Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36925638", "kind": "science-record", "revision": 2, "content_hash": "sha256:359571f76f06c07cbc8418a226aafc82c39d948c50bc71ef4917d502b06af3c8", "created_at": "2026-11-01T19:01:34Z", "updated_at": "2026-11-01T21:30:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive genomics analysis of aging related gene signature to predict the prognosis and drug resistance of colon adenocarcinoma.", "doi": "10.3389/fphar.2023.1121634", "pmid": "36925638", "authors": ["Jubin Feng", "Fengyihuan Fu", "Yuqiang Nie"], "journal": "Frontiers in pharmacology", "year": 2023, "abstract": "Background: Colon adenocarcinoma (COAD) is a heterogeneous tumor and senescence is crucial in the occurrence of cancer. This study aimed to identify senescence-based subtypes and construct a prognostic signature to predict the prognosis and guide immunotherapy or chemotherapy decisions for COAD patients. Methods: Based on the single-cell RNA sequencing (scRNA-seq) data of 13 samples from the Gene Expression Omnibus (GEO) database, we assessed cellular senescence characteristics. Transcriptome data, copy number variations (CNVs) and single nucleotide variations (SNVs) data were obtained from The Cancer Genome Atlas (TCGA) database. GSE39582 and GSE17537 were used for validation. Senescence subtypes were identified using unsupervised consensus clustering analysis, and a prognostic signature was developed using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO). Response of risk groups to chemotherapy was predicted using the half-maximal inhibitory concentration (IC50) values. We further analyzed the relationship between risk gene expression and methylation level. The prediction performance was assessed by nomogram. Results: Senescence-related pathways were highly enriched in malignant cells and bulk RNA-seq verified cellular senescence. Three senescence subtypes were identified, in which patients in clust3 had poorest prognosis and higher T stage, accompanied with higher tumor mutation burden (TMB) and mutations, activated inflammatory response, more immune cell infiltration, and higher immune escape tendency. A senescence-based signature using 11 genes (MFNG, GPRC5B, TNNT1, CCL22, NOXA1, PABPC1L, PCOLCE2, MID2, CPA3, HSPA1A, and CALB1) was established, and accurately predicted a lower prognosis in high risk patients. Its robustness was validated by external cohort. Low risk patients were more sensitive to small molecule drugs including Erlotinib, Sunitinib, MG-132, CGP-082996, AZ628, Sorafenib, VX-680, and Z-LLNle-CHO. Risk score was an independent prognostic factor and nomogram confirmed its reliability. Four risk genes (CALB1, CPA3, NOXA1, and TNNT1) had significant positive correlation with their methylation level, while six genes (CCL22, GPRC5B, HSPA1A, MFNG, PABPC1L, and PCOLCE2) were negatively correlated with their methylation level. Conclusion: This study provides novel understanding of heterogeneity in COAD from the perspective of senescence, and develops signatures for prognosis prediction in COAD.", "claims": [{"id": "claim-method", "statement": "A consensus clustering analysis was performed to categorize the 432 TCGA-COAD samples based on the expression profiles of the 16 senescence-related genes using “Consensus ClusterPlus” package (Wilkerson et al., 2013) with “Partitioning Around Medoids” (PAM) algorithm (Kaufman and Rousseeuw, 1990) and Euclidean distancing, in procedures with 500 bootstraps containing 80% COAD patients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36925638", "locator": "sections[9] \"Identification of senescence subtypes\" paragraph 2", "quote": "A consensus clustering analysis was performed to categorize the 432 TCGA-COAD samples based on the expression profiles of the 16 senescence-related genes using “Consensus ClusterPlus” package (Wilkerson et al., 2013) with “Partitioning Around Medoids” (PAM) algorithm (Kaufman and Rousseeuw, 1990) and Euclidean distancing, in procedures with 500 bootstraps containing 80% COAD patients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36925638", "locator": "sections[36] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Kaplan-Meier analysis showed significant survival differences among the three senescence subtypes, with clust3 having the lowest survival probability and clust1 the best prognosis in TCGA (p = 0.0021).", "kind": "quantitative", "target": {"value": 0.0021, "unit": "p-value", "tolerance": null, "source": "Next, Kaplan-Meier curves revealed significant variations among the three subtypes, and clust3 had the lowest survival probability while clust1 had the best prognosis in TCGA (p = 0.0021) (Figure 4D)."}, "evidence": [{"asset": "source:pmid:36925638", "locator": "sections[23] \"Three senescence subtypes were identified\" paragraph 2", "quote": "Next, Kaplan-Meier curves revealed significant variations among the three subtypes, and clust3 had the lowest survival probability while clust1 had the best prognosis in TCGA (p = 0.0021) (Figure 4D)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Kaplan-Meier analysis showed significant survival differences among the three senescence subtypes, with clust3 having the lowest survival probability and clust1 the best prognosis in TCGA (p = 0.0021).", "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:36925638"], "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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