ArticleBreast cancer (Tokyo, Japan)2023
Characterization of protein-based risk signature to predict prognosis and evaluate the tumor immune environment in breast cancer.
Article in Breast cancer (Tokyo, Japan), 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.
- Therapeutic targets for lung cancer: genome-wide Mendelian randomization and colocalization analyses.Frontiers in pharmacology · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProteomics profiles have enabled a systematic insight into the prognosis of cancer. This study aimed to establish a valuable protein-based risk signature to assess the prognosis and immune status in patients with breast cancer (BC).
methodsProtein expression profile, RNA expression data, and clinical information were acquired from The Cancer Genome Atlas (TCGA). The whole cohort was randomly split into two cohorts, one for establishing the risk signature and the other for testing. Univariate Cox analysis and Least absolute shrinkage and selection operator (LASSO) Cox regression were utilized to construct the protein-based risk signature. All cohorts were divided into high- and low-risk groups, which were applied to investigate the clinical relevance, tumor microenvironment, and therapeutic response.
resultsThe prognostic proteomics signature was established based on prognostic proteins, thus categorizing patients into low-risk and high-risk groups with different prognoses. A predictive nomogram was also developed to predict 1, 3, and 5-year survival possibility for BC patients, and the calibration curves confirmed the predictive significance of this signature. Afterward, the low-risk group displayed higher immune activities, immune checkpoint expression, and immunotherapeutic response. Moreover, GSEA analysis indicated that immune-associated pathways were rich in the low-risk group. Additionally, this prognostic signature demonstrated potential predict significance for chemotherapeutic agents.
conclusionThis study established an effective prognostic proteomics signature with reliable predictive performance for survival, immune activity, and drug sensitivity. It might provide a novel perspective into the protein function in BC, and guide the individual treatment strategies for BC patients.
Indexed as
Identifiers
36732487What 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.