ArticleBMC cancer2011
Protein expression based multimarker analysis of breast cancer samples.
Article in BMC cancer, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 39 citations in OpenAlex.
- Trial
- FOXP3 transcription factor: a candidate marker for susceptibility and prognosis in triple negative breast cancer.BioMed research international · 2014Trial
- Circulating microRNAs and serum proteins in breast cancer patients: Diagnostic relevance and grade-specific expression patterns.World journal of experimental medicine · 2025Article
- Reciprocal expression of the immune response genes CXCR3 and IFI44L as module hubs are associated with patient survivals in primary central nervous system lymphoma.International journal of clinical oncology · 2023Article
- Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses.Scientific reports · 2022Article
- Article
- Identification of Key Pathways and Genes Related to Immunotherapy Resistance of LUAD Based on WGCNA Analysis.Frontiers in oncology · 2021Article
- Epigenetic Alteration and its Association With Downregulated FOXP3 Gene in Indian Breast Cancer Patients.Frontiers in genetics · 2021Article
- Immunohistochemical Expression of Five Protein Combinations Revealed as Prognostic Markers in Asian Oral Cancer.Frontiers in genetics · 2021Article
- Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma.Frontiers in genetics · 2020Article
- Imprinting effects of UBE3A loss on synaptic gene networks and Wnt signaling pathways.Human molecular genetics · 2019Article
- Identification of lncRNA TRPM2-AS/miR-140-3p/PYCR1 axis's proliferates and anti-apoptotic effect on breast cancer using co-expression network analysis.Cancer biology & therapy · 2019Article
- Article
- Application of weighted gene co-expression network analysis to identify key modules and hub genes in oral squamous cell carcinoma tumorigenesis.OncoTargets and therapy · 2018Article
- Application of a co‑expression network for the analysis of aggressive and non‑aggressive breast cancer cell lines to predict the clinical outcome of patients.Molecular medicine reports · 2017Article
- Oncosis and apoptosis induction by activation of an overexpressed ion channel in breast cancer cells.Oncogene · 2017Article
- Identification of two novel biomarkers of rectal carcinoma progression and prognosis via co-expression network analysis.Oncotarget · 2017Article
- Weighted gene co-expression network analysis of gene modules for the prognosis of esophageal cancer.Journal of Huazhong University of Science and Technology. Medical sciences = Hua zhong ke ji da xue xue bao. Yi xue Ying De wen ban = Huazhong keji daxue xuebao. Yixue Yingdewen ban · 2017Article
- Comprehensive Analyses of Tissue-Specific Networks with Implications to Psychiatric Diseases.Methods in molecular biology (Clifton, N.J.) · 2017Article
- Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundTissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.
methodsWe apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.
resultsWe find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.
conclusionsWe show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.
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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.