Dissecting Oncogenic RTK Pathways in Colorectal Cancer Initiation and Progression

Support Vector Machine-Based Prediction Models for Drug Repurposing and Designing Novel Drugs for Colorectal Cancer

Colorectal cancer (CRC) has witnessed a concerning increase in incidence and poses a significant therapeutic challenge due to its poor prognosis. There is a pressing demand to identify novel drug therapies to combat CRC. In this study, we addressed this need by utilizing the pharmacological profiles of anticancer drugs from the Genomics of Drug Sensitivity in Cancer (GDSC) database and developed QSAR models using the Support Vector Machine (SVM) algorithm for prediction of alternative and promiscuous anticancer compounds for CRC treatment. Our QSAR models demonstrated their robustness by achieving a high correlation of determination (R2) after 10-fold cross-validation. For 12 CRC cell lines, R2 ranged from 0.609 to 0.827. The highest performance was achieved for SW1417 and GP5d cell lines with R2 values of 0.827 and 0.786, respectively. Further, we listed the most common chemical descriptors in the drug profiles of the CRC cell lines and we also further reported the correlation of these descriptors with drug activity. The KRFP314 fingerprint was the predominantly occurring descriptor, with the KRFPC314 fingerprint following closely in prevalence within the drug profiles of the CRC cell lines. Beyond predictive modeling, we also confirmed the applicability of our developed QSAR models via in silico methods by conducting descriptor-drug analyses and recapitulating drug-to-oncogene relationships. We also identified two potential anti-CRC FDA-approved drugs, viomycin and diamorphine, using QSAR models. To ensure the easy accessibility and utility of our research findings, we have incorporated these models into a user-friendly prediction Web server named "ColoRecPred", available at https://project.iith.ac.in/cgntlab/colorecpred. We anticipate that this Web server can be used for screening of chemical libraries to identify potential anti-CRC drugs.

Relationship between apical junction proteins, gene expression and cancer

The apical junctional complex (AJC) is a cell-cell adhesion system present at the upper portion of the lateral membrane of epithelial cells integrated by the tight junction (TJ) and the adherens junction (AJ). This complex is crucial to initiate and stabilize cell-cell adhesion, to regulate the paracellular transit of ions and molecules and to maintain cell polarity. Moreover, we now consider the AJC as a hub of signal transduction that regulates cell-cell adhesion, gene transcription and cell proliferation and differentiation. The molecular components of the AJC are multiple and diverse and depending on the cellular context some of the proteins in this complex act as tumor suppressors or as promoters of cell transformation, migration and metastasis outgrowth. Here, we describe these new roles played by TJ and AJ proteins and their potential use in cancer diagnostics and as targets for therapeutic intervention.

Analysis of TGCA data reveals genetic and epigenetic changes and biological function of MUC family genes in colorectal cancer

Aim: Few studies focused on functions and regulatory networks of MUC family members in colorectal cancer based on comprehensive analysis of online database. Materials & methods: Copy number variation, methylation, pathway analysis and drug influence on MUC expression were analyzed based on The Cancer Genome Atlas and GTEx database. Results: Copy number variation analysis showed MUC heterozygous amplification and heterozygous deletion predominate. Methylation of MUC17, MUC12 and MUC4 were found related to gene expression. Function of MUC family genes mainly affects pathways such as apoptosis, cell cycle, DNA damage and EMT pathways. PLX4720, dabrafenib, gefitinib, afatinib and austocystin D can alter the expression of MUC gene. Conclusion: The genetic and epigenetic changes of MUC are related to the level of MUC expression in colorectal cancer.