Evaluation Study Journal Article Validation Study Epublish 1 citation
PubMed: 40055377 DOI: 10.1038/s41598-025-91475-1
Development of a MVI associated HCC prognostic model through single cell transcriptomic analysis and 101 machine learning algorithms.
Sci Rep 15(1):7977
Authors: Jiayi Zhang, Zheng Zhang, Chenqing Yang, Qingguang Liu, Tao Song
Affiliations
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
- Department of Gynaecology and Obstetrics Department, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi Province, China.
Abstract
Hepatocellular carcinoma (HCC) is an exceedingly aggressive form of cancer that often carries a poor prognosis, especially when it is complicated by the presence of microvascular invasion (MVI). Identifying patients at high risk of MVI is crucial for personalized treatment strategies. Utilizing the single-cell RNA-sequencing dataset (GSE242889) of HCC, we identified malignant cell subtypes associated with microvascular invasion (MVI), in conjunction with the TCGA dataset, selected a set of MVI-related genes (MRGs). We developed an optimal prognostic model comprising 11 genes (NOP16, YIPF1, HMMR, NDC80, DYNLL1, CDC34, NLN, KHDRBS3, MED8, SLC35G2, RAB3B) based on MVI-related signature genes by integrating single-cell transcriptomic analysis with 101 machine learning algorithms. This model is meticulously crafted to forecast the prognosis of individuals afflicted with hepatocellular carcinoma (HCC). Additionally, we affirmed the predictive precision and superiority of our model through a meta-analysis against existing HCC models. Furthermore, we explored the differences between high- and low-risk groups through mutation and immune infiltration analyses. Lastly, we investigated immunotherapy responses and drug sensitivities between risk groups, providing novel therapeutic insights for liver cancer.
MeSH
Cells, Genes, Neoplasms, Algorithms, Machine Learning, Natural Science Disciplines, Therapeutics, Learning, Prognosis, Machine Learning Algorithms