Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases

Abstract

Background: Epidemiological and clinical evidence points cancer comorbidity with pulmonary chronic disease. The acquisition of some hallmarks of cancer by cells affected with lung pathologies as a cell adaptive mechanism to a shear stress, suggests that could be associated with the establishment of tumoral processes. Objective: To propose a bioinformatic pipeline for the identification of all deregulated genes and the transcriptional regulators (TFs) that are coexpressed during lung cancer establishment, and therefore could be important for the acquisition of the hallmarks of cancer. Methods: Ten microarray datasets (six of lung cancer, four of lung diseases) comparing normal and diseases-related lung tissue were selected to identify hub differentiated expressed genes (DEGs) in common between lung pathologies and lung cancer, along with transcriptional regulators through the utilization of specialized libraries from R language. DAVID bioinformatics tool for gene enrichment analyses was used to identify genes with experimental evidence associated to tumoral processes and signaling pathways. Coexpression networks of DEGs and TFs in lung cancer establishment were created with Coexnet library, and a survival analysis of the main hub genes was made. Results: Two hundred ten DEGs were identified in common between lung cancer and other lung diseases related to the acquisition of tumoral characteristics, which are coexpressed in a lung cancer network with TFs, suggesting that could be related to the establishment of the tumoral pathology in lung. The comparison of the coexpression networks of lung cancer and other lung diseases allowed the identification of common connectivity patterns (CCPs) with DEGs and TFs correlated to important tumoral processes and signaling pathways, that haven´t been studied to experimentally validate their role in the early stages of lung cancer. Some of the TFs identified showed a correlation between its expression levels and the survival of lung cancer patients. Conclusion: Our findings indicate that lung diseases share genes with lung cancer which are coexpressed in lung cancer, and might be able to explain the epidemiological observations that point to direct and inverse comorbid associations between some chronic lung diseases and lung cancer and represent a complex transcriptomic scenario.

Identification of Two Exosomal miRNAs in Circulating Blood of Cancer Patients by Using Integrative Transcriptome and Network Analysis

“…Given the complexity of molecular interactions, focusing only on a single type of biomolecule for the understanding of a complex disease such as cancer creates difficulties in the understanding of biological processes and disease development. Thus, it is vital to apply systems biology and network analysis, including protein–protein interaction networks, metabolites, and transcription factor genes [ 26 , 27 , 28 ]. Recent advances in systems biology and bioinformatics have also increased our understanding of heterogeneous interaction networks in cancer [ 29 ] and have highlighted the importance of miRNAs in cancer.…”

Inflammation, ageing and diseases of the lung: Potential therapeutic strategies from shared biological pathways

“…Patients with non‐small cell lung cancer (NSCLC) are also known to have a high rate of DNA damage due to the limitation of their DNA repair processes (Orlow et al, 2008). Transcriptomic analysis shows that genes involved in DNA replication, cell cycle, mismatch repair and p53 signalling pathway are up‐regulated in lung cancer and other lung diseases suggesting an overlap of DNA‐associated cellular dysfunction across the spectrum of lung disease (Otálora‐Otálora et al, 2019). …”

Potential biomarkers for lung adenocarcinoma identified by integrative transcriptomics analysis

“…The targeted therapies in patients with EGFR mutations and/or ALK translocations improved survival (Patel et al, 2015), suggesting that the genomic biomarkers can serve not only for early diagnosis of the disease, but also for targeted therapies (Li et al, 2019;Villalobos & Wistuba, 2017). Moreover, the identification of the mutations, gene amplifications, deletions, or the presence of the fusion genes, which are accepted as the genetic risk factors associated with lung cancer would lead to developing treatments (Otálora-Otálora et al, 2019). Therefore, the search for novel biomarkers is of great significance and new methods to identify biomarkers with high prognostic performance need to be developed.…”

“…Microarray technology has been extensively used to identify genes with altered expressions during tumorigenesis, and therefore, enhances our understanding of the genetics and the molecular biology of cancer (Cieślik & Chinnaiyan, 2018). The identification of the transcriptionally dysregulated genes and their associated biological processes and signaling pathways allow us to understand the expression patterns related to tumor's biological state and the patients' survival (Otálora-Otálora et al, 2019). Moreover, the integration of transcriptome with biological and regulatory networks provide an opportunity to discover novel prognostic biomarkers and therapeutic strategies (Gov et al, 2017).…”