Gene Regulation Network Analysis on Human Prostate Orthografts Highlights a Potential Role for the JMJD6 Regulon in Clinical Prostate Cancer

WITHDRAWN: JUN and ATF3 are deficient in prostate cancer patients and their delivery in vivo via lipid nanoparticles has therapeutic efficacy by enhancing immune surveillance

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Elucidating the role of PRMTs in prostate cancer using open access databases and a patient cohort dataset

Protein arginine methylation is an understudied epigenetic mechanism catalyzed by enzymes known as Protein Methyltransferases of Arginine (PRMTs), while the opposite reaction is performed by Jumonji domain- containing protein 6 (JMJD6). There is increasing evidence that PRMTs are deregulated in prostate cancer (PCa). In this study, the expression of two PRMT members, PRMT2 and PRMT7 as well as JMJD6, a demethylase, was analyzed in PCa. Initially, we retrieved data from The Cancer Genome Atlas (TCGA) project and the Gene Expression Omnibus (GEO) database to explore the differential expression of various PRMT family members in patients with PCa and then applied immunohistochemistry in a patient cohort across the spectrum of PCa, including non-neoplastic prostate tissue and lymph node metastatic foci. The results from the TCGA analysis revealed that PRMT7, PRMT6 and PRMT3 expression increased while PRMT2, PRMT9 and JMJD6 levels decreased in the tumor compared to non-neoplastic prostate. Results from the GEO datasets were similar, albeit not identical with the TCGA results, with PRMT7 and PRMT3 being upregulated and PRMT2 and JMJD6 being downregulated in the tumor compared to non-neoplastic tissue in some of them. In addition, PRMT7 levels decreased with stage and grade progression in the TCGA analysis. In the patient cohort, both PRMTs and JMJD6 were overexpressed in PCa compared to non-neoplastic tissue, and nuclear PRMT2 and JMJD6 were upregulated in lymph node metastasis, too. PRMT7 and JMJD6 expression were upregulated with the progression of stage and JMJD6 was also increased with the elevation of grade. After androgen ablation therapy, nuclear expression of PRMT7 and JMJD6 were elevated compared to untreated tumors. PRMT2, PRMT7 and JMD6 were also correlated with markers of EMT and cell cycle regulators. Finally, our findings indicate that PRMTs and JMJD6 are involved in prostate cancer progression and revealed a potential interplay of PRMTs with EMT mediators, underscoring the need for therapeutic targeting of arginine methylation in prostate cancer.

Comprehensive Characterization of the Regulatory Landscape of Adrenocortical Carcinoma: Novel Transcription Factors and Targets Associated with Prognosis

We reconstructed a transcriptional regulatory network for adrenocortical carcinoma (ACC) using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA)-ACC cohort. We investigated the association of transcriptional regulatory units (regulons) with overall survival, molecular phenotypes, and immune signatures. We annotated the ACC regulons with cancer hallmarks and assessed single sample regulon activities in the European Network for the Study of Adrenal Tumors (ENSAT) cohort. We found 369 regulons associated with overall survival and subdivided them into four clusters: RC1 and RC2, associated with good prognosis, and RC3 and RC4, associated with worse outcomes. The RC1 and RC3 regulons were highly correlated with the 'Steroid Phenotype,' while the RC2 and RC4 regulons were highly correlated with a molecular proliferation signature. We selected two regulons, NR5A1 (steroidogenic factor 1, SF-1) and CENPA (Centromeric Protein A), that were consistently associated with overall survival for further downstream analyses. The CENPA regulon was the primary regulator of MKI-67 (a marker of proliferation KI-67), while the NR5A1 regulon is a well-described transcription factor (TF) in ACC tumorigenesis. We also found that the ZBTB4 (Zinc finger and BTB domain-containing protein 4) regulon, which is negatively associated with CENPA in our transcriptional regulatory network, is also a druggable anti-tumorigenic TF. We anticipate that the ACC regulons may be used as a reference for further investigations concerning the complex molecular interactions in ACC tumors.

Discovering gene regulatory networks of multiple phenotypic groups using dynamic Bayesian networks

Dynamic Bayesian networks (DBNs) can be used for the discovery of gene regulatory networks (GRNs) from time series gene expression data. Here, we suggest a strategy for learning DBNs from gene expression data by employing a Bayesian approach that is scalable to large networks and is targeted at learning models with high predictive accuracy. Our framework can be used to learn DBNs for multiple groups of samples and highlight differences and similarities in their GRNs. We learn these DBN models based on different structural and parametric assumptions and select the optimal model based on the cross-validated predictive accuracy. We show in simulation studies that our approach is better equipped to prevent overfitting than techniques used in previous studies. We applied the proposed DBN-based approach to two time series transcriptomic datasets from the Gene Expression Omnibus database, each comprising data from distinct phenotypic groups of the same tissue type. In the first case, we used DBNs to characterize responders and non-responders to anti-cancer therapy. In the second case, we compared normal to tumor cells of colorectal tissue. The classification accuracy reached by the DBN-based classifier for both datasets was higher than reported previously. For the colorectal cancer dataset, our analysis suggested that GRNs for cancer and normal tissues have a lot of differences, which are most pronounced in the neighborhoods of oncogenes and known cancer tissue markers. The identified differences in gene networks of cancer and normal cells may be used for the discovery of targeted therapies.

Role of the Epigenetic Modifier JMJD6 in Tumor Development and Regulation of Immune Response

JMJD6 is a member of the Jumonji (JMJC) domain family of histone demethylases that contributes to catalyzing the demethylation of H3R2me2 and/or H4R3me2 and regulating the expression of specific genes. JMJD6-mediated demethylation modifications are involved in the regulation of transcription, chromatin structure, epigenetics, and genome integrity. The abnormal expression of JMJD6 is associated with the occurrence and development of a variety of tumors, including breast carcinoma, lung carcinoma, colon carcinoma, glioma, prostate carcinoma, melanoma, liver carcinoma, etc. Besides, JMJD6 regulates the innate immune response and affects many biological functions, as well as may play key roles in the regulation of immune response in tumors. Given the importance of epigenetic function in tumors, targeting JMJD6 gene by modulating the role of immune components in tumorigenesis and its development will contribute to the development of a promising strategy for cancer therapy. In this article, we introduce the structure and biological activities of JMJD6, followed by summarizing its roles in tumorigenesis and tumor development. Importantly, we highlight the potential functions of JMJD6 in the regulation of tumor immune response, as well as the development of JMJD6 targeted small-molecule inhibitors for cancer therapy.

A Personalized Genomics Approach of the Prostate Cancer

Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.