Identification and validation of an epithelial mesenchymal transition-related gene pairs signature for prediction of overall survival in patients with skin cutaneous melanoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35116193", "kind": "science-record", "revision": 4, "content_hash": "sha256:7f44c5e8e7b98ed885a2af6ebfdf7c30270156dc4482827e66caa4ac1accc613", "created_at": "2026-11-01T17:36:50Z", "updated_at": "2026-11-01T22:05:44Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of an epithelial mesenchymal transition-related gene pairs signature for prediction of overall survival in patients with skin cutaneous melanoma.", "doi": "10.7717/peerj.12646", "pmid": "35116193", "authors": ["Yucang Shi", "Zhanpeng Li", "Zhihong Zhou", "Simu Liao", "Zhiyuan Wu", "Jie Li", "Jiasheng Yin", "Meng Wang", "Meilan Weng"], "journal": "PeerJ", "year": 2022, "abstract": "BACKGROUND: We aimed to construct a novel epithelial-mesenchymal transition (EMT)-related gene pairs (ERGPs) signature to predict overall survival (OS) in skin cutaneous melanoma (CM) patients.\nMETHODS: Expression data of the relevant genes, corresponding clinicopathological parameters, and follow-up data were obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis was utilized to identify ERGPs significantly associated with OS, and LASSO analysis was used to identify the genes used for the construction of the ERGPs signature. The optimal cutoff value determined by the receiver operating characteristic curve was used to classify patients into high-risk and low-risk groups. Survival curves were generated using the Kaplan-Meier method, and differences between the two groups were estimated using the log-rank test. The independent external datasets GSE65904 and GSE19234 were used to verify the performance of the ERGPs signature using the area under the curve (AUC) values. In addition, we also integrated clinicopathological parameters and risk scores to develop a nomogram that can individually predict the prognosis of patients with CM.\nRESULTS: A total of 104 ERGPs related to OS were obtained, of which 21 ERGPs were selected for the construction of the signature. All CM patients were stratified into high-and low-risk groups based on an optimal risk score cutoff value of 0.281. According to the Kaplan-Meier analysis, the mortality rate in the low-risk group was lower than that in the high-risk group in the TCGA cohort (P < 0.001), GSE65904 cohort (P = 0.006), and GSE19234 cohort (P = 0.002). Multivariate Cox regression analysis indicated that our ERGP signature was an independent risk factor for OS in CM patients in the three cohorts (for TCGA: HR, 2.560; 95% CI [1.907-3.436]; P < 0.001; for GSE65904: HR = 2.235, 95% CI [1.492-3.347], P < 0.001; for GSE19234: HR = 2.458, 95% CI [1.065-5.669], P = 0.035). The AUC value for predicting the 5-year survival rate of patients with CM of our developed model was higher than that of two previously established prognostic signatures. Both the calibration curve and the C-index (0.752, 95% CI [0.678-0.826]) indicated that the developed nomogram was highly accurate. Most importantly, the decision curve analysis results showed that the nomogram had a higher net benefit than that of the American Joint Committee on Cancer stage system.\nCONCLUSION: Our study established an ERGPs signature that could be potentially used in a clinical setting as a genetic biomarker for risk stratification of CM patients. In addition, the ERGPs signature could also predict which CM patients will benefit from PD-1 and PD-L1 inhibitors.", "claims": [{"id": "claim-method", "statement": "In the discovery cohort (TCGA dataset), 104 ERGPs were significantly associated with prognosis in CM patients according to univariate Cox regression analysis. Among these, 21 ERGPs were further selected using Lasso-Cox regression analysis to establish a prognostic risk signature (Figs. 1A–1B).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35116193", "locator": "sections[14] \"Establishment and evaluation of the ERGPs signature\" paragraph 2", "quote": "In the discovery cohort (TCGA dataset), 104 ERGPs were significantly associated with prognosis in CM patients according to univariate Cox regression analysis. Among these, 21 ERGPs were further selected using Lasso-Cox regression analysis to establish a prognostic risk signature (Figs. 1A–1B)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:Data is available at NCBI GEO: GSE65904.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35116193", "locator": "sections[31] \"Data Availability\" paragraph 2", "quote": "The following information was supplied regarding data availability:Data is available at NCBI GEO: GSE65904."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study developed an EMT-related gene-pair signature that stratified cutaneous melanoma patients by overall-survival risk and was independently prognostic in TCGA and two independent GEO cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35116193", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Our study established an ERGPs signature that could be potentially used in a clinical setting as a genetic biomarker for risk stratification of CM patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study developed an EMT-related gene-pair signature that stratified cutaneous melanoma patients by overall-survival risk and was independently prognostic in TCGA and two independent GEO cohorts.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35116193"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE19234"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE19234"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35116193", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.12646", "sha256": 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