ReviewFunctional & integrative genomics2025
Computational identification and validation of non-coding rna biomarkers in gastrointestinal cancer.
Review in Functional & integrative genomics, 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.
- Interpretable machine learning-based survival prediction and key gene identification in cancer using gene expression and clinical data.Translational cancer research · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Non-coding RNAs (ncRNAs) are showing great potential as clinical indicators and are becoming essential regulators in gastrointestinal (GI) cancers. The computational discovery and subsequent confirmation of specific ncRNAs, such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), in the pathophysiology of GI cancer (GIC) is the primary focus of this study. We reviewed a rigorous, multi-step validation workflow that includes RNA sequencing and bioinformatic analysis for initial discovery, quantitative PCR for confirmation in tissue and liquid biopsies, and receiver operating characteristic (ROC) and survival analyses for evaluating clinical usefulness. We highlight, as a specific result, that a panel of lncRNAs (e.g., H19, NEAT1, OIP5-AS, MALAT1) and miRNAs (e.g., miR-21, miR-92a) have been consistently validated with excellent diagnostic accuracy and are strongly associated with poor overall survival. According to our findings, these computationally generated ncRNA signatures are practical tools for GIC prognosis and early diagnosis, opening the door for their incorporation into personalized oncology.
Indexed as
Identifiers
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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.