A novel in silico reverse-transcriptomics-based identification and blood-based validation of a panel of sub-type specific biomarkers in lung cancer

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.

Figure 1

Flow-diagram showing entire strategy that is applied to identify TF biomarkers in Lung cancer based on miRNA profiles.

Figure 2

Cell cycle specific 26 key interacting TFs that are targets of miRNAs involved in common events in lung cancer as well as in NSCLC and SCLC. The network is created as described in the text. As per our hypothesis, this network also represents interactions of cell cycle regulating miRNAs associated with NSCLC, SCLC, and common events of lung cancer. TFs circled in red are shared by both NSCLC and SCLC. Molecules marked in hexagon are unique to common events. Other molecules in the map are shared by NSCLC and common events of lung cancers.

Figure 3

Cell cycle specific 9 key interacting TFs that are targets of miRNAs involved in SCLC. As per our hypothesis, this network represents interaction of cell cycle regulating miRNAs associated with SCLC. For detail, please see the text.

Figure 4

Interactions of TFs (as per our hypothesis miRNAs) associated with NSCLC and SCLC. TFs circled in red are shared by both NSCLC and SCLC. Molecules marked in star are unique to NSCLC. Other molecules in the map are shared by NSCLC and general events of lung cancers.

Figure 5

Blood based qPCR results for selected seven NSCLC specific markers. As compared to the control; HMGA1, TFPD1, E2F6, and IRF1 are upregulated and SUV39H1, RBL1, and HNRPD are downregulated or not expressed in all tested samples.

Figure 6

The correlations of identified seven TF markers and interacting miRNAs. The interactions provide better insights of molecular events and mechanisms during lung cancer tumorigenesis. For detail, please see the text.