ArticleMedicine2026
A prognostic risk model based on programmed cell death genes for breast cancer and its potential clinical application.
Article in Medicine, 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
This study aims to identify programmed cell death (PCD)-associated genes linked to breast cancer prognosis for the construction of a prognostic model. Transcriptomic and clinical information was imported from the The Cancer Genome Atlas (TCGA) and GEO databases. Modulatory genes related to 18 types of PCD were evaluated. Furthermore, the TCGA and GEO datasets were employed as the training and validation datasets, respectively. A risk score prognostic model based on PCD-related genes was generated via univariate, LASSO, and multivariate Cox regression analyses. Further, differences in drug sensitivity, tumor mutation burden (TMB), immune-related pathways and cell infiltration, and immune checkpoints were compared between high-risk and low-risk cohorts to elucidate the clinical applicability of the model. Lastly, the model gene's expression was verified by RT-PCR. The data revealed 1480 PCD-related genes from the TCGA breast cancer gene expression matrix. Differential expression analysis identified 186 differentially expressed PCD genes. Furthermore, a prognostic model was generated according to the risk scores using multivariate Cox regression. The model comprised 8 PCD-related genes (BRSK2, CD24, IFNG, LAMB3, PDX1, PMAIP1, SLC7A11, and TRIML2). Moreover, breast cancer cases were divided into high-risk and low-risk cohorts per the median risk score. The results indicated that low-risk patients had better prognoses, and the model showed good predictive performance in the GEO validation cohort. The area under the curve values were 0.819, 0.731, and 0.674 for the nomogram's 3, 5, and 8 years overall survival, respectively. Functional enrichment analysis revealed that the prognostic model was markedly linked with the modulation of the immune microenvironment and tumor progression in breast cancer. Immune infiltration assessment revealed that low-risk patients had increased activity in immune-related pathways and infiltration of immune cells. In addition, the low-risk cohort had lower TMB and elevated immune checkpoint-related levels. Correlation analysis between immune checkpoints and risk scores indicated that low-risk patients were more responsive to immunotherapy. Drug sensitivity analysis showed variations in the IC50 values between the risk cohorts, suggesting potential variations in drug efficacy across different risk cohorts. Gene expression was verified by RT-PCR.A prognostic risk model for breast cancer based on 8 PCD-related genes was constructed and its predictive value was validated. The established model may provide novel biomarkers and effective therapeutic targets for breast cancer diagnosis and treatment.
Indexed as
Identifiers
What Socratic 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 Socratic graph.