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A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics

Microvascular invasion (MVI) adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma (HCC). There is no study addressing genetic changes in HCC patients with MVI. | Wang et al. BMC Cancer 2021 21 1337 https doi.org 10.1186 s12885-021-09047-1 RESEARCH Open Access A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics Jin Wang1 Zhi Wen Ding2 Kuang Chen1 Yan Zhe Liu1 Nan Li2 and Ming Gen Hu1 Abstract Background Microvascular invasion MVI adversely affects postoperative long-term survival outcomes in patients with hepatocellular carcinoma HCC . There is no study addressing genetic changes in HCC patients with MVI. We first screened differentially expressed genes DEGs in patients with and without MVI based on TCGA data established a prediction model and explored the prognostic value of DEGs for HCC patients with MVI. Methods In this paper gene expression and clinical data of liver cancer patients were downloaded from the TCGA database. The DEG analysis was conducted using DESeq2. Using the least absolute shrinkage and selection operator MVI-status-related genes were identified. A Kaplan-Meier survival analysis was performed using these genes. Finally we validated two genes HOXD9 and HOXD10 using two sets of HCC tissue microarrays from 260 patients. Results Twenty-three MVI-status-related key genes were identified. Based on the key genes we built a classification model using random forest and time-dependent receiver operating characteristic ROC which reached 0.814. Then we performed a survival analysis and found ten genes had a significant difference in survival time. Simultaneously using two sets of 260 patients HCC tissue microarrays we validated two key genes HOXD9 and HOXD10. Our study indicated that HOXD9 and HOXD10 were overexpressed in HCC patients with MVI compared with patients without MVI and patients with MVI with HOXD9 and 10 overexpression had a poorer prognosis than patients with MVI with low expression of HOXD9 and 10. Conclusion We established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic