A 65‑gene signature for prognostic prediction in colon adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29393333", "kind": "science-record", "revision": 7, "content_hash": "sha256:0988f8c665ee350824c482c0ae6c57430d769041c6e5c9bb71afebbc4acb697d", "created_at": "2026-11-01T15:40:31Z", "updated_at": "2026-11-01T21:21:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A 65‑gene signature for prognostic prediction in colon adenocarcinoma.", "doi": "10.3892/ijmm.2018.3401", "pmid": "29393333", "authors": ["Hui Jiang", "Jun Du", "Jiming Gu", "Liugen Jin", "Yong Pu", "Bojian Fei"], "journal": "International journal of molecular medicine", "year": 2018, "abstract": "The aim of the present study was to examine the molecular factors associated with the prognosis of colon cancer. Gene expression datasets were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases to screen differentially expressed genes (DEGs) between colon cancer samples and normal samples. Survival‑related genes were selected from the DEGs using the Cox regression method. A co‑expression network of survival‑related genes was then constructed, and functional clusters were extracted from this network. The significantly enriched functions and pathways of the genes in the network were identified. Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples. The discrimination efficacy of the system was validated in the GSE17538 dataset using Kaplan‑Meier survival analysis. A total of 636 and 1,892 DEGs between the colon cancer samples and normal samples were screened from the TCGA and GSE44861 dataset, respectively. There were 155 survival‑related genes selected. The co‑expression network of survival‑related genes included 138 genes, 534 lines (connections) and five functional clusters, including the signaling pathway, cellular response to cAMP, and immune system process functional clusters. The molecular function, cellular components and biological processes were the significantly enriched functions. The peroxisome proliferator‑activated receptor signaling pathway, Wnt signaling pathway, B cell receptor signaling pathway, and cytokine‑cytokine receptor interactions were the significant pathways. A prognostic prediction system based on a 65‑gene signature was established using this co‑expression network. Its discriminatory effect was validated in the TCGA dataset (P=3.56e‑12) and the GSE17538 dataset (P=1.67e‑6). The 65‑gene signature included kallikrein‑related peptidase 6 (KLK6), collagen type XI α1 (COL11A1), cartilage oligomeric matrix protein, wingless‑type MMTV integration site family member 2 (WNT2) and keratin 6B. In conclusion, a 65‑gene signature was screened in the present study, which showed a prognostic prediction effect in colon adenocarcinoma. KLK6, COL11A1, and WNT2 may be suitable prognostic predictors for colon adenocarcinoma.", "claims": [{"id": "claim-method", "statement": "Based on the survival information of the samples in the TCGA dataset (272 tumor samples and 39 samples), survival-associated genes were selected from the above overlapping DEGs using univariate Cox regression analysis of survival (13) with the survival package in R3.1.0 language (bioconductor.org/packages/survivalr/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29393333", "locator": "sections[5] \"Survival-associated genes\" paragraph 2", "quote": "Based on the survival information of the samples in the TCGA dataset (272 tumor samples and 39 samples), survival-associated genes were selected from the above overlapping DEGs using univariate Cox regression analysis of survival (13) with the survival package in R3.1.0 language (bioconductor.org/packages/survivalr/)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "mRNA expression data of colon adenocarcinoma samples were downloaded from the TCGA database (gdc-portal.nci.nih.gov/) on 12 Dec. 2016, which included 286 tumor samples and 41 normal samples. The dataset was based on the Illumina HiSeq 2000 RNA sequencing platform.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29393333", "locator": "sections[3] \"Microarray data\" paragraph 2", "quote": "mRNA expression data of colon adenocarcinoma samples were downloaded from the TCGA database (gdc-portal.nci.nih.gov/) on 12 Dec. 2016, which included 286 tumor samples and 41 normal samples. The dataset was based on the Illumina HiSeq 2000 RNA sequencing platform."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used Bayesian discriminant analysis to establish a 65-gene prognostic prediction system from the TCGA co-expression network, which distinguished good- from bad-prognosis colon cancer samples in TCGA (P=3.56e–12) and GSE17538 (P=1.67e–6).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:29393333", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Using Bayesian discriminant analysis, a prognostic prediction system was established to distinguish the positive from negative prognostic samples."}, {"asset": "source:pmid:29393333", "locator": "sections[13] \"Prognostic prediction system\" paragraph 2", "quote": "Based on these and on the co-expression network, a 65-gene signature-based prognostic prediction system was established."}, {"asset": "source:pmid:29393333", "locator": "sections[13] \"Prognostic prediction system\" paragraph 5", "quote": "The results showed that patients with a good prognosis had significantly longer overall survival rates, compared with those with a bad prognosis (P=3.56e–12; Fig. 5A)."}, {"asset": "source:pmid:29393333", "locator": "sections[13] \"Prognostic prediction system\" paragraph 5", "quote": "The discriminative effect of the 65-gene signature was assessed on the GSE17538 dataset (validation set)."}, {"asset": "source:pmid:29393333", "locator": "sections[13] \"Prognostic prediction system\" paragraph 5", "quote": "As presented in Fig. 5, the good prognostic samples and bad prognostic samples in the GSE17538 dataset had significantly different overall survival rates (P=1.67e–6; Fig. 5B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used Bayesian discriminant analysis to establish a 65-gene prognostic prediction system from the TCGA co-expression network, which distinguished good- from bad-prognosis colon cancer samples in TCGA (P=3.56e–12) and GSE17538 (P=1.67e–6).", "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:29393333"], "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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