Establishment of a 12-gene expression signature to predict colon cancer prognosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29915691", "kind": "science-record", "revision": 7, "content_hash": "sha256:3aa96f4f61c4d241723383f388d12d666a9bda34b2e23cc8317887cb68c313ad", "created_at": "2026-11-01T15:40:34Z", "updated_at": "2026-11-01T21:59:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Establishment of a 12-gene expression signature to predict colon cancer prognosis.", "doi": "10.7717/peerj.4942", "pmid": "29915691", "authors": ["Dalong Sun", "Jing Chen", "Longzi Liu", "Guangxi Zhao", "Pingping Dong", "Bingrui Wu", "Jun Wang", "Ling Dong"], "journal": "PeerJ", "year": 2018, "abstract": "A robust and accurate gene expression signature is essential to assist oncologists to determine which subset of patients at similar Tumor-Lymph Node-Metastasis (TNM) stage has high recurrence risk and could benefit from adjuvant therapies. Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD) prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA). The predictive performance of the 12-gene signature was validated with two independent gene expression microarray datasets: GSE39582 includes 566 COAD cases for the development of six molecular subtypes with distinct clinical, molecular and survival characteristics; GSE17538 is a dataset containing 232 colon cancer patients for the generation of a metastasis gene expression profile to predict recurrence and death in COAD patients. The signature could effectively separate the poor prognosis patients from good prognosis group (disease specific survival (DSS): Kaplan Meier (KM) Log Rank p = 0.0034; overall survival (OS): KM Log Rank p = 0.0336) in GSE17538. For patients with proficient mismatch repair system (pMMR) in GSE39582, the signature could also effectively distinguish high risk group from low risk group (OS: KM Log Rank p = 0.005; Relapse free survival (RFS): KM Log Rank p = 0.022). Interestingly, advanced stage patients were significantly enriched in high 12-gene score group (Fisher's exact test p = 0.0003). After stage stratification, the signature could still distinguish poor prognosis patients in GSE17538 from good prognosis within stage II (Log Rank p = 0.01) and stage II & III (Log Rank p = 0.017) in the outcome of DFS. Within stage III or II/III pMMR patients treated with Adjuvant Chemotherapies (ACT) and patients with higher 12-gene score showed poorer prognosis (III, OS: KM Log Rank p = 0.046; III & II, OS: KM Log Rank p = 0.041). Among stage II/III pMMR patients with lower 12-gene scores in GSE39582, the subgroup receiving ACT showed significantly longer OS time compared with those who received no ACT (Log Rank p = 0.021), while there is no obvious difference between counterparts among patients with higher 12-gene scores (Log Rank p = 0.12). Besides COAD, our 12-gene signature is multifunctional in several other cancer types including kidney cancer, lung cancer, uveal and skin melanoma, brain cancer, and pancreatic cancer. Functional classification showed that seven of the twelve genes are involved in immune system function and regulation, so our 12-gene signature could potentially be used to guide decisions about adjuvant therapy for patients with stage II/III and pMMR COAD.", "claims": [{"id": "claim-method", "statement": "Survival comparisons between high and low risk groups were conducted by Kaplan–Meier plotting. Log Rank p value <0.05 was considered as significantly different.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29915691", "locator": "sections[12] \"Validation stage\" paragraph 2", "quote": "Survival comparisons between high and low risk groups were conducted by Kaplan–Meier plotting. Log Rank p value <0.05 was considered as significantly different."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:\nDetailed clinical information of TCGA patients enrolled in phase I and phase II training stage were listed in Tables S1 and S2, respectively.\nThe detailed clinical information of TCGA patients enrolled in phase I and phase II training stages are listed in Tables S1–S3.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29915691", "locator": "sections[32] \"Data Availability Statement\" paragraphs 1-4", "quote": "The following information was supplied regarding data availability:\nDetailed clinical information of TCGA patients enrolled in phase I and phase II training stage were listed in Tables S1 and S2, respectively.\nThe detailed clinical information of TCGA patients enrolled in phase I and phase II training stages are listed in Tables S1–S3."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The paper used a two-step supervised machine-learning method trained with TCGA COAD RNA-seq data and validated in independent GEO microarray cohorts; the resulting 12-gene signature separated prognosis groups, including in GSE17538 (DSS: KM Log Rank p = 0.0034; OS: KM Log Rank p = 0.0336) and in GSE39582 pMMR patients (OS: KM Log Rank p = 0.005; RFS: KM Log Rank p = 0.022).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:29915691", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Here we applied a two-step supervised machine-learning method and established a 12-gene expression signature to precisely predict colon adenocarcinoma (COAD) prognosis by using COAD RNA-seq transcriptome data from The Cancer Genome Atlas (TCGA)."}, {"asset": "source:pmid:29915691", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "The signature could effectively separate the poor prognosis patients from good prognosis group (disease specific survival (DSS): Kaplan Meier (KM) Log Rank p = 0.0034; overall survival (OS): KM Log Rank p = 0.0336) in GSE17538."}, {"asset": "source:pmid:29915691", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "For patients with proficient mismatch repair system (pMMR) in GSE39582, the signature could also effectively distinguish high risk group from low risk group (OS: KM Log Rank p = 0.005; Relapse free survival (RFS): KM Log Rank p = 0.022)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The paper used a two-step supervised machine-learning method trained with TCGA COAD RNA-seq data and validated in independent GEO microarray cohorts; the resulting 12-gene signature separated prognosis groups, including in GSE17538 (DSS: KM Log Rank p = 0.0034; OS: KM Log Rank p = 0.0336) and in GSE39582 pMMR patients (OS: KM Log Rank p = 0.005; RFS: KM Log Rank p = 0.022).", "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:29915691"], "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. 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