A novel cell senescence-related IncRNA survival model associated with the tumor immune environment in colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36275644", "kind": "science-record", "revision": 4, "content_hash": "sha256:49edb322052e40f801adb13a58f68a4682f62252f5e720d4ce927bdca6972726", "created_at": "2026-11-01T18:15:40Z", "updated_at": "2026-11-01T22:07:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel cell senescence-related IncRNA survival model associated with the tumor immune environment in colorectal cancer.", "doi": "10.3389/fimmu.2022.1019764", "pmid": "36275644", "authors": ["Chengfei Xu", "Fanghan Li", "Zilin Liu", "Chuanjing Yan", "Jiangwei Xiao"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Long noncoding RNAs have a major role in tumorigenesis, development, and metastasis in colorectal cancer (CRC), participate in the regulation of cell senescence and are related to the prognosis of CRC. Therefore, it is important to validate cell senescence-related lncRNAs that correlate with prognosis in CRC.\nMethods: CRC expression profile data and clinical information were downloaded from TCGA. A gene list related to cellular senescence was obtained from Human Aging Genomic Resources. A coexpression network of cell senescence-related mRNA-lncRNA was explored with R. Six cell senescence-related lncRNA signatures were identified by univariate and multivariate analyses. The cell senescence-related risk model was generated by using six cell senescence-related lncRNAs, and the risk score was calculated. Furthermore, an internal validation set and GSE17537 were used to verify the risk model. The risk model demonstrated good stability and accuracy. Finally, we investigated the correlation between cell senescence-related risk scores and immune infiltration, immune function, immune checkpoints, and drug sensitivity.\nResult: We established a signature of six cell senescence-related lncRNAs. The cell senescence-related risk model revealed an exceptional ability to assess the prognosis of colorectal cancer and was correlated with clinical features. Additionally, we observed that risk models correlate with the tumor microenvironment and immune checkpoints, potentially predicting patient response to clinical immunotherapy. Finally, we validated the correlation between the cell senescence-related risk model and drug susceptibility. Our findings indicated that AICAR, cisplatin, nilotinib, and bexarotene exhibited lower IC50 values in the high-risk group.\nConclusion: Our current study identified 6 cell senescence-associated lncRNA signatures that may be vital biomarkers to predict the prognostic features and immune and chemotherapy responses in CRC.", "claims": [{"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:36275644", "locator": "sections[22] \"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": "The six-lncRNA risk model had an AUC of 0.714 and was an independent prognostic factor for CRC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36275644", "locator": "sections[17] \"Development of a nomogram and evaluation of the survival model for CRC\" paragraph 2", "quote": "The AUC point of the risk score was 0.714, indicating that the risk model was a robust prognostic risk model for CRC ( Figure 9C )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The six-lncRNA risk model had an AUC of 0.714 and was an independent prognostic factor for CRC.", "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:36275644"], "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:GSE17537"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE17537"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36275644", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.1019764", "sha256": "sha256:371bf0e0813b4f061f6d3d0b8c01f70d76a0c3a27f92e3191844f20c381fb288", "bytes": 38299, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.1019764", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9583265", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE17537", "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=GSE17537", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE17537", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The original contributions presented in the study are included in the article/ Supplementary Material . 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