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Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression
Liang Yu1, Fengdan Xu1, Lin Gao1
1School of Computer Science and Technology, Xidian University, Xi'an, China.
Abstract:
Hepatocellular carcinoma (HCC) is the fourth most common primary liver tumor and is an important medical problem worldwide. However, the use of current therapies for HCC is no possible to be cured, and despite numerous attempts and clinical trials, there are not so many approved targeted treatments for HCC. So, it is necessary to identify additional treatment strategies to prevent the growth of HCC tumors. We are looking for a systematic drug repositioning bioinformatics method to identify new drug candidates for the treatment of HCC, which considers not only aberrant genomic information, but also the changes of transcriptional landscapes. First, we screen the collection of HCC feature genes, i.e., kernel genes, which frequently mutated in most samples of HCC based on human mutation data. Then, the gene expression data of HCC in TCGA are combined to classify the kernel genes of HCC. Finally, the therapeutic score (TS) of each drug is calculated based on the kolmogorov-smirnov statistical method. Using this strategy, we identify five drugs that associated with HCC, including three drugs that could treat HCC and two drugs that might have side-effect on HCC. In addition, we also make Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets. All these findings suggest that our approach is effective for accurate predicting novel therapeutic options for HCC and easily to be extended to other tumors.
Insights
This study developed a bioinformatics method to identify new drug candidates for hepatocellular carcinoma (HCC). The approach successfully identified three potential HCC treatments and two drugs with possible side effects, aiding future liver cancer therapy development.
Area of Science:
- Oncology
- Bioinformatics
- Pharmacology
Background:
- Hepatocellular carcinoma (HCC) is a prevalent and challenging liver cancer with limited effective targeted therapies.
- Existing treatments for HCC often fail to achieve a cure, necessitating the exploration of novel therapeutic strategies.
Purpose of the Study:
- To identify novel drug candidates for hepatocellular carcinoma (HCC) treatment using a systematic drug repositioning bioinformatics approach.
- To integrate genomic and transcriptional data for a comprehensive analysis of potential HCC therapeutics.
Main Methods:
- Screening of HCC kernel genes based on human mutation data.
- Classification of HCC kernel genes using The Cancer Genome Atlas (TCGA) gene expression data.
- Calculation of drug therapeutic scores (TS) using the Kolmogorov-Smirnov statistical method.
Main Results:
- Identification of five drugs associated with HCC, including three with potential therapeutic benefits and two with possible adverse effects.
- Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis were performed on drug targets.
- The developed approach demonstrated effectiveness in predicting novel therapeutic options for HCC.
Conclusions:
- The proposed bioinformatics strategy is effective for identifying potential new drug candidates for HCC.
- This method can be readily extended to discover therapeutic options for other types of cancer.
- The findings offer promising avenues for advancing HCC treatment and drug development.
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