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Incorporating Differential Gene Expression Analysis with Predictive Biomarkers to Identify Novel Therapeutic Drugs
Huaming Wen1,2, Ryan A Gallo3, Xiaosheng Huang4,5
1Department of Ophthalmology, Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen 518020, Guangdong, China.
Purpose:
Based on the differential gene expression analysis for predictive biomarkers with RNA-Sequencing data from Fuchs endothelial corneal dystrophy (FECD) patients, we are aiming to evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify novel pharmacotherapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotype in FECD.
Methods:
A publicly available RNA-seq dataset was used to compare corneal endothelial specimens from controls and patients with FECD. Based on the differential gene expression analysis for predictive biomarkers, we evaluated the efficacy of LINCS perturbagen prediction software to identify novel therapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotypes in FECD.
Results:
The RNA-seq dataset of the corneal endothelial cells from FECD patients revealed the differential gene expression signatures of FECD. Many of the differential expressed genes are related to canonical pathways of the FECD pathogenesis, such as extracellular matrix reorganization and immunological response. The expression levels of genes VSIG2, IL18, and ITGB8 were significantly increased in FECD compared with control. Meanwhile, the expression levels of CNGA3, SMOX, and CERS1 were significantly lower in the FECD than in control. We employed LINCS L1000 Characteristic Direction Signature Search Engine (L1000-CDS2) to investigate pathway-based molecular treatment. L1000-CDS2 predicted that small molecule drugs such as histone deacetylase (HDAC) inhibitors might be a potential candidate to reverse the pathological gene expression signature in FECD.
Conclusions:
Based on differential gene expression signatures, several candidate drugs have been identified to reverse the disease phenotypes in FECD. Gene expression signature with LINCS small molecule prediction software can discover novel preclinical drug candidates for FECD.
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