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Development of a prognostic model for hepatocellular carcinoma based on microvascular invasion characteristic genes
Xiaolan Mu1, Lili Pan1, Xicheng Wang1
1Institute for Regenerative Medicine, Medical Innovation Center and State Key Laboratory of Cardiology, Shanghai East Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.
Abstract:
Microvascular invasion (MVI) is an independent risk factor for the recurrence and metastasis of hepatocellular carcinoma (HCC), associated with poor prognosis. Thus, MVI has significant clinical value for the treatment selection and prognosis assessment of patients with HCC. However, there is no reliable and precise method for assessing the postoperative prognosis of MVI patients. This study aimed to develop a new HCC prognosis prediction model based on MVI characteristic genes through spatial transcriptomics sequencing, distinguishing between high-risk and low-risk patients and evaluating patient prognosis. In this study, four MVI samples with different grades were selected for spatial transcriptomic sequencing to screen for MVI region-specific genes. On this basis, an HCC prognostic model was constructed using univariate Cox regression analysis, LASSO regression analysis, random survival forest, and stepwise multivariate Cox regression analysis methods. We constructed a 7-gene prognostic model based on MVI characteristic genes and demonstrated its applicability for predicting the prognosis of HCC patients in three external validation cohorts. Furthermore, our model showed superior predictive performance compared with three published HCC prediction prognostic models and could serve as an independent prognostic factor for HCC. Additionally, single nucleus RNA sequencing analysis and multiple immunofluorescence images revealed an increased proportion of macrophages in high-risk patient samples, suggesting that HCC tumor cells may promote HCC metastasis through MIF-CD74 cell interactions. To sum up, we have developed a 7-gene biomarker based on MVI that can predict the survival rate of HCC patients at different stages. This predictive model can be used to categorize into high- and low- risk groups, which is of great significance for the prognostic assessment and personalized treatment of HCC patients.
