Glycolysis related lncRNA SNHG3 / miR-139-5p / PKM2 axis promotes castration-resistant prostate cancer (CRPC) development and enzalutamide resistance.

Although androgen deprivation therapy (ADT) by the anti-androgen drug enzalutamide (Enz) may improve the survival level of patients with castration-resistant prostate cancer (CRPC), most patients may eventually fail due to the acquired resistance. The reprogramming of glucose metabolism is one type of the paramount hallmarks of cancers. PKM2 (Pyruvate kinase isozyme typeM2) is a speed-limiting enzyme in the glycolytic mechanism, and has high expression in a variety of cancers. Emerging evidence has unveiled that microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have impact on tumor development and therapeutic efficacy by regulating PKM2 expression. Herein, we found that lncRNA SNHG3, a highly expressed lncRNA in CRPC via bioinformatics analysis, promoted the invasive ability and the Enz resistance of the PCa cells. KEGG pathway enrichment analysis indicated that glucose metabolic process was tightly correlated with lncRNA SNHG3 level, suggesting lncRNA SNHG3 may affect glucose metabolism. Indeed, glucose uptake and lactate content determinations confirmed that lncRNA SNHG3 promoted the process of glycolysis. Mechanistic dissection demonstrated that lncRNA SNHG3 facilitated the advance of CRPC by adjusting the expression of PKM2. Further explorations unraveled the role of lncRNA SNHG3 as a 'sponge' of miR-139-5p and released its binding with PKM2 mRNA, leading to PKM2 up-regulation. Together, Our studies suggest that lncRNA SNHG3 / miR-139-5p / PKM2 pathway promotes the development of CRPC via regulating glycolysis process and provides valuable insight into a novel therapeutic approach for the disordered disease.

Identification of hub genes predicting the development of prostate cancer from benign prostate hyperplasia and analyzing their clinical value in prostate cancer by bioinformatic analysis.

Prostate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in males. Studies showed that genetic factors are responsible for the occurrences of both diseases. However, the genetic association between them is still unclear. Gene Expression Omnibus (GEO) database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were utilized to find pathways DEGs enriched. The STRING database can provide a protein-protein interaction (PPI) network, and find hub genes in PPI network. R software was used to analyze the clinical value of hub genes in PCa. Finally, the function of these hub genes was tested in different databases, clinical samples, and PCa cells. Fifteen up-regulated and forty-five down-regulated genes were found from GEO database. Seven hub genes were found in PPI network. The expression and clinical value of hub genes were analyzed by The Cancer Genome Atlas (TCGA) data. Except CXCR4, all hub genes expressed differently between tumor and normal samples. Exclude CXCR4, other hub genes have diagnostic value in predicting PCa and their mutations can cause PCa. The expression of CSRP1, MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect disease-free survival (DFS). Same results reflected in different databases. The expression and function of MYC, MYL9, and SNAI2, were validated in clinical samples and PCa cells. In conclusion, seven hub genes among sixty DEGs may be achievable targets for predicting which BPH patients may later develop PCa and they can influence the progression of PCa.

Exploration of the Tumor Mutational Burden as a Prognostic Biomarker and Related Hub Gene Identification in Prostate Cancer.

To explore the signature function of the tumor mutational burden (TMB) and potential biomarkers in prostate cancer (PCa), transcriptome profiles, somatic mutation data, and clinicopathologic feature information were downloaded from The Cancer Genome Atlas (TCGA) database. R software package was used to generate a waterfall plot to summarize the specific mutation information and calculate the TMB value of PCa. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select the hub genes related to the TMB from the ImmPort network to build a risk score (RS) model to evaluate prognostic values and plot Kaplan-Meier (K-M) curves to predict PCa patients survival. The results showed that PCa patients with a high TMB exhibited higher infiltration of CD8+ T cells and CD4+ T cells and better overall survival (OS) than those with a low TMB. The anti-Mullerian hormone (AMH), baculoviral IAP repeat-containing 5 (BIRC5), and opoid receptor kappa 1 (OPRK1) genes were three hub genes and their copy number variation (CNV) was relatively likely to affect the infiltration of immune cells. Moreover, PCa patients with low AMH or BIRC5 expression had a longer survival time and lower cancer recurrence, while elevated AMH or BIRC5 expression favored PCa progression. In contrast, PCa patients with low OPRK1 expression had poorer OS in the early stage of PCa and a higher recurrent rate than those with high expression. Taken together, these results suggest that the TMB may be a promising prognostic biomarker for PCa and that AMH, OPRK1, and BIRC5 are hub genes affecting the TMB; AMH, OPRK1, and BIRC5 could serve as potential immunotherapeutic targets for PCa treatment.

Corrigendum: Increased CDC6 Expression Associates With Poor Prognosis in Patients With Clear Cell Renal Cell Carcinoma.

[This corrects the article DOI: 10.3389/fonc.2021.666418.].