Dataset Information

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.

SUBMITTER: Otalora-Otalora BA

PROVIDER: S-EPMC6908522 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases.

Otálora-Otálora Beatriz Andrea BA Florez Mauro M López-Kleine Liliana L Canas Arboleda Alejandra A Grajales Urrego Diana Marcela DM Rojas Adriana A

Frontiers in genetics 20191206

<b>Background:</b> 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. <b>Objective:</b> To propose a bioinformatic pipeline for the identification of all deregulated genes and the transcriptional regulators (TFs) that are coexpressed during lun ...[more]

PMID: 31867044

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Project description:The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID's tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan-Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Project description:ObjectiveLung cancer remains the number one cause of cancer-related mortality worldwide, but less known is that lung cancer patients are among the most psychologically disabled of all cancer groups. Patients with stage IV non-small cell lung cancer (NSCLC) were studied to test the hypothesis that trajectories of depression and/or anxiety symptoms after diagnosis would show an adverse relationship with survival, beyond relevant controls.MethodsPatients with stage IV NSCLC (n = 157) were enrolled (ClinicalTrials.gov Identifier: NCT03199651) at diagnosis and completed validated measures for depressive symptoms (Patient Health Questionnaire-9) and anxiety symptoms (Generalized Anxiety Disorder-7). Patients were reassessed every 1 to 2 months through 24 months (16 assessments; 80% average completion rate) and survival monitored. Joint statistical models provided simultaneous modeling of longitudinal (psychological) and time-to-event (survival) processes. Control variables were age, sex, marital status, education, smoking status, cancer type, and treatment received.ResultsDepression and anxiety symptoms significantly decreased with time since diagnosis. The 2-year trajectory of depressive symptoms was significantly associated with cancer survival after adjustment for covariates (hazard ratio = 1.09 per unit increase in the Patient Health Questionnaire-9, 95% confidence interval = 1.03-1.15, p = .002). Anxiety was marginally significant in the unadjusted (p = .053) but not the adjusted (p = .39) model.ConclusionsFor the first time, joint model analyses test the interaction of a longitudinal trajectory of psychological symptoms, assessed from diagnosis to 24 months, and cancer survival. New data show the continuation of depressive and anxiety symptoms through treatment and thereafter. Immunotherapy and targeted therapies have dramatically improved survival for patients with advanced NSCLC; however, novel data suggest their benefit may be constrained by depressive symptoms.

Dataset Information

Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases.

Publications

Joint Transcriptomic Analysis of Lung Cancer and Other Lung Diseases.

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