Identifying a CD8T cell signature in the tumor microenvironment to forecast gastric cancer outcomes from sequencing data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39554559", "kind": "science-record", "revision": 2, "content_hash": "sha256:3ba98dbb6e67735855d68a8030663205d8a50598e2db599b0b89133d9ac1cc68", "created_at": "2026-11-01T20:29:49Z", "updated_at": "2026-11-01T21:34:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying a CD8T cell signature in the tumor microenvironment to forecast gastric cancer outcomes from sequencing data.", "doi": "10.21037/jgo-24-603", "pmid": "39554559", "authors": ["Xiangyue Zeng", "Tiannake Shapaer", "Jianguo Tian", "Abulajiang Abudoukelimu", "Zeliang Zhao", "Paerhati Shayimu", "Binlin Ma"], "journal": "Journal of gastrointestinal oncology", "year": 2024, "abstract": "Background: The tumor microenvironment (TME) could be critical in carcinogenesis, immune evasion, and treatment response. TME-related genes are limited in their ability to predict gastric cancer (GC) outcomes. We utilized data from The Cancer Genome Atlas (TCGA) to investigate the functional roles of TME-related genes in GC.\nMethods: We acquired single-cell data, bulk sequencing data, and clinical characteristics of GC patients from the TCGA database. The CD8T cell genes associated with the TME were selected for bioinformatic analysis in GC. Tumor classification of GC was established through consistent cluster analysis. We then evaluated the prognosis and immune cell infiltration in connection with a CD8T cell-related model for GC.\nResults: The single-cell messenger RNA (mRNA) sequencing (scRNA-Seq) dataset of GSE134520 was utilized to investigate the pathogenesis and disease-specific cell types in GC. Interestingly, compared to healthy tissue, the proportions of CD8Tex cells, malignant cells, and gland mucous increased in GC, whereas the proportion of pit mucous decreased in GC. Since CD8Tex cells may play a vital role in pancreatic adenocarcinoma (PAAD), based on the 612 differentially expressed genes (DEGs) involved in CD8Tex cells, TCGA-GC patients were stratified into low- and high-risk groups. The downregulated DEGs in the low-risk G1 group were associated with proteoglycans in cancer, the cGMP-PKG signaling pathway, focal adhesion, and cell adhesion molecules (CAMs), whereas the upregulated DEGs were associated with viral protein interaction with cytokine and cytokine receptors, the tumor necrosis factor (TNF) signaling pathway, the interleukin (IL)-17 signaling pathway, and the chemokine signaling pathway. Combined with univariate Cox analysis, we ultimately identified 23 CD8T cell-related prognostic genes: TCIM, AADAC, SLC2A3, ZNF331, TSC22D3, CMTM3, ZFP36, VIM, CLDND1, GABARAPL1, SOCS3, RGS1, TCEAL9, RGS2, CD59, SPRY1, EMP3, ZEB2, PDE4B, GLIPR1, ERRFI1, and LBH. Using the Cox regression model to prioritize the 23 CD8T cell-related genes, we finally selected 7 genes: CXCR4, AADAC, SLC2A3, CMTM3, RGS2, CD59, and ZEB2.\nConclusions: CD8T cell-related genes have a strong association with tumor classification and immune response in GC patients. A CD8T cell-related signature demonstrated robust prognostic predictive performance for GC. Our findings may reveal novel insights into the diagnosis and treatment of GC.", "claims": [{"id": "claim-method", "statement": "Through Cox regression analysis, 23 prognostic genes for CD8T cells in GC were selected (Figure 6A).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39554559", "locator": "sections[18] \"The development of a prognostic model based on key CD8T cell-related genes\" paragraph 2", "quote": "Through Cox regression analysis, 23 prognostic genes for CD8T cells in GC were selected (Figure 6A)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "To annotate cell types, we searched the Gene Expression Omnibus (GEO) database for data from the GSE134520 database.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39554559", "locator": "sections[4] \"Single-cell sequencing analysis of GC\" paragraph 2", "quote": "To annotate cell types, we searched the Gene Expression Omnibus (GEO) database for data from the GSE134520 database."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A CD8T cell-related signature demonstrated robust prognostic predictive performance for GC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39554559", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "A CD8T cell-related signature demonstrated robust prognostic predictive performance for GC."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A CD8T cell-related signature demonstrated robust prognostic predictive performance for GC.", "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:39554559"], "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:GSE134520"], "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:GSE134520"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39554559", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jgo-24-603", "sha256": "sha256:e35ec893c14cbab58deab9f14bccac28ee501ddb8dfe63180b78931d2ec6d93b", "bytes": 26363, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/jgo-24-603", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11565103", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE134520", "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=GSE134520", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE134520", "version": null, "note": "To annotate cell types, we searched the Gene Expression Omnibus (GEO) database for data from the GSE134520 database.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "To annotate cell types, we searched the Gene Expression Omnibus (GEO) database for data from the GSE134520 database.", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM3954946", "GSM3954947", "GSM3954948", "GSM3954949", "GSM3954950", "GSM3954951", "GSM3954952", "GSM3954953", "GSM3954954", "GSM3954955", "GSM3954956", "GSM3954957", "GSM3954958"], "sample_count": 13, "group_sizes": null, "group_sizes_basis": null, "platform": "20795", "taxon": "Homo sapiens", "title": "Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer [10x genomics]", "resolution_checked_at": "2026-11-01T17:30:42Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE134520 not downloaded", "asset:data:geo:GSE134520 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Through Cox regression analysis, 23 prognostic genes for CD8T cells in GC were selected (Figure 6A)."}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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