ReviewJournal of cancer prevention2016
Identification of Biomarkers for Breast Cancer Using Databases.
Review in Journal of cancer prevention, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed.
- Review
- Breast cancer stem cells: A review of their characteristics and the agents that affect them.Molecular carcinogenesis · 2021Review
- Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis.Bioscience reports · 2021Article
- Highly robust model of transcription regulator activity predicts breast cancer overall survival.BMC medical genomics · 2020Article
- Six-gene-based prognostic model predicts overall survival in patients with uveal melanoma.Cancer biomarkers : section A of Disease markers · 2020Article
- Matrix Metalloproteinase 11 Is a Potential Therapeutic Target in Lung Adenocarcinoma.Molecular therapy oncolytics · 2019Article
- Developments in Point-of-Care Diagnostic Technology for Cancer Detection.Diagnostics (Basel, Switzerland) · 2018Review
- Identifying genes as potential prognostic indicators in patients with serous ovarian cancer resistant to carboplatin using integrated bioinformatics analysis.Oncology reports · 2018Article
- Twenty-gene-based prognostic model predicts lung adenocarcinoma survival.OncoTargets and therapy · 2018Article
- Transforming Growth Factor-β Drives the Transendothelial Migration of Hepatocellular Carcinoma Cells.International journal of molecular sciences · 2017Article
- Tumor Heterogeneity in Breast Cancer.Frontiers in medicine · 2017Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is one of the major causes of cancer death in women. Many studies have sought to identify specific molecules involved in breast cancer and understand their characteristics. Many biomarkers which are easily measurable, dependable, and inexpensive, with a high sensitivity and specificity have been identified. The rapidly increasing technology development and availability of epigenetic informations play critical roles in cancer. The accumulated data have been collected, stored, and analyzed in various types of databases. It is important to acknowledge useful and available data and retrieve them from databases. Nowadays, many researches utilize the databases, including The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Surveillance, Epidemiology and End Results (SEER), and Embase, to find useful informations on biomarkers for breast cancer. This review summarizes the current databases which have been utilized for identification of biomarkers for breast cancer. The information provided by this review would be beneficial to seeking appropriate strategies for diagnosis and treatment of 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.