ArticleEuropean journal of breast health2022
An
Article in European journal of breast health, 2022. 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.
- Imaging-Based Prediction of Key Breast Cancer Biomarkers Using Deep Learning on Digital Breast Tomosynthesis.European journal of breast health · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Breast cancer is the leading cause of morbidity and mortality in women worldwide. This malignant neoplasm can be classified into four clinically relevant subtypes according to the expression of a number of biomarkers. However, these tumors show considerable intratumoral heterogeneity and multidrug resistance. Members of the pleckstrin homology-like domain, family B (PHLDB) play a critical role in the regulation of p53 and AKT signaling pathways, important for cancer and cellular metabolism. The present study was performed to evaluate the expression pattern of PHLDB family members in breast cancer and its potential prognostic and predictive value for therapeutic response using bioinformatics tools. Materials and Methods: This in silico analysis was performed using several online repositories, including UALCAN, GEPIA2, bc-GenExMiner, KM Plotter, PrognoScan and ROC Plotter. Results: PHLDB family genes were found to be differentially expressed in tumor samples when compared to healthy breast tissue samples. Furthermore, epigenetic regulation may be one of the regulatory mechanisms for the expression of these markers. The PHLDB family of genes proved to be potential markers for predicting the development of lymph node metastasis ( Conclusion: Our cumulative results demonstrate that PHLDB family members may be promising biomarkers for predicting prognosis and therapeutic response in breast cancer patients.
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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.