ArticleFrontiers in oncology2014
Hidden treasures in "ancient" microarrays: gene-expression portrays biology and potential resistance pathways of major lung cancer subtypes and normal tissue.
Article in Frontiers in oncology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Systematic comparison of ranking aggregation methods for gene lists in experimental results.Bioinformatics (Oxford, England) · 2022Pooled it
- Integrated Analysis of Bulk and Single-Cell Transcriptomics Identifies Prognostic Biomarkers in Breast Cancer Metastasis.Biochemical genetics · 2026Article
- Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.Bioengineering (Basel, Switzerland) · 2023Article
- LINC02126 is a potential diagnostic, prognostic and immunotherapeutic target for lung adenocarcinoma.BMC pulmonary medicine · 2022Article
- Pathway analysis of smoking-induced changes in buccal mucosal gene expression.The Egyptian journal of medical human genetics · 2022Article
- Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning.Journal of clinical medicine · 2020Article
- Analysis of genes associated with prognosis of lung adenocarcinoma based on GEO and TCGA databases.Medicine · 2020Article
- A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications.Briefings in bioinformatics · 2019Article
- A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies.Statistics in medicine · 2018Article
- Gene set selection via LASSO penalized regression (SLPR).Nucleic acids research · 2017Article
- GEN3VA: aggregation and analysis of gene expression signatures from related studies.BMC bioinformatics · 2016Article
- Association between EPHX1 rs1051740 and lung cancer susceptibility: a meta-analysis.International journal of clinical and experimental medicine · 2015Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveNovel statistical methods and increasingly more accurate gene annotations can transform "old" biological data into a renewed source of knowledge with potential clinical relevance. Here, we provide an in silico proof-of-concept by extracting novel information from a high-quality mRNA expression dataset, originally published in 2001, using state-of-the-art bioinformatics approaches.
methodsThe dataset consists of histologically defined cases of lung adenocarcinoma (AD), squamous (SQ) cell carcinoma, small-cell lung cancer, carcinoid, metastasis (breast and colon AD), and normal lung specimens (203 samples in total). A battery of statistical tests was used for identifying differential gene expressions, diagnostic and prognostic genes, enriched gene ontologies, and signaling pathways.
resultsOur results showed that gene expressions faithfully recapitulate immunohistochemical subtype markers, as chromogranin A in carcinoids, cytokeratin 5, p63 in SQ, and TTF1 in non-squamous types. Moreover, biological information with putative clinical relevance was revealed as potentially novel diagnostic genes for each subtype with specificity 93-100% (AUC = 0.93-1.00). Cancer subtypes were characterized by (a) differential expression of treatment target genes as TYMS, HER2, and HER3 and (b) overrepresentation of treatment-related pathways like cell cycle, DNA repair, and ERBB pathways. The vascular smooth muscle contraction, leukocyte trans-endothelial migration, and actin cytoskeleton pathways were overexpressed in normal tissue.
conclusionReanalysis of this public dataset displayed the known biological features of lung cancer subtypes and revealed novel pathways of potentially clinical importance. The findings also support our hypothesis that even old omics data of high quality can be a source of significant biological information when appropriate bioinformatics methods are used.
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