Artificial intelligence-based prediction of molecular and genetic markers for hepatitis C-related hepatocellular
Cemil Colak1, Zeynep Kucukakcali1, Sami Akbulut1,2
1Department of Biostatistics and Medical Informatics.
Insights
This study used machine learning to identify key genes for Hepatitis C virus-related hepatocellular carcinoma (HCC). The XGboost model achieved high accuracy, pinpointing HAO2, TOMM20, GPC3, and PSMB4 as potential biomarkers for HCC.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Hepatocellular carcinoma (HCC) is a leading global cause of cancer mortality.
- Hepatitis C virus (HCV) infection is a significant risk factor for HCC development.
- Distinguishing between HCV-related HCC and chronic HCV without HCC is crucial for patient management.
Purpose of the Study:
- To classify gene expression data from patients with HCV-related HCC and chronic HCV.
- To identify key genes associated with the development of HCC in HCV-infected patients.
- To leverage machine learning for biomarker discovery in HCC.
Main Methods:
- Retrospective case-control study utilizing public gene expression data.
- Application of the XGboost machine learning algorithm for classification.
- 10-fold cross-validation and performance metrics including accuracy, sensitivity, and specificity were employed.
Main Results:
- The XGboost model demonstrated high performance with an overall accuracy of 98.1%.
- Key genes identified as potential biomarkers for HCV-related HCC include HAO2, TOMM20, GPC3, and PSMB4.
- The model achieved excellent sensitivity (100%) and specificity (94.1%).
Conclusions:
- Machine learning effectively identified potential biomarkers for HCV-related HCC.
- The identified genes (HAO2, TOMM20, GPC3, PSMB4) warrant further clinical investigation.
- Future research should focus on validating these biomarkers for therapeutic and diagnostic applications.
Background:
Hepatocellular carcinoma (HCC) is the main cause of mortality from cancer globally. This paper intends to classify public gene expression data of patients with Hepatitis C virus-related HCC (HCV+HCC) and chronic HCV without HCC (HCV alone) through the XGboost approach and to identify key genes that may be responsible for HCC.
Methods:
The current research is a retrospective case-control study. Public data from 17 patients with HCV+HCC and 35 patients with HCV-alone samples were used in this study. An XGboost model was established for the classification by 10-fold cross-validation. Accuracy (AC), balanced accuracy (BAC), sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were utilized for performance assessment.
Results:
AC, BAC, sensitivity, specificity, positive predictive value, negative predictive value, and F1 scores from the XGboost model were 98.1, 97.1, 100, 94.1, 97.2, 100, and 98.6%, respectively. According to the variable importance values from the XGboost, the HAO2, TOMM20, GPC3, and PSMB4 genes can be considered potential biomarkers for HCV-related HCC.
Conclusion:
A machine learning-based prediction method discovered genes that potentially serve as biomarkers for HCV-related HCC. After clinical confirmation of the acquired genes in the following medical study, their therapeutic use can be established. Additionally, more detailed clinical works are needed to substantiate the significant conclusions in the current study.
More Related Videos
00:06An Oncogenic Hepatocyte-Induced Orthotopic Mouse Model of Hepatocellular Cancer Arising in the Setting of Hepatic Inflammation and Fibrosis
Published on: September 12, 2019
12:24A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
