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Objective: This study aimed to classify open-access gene expression data of patients with hepatitis B virus-related hepatocellular carcinoma (HBV + HCC) and chronic HBV without HCC (HBV alone) using the XGBoost method, one of the machine learning methods, and reveal important genes that may cause HCC. Methods: This case-control study used the open-access gene expression data of patients with HBV + HCC and HBV alone. Data from 17 patients with HBV + HCC and 36 patients with HBV were included. XGBoost was constructed for the classification via 10-fold cross-validation. Accuracy, balanced accuracy, sensitivity, selectivity, positive-predictive value, and negative-predictive value performance metrics were evaluated for model performance. Results: According to the feature-selection method, 18 genes were selected, and modeling was performed with these input variables. Accuracy, balanced accuracy, sensitivity, specificity, positive-predictive value, negative-predictive value, and F1 score obtained from XGBoost model were 98.1%, 98.6%, 100%, 97.2%, 94.4%, 100%, and 97.1%, respectively. Based on the predictor importance findings acquired from XGBoost, the RNF26, FLJ10233, ACBD6, RBM12, PFAS, H3C11, and GKP5 can be employed as potential biomarkers of HBV-related HCC. Conclusions: In this study, genes that may be possible biomarkers of HBV-related HCC were determined using a machine learning-based prediction approach. After the reliability of the obtained genes are clinically verified in subsequent research, therapeutic procedures can be established based on these genes, and their usefulness in clinical practice may be documented. [ABSTRACT FROM AUTHOR]

Background: Comparing gene expression profiles using gene expression datasets of different types of tumors is frequently used to identify molecular mechanisms of cancer. This study aimed to find shared and type specific gene expression profiles of hepatocellular carcinoma (HCC) and B-cell chronic lymphocytic leukemia (B-CLL). Material and methods: Gene expression microarrays for HCC and B-CLL and RNA-sequencing expression data for liver HCC and lymphoid neoplasm diffuse large B-cell lymphoma (DLBC) were analyzed and differentially expressed probe sets or genes for each cancer type were detected. Probe sets and genes that were shared or specifically expressed in both cancer types were identified. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) terms for Biological Process (BP) annotations were performed. Results: According to our analysis shared upregulated genes were mainly annotated in cell cycle processes. Some of the genes that changed only in HCC were annotated in cell cycle and metabolic processes, and some of the genes, altered only in B-CLL, were annotated in immune response and hemopoiesis. Conclusion: These results contribute to cancer research that aim to find the conserved gene expression profiles in different cancer types and widen the knowledge of HCC and B-CLL specific mechanisms. [ABSTRACT FROM AUTHOR]