ArticleFrontiers in oncology2023
Prognosis stratification and response to treatment in breast cancer based on one-carbon metabolism-related signature.
Article in Frontiers in oncology, 2023. 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.
- Molecular Mechanisms Linking Genes and Vitamins of the Complex B Related to One-Carbon Metabolism in Breast Cancer: An In Silico Functional Database Study.International journal of molecular sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Breast cancer (BC) is the most common malignant tumor in the female population. Despite staging and treatment consensus guidelines, significant heterogeneity exists in BC patients' prognosis and treatment efficacy. Alterations in one-carbon (1C) metabolism are critical for tumor growth, but the value of the role of 1C metabolism in BC has not been fully investigated. Methods: To investigate the prognostic value of 1C metabolism-related genes in BC, 72 1C metabolism-related genes from GSE20685 dataset were used to construct a risk-score model via univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression algorithm, which was validated on three external datasets. Based on the risk score, all BC patients were categorized into high-risk and low-risk groups. The predictive ability of the model in the four datasets was verified by plotting Kaplan-Meier curve and receiver operating characteristic (ROC) curve. The candidate genes were then analyzed in relation to gene mutations, gene enrichment pathways, immune infiltration, immunotherapy, and drug sensitivity. Results: We identified a 7-gene 1C metabolism-related signature for prognosis and structured a prognostic model. ROC analysis demonstrated that the model accurately predicted the 2-, 3-, and 5-year overall survival rate of BC patients in the four cohorts. Kaplan-Meier analysis revealed that survival time of high-risk patients was markedly shorter than that of low-risk patients (p < 0.05). Meanwhile, high-risk patients had a higher tumor mutational burden (TMB), enrichment of tumor-associated pathways such as the IL-17 signaling pathway, lower levels of T follicular helper (Tfh) and B cells naive infiltration, and poorer response to immunotherapy. Furthermore, a strong correlation was found between MAT2B and CHKB and immune checkpoints. Discussion: These findings offer new insights into the effect of 1C metabolism in the onset, progression, and therapy of BC and can be used to assess BC patients' prognosis, study immune infiltration, and develop potentially more effective clinical treatment options.
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.