Integrative analysis of single-cell and bulk RNA-sequencing data revealed disulfidptosis genes-based molecular subtypes and a prognostic signature in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38319721", "kind": "science-record", "revision": 2, "content_hash": "sha256:8c94a3d92a8a144a47bf283c36bab45ecbcdfa088e228ddc741cf3d491381154", "created_at": "2026-11-01T19:20:15Z", "updated_at": "2026-11-01T21:32:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative analysis of single-cell and bulk RNA-sequencing data revealed disulfidptosis genes-based molecular subtypes and a prognostic signature in lung adenocarcinoma.", "doi": "10.18632/aging.205509", "pmid": "38319721", "authors": ["Haixia Wang", "Xuemei Zhu", "Fangchao Zhao", "Pengfei Guo", "Jing Li", "Jingfang Du", "Guoyong Shan", "Yishuai Li", "Juan Li"], "journal": "Aging", "year": 2024, "abstract": "BACKGROUND: Disulfidoptosis is an unconventional form of programmed cell death that distinguishes itself from well-established cell death pathways like ferroptosis, pyroptosis, and necroptosis.\nMETHODS: Initially, we conducted a single-cell analysis of the GSE131907 dataset from the GEO database to identify disulfidoptosis-related genes (DRGs). We utilized differentially expressed DRGs to classify TCGA samples with an unsupervised clustering algorithm. Prognostic models were built using Cox regression and LASSO regression.\nRESULTS: Two DRG-related clusters (C1 and C2) were identified based on the DEGs from single-cell sequencing data analysis. In comparison to C1, C2 exhibited significantly worse overall prognosis, along with lower expression levels of immune checkpoint genes (ICGs) and chemoradiotherapy sensitivity-related genes (CRSGs). Furthermore, C2 displayed a notable enrichment in metabolic pathways and cell cycle-associated mechanisms. C2 was also linked to the development and spread of tumors. We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs. Patients were categorized into high-risk and low-risk groups depending on their DRG score, with the former group being linked to a poorer prognosis and higher TMB score. Moreover, the DRG score displayed significant correlations with CRSGs, ICGs, the tumor immune dysfunction and exclusion (TIDE) score, and chemotherapeutic sensitivity. Subsequently, we identified a significant correlation between the DRG score and monocyte macrophages. Additionally, crucial DRGs were additionally validated using qRT-PCR.\nCONCLUSIONS: Our new DRG score can predict the immune landscape and prognosis of LUAD, serving as a reference for immunotherapy and chemotherapy.", "claims": [{"id": "claim-method", "statement": "We used LASSO and multivariate Cox regression analyses on 1,421 DEGs associated with DRG clusters to create a DRG prognostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38319721", "locator": "sections[20] \"Construction and evaluation of the DRG prognostic model\" paragraph 2", "quote": "We used LASSO and multivariate Cox regression analyses on 1,421 DEGs associated with DRG clusters to create a DRG prognostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We downloaded the LUAD single-cell dataset GSE131907 from the GEO database, which consists of 11 samples.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38319721", "locator": "sections[3] \"Single cell sequencing data download and processing\" paragraph 2", "quote": "We downloaded the LUAD single-cell dataset GSE131907 from the GEO database, which consists of 11 samples."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38319721", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We created a prognostic risk model known as the DRG score, which relies on the expression levels of five DRGs.", "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:38319721"], "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:GSE131907"], "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:GSE131907"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38319721", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.205509", "sha256": 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