Identification and Interaction Analysis of Significant Genes and MicroRNAs in Pterygium

Key Genes of Immunity Associated with Pterygium and Primary Sjögren's Syndrome

Pterygium and primary Sjögren's Syndrome (pSS) share many similarities in clinical symptoms and ocular pathophysiological changes, but their etiology is unclear. To identify the potential genes and pathways related to immunity, two published datasets, GSE2513 containing pterygium information and GSE176510 containing pSS information, were selected from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) of pterygium or pSS patients compared with healthy control conjunctiva, and the common DEGs between them were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted for common DEGs. The protein-protein interaction (PPI) network was constructed using the STRING database to find the hub genes, which were verified in clinical samples. There were 14 co-upregulated DEGs. The GO and KEGG analyses showed that these common DEGs were enriched in pathways correlated with virus infection, antigen processing and presentation, nuclear factor-kappa B (NF-κB) and Th17 cell differentiation. The hub genes (IL1R1, ICAM1, IRAK1, S100A9, and S100A8) were selected by PPI construction. In the era of the COVID-19 epidemic, the relationship between virus infection, vaccination, and the incidence of pSS and pterygium growth deserves more attention.

Evaluation of JUN, FN1 and LAMB1 polymorphisms in pterygium in a Chinese Han population

PURPOSE

To explore the underlying molecular mechanism of pterygium and identify the key genes regulating the development of pterygium.

METHODS

Differentially expressed mRNAs were obtained from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the DAVID (http://david.abcc.ncifcrf.gov/). The differential expressions of hub genes were verified using the reverse transcription-real-time fluorescent quantitative PCR (RT-qPCR). The function of the hub genes was further confirmed based on associations between the single nucleotide polymorphisms (SNPs) in hub genes and pterygium. The genotyping results were analyzed using SNPStats online software in five gene models, including codominant, dominant, recessive, overdominant, and log-additive. Five gene models were analyzed using SNPStats.

RESULTS

We found that 240 genes were significantly differentially expressed. Functional enrichment analysis showed that focal adhesion pathway is extremely meaningful, among which JUN, FN1, and LAMB1 were verified to significantly differentially express in pterygium (P = 0.0011, P = 0.0018, and P = 0.0050, respectively). However, the all nine candidate SNPs (rs11688, rs3748814 in JUN; rs1263, rs1132741, rs1250259 in FN1; rs20556, rs35710474, rs25659, rs4320486 in LAMB1), were not statistically associated with pterygium.

CONCLUSION

Our results demonstrated that JUN, FN1, and LAMB1 polymorphisms were not associated with susceptibility to pterygium in Chinese Han population. Considering the fact that these three genes are differentially expressed in pterygium, further research is needed to explain its involvement in pterygium.

Characterization of the Cellular Microenvironment and Novel Specific Biomarkers in Pterygia Using RNA Sequencing

With a worldwide prevalence of ~12%, pterygium is a common degenerative and environmentally triggered ocular surface disorder characterized by wing-shaped growth of conjunctival tissue onto the cornea that can lead to blindness if left untreated. This study characterizes the transcriptional profile and the cellular microenvironment of conjunctival pterygia and identifies novel pterygia-specific biomarkers. Formalin-fixed and paraffin-embedded pterygia as well as healthy conjunctival specimens were analyzed using MACE RNA sequencing (n = 8 each) and immunohistochemistry (pterygia n = 7, control n = 3). According to the bioinformatic cell type enrichment analysis using xCell, the cellular microenvironment of pterygia was characterized by an enrichment of myofibroblasts, T-lymphocytes and various antigen-presenting cells, including dendritic cells and macrophages. Differentially expressed genes that were increased in pterygia compared to control tissue were mainly involved in autophagy (including DCN, TMBIM6), cellular response to stress (including TPT1, DDX5) as well as fibroblast proliferation and epithelial to mesenchymal transition (including CTNNB1, TGFBR1, and FN1). Immunohistochemical analysis confirmed a significantly increased FN1 stromal immunoreactivity in pterygia when compared to control tissue. In addition, a variety of factors involved in apoptosis were significantly downregulated in pterygia, including LCN2, CTSD, and NISCH. Furthermore, 450 pterygia-specific biomarkers were identified by including transcriptional data of different ocular surface pathologies serving as controls (training group), which were then validated using transcriptional data of cultured human pterygium cells. Among the most pterygia-specific factors were transcripts such as AHNAK, RTN4, TPT1, FSTL1, and SPARC. Immunohistochemical validation of SPARC revealed a significantly increased stromal immunoreactivity in pterygia when compared to controls, most notably in vessels and intravascular vessel wall-adherent mononuclear cells. Taken together, the present study provides new insights into the cellular microenvironment and the transcriptional profile of pterygia, identifies new and specific biomarkers and in addition to fibrosis-related genes, uncovers autophagy, stress response and apoptosis modulation as pterygium-associated processes. These findings expand our understanding of the pathophysiology of pterygia, provide new diagnostic tools, and may enable new targeted therapeutic options for this common and sight-threatening ocular surface disease.

Transcriptomics and network analysis highlight potential pathways in the pathogenesis of pterygium

Pterygium is a common ocular surface condition frequently associated with irritative symptoms. The precise identity of its critical triggers as well as the hierarchical relationship between all the elements involved in the pathogenesis of this disease are not yet elucidated. Meta-analysis of gene expression studies represents a novel strategy capable of identifying key pathogenic mediators and therapeutic targets in complex diseases. Samples from nine patients were collected during surgery after photo documentation and clinical characterization of pterygia. Gene expression experiments were performed using Human Clariom D Assay gene chip. Differential gene expression analysis between active and atrophic pterygia was performed using limma package after adjusting variables by age. In addition, a meta-analysis was performed including recent gene expression studies available at the Gene Expression Omnibus public repository. Two databases including samples from adults with pterygium and controls fulfilled our inclusion criteria. Meta-analysis was performed using the Rank Production algorithm of the RankProd package. Gene set analysis was performed using ClueGO and the transcription factor regulatory network prediction was performed using appropriate bioinformatics tools. Finally, miRNA-mRNA regulatory network was reconstructed using up-regulated genes identified in the gene set analysis from the meta-analysis and their interacting miRNAs from the Brazilian cohort expression data. The meta-analysis identified 154 up-regulated and 58 down-regulated genes. A gene set analysis with the top up-regulated genes evidenced an overrepresentation of pathways associated with remodeling of extracellular matrix. Other pathways represented in the network included formation of cornified envelopes and unsaturated fatty acid metabolic processes. The miRNA-mRNA target prediction network, also reconstructed based on the set of up-regulated genes presented in the gene ontology and biological pathways network, showed that 17 target genes were negatively correlated with their interacting miRNAs from the Brazilian cohort expression data. Once again, the main identified cluster involved extracellular matrix remodeling mechanisms, while the second cluster involved formation of cornified envelope, establishment of skin barrier and unsaturated fatty acid metabolic process. Differential expression comparing active pterygium with atrophic pterygium using data generated from the Brazilian cohort identified differentially expressed genes between the two forms of presentation of this condition. Our results reveal differentially expressed genes not only in pterygium, but also in active pterygium when compared to the atrophic ones. New insights in relation to pterygium's pathophysiology are suggested.

Expression profiling suggests the involvement of hormone-related, metabolic, and Wnt signaling pathways in pterygium progression

BACKGROUND

Pterygium is an ocular surface disease that can cause visual impairment if it progressively invades the cornea. Although many pieces of research showed ultraviolet radiation is a trigger of pterygium pathological progress, the underlying mechanism in pterygium remains indistinct.

METHODS

In this study, we used microarray to evaluate the changes of transcripts between primary pterygium and adjacent normal conjunctiva samples in China. Then, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Moreover, we constructed protein-protein interaction (PPI) and miRNA-mRNA regulatory networks to predict possible regulatory relationships. We next performed gene set enrichment analysis (GSEA) to explore the similarities and differences of transcripts between Asian studies from the Gene Expression Omnibus database. Furthermore, we took the intersection of differentially expressed genes (DEGs) with other data and identified hub genes of the development of pterygium. Finally, we utilized real-time quantitative PCR to verify the expression levels of candidate genes.

RESULTS

A total of 49 DEGs were identified. The enrichment analyses of DEGs showed that pathways such as the Wnt-signaling pathway and metabolism-related pathways were upregulated, while pathways such as hormone-related and transcription factor-associated pathways were downregulated. The PPI and miRNA-mRNA regulatory networks provide ideas for future research directions. The GSEA of selecting Asian data revealed that epithelial-mesenchymal transition and myogenesis existed in the pathology of pterygium in the Asian group. Furthermore, five gene sets (interferon-gamma response, Wnt beta-catenin signaling, oxidative phosphorylation, DNA repair, and MYC targets v2) were found only in our Chinese datasets. After taking an intersection between selecting datasets, we identified two upregulated (SPP1 and MYH11) and five downregulated (ATF3, FOS, EGR1, FOSB, and NR4A2) hub genes. We finally chose night genes to verify their expression levels, including the other two genes (SFRP2 and SFRP4) involved in Wnt signaling; Their expression levels were significantly different between pterygium and conjunctiva.

CONCLUSIONS

We consider hormone-related, metabolic, and Wnt signaling pathways may be important in the pathology of pterygium development. Nine candidate genes we identified deserve further study and can be potential therapeutic targets.

Comparative Transcriptomic Analysis to Identify the Important Coding and Non-coding RNAs Involved in the Pathogenesis of Pterygium

Pterygium is a common ocular surface disease characterized by abnormal fibrovascular proliferation and invasion, similar to tumorigenesis. The formation of tumors is related to a change in the expression of various RNAs; however, whether they are involved in the formation and development of pterygium remains unclear. In this study, transcriptome analysis of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs) of paired pterygium and normal conjunctiva was performed to explore key genes regulating the development of pterygium. In total, 579 mRNAs, 275 lncRNAs, and 21 circRNAs were differentially expressed (DE) in pterygium compared with paired conjunctival tissues. Functional enrichment analysis indicated that DE RNAs were associated with extracellular matrix organization, blood vessel morphogenesis, and focal adhesion. Furthermore, through protein-protein interaction network and mRNA-lncRNA co-expression network analysis, key mRNAs including FN1, VCAM1, and MMP2, and key lncRNAs including MIR4435-2HG and LINC00968 were screened and might be involved in the pathogenesis of pterygium. In addition, several circRNAs including hsa_circ_0007482 and hsa_circ_001730 were considered to be involved in the pterygium development. This study provides a scientific basis for elucidating the pathogenesis of pterygium and will be beneficial for the development of preventive and therapeutic strategies.

Comparing Adjuvant Beta Radiation, Mitomycin C, and Conjunctival Autograft in Primary Pterygium Treatment, a Three-year Follow-up Study

Purpose:

To compare the recurrence rate of primary pterygium surgery after the adjuvant use of Beta radiation, Mitomycin C, and conjunctival autograft.

Methods:

180 eyes of 180 patients were included in the study. All cases had primary pterygium excision following the use of adjuvant therapy of Beta radiation or Mitomycin C (0.02% for 5 minutes) or conjunctival autograft. The study was conducted at Fayoum University Hospital, Fayoum, Egypt, and Misr University Hospital. The patients were randomly divided into three groups, with each group comprising 60 patients. Group (A) included patients treated with Beta radiation following Pterygium excision, group (B) patients had primary pterygium excision with the application of 0.02% Mitomycin C for 5 minutes, and group (C) patients had conjunctival autograft to cover the bare area after pterygium excision. Patients were followed up for three years postoperatively.

Results:

group A had the highest recurrence rate (33.3%) followed by group B (13.3%), and finally group C presented a recurrence rate of 6.7%. Group B showed the highest rate of intra-ocular postoperative complications, while no intra-ocular complications were recorded in group C. Common complications in groups A and B were scleral melting, keratitis, and Dellen formation.

Conclusion:

Using conjunctival autograft after primary pterygium excision gives the best results regarding the rate of recurrence and postoperative complications. Meanwhile, B-radiation or Mitomycin C did not prove to be as good.

Identification of Functional Genes in Pterygium Based on Bioinformatics Analysis

Purpose

The competing endogenous RNA (ceRNA) network regulatory has been investigated in the occurrence and development of many diseases. This research aimed at identifying the key RNAs of ceRNA network in pterygium and exploring the underlying molecular mechanism.

Methods

Differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs were obtained from the Gene Expression Omnibus (GEO) database and analyzed with the R programming language. LncRNA and miRNA expressions were extracted and pooled by the GEO database and compared with those in published literature. The lncRNA-miRNA-mRNA network was constructed of selected lncRNAs, miRNAs, and mRNAs. Metascape was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on mRNAs of the ceRNA network and to perform Protein-Protein Interaction (PPI) Network analysis on the String website to find candidate hub genes. The Comparative Toxicogenomic Database (CTD) was used to find hub genes closely related to pterygium. The differential expressions of hub genes were verified using the reverse transcription-real-time fluorescent quantitative PCR (RT-qPCR).

Result

There were 8 lncRNAs, 12 miRNAs, and 94 mRNAs filtered to construct the primary ceRNA network. A key lncRNA LIN00472 ranking the top 1 node degree was selected to reconstruct the LIN00472 network. The GO and KEGG pathway enrichment showed the mRNAs in ceRNA networks mainly involved in homophilic cell adhesion via plasma membrane adhesion molecules, developmental growth, regulation of neuron projection development, cell maturation, synapse assembly, central nervous system neuron differentiation, and PID FOXM1 PATHWAY. According to the Protein-Protein Interaction Network (PPI) analysis on mRNAs in LINC00472 network, 10 candidate hub genes were identified according to node degree ranking. Using the CTD database, we identified 8 hub genes closely related to pterygium; RT-qPCR verified 6 of them were highly expressed in pterygium.

Conclusion

Our research found LINC00472 might regulate 8 hub miRNAs (miR-29b-3p, miR-183-5p, miR-138-5p, miR-211-5p, miR-221-3p, miR-218-5p, miR-642a-5p, miR-5000-3p) and 6 hub genes (CDH2, MYC, CCNB1, RELN, ERBB4, RB1) in the ceRNA network through mainly PID FOXM1 PATHWAY and play an important role in the development of pterygium.