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Background: Predicting the survival of cancer patients provides prognostic information and therapeutic guidance. However, improved prediction models are needed for use in diagnosis and treatment. Objective: This study aimed to identify genomic prognostic biomarkers related to colon cancer (CC) based on computational data and to develop survival prediction models. Methods: We performed machine‐learning (ML) analysis to screen pathogenic survival‐related driver genes related to patient prognosis by integrating copy number variation and gene expression data. Moreover, in silico system analysis was performed to clinically assess data from ML analysis, and we identified RABGAP1L, MYH9, and DRD4 as candidate genes. These three genes and tumor stages were used to generate survival prediction models. Moreover, the genes were validated by experimental and clinical analyses, and the theranostic application of the survival prediction models was assessed. Results: RABGAP1L, MYH9, and DRD4 were identified as survival‐related candidate genes by ML and in silico system analysis. The survival prediction model using the expression of the three genes showed higher predictive performance when applied to predict the prognosis of CC patients. A series of functional analyses revealed that each knockdown of three genes reduced the protumor activity of CC cells. In particular, validation with an independent cohort of CC patients confirmed that the coexpression of MYH9 and DRD4 gene expression reflected poorer clinical outcomes in terms of overall survival and disease‐free survival. Conclusions: Our survival prediction approach will contribute to providing information on patients and developing a therapeutic strategy for CC patients. [ABSTRACT FROM AUTHOR]

In this study, the potential effects of pyronaridine, an antimalarial agent, on airway MUC5AC mucin gene expression were investigated. The human pulmonary epithelial NCI-H292 cells were pretreated with pyronaridine for 30 min and then stimulated with phorbol 12-myristate 13-acetate (PMA) for 24 h. The effect of pyronaridine on the PMA-induced nuclear factor kappa B (NF-κB) signaling pathway was also examined. Pyronaridine inhibited glycoprotein production and mRNA expression of MUC5AC mucins induced by PMA through the inhibition of degradation of inhibitory kappa Bα and NF-κB p65 nuclear translocation. These results suggest that pyronaridine suppresses gene expression of mucin through regulation of the NF-κB signaling pathway in human pulmonary epithelial cells.

Dysregulation of cancer cell motility is a key driver of invasion and metastasis. High dysadherin expression in cancer cells is correlated with invasion and metastasis. Here, we found the molecular mechanism by which dysadherin regulates the migration and invasion of colon cancer (CC). Comprehensive analysis using single-cell RNA sequencing data from CC patients revealed that high dysadherin expression in cells is linked to cell migration-related gene signatures. We confirmed that the deletion of dysadherin in tumor cells hindered local invasion and distant migration using in vivo tumor models. In this context, by performing cell morphological analysis, we found that aberrant cell migration resulted from impaired actin dynamics, focal adhesion turnover and protrusive structure formation upon dysadherin expression. Mechanistically, the activation of focal adhesion kinase (FAK) was observed in dysadherin-enriched cells. The dysadherin/FAK axis enhanced cell migration and invasion by activating the FAK downstream cascade, which includes the Rho family of small GTPases. Overall, this study illuminates the role of dysadherin in modulating cancer cell migration by forcing actin dynamics and protrusive structure formation via FAK signaling, indicating that targeting dysadherin may be a potential therapeutic strategy for CC patients., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)