ArticleScientific reports2025
Machine learning-based integration develops relapse related signature for predicting prognosis and indicating immune microenvironment infiltration in breast cancer.
Article in Scientific reports, 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.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Breast cancer is the most common type of cancer in women, and while current treatments can cure the majority of early-stage primary BC cases, recurrence remains a significant challenge. Traditional methods of assessing patient prognosis, such as AJCC, TNM staging, and biochemical markers, are no longer sufficient in the era of precision medicine. Existing tumor models often rely on single selection and simpler algorithms, which can lead to poor effectiveness or overfitting. To address these limitations, this study systematically analyzed RNA-seq high-throughput data and combined 10 machine learning algorithms to construct 117 models. The optimal algorithm combination, StepCox[both] and ridge regression, was identified, and an immune-related gene signature (IRGS) composed of 12 genes was developed. The IRGS demonstrated outstanding predictive performance across multiple datasets and surpassed 10 previously published signatures. GSEA analysis revealed significant enrichment differences in cellular processes, diseases, and immune-related pathways between high- and low-risk recurrence patients. The low recurrence risk group based on IRGS exhibited a stronger immune phenotype and better survival prognosis, which may be associated with higher infiltration of CD4 + and CD8 + T cells. However, high M2 macrophage infiltration suggests potential immune escape in low recurrence risk patients. Combined with immune checkpoint expression levels and TIDE results, it is suggested that low-risk patients may respond positively to immunotherapy. Through drug sensitivity analysis, potential drugs that are more effective for both high- and low-risk groups have been identified. Therefore, the IRGS developed in this study can serve as an adjunct tool for assessing the recurrence risk of breast cancer, potentially enhancing personalized treatment planning, and improving the clinical management of patients with breast cancer.
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.