Abstract
Lung cancer accounts for the highest number of cancer-related deaths worldwide. Early diagnosis significantly increases the disease-free survival rate and a large amount of effort has been expended in screening trials and the development of early molecular diagnostics. However, a gold standard diagnostic strategy is not yet available. Here, based on miRNA expression profile in lung cancer and using a novel in silico reverse-transcriptomics approach, followed by analysis of the interactome; we have identified potential transcription factor (TF) markers that would facilitate diagnosis of subtype specific lung cancer. A subset of seven TF markers has been used in a microarray screen and was then validated by blood-based qPCR using stage-II and IV non-small cell lung carcinomas (NSCLC). Our results suggest that overexpression of HMGA1, E2F6, IRF1, and TFDP1 and downregulation or no expression of SUV39H1, RBL1, and HNRPD in blood is suitable for diagnosis of lung adenocarcinoma and squamous cell carcinoma sub-types of NSCLC. Here, E2F6 was, for the first time, found to be upregulated in NSCLC blood samples. The miRNA-TF-miRNA interaction based molecular mechanisms of these seven markers in NSCLC revealed that HMGA1 and TFDP1 play vital roles in lung cancer tumorigenesis. The strategy developed in this work is applicable to any other cancer or disease and can assist in the identification of potential biomarkers.
Highlights
Lung cancer is the leading cause among cancer related deaths worldwide, constituting 17% of new cancer cases and 23% of deaths from cancer
In this study we have developed a novel in silico reverse-transcriptomics strategy followed by interactome analysis to identify the sub-type specific diagnostic transcription factor (TF) markers in lung cancer
The list of common miRNAs involved in lung cancer along with their corresponding GO terms is presented in Additional file 5. miRNAs involved uniquely in either non-small cell lung carcinomas (NSCLC) or small cell lung cancer (SCLC) and their corresponding GO terms were defined
Summary
Lung cancer is the leading cause among cancer related deaths worldwide, constituting 17% of new cancer cases and 23% of deaths from cancer. Lung cancer is mainly divided into two Numerous studies have utilized different “-omics”based approaches to identify molecular signatures in lung cancer with diagnostic or prognostic value while using minimally invasive processes. Some of these are as follows: 34 miRNA signatures [6], expression profiles of 11 miRNAs (miR-106a, miR-15b, miR-27b, miR-142-3p, miR-26b, miR-182, miR-126, let7g, let-7i and miR-30e5p) from serum [7], 7 miRNA signatures [8], overexpression of six snoRNAs [9], and expression of 3 miRs (miR-205, miR-210 and miR-708) in sputum [10]. None of these have progressed sufficiently to provide the necessary specificity and sensitivity required for clinical implementation
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