Up-regulation of Biglycan is Associated with Poor Prognosis and PTEN Deletion in Patients with Prostate Cancer

Network analysis based on low-rank method for mining information on integrated data of multi-cancers

The noise problem of cancer sequencing data has been a problem that can't be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.

Integrative metabolic and transcriptomic profiling of prostate cancer tissue containing reactive stroma

Reactive stroma is a tissue feature commonly observed in the tumor microenvironment of prostate cancer and has previously been associated with more aggressive tumors. The aim of this study was to detect differentially expressed genes and metabolites according to reactive stroma content measured on the exact same prostate cancer tissue sample. Reactive stroma was evaluated using histopathology from 108 fresh frozen prostate cancer samples gathered from 43 patients after prostatectomy (Biobank1). A subset of the samples was analyzed both for metabolic (n = 85) and transcriptomic alterations (n = 78) using high resolution magic angle spinning magnetic resonance spectroscopy (HR-MAS MRS) and RNA microarray, respectively. Recurrence-free survival was assessed in patients with clinical follow-up of minimum five years (n = 38) using biochemical recurrence (BCR) as endpoint. Multivariate metabolomics and gene expression analysis compared low (≤15%) against high reactive stroma content (≥16%). High reactive stroma content was associated with BCR in prostate cancer patients even when accounting for the influence of Grade Group (Cox hazard proportional analysis, p = 0.013). In samples with high reactive stroma content, metabolites and genes linked to immune functions and extracellular matrix (ECM) remodeling were significantly upregulated. Future validation of these findings is important to reveal novel biomarkers and drug targets connected to immune mechanisms and ECM in prostate cancer. The fact that high reactive stroma grading is connected to BCR adds further support for the clinical integration of this histopathological evaluation.

An integrated study on TFs and miRNAs in colorectal cancer metastasis and evaluation of three co-regulated candidate genes as prognostic markers

Molecular alterations that occur in cancer have the potential to be considered as either cancer biomarkers or targeted therapies or even both. In the presented study, we aimed to elucidate the gene regulatory network of metastatic colorectal cancer using data acquired from microarrays to reach the most common DEGs in colorectal cancer metastasis and find their possible regulatory mechanism by DETFs and DEmiRs. In this regards, seven microarray datasets were employed to assess the most important DEGs, DETFs and DEmiRs in colorectal cancer metastasis. Afterward, GRN based on DETFs and DEmiRs were constructed. Also ARACNE algorithm was used to construct an accurate GRN. GRN was analyzed structurally and then, two DETFs (LEF1 and ETV4) and a less-well known DEG (FABP6) by real time qRT-PCR in 50 patients with colorectal cancer were quantified. The constructed GRN highlighted the importance of some DETFs and DEmiRs in colorectal cancer metastasis. Interestingly the gene expression analysis by qRT-PCR on three candidate genes (LEF1, ETV4 and FABP6) indicated that the three genes were co-expressed in tumor samples, and were significantly associated with metastasis in colorectal cancer. Therefore, our experimental results proved a part of our comprehensive data analysis and system biology results. In summary, according to our empirical study we found the importance of three candidate genes as the potent prognostic factors in colorectal cancer metastasis. Also our study in a holistic insight on gene regulatory mechanism revealed the importance of some gene regulatory factors (DETFs and DEmiRs) and their potential as prognostic factors and/or targets in molecular targeted therapies in colorectal cancer.

Quantitative Proteomics of TRAMP Mice Combined with Bioinformatics Analysis Reveals That PDGF-B Regulatory Network Plays a Key Role in Prostate Cancer Progression

Transgenic adenocarcinoma of the mouse prostate (TRAMP) mice is a widely used transgenic animal model of prostate cancer (PCa). We performed a label-free quantitative proteomics analysis combined with a bioinformatics analysis on the entire prostate protein extraction from TRAMP mice and compared it with WT littermates. From 2379 total identified proteins, we presented a modest mice prostate reference proteome containing 919 proteins. 61 proteins presented a significant expression difference between two groups. The integrative bioinformatics analysis predicted the overexpression of platelet-derived growth factor B (PDGF-B) in tumor tissues and supports the hypothesis of the PDGF-B signaling network as a key upstream regulator in PCa progression. Furthermore, we demonstrated that Crenolanib, a novel PDGF receptor inhibitor, inhibited PCa cell proliferation in a dose-dependent manner. Finally, we revealed the importance of PDGF-B regulatory network in PCa progression, which will assist us in understanding the role and mechanisms of PDGF-B in promoting cancer growth and provide valuable knowledge for future research on anti-PDGF therapy.