ArticlePeerJ2026
Identification of colorectal cancer immune biomarkers
Article in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide. The identification of effective molecular targets is crucial for advancing precision medicine and prognostic strategies. This study aims to uncover key CRC biomarkers through integrative bioinformatics analyses, providing mechanistic insights for therapeutic development. Methods: We analyzed three CRC datasets from the Gene Expression Omnibus (GEO) database. Expression quantitative trait loci (eQTL) analysis was performed to identify instrumental variables (IVs), which were subsequently used in Mendelian Randomization (MR) analysis with CRC Genome-Wide Association Study (GWAS) data. MR-associated genes were intersected with differentially expressed genes (DEGs) to screen disease-related key genes. Functional enrichment analyses were conducted using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). Additionally, immune cell infiltration and gene-immune correlation analyses were performed. Finally, validation was performed using independent GEO, The Cancer Genome Atlas (TCGA) datasets, summary data-based Mendelian randomization (SMR) and quantitative reverse transcription polymerase chain reaction (qRT-PCR) in CRC cell lines. Results: A total of 776 upregulated and 981 downregulated DEGs were identified. Nine key genes were prioritized: Discussion: This study suggests that
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.