Integrated Single-Cell RNA-Seq Reveals Immunosuppressive Mechanisms of Treg Cell Differentiation and Tumor Microenvironment Interactions in Colorectal Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40891683", "kind": "science-record", "revision": 2, "content_hash": "sha256:8f5c4a292b3e8f0c1027e4f6563091431ca470001205e10bf9f97383aa4d30e2", "created_at": "2026-11-01T20:31:00Z", "updated_at": "2026-11-01T21:35:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated Single-Cell RNA-Seq Reveals Immunosuppressive Mechanisms of Treg Cell Differentiation and Tumor Microenvironment Interactions in Colorectal Cancer.", "doi": "10.1002/cam4.71202", "pmid": "40891683", "authors": ["Dongwei Li", "Jingya Liu", "Lixian Yu", "Xina Luo", "Xiaomei Wu", "Ronghui Chen"], "journal": "Cancer medicine", "year": 2025, "abstract": "BACKGROUND: Colorectal cancer (CRC) remains a major challenge in oncology, necessitating the identification of new therapeutic targets. This study aimed to explore the CRC microenvironment and uncover potential targets using single-cell RNA sequencing (scRNA-seq).\nMETHODS: Single-cell RNA sequencing data from GEO (GSE164522, GSE132465, and GSE144735) were integrated and stratified based on CRC clinical stages and tumor grades. Cell clustering analysis identified distinct cellular subsets, while survival and functional enrichment analyses evaluated the prognostic significance and biological activity of differentially expressed genes. Additionally, transcription factor activity and pseudotime trajectory analyses elucidated cellular dynamics during CRC progression. Cell-cell communication analysis identified key ligand-receptor interactions involved in immune regulation. Finally, multiplex immunofluorescence was used to validate the expression of key markers and cells in CRC tissues from patients.\nRESULTS: In the low differentiated colorectal adenocarcinoma (LDCA) grade of CRC, the proportion of T cells significantly increased, accompanied by the enrichment of Treg cells and the upregulation of genes associated with immunosuppression, proliferation, and differentiation. Notably, TGFβ1+ Tregs significantly expanded in LDCA, accumulating in late tumor grades with enhanced immunosuppressive capacity and self-proliferation. Pseudotime analysis confirmed their dominance in late CRC, reinforcing immunosuppression through signal amplification and sustained expansion. Concurrently, CD8+ T cells in the LDCA grade exhibited a progressive loss of cytotoxic function, transitioning into an exhausted state while concurrently activating apoptotic pathways, leading to a profound impairment of antitumor immunity. Cell-cell communication analysis further demonstrated that TGFβ1+ Treg exhibits the strongest interactions with CD8+ T cells, with KLRB1-CLEC2D, LGALS9-HAVCR2, and TNFSF10-TNFRSF10B emerging as pivotal ligand-receptor pairs displaying significantly enhanced signaling in the LDCA grade.\nCONCLUSION: Collectively, TGFβ1+ Treg may mediate CD8+ T cell dysfunction through these ligand-receptor interactions, accelerating T cell exhaustion and apoptosis, thereby fostering a profoundly immunosuppressive tumor microenvironment that ultimately drives CRC immune evasion and malignant progression.", "claims": [{"id": "claim-method", "statement": "The survfit function was used to perform Kaplan–Meier survival analysis, with a p‐value of < 0.05 considered significant.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40891683", "locator": "sections[5] \"Survival Analysis\" paragraph 2", "quote": "The survfit function was used to perform Kaplan–Meier survival analysis, with a p‐value of < 0.05 considered significant."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used in this study were obtained from the public database GEO, dataset numbers GSE164522, GSE132465, and GSE144735, which are publicly accessible to any researcher through the GEO database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40891683", "locator": "sections[30] \"Data Availability Statement\" paragraph 2", "quote": "The data used in this study were obtained from the public database GEO, dataset numbers GSE164522, GSE132465, and GSE144735, which are publicly accessible to any researcher through the GEO database."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:40891683"], "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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