Splice variant PRKC-ζ(-PrC) is a novel biomarker of human prostate cancer

Genetic polymorphism in untranslated regions of PRKCZ influences mRNA structure, stability and binding sites

BACKGROUND

Variations in untranslated regions (UTR) alter regulatory pathways impacting phenotype, disease onset, and course of disease. Protein kinase C Zeta (PRKCZ), a serine-threonine kinase, is implicated in cardiovascular, neurological and oncological disorders. Due to limited research on PRKCZ, this study aimed to investigate the impact of UTR genetic variants' on binding sites for transcription factors and miRNA. RNA secondary structure, eQTLs, and variation tolerance analysis were also part of the study.

METHODS

The data related to PRKCZ gene variants was downloaded from the Ensembl genome browser, COSMIC and gnomAD. The RegulomeDB database was used to assess the functional impact of 5' UTR and 3'UTR variants. The analysis of the transcription binding sites (TFBS) was done through the Alibaba tool, and the Kyoto Encyclopaedia of Genes and Genomes (KEGG) was employed to identify pathways associated with PRKCZ. To predict the effect of variants on microRNA binding sites, PolymiRTS was utilized for 3' UTR variants, and the SNPinfo tool was used for 5' UTR variants.

RESULTS

The results obtained indicated that a total of 24 variants present in the 3' UTR and 25 variants present in the 5' UTR were most detrimental. TFBS analysis revealed that 5' UTR variants added YY1, repressor, and Oct1, whereas 3' UTR variants added AP-2alpha, AhR, Da, GR, and USF binding sites. The study predicted TFs that influenced PRKCZ expression. RNA secondary structure analysis showed that eight 5' UTR and six 3' UTR altered the RNA structure by either removal or addition of the stem-loop. The microRNA binding site analysis highlighted that seven 3' UTR and one 5' UTR variant altered the conserved site and also created new binding sites. eQTLs analysis showed that one variant was associated with PRKCZ expression in the lung and thyroid. The variation tolerance analysis revealed that PRKCZ was an intolerant gene.

CONCLUSION

This study laid the groundwork for future studies aimed at targeting PRKCZ as a therapeutic target.

LogLoss-BERAF: An ensemble-based machine learning model for constructing highly accurate diagnostic sets of methylation sites accounting for heterogeneity in prostate cancer

Although modern methods of whole genome DNA methylation analysis have a wide range of applications, they are not suitable for clinical diagnostics due to their high cost and complexity and due to the large amount of sample DNA required for the analysis. Therefore, it is crucial to be able to identify a relatively small number of methylation sites that provide high precision and sensitivity for the diagnosis of pathological states. We propose an algorithm for constructing limited subsamples from high-dimensional data to form diagnostic panels. We have developed a tool that utilizes different methods of selection to find an optimal, minimum necessary combination of factors using cross-entropy loss metrics (LogLoss) to identify a subset of methylation sites. We show that the algorithm can work effectively with different genome methylation patterns using ensemble-based machine learning methods. Algorithm efficiency, precision and robustness were evaluated using five genome-wide DNA methylation datasets (totaling 626 samples), and each dataset was classified into tumor and non-tumor samples. The algorithm produced an AUC of 0.97 (95% CI: 0.94-0.99, 9 sites) for prostate adenocarcinoma and an AUC of 1.0 (from 2 to 6 sites) for urothelial bladder carcinoma, two types of kidney carcinoma and colorectal carcinoma. For prostate adenocarcinoma we showed that identified differential variability methylation patterns distinguish cluster of samples with higher recurrence rate (hazard ratio for recurrence = 0.48, 95% CI: 0.05-0.92; log-rank test, p-value < 0.03). We also identified several clusters of correlated interchangeable methylation sites that can be used for the elaboration of biological interpretation of the resulting models and for further selection of the sites most suitable for designing diagnostic panels. LogLoss-BERAF is implemented as a standalone python code and open-source code is freely available from https://github.com/bioinformatics-IBCH/logloss-beraf along with the models described in this article.

Inflammation and NF-κB Signaling in Prostate Cancer: Mechanisms and Clinical Implications

Prostate cancer is a highly prevalent form of cancer that is usually slow-developing and benign. Due to its high prevalence, it is, however, still the second most common cause of death by cancer in men in the West. The higher prevalence of prostate cancer in the West might be due to elevated inflammation from metabolic syndrome or associated comorbidities. NF-κB activation and many other signals associated with inflammation are known to contribute to prostate cancer malignancy. Inflammatory signals have also been associated with the development of castration resistance and resistance against other androgen depletion strategies, which is a major therapeutic challenge. Here, we review the role of inflammation and its link with androgen signaling in prostate cancer. We further describe the role of NF-κB in prostate cancer cell survival and proliferation, major NF-κB signaling pathways in prostate cancer, and the crosstalk between NF-κB and androgen receptor signaling. Several NF-κB-induced risk factors in prostate cancer and their potential for therapeutic targeting in the clinic are described. A better understanding of the inflammatory mechanisms that control the development of prostate cancer and resistance to androgen-deprivation therapy will eventually lead to novel treatment options for patients.

Novel potential serological prostate cancer biomarkers using CT100+ cancer antigen microarray platform in a multi-cultural South African cohort

There is a growing need for high throughput diagnostic tools for early diagnosis and treatment monitoring of prostate cancer (PCa) in Africa. The role of cancer-testis antigens (CTAs) in PCa in men of African descent is poorly researched. Hence, we aimed to elucidate the role of 123 Tumour Associated Antigens (TAAs) using antigen microarray platform in blood samples (N = 67) from a South African PCa, Benign prostatic hyperplasia (BPH) and disease control (DC) cohort. Linear (fold-over-cutoff) and differential expression quantitation of autoantibody signal intensities were performed. Molecular signatures of candidate PCa antigen biomarkers were identified and analyzed for ethnic group variation. Potential cancer diagnostic and immunotherapeutic inferences were drawn. We identified a total of 41 potential diagnostic/therapeutic antigen biomarkers for PCa. By linear quantitation, four antigens, GAGE1, ROPN1, SPANXA1 and PRKCZ were found to have higher autoantibody titres in PCa serum as compared with BPH where MAGEB1 and PRKCZ were highly expressed. Also, p53 S15A and p53 S46A were found highly expressed in the disease control group. Statistical analysis by differential expression revealed twenty-four antigens as upregulated in PCa samples, while 11 were downregulated in comparison to BPH and DC (FDR = 0.01). FGFR2, COL6A1and CALM1 were verifiable biomarkers of PCa analysis using urinary shotgun proteomics. Functional pathway annotation of identified biomarkers revealed similar enrichment both at genomic and proteomic level and ethnic variations were observed. Cancer antigen arrays are emerging useful in potential diagnostic and immunotherapeutic antigen biomarker discovery.

Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted.

Atypical protein kinase C induces cell transformation by disrupting Hippo/Yap signaling

Epithelial cells are major sites of malignant transformation. Atypical protein kinase C (aPKC) isoforms are overexpressed and activated in many cancer types. Using normal, highly polarized epithelial cells (MDCK and NMuMG), we report that aPKC gain of function overcomes contact inhibited growth and is sufficient for a transformed epithelial phenotype. In 2D cultures, aPKC induced cells to grow as stratified epithelia, whereas cells grew as solid spheres of nonpolarized cells in 3D culture. aPKC associated with Mst1/2, which uncoupled Mst1/2 from Lats1/2 and promoted nuclear accumulation of Yap1. Of importance, Yap1 was necessary for aPKC-mediated overgrowth but did not restore cell polarity defects, indicating that the two are separable events. In MDCK cells, Yap1 was sequestered to cell-cell junctions by Amot, and aPKC overexpression resulted in loss of Amot expression and a spindle-like cell phenotype. Reexpression of Amot was sufficient to restore an epithelial cobblestone appearance, Yap1 localization, and growth control. In contrast, the effect of aPKC on Hippo/Yap signaling and overgrowth in NMuMG cells was independent of Amot. Finally, increased expression of aPKC in human cancers strongly correlated with increased nuclear accumulation of Yap1, indicating that the effect of aPKC on transformed growth by deregulating Hippo/Yap1 signaling may be clinically relevant.

Altered endosome biogenesis in prostate cancer has biomarker potential

Prostate cancer is the second most common form of cancer in males, affecting one in eight men by the time they reach the age of 70 years. Current diagnostic tests for prostate cancer have significant problems with both false negatives and false positives, necessitating the search for new molecular markers. A recent investigation of endosomal and lysosomal proteins revealed that the critical process of endosomal biogenesis might be altered in prostate cancer. Here, a panel of endosomal markers was evaluated in prostate cancer and nonmalignant cells and a significant increase in gene and protein expression was found for early, but not late endosomal proteins. There was also a differential distribution of early endosomes, and altered endosomal traffic and signaling of the transferrin receptors (TFRC and TFR2) in prostate cancer cells. These findings support the concept that endosome biogenesis and function are altered in prostate cancer. Microarray analysis of a clinical cohort confirmed the altered endosomal gene expression observed in cultured prostate cancer cells. Furthermore, in prostate cancer patient tissue specimens, the early endosomal marker and adaptor protein APPL1 showed consistently altered basement membrane histology in the vicinity of tumors and concentrated staining within tumor masses. These novel observations on altered early endosome biogenesis provide a new avenue for prostate cancer biomarker investigation and suggest new methods for the early diagnosis and accurate prognosis of prostate cancer.

IMPLICATIONS

This discovery of altered endosome biogenesis in prostate cancer may lead to novel biomarkers for more precise cancer detection and patient prognosis.

Increased aPKC Expression Correlates with Prostatic Adenocarcinoma Gleason Score and Tumor Stage in the Japanese Population

Background. Levels of the protein kinase aPKC have been previously correlated with prostate cancer prognosis in a British cohort. However, prostate cancer incidence and progression rates, as well as genetic changes in this disease, show strong ethnic variance, particularly in Asian populations. Objective. The aim of this study was to validate association of aPKC expression with prostatic adenocarcinoma stages in a Japanese cohort. Methods. Tissue microarrays consisting of 142 malignant prostate cancer cases and 21 benign prostate tissues were subject to immunohistological staining for aPKC. aPKC staining intensity was scored by three independent pathologists and categorized as absent (0), dim (1+), intermediate (2+), and bright (3+). aPKC staining intensities were correlated with Gleason score and tumor stage. Results. Increased aPKC staining was observed in malignant prostate cancer, in comparison to benign tissue. Additionally, aPKC staining levels correlated with Gleason score and tumor stage. Our results extend the association of aPKC with prostate cancer to a Japanese population and establish the suitability of aPKC as a universal prostate cancer biomarker that performs consistently across ethnicities.

Rewiring cell polarity signaling in cancer

Disrupted cell polarity is a feature of epithelial cancers. The Crumbs, Par and Scribble polarity complexes function to specify and maintain apical and basolateral membrane domains, which are essential to organize intracellular signaling pathways that maintain epithelial homeostasis. Disruption of apical-basal polarity proteins facilitates rewiring of oncogene and tumor suppressor signaling pathways to deregulate proliferation, apoptosis, invasion and metastasis. Moreover, apical-basal polarity integrates intracellular signaling with the microenvironment by regulating metabolic signaling, extracellular matrix remodeling and tissue level organization. In this review, we discuss recent advances in our understanding of how polarity proteins regulate diverse signaling pathways throughout cancer progression from initiation to metastasis.