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Screening of Therapeutic Targets for Pancreatic Cancer by Bioinformatics Methods
Xiaojie Xiao1, Zheng Wan1, Xinmei Liu2
1Department of Oncology and Vascular Interventional Radiology, Zhongshan Hospital Xiamen University, Xiamen, China.
Abstract:
Pancreatic cancer (PC) has the lowest survival rate and the highest mortality rate among all cancers due to lack of effective treatments. The objective of the current study was to identify potential therapeutic targets in PC. Three transcriptome datasets, namely GSE62452, GSE46234, and GSE101448, were analyzed for differentially expressed genes (DEGs) between cancer and normal samples. Several bioinformatics methods, including functional analysis, pathway enrichment, hub genes, and drugs were used to screen therapeutic targets for PC. Fisher's exact test was used to analyze functional enrichments. To screen DEGs, the paired t-test was employed. The statistical significance was considered at p <0.05. Overall, 60 DEGs were detected. Functional enrichment analysis revealed enrichment of the DEGs in "multicellular organismal process", "metabolic process", "cell communication", and "enzyme regulator activity". Pathway analysis demonstrated that the DEGs were primarily related to "Glycolipid metabolism", "ECM-receptor interaction", and "pathways in cancer". Five hub genes were examined using the protein-protein interaction (PPI) network. Among these hub genes, 10 known drugs targeted to the CPA1 gene and CLPS gene were found. Overall, CPA1 and CLPS genes, as well as candidate drugs, may be useful for PC in the future.
Insights
This study identified CPA1 and CLPS genes as potential therapeutic targets for pancreatic cancer (PC). Candidate drugs targeting these genes show promise for future PC treatment strategies.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Pancreatic cancer (PC) exhibits low survival and high mortality rates due to limited effective treatments.
- Identifying novel therapeutic targets is crucial for improving PC patient outcomes.
Purpose of the Study:
- To identify potential therapeutic targets for pancreatic cancer (PC).
- To screen differentially expressed genes (DEGs) and associated drugs for PC treatment.
Main Methods:
- Analysis of three transcriptome datasets (GSE62452, GSE46234, GSE101448) to identify DEGs between PC and normal samples.
- Application of bioinformatics approaches including functional enrichment, pathway analysis, and protein-protein interaction (PPI) network construction.
- Utilized Fisher's exact test and paired t-test for statistical analysis (p < 0.05).
Main Results:
- Identified 60 differentially expressed genes (DEGs) in pancreatic cancer.
- Functional enrichment analysis highlighted DEGs involved in multicellular organismal processes, metabolic processes, cell communication, and enzyme regulator activity.
- Pathway analysis revealed enrichment in Glycolipid metabolism, ECM-receptor interaction, and pathways in cancer.
- Protein-protein interaction network analysis identified five hub genes, with CPA1 and CLPS showing significant potential.
- Discovered 10 known drugs targeting the CPA1 and CLPS genes.
Conclusions:
- The CPA1 and CLPS genes represent promising therapeutic targets for pancreatic cancer.
- Candidate drugs targeting CPA1 and CLPS may offer future treatment strategies for PC.
- This study provides a foundation for developing novel therapeutic interventions for pancreatic cancer.
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