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Molecular heterogeneity in prostate cancer (PCa) is one of the key reasons underlying the differing likelihoods of recurrence after surgical treatment in individual patients of the same clinical category. In this study, we performed RNA-Seq profiling of 58 localized PCa and 43 locally advanced PCa tissue samples obtained as a result of radical prostatectomy on a cohort of Russian patients. Based on bioinformatics analysis, we examined features of the transcriptome profiles within the high-risk group, including within the most commonly represented molecular subtype, TMPRSS2-ERG. The most significantly affected biological processes in the samples were also identified, so that they may be further studied in the search for new potential therapeutic targets for the categories of PCa under consideration. The highest predictive potential was found with the EEF1A1P5, RPLP0P6, ZNF483, CIBAR1, HECTD2, OGN, and CLIC4 genes. We also reviewed the main transcriptome changes in the groups at intermediate risk of PCa—Gleason Score 7 (groups 2 and 3 according to the ISUP classification)—on the basis of which the LPL, MYC, and TWIST1 genes were identified as promising additional prognostic markers, the statistical significance of which was confirmed using qPCR validation. [ABSTRACT FROM AUTHOR]

Radical prostatectomy is the gold standard treatment for prostate cancer (PCa); however, it does not always completely cure PCa, and patients often experience a recurrence of the disease. In addition, the clinical and pathological parameters used to assess the prognosis and choose further tactics for treating a patient are insufficiently informative and need to be supplemented with new markers. In this study, we performed RNA-Seq of PCa tissue samples, aimed at identifying potential prognostic markers at the level of gene expression and miRNAs associated with one of the key signs of cancer aggressiveness—lymphatic dissemination. The relative expression of candidate markers was validated by quantitative PCR, including an independent sample of patients based on archival material. Statistically significant results, derived from an independent set of samples, were confirmed for miR-148a-3p and miR-615-3p, as well as for the CST2, OCLN, and PCAT4 genes. Considering the obtained validation data, we also analyzed the predictive value of models based on various combinations of identified markers using algorithms based on machine learning. The highest predictive potential was shown for the "CST2 + OCLN + pT" model (AUC = 0.863) based on the CatBoost Classifier algorithm. [ABSTRACT FROM AUTHOR]

Following radical surgery, patients may suffer a relapse. It is important to identify such patients so that therapy tactics can be modified appropriately. Existing stratification schemes do not display the probability of recurrence with enough precision since locally advanced prostate cancer (PCa) is classified as high-risk but is not ranked in greater detail. Between 40 and 50% of PCa cases belong to the TMPRSS2-ERG subtype that is a sufficiently homogeneous group for high-precision prognostic marker search to be possible. This study includes two independent cohorts and is based on high throughput sequencing and qPCR data. As a result, we have been able to suggest a perspective-trained model involving a deep neural network based on both qPCR data for mRNA and miRNA and clinicopathological criteria that can be used for recurrence risk forecasts in patients with TMPRSS2-ERG-positive, locally advanced PCa (the model uses ALDH3A2 + ODF2 + QSOX2 + hsa-miR-503-5p + ISUP + pT, with an AUC = 0.944). In addition to the prognostic model's use of identified differentially expressed genes and miRNAs, miRNA–target pairs were found that correlate with the prognosis and can be presented as an interactome network. [ABSTRACT FROM AUTHOR]

The NETO2 gene (neuropilin and tolloid-like 2) encodes a protein that acts as an accessory subunit of kainate receptors and is predominantly expressed in the brain. Upregulation of NETO2 has been observed in several tumors; however, its role in tumorigenesis remains unclear. In this study, we investigated NETO2 expression in breast, prostate, and colorectal cancer using quantitative PCR (qPCR), as well as the effect of shRNA-mediated NETO2 silencing on transcriptome changes in colorectal cancer cells. In the investigated tumors, we observed both increased and decreased NETO2 mRNA levels, presenting no correlation with the main clinicopathological characteristics. In HCT116 cells, NETO2 knockdown resulted in the differential expression of 17 genes and 2 long non-coding RNAs (lncRNAs), associated with the upregulation of circadian rhythm and downregulation of several cancer-associated pathways, including Wnt, transforming growth factor (TGF)-β, Janus kinase (JAK)-signal transducer and activator of transcription (STAT), mitogen-activated protein kinase (MAPK), and phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) pathways. Furthermore, we demonstrated the possibility to utilize a novel model organism, short-lived fish Nothobranchius furzeri , for evaluating NETO2 functions. The ortholog neto2b in N. furzeri demonstrated a high similarity in nucleotide and amino acid sequences with human NETO2 , as well as was characterized by stable expression in various fish tissues. Collectively, our findings demonstrate the deregulation of NETO2 in the breast, prostate, and colorectal cancer and its participation in the tumor development primarily through cellular signaling. [ABSTRACT FROM AUTHOR]

Currently, seven molecular subtypes of prostate cancer (PCa) are known, the most common of which being the subtype characterized by the presence of the TMPRSS2–ERG fusion transcript. While there is a considerable amount of work devoted to the influence of this transcript on the prognosis of the disease, data on its role in the progression and prognosis of PCa remain controversial. The present study is devoted to the analysis of the association between the TMPRSS2–ERG transcript and the biochemical recurrence of PCa. The study included two cohorts: the RNA–Seq sample of Russian patients with PCa (n = 72) and the TCGA–PRAD data (n = 203). The results of the analysis of the association between the TMPRSS2–ERG transcript and biochemical recurrence were contradictory. The differential expression analysis (biochemical recurrence cases versus biochemical recurrence-free) and the gene set enrichment analysis revealed a list of genes involved in major cellular pathways. The GNL3, QSOX2, SSPO, and SYS1 genes were selected as predictors of the potential prognostic model (AUC = 1.000 for a cohort of Russian patients with PCa and AUC = 0.779 for a TCGA–PRAD cohort). [ABSTRACT FROM AUTHOR]