Exploration of bacterial lipopolysaccharide-related genes signature based on T cells for predicting prognosis in colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39115879", "kind": "science-record", "revision": 2, "content_hash": "sha256:e884811b6d7edc4a1d0b017c845d77434fd67353fd35b298486be1944c4eae3d", "created_at": "2026-11-01T20:29:12Z", "updated_at": "2026-11-01T21:33:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploration of bacterial lipopolysaccharide-related genes signature based on T cells for predicting prognosis in colorectal cancer.", "doi": "10.18632/aging.206041", "pmid": "39115879", "authors": ["Lichao Cao", "Ying Ba", "Fang Chen", "Shenrui Zhang", "Hezi Zhang"], "journal": "Aging", "year": 2024, "abstract": "PURPOSE: The intratumoral microorganisms participates in the progression and immunotherapy of colorectal cancer (CRC). However, due to technical limitations, the impact of microorganisms on CRC has not been fully understood. Therefore, we conducted a systematic analysis of relationship between bacterial lipopolysaccharide (LPS)-associated genes and immune cells to explore new biomarkers for predicting the prognosis of CRC.\nMETHODS: The single-cell RNA sequencing data and the Comparative Toxicogenomics Database were used to screen T cells-associated LPS-related genes (TALRGs). Then, we established and validated the TALRGs risk signature in The Cancer Genome Atlas Colon Adenocarcinoma (TCGA-COAD) cohort and GSE39582 cohort. Besides, we compared the differences in tumor-infiltrating immune cell types, immunotherapeutic response, somatic mutation profiles, and tumor mutation burden (TMB) between high-risk group and low-risk group. In addition, the immunotherapeutic cohort (Imvigor210) treated with an anti-PD-L1 agent was performed to explore the potential value of the TALRGs signature on immunotherapy.\nRESULTS: Five prognostic TALRGs were identified and selected to build the prognostic model. The high-risk group had poor prognosis in both TCGA-COAD cohort (P < 0.0001) and GSE39582 cohort (P = 0.00019). The areas under the curves (AUCs) of TALRGs signature were calculated (TCGA-COAD cohort: 0.624 at 1 years, 0.639 at 3 years, 0.648 at 5 years; anti-PD-L1 cohort was 0.59). The high-risk group had advanced pathological stages and higher TMN stages in both TCGA-COAD cohort and GSE39582 cohort. The high-risk group had the higher infiltration of immunosuppressive cells, the expressions of immune checkpoint molecules, the IC50 values of chemotherapy drugs, and TP53 mutation rate (P < 0.05). In addition, patients with high TMB had worse prognosis (P < 0.05). Furthermore, the Imvigor210 also showed patients with high-risk scores had poor prognosis (platinum-treated cohort: P = 0.0032; non-platinum-treated cohort: P = 0.00017).\nCONCLUSIONS: Microorganisms are closely related to the tumor microenvironment to influence the progression and immune response of CRC via stimulating T cells through LPS-related genes. The TALRGs signature contributed to predict the prognosis and immunotherapy of CRC, and became new therapeutic targets and biomarkers of CRC.", "claims": [{"id": "claim-method", "statement": "Univariate Cox analysis was used to screen the prognostic signature using Survival R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39115879", "locator": "sections[5] \"Development and validation of the TALRGs prognostic model\" paragraph 2", "quote": "Univariate Cox analysis was used to screen the prognostic signature using Survival R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The scRNA-seq dataset GSE200997 was obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov) database, which included 16 CRC samples and 7 adjacent samples of normal colon tissue.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39115879", "locator": "sections[3] \"Acquisition and processing of data\" paragraph 2", "quote": "The scRNA-seq dataset GSE200997 was obtained from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov) database, which included 16 CRC samples and 7 adjacent samples of normal colon tissue."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five prognostic TALRGs were identified and selected to build the prognostic model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39115879", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Five prognostic TALRGs were identified and selected to build the prognostic model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Five prognostic TALRGs were identified and selected to build 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:39115879"], "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:GSE200997"], "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:GSE200997"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39115879", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.206041", "sha256": 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