ArticleJournal of gastrointestinal oncology2026
Immunological characteristics of calmodulin-related genes in hepatocellular carcinoma and their potential for prognostic prediction and diagnostic applications.
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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: As a primary liver cancer with high malignancy, hepatocellular carcinoma (HCC) is notoriously difficult to detect early, which often leads to an unfavorable prognosis for patients. Calmodulin-related genes (CRGs) participate in the regulation of cellular calcium signaling, yet their roles in HCC development, immune microenvironment remodeling, and clinical application value remain incompletely understood. This study aims to systematically screen key CRGs and evaluate their clinical value as prognostic biomarkers for HCC. Methods: Intersection analysis identified differentially expressed calmodulin-related genes (DECRGs). Integration of three algorithms-least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest-identified core diagnostic genes, whose efficacy was validated by receiver operating characteristic (ROC) curves and a diagnostic nomogram. Based on DECRGs, we screened prognosis-related genes and constructed prognosis risk models using LASSO regression and multivariate Cox regression. Model performance was validated in the external GSE14520 cohort. We investigated differences in immune infiltration and gene enrichment features across risk groups, analyzed associations between risk scores and clinical characteristics, and assessed predictive value for immune therapy response. Results: Through analysis of 51 DECRGs, we identified a five-gene HCC diagnostic signature demonstrating robust diagnostic performance in both training and validation cohorts. Concurrently, a nine-gene prognostic risk model based on DECRGs was constructed, validated in an external cohort, with its associated nomogram successfully predicting patient survival. High- and low-risk groups exhibited distinct biological characteristics: the low-risk group displayed an active immune phenotype and predicted superior response to immune checkpoint inhibitors, while the high-risk group enriched cell cycle pathways and metabolic processes. Conclusions: The calmodulin-related core genes identified in this study may serve as potential diagnostic biomarkers for HCC. The prognostic risk model constructed based on these CRGs effectively enables risk stratification and prognosis assessment in HCC patients. Furthermore, CRG expression is closely associated with the HCC immune microenvironment and response to immunotherapy, offering new insights for optimizing HCC diagnosis and treatment strategies.
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.