Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39587687", "kind": "science-record", "revision": 3, "content_hash": "sha256:d384a42051112f9d73d53fd9d35d52f9a840452bb5a487feaf6d211d05477d18", "created_at": "2026-11-01T20:29:51Z", "updated_at": "2026-11-01T22:13:11Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer.", "doi": "10.1186/s40246-024-00698-2", "pmid": "39587687", "authors": ["Qunfang Zhou", "Jingqiang Wu", "Jiaxin Bei", "Zixuan Zhai", "Xiuzhen Chen", "Wei Liang", "Jing Meng", "Mingyu Liu"], "journal": "Human genomics", "year": 2024, "abstract": "BACKGROUND: Liver cancer has a high global incidence, particularly in East Asia. Early detection difficulties lead to poor prognosis. Single-cell sequencing precisely identifies gene expression differences in specific cell types, making it valuable in tumor microenvironment research and immune drug development. However, the characteristics of tumor cells themselves are equally important for patient prognosis and treatment.\nMETHODS: We downloaded single-cell sequencing data from GSE189903, grouped cells by cluster markers, and classified epithelial cells into adjacent non-tumor, normal, and tumor cells. Differential gene and survival analyses identified significant differential genes. Using TCGA-LIHC data, we divided 370 patients into test and training sets. We constructed and validated a LASSO model based on these genes in both sets and two external datasets. Functional, immune infiltration, and mutation analyses were performed on high and low-risk groups. We also used RNA-seq and IC50 data of 15 liver cancer cell lines from GDSC, scoring them with our prognostic model to identify potential drugs for high-risk patients.\nRESULTS: Dimensionality reduction and clustering of 34 single-cell samples identified five subgroups, with epithelial cells further classified. Differential gene analysis identified 124 significant genes. An 11-gene prognostic model was constructed, effectively stratifying patient prognosis (p < 0.05) and achieving an AUC above 0.6 for 5 year survival prediction in multiple cohorts. Functional analysis revealed that upregulated genes in high-risk groups were enriched in cell adhesion pathways, while downregulated genes were enriched in metabolic pathways. Mutation analysis showed more TP53 mutations in the high-risk group and more CTNNB1 mutations in the low-risk group. Immune infiltration analysis indicated higher immune scores and less CD8 + naive T cell infiltration in the high-risk group. Drug sensitivity analysis identified 14 drugs with lower IC50 in the high-risk group, including clinically approved Sorafenib and Axitinib for treating unresectable HCC.\nCONCLUSION: We established an 11-gene prognostic model that effectively stratifies liver cancer patients based on differentially expressed genes between tumor and adjacent non-tumor cells clustered by scRNA-seq data. The two risk groups had significantly different molecular characteristics. We identified 14 drugs that might be effective for high-risk HCC patients. Our study provides novel insights into tumor cell characteristics, aiding in research on tumor development and treatment.", "claims": [{"id": "claim-method", "statement": "Using the glmnet R package, we constructed a LASSO-Cox regression model in the training group based on the candidate genes, and tenfold cross validation was used to determine the optimal regularization parameters.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39587687", "locator": "sections[6] \"Establishment and validation of a prognostic model\" paragraph 3", "quote": "Using the glmnet R package, we constructed a LASSO-Cox regression model in the training group based on the candidate genes, and tenfold cross validation was used to determine the optimal regularization parameters."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analysed during the current study are available in the GEO repository https://www.ncbi.nlm.nih.gov/, the TCGA repository https://portal.gdc.cancer.gov/, the NODE repository https://www.biosino.org/node.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39587687", "locator": "sections[22] \"notes\" paragraph 1", "quote": "The datasets generated and/or analysed during the current study are available in the GEO repository https://www.ncbi.nlm.nih.gov/, the TCGA repository https://portal.gdc.cancer.gov/, the NODE repository https://www.biosino.org/node."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An 11-gene LASSO-Cox prognostic model was established and validated to stratify liver cancer patient prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39587687", "locator": "sections[12] \"Establishment and validation of a prognostic model in TCGA-LIHC\" paragraph 2", "quote": "A model containing eleven genes was established, including CCT3, CRYAB, ENO1, LDHA, MARCKSL1, MMP7, SAT2, SPP2, STOM, TMEM205, and TRAPPC9 (Fig. 2A)."}, {"asset": "source:pmid:39587687", "locator": "sections[12] \"Establishment and validation of a prognostic model in TCGA-LIHC\" paragraph 2", "quote": "The AUC for 1-year, 3-year, and 5-year survival were 0.77, 0.76, and 0.73, respectively (Fig. 2C), suggesting good predictive efficiency."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An 11-gene LASSO-Cox prognostic model was established and validated to stratify liver cancer patient prognosis.", "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:39587687"], "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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