ArticleBioMed research international2015
Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach.
Article in BioMed research international, 2015. 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.
- Multi-omic data integration and exploiting metabolic models using systems biology approach increase precision in subtyping and early diagnosis of cancer.Quantitative biology (Beijing, China) · 2025Article
- Advancing Non-Small-Cell Lung Cancer Management Through Multi-Omics Integration: Insights from Genomics, Metabolomics, and Radiomics.Diagnostics (Basel, Switzerland) · 2025Review
- Dichotomous roles of ACBD3 in NSCLC growth and metastasis.Oncogene · 2025Article
- APOBEC3 Activity Promotes the Survival and Evolution of Drug-Tolerant Persister Cells during EGFR Inhibitor Resistance in Lung Cancer.Cancer research communications · 2025Article
- Genomic profiling of NSCLC tumors with the TruSight oncology 500 assay provides broad coverage of clinically actionable genomic alterations and detection of known and novel associations between genomic alterations, TMB, and PD-L1.Frontiers in oncology · 2024Article
- Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types.International journal of medical sciences · 2024Review
- Overexpression of miR-425-5p is associated with poor prognosis and tumor progression in non-small cell lung cancer.Cancer biomarkers : section A of Disease markers · 2020Article
- Hypoxia‑induced internalization of connexin 26 and connexin 43 in pulmonary epithelial cells is involved in the occurrence of non‑small cell lung cancer via the P53/MDM2 signaling pathway.International journal of oncology · 2019Article
- Identification of high expression profiles of miR-31-5p and its vital role in lung squamous cell carcinoma: a survey based on qRT-PCR and bioinformatics analysis.Translational cancer research · 2019Article
- Identification of differentially expressed genes in small and non-small cell lung cancer based on meta-analysis of mRNA.Heliyon · 2019Article
- A review of metabolism-associated biomarkers in lung cancer diagnosis and treatment.Metabolomics : Official journal of the Metabolomic Society · 2018Article
- Candidate Biomarkers and Molecular Mechanism Investigation for Glioblastoma Multiforme Utilizing WGCNA.BioMed research international · 2018Article
- A Computational Method of Defining Potential Biomarkers based on Differential Sub-Networks.Scientific reports · 2017Article
- Article
- Predicting Diagnostic Gene Biomarkers for Non-Small-Cell Lung Cancer.BioMed research international · 2016Article
- The Emerging Role of Radiomics in COPD and Lung Cancer.Respiration; international review of thoracic diseasesReview
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5 authors.
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Abstract
Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network. From the network, we obtain gene modules in which genes are highly functionally associated with each other and are either positively or negatively coexpressed in the three conditions. Then, we identify gene modules that not only are differentially expressed between cancer and normal samples, but also show distinctive expression patterns between SCLC and NSCLC. Finally, we select genes inside those modules with discriminating coexpression patterns between the two lung cancer subtypes and predict them as candidate biomarkers that are of diagnostic use.
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