Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0376358 (prostate cancer)
 59,338 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

MicroRNA (miRNA)-gene interactions are well-recognized as involved in the progression of almost all cancer types including prostate cancer, which is one of the most common cancers in men. This study explored the significantly dysregulated genes and miRNAs and elucidated the potential miRNA-gene regulatory network in prostate cancer. Integrative analysis of prostate cancer and normal prostate transcriptomic data in The Cancer Genome Atlas dataset was conducted using both differential expression analysis and weighted correlation network analysis (WGCNA). Thirteen genes (RRM2, ORC6, CDC45, CDKN2A, E2F2, MYBL2, CCNB2, PLK1, FOXM1, CDC25C, PKMYT1, GTSE1, and CDC20) were potentially correlated with prostate cancer based on functional enrichment analyses. MiRNAs targeting these genes were predicted and eight miRNAs were intersections between those miRNAs and the hub miRNAs obtained from miRNA WGCNA analysis. Three genes (E2F2, RRM2, and PKMYT1) and four miRNAs (hsa-mir-17-5p, hsa-mir-20a-5p, hsa-mir-92a-3p, and hsa-mir-93-5p) were key factors according to the interaction network. RRM2 and PKMYT1 were significantly related to survival. These findings partially elucidated the dysregulation of gene expressions in prostate cancer. Efficient manipulations of the miRNA-gene interactions in prostate cancer may be exploited as promising therapeutics.
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PMID:Integrative Analysis of MicroRNA and Gene Interactions for Revealing Candidate Signatures in Prostate Cancer. 3218 Aug 4

Prostate cancer (PCa) is the third most common malignancy worldwide. Novel and effective therapeutic targets are needed for PCa. The purpose of this study was to discover novel therapeutic targets for PCa by performing advanced analysis on PCa RNA sequencing (RNAseq) data from The Cancer Genome Atlas (TCGA). Weighted correlation-network analysis (WGCNA) was performed on the RNAseq data of tumor samples, and the module most relevant to the Gleason score was identified. Combining differential gene-expression analysis and survival analysis, we narrowed down potential therapeutic target genes and found that PKMYT1 might be one. Subsequently, functional studies (i.e., cell-proliferation assays, cell cycle analysis, and colony-formation assays) demonstrated that knockdown of PKMYT1 significantly inhibited the growth of PCa cells. Further investigation illustrated that PKMYT1 promoted the growth of PCa cells through targeting CCNB1 and CCNE1 expression. In addition, fostamatinib, an inhibitor of PKMYT1, effectively inhibited the proliferation of PCa cells. Taken together, our results suggest that PKMYT1 is a gene associated with malignancy of PCa and is a novel therapeutic target.
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PMID:PKMYT1 is associated with prostate cancer malignancy and may serve as a therapeutic target. 3223 41