Frontiers in Cell and Developmental Biology · 2021 · 12 citations · 60 references
Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and heterogeneity of HCC is the major barrier in improving patient outcome. To stratify HCC patients with different degrees of malignancy and provide precise treatment strategies, we reconstructed the tumor evolution trajectory with the help of scRNA-seq data and established a 30-gene prognostic model to identify the malignant state in HCC. Patients were divided into high-risk and low-risk groups. C-index and receiver operating characteristic (ROC) curve confirmed the excellent predictive value of this model. Downstream analysis revealed the underlying molecular and functional characteristics of this model, including significantly higher genomic instability and stronger proliferation/progression potential in the high-risk group. In summary, we established a novel prognostic model to overcome the barriers caused by HCC heterogeneity and provide the possibility of better clinical management for HCC patients to improve their survival outcomes.
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GSVA: gene set variation analysis for microarray and RNA-Seq data
Sonja Hänzelmann, Robert Castelo, Justin Guinney · BMC Bioinformatics · 2013 · 15.6K citations · Full text
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Andrew Butler, Paul Hoffman, Peter Smibert et al. · Nature Biotechnology · 2018 · 14.1K citations · Full text
Engineering, Genetics, Multiomics +17