BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery

Abstract

Single-nucleotide variation and gene expression of disease samples represent important resources for biomarker discovery. Many databases have been built to host and make available such data to the community, but these databases are frequently limited in scope and/or content. BioMuta, a database of cancer-associated single-nucleotide variations, and BioXpress, a database of cancer-associated differentially expressed genes and microRNAs, differ from other disease-associated variation and expression databases primarily through the aggregation of data across many studies into a single source with a unified representation and annotation of functional attributes. Early versions of these resources were initiated by pilot funding for specific research applications, but newly awarded funds have enabled hardening of these databases to production-level quality and will allow for sustained development of these resources for the next few years. Because both resources were developed using a similar methodology of integration, curation, unification, and annotation, we present BioMuta and BioXpress as allied databases that will facilitate a more comprehensive view of gene associations in cancer. BioMuta and BioXpress are hosted on the High-performance Integrated Virtual Environment (HIVE) server at the George Washington University at https://hive.biochemistry.gwu.edu/biomuta and https://hive.biochemistry.gwu.edu/bioxpress, respectively.

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

“…miRNAs with significant adjusted p-values generated by both DESeq2 and edgeR were considered to be differentially expressed in a given cancer type. Results from differential expression analysis for paired and tumor-only expression data were integrated into BioXpress version 3.0 [80]. The Heatmap with clustering for significantly differentially expressed miRNAs across different cancer types was generated by R package gplots 3.0.1.…”

“…CancerMiner data were included only for records not found in Tarbase or miRWalk. Expression changes of all tissue-specific target genes in corresponding cancer types were extracted from BioXpress v3.0 [80]. For the non-tissuespecific target group, we first excluded those targets that were also in the tissue-specific target section.…”

Identification of Key Differentially Expressed MicroRNAs in Cancer Patients Through Pan-cancer Analysis

“…miRNAs with significant adjusted p-values generated by both DESeq2 and edgeR were considered to be differentially expressed in a given cancer type. Results from differential expression analysis for paired and tumor-only expression data were integrated into BioXpress version 3.0 [75]. The Heatmap with clustering for significantly differentially expressed miRNAs across different cancer types was generated by R package gplots 3.0.1.…”

“…CancerMiner data were included only for records not found in Tarbase or miRWalk. Expression changes of all tissue-specific target genes in corresponding cancer types were extracted from BioXpress v3.0 [75]. For the non-tissuespecific target group, we first excluded those targets that were also in the tissue-specific target section.…”

Population-specific sequence and expression differentiation in Europeans

“…NPIPA2 is part of the nuclear pore complex of the cell membrane, which regulates exchange between the nucleus and cytoplasm (Strambio-De-Castillia et al 2010). Polymorphisms in NPIPA2 are associated with several diverse classes of cancer (Dingerdissen et al 2017), and its deletion is common in earlyonset colorectal cancer (Perea et al 2017). Moreover, both the localization of NPIPA2 to the cell membrane and its function in transport across the membrane make it a likely target of natural selection (Tang and Presgraves 2009;Tracy et al 2010).…”