Development and validation of a programmed cell death index to predict the prognosis and drug sensitivity of gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39744132", "kind": "science-record", "revision": 3, "content_hash": "sha256:23cc355da74b047d8f2e8cc2f1e53f4e4abc312fa9e99f4937899f2bdcd016db", "created_at": "2026-11-01T20:30:04Z", "updated_at": "2026-11-01T22:13:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of a programmed cell death index to predict the prognosis and drug sensitivity of gastric cancer.", "doi": "10.3389/fphar.2024.1477363", "pmid": "39744132", "authors": ["Feizhi Lin", "Xiaojiang Chen", "Chengcai Liang", "Ruopeng Zhang", "Guoming Chen", "Ziqi Zheng", "Bowen Huang", "Chengzhi Wei", "Zhoukai Zhao", "Feiyang Zhang", "Zewei Chen", "Shenghang Ruan", "Yongming Chen", "Runcong Nie", "Yuangfang Li", "Baiwei Zhao"], "journal": "Frontiers in pharmacology", "year": 2024, "abstract": "Aim: Programmed cell death (PCD) critically influences the tumor microenvironment (TME) and is intricately linked to tumor progression and patient prognosis. This study aimed to develop a novel prognostic indicator and marker of drug sensitivity in patients with gastric cancer (GC) based on PCD.\nMethods: We analyzed genes associated with 14 distinct PCD patterns using bulk transcriptome data and clinical information from TCGA-STAD for model construction with univariate Cox regression and LASSO regression analyses. Microarray data from GSE62254, GSE15459, and GSE26901 were used for validation. Single-cell transcriptome data from GSE183904 were analyzed to explore the relationship between TME and the newly constructed model, named PCD index (PCDI). Drug sensitivity comparisons were made between patients with high and low PCDI scores.\nResults: We developed a novel twelve-gene signature called PCDI. Upon validation, GC patients with higher PCDI scores had poorer prognoses. A high-performance nomogram integrating the PCDI with clinical features was also established. Additionally, single-cell transcriptome data analysis suggested that PCDI might be linked to critical components of the TME. Patients with high PCDI scores exhibited resistance to standard adjuvant chemotherapy and immunotherapy but might benefit from targeted treatments with NU7441, Dasatinib, and JQ1.\nConclusion: The novel PCDI model shows significant potential in predicting clinical prognosis and drug sensitivity of GC, thereby facilitating personalized treatment strategies for patients with GC.", "claims": [{"id": "claim-method", "statement": "Significant survival-related genes were identified using univariate Cox regression analysis (p < 0.05). LASSO (least absolute shrinkage and selection operator) regression, implemented through the “glmnet” package, was further employed to refine variables, thereby avoiding collinearity and overfitting (Engebretsen and Bohlin, 2019).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39744132", "locator": "sections[5] \"2.3 Prognostic gene signature development for GC patients\" paragraph 2", "quote": "Significant survival-related genes were identified using univariate Cox regression analysis (p < 0.05). LASSO (least absolute shrinkage and selection operator) regression, implemented through the “glmnet” package, was further employed to refine variables, thereby avoiding collinearity and overfitting (Engebretsen and Bohlin, 2019)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA: https://www.cancer.gov/ccg/research/genome-sequencing/tcga GEO: https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39744132", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: TCGA: https://www.cancer.gov/ccg/research/genome-sequencing/tcga GEO: https://www.ncbi.nlm.nih.gov/geo/."}], "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:39744132"], "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:GSE15459"], "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:GSE15459"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39744132", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2024.1477363", "sha256": "sha256:47b2be9bd5f65b8a3c96ba234cfb01ac8b8ec4b56c506eacc9ae8c3967057c2e", "bytes": 57977, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fphar.2024.1477363", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11688280", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE15459", "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=GSE15459", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE15459", "version": null, "note": "Publicly available datasets were analyzed in this study. 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