Integrative transcriptome analysis identifies a crotonylation gene signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39428564", "kind": "science-record", "revision": 1, "content_hash": "sha256:244a9025e4a2228c1f33ce08f43b407499a020e51fbeb5c13a0b7f81379c277f", "created_at": "2026-11-01T20:29:40Z", "updated_at": "2026-11-01T20:29:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative transcriptome analysis identifies a crotonylation gene signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma.", "doi": "10.1111/jcmm.70083", "pmid": "39428564", "authors": ["Bailu Yang", "Fukai Wen", "Yifeng Cui"], "journal": "Journal of cellular and molecular medicine", "year": 2024, "abstract": "Hepatocellular carcinoma (HCC) stands as the most prevalent and treatment-resistant malignant tumour, characterized by a dismal prognosis. Croton acylation (CA) has recently gained attention as a critical factor in cancer pathogenesis. This study sought to rapidly identify prognostic features of HCC linked to CA. Differential analysis was conducted between tumour tissues and adjacent non-tumour tissues in the TCGA-LIHC and GSE76427 datasets to uncover differentially expressed genes (DEG1 and DEG2). The intersection of DEG1 and DEG2 highlighted DEGs with consistent expression patterns. Single-sample gene set enrichment analysis scores were calculated for 18 lysine crotonylation-related genes (LCRGs) identified in prior research, showing significant differences between tumour and normal groups. Subsequently, weighted gene co-expression network analysis was employed to identify key module genes correlated with the LCRG score. Candidate genes were identified by overlapping consistently expressed DEGs with key module genes. Prognostic features were identified, and risk scores were determined via regression analysis. Patients were categorized into risk groups based on the optimal cutoff value. Gene set enrichment analysis (GSEA) and immunoassays were also performed. The prognostic features were further validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 88 candidate genes were identified from 1179 consistently expressed DEGs and 4200 key module genes. Seven prognostic features were subsequently identified: TMCO3, RAP2A, ITGAV, ZFYVE26, CHST9, HMGN4, and KLHL21. GSEA revealed that DEGs between risk groups were primarily associated with chylomicron metabolism, among other pathways. Additionally, activated CD4+ T cells demonstrated the strongest positive correlation with risk scores, and most immune checkpoints showed significant differences between risk groups, with ASXL1 exhibiting the strongest correlation with risk scores. The Tumour Immune Dysfunction and Exclusion score was notably higher in the high-risk group. Moreover, in both the TCGA-LIHC and ICGC-LIRI-JP datasets, the expression of other prognostic features was elevated in tumour tissues, with the exception of CHST9. RT-qPCR confirmed the increased expression of TMCO3, RAP2A, ITGAV, ZFYVE26, and HMGN4. This study establishes a risk model for HCC based on seven crotonylation-associated prognostic features, offering a theoretical framework for the diagnosis and treatment of HCC.", "claims": [{"id": "claim-data-access", "statement": "The data utilized in this study are accessible through the UCSC Xena (http://xena.ucsc.edu/), TCGA (http://cancergenome.nih.gov/, TCGA‐LIHC), GEO (https://www.ncbi.nlm.nih.gov/geo/, GSE76427), and ICGC (https://gwas.mrcieu.ac.uk/, ICGC‐LIRI‐JP) databases.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39428564", "locator": "sections[34] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "The data utilized in this study are accessible through the UCSC Xena (http://xena.ucsc.edu/), TCGA (http://cancergenome.nih.gov/, TCGA‐LIHC), GEO (https://www.ncbi.nlm.nih.gov/geo/, GSE76427), and ICGC (https://gwas.mrcieu.ac.uk/, ICGC‐LIRI‐JP) databases."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Seven prognostic characteristics—TMCO3, RAP2A, ITGAV, ZFYVE26, CHST9, HMGN4, and KLHL21—were selected through 10-fold cross-validation to construct an HCC risk model.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39428564", "locator": "sections[19] \"Construction of a risk model based on seven prognostic characteristics\" paragraph 2", "quote": "Subsequently, seven prognostic markers—transmembrane and coiled‐coil domain 3 (TMCO3), RAP2A, integrin subunit alpha‐V (ITGAV), ZFYVE26, CHST9, HMGN4, and Kelch‐like protein 21 (KLHL21)—were selected through 10‐fold cross‐validation using the optimal model parameter λ = 0.01201314, and a risk model was developed (Figure 4B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Seven prognostic characteristics—TMCO3, RAP2A, ITGAV, ZFYVE26, CHST9, HMGN4, and KLHL21—were selected through 10-fold cross-validation to construct an HCC risk model.", "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:39428564"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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