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Detecting the Molecular System Signatures of Idiopathic Pulmonary Fibrosis through Integrated Genomic Analysis.

ABSTRACT: Idiopathic Pulmonary Fibrosis (IPF) is an incurable progressive fibrotic disease of the lungs. We currently lack a systematic understanding of IPF biology and a systems approach may offer new therapeutic insights. Here, for the first time, a large volume of high throughput genomics data has been unified to derive the most common molecular signatures of IPF. A set of 39 differentially expressed genes (DEGs) was found critical to distinguish IPF. Using high confidence evidences and experimental data, system level networks for IPF were reconstructed, involving 737 DEGs found common across at least two independent studies. This all provided one of the most comprehensive molecular system views for IPF underlining the regulatory and molecular consequences associated. 56 pathways crosstalks were identified which included critical pathways with specified directionality. The associated steps gained and lost due to crosstalk during IPF were also identified. A serially connected system of five crucial genes was found, potentially controlled by nine miRNAs and eight transcription factors exclusively in IPF when compared to NSIP and Sarcoidosis. Findings from this study have been implemented into a comprehensive molecular and systems database on IPF to facilitate devising diagnostic and therapeutic solutions for this deadly disease.

SUBMITTER: Gangwar I

PROVIDER: S-EPMC5431532 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Detecting the Molecular System Signatures of Idiopathic Pulmonary Fibrosis through Integrated Genomic Analysis.

Gangwar Indu I Kumar Sharma Nitesh N Panzade Ganesh G Awasthi Supriya S Agrawal Anurag A Shankar Ravi R

Scientific reports 20170508 1

Idiopathic Pulmonary Fibrosis (IPF) is an incurable progressive fibrotic disease of the lungs. We currently lack a systematic understanding of IPF biology and a systems approach may offer new therapeutic insights. Here, for the first time, a large volume of high throughput genomics data has been unified to derive the most common molecular signatures of IPF. A set of 39 differentially expressed genes (DEGs) was found critical to distinguish IPF. Using high confidence evidences and experimental da ...[more]

PMID: 28484236

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Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is a devastating disease with a high clinical burden. The molecular signatures of IPF were analyzed to distinguish molecular subgroups and identify key driver genes and therapeutic targets.MethodsThirteen datasets of lung tissue transcriptomics including 585 IPF patients and 362 normal controls were obtained from the databases and subjected to filtration of differentially expressed genes (DEGs). A functional enrichment analysis, agglomerative hierarchical clustering, network-based key driver analysis, and diffusion scoring were performed, and the association of enriched pathways and clinical parameters was evaluated.ResultsA total of 2,967 upregulated DEGs was filtered during the comparison of gene expression profiles of lung tissues between IPF patients and healthy controls. The core molecular network of IPF featured p53 signaling pathway and cellular senescence. IPF patients were classified into two molecular subgroups (C1, C2) via unsupervised clustering. C1 was more enriched in the p53 signaling pathway and ciliated cells and presented a worse prognostic score, while C2 was more enriched for cellular senescence, profibrosing pathways, and alveolar epithelial cells. The p53 signaling pathway was closely correlated with a decline in forced vital capacity and carbon monoxide diffusion capacity and with the activation of cellular senescence. CDK1/2, CKDNA1A, CSNK1A1, HDAC1/2, FN1, VCAM1, and ITGA4 were the key regulators as evidence by high diffusion scores in the disease module. Currently available and investigational drugs showed differential diffusion scores in terms of their target molecules.ConclusionsAn integrative molecular analysis of IPF lungs identified two molecular subgroups with distinct pathobiological characteristics and clinical prognostic scores. Inhibition against CDKs or HDACs showed great promise for controlling lung fibrosis. This approach provided molecular insights to support the prediction of clinical outcomes and the selection of therapeutic targets in IPF patients.

Dataset Information

Detecting the Molecular System Signatures of Idiopathic Pulmonary Fibrosis through Integrated Genomic Analysis.

Publications

Detecting the Molecular System Signatures of Idiopathic Pulmonary Fibrosis through Integrated Genomic Analysis.

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