Exploring the Immune Infiltration Landscape and M2 Macrophage-Related Biomarkers of Proliferative Diabetic Retinopathy

Zhishang Meng; Yanzhu Chen; Wenyi Wu; Bin Yan; Yongan Meng; Youling Liang; Xiaoxi Yao; Jing Luo

doi:10.3389/fendo.2022.841813

Exploring the Immune Infiltration Landscape and M2 Macrophage-Related Biomarkers of Proliferative Diabetic Retinopathy

Front Endocrinol (Lausanne). 2022 May 27:13:841813. doi: 10.3389/fendo.2022.841813. eCollection 2022.

Authors

Zhishang Meng¹, Yanzhu Chen², Wenyi Wu³, Bin Yan¹, Yongan Meng¹, Youling Liang¹, Xiaoxi Yao⁴, Jing Luo¹

Affiliations

¹ Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, China.
² Department of Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China.
³ Department of Ophthalmology, Xiangya Hospital, Central South University, Changsha, China.
⁴ Shenzhen College of International Education, Shenzhen, China.

Abstract

Backgrounds: Diabetic retinopathy (DR), especially proliferative diabetic retinopathy (PDR), is the major cause of irreversible blindness in the working-age population. Increasing evidence indicates that immune cells and the inflammatory microenvironment play an important role during PDR development. Herein, we aim to explore the immune landscape of PDR and then identify potential biomarkers correlated with specific infiltrating immune cells.

Methods: We mined and re-analyzed PDR-related datasets from the Gene Expression Omnibus (GEO) database. Using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm, we investigated the infiltration of 22 types of immune cells in all selected samples; analyses of differences and correlations between infiltrating cells were used to reveal the immune landscape of PDR. Thereafter, weighted gene co-expression network analysis (WGCNA) and differential expression analysis were applied to identify the hub genes on M2 macrophages that may affect PDR progression.

Results: Significant differences were found between infiltration levels of immune cells in fibrovascular membranes (FVMs) from PDR and normal retinas. The percentages of follicular helper T cells, M1 macrophages, and M2 macrophages were increased significantly in FVMs. Integrative analysis combining the differential expression and co-expression revealed the M2 macrophage-related hub genes in PDR. Among these, COL5A2, CALD1, COL6A3, CORO1C, and CALU showed increased expression in FVM and may be potential biomarkers for PDR.

Conclusions: Our findings provide novel insights into the immune mechanisms involved in PDR. COL5A2, CALD1, COL6A3, CORO1C, and CALU are M2 macrophage-related biomarkers, further study of these genes could inform novel ideas and basis for the understanding of disease progression and targeted treatment of PDR.

Keywords: M2 macrophage; bioinformatics; biomarkers; immune landscape; proliferative diabetic retinopathy.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomarkers
Diabetes Mellitus*
Diabetic Retinopathy* / genetics
Diabetic Retinopathy* / metabolism
Humans
Macrophages / metabolism
RNA

Substances

Biomarkers
RNA