Identification and Validation of a Novel DNA Damage and DNA Repair Related Genes Based Signature for Colon Cancer Prognosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33719345", "kind": "science-record", "revision": 7, "content_hash": "sha256:f278c330d8c6f2e59afc5520e0df24ce22bf14dd1117767a18d5ff74ffbf0703", "created_at": "2026-11-01T15:41:28Z", "updated_at": "2026-11-01T22:03:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Validation of a Novel DNA Damage and DNA Repair Related Genes Based Signature for Colon Cancer Prognosis.", "doi": "10.3389/fgene.2021.635863", "pmid": "33719345", "authors": ["Xue-Quan Wang", "Shi-Wen Xu", "Wei Wang", "Song-Zhe Piao", "Xin-Li Mao", "Xian-Bin Zhou", "Yi Wang", "Wei-Dan Wu", "Li-Ping Ye", "Shao-Wei Li"], "journal": "Frontiers in genetics", "year": 2021, "abstract": "Backgrounds: Colorectal cancer (CRC) with high incidence, has the third highest mortality of tumors. DNA damage and repair influence a variety of tumors. However, the role of these genes in colon cancer prognosis has been less systematically investigated. Here, we aim to establish a corresponding prognostic signature providing new therapeutic opportunities for CRC. Method: After related genes were collected from GSEA, univariate Cox regression was performed to evaluate each gene's prognostic relevance through the TCGA-COAD dataset. Stepwise COX regression was used to establish a risk prediction model through the training sets randomly separated from the TCGA cohort and validated in the remaining testing sets and two GEO datasets (GSE17538 and GSE38832). A 12-DNA-damage-and-repair-related gene-based signature able to classify COAD patients into high and low-risk groups was developed. The predictive ability of the risk model or nomogram were evaluated by different bioinformatics- methods. Gene functional enrichment analysis was performed to analyze the co-expressed genes of the risk-based genes. Result: A 12-gene based prognostic signature established within 160 significant survival-related genes from DNA damage and repair related gene sets performed well with an AUC of ROC 0.80 for 5 years in the TCGA-CODA dataset. The signature includes CCNB3, ISY1, CDC25C, SMC1B, MC1R, LSP1P4, RIN2, TPM1, ELL3, POLG, CD36, and NEK4. Kaplan-Meier survival curves showed that the prognosis of the risk status owns more significant differences than T, M, N, and stage prognostic parameters. A nomogram was constructed by LASSO regression analysis with T, M, N, age, and risk as prognostic parameters. ROC curve, C-index, Calibration analysis, and Decision Curve Analysis showed the risk module and nomogram performed best in years 1, 3, and 5. KEGG, GO, and GSEA enrichment analyses suggest the risk involved in a variety of important biological processes and well-known cancer-related pathways. These differences may be the key factors affecting the final prognosis. Conclusion: The established gene signature for CRC prognosis provides a new molecular tool for clinical evaluation of prognosis, individualized diagnosis, and treatment. Therapies based on targeted DNA damage and repair mechanisms may formulate more sensitive and potential chemotherapy regimens, thereby expanding treatment options and potentially improving the clinical outcome of CRC patients.", "claims": [{"id": "claim-method", "statement": "Univariate Cox regression analysis (Cox, 1972) was first performed with DNA damage and DNA repair related genes, and genes with a p value of less than 0.05 were considered a statistically significant difference.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33719345", "locator": "sections[4] \"Construction of the DNA Damage and DNA Repair Related Gene Signature\" paragraph 2", "quote": "Univariate Cox regression analysis (Cox, 1972) was first performed with DNA damage and DNA repair related genes, and genes with a p value of less than 0.05 were considered a statistically significant difference."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33719345", "locator": "sections[17] \"Data Availability Statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33719345"], "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:GSE17538"], "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:GSE17538"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33719345", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fgene.2021.635863", "sha256": "sha256:5e5a0b26b8d6c844a515cb41f4044145769bb5cec6f0266654a09268519e3eaf", "bytes": 56016, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fgene.2021.635863", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7943631", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17538", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17538", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17538", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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