ArticleCancer cell international2022
A novel prognostic model based on single-cell RNA sequencing data for hepatocellular carcinoma.
Article in Cancer cell international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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Who cites it
36 citing papers in PubMed, 1 synthesis or guideline pooled it, 57 citations in OpenAlex.
- A Risk Model Based on Ferroptosis-Related Genes OSMR, G0S2, IGFBP6, IGHG2, and FMOD Predicts Prognosis in Glioblastoma Multiforme.CNS neuroscience & therapeutics · 2025Pooled it
- Construction of a prognostic model and multidimensional analysis of hepatocellular carcinoma based on palmitoylation-related genes.Discover oncology · 2026Article
- The efferocytosis-related genes of SLC26A6, TYRO3, and PDK4 have been identified as predictors of prognosis in hepatocellular carcinoma and are associated with the immune status.International journal of medical sciences · 2026Article
- All-Trans Retinoic Acid Suppresses Hepatocellular Carcinoma Progression via the CSTB/CYTB Axis.Journal of cellular and molecular medicine · 2026Article
- Identification of coagulation-related hub genes in ischemic stroke based on bioinformatics integration analysis and investigation of their immune regulatory mechanisms.European journal of medical research · 2025Article
- Study of the Immune Infiltration and Sonic Hedgehog Expression Mechanism in Synovial Tissue of Rheumatoid Arthritis-Related Interstitial Lung Disease under Machine Learning CIBERSORT Algorithm.Molecular biotechnology · 2025Article
- Identification of a fibroblast-derived gene signature reveals prognostic and therapeutic insights in pancreatic cancer.Clinical and experimental medicine · 2025Article
- Identification of a novel FOXO3‑associated prognostic model in hepatocellular carcinoma.Oncology letters · 2025Article
- Multi-omics analyses develop and validate the optimal prognostic model on overall survival prediction for resectable hepatocellular carcinoma.Journal of gastrointestinal oncology · 2025Article
- Multiple machine learning algorithms identified SLC6A8 as a diagnostic biomarker of the late stage of Hepatocellular carcinoma.Discover oncology · 2025Article
- Exploring the tumor microenvironment of breast cancer to develop a prognostic model and predict immunotherapy responses.Scientific reports · 2025Article
- The role of hypoxia-senescence co-related molecular subtypes and prognostic characteristics in hepatocellular carcinoma.Scientific reports · 2025Article
- Integrated analysis of single-cell and bulk RNA-sequencing to predict prognosis and therapeutic response for colorectal cancer.Scientific reports · 2025Article
- Expression profiles and clinical significance of cystatin family genes in transitional cell carcinoma of the urinary bladder.Bladder (San Francisco, Calif.) · 2025Article
- Machine learning-based identification of core regulatory genes in hepatocellular carcinoma: insights from lactylation modification and liver regeneration-related genes.Frontiers in oncology · 2025Article
- Establishment of a Lactylation-Related Gene Signature for Hepatocellular Carcinoma Applying Bulk and Single-Cell RNA Sequencing Analysis.International journal of genomics · 2025Article
- Identifying and Diagnosing Lytic Cell Death Genes in Atherosclerosis Using Machine Learning and Bioinformatics.Journal of inflammation research · 2025Article
- Machine learning-based tumor associated macrophages polarity signature predicts prognosis and treatment response in hepatocellular carcinoma.Frontiers in immunology · 2025Article
- Unraveling the Heterogeneity of Tumor Immune Microenvironment in Hepatocellular Carcinoma by SingleCell RNA Sequencing and its Implications for Prognosis and Therapeutic Response.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2024Review
- Identification of Cuproptosis-Associated Prognostic Gene Expression Signatures from 20 Tumor Types.Biology · 2024Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe tumour heterogeneous make-up of immune cell infiltrates is a key factor for the therapy response and prognosis of hepatocellular carcinoma (HCC). However, it is still a major challenge to comprehensively understand the tumour immune microenvironment (TIME) at the genetic and cellular levels.
methodsHCC single-cell RNA sequencing (scRNA-seq) data were downloaded from the Gene Expression Omnibus (GEO) database, and gene expression data were retrieved from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was performed to evaluate the abundance of immune infiltrating cells. We employed weighted gene coexpression network analysis (WGCNA) to construct a gene coexpression network. Univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were further used to construct a risk model. Moreover, the expression levels of model genes were assessed by qPCR.
resultsWe defined 25 cell clusters based on the scRNA-seq dataset (GSE149614), and the clusters were labelled as various cell types by marker genes. Then, we constructed a weighted coexpression network and identified a total of 6 modules, among which the brown module was most highly correlated with tumours. Moreover, we found that the brown module was most closely related to monocytes (cluster 21). Through univariate Cox and LASSO analyses, we constructed a 3-gene risk model (RiskScore = 0.257*Expression
conclusionsWe developed a 3-gene signature (including CLTA, TALDO1 and CSTB) based on the heterogeneity of the TIME to predict the survival outcome and immunotherapy response.
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