| Summary: | Objective: Novel 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. Methods: The dataset consists of histologically defined cases of lung adenocarcinoma, squamous cell carcinoma, small-cell lung cancer, carcinoid, metastasis (breast and colon adenocarcinoma) 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. Results: Our results showed that gene expressions faithfully recapitulate immunohistochemical subtype markers, as chromogranin A in carcinoids, cytokeratin 5, p63 in squamous 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 where 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 transendothelial migration and actin cytoskeleton pathways were overexpressed in normal tissue. Conclusion: Reanalysis 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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