ArticlePloS one2025
Integrative GWAS and RNA-Seq analysis for target identification and virtual drug screening in colorectal cancer.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Application of Systems Genetics to Investigate Molecular Pathology of Early-Onset Colorectal Cancer.Chemical biology & drug design · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundColorectal cancer (CRC) is a leading cause of global cancer-related mortality, necessitating the identification of novel therapeutic targets. Integrating genetic and transcriptomic data may reveal key molecular drivers of CRC progression and treatment opportunities.
methodsWe performed a multiomics analysis combining genome-wide association study (GWAS) data (p < 1e-6) and RNA-seq data from the TCGA. Differential expression analysis (Limma) identified 24 consistently dysregulated genes (17 mRNAs, 7 lncRNAs) in CRC. Survival analysis was used to evaluate their prognostic impact on overall survival (OS), relapse-free survival (RFS), and post progression survival (PPS). Drug‒gene interactions were explored via Enrichr, and virtual screening (PubChem) prioritized high-affinity compounds that target PYGL, a metabolic regulator.
resultsIntegration of GWAS and RNA-seq revealed that 24 CRC-associated genes, including PYGL, SMAD7, and TCF7L2, are involved in tumor metabolism and Wnt/TCF signaling. Survival analysis revealed that five genes (CDKN2B, BOC, METRNL, etc.) were significantly correlated with OS, RFS, and PPS. Ten small-molecule candidates targeting PYGL exhibited high binding affinity, suggesting their therapeutic potential.
conclusionThis study identified CRC-linked genes through GWASs and transcriptomics, highlighting their prognostic and druggable relevance. Computational drug repurposing pinpoints PYGL inhibitors as promising candidates, offering a translational framework for CRC therapy development.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.