Hepatocellular carcinoma (HCC) is a primary liver malignancy that is responsible for a significant number of cancer-related deaths worldwide. Unfortunately, currently used therapies do not offer a cure and there are limited approved targeted therapies for HCC despite numerous attempts and clinical trials. To address this challenge, Liang Yu, Fengdan Xu and Lin Gao sought to use a systematic drug repositioning bioinformatics approach to identify novel candidate drugs to treat HCC. Their approach considered not only aberrant genomic information but also changes in transcriptional landscapes associated with the disease. The researchers began by screening the collection of HCC feature genes that frequently mutated in most samples of HCC based on human mutation data. They then combined gene expression data from The Cancer Genome Atlas (TCGA) to classify the kernel genes of HCC and calculated the therapeutic score (TS) of each drug using the Kolmogorov-Smirnov statistical method. Using this strategy, Yu et al. identified five drugs associated with HCC: three drugs that could treat HCC and two drugs that might have side effects on HCC. In addition, they conducted Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets. Their findings suggest that their approach is effective for accurately discovering novel therapeutic options for HCC and can be easily extended to other tumors. This research provides hope for new treatments that could improve outcomes for patients with this devastating disease while still many challenges remain in treating liver cancer effectively. With their systematic drug repositioning bioinformatics approach, Yu et al have opened up new possibilities for discovering novel therapeutic options for hepatocellular carcinoma which may lead to improved outcomes in the future.
Hepatocellular carcinoma (HCC) is a type of liver cancer that causes many deaths around the world. There are not many good treatments for HCC yet. Scientists used a special way to find new drugs that could help treat HCC. They looked at how the genes in HCC cells were different from normal cells and found five drugs that might work. They tested these drugs to see if they could help or hurt people with HCC. This is an important discovery, but there is still more work to do to find better ways to treat liver cancer.
Definitions
- Hepatocellular carcinoma (HCC): A type of liver cancer.
- Malignancy: A disease that can grow and spread quickly in the body.
- Bioinformatics: Using computers and math to study biology and medicine.
- Genomic information: Information about all the genes in a person's body.
- Transcriptional landscapes: The pattern of which genes are turned on or off in a cell.
- Therapeutic score: A number that shows how likely a drug is to be helpful for treating a disease.
- Kolmogorov-Smirnov statistical method: A way of comparing two sets of data to see if they are similar or different.
- Connectivity Map (CMap): A tool for comparing how different drugs affect cells in the body.
- KEGG enrichment analysis: A way of looking at which biological pathways are affected by certain genes or drugs.
Exploring Novel Therapeutic Options for Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is a primary liver malignancy that has become one of the leading causes of cancer-related deaths worldwide. Despite numerous attempts and clinical trials, there are limited approved targeted therapies available to treat HCC, making it difficult to provide effective treatment options for patients with this devastating disease. To address this challenge, Liang Yu, Fengdan Xu and Lin Gao sought to use a systematic drug repositioning bioinformatics approach to identify novel candidate drugs that could be used to treat HCC.
Systematic Drug Repositioning Bioinformatics Approach
The researchers began by screening the collection of HCC feature genes that frequently mutated in most samples of HCC based on human mutation data. They then combined gene expression data from The Cancer Genome Atlas (TCGA) to classify the kernel genes of HCC and calculated the therapeutic score (TS) of each drug using the Kolmogorov-Smirnov statistical method. Using this strategy, Yu et al. identified five drugs associated with HCC: three drugs that could treat HCC and two drugs that might have side effects on HCC. In addition, they conducted Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets.
Findings & Implications
Their findings suggest that their approach is effective for accurately discovering novel therapeutic options for HCC and can be easily extended to other tumors. This research provides hope for new treatments that could improve outcomes for patients with this devastating disease while still many challenges remain in treating liver cancer effectively. With their systematic drug repositioning bioinformatics approach, Yu et al have opened up new possibilities for discovering novel therapeutic options for hepatocellular carcinoma which may lead to improved outcomes in the future.
Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and is a leading cause of cancer-related death worldwide. However, cure is not possible with currently used therapies, and there is not so much approved targeted therapy for HCC despite numerous attempts and clinical trials. So, it is essential to identify additional therapeutic strategies to block the growth of HCC tumors. As a cancer disease, it is associated with aberrant genomic and transcriptional landscapes. We sought to use a systematic drug repositioning bioinformatics approach to identify novel candidate drugs to treat HCC, which considers not only aberrant genomic information, but also the changes of transcriptional landscapes. First, we screen the collection of HCC feature genes that 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 identified 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 discovering novel therapeutic options for HCC and easily to be extended to other tumors.
