Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38654290", "kind": "science-record", "revision": 4, "content_hash": "sha256:eb865a2f69c3431bbbbe1153dac38988f89f09b4402d10da6b2a9cce515edf5e", "created_at": "2026-11-01T19:20:37Z", "updated_at": "2026-11-01T22:11:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing.", "doi": "10.1186/s12920-024-01871-1", "pmid": "38654290", "authors": ["Yujian He", "Wei Qi", "Xiaoli Xie", "Huiqing Jiang"], "journal": "BMC medical genomics", "year": 2024, "abstract": "BACKGROUND: Hepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles.\nMETHODS: The GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530.\nRESULTS: Our findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease.\nCONCLUSION: Our research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.", "claims": [{"id": "claim-method", "statement": "To refine the gene selection process and enhance predictive accuracy, the “glmnet” package was employed to conduct Lasso Cox regression analysis using the training cohort [25], and the optimal genes were assessed through 10-fold cross validation.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38654290", "locator": "sections[8] \"Construction and evaluation of predictive signature based on hepatocyte-specific genes\" paragraph 2", "quote": "To refine the gene selection process and enhance predictive accuracy, the “glmnet” package was employed to conduct Lasso Cox regression analysis using the training cohort [25], and the optimal genes were assessed through 10-fold cross validation."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the results of this study were available from public databases, including TCGA (TCGA-LIHC cohort) database (https://portal.gdc.cancer.gov/), ICGC (ICGC-LIRI-JP cohort) database (https://dcc.icgc.org/), GEO (bulk RNA-sequencing data: GSE135631, GSE135251 and GSE142530. Single cell omic data: GSE115469) database (https://www.ncbi.nlm.nih.gov/geo/), China National Gene Bank (CNP0000650) database (https://db.cngb.org/). The human genetic information was obtained from the https://data.broadinstitute.org/Trinity/CTAT/cnv/. The TIDE data retrieved from http://tide.dfci.harvard.edu/. The data pertaining to an immunotherapy cohort involving CTLA-4 inhibitors and PD-1 inhibitors was acquired from the https://tcia.at/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38654290", "locator": "sections[31] \"notes\" paragraph 1", "quote": "The data used to support the results of this study were available from public databases, including TCGA (TCGA-LIHC cohort) database (https://portal.gdc.cancer.gov/), ICGC (ICGC-LIRI-JP cohort) database (https://dcc.icgc.org/), GEO (bulk RNA-sequencing data: GSE135631, GSE135251 and GSE142530. Single cell omic data: GSE115469) database (https://www.ncbi.nlm.nih.gov/geo/), China National Gene Bank (CNP0000650) database (https://db.cngb.org/). The human genetic information was obtained from the https://data.broadinstitute.org/Trinity/CTAT/cnv/. The TIDE data retrieved from http://tide.dfci.harvard.edu/. The data pertaining to an immunotherapy cohort involving CTLA-4 inhibitors and PD-1 inhibitors was acquired from the https://tcia.at/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38654290", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group.", "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:38654290"], "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. 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The human genetic information was obtained from the https://data.broadinstitute.org/Trinity/CTAT/cnv/. The TIDE data retrieved from http://tide.dfci.harvard.edu/. 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