Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38535087", "kind": "science-record", "revision": 2, "content_hash": "sha256:255df66d135341ecf4a46a9e5f4f55e1c212a5f4fc2343c84bfe3085bc57983c", "created_at": "2026-11-01T19:20:29Z", "updated_at": "2026-11-01T21:32:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data.", "doi": "10.3390/diagnostics14060667", "pmid": "38535087", "authors": ["Yaowu Zhu", "Li Tan", "Danju Luo", "Xiong Wang"], "journal": "Diagnostics (Basel, Switzerland)", "year": 2024, "abstract": "PURPOSE: Pancreatic cancer (PACA) is one of the most fatal malignancies worldwide. Immunotherapy is largely ineffective in patients with PACA. T-cell exhaustion contributes to immunotherapy resistance. We investigated the prognostic potential of T-cell exhaustion-related genes (TEXGs).\nMETHODS: A single-cell RNA (scRNA) sequencing dataset from Tumor Immune Single-Cell Hub (TISCH) and bulk sequencing datasets from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to screen differentially expressed TEXGs. Kaplan-Meier survival, LASSO regression, and univariate/multivariate Cox regression analyses were performed to construct a TEXG risk model. This model was used to predict the prognosis, tumor immune microenvironment, and immunotherapy response. The PACA cohorts from the ICGC and GSE71729 datasets were used to validate the risk model. Pan-cancer expression of SPOCK2 was determined using the TISCH database.\nRESULTS: A six-gene (SPOCK2, MT1X, LIPH, RARRES3, EMP1, and MEG3) risk model was constructed. Patients with low risk had prolonged survival times in both the training (TCGA-PAAD, n = 178) and validation (ICGC-PACA-CA, ICGC-PAAD-US, and GSE71729, n = 412) datasets. Multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic variable for PACA. High-risk patients correlated with their immunosuppressive status. Immunohistochemical staining confirmed the changes in TEXGs in clinical samples. Moreover, pan-cancer scRNA sequencing datasets from TISCH analysis indicated that SPOCK2 may be a novel marker of exhausted CD8+ T-cells.\nCONCLUSION: We established and validated a T-cell exhaustion-related prognostic signature for patients with PACA. Moreover, our study suggests that SPOCK2 is a novel marker of exhausted CD8+ T cells.", "claims": [{"id": "claim-method", "statement": "A single-cell RNA (scRNA) sequencing dataset from Tumor Immune Single-Cell Hub (TISCH) and bulk sequencing datasets from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to screen differentially expressed TEXGs. Kaplan–Meier survival, LASSO regression, and univariate/multivariate Cox regression analyses were performed to construct a TEXG risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38535087", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A single-cell RNA (scRNA) sequencing dataset from Tumor Immune Single-Cell Hub (TISCH) and bulk sequencing datasets from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to screen differentially expressed TEXGs. Kaplan–Meier survival, LASSO regression, and univariate/multivariate Cox regression analyses were performed to construct a TEXG risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The TCGA and GTEx integrated data were downloaded from Xena (https://xenabrowser.net/datapages/?cohort=TCGA%20TARGET%20GTEx&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443, accessed on 1 January 2024). ICGC-PACA-CA was downloaded from ICGC (https://dcc.icgc.org/projects/PACA-CA, accessed on 1 January 2024). ICGC-PAAD-US was downloaded from ICGC (https://dcc.icgc.org/projects/PAAD-US, accessed on 1 January 2024). The GSE71729 dataset was downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71729, accessed on 1 January 2024). Other data and results are displayed in the manuscript and Supplementary Data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38535087", "locator": "sections[29] \"notes\" paragraph 1", "quote": "The TCGA and GTEx integrated data were downloaded from Xena (https://xenabrowser.net/datapages/?cohort=TCGA%20TARGET%20GTEx&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443, accessed on 1 January 2024). ICGC-PACA-CA was downloaded from ICGC (https://dcc.icgc.org/projects/PACA-CA, accessed on 1 January 2024). ICGC-PAAD-US was downloaded from ICGC (https://dcc.icgc.org/projects/PAAD-US, accessed on 1 January 2024). The GSE71729 dataset was downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71729, accessed on 1 January 2024). Other data and results are displayed in the manuscript and Supplementary Data."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We established and validated a T-cell exhaustion-related prognostic signature for patients with PACA.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38535087", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We established and validated a T-cell exhaustion-related prognostic signature for patients with PACA."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We established and validated a T-cell exhaustion-related prognostic signature for patients with PACA.", "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:38535087"], "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:GSE71729"], "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:GSE71729"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38535087", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3390/diagnostics14060667", "sha256": "sha256:64a8f24eb5376b1ee80930f0e05c8fcb7ab310325b1631a11f38b46058049755", "bytes": 37266, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3390/diagnostics14060667", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10968840", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE71729", "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=GSE71729", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE71729", "version": null, "note": "The TCGA and GTEx integrated data were downloaded from Xena (https://xenabrowser.net/datapages/?cohort=TCGA%20TARGET%20GTEx&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443, accessed on 1 January 2024). ICGC-PACA-CA was downloaded from ICGC (https://dcc.icgc.org/projects/PACA-CA, accessed on 1 January 2024). ICGC-PAAD-US was downloaded from ICGC (https://dcc.icgc.org/projects/PAAD-US, accessed on 1 January 2024). The GSE71729 dataset was downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71729, accessed on 1 January 2024). Other data and results are displayed in the manuscript and Supplementary Data.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The TCGA and GTEx integrated data were downloaded from Xena (https://xenabrowser.net/datapages/?cohort=TCGA%20TARGET%20GTEx&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443, accessed on 1 January 2024). ICGC-PACA-CA was downloaded from ICGC (https://dcc.icgc.org/projects/PACA-CA, accessed on 1 January 2024). ICGC-PAAD-US was downloaded from ICGC (https://dcc.icgc.org/projects/PAAD-US, accessed on 1 January 2024). The GSE71729 dataset was downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71729, accessed on 1 January 2024). 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Kaplan–Meier survival, LASSO regression, and univariate/multivariate Cox regression analyses were performed to construct a TEXG risk model."}}, {"id": "method-2", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-3", "family": "survival_analysis", "specific": "log_rank_test", "label": "Survival analysis", "dsl_operations": ["logrank_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "log-rank"}}, {"id": "method-4", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}], "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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