Evaluation of gene interaction and similarity in 17 different cancer pathways

Abstract

This study investigates similarities and gene interactions in 17 different cancer types using Kyoto University's KEGG cancer pathways. Using Python software and the Google Colab platform, gene similarities and interactions within cancer pathways were calculated through Jaccard similarity indices and interaction analyses. The results reveal important genes and pathways shared between cancer types, providing insights into common molecular mechanisms underlying cancer development and progression. These findings may contribute to the identification of potential therapeutic targets by understanding the biological processes shared between cancers. In comparisons between different cancer types, gene similarities ranged between 43% and 47% and pathway similarities ranged between 25% and 46%. These results reveal that while some cancer types are genetically similar, they show differences in biochemical processes. In the gene interaction study among 17 different cancer pathways, the highest interaction rates were observed in colorectal cancer between entries ‘43-40’ with ('activation'), in pancreatic cancer between entries ‘113- 6’ with ('activation'), and in hepatocellular carcinoma between entries ‘122-224’ with ('activation'), showing nearly 100% interaction. On the other hand, the lowest interaction rates were found in colorectal cancer between entries ‘39-135’ with ('missing interaction') and in melanoma between entries ‘95-106’ with ('missing interaction'), showing 0% interaction.

Keywords

• Cancer pathways
• Python
• Jaccard similarity
• gene interactions

References

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