Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments

Acosta, Juan Pablo; Restrepo, Silvia; Henao, Juan David Velásquez; López‐Kleine, Liliana

doi:10.1089/cmb.2018.0013

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…In order to identify the differentially expressed genes (DEGs), we employed three independent methods: (a) an empirical Bayesian method (eBayes) using the Benjamini–Hochberg procedure with adjusted p value < 0.01 as the significance threshold (R package limma) [ 20 ]; (b) the Significance Analysis of Microarray (SAM) method, with false discovery rate (FDR) < 0.01 as the significance threshold (R package EMA) [ 21 ]; (c) multivariate inferential analysis method, with false discovery rate (FDR) < 0.01 as the significance threshold (R package acde) [ 22 ]. An absolute value of fold change > 1.5 was considered as DEGs.…”

Section: Methodsmentioning

confidence: 99%

Integrative analysis of lung molecular signatures reveals key drivers of idiopathic pulmonary fibrosis

et al. 2021

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Background Idiopathic 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. Methods Thirteen 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. Results A 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. Conclusions An 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.

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Section: Methodsmentioning

confidence: 99%