TP63-TRIM29 axis regulates enhancer methylation and chromosomal instability in prostate cancer.

BACKGROUND: Prostate adenocarcinoma (PRAD) is the second leading cause of cancer-related deaths in men. High variability in DNA methylation and a high rate of large genomic rearrangements are often observed in PRAD. RESULTS: To investigate the reasons for such high variance, we integrated DNA methylation, RNA-seq, and copy number alterations datasets from The Cancer Genome Atlas (TCGA), focusing on PRAD, and employed weighted gene co-expression network analysis (WGCNA). Our results show that only single cluster of co-expressed genes is associated with genomic and epigenomic instability. Within this cluster, TP63 and TRIM29 are key transcription regulators and are downregulated in PRAD. We discovered that TP63 regulates the level of enhancer methylation in prostate basal epithelial cells. TRIM29 forms a complex with TP63 and together regulates the expression of genes specific to the prostate basal epithelium. In addition, TRIM29 binds DNA repair proteins and prevents the formation of the TMPRSS2:ERG gene fusion typically observed in PRAD. CONCLUSION: Our study demonstrates that TRIM29 and TP63 are important regulators in maintaining the identity of the basal epithelium under physiological conditions. Furthermore, we uncover the role of TRIM29 in PRAD development.

Differences in Protein Secretion by Multipotent Mesenchymal Stromal Cells Effective and Ineffective in the Prevention of Acute Graft-Versus-Host Disease after Allogeneic Hematopoietic Stem Cell Transplantation.

Multipotent mesenchymal stromal cells (MSC) were administered to patients after allogeneic hematopoietic stem cell transplantation to prevent the development of acute graft-versus- host disease (GVHD). The injection of MSC did not always prevent the development of GVHD. The aim of the work was to compare the secretome of MSC effective and ineffective in the prevention of GVHD. MSC were obtained from the bone marrow of hematopoietic stem cells donors. The secretome was studied using a TripleTOF 5600+ mass spectrometer with a NanoSpray III ion source coupled to a NanoLC Ultra 2D Plus nano-HPLC System. A total of 1,965 proteins were analyzed. Analysis of the secretome of effective and ineffective MSC samples revealed significant differences in the secretion of 1,119 proteins associated with ribosomes, exosomes, focal contacts, and others. Analysis of proteins secreted by MSC can be used to identify prognostically effective samples.

Expression and Intracellular Localization of Paraoxonase 2 in Different Types of Malignancies.

PON2 belongs to the paraoxonase protein family that consists of lactone hydrolyzing enzymes with different substrate specificities. Unlike other members of the family, PON2 exhibits substantial antioxidant activity, is localized predominantly inside the cell, and is ubiquitously expressed in all human tissues. Previously, it was proffered that defense against pathogens, such as Pseudomonas aeruginosa, is the main function of paraoxonases. However, recent findings have highlighted the important role played by PON2 in protection against oxidative stress, inhibition of apoptosis, and progression of various types of malignancies. In the current study, we performed a bioinformatic analysis of RNA and DNA sequencing data extracted from tumor samples taken from more than 10,000 patients with 31 different types of cancer and determined expression levels and mutations in the PON2 gene. Next, we investigated the intracellular localization of PON2 in multiple cancer cell lines and identified the proteins interacting with PON2 using the LC-MS/MS technique. Our data indicate that a high PON2 expression level correlates with a worse prognosis for patients with multiple types of solid tumors and suggest that PON2, when localized on the nuclear envelope and endoplasmic reticulum, may protect cancer cells against unfavorable environmental conditions and chemotherapy.

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.

[Serum proteome profiling for ovarion cancer diagnosis using ClinProt magnetic bead technique and MALDI-TOF-mass-spectrometry].

Using reverse-phase (MB-HIC 8 and HB-HIC 18) weak cation exchange (MB-WCX) and metal affinity ClinProt magnetoc beads peptides and protein factions were obtained from human sera for their profiling by MALDI-TOF mass spectrometry. Proteome profiling of sera from I-IV stage ovarian cancer patients (47 women, average age 51) and from healthy women (47 subjects, average age 49) using MB-WCX beads allowed calculation of the best diagnostic models based on the Genetic Algorithm and Supervised Neural Network classifiers; these model generated 100% sensitivity and specificity when the test set of subjects was analyzed. Introduction of additional sera from patients with colorectal cancer (19) and ulcerous colitis (5) to the statistical model confirmed 100% ovarian cancer recognition. Statistical mass-spectrometry analysis of mass-spectrometry peak areas included to the diagnostic classifiers showed 3 peaks distinctive for ovarian cancer and 4 peaks distinctive for ovarian and colorectal cancer.