Recent advances in sequencing technology have led to a tremendous surge in generation genomics and transcriptomics data. The Cancer Genome Atlas (TCGA) consortium has utilized such modern technologies to sequence samples from thousands of cancer patients, with a wide variety of cancers.
Big data analysis from the project has led to the comprehensive molecular characterization of multiple cancer types and identification of potential biomarkers. Such high volume of data present an excellent opportunity for cancer researchers and clinicians to raise questions associated with tumor heterogeneity, racial disparity and to unearth novel cancer subtype specific markers. Systematic cancer data exploration by cancer researchers and clinicians requires analysis platforms with user-friendly features.
While there are computational tools available to aid researchers in carrying out specific TCGA data analyses, there is a need for a resource to facilitate the analysis of gene expression and survival profiles for tumor subgroups and for molecular subtypes of cancers.
In order to address this issue, a group at the UAB Department of Pathology led by Sooryanarayana Varambally, Ph.D., Associate Professor, Molecular and Cellular Pathology and Director, Translational Oncologic Pathology Research, and the UAB Comprehensive Cancer Center developed the University of Alabama Cancer Database, or UALCAN, an easy to use, interactive web-portal to perform to in-depth analyses of TCGA gene expression data.
UALCAN uses TCGA RNA-sequencing and patients' clinical data from 33 different cancer types, including several metastatic tumors. The web-based platform's user-friendly features facilitate:
1) relative expression analysis of a query gene(s) across tumor and normal samples, as well as in various tumor sub-groups based on individual cancer stages, tumor grade, race, body weight or other clinico-pathologic features
2) understanding the combined impact of gene expression level and clinico-pathologic features on patient survival
3) identification of the top over- and under-expressed genes in individual cancer types
This resource aids in in silico validation of target genes and for identifying tumor sub-group specific candidate biomarkers.
UALCAN allows users to export results of gene expression and survival analysis as publication-ready graphical images in png, jpeg, and PDF formats. The precompiled list of the top 250 over-/under-expressed genes for major cancers (with large sample size) as well as popular cancer subtypes (e.g. triple negative breast cancer [TNBC], prostate tumors with ETS-fusion) is provided via heatmap feature. This serves as a ready-to-use list of potential markers for further exploration.
Using UALCAN, it is possible to explore/validate the pan cancer expression pattern of hundreds of use-defined gene via “Scan by gene classes” option. UALCAN serves as a one-stop-shop by providing easy access to external resources such as GeneCards, Human Protein Reference Database (HPRD) (to explore relevant protein interactions), PubMed, TargetScan (to find predicted microRNA that potentially regulate the gene of interest) and Human Protein Atlas (to investigate protein expression in various cancers).
Since its release to the public in 2017, UALCAN has been visited over 50,000 times all across the world and is being highly cited. We believe that UALCAN will be extremely helpful in accelerating cancer biomarker and therapeutic target identification. We will add new functionality to UALCAN including non-coding RNA and microRNA expression analyses in the future.
This work was supported by UAB Pathology, the UAB Comprehensive Cancer Center, UAB Heersink School of Medicine and the Breast Cancer Research Foundation of Alabama (BCRFA).
Others contributors to this project include: Darshan Chandrashekar, Ph.D., Bioinformatics; Bhuwan Bashel, software engineer, Planet Fundraiser; Sai Akashaya Hodigere Balasubramanya; Israel Ponce-Rodriguez, IT Manager, UAB; Balabhadrapatruni Chakravarthi, Ph.D., UAB; and Chad Creighton, Ph.D., Baylor College of Medicine.
UALCAN is publicly available at: http://ualcan.path.uab.edu.
An article in the journal Neoplasia describing this web portal is here: https://www.ncbi.nlm.nih.gov/pubmed/28732212
UALCAN has been featured in RNA-Seq blog site: http://www.rna-seqblog.com/tag/ualcan/