T-cell exhaustion indicator characterizes the tumor microenvironment landscape and predicts colon adenocarcinoma prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41530714", "kind": "science-record", "revision": 3, "content_hash": "sha256:254e34cb1d23f68a2fdc35b5602f0bc8d17155f9d49069298f8c054d4c670c5e", "created_at": "2026-11-01T20:31:29Z", "updated_at": "2026-11-01T22:15:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "T-cell exhaustion indicator characterizes the tumor microenvironment landscape and predicts colon adenocarcinoma prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing.", "doi": "10.1186/s12885-025-15535-5", "pmid": "41530714", "authors": ["Haifeng Sun", "Ning Hu", "Shaowei Ma", "Yu Zheng", "Ren Niu", "Wenya Zhu", "Shaofan Qiu"], "journal": "BMC cancer", "year": 2026, "abstract": "BACKGROUND: Colon adenocarcinoma (COAD) is the most common type of colon cancer, posing a significant threat to public health. In the tumor microenvironment (TME), T cells differentiate into terminally exhausted T cells (TEX), but the relationship between TEX and COAD has not been fully elucidated.\nMETHODS: To identify TEX-related signatures, we integrated transcriptomic data from TCGA and GEO databases (GSE103479, GSE17536). A prognostic model was constructed using GSVA, univariate Cox, LASSO, and random forest algorithms. The tumor immune microenvironment was characterized using CIBERSORTx and GSEA. The functional role of FAT4 was validated in vitro using FAT4-knockdown COAD cell lines assessed by flow cytometry and RT-qPCR.\nRESULTS: We developed a prognostic signature based on five TEX-related genes (IL21R, FCRL3, TIFAB, TNFSF14, SLAMF1). Patients in the high-risk group showed significantly poorer overall survival and distinct immune cell infiltration patterns, characterized by decreased CD8 + T cells and M1 macrophages. At single-cell resolution, CD8 + TEX cells exhibited high expression of immune checkpoints like LAG3. Furthermore, in vitro experiments demonstrated that knockdown of FAT4, a frequently mutated gene in COAD, promoted apoptosis and induced G0/G1 cell cycle arrest in COAD cells.\nCONCLUSION: We proposed a non-invasive prediction method based on TEX-related genes, which effectively predicts survival outcomes and therapeutic responses in COAD patients. Additionally, FAT4 was found to regulate proliferative and apoptotic phenotypes in COAD.", "claims": [{"id": "claim-method", "statement": "A prognostic model was constructed using GSVA, univariate Cox, LASSO, and random forest algorithms.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41530714", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A prognostic model was constructed using GSVA, univariate Cox, LASSO, and random forest algorithms."}], "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.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41530714", "locator": "sections[24] \"Data availability\" 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."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five TEX-related genes (IL21R, FCRL3, TIFAB, TNFSF14, and SLAMF1) were selected to construct the prognostic model.", "kind": "quantitative", "target": {"value": 0.01, "unit": "p-value", "tolerance": null, "source": "Univariate COX regression analysis was implemented to retain the survival-related genes (p < 0.01), which was further selected by the random forest algorithm, generating a final gene set of 5 key genes (IL21R, FCRL3, TIFAB, TNFSF14, SLAMF1) (Fig. 1A)."}, "evidence": [{"asset": "source:pmid:41530714", "locator": "sections[14] \"Construction of TEX based prognostic model\" paragraph 2", "quote": "Univariate COX regression analysis was implemented to retain the survival-related genes (p < 0.01), which was further selected by the random forest algorithm, generating a final gene set of 5 key genes (IL21R, FCRL3, TIFAB, TNFSF14, SLAMF1) (Fig. 1A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Five TEX-related genes (IL21R, FCRL3, TIFAB, TNFSF14, and SLAMF1) were selected to construct the prognostic model.", "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:41530714"], "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:GSE103479"], "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:machine_learning_random_forest", "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:GSE103479"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:41530714", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-025-15535-5", "sha256": "sha256:dd08c0e37368c346dee9f5382a0452f9cdc263b057c9e85b615685d6a82cea01", "bytes": 41140, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-025-15535-5", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12888615", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE103479", "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=GSE103479", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE103479", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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