Cancer genomic, transcriptomic, and proteomic profiling has generated extensive data that necessitate the development of tools for its analysis and dissemination. We developed UALCAN to provide a portal for easy exploring, analyzing, and visualizing these data, allowing users to integrate the data to better understand the gene, proteins, and pathways perturbed in cancer and make discoveries. UALCAN web portal enables analyzing and delivering cancer transcriptome, proteomics, and patient survival data to the cancer research community. With data obtained from The Cancer Genome Atlas (TCGA) project, UALCAN has enabled users to evaluate protein-coding gene expression and its impact on patient survival across 33 types of cancers. The web portal has been used extensively since its release and received immense popularity, underlined by its usage from cancer researchers in more than 100 countries. The present manuscript highlights the task we have undertaken and updates that we have made to UALCAN since its release in 2017. Extensive user feedback motivated us to expand the resource by including data on a) microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and promoter DNA methylation from TCGA and b) mass spectrometry-based proteomics from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). UALCAN provides easy access to pre-computed, tumor subgroup-based gene/protein expression, promoter DNA methylation status, and Kaplan-Meier survival analyses. It also provides new visualization features to comprehend and integrate observations and aids in generating hypotheses for testing. UALCAN is accessible at http://ualcan.path.uab.edu

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Background: Long noncoding RNAs (lncRNAs) are RNA molecules with over 200 nucleotides that do not code for proteins, but are known to be widely expressed and have key roles in gene regulation and cellular functions. They are also found to be involved in the onset and development of various cancers, including prostate cancer (PCa). Since PCa are commonly driven by androgen regulated signaling, mainly stimulated pathways, identification and determining the influence of lncRNAs in androgen response is useful and necessary. LncRNAs regulated by the androgen receptor (AR) can serve as potential biomarkers for PCa. In the present study, gene expression data analysis were performed to distinguish lncRNAs related to the androgen response pathway. Methods and Results:We used publicly available RNA-sequencing and ChIP-seq data to identify lncRNAs that are associated with the androgen response pathway. Using Universal Correlation Coefficient (UCC) and Pearson Correlation Coefficient(PCC) analyses, we found 15 lncRNAs that have (a) highly correlated expression with androgen response genes in PCa and are (b) differentially expressed in the setting of treatment with an androgen agonist as well as antagonist compared to controls.Using publicly available ChIP-seq data, we investigated the role of androgen/AR axis in regulating expression of these lncRNAs. We observed AR binding in the promoter regions of 5 lncRNAs (MIR99AHG, DUBR, DRAIC, PVT1, and COLCA1), showing the direct influence of AR on their expression and highlighting their association with the androgen response pathway. Conclusion:By utilizing publicly available multiomics data and by employing in silico methods, we identified five candidate lncRNAs that are involved in the androgen response pathway. These lncRNAs should be investigated as potential biomarkers for PCa.

Breast cancer (BC) accounted for 15% cancer related death among women, in the United States last year. It is a highly heterogeneous and complex diseases with multiple molecular events occurring during cancer initiation and progression. It is important to unravel the heterogeneity of breast cancer and the molecular events to develop diagnostic biomarkers and targeted therapies. There is need for comprehensive and user-friendly tool for the utilization of available breast cancer data including metastatic and treatment response data. We have earlier created a highly used pan-cancer proteogenomic portal called UALCAN (ualcan.path.uab.edu). Based on the current unmet need for comprehensive BC data analysis and visualization resource, we developed an integrated proteo-genomicbreast cancer data analysis portal, called Mammonc-DB (http://resource.path.uab.edu/MammOnc-ge.html). For Mammonc-DB, we collected data from public repositories like NCBI Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) for collecting and analysis of genomics and epigenetics data, and, PRIDE, and, ProteomeXchange for proteomics data. We procured and processed multi-omics studies related to normal, primary, and metastatic human breast cancer data with other clinical information available, for different clinical models. We also collected and analyzed different therapy, and treatment studies that are currently employed in treating BC patients. Mammonc-DB has user friendly interface that aid breast cancer researchers and clinicians in, a) allows users to identify potential biomarkers in breast cancer, b) accessing publicly available breast cancer multi-omics data, c) Visualizing the expression pattern of gene or protein of interest with clinical feature stratification in each dataset among others This resource serves as a one-stop resource to study gene expression (protein-coding and non-coding) analysis along with epigenetic regulation, and protein expression patterns in BC. With this platform, we tend to create global user base that will accelerate discovery of biomarkers in BC that might be involved in diagnosis, therapeutic target identification and treatments of breast cancer. Citation Format: Santhosh Kumar Karthikeyan, Darshan S. Chandrashekar, Upender Manne, Chad Creighton, Zhaohui S. Qin, Sidharth Kumar, Sooryanaraya Varambally. Mammonc-DB: A web based user-friendly tool for comprehensive multi-omics data analysis in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6571.