Identification of a new RNA-binding proteins-based signature for prognostic prediction in gastric cancer

The Musashi RNA-binding proteins in female cancers: insights on molecular mechanisms and therapeutic relevance

RNA-binding proteins have increasingly been identified as important regulators of gene expression given their ability to bind distinct RNA sequences and regulate their fate. Mounting evidence suggests that RNA-binding proteins are involved in the onset and progression of multiple malignancies, prompting increasing interest in their potential for therapeutic intervention.The Musashi RNA binding proteins Musashi-1 and Musashi-2 were initially identified as developmental factors of the nervous system but have more recently been found to be ubiquitously expressed in physiological tissues and may be involved in pathological cell behavior. Both proteins are increasingly investigated in cancers given dysregulation in multiple tumor entities, including in female malignancies. Recent data suggest that the Musashi proteins serve as cancer stem cell markers as they contribute to cancer cell proliferation and therapy resistance, prompting efforts to identify mechanisms to target them. However, as the picture remains incomplete, continuous efforts to elucidate their role in different signaling pathways remain ongoing.In this review, we focus on the roles of Musashi proteins in tumors of the female - breast, endometrial, ovarian and cervical cancer - as we aim to summarize current knowledge and discuss future perspectives.

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

This study aimed to establish an artificial neural network (ANN) model based on prostate cancer signature genes (PCaSGs) to predict the patients with prostate cancer (PCa). In the present study, 270 differentially expressed genes (DEGs) were identified between PCa and normal prostate (NP) groups by differential gene expression analysis. Next, we performed Metascape gene annotation, pathway and process enrichment analysis, and PPI enrichment analysis on all 270 DEGs. Then, we identified and screened out 30 PCaSGs based on the random forest analysis and constructed an ANN model based on the gene score matrix consisting of 30 PCaSGs. Lastly, analysis of microarray dataset GSE46602 showed that the accuracy of this model for predicating PCa and NP samples was 88.9 and 78.6%, respectively. Our results suggested that the ANN model based on PCaSGs can be used for effectively predicting the patients with PCa and will be helpful for early PCa diagnosis and treatment.