA
1.0 -
CDKNIA
Low
Low
High
High
Low
High
0.8
0.8
Survival probability
0.8
Survival probability
Survival probability
0.6
0.6
0.6
0.4
0.4
0.4
0.2
Progress Free Interval
0.2
Progress Free Interval
0.2
HR -2.67(1.39-5.13)
HR-1.59(1.12-2.26)
Progress Free lanerval
HR - 2.11 (1,47-3.03)
0,0
₱=0:003
0.0
P-001
0.0
₱€ 0.001
0
50
100
150
0
50
100
150
0
50
100
150
200
Time (months)
Time (months)
Time (months)
Cancer: KIRC
Cancer: PRAD
Cancer: LIHC
1.0
FGL2
1.0 -
CDKNZA
1.0 - J
CDKNZA
Low
Low
Low
High
High
High
0.8
0.8
0.8
Survival probability
Survival probability
Survival probability
0.6
0.6
0.6
0.4
0,4
0.4
0.2
Progress Free Interval HR -0.49 (0.50-0.94)
0.2
0.5
1.0
1.5
2.0
Progress Free Interval HR -2.04(1.34-3.12)
0.2
Progress Free Intervall HR- 158(1.48-2.11)
0.0
P= 0.02
0.0
P=0.001
0.0
- 0.002
0
50
100
0
40
80
120
160
0
30
60
90
120
Time (months)
Time (months)
Time (months)
| Characteristics | N (%) | HR (95% CI) | P value | |
|---|---|---|---|---|
| ACC | 77 | 1.836 (1.231-2.740) | 0.003 | |
| BLCA | 399 | 0.940 (0.866-1.020) | 0.139 | |
| BRCA | 1082 | 0.86 (0.56-1.32) | 0.492 | |
| CESC | 302 | 0.906 (0.744-1.104) | 0.326 | |
| CHOL | 35 | 0.854 (0.490-1.489) | 0.577 | |
| COAD | 461 | 1.447 (1.200-1.746) | <0.001 | |
| DLBC | 48 | 0.504 (0.168-1.509) | 0.221 | |
| ESAD | 79 | 1.054 (0.834-1.410) | 0.546 | |
| ESCC | 82 | 0.835 (0.643-1.085) | 0.178 | |
| GBM | 155 | 0.954 (0.873-1.042) | 0.205 | |
| HNSC | 476 | 0.914 (0.837-0.998) | 0.045 | |
| KICH | 64 | 2.510 (1.343-4.691) | 0.004 | |
| KIRC | 528 | 1.984 (1.587-2.480) | 20.001 | |
| KIRP | 284 | 1.330 (0.822-2.151) | 0.245 | |
| LGG | 519 | 0.865 (0.714-1.047) | 0.137 | |
| LIHC | 365 | 1.221 (1.048-1.421) | 0.01 | |
| LUAD | 491 | 1.085 (0.967-1.218) | 0.166 | |
| LUSC | 444 | 1.011 (0.901-1.136) | 0.85 | |
| OV | 352 | 0.948 (0.883-1.018) | 0.142 | |
| PAAD | 172 | 0.959 (0.838-1.096) | 0.537 | |
| PRAD | 497 | 3.065 (0.917-10.238) | 0.069 | |
| READ | 160 | 1.474 (0.926-2.347) | 0.102 | |
| SARC | 257 | 0.999 (0.888-1.123) | 0.981 | |
| SKCM | 450 | 0.956 (0.906-1.073) | 0.739 | |
| STAD | 349 | 1.019 (0.894-1.162) | 0.774 | |
| TGCT | 139 | 1.929 (0.197-18.910) | 0.573 | |
| THYM | 118 | 1.345 (0.433-4.175) | 0.608 | |
| UCEC | 549 | 1.408 (1.229-1.612) | <0.001 | |
| UCS | 54 | 0.977 (0.740-1.291) | 0.871 |
| Characteristics | N (%) | HR (95% CI) | P value | |
|---|---|---|---|---|
| ACC | 79 | 1.469 (1.122-1.924) | 0.005 | |
| BLCA | 414 | 0.947 (0.885-1.013) | 0 | 0.115 |
| BRCA | 1082 | 1.021 (0.900-1.158) | 0.75 | |
| CESC | 306 | 0.879 (0.746-1.036) | 0.125 | |
| CHOL | 36 | 0.822 (0.520-1.301) | 0.403 | |
| COAD | 477 | 1.248 (1.072-1.452) | 0.004 | |
| DLDC | 48 | 0.050 (0.500-1.449) | 0.507 | |
| ESAD | 80 | 0.949 (0.758-1.189) | 0.651 | |
| ESCC | 82 | 0.884 (0.736-1.062) | 0.188 | |
| GBM | 168 | 0.937 (0.862-1.018) | 0.125 | |
| HNSC | 501 | 0.939 (0.875-1.007) | 0.079 | |
| KICH | 64 | 1.909 (1.099-3.317) | 0.022 | |
| KIRC | 537 | 1.790 (1.464-2.189) | 40.001 | |
| KIRP | 207 | 1.390 (0.987-1.958) | 0.06 | |
| LGG | 527 | 1.057 (0.914-1.223) | 0.457 | |
| LIHC | 373 | 1.246 (1.102-1.409) | <0.001 | |
| LUAD | 526 | 1.045 (0.959-1.139) | 0.315 | |
| LUSC | 497 | 0.983 (0.899-1.075) | 0.711 | |
| OV | 377 | 1.002 (0.941-1.067) | 0.949 | |
| PAAD | 178 | 1.051 (0.921-1.200) | 0.461 | |
| PRAD | 499 | 1.552 (1.090-2.208) | 0.015 | |
| READ | 166 | 0.999 (0.704-1.417) | 0.994 | |
| SARC | 263 | 1.001 (0.917-1.093) | 0.985 | |
| SKCM | 457 | 1.033 (0.966-1.103) | 0.323 | |
| STAD | 372 | 0.977 (0.872-1.094) | 0.685 | |
| THCA | 510 | 1.411 (1.054-1,889) | 0.021 | |
| TGCT | 139 | 0.973 (0.463-2.045) | 0.943 | |
| THYM | 118 | 1.828 (1.147-2.913) | 0.011 | |
| UCEC | 551 | 1.260 (1.146-1.385) | <0.001 | |
| UCS | 56 | 0.998 (0.765-1.302) | 0.989 |
Figure 4. Prognostic value of CDKN2A expression in pan-cancer. The Cox regression and Kaplan-Meier analysis of CDKN2A expression in OS (A), DSS (B), and PFI (C). 0 < HR (95% CI) < 1 means that CDKN2A may play a protective role in cancer, and HR (95% CI) > 1 indicates CDKN2A may play an adverse role in pan-cancer. CDKN2A = cyclin dependent kinase inhibitor 2A, DSS = disease specific survival, OS = overall survival, PFI = progress free interval.
time was similar between high and low CDKN2A (see Figure S2B, Supplemental Digital Content 2, http://links.lww.com/ MD/1755, Supplemental Content, which indicated the effect of CDKN2A on disease outcomes). Forest plot indicated that high level of CDKN2A portended shorten progress-free inter- val in ACC (P = . 005, Fig. 4C), COAD (P = . 004, Fig. 4C), KICH (P = . 022, Fig. 4C), KIRC (P < . 001, Fig. 4C), LIHC (P < . 001, Fig. 4C), PRAD (P = . 015, Fig. 4C), THYM (P =. 011, Fig. 4C) and UCEC (P <. 001, Fig. 4C). However, the Kaplan- Meier curve found that KICH and THYM are not statistically significant (see Figure S2C, Supplemental Digital Content 2, http://links.lww.com/MD/I755 Supplemental Content, which indicated the effect of CDKN2A on disease outcomes).
3.4. CDKN2A genetic alteration analysis
CDKN2A genetic alteration in pan-cancer was explored to elu- cidate the possible mechanisms that affected the expression. Figure 5A indicated that the highest alteration frequency of CDKN2A (>50%) happened in GBM patients, of which “Deep Deletion” was the primary alteration type. HNSC patients had the highest “mutation” frequency (almost 20%). As presented in Figure 5B, 382 mutations were found in the CDKN2A sequence, of which truncating seems to be the main mutation type. Moreover, most of the mutation was located in the “Ank_2” (Ankyrin repeats, 52-133) domain. The R80*/Q alteration with the highest alteration frequency was detected in 45 cases of car- cinoma, and its mutation site was presented in the 3D structure
A
C
Mutation
Structural Variant
Amplification
Deep Deletion
Multiple Alterations
50%
Aberation Frequency
-
30%
20%
Suchesi variant dele
ÇNA dela
Griblastone M/Mforme (ICDA, PinCancer Atas)
Persons Adenocarcinoma (TCGA, PanCancer Altas)
Esophageal Adenocarcinoma (TCSA PreCancer Atas)
Mesithetoma (YOGA, PanCarcer Aties)
Long Squamous Ces Cantinone (FCIGA, PanCareer Allas)
pran biren &-Cet Lymphoma (TCCA, ParCances ADEU
seocartin (TCGA ParCancer Adas)
Slumsach Adenocarcinoma (TOGA, PaCenses Adas;
Conom ICDA ParCancer Attas: Bingen Lower Gradte Otome (TCOA, PeConce Adas)
Laver Hepatocelular Carcinome (TCCA, PanCanone Asas)
Ovarien Sieruns Cystasenocarunema (TCGA, ParCancer Afas)
Paymonus (ICCA PuCancer Allasi
Katves Hanal Papmary Col Carcinoma (TOGA, ParCancer ABas)
na (ICGA PinCancer Anas
Wages Penal Des Caf Carcinoma (TOGA, PanCancer Afas) Chpestupendos Cel Cantinone (TOGA, PanCareer Adas)
Uterne Corpus Endomenar Carcinoma (TCGA, ParCancer Alan)
Vadoes Croixghete (TCDA, Ag:)
Pagoio Carcinoma (TOGA Pa-Cances Alas) happenedbypar Cat Tamers (TCGA, ParCancu Atus)
tegeven and Paragangiona (TCGA, ParCancer Adas)
Ovest Metanouna (TCCA, PanCancer ABas)
B
119
Missense
R80*/Q (n=45)
# CDKN2A Mutations
45
207
Truncating
7
Inframe
35
Splice
14
SV/Fusion
0
…
Ank
Ank_2
D
0
100
156aa
OS
DFS
10
DFS
DFS
-im
-
-
-
-
-
ACC
COAD
LIHC
PRAD
GEGEGGGREE
OS
DFS
-
OS
PFI
—
-
GBM
GBM
SKCM
SKCM
OS
DFS
DSS
PFI
—
KIRC
KIRC
KIRC
KIRC
of CDKN2A protein (Fig. 5C). The survival analysis showed that the CDKN2A alteration resulted in a poor prognosis in ACC, COAD, LIHC, PRAD, GBM, SKCM, and KIRC patients (Fig. 5D).
3.5. DNA methylation analysis
Numerous types of research have indicated that DNA methyl- ation, as an epigenetic modification, contributed to regulating the expression of cancer-related genes.[31] Thus, the promoter methylation level of CDKN2A between tumors and normal tissues was explored via the “CPTAC” dataset. As displayed in Figure 6A, compared with normal samples, the promoter methyl- ation level of CDKN2A was dramatically increased in most can- cers (e.g., BLCA, COAD, KIRC, LIHC, and so on), except KIRP with decreased methylation level. No obvious change in meth- ylation was found in TGCT, THCA, stomach adenocarcinoma,
and THYM. Then, the “MethSurv” web tool was performed to further excavate the effect of CDKN2A methylation on sur- vival. Figure 6B indicated that higher CDKN2A methylation (both island and N_shore region) led to poor prognosis in ACC patients. For KIRC, DNA methylation in the N_shore region induced decreased survival probability. There is no significant survival difference between the lower versus higher CDKN2A methylation group in COAD, SKCM, UCEC, and LIHC.
3.6. The correlation analysis between CDKN2A expression and immune infiltration
As we all know, the tumor microenvironment affects tumor occurrence and development, in which the infiltration of immune cells, especially macrophages and CAF, plays crucial roles. So, here we explored the relationship between CDKN2A expres- sion and immune infiltration in TCGA tumors by the “Immune”
A
Proonatet methylation level of CERCHIZA in BLCA
Promoter methylation level of CDKN2A is COAD
Promoter methylation level of CD822A in FORC
Promoter methylation level of CDANZA in BRCA
Promoter methylation level of CDKNZA is CHOL
Median: 0.072
Median: 0,085
Median: 0.059
Median: 0.063
Median: 0.063
Median: 0.061
Median: 0.054
Median: 0.053
Median: 0.062
Median: 0.053
p = 3.29E-07
p = 1.62E-12
p = 2.16E-10
p = 1.63E-12
p = 4.38E-05
TOGA samples
=
TOCA samples
TOCA sangles
TEBA sungkes
TOCA tangles
Promoter methylation level of CDKONZA.in CHOL
Promoter methylation level nif CDKNÍŽA in ESCA
Promoter methylation level of CDKN2A in Ckat
Promoter methylation level of CORNZA in HNSC
Promitar methylation level of CDON02 A in kiky
Mediarc 0.081
Median: 0.676
Median: 0.076
Median: 0.075
Median: 0.053
Median: 0.000
Median: 0.063
Median: 0.049
Median: 0.063
Median: 0.061
p = 0.008
p = 3.88E-05
p = 0.41
p < 1E-12
p = 0.049
-
TCCA samples
TOCA samples
TOCA comptes
FCGA samples
TOGA sangle
Promoter methylation level of CDKN2A in KiRP
Promoter methylation level of CDKNZA In LUAD
Promoter methylation level of COKN2A in LUSC
Promoter methylation level of CDKNZA in FAAD
Promoter methylation level of CDKENZA in PRAD
Median: 0.064
-
Median: 0.10/
Median: 0,07
Median: 0.067
Median: 0.067
Median: 0.057
Median: 0.053
Median: 0,049
Median: 0.092
Median: 0.068
p = 2.53E-07
p = 1.77E-06
p < 1E-12
p = 2.00E-05
p = 7.67E-05
=
TOCA samples
=
-
TOCA Hempler
TELA samples
TOCA samples
Promoter methylation level of CDR22A in ASAD
Promoter methylation level of CDENZA in SARC
Promoter methylation level of CDANZA is TGCT
Promoter methylation level of CDANZA In STAD
Promoter methylation level of CDKNZA in THCA
Median: 0.075
Median: 0.052
Median: 0.051
Median: 0.879
Median: 0.859
Mediar: 0.058
Median: 0.048
—
Median: 0.064
Median: 0.046
Median: 0.069
p = 3.72E-08
p = 1.84E-04
p = 0.0503
p = 0.4650
p = 0.2728
=
=
CGA samples
TCCA samples
TOGA sengles
TOCA samples
1CCA samples
Promoter methylation level of CDANZA In THYM
Promoter misthylation level of CDKNZA In UCEC
Median: 0.072
Median: 0.051
Medien: 0.064
Median: 0.051
p = 0.7222
p = 1.22E-11
B
Cancer: ACC
Cancer: KIRC
COKINZA + 1450000 Hody-40and-491.3001 799
CEKAIRA - HAR Rin, Body & Shore(go-3004.7)
CDENJA - Body, SIFR-N More-4937810/19
CDKNJA - ISExon Body-island-(01 1001 799
CORNJA - PAPP XDIŞ Hoy-N Shore-(00-1000079
=
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tunivel time Here!
Cancer: COAD
Cancer: SKCM
CUANTA + 1SFF wie Body-edand-( q1 300: 39
CERNIA - ISTE xong Boden Shore-egosorsa rs
KORNJA - Body: TUINN Shore-2017810/19
CARNICERNUTRAS - Estizone 13.1500-and-egoso/sos CDen//c(DRN/BAS , stixon: 19:500-hland-go/5679
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Cancer: LIHC
=
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4
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1
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2000
500
1000
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Survival time liduyağ
module of TIMER web serve. Heatmap data and Scatter plot data indicated a significantly positive association between CDKN2A expression and the infiltration of macrophages (MO and M1) in BLCA, PARD and THCA. For BRCA, we found a positive
relationship between CDKN2A level and the infiltration of M0 and M1 cell but noted a negative association in M2 cell infiltra- tion. Moreover, the CDKN2A expression in CESC, LUAD, and OV was positively correlated with the estimated infiltration of
A
S . CDKN2A Expression Level (log2 TPM) CDKN2A Expression Level (log2 TPM) CDKN2A Expression Level (log2 TPM) CDKN2A Expression Level (log2 TPM) COKN2A Expression Level (log2 TPM) 1
CDKN2A Expression Level (log2 TPM) CDKN2A Expression Level [log2 TPM) CDKN2A Expression Level (log2 TPM) CDKN2A Expression Level (log2 TPM; COKN2A Expression Level (Jog2 TPM) 4
CDKN2A Expression Level (log2 TPM) CDKN2A Expression Level (log2 TPM) CDKN2A Expression Level (log2 TPM)
Portty
Macrophage MO CIBERSORT-ABS
Purty
Macrophage MI QUANTISEO
Purity
Macrophage MI_QUANTISEO
10.0-
98-1
10.0
ACC
E
BLCA
…
7.5
10
7.5
BRCA
..
.. ..
5.0
BLCA
BLCA
5.0
CESC
BRCA-Basal
A
BRCA-Her2
2.5
2.5
BRCA-LumA
.. ..
0.0
0
0.0
.
BRCA-LumB
.
.
0.25
0.50
0.75
1.000.0
0.1
0.2
0.3
0.25
8.50
0.75
1.000.0
01
82
0.25
0.50
0.75
1.000.00
0.05
0.10 0.
0.20
Purity
Infiltration Level
Purity
Infiltration Level
Purity
Infiltration Level
CESC
… .
CHOL
.
Purity
Macrophage MD_CIRERSORT ABS
Purity
Macrophago MI_XCELL
Purity
Macrophage M2_TIDE
COAD
·
*
5
₹
.
DLBC
.
1.5
5
1.5
ESCA
.
S.
BACA
O
BACA
BACA
GBM
.
HNSC
.. ..
. ..
* p <0.05
2.5
.5
2.5-
HNSC-HPV-
-
** p<0.01
HNSC HPV+
**
Correlation
0.0
1.0
0.50
0,75
1.00 0.0
0.2
04
0.0
0.25
0.25
0.50
0.75
1.000/00
0,05
0.10
0.15
0.25
0.50
0.75
1.000.15 -0.10 -0.05 0.00 0.05 0.10
KICH
.. ..
Purity
Infiltration Level
Purity
Infiltration Level
Purity
Infiltration Level
KIRC
0.5
KIRP
*
0.0
Purity
Macrophage_XCELL
Purity
Macrophage M1 CIBERSORT
Purity
Macrophage MI_QUANTISEO
·
95884
10.0
LGG
… .. ..
-0.5
.
LIHC
**
++
-1.0
2
“.
2
7,3
LUAD
… ..
..
COAD
0
LEAD
6
LUSC
5.0
OV
… ..
.. ..
*
2.5
PAAD
.
%
PRAD
… .. ..
*
0.25
0.50
0.75
1.000.00
0.05
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MO CIBERSORT
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CDKN2A Expression Level (log2 TPM)
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CDKN2A Expression Level (log2 TPM)
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M1 macrophages based on all algorithms (Fig. 7A). Besides, the expression of CDKN2A in BRCA-lumA, COAD, KIRP, TGCT, and THCA was positively associated with the CAF infiltration, while CESC was negatively correlated (Fig. 7B).
3.7. Enrichment and co-expression analysis of CDKN2A
To elucidate the function of CDKN2A, GEPIA2, and xiantao bioinformatic toolbox were carried out. At the gene expression level, GEPIA2 was used to find similar genes. As represented in Figure 8B, the CDKN2A expression was positively correlated with identical genes, including ASF, CDC20, MCM2, replica- tion factor C subunit 4, RNSSEH2A and Mago homolog, exon junction complex subunit. The correlation analysis suggested that genes-ASF, CDC20, MCM2, replication factor C subunit 4, RNSSEH2A and Mago homolog, exon junction complex subunit- were positively related to most cancer, especially ACC, BLCA, HNSC-HPV+, SARC, and LIHC (Fig. 8C). However, the above genes seemed to play a weakly negative role in TGCT (Fig. 8C, blue square). Moreover, Protein-Protein Network Interaction (Fig. 8A) was established to display identified CDKN2A-binding molecules intuitively. The top 20 molecules (core molecules) were chosen for further GO and KEGG enrich- ment analysis. Go enrichment analysis results indicated that CDKN2A and the core molecules mainly involved biological process, such as G1/S transition of mitotic cell cycle, cell cycle G1/S phase transition, DNA replication initiation, and DNA- dependent DNA replication (Fig. 8D, blue columns). For CC analysis, core molecules were mostly involved in the chromo- somal region, chromosome, telomeric region, MCM complex and nuclear chromosome, and telomeric region (Fig. 8D, red col- umns). The molecular function showed that the core molecules were mainly involved in DNA helicase activity, single-stranded DNA binding, 3’-5’ DNA helicase activity and DNA replica- tion origin binding (Fig. 8D, green columns). KEGG analysis figured out that core molecules mainly participated in the p53 signaling pathway, non-small cell lung cancer, Cellular senes- cence, Endocrine resistance, Chronic myeloid leukemia, Glioma, Melanoma, Bladder cancer, DNA replication and Cell cycle signaling pathways (Fig. 8E). Moreover, single-gene GSEA was performed to analyze CDKN2A-relevant pathways in ACC, COAD, KIRC, LIHC, PRAD, SKCM, THCA, and UCEC. The top 5 most frequently enriched pathways were visualized (see Figure S3A-H, Supplemental Digital Content 3, http://links. lww.com/MD/I756, Supplemental Content, which suggested CDKN2A-relevant pathways in pan-cancer).
As we all know, the cell cycle, immune response and metabo- lism affect the occurrence and development of pan-cancer. The relevant pathways related to cell cycle, immune regulation (e.g., signaling by interleukins, integrin cell surface interactions, inte- grin-1 pathway and so on) and metabolisms relevant to oxida- tive stress, mitochondrial activity and fatty acid were conducted. GSEA results showed that CDKN2A was positively enriched in the cell cycle in ACC and PRAD (Fig. 9A and C). Furthermore, CDKN2A was found to be positively associated with the path- ways related to oxidative response, fatty acid metabolism and mitochondrial metabolism in ACC (Fig. 9A), COAD (Fig. 9B), PRAD (Fig. 9C), and SKCM (Fig. 9D). Moreover, CDKN2A was positively related to immune-related pathways in ACC (Fig. 9A) and PRAD (Fig. 9E). Conversely, the aforementioned pathways in SKCM and THCA were negatively regulated. For KIRC, LIHC and UCEC, the pathways mentioned above were not dramatically enriched (see Figure S4A-C, Supplemental Digital Content 4, http://links.lww.com/MD/I757, Supplemental Content, which indicated CDKN2A-relavant pathways related to cell cycle, immune and metabolisms in KIRC, LIHC, and UCEC). All of the above results indicated that the CDKN2A expression was linked to some critical pathways in cancer for- mation, occurrence and metastases.
3.8. CDKN2A correlated with the majority of cuproptosis- related genes and immune-regulatory genes
To further validate the roles of CDKN2A in different cancers, the co-expression analysis related to cuproptosis, immune check- point and immune regulation was also conducted. As presented in Figure 10A, cuproptosis-related genes in ACC were positively correlated with CDKN2A expression while negatively related in KIRC, PRAD, and THCA. Moreover, the correlation analysis of immune checkpoints showed that most genes were significantly positively associated with CDKN2A in BLCA, BRCA, HNSC, KICH, KIRP, LIHC, TGCT, and THCA. Conversely, some immune checkpoint genes in UCEC were negatively associated with CDKN2A (Fig. 10B). In addition, the correlation analy- sis of immune-regulatory genes results was simultaneous with the previous ones (Fig. 10C and D). Consequently, CDKN2A might play a pivotal role in pan-cancer copper metabolism and immune infiltration.
4. Discussion
CDKN2A, a tumor suppressor and cell cycle regulator, was verified to take part in the cell cycle and p53 signaling path- way.[13] CDKN2A encoded 2 proteins, named p16INK4a and p14ARF.[32] It is reported that p14ARF regulated the cell cycle by preventing p53 inactivation, while p16INK4a prevented the phosphorylation of Rb proteins. [32,33] Previous studies have revealed the suppressive roles of CDKN2A in some cancer types.[20,34-36] Disruption of CDKN2A (deletion or methylation) has been reported to be a frequent event in tumorigenesis, which affected the clinical characteristics and patient outcomes.[14-16] Alhejaily and his colleagues revealed that silence of CDKN2A by deletion or methylation was correlated with worse clinical outcome in follicular lymphoma.[14] A meta-analysis from Xing showed that CDKN2A hypermethylation was significantly associated with unfavorable prognosis in CRC patients.[15] Similar results were also discovered in PDAC, thymic carci- noma, HNSC and MIBC.[16-19] Although the tumorigenic effects of disruption of CDKN2A were well confirmed, unexpectedly high CDKN2A indicated a poor clinical outcome in some can- cer, including COAD, BLCA and LIHC.[17,20-22] The mechanism by which CDKN2A serves as a tumor suppressor but results in unfavorable prognosis is speculated that CDKN2A may involve in tumor initiation and progression as an anti-cupropto- sis gene by performing genome-wide CRISPR/Cas9 knock-out screens.[8,23-25] However, as an anti-cuproptosis gene, the roles or signatures and the regulatory mechanisms of CDKN2A in pan-cancer have not yet been explored in depth. Thus, we con- ducted this pan-cancer analysis for CDKN2A.
In this study, overexpressed CDKN2A was observed in the majority of cancer tissues (e.g., ACC, BLCA, CESC, lymphoid neoplasm diffuse large B-cell lymphoma, esophageal carcinoma, UCEC and so on), while downregulated CDKN2A was found in TGCT tissues by the assessment of CDKN2A mRNA level. These results indicated that CDKN2A might involve in different pathways in cancer initiation and progression. Consistent with the transcriptional level, an augmented translational level of CDKN2A was found in KIRC, UCEC, LUAD, and LIHC sam- ples. Curiously, the protein expression of CDKN2A in HNSC was not consistent, which might be due to metabolism or post- transcriptional protein modification. Moreover, it is verified that the expression of CDKN2A was related to the tumor patholog- ical stages in ACC, COAD, KICH, UCEC, KIRC, KIRP, LIHC, and THCA, which suggested the potential of CDKN2A as a bio- marker for the clinical stage.
The Cox regression and Kaplan-Meier analysis revealed that upregulated CDKN2A predicted a poor prognosis for ACC, COAD, KIRC, LIHC, PRAD, SKCM, THCA, and UCEC. Concordant with this, recent convincing research also
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ACC (n=79)
BLCA (n=408)
BRCA (n=1100)
BRCA-Basal (n=191)
BRCA-Her2 (n=82)
BRCA-LumA (n=568)
BRCA-LumB (n=219)
CESC (n=306)
CHOL (n=36)
COAD (n=458)
DLBC (n=48)
ESCA (n=185)
HNSC (n=522) GBM (n=153)
HNSC-HPV- (n=422)
HNSC-HPV+ (n=98)
KICH (n=66)
KIRC (n=533)
KIRP (0=290)
LIHC (n=371)
LUAD (n=515)
LUSC (n=501)
OV (n=303)
PAAD (n=179)
PRAD (n=498) READ (n=166)
SARC (n=260) SKCM (n=471)
SKCM-Metastasis (n=368)
SKCM-Primary (n=103)
STAD (n=415)
TGCT (n=150)
THCA (h)=509)
THYM (n=120)
UCEC (n=545)
UCS (n=57)
D
DNA helicase activity
single-stranded DNA binding
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DNA replication origin binding
chromosomal region
BP
chromosome, telomeric region
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Gene.Ratio
| Ontology | ID | Description | GeneRatio | pvalue | p.adjust | qvalue |
|---|---|---|---|---|---|---|
| BP | GO:0000082 | G1/S transition of mitotic cell cycle | 17/20 | 6.20e-29 | 7.75e-26 | 3.64e-26 |
| BP | GO:0044843 | cell cycle G1/S phase transition | 17/20 | 1.96e-28 | 1.22e-25 | 5.74e-26 |
| BP | GO:0006270 | DNA replication initiation | 11/20 | 5.92e-26 | 2.46e-23 | 1.16e-23 |
| BP | GO:0006261 | DNA-dependent DNA replication | 11/20 | 1.23e-18 | 3.83e-16 | 1.80e-16 |
| CC | GO:0000784 | nuclear chromosome telomeric region | 8/20 | 2.46€-13 | 1.890-11 | 1.190-11 |
| CC | GO:0042555 | MCM complex | 5/20 | 4.93e-13 | 1.90e-11 | 1.19e-11 |
| CC | GO:0000781 | chromosome, telomeric region | 8/20 | 1.92e-12 | 4.94e-11 | 3.10e-11 |
| CC | GO:0098687 | chromosomal region | 8/20 | 9.31e-10 | 1.79e-08 | 1.13e-08 |
| MF | GO:0003688 | DNA replication origin binding | 11/20 | 3.13e-28 | 3.10€-26 | 1.52e-26 |
| MF | GO:0043138 | 3'-5' DNA helicase activity | 5/20 | 1.65e-11 | 8.14e-10 | 3.98e-10 |
| MF | GO:0003697 | single-stranded DNA binding | 7/20 | 2.60e-11 | 8.56e-10 | 4.19e-10 |
| MF | GO:0003678 | DNA helicase activity | 6/20 | 2.80e-10 | 6.94e-09 | 3.39e-09 |
| Ontology | ID | Description | GeneRatio | pvalue | p.adjust | qvalue |
|---|---|---|---|---|---|---|
| KEGG | hsa04110 | Cell cycle | 16/19 | 3.29e-27 | 2.53e-25 | 1.35e-25 |
| KEGG | hsa03030 | DNA replication | 5/19 | 1.47e-08 | 5.64e-07 | 3.01e-07 |
| KEGG | hsa05219 | Bladder cancer | 5/19 | 2.89e-08 | 7.42e-07 | 3.96e-07 |
| KEGG | hsa05218 | Melanoma | 5/19 | 5.16e-07 | 8.700-06 | 4.64e-06 |
| KEGG | hsa05214 | Glioma | 5/19 | 6.34e-07 | 8.70e-06 | 4.64e-06 |
| KEGG | hsa05220 | Chronic myeloid leukemia | 5/19 | 6.78e-07 | 8.70e-06 | 4.64e-06 |
| KEGG | hsa01522 | Endocrine resistance | 5/19 | 2.41e-06 | 2.65e-05 | 1.42e-05 |
| KEGG | hsa05223 | Non-small cell lung cancer | 4/19 | 2.03e-05 | 1.49e-04 | 7.96e-05 |
| KEGG | hsa04115 | p53 signaling pathway | 4/19 | 2.15e-05 | 1.49e-04 | 7.96e-05 |
| KEGG | hsa04218 | Cellular senescence | 5/19 | 2.36e-05 | 1.49e-04 | 7.96e-05 |
Figure 8. Co-expression and enrichment analysis of CDKN2A in pan-cancer. (A) Experimentally identified CDKN2A binding molecules were exhibited in PPI networks established by STRING. (B) Core CDKN2A-related genes, including ASF, CDC20, MCM2, RFC4, RNSSEH2A and MAGOH, were analyzed by GEPIA2. (C) The correlation between CDKN2A expression and selected CDKN2A-related genes (ASF, CDC20, MCM2, RFC4, RNSSEH2A and MAGOH) in pan-cancer was represented via heatmap. GO (D) and KEGG (E) enrichment for the top 20 experimental identified CDKN2A binding molecules from PPI network. BP = biological process, CC = cellular component, CDKN2A = cyclin dependent kinase inhibitor 2A, GEPIA2 = gene expression profiling interactive analysis, GO = gene ontology, KEGG = Kyoto encyclopedia of genes and genomes, MF = molecular function.
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1000
2000
1000
4000
1000
2000
3000
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8000
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Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
Rank in Ordered Dataset
| 0 1 | |||
|---|---|---|---|
| REACTOME CHLA CICLI 0 378 | 1.834 | 0.048 | |
| KEGG, CHIL CYCLE 0.457 | 1.905 | ||
| WP_CILL CYCLE | 0000 | 0028 |
| NES | FOR | ||
|---|---|---|---|
| WP GRIDATIVE PHOS_0670 | 2.122 | 0041 | |
| WP OXIDATION WY C. 0-651 | 2.228 | 6.646 | 6041 |
| REACTOME_FATTY_AC .. 4-445 | -1.314 | 0012 | 0.048 |
| REACTOME THE CITIL_ 4524 | -2257 | 0052 | 0044 |
| WP_MITOCHONDRIAL, - 6-170 | 1.500 |
| ID RS | NES | paint | |
|---|---|---|---|
| REACTOME INTIGRIN, 0559 | 1.990 | 0.046 | 0:038 |
| PID INTEGAINT_PAT. 0.585 | 0.005 | 0018 | |
| BLACTOME SIGNALIN, 0.335 | 0176 | 0.147 |
| NES | patjust | FOR | |
|---|---|---|---|
| REACTOME_DKCOATN .- 6.60% | -4.585 | 0044 | 00M |
| REACTOME ACTIVATE_ 0695 | 1.770 | 0014 | 0016 |
| REACTOME, APOPTOSE. 0.829 | -1.715 | 0649 | 0.040 |
| 0 ES | MIS | DaGust | FOR |
|---|---|---|---|
| WP_COMPLIMINT_AND_0.593 | 2010 | 0:044 | 0:040 |
| REACTOME NEGATIVE. 8538 | 1848 | 0044 | 0040 |
| KIGQ_DRUG_METABOL_ 0617 | 2077 | 0.044 | 0:040 |
| REACTOME CELL CYCLE 0 357 | 1.290 | 0044 | 0.040 |
| NES | padust | FOR | |
|---|---|---|---|
| REACTOME CELL SUR 0.551 | 2130 | 0 014 | 0.025 |
| REACTOME IMMUNORE: - 0.558 | -2171 | 0014 | 0:026 |
| REACTOME_SIGNALIN -0.691 | 2.525 | 0 034 | |
| BLACTOME ANTIGEN _ 0.726 | 2568 | 0.014 |
| 0 | NES | DAGIt | IDE |
|---|---|---|---|
| REACTOME ROLE_OF _- - 4725 | -2.509 | 0.054 | 0.0.26 |
| KEOG ONDATIVE PHL.0524 | 2112 | 0.046 | 0.036 |
| REACTOME CELE CYC 0412 | 2076 | POST | 0010 |
| REACTOME MITOCHON.0.546 | 0082 | 0064 |
| NES | paquet | FOR | |
|---|---|---|---|
| REACTOME SIGNAUN_0.491 | 1.909 | 0.026 | 0022 |
| REACTOME NEUTROPH , 0.511 | 1.995 | 0.006 | 0.022 |
| K10G CYTOKINE CYT.0586 | 2.218 | 0:025 | 0022 |
| REACTOME CELL SUIL: 0 725 | 2.634 | 0026 | 00/2 |
| REACTOME INTERFER,. 0-489 | 0006 | 6022 |
Figure 9. Gene set enrichment analysis. The roles of CDKN2A in ACC (A), COAD (B), PRAD (C), SKCM (D), THCA (E) by performing Gene Set Enrichment Analysis (GSEA). ACC = adrenocortical carcinoma, CDKN2A = cyclin dependent kinase inhibitor 2A, COAD = colon adenocarcinoma, PRAD = prostate adeno- carcinoma, SKCM = skin cutaneous melanoma, THCA = thyroid carcinoma.
discovered that overexpressed CDKN2A was correlated with the poor prognosis in CRC.[21,34,37] These results revealed the potential of CDKN2A as an original prognostic predictor for some cancer types.
Deneka et al(38] found the relationship between CDKN2A mutation and tumor mutation burden. Liu and his colleagues demonstrated that the presence of CDKN2A deletion might induce the progression of ESCC.[39] In T-cell acute lymphoblastic leukemia, CDKN2A deletion was an independent poor prog- nostic factor.[40] Similar to the above findings, deep deletion hap- pened in most cancer types. Besides, CDKN2A mutation played important roles in certain cancers. Our results showed that the CDKN2A amplification has arisen in ACC, OV, UCEC and TGCT, and the alteration of CDKN2A implicated inferior out- comes in cancer such as ACC, COAD, LIHC, KIRC, and so on. These results were seemly contradictory to the aforementioned CDKN2A expression levels. Possible reasons for these conflict- ing results are as follows: different change types occurred in tumors. For instance, compared with deep deletion, a mutation might have a greater impact on tumorigenesis in certain human tumors, which resulted in a loss of function. Then compensatory overexpression of CDKN2A was measured. Moreover, amplifi- cation might occupy center stage in some cancers, leading to the upregulated expression.
As an epigenetic modification, DNA methylation contrib- utes to regulating the expression of cancer-related genes.[31] A meta-analysis performed by Zhou pointed out that CDKN2A methylation had a crucial role in the occurrence of esopha- geal cancer.[41] However, Cao et al indicated that CDKN2A
methylation was not significantly correlated with the progres- sion of PRAD.[42] This study explored that obviously increased promoter methylation level of CDKN2A happened in most can- cers, which led to poor outcomes in ACC and KIRC. Therefore, we hypothesized that aberrant methylation of CDKN2A might take part in tumor progression and prognosis. Furthermore, enrichment analysis results suggested CDKN2A was closely correlated to cell cycle, immune responses and DNA replica- tion. Especially, CDKN2A was positively enriched in cell cycle, oxidative response, fatty acid metabolism and mitochondrial metabolisms and immune-related pathways in ACC and PRAD. Thus, the results conformed to the existing mechanisms of can- cer formation, occurrence and metastases.[43] Recent studies have revealed the indispensable and integral roles of tumor microen- vironment (TME) in tumor physiology.[44,45] Growing evidence discovered that immune cells, transformed ECM and soluble factors presented in TME gave rise to tumor progression and metastasis.[44,46,47] Macrophages, the most abundant immune cells residing in TME, reflected the Th1/Th2 paradigm via anti- gen presentation and pathogen phagocytosis, which played crucial roles in immune homeostasis.[44] Tumor-associated mac- rophages (TAMs) were found to be correlated with shorter sur- vival in cancer patients.[48,49] The analysis from Luo et al revealed that CDKN2A was positively related to the infiltration of mac- rophages in LIHC,[22] which indicated its value for prognosis or immunotherapy.[50] Xiao and his team discovered that high-risk PDAC group had higher CDKN2A mutations with mounted infiltration of macrophages MO and M2.[51] CAF, as the most essential components of the TME, have also been verified to
A
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have a critical role in tumorigenesis and progression.[52] CAFs interact with immune cells by the secretion of growth factors, cytokines and other molecules, and induce an immunosuppres- sive TME that facilitates the growth of tumor cells.[52-54] Similar to the previous studies, the immune infiltration analysis in our
study suggested that CDKN2A expression was positively asso- ciated with the infiltration of macrophages or CAFs in pan-can- cer, which exposed the important roles of CDKN2A in tumor immunology. It is noteworthy that more macrophages M1 infil- tration was found in some CDKN2A overexpressed samples,
which seemly contradictory to the aforementioned prognostic evaluation of CDKN2A. These conflicting results may be due to the double-edged roles of CDKN2A in pan-cancer. Furthermore, our study also analyzes the relationship between CDKN2A expression and immune-related genes, including the immune checkpoint, immunosuppressive and immune activated genes. Fascinatingly, consistent with enrichment analysis results, a strong correlation between CDKN2A and immune-related genes was found in diverse cancers. These exciting results offered new insights and orientations for cancer treatment. Moreover, CDKN2A associated with the majority of cuproptosis-related genes in ACC, KIRC, PRAD and THCA, which implied the vital roles of CDKN2A in copper metabolism.
Nevertheless, there are some limitations in this study. Firstly, this study had not yet explored the definite mechanisms of CDKN2A expression in copper metabolisms. How CDKN2A expression affected immune infiltration and tumor progression needs to be verified in later research. Secondly, we explored the double-edged roles of CDKN2A as tumor suppressors or oncogenes in pan-cancer, and it might be induced by the diverse origin of cancers and tumor heterogeneity. In addition, more Vivo and Vitro experiments are further needed for verification.
In summary, our study pointed out the potential of CDKN2A as a predictor and biomarker associated with prognosis. The results provided new insights and orientations for novel anti-cancer treatments via facilitating tumor silence or regulat- ing cuproptosis.
Acknowledgments
We would like to thank the Tianjin Union Medical Center and Tianjin Institute of Coloproctology for the support. In addi- tion, we thank all tools used in this study for data analysis and visualization.
Author contributions
Conceptualization: Xipeng Zhang.
Data curation: Di Zhang.
Formal analysis: Di Zhang, Xipeng Zhang.
Investigation: Di Zhang, Tao Wang.
Methodology: Yi Zhou.
Project administration: Xipeng Zhang.
Resources: Di Zhang, Yi Zhou.
Software: Tao Wang.
Supervision: Xipeng Zhang.
Validation: Xipeng Zhang.
Visualization: Di Zhang, Tao Wang.
Writing - original draft: Di Zhang.
Writing - review & editing: Xipeng Zhang.
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