Evidence map›Paper›PMID 40950112›Full record

ArticlebioRxiv : the preprint server for biology2025

UALCAN Mobile, an app for cancer proteogenomic data analysis.

David Rubey, Darshan Shimoga Chandrashekar, Ahmedur Rahman Shovon, Gopi Chand Puli, Santhosh Kumar Karthikeyan, Upender Manne, Chad J Creighton, Sidharth Kumar, Sooryanarayana Varambally

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

David RubeyDepartment of Computer Science, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Darshan Shimoga ChandrashekarDepartment of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Ahmedur Rahman ShovonDepartment of Computer Science, University of Illinois, Chicago, 851 S. Morgan, MC 152, Chicago, IL 60607, USA.
Gopi Chand PuliDepartment of Computer Science, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Santhosh Kumar KarthikeyanDepartment of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Upender ManneDepartment of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
Chad J CreightonDepartment of Medicine, Dan L. Duncan Comprehensive Cancer Center, and Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.
Sidharth KumarDepartment of Computer Science, University of Illinois, Chicago, 851 S. Morgan, MC 152, Chicago, IL 60607, USA.
Sooryanarayana VaramballyDepartment of Pathology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.ORCID 0000-0002-2277-1127

Funding

TRAINING AND CAREER DEVELOPMENTU54CA118948 · NCI · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI Timiya S Nolan · 2005 to 2026
$26.6M
NCI NIH HHS U54 CA118948
6 · The paper itself

Abstract

Cancer is a complex disease affecting various organs and is a major cause of death worldwide. During cancer initiation, disease progression, and tumor metastasis, various genomic and proteomic alterations are observed. Recent technological advances have led to the generation of large amounts of molecular data, including genomics and transcriptomics. These large-scale datasets can be utilized to analyze and identify sub-class-specific cancer biomarkers and targets. However, there is a need for the development of user-friendly tools for large-scale data analysis, disseminating the analyzed data in a visualizable format to cancer researchers with no programming skills. We developed UALCAN, a comprehensive platform that allows users to integrate disparate data to better understand the genes, proteins, and pathways perturbed in cancer and make discoveries of potential biomarkers and targets. In the current study, we describe the development of the UALCAN Mobile application (app) that will provide cancer transcriptomic data obtained from The Cancer Genome Atlas (TCGA) project to evaluate protein-coding gene expression based on various stratifications, including stage, grade, race, gender, and molecular-subtypes across over 30 types of cancers. In addition, the UALCAN mobile provides data analysis options for epigenetic changes due to DNA promoter methylation and Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer proteomic data. The app provides access to large cancer molecular datasets on the go. To find changes in the expression of causative genes and proteins and to identify biomarkers and therapeutic targets, UALCAN mobile app will be extremely valuable. The "UALCAN Mobile" app is free to use and can be downloaded from both the iOS/Apple and the Android Play Store and has been downloaded over 100 times in each of iOS and android app stores.

Indexed as

appcancerdata analysisgene expression

Identifiers

PMID40950112
PMCPMC12424836

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.