NCDN is a Potential Biomarker and Therapeutic Target for Glioblastoma

Background: Glioblastoma (GBM) is a type of central nervous system malignancy. In our study, we determined the effect of NCDN in GBM patients through The Cancer Genome Atlas (TCGA) data analysis, and studied the effects of NCDN on GBM cell function to estimate its potential as a therapeutic target. Methods: Gene expression profiles of glioblastoma cohort were acquired from TCGA database and analyzed to look for central genes that may serve as GBM therapeutic targets. Then the cell function of NCDN in glioblastoma cell was explored through in vitro cell experiments. Results: Through gene ontology (GO) analysis, weighted gene co-expression network analysis (WGCNA), and survival analysis, we identified three key genes (NCDN, PAK1 and SPRYD3) associated with poor prognosis in glioblastoma. In vitro experiments showed impaired cell migration, apoptosis, and cell cycle arrest in NCDN knockdown cells. Conclusion: NCDN affects the progress and prognosis of glioblastoma by promoting cell migration and inhibiting apoptosis.

rPAC: Route based pathway analysis for cohorts of gene expression data sets

Pathway analysis is a popular method aiming to derive biological interpretation from high-throughput gene expression studies. However, existing methods focus mostly on identifying which pathway or pathways could have been perturbed, given differential gene expression patterns. In this paper, we present a novel pathway analysis framework, namely rPAC, which decomposes each signaling pathway route into two parts, the upstream portion of a transcription factor (TF) block and the downstream portion from the TF block and generates a pathway route perturbation analysis scheme examining disturbance scores assigned to both parts together. This rPAC scoring is further applied to a cohort of gene expression data sets which produces two summary metrics, "Proportion of Significance" (PS) and "Average Route Score" (ARS), as quantitative measures discerning perturbed pathway routes within and/or between cohorts. To demonstrate rPAC's scoring competency, we first used a large amount of simulated data and compared the method's performance against those by conventional methods in terms of power curve. Next, we performed a case study involving three epithelial cancer data sets from The Cancer Genome Atlas (TCGA). The rPAC method revealed specific pathway routes as potential cancer type signatures. A deeper pathway analysis of sub-groups (i.e., age groups in COAD or cancer sub-types in BRCA) resulted in pathway routes that are known to be associated with the sub-groups. In addition, multiple previously uncharacterized pathways routes were identified, potentially suggesting that rPAC is better in deciphering etiology of a disease than conventional methods particularly in isolating routes and sections of perturbed pathways in a finer granularity.

Identification of miRNA Master Regulators in Breast Cancer

Breast cancer is the neoplasm with the highest number of deaths in women. Although the molecular mechanisms associated with the development of this tumor have been widely described, metastatic disease has a high mortality rate. In recent years, several studies show that microRNAs or miRNAs regulate complex processes in different biological systems including cancer. In the present work, we describe a group of 61 miRNAs consistently over-expressed in breast cancer (BC) samples that regulate the breast cancer transcriptome. By means of data mining from TCGA, miRNA and mRNA sequencing data corresponding to 1091 BC patients and 110 normal adjacent tissues were downloaded and a miRNA-mRNA network was inferred. Calculations of their oncogenic activity demonstrated that they were involved in the regulation of classical cancer pathways such as cell cycle, PI3K-AKT, DNA repair, and k-Ras signaling. Using univariate and multivariate analysis, we found that five of these miRNAs could be used as biomarkers for the prognosis of overall survival. Furthermore, we confirmed the over-expression of two of them in 56 locally advanced BC samples obtained from the histopathological archive of the National Cancer Institute of Mexico, showing concordance with our previous bioinformatic analysis.

Quantitative Estimation of Oxidative Stress in Cancer Tissue Cells Through Gene Expression Data Analyses

Quantitative assessment of the intracellular oxidative stress level is a very important problem since it is the basis for elucidation of the fundamental causes of metabolic changes in diseased human cells, particularly cancer. However, the problem proves to be very challenging to solve in vivo because of the complex nature of the problem. Here a computational method is presented for predicting the quantitative level of the intracellular oxidative stress in cancer tissue cells. The basic premise of the predictor is that the genomic mutation level is strongly associated with the intracellular oxidative stress level. Based on this, a statistical analysis is conducted to identify a set of enzyme-encoding genes, whose combined expression levels can well explain the mutation rates in individual cancer tissues in the TCGA database. We have assessed the validity of the predictor by assessing it against genes that are known to have anti-oxidative functions for specific types of oxidative stressors. Then the applications of the predictor are conducted to illustrate its utility.

PR/SET Domain Family and Cancer: Novel Insights from the Cancer Genome Atlas

The PR/SET domain gene family (PRDM) encodes 19 different transcription factors that share a subtype of the SET domain [Su(var)3-9, enhancer-of-zeste and trithorax] known as the PRDF1-RIZ (PR) homology domain. This domain, with its potential methyltransferase activity, is followed by a variable number of zinc-finger motifs, which likely mediate protein⁻protein, protein⁻RNA, or protein⁻DNA interactions. Intriguingly, almost all PRDM family members express different isoforms, which likely play opposite roles in oncogenesis. Remarkably, several studies have described alterations in most of the family members in malignancies. Here, to obtain a pan-cancer overview of the genomic and transcriptomic alterations of PRDM genes, we reanalyzed the Exome- and RNA-Seq public datasets available at The Cancer Genome Atlas portal. Overall, PRDM2, PRDM3/MECOM, PRDM9, PRDM16 and ZFPM2/FOG2 were the most mutated genes with pan-cancer frequencies of protein-affecting mutations higher than 1%. Moreover, we observed heterogeneity in the mutation frequencies of these genes across tumors, with cancer types also reaching a value of about 20% of mutated samples for a specific PRDM gene. Of note, ZFPM1/FOG1 mutations occurred in 50% of adrenocortical carcinoma patients and were localized in a hotspot region. These findings, together with OncodriveCLUST results, suggest it could be putatively considered a cancer driver gene in this malignancy. Finally, transcriptome analysis from RNA-Seq data of paired samples revealed that transcription of PRDMs was significantly altered in several tumors. Specifically, PRDM12 and PRDM13 were largely overexpressed in many cancers whereas PRDM16 and ZFPM2/FOG2 were often downregulated. Some of these findings were also confirmed by real-time-PCR on primary tumors.

Pan-Cancer Mutational and Transcriptional Analysis of the Integrator Complex

The integrator complex has been recently identified as a key regulator of RNA Polymerase II-mediated transcription, with many functions including the processing of small nuclear RNAs, the pause-release and elongation of polymerase during the transcription of protein coding genes, and the biogenesis of enhancer derived transcripts. Moreover, some of its components also play a role in genome maintenance. Thus, it is reasonable to hypothesize that their functional impairment or altered expression can contribute to malignancies. Indeed, several studies have described the mutations or transcriptional alteration of some Integrator genes in different cancers. Here, to draw a comprehensive pan-cancer picture of the genomic and transcriptomic alterations for the members of the complex, we reanalyzed public data from The Cancer Genome Atlas. Somatic mutations affecting Integrator subunit genes and their transcriptional profiles have been investigated in about 11,000 patients and 31 tumor types. A general heterogeneity in the mutation frequencies was observed, mostly depending on tumor type. Despite the fact that we could not establish them as cancer drivers, INTS7 and INTS8 genes were highly mutated in specific cancers. A transcriptome analysis of paired (normal and tumor) samples revealed that the transcription of INTS7, INTS8, and INTS13 is significantly altered in several cancers. Experimental validation performed on primary tumors confirmed these findings.