Identification of proliferative diabetic retinopathy-associated genes on the protein–protein interaction network by using heat diffusion algorithm

Zhang, Jian; Zhang, Meihua; Zhao, Huijie; Xu, Xun

doi:10.1016/j.bbadis.2020.165794

Cited by 10 publications

(6 citation statements)

References 64 publications

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“…CSF3, COL18A1, CXCR2, CCR1, FGF23, CXCL11, and IL13 were previously reported as related to PDR pathogenesis based on a Laplacian heat diffusion algorithm [20]. Therapeutic strategies targeting MIP1γ to inhibit CCR1-related signaling in retinal endothelial cells might have potential against DR progression [21].…”

Section: Discussionmentioning

confidence: 99%

Identification of immune associated potential molecular targets in proliferative diabetic retinopathy

et al. 2023

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Background Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes and causes of blindness in developed countries. Our study was designed to identify immune-related genes involved in the progression of proliferative diabetic retinopathy (PDR). Methods The “GSE102485” dataset of neovascular membrane samples (NVMs) from type 1 and 2 diabetes mellitus patients was downloaded from the Gene Expression Omnibus database. Functional enrichment analyses, protein–protein interaction network (PPI) construction, and module analysis of immune pathways in NVMs and controls were conducted via Gene Set Enrichment Analysis and Metascape. Results The significantly upregulated hallmark gene sets in DR2 and DR1 groups were involved in five immune pathways. Only CCR4, CXCR6, C3AR1, LPAR1, C5AR1, and P2RY14 were not previously reported in the context of PDR molecular pathophysiology. Except for P2RY14, all of the above were upregulated in retinal samples from experimental diabetes mouse models and human retina microvascular endothelial cells (HRMECs) treated with high glucose (HG) by quantitative Real Time Polymerase Chain Reaction (qRT-PCR). Conclusion The genes identified herein provide insight into immune-related differential gene expression during DR progression.

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Section: Discussionmentioning

confidence: 99%

Identification of immune associated potential molecular targets in proliferative diabetic retinopathy

et al. 2023

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“…So far, FHRs haven't been reported for vitreous, aqueous humor, or tears, but this could be of major interest. Lastly, the role of the complement system has been implicated in various other ocular diseases namely autoimmune uveitis, glaucoma, and diabetes retinopathy and myopia, but the involvement of the FH protein family has thus far not been investigated (Bansal and Gupta, 2020;Clark and Bishop, 2018;Fiedorowicz et al, 2021;García-Gen et al, 2021;Gui et al, 2020;Jha et al, 2007;Rajagopal et al, 2021;Sundstrom et al, 2018;Torok et al, 2013;Youngblood et al, 2019;Zhang et al, 2020).…”

Section: Eye Diseasementioning

confidence: 99%

Evaluating the clinical utility of measuring levels of factor H and the related proteins

Banerjee¹,

Veuskens

Jorge

et al. 2022

Molecular Immunology

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“…Additionally, a number of computational studies have employed network algorithms such as random walk with restart, shortest path and Laplacian heat diffusion to evaluated PPI networks in the STRING database to infer genes associated with the pathogenesis of proliferative diabetic retinopathy (DR; J. Zhang et al, 2020). From the omics perspective, high-throughput approaches (e.g., ligandomics) have been used in the identification of novel and disease-specific drug targets that control angiogenesis and vascular leakage in ocular diseases (LeBlanc et al, 2015;Rong et al, 2019).…”

Section: Omics Data-driven Systems Biology Studies Of Angiogenesis In...mentioning

confidence: 99%

“…A systematic analysis based on protein–protein interaction (PPI) network of angiogenesis‐related proteins suggested that the therapeutic regulation of multiple targets instead of a single factor like VEGF might be more effective in the treatment of pathological ocular neovascularization (Cabral et al, 2017). Additionally, a number of computational studies have employed network algorithms such as random walk with restart, shortest path and Laplacian heat diffusion to evaluated PPI networks in the STRING database to infer genes associated with the pathogenesis of proliferative diabetic retinopathy (DR; J. Zhang et al, 2020). From the omics perspective, high‐throughput approaches (e.g., ligandomics) have been used in the identification of novel and disease‐specific drug targets that control angiogenesis and vascular leakage in ocular diseases (LeBlanc et al, 2015; Rong et al, 2019).…”

Section: Omics Data‐driven Systems Biology Studies Related To Angioge...mentioning

confidence: 99%

Systems biology of angiogenesis signaling: Computational models and omics

Zhang

Wang

Oliveira

et al. 2021

WIREs Mechanisms of Disease

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Angiogenesis is a highly regulated multiscale process that involves a plethora of cells, their cellular signal transduction, activation, proliferation, differentiation, as well as their intercellular communication. The coordinated execution and integration of such complex signaling programs is critical for physiological angiogenesis to take place in normal growth, development, exercise, and wound healing, while its dysregulation is critically linked to many major human diseases such as cancer, cardiovascular diseases, and ocular disorders; it is also crucial in regenerative medicine. Although huge efforts have been devoted to drug development for these diseases by investigation of angiogenesis‐targeted therapies, only a few therapeutics and targets have proved effective in humans due to the innate multiscale complexity and nonlinearity in the process of angiogenic signaling. As a promising approach that can help better address this challenge, systems biology modeling allows the integration of knowledge across studies and scales and provides a powerful means to mechanistically elucidate and connect the individual molecular and cellular signaling components that function in concert to regulate angiogenesis. In this review, we summarize and discuss how systems biology modeling studies, at the pathway‐, cell‐, tissue‐, and whole body‐levels, have advanced our understanding of signaling in angiogenesis and thereby delivered new translational insights for human diseases. This article is categorized under: Cardiovascular Diseases > Computational Models Cancer > Computational Models

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Identification of proliferative diabetic retinopathy-associated genes on the protein–protein interaction network by using heat diffusion algorithm

Cited by 10 publications

References 64 publications

Identification of immune associated potential molecular targets in proliferative diabetic retinopathy

Identification of immune associated potential molecular targets in proliferative diabetic retinopathy

Evaluating the clinical utility of measuring levels of factor H and the related proteins

Systems biology of angiogenesis signaling: Computational models and omics

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