ArticleScientific reports2020
An in-silico method leads to recognition of hub genes and crucial pathways in survival of patients with breast cancer.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Article
- Bioinformatics analysis identifies dysregulation of miR-548F-3p and its hub gene in triple-negative breast cancer.Iranian journal of basic medical sciences · 2025Article
- LINC01089 in cancer: multifunctional roles and therapeutic implications.Journal of translational medicine · 2024Review
- Article
- Identification of Hub of the Hub-Genes From Different Individual Studies for Early Diagnosis, Prognosis, and Therapies of Breast Cancer.Bioinformatics and biology insights · 2024Article
- Molecular docking and MD simulation approach to identify potential phytochemical lead molecule against triple negative breast cancer.F1000Research · 2024Article
- Utilizing an In-silico Approach to Pinpoint Potential Biomarkers for Enhanced Early Detection of Colorectal Cancer.Cancer informatics · 2024Article
- Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer.Medicina (Kaunas, Lithuania) · 2023Article
- Integrated analysis of inflammatory mRNAs, miRNAs, and lncRNAs elucidates the molecular interactome behind bovine mastitis.Scientific reports · 2023Article
- Article
- Frankincense myrrh attenuates hepatocellular carcinoma by regulating tumor blood vessel development through multiple epidermal growth factor receptor-mediated signaling pathways.World journal of gastrointestinal oncology · 2022Article
- Comparative transcriptomic analysis of long noncoding RNAs inFrontiers in genetics · 2022Article
- Long non-coding RNA-associated competing endogenous RNA axes in the olfactory epithelium in schizophrenia: a bioinformatics analysis.Scientific reports · 2021Article
- Bioinformatics analysis of long non-coding RNA-associated competing endogenous RNA network in schizophrenia.Scientific reports · 2021Article
- Long Non-Coding RNA- Associated Competing Endogenous RNA Axes in T-Cells in Multiple Sclerosis.Frontiers in immunology · 2021Article
- Mechanistic Insight Into the Regulation of Immune-Related Genes Expression in Autism Spectrum Disorder.Frontiers in molecular biosciences · 2021Article
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3 authors.
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Abstract
Breast cancer is a highly heterogeneous disorder characterized by dysregulation of expression of numerous genes and cascades. In the current study, we aim to use a system biology strategy to identify key genes and signaling pathways in breast cancer. We have retrieved data of two microarray datasets (GSE65194 and GSE45827) from the NCBI Gene Expression Omnibus database. R package was used for identification of differentially expressed genes (DEGs), assessment of gene ontology and pathway enrichment evaluation. The DEGs were integrated to construct a protein-protein interaction network. Next, hub genes were recognized using the Cytoscape software and lncRNA-mRNA co-expression analysis was performed to evaluate the potential roles of lncRNAs. Finally, the clinical importance of the obtained genes was assessed using Kaplan-Meier survival analysis. In the present study, 887 DEGs including 730 upregulated and 157 downregulated DEGs were detected between breast cancer and normal samples. By combining the results of functional analysis, MCODE, CytoNCA and CytoHubba 2 hub genes including MAD2L1 and CCNB1 were selected. We also identified 12 lncRNAs with significant correlation with MAD2L1 and CCNB1 genes. According to The Kaplan-Meier plotter database MAD2L1, CCNA2, RAD51-AS1 and LINC01089 have the most prediction potential among all candidate hub genes. Our study offers a framework for recognition of mRNA-lncRNA network in breast cancer and detection of important pathways that could be used as therapeutic targets in this kind of cancer.
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