ArticleDiscover oncology2025
Bioinformatics-based screening and prediction of compounds and antigen epitopes for immune targets with point mutations in colitis-associated colorectal cancer.
Article in Discover oncology, 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to identify and predict bioactive small molecules and immunogenic antigen epitopes targeting proteins with point mutations in colitis-associated colorectal cancer (CAC), using integrative bioinformatics approaches.
methodsGenes associated with colorectal cancer (CRC), ulcerative colitis (UC), diabetes mellitus (DM), hyperlipidemia (HL), gout, and functional dyspepsia (FD) were obtained from disease databases and GEO. Overlapping genes were identified using jvenn. CIBERSORT was used for immune infiltration analysis. Prognostic significance was evaluated using KM Plotter, and gene co-expression correlations in CRC were assessed via TIMER. cBioPortal was employed to analyze mutation profiles, tumor mutational burden (TMB), and microsatellite instability (MSI). Allosteric sites were predicted using AlloDriver. Molecular docking (AutoDock Vina v1.2.3) and 100-ns molecular dynamics simulations (GROMACS v2022.03) assessed protein-ligand interactions. Neoantigen and B-cell epitope immunogenicity were predicted using NetMHCpan-4.1 and IEDB, respectively.
resultsSERPINE1, SELE, SPP1, PLAU, COL1A1, PTGES, IL6, KDR, and ICAM1 were significantly upregulated in CAC. Lower expression correlated with improved relapse-free and overall survival (log-rank P < 0.05) and was associated with CRC molecular subtypes and biomarkers (P < 0.05). Mutations PLAU
conclusionBased on bioinformatics analysis, this study identified nine genes significantly upregulated in CAC and associated with prognosis. Allosteric-site mutations in PLAU, SERPINE1, and KDR affect the binding stability of small-molecule drugs (Anacetrapib, LY2090314), suggesting their role in drug response. Four mutation-specific B-cell epitopes (COL1A1
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.