ArticleJournal of gastrointestinal oncology2026
Mining and experimental validation of machine learning-based immune-related diagnostic biomarkers for hepatocellular carcinoma.
Article in Journal of gastrointestinal oncology, 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: Hepatocellular carcinoma (HCC) is a highly malignant and aggressive tumor. Immune-related genes (IRGs) expression correlates closely with the HCC immune microenvironment, and this study aims to identify immune-related diagnostic markers in HCC. Methods: HCC-related datasets and IRGs were obtained from public databases (TCGA, ICGC, TISIDB, and InnateDB). Differential expression analysis screened differentially expressed genes (DEGs) between HCC and control samples, while weighted gene co-expression network analysis identified key module genes correlated with immune scoring systems. Candidate genes were obtained via the intersection of DEGs, IRGs, and key module genes. Hub genes were then determined through protein-protein interaction analysis, followed by correlation and survival analyses. Moreover, three machine learning algorithms identified diagnostic biomarkers, which were evaluated via a logit model and receiver operating characteristic curve analysis for HCC diagnostic efficacy. Finally, biomarker expression was validated in clinical samples. Results: After identifying 8,800 DEGs and 2,337 key module genes, we intersected them with IRGs to obtain 87 candidate genes. Thereafter, 33 hub genes were obtained. The hub genes showed a notable positive correlation, suggesting their potential involvement in regulating common biological processes. Additionally, six hub genes (e.g., Conclusions: This study identified three immune-related diagnostic biomarkers for HCC, which may provide novel insights into HCC prognostic management.
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.