Purpose: Prostate cancer (PCa) causes a common male urinary system malignant tumour, and the molecular mechanisms of PCa remain poorly understood. This study aims to investigate the underlying molecular mechanisms of PCa with bioinformatics.
Methods:Original gene expression profiles were obtained from the GSE64318 and GSE46602 datasets in the Gene Expression Omnibus (GEO). We conducted differential screens of the expression of genes (DEGs) between two groups using the R software limma package. The interactions between the differentially expressed miRNAs, mRNAs and lncRNAs were predicted and merged with the target genes.Co-expression of the miRNAs, lncRNAs and mRNAs were selected to construct the mRNA-miRNA and -lncRNA interaction networks. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for the DEGs. The protein-protein interaction (PPI) networks were constructed, and the transcription factors were annotated. The expression of hub genes in the TCGA datasets was verified to improve the reliability of our analysis.
Results:The results demonstrated that 60 miRNAs, 1578 mRNAs and 61 lncRNAs were differentially expressed in PCa. The mRNA-miRNA-lncRNA networks were composed of 5 miRNA nodes, 13 lncRNA nodes, and 45 mRNA nodes. The DEGs were mainly enriched in the nuclei and cytoplasm and were involved in the regulation of transcription, related to sequence-specific DNA binding, and participated in the regulation of the PI3K-Akt signalling pathway. These pathways are related to cancer and focal adhesion signalling pathways. Furthermore, we found that 5 miRNAs, 6 lncRNAs, 6 mRNAs and 2 TFs play important regulatory roles in the interaction network. The expression levels of EGFR, VEGFA, PIK3R1, DLG4, TGFBR1 and KIT were significantly different between PCa and normal prostate tissue.
Conclusion:Based on the current study, large-scale effects of interrelated mRNAs, miRNAs, lncRNAs, and TFs were revealed and a model for predicting the mechanism of PCa was provided. This study provides new insight for the exploration of the molecular mechanisms of PCa and valuable clues for further research.
Identification of differentially expressed genesWe used the robust multi-array average algorithm to perform background correction and quartile data normalization of the downloaded data [12]. Probes without a corresponding gene symbol were then filtered, and the average value of the gene symbols with multiple probes was then calculated. Student's t-tests and fold-change (FC) filtering were conducted to screen for the differentially expressed genes (DEGs) between the two groups using the R software limma package [13]. With a threshold P-value <0.05 and an absolute value of FC> 2, volcano plot filtering was performed using the R software ggplot2 package to identify the DEGs with statistical significance between the two groups. Hierarchical clustering and combined analyses were performed for the DEGs.
Prediction of the mRNA-miRNA-lncRNA interactionsThe interactions between the ...