Statistical network modeling techniques are increasingly important tools to analyze cancer genomics data. However, current tools and resources are not designed to work across multiple diagnoses and technical platforms, thus limiting their applicability to comprehensive pan-cancer datasets such as The Cancer Genome Atlas (TCGA). To address this, we describe a new data driven modeling method, bas...

BACKGROUND Mutational signatures have been identified by the broad sequencing of cancer genomes and reflect underlying processes of mutagenesis. The clinical application of mutational signatures is not well defined. Here we aim to assess the prognostic utility of mutational signatures in ovarian high-grade serous carcinoma. METHODS Open access data of 15,439 somatic mutations of 310 ovarian h...

The Cancer Genome Atlas (TCGA) microRNA (miRNA) initiative has revealed a pivotal role for miRNAs in cancer. Utilizing the TCGA raw data, we performed the first mapping of viral miRNA sequences within cancer and adjacent normal tissues. Results were integrated with TCGA RNA-seq to link the expression of viral miRNAs to the phenotype. Using clinical data and viral miRNA mapping results we also p...

BACKGROUND Previously, we have used clinical and gene expression data from The Cancer Genome Atlas (TCGA) to model a pathway-based index predicting outcomes in ovarian carcinoma. This data were obtained from snap-frozen tissue measured with the Affymetrix U133 platform. In the current study, we correlate the data used to model with data derived from TaqMan qPCR both snap frozen and paraffin emb...

Isoforms of human miRNAs (isomiRs) are constitutively expressed with tissue- and disease-subtype-dependencies. We studied 10 271 tumor datasets from The Cancer Genome Atlas (TCGA) to evaluate whether isomiRs can distinguish amongst 32 TCGA cancers. Unlike previous approaches, we built a classifier that relied solely on 'binarized' isomiR profiles: each isomiR is simply labeled as 'present' or '...

Ovarian cancer presents as an aggressive, advanced stage cancer with widespread metastases that depend primarily on multicellular spheroids in the peritoneal fluid. To identify new druggable pathways related to metastatic progression and spheroid formation, we integrated microRNA and mRNA sequencing data from 293 tumors from The Cancer Genome Atlas (TCGA) ovarian cancer cohort. We identified mi...

Aberrant activation of DNA repair is frequently associated with tumor progression and response to therapy in hepatocellular carcinoma (HCC). Bioinformatics analyses of HCC data in the Cancer Genome Atlas (TCGA) were performed to define DNA repair based molecular classification that could predict the prognosis of patients with HCC. Furthermore, we tested its predictive performance in 120 indepen...

[Objective] ZEB2 is known to be participated in several cancers, yet there no comprehensive analysis about its function pan-cancer. This study aimed investigate the role plays [Methods] The data was downloaded from University of California Santa Cruz (UCSC) Xena and Cancer Genome Atlas (TCGA). mRNA expression status studied TCGA_GTEx samples, TCGA samples paired TCGA, respectively, protein obta...

The goal of this work was to explore the most effective miRNAs affecting glioblastoma multiforme (GBM) phenotype transition and malignant progression. We annotated 491 TCGA samples' miRNA expression profiles according to their mRNA-based subtypes and found that the mesenchymal tumors had significantly decreased miR-181 family expression compared with the other three subtypes while the proneural...

The Cancer Genome Atlas (TCGA) has accrued RNA-Seq-based transcriptome data for more than 4000 cancer tissue samples across 12 cancer types, translating these data into biological insights remains a major challenge. We analyzed and compared the transcriptomes of 4043 cancer and 548 normal tissue samples from 21 TCGA cancer types, and created a comprehensive catalog of gene expression alteration...