ArticleDiscover oncology2025
Bioinformatics mining and experimental validation of prognostic biomarkers in 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.
- Discovery and validation of a multi-protein panel for predicting non-fatal major adverse cardiovascular events in diabetic kidney disease.Frontiers in endocrinology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
Colorectal cancer (CRC) is a prevalent condition with increasing incidence and mortality rates. The identification of robust prognostic gene signatures remains an unmet clinical need in CRC treatment. In this study, data from the GEO and TCGA databases were utilized to identify 2,779 upregulated and 2,629 downregulated genes in CRC tissues compared to adjacent normal tissues. WGCNA analysis highlighted the MEbrown module, which comprised 1,639 genes that exhibited strong correlations with CRC progression. Subsequently, an intersection analysis was conducted to further refine the candidate gene set, resulting in the selection of 926 differentially expressed CRC-related genes for subsequent analysis. Through univariate Cox regression, LASSO regularization, and multivariate Cox regression, a five-gene prognostic signature (TIMP1, PCOLCE2, MEIS2, HDC, CXCL13) was established, demonstrating consistent predictive accuracy in external (GSE32323) and internal validation cohorts. Mutational profiling showed predominant missense mutations across signature genes, with TIMP1 exhibiting the highest variant allele frequency. Functional enrichment analysis linked TIMP1 to critical CRC pathways including type I interferon receptor binding, oxidative phosphorylation, and Notch signaling pathways. High expression of TIMP1 was associated with poor prognosis in patients with CRC. Additionally, using siRNA technology, the impact of TIMP1 on cellular proliferation, metastasis and apoptosis in CRC cell lines (HCT116 and HT29) was investigated, showing that TIMP1 knockdown significantly inhibited CRC cell proliferation, metastasis, and promoted apoptosis. These experimental results were consistent with the conclusions drawn from the bioinformatics analysis. This research presents a prognostic risk model for CRC, further highlights TIMP1 as a potential biomarker and therapeutic target for the disease.
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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.