ArticleScientific reports2025
Comprehensive bioinformatic analysis reveals prognostic significance and functional insights of candidate gene expression in colorectal cancer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- An epithelial mesenchymal transition associated gene signature stratifies risk of ulcerative colitis associated colorectal cancer.Discover oncology · 2026Article
- Article
- MARCHF1-mediated SULF1 degradation blocks THBS2/TGF-β/SMAD2/3 signaling to reverse colon cancer metastasis and 5-FU resistance.iScience · 2026Article
- Bioinformatic Approach to Identify Positive PrognosticInternational journal of molecular sciences · 2025Article
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
The purpose of this study was to investigate biomarkers associated with poor clinical outcomes in colorectal cancer (CRC) by utilizing comprehensive datasets from the gene expression omnibus (GEO) databases GSE41258, GSE39582, and GSE44861. We initially identified differentially expressed genes (DEGs) and applied weighted gene co-expression network analysis (WGCNA) to the GSE41258 dataset to reveal key gene modules associated with CRC. Enrichment analyses were conducted to gain insights into the underlying biology of CRC, particularly focusing on pathways linked to the identified gene modules. Our analysis unveiled a distinct module strongly correlated with CRC carcinogenesis, with significant pathways related to extracellular matrix organization and vasculature development. Furthermore, we identified nine candidate genes (CDH11, COL1A1, COL1A2, COL5A1, COL5A2, FAP, SPARC, SULF1, and THY1) as potential crosstalk genes across various datasets. Notably, eight of these candidate genes exhibited a significant correlation with poor overall survival (OS) and recurrence-free survival (RFS) in CRC patients, suggesting their potential as prognostic biomarkers. Experimental validation using short hairpin RNA (shRNA)-mediated knockdown in HCT116 cells demonstrated that silencing of these candidate genes significantly impaired cancer cell proliferation, providing biological evidence supporting their functional roles in CRC progression. Our integrative approach offers a comprehensive understanding of the molecular landscape of CRC and identifies promising biomarkers for further exploration and validation.
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.