ArticleBMC cancer2021
A predictive and prognostic model for hepatocellular carcinoma with microvascular invasion based TCGA database genomics.
Article in BMC cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 14 citations in OpenAlex.
- Article
- Advances in multi-omics studies of microvascular invasion in hepatocellular carcinoma.European journal of medical research · 2025Review
- Protein structural context of cancer mutations reveals molecular mechanisms and candidate driver genes.Cell reports · 2024Article
- Construction and validation of a prognostic signature based on microvascular invasion and immune-related genes in hepatocellular carcinoma.Scientific reports · 2024Article
- Clinical Nomogram Model for Pre-Operative Prediction of Microvascular Invasion of Hepatocellular Carcinoma before Hepatectomy.Medicina (Kaunas, Lithuania) · 2024Article
- Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Macrophage Polarization-Related Genes.Journal of hepatocellular carcinoma · 2024Article
- Screening for aberrantly methylated and differentially expressed genes in nonalcoholic fatty liver disease of hepatocellular carcinoma patients with cirrhosis.World journal of surgical oncology · 2022Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
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Abstract
backgroundMicrovascular 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.
methodsIn 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.
resultsTwenty-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.
conclusionWe established an accurate TCGA database-based genomics prediction model for preoperative MVI risk and studied the prognostic value of DEGs for HCC patients with MVI. These DEGs that are related to MVI warrant further study regarding the occurrence and development of MVI.
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