Using structural controllability to analyze signaling pathways and PPI networks for the identification of therapeutic targets in colorectal cancer

Colorectal cancer is the third most common cancer and the second most common cause of cancer deaths worldwide. In ۲۰۲۲, there were an estimated ۱.۹۳ million new cases and ۹۰۴,۰۰۰ deaths reported worldwide. According to the International Agency for Research on Cancer (IARC), projects an increase in new cases annually to reach ۳.۲ million, coupled with ۱.۶ million related deaths by the year ۲۰۴۰ (Bray, F., et al., ۲۰۲۳). Early diagnosis and timely intervention in this malignancy go a long way in securing better survival and improvement in quality of life. Colorectal cancer is a complex disease that results from extensive interactions of genetic, epigenetic, and environmental factors. For that reason, systems biology approaches may provide more profound insights into such processes and thus more effective treatments than traditional methods using complex network-based molecular interaction modeling. This project adopted a comprehensive analysis using omics data for the identification of effective treatment strategies. Gene expression data, GSE۲۶۱۸۸۸, were obtained from the GEO database (Escrich, V., et al., ۲۰۲۴). The study first made a comparison between healthy samples and early-stage disease samples. Significant genes were identified by applying two statistical criteria: p-value < ۰.۰۵ and |log FC|> ۲. These genes were then mapped to the colorectal cancer signaling pathway in the KEGG database and considered as target control genes. Subsequently, a control algorithm was applied to the signaling pathway network, which led to the identification of three driver genes. Additionally, to investigate disease progression, a comparison was made between early-stage and advanced-stage disease samples. Significant genes from this comparison were also identified using the aforementioned criteria. To perform network analysis, a protein-protein interaction (PPI) network was constructed for the identified genes using the STRING database. Hub centrality analysis of this network led to the identification of three hub proteins. Finally, drug-gene interaction analysis was performed for the six identified proteins — three driver proteins and three hub proteins — to identify potential therapeutic targets. These analyses can be utilized in the design of effective drugs for colorectal cancer. In the drug-gene interaction analysis, it was found that the drug Tetradecanoylphorbol Acetate targets the genes CXCL۸, MYC, and FOS; the drug Cetuximab targets the genes AREG and CXCL۸; the drug Tretinoin targets the genes SHH and CXCL۸; and the drug Methotrexate targets the IL۱RN gene.

کلیدواژه ها:

colorectal cancer ، network analysis ، controllability ، PPI network ، drug target ، expression data