ArticleFrontiers in immunology2026
Identification of prognostic markers related to homologous recombination deficiency in cholangiocarcinoma using CoxBoost and LASSO machine learning techniques.
Article in Frontiers in immunology, 2026. 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.
- Machine learning-based programmed cell death gene signature for prognosis and drug sensitivity in breast cancer.Oncology letters · 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Cholangiocarcinoma (CHOL) is a highly aggressive malignancy with a poor prognosis. Homologous recombination deficiency (HRD) is associated with genomic instability and cancer progression, making it a potential therapeutic target. The aim of this study is to develop novel potential prognostic biomarkers and construct an HRD-based prognostic risk prediction model for CHOL to enhance clinical precision medicine. Methods: We analyzed HRD across cancers using multiple datasets including TCGA-CHOL, TCGA-LIHC, GDC TARGET-OS, and IMvigor210. HRD scores were calculated using data from Thorsson et al. and the maftools R package was used for mutation data visualization and tumor mutational burden (TMB) calculation. Differential gene expression analysis identified HRD-related genes, validated in tumor and adjacent non-tumor tissues using RT-PCR. 10 machine-learning algorithms including RSF, LASSO, GBM, Survival-SVM, SuperPC, Ridge, plsRcox, CoxBoost, Stepwise Cox, Enet were selected to construct a prognostic model and validated in the E-MTAB-6389 and GSE107943. Among them, RSF, LASSO, CoxBoost and Stepwise Cox have the functions of dimension reduction and variable screening. Results: Comparative analysis demonstrated significant associations between HRD scores and genomic instability markers. High HRD scores independently predicted poorer overall survival (log-rank p = 0.043) and progression-free interval (log-rank p = 0.028). Immune infiltration analysis revealed higher levels of active B cells and regulatory T cells in the low-risk group, suggesting differential immune landscapes between risk groups. We identified the CoxBoost and LASSO algorithms as the optimal combination for creating a CoxBoost+ LASSO prognostic model. Using this model, we identified six genes (ANXA2P1, BBOX1, KLHL33, MN1, OR51A4, and TRDN) with significant differential expression. Conclusions: Our HRD-based prediction model offers a reliable tool for CHOL prognosis, suggesting new potential for six candidate genes as prognostic biomarkers. It highlights potential therapeutic targets and drug sensitivities, providing new insights into personalized treatment strategies for CHOL 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.