SynthesisOncotarget2017
Genome-wide screen identifies a novel prognostic signature for breast cancer survival.
Synthesis in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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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.
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Who cites it
13 citing papers in PubMed, 31 citations in OpenAlex.
- Comprehensive research into prognostic and immune signatures of transcription factor family in breast cancer.BMC medical genomics · 2023Article
- ZNF92, an unexplored transcription factor with remarkably distinct breast cancer over-expression associated with prognosis and cell-of-origin.NPJ breast cancer · 2022Article
- iCEMIGE: Integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers.World journal of clinical oncology · 2022Article
- Identification of a novel cancer microbiome signature for predicting prognosis of human breast cancer patients.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2022Article
- Identification of a Transcription Factor Signature That Can Predict Breast Cancer Survival.Computational and mathematical methods in medicine · 2021Article
- Lipid Phosphate Phosphatases and Cancer.Biomolecules · 2020Review
- Role of Adipose Tissue-Derived Autotaxin, Lysophosphatidate Signaling, and Inflammation in the Progression and Treatment of Breast Cancer.International journal of molecular sciences · 2020Review
- Review
- Association of FOXA1 and EMT markers (Twist1 and E-cadherin) in breast cancer.Molecular biology reports · 2019Article
- Associations in tumor infiltrating lymphocytes between clinicopathological factors and clinical outcomes in estrogen receptor-positive/human epidermal growth factor receptor type 2 negative breast cancer.Oncology letters · 2019Article
- A 10‑microRNA prognosis scoring system in esophageal squamous cell carcinoma constructed using bioinformatic methods.Molecular medicine reports · 2018Article
- Review
- Elevated mRNA Levels of AURKA, CDC20 and TPX2 are associated with poor prognosis of smoking related lung adenocarcinoma using bioinformatics analysis.International journal of medical sciences · 2018Article
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 at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large genomic datasets in combination with clinical data can be used as an unbiased tool to identify genes important in patient survival and discover potential therapeutic targets. We used a genome-wide screen to identify 587 genes significantly and robustly deregulated across four independent breast cancer (BC) datasets compared to normal breast tissue. Gene expression of 381 genes was significantly associated with relapse-free survival (RFS) in BC patients. We used a gene co-expression network approach to visualize the genetic architecture in normal breast and BCs. In normal breast tissue, co-expression cliques were identified enriched for cell cycle, gene transcription, cell adhesion, cytoskeletal organization and metabolism. In contrast, in BC, only two major co-expression cliques were identified enriched for cell cycle-related processes or blood vessel development, cell adhesion and mammary gland development processes. Interestingly, gene expression levels of 7 genes were found to be negatively correlated with many cell cycle related genes, highlighting these genes as potential tumor suppressors and novel therapeutic targets. A forward-conditional Cox regression analysis was used to identify a 12-gene signature associated with RFS. A prognostic scoring system was created based on the 12-gene signature. This scoring system robustly predicted BC patient RFS in 60 sampling test sets and was further validated in TCGA and METABRIC BC data. Our integrated study identified a 12-gene prognostic signature that could guide adjuvant therapy for BC patients and includes novel potential molecular targets for therapy.
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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.