Gene expression profiles following active HE4 stimulation in epithelial ovarian cancer cells: microarray study and comprehensive bioinformatics analysis

2020 ◽  
Author(s):  
Liancheng Zhu ◽  
Mingzi Tan ◽  
Haoya Xu ◽  
Bei Lin
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Coexpression Analysis ◽  
Pathway Enrichment

Abstract Background: Human epididymis protein 4 (HE4) is a novel serum biomarker for diagnosing epithelial ovarian cancer (EOC) with high specificity and sensitivity, compared with CA125. Recent studies have focused on the roles of HE4 in promoting carcinogenesis and chemoresistance in EOC; however, the molecular mechanisms underlying its action remain poorly understood. This study was conducted to determine the molecular mechanisms underlying HE4 stimulation and identifying key genes and pathways mediating carcinogenesis in EOC by microarray and bioinformatics analysis.Methods: We established a stable HE4-silenced ES-2 ovarian cancer cell line labeled as “S”; the S cells were stimulated with the active HE4 protein, yielding cells labeled as “S4”. Human whole-genome microarray analysis was used to identify differentially expressed genes (DEGs) in S4 and S cells. The “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal was used for WFDC2 coexpression analysis. The GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction were used to validate the results. Protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape, respectively. Results: In total, 713 DEGs were identified (164 upregulated and 549 downregulated) and further analyzed by GO, pathway enrichment, and PPI analyses. We found that the MAPK pathway accounted for a significant large number of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2-coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) whose expression levels were dramatically altered in S4 cells; this was validated using the GSE51088 dataset. Kaplan–Meier survival statistics revealed that all 10 target genes were clinically significant. Finally, in the PPI network, 16 hub genes and 8 molecular complex detections (MCODEs) were identified; the seeds of the five most significant MCODEs were subjected to GO and KEGG enrichment analyses and their clinical relevance was evaluated.Conclusions: Through microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network following active HE4 stimulation in EOC cells. We proposed several possible mechanisms underlying the action of HE4 and identified the therapeutic and prognostic targets of HE4 in EOC.

scholarly journals Gene Expression Profile of Active HE4 Stimulation in Epithelial Ovarian Cancer Cells: Microarray Study and Comprehensive Bioinformatics Analysis

2020 ◽  
Author(s):  
Liancheng Zhu ◽  
Mingzi Tan ◽  
Haoya Xu ◽  
Bei Lin
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Clinical Significance ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Coexpression Analysis ◽  
Pathway Enrichment

Abstract Background.Human Epididymis Protein 4 (HE4) is a novel serum biomarker for diagnosis of epithelial ovarian cancer (EOC) with high specificity and sensitivity compared with CA125, and the increasing researches have been carried out on its roles in promoting carcinogenesis and chemoresistance in EOC in recent years, however, its underlying molecular mechanisms remain poorly understood. The aim of this study was to elucidate the molecular mechanisms of HE4 stimulation and to identify the key genes and pathways mediating carcinogenesis in EOC using microarray and bioinformatics analysis.Methods. We established a stable HE4-silence ES-2 ovarian cancer cell line labeled as “S”, and its active HE4 protein stimulated cells labeled as “S4”. Human whole genome microarray analysis was used to identify deferentially expressed genes (DEGs) from triplicate samples of S4 and S cells. “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis (GSEA) were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal for WFDC2 coexpression analysis. GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction (qRT-PCR) was applied for validation. The protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape. Results.In total, 713 DEGs were found (164 up regulated and 549 down regulated) and further analyzed by GO, pathway enrichment and PPI analyses. We found that MAPK pathway accounted for a significant portion of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2 coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) that were also dramatically changed in S4 cells and validated by dataset GSE51088. Kaplan–Meier survival statistics revealed clinical significance for all of the 10 target genes. Finally, PPI was constructed, sixteen hub genes and eight molecular complex detections (MCODEs) were identified, the seeds of five most significant MCODEs were subjected to GO and KEGG enrichment analysis and their clinical significance was evaluated.Conclusions.By applying microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of active HE4 stimulation in EOC cells. We offered several possible mechanisms and identified therapeutic and prognostic targets of HE4 in EOC.

scholarly journals Screening of Osteoarthritis-Related Genes and Function Determination Based on Bioinformatics Tools

2021 ◽  
Author(s):  
Zheng Fu ◽  
Weiqian Jiang ◽  
Wenlong Yan ◽  
Fei Xie ◽  
Yu Chen ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Experimental Studies ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Pathway Enrichment ◽  
Early Diagnostic

Abstract Background:Osteoarthritis(OA), commonly seen in the middle-aged and elderly population, imposes a heavy burden on patients from the clinical, humanistic and economic aspects. Our work aims at the discovery of early diagnostic and therapeutic targets for OA and new candidate biomarkers for experimental studies on OA via bioinformatics analysis.Methods:The dataset GSE114007 was downloaded from GEO to identify differentially expressed genes(DEGs) in R using 3 different algorithms. Overlapping DEGs were subject to GO and KEGG pathway enrichment analysis and functional annotation. Following the identification of DEGs, a protein-protein interaction(PPI) network was established and imported into Cytoscape to screen for hubgenes. The expression of each hubgene was verified in two other datasets and create miRNA-mRNA regulatory networks.Results:174 upregulated genes and 117 downregulated genes were identified among the overlapping DEGs. According to the results of GO enrichment analysis,MF enrichment was basically found in ECM degradation and collagen breakdown; enrichment was also present in the development, ossification, and differentiation of cells. The KEGG pathway enrichment analysis suggested significant enrichment in such pathways as PI3K-AKT, P53, TNF, and FoxO. 23 hubgenes were obtained from the PPI network, and 11 genes were identified as DEGs through verification. 8 genes were used for the establishment of miRNA-mRNA regulatory networks.Conclusion:OA-related genes, proteins, pathways and miRNAs that were identified through bioinformatics analysis may provide a reference for the discovery of early diagnostic and therapeutic targets for OA, as well as candidate biomarkers for experimental studies on OA.

scholarly journals Bioinformatics analysis of key genes and pathways in Hashimoto thyroiditis tissues

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Long Zheng ◽  
Xiaojie Dou ◽  
Huijia Song ◽  
Pengwei Wang ◽  
Wei Qu ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Interaction Analysis ◽  
Differentially Expressed Gene ◽  
Enrichment Analysis ◽  
Hashimoto Thyroiditis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Abstract Hashimoto thyroiditis (HT) is one of the most common autoimmune diseases, and the incidence of HT continues to increase. Long-term, uncontrollable HT results in thyroid dysfunction and even increases carcinogenesis risks. Since the origin and development of HT involve many complex immune processes, there is no effective therapy for HT on a pathogenesis level. Although bioinformatics analysis has been utilized to seek key genes and pathways of thyroid cancer, only a few bioinformatics studies that focus on HT pathogenesis and mechanisms have been reported. In the present study, the Gene Expression Omnibus dataset (GSE29315) containing 6 HT and 8 thyroid physiological hyperplasia samples was downloaded, and differentially expressed gene (DEG) analysis, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, protein–protein interaction analysis, and gene set enrichment analysis were performed. In total, 85 DEGs, containing 76 up-regulated and 9 down-regulated DEGS, were identified. The DEGs were mainly enriched in immune and inflammatory response, and the signaling pathways were involved in cytokine interaction and cytotoxicity. Moreover, ten hub genes were identified, and IFN-γ, IFN-α, IL6/JAK/STAT3, and inflammatory pathways may promote the origin and progression of HT. The present study indicated that exploring DEGs and pathways by bioinformatics analysis has important significance in understanding the molecular mechanisms of HT and providing potential targets for the prevention and treatment of HT.

scholarly journals Bioinformatics analysis and identification of genes and molecular pathways in steroid-induced osteonecrosis of the femoral head

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Tianye Lin ◽  
Weijian Chen ◽  
Peng Yang ◽  
Ziqi Li ◽  
Qiushi Wei ◽  
...  
Keyword(s):  
Femoral Head ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Peripheral Blood ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Pathway Enrichment ◽  
Qrt Pcr

Abstract Background Steroid-induced osteonecrosis of the femoral head (ONFH) is a common hip joint disease and is difficult to be diagnosed early. At present, the pathogenesis of steroid-induced ONFH remains unclear, and recognized and effective diagnostic biomarkers are deficient. The present study aimed to identify potentially important genes and signaling pathways involved in steroid-induced ONFH and investigate their molecular mechanisms. Methods Microarray data sets GSE123568 (peripheral blood) and GSE74089 (cartilage) were obtained from the Gene Expression Omnibus database, including 34 ONFH samples and 14 control samples. Morpheus software and Venn diagram were used to identify DEGs and co-expressed DEGs, respectively. Besides, we conducted Kyoto Encyclopedia of Genome (KEGG) and gene ontology (GO) pathway enrichment analysis. We construct a protein-protein interaction (PPI) network through GEO2R and used cytoHubba to divide the PPI network into multiple sub-networks. Additionally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed to verify the bioinformatics analysis results. Results A total of 118 intersecting DEGs were obtained between the peripheral blood and cartilage samples, including 40 upregulated genes and 78 downregulated genes. Then, GO and KEGG pathway enrichment analysis revealed that upregulated DEGs focused on the signaling pathways related to staphylococcus aureus infection, leishmaniasis, antigen processing, and presentation, as well as asthma and graft-versus-host disease. Downregulated genes were concentrated in the FoxO signaling pathway, AMPK signaling pathway, signaling pathway regulating stem cell pluripotency, and mTOR signaling pathway. Some hub genes with high interactions such as CXCR1, FPR1, MAPK1, FOXO3, FPR2, CXCR2, and TYROBP were identified in the PPI network. The results of qRT-PCR demonstrated that CXCR1, FPR1, and TYROBP were upregulated while MAPK1 was downregulated in peripheral blood of steroid-induced ONFH patients. This was consistent with the bioinformatics analysis. Conclusions The present study would provide novel insight into the genes and associated pathways involved in steroid-induced ONFH. CXCR1, FPR1, TYROBP, and MAPK1 may be used as potential drug targets and biomarkers for the diagnosis and prognosis of steroid-induced ONFH.

scholarly journals Identification of Key Deregulated RNA-Binding Proteins in Pancreatic Cancer by Meta-Analysis and Prediction of Their Role as Modulators of Oncogenesis

2021 ◽  
Vol 9 ◽  
Author(s):  
Moumita Mukherjee ◽  
Srikanta Goswami
Keyword(s):  
Pancreatic Cancer ◽  
Cell Migration ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Pathway Enrichment

RNA-binding proteins (RBPs) play a significant role in multiple cellular processes with their deregulations strongly associated with cancer. However, there are not adequate evidences regarding global alteration and functions of RBPs in pancreatic cancer, interrogated in a systematic manner. In this study, we have prepared an exhaustive list of RBPs from multiple sources, downloaded gene expression microarray data from a total of 241 pancreatic tumors and 124 normal pancreatic tissues, performed a meta-analysis, and obtained differentially expressed RBPs (DE-RBPs) using the Limma package of R Bioconductor. The results were validated in microarray datasets and the Cancer Genome Atlas (TCGA) RNA sequencing dataset for pancreatic adenocarcinoma (PAAD). Pathway enrichment analysis was performed using DE-RBPs, and we also constructed the protein–protein interaction (PPI) network to detect key modules and hub-RBPs. Coding and noncoding targets for top altered and hub RBPs were identified, and altered pathways modulated by these targets were also investigated. Our meta-analysis identified 45 upregulated and 15 downregulated RBPs as differentially expressed in pancreatic cancer, and pathway enrichment analysis demonstrated their important contribution in tumor development. As a result of PPI network analysis, 26 hub RBPs were detected and coding and noncoding targets for all these RBPs were categorized. Functional exploration characterized the pathways related to epithelial-to-mesenchymal transition (EMT), cell migration, and metastasis to emerge as major pathways interfered by the targets of these RBPs. Our study identified a unique meta-signature of 26 hub-RBPs to primarily modulate pancreatic tumor cell migration and metastasis in pancreatic cancer. IGF2BP3, ISG20, NIP7, PRDX1, RCC2, RUVBL1, SNRPD1, PAIP2B, and SIDT2 were found to play the most prominent role in the regulation of EMT in the process. The findings not only contribute to understand the biology of RBPs in pancreatic cancer but also to evaluate their candidature as possible therapeutic targets.

System Pharmacological Mechanism and Compatibility Laws of Jianghuang from Traditional Chinese Medicine In Blood-Regulating Formulae

2020 ◽  
Author(s):  
Zhiqiang Liu ◽  
Bolong Wang
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Kegg Pathway ◽  
Inflammatory Diseases ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Pharmacological Effects ◽  
Ppi Network ◽  
Pathway Enrichment ◽  
Pharmacological Mechanism

Abstract Background: Jianghuang (JH) is a popular ingredient in blood-regulating traditional Chinese Medicine (TCM) that could be effective for the treatment of various diseases. We demonstrate the compatibility laws and system pharmacological mechanisms of the key formula containing JH by leveraging data mining of bioinformatics databases.Material/Methods: The compatibility laws of blood-regulating formulae containing JH from the Chinese Traditional Medicine Formula Dictionary were analyzed using a generalized rule induction (GRI) algorithm implemented. The putative target gene and miRNA were retrieved via a combination of the Arrowsmith knowledge discovery tool and FunRich 3.1.3. System pharmacological mechanisms are traced by their protein-protein interaction (PPI) network, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted using Uniprot, the Human Protein Atlas (HPA), STRING 11.0, and KOBAS 3.0.Results: We found that the JH-CX-DG formula (Ligusticum chuanxiong-Angelica sinensis) could represent a key formula containing JH in blood-regulating TCM formulae. The JH-CX-DG formula was observed to directly target AKT, TLR4, caspase-3, PI3K, mTOR, p38 MAPK, VEGF, iNOS, Nrf2, BDNF, NF-κB, Bcl-2, and Bax 13 targets and regulate targets through 13 miRNA. The PPI network and KEGG pathway enrichment analysis showed that the JH-CX-DG formula possess potential pharmacological effects including anti-inflammatory, improving microcirculation, and anti-tumor through the regulation of multiple pathways including PI3K/Akt, MAPK, Toll-like receptor, T cell receptor, EGFR, VEGFR, Apoptosis, HIF-1 (p < 0.05).Conclusion: The JH-CX-DG formula can exert beneficial pharmacological effects through multi-target and multi-pathway interactions. It can be effectively administered for the treatment of inflammatory diseases, microcirculation disorders, cardiovascular disease, and cancer. We found a new effective drug formula through analyzing the compatibility law and systemic pharmacological mechanism of JH. Our study provides a theoretical basis and directions for subsequent research on the JH-CX-DG formula.

scholarly journals The G Protein-Coupled Estrogen Receptor (GPER) Expression Correlates with Pro-Metastatic Pathways in ER-Negative Breast Cancer: A Bioinformatics Analysis

Cells ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 622 ◽  
Author(s):  
Marianna Talia ◽  
Ernestina De Francesco ◽  
Damiano Rigiracciolo ◽  
Maria Muoio ◽  
Lucia Muglia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Signaling Pathways ◽  
G Protein ◽  
Bioinformatics Analysis ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
G Protein Coupled

The G protein-coupled estrogen receptor (GPER, formerly known as GPR30) is a seven-transmembrane receptor that mediates estrogen signals in both normal and malignant cells. In particular, GPER has been involved in the activation of diverse signaling pathways toward transcriptional and biological responses that characterize the progression of breast cancer (BC). In this context, a correlation between GPER expression and worse clinical-pathological features of BC has been suggested, although controversial data have also been reported. In order to better assess the biological significance of GPER in the aggressive estrogen receptor (ER)-negative BC, we performed a bioinformatics analysis using the information provided by The Invasive Breast Cancer Cohort of The Cancer Genome Atlas (TCGA) project and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets. Gene expression correlation and the statistical analysis were carried out with R studio base functions and the tidyverse package. Pathway enrichment analysis was evaluated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway on the Database for Annotation, Visualization and Integrated Discovery (DAVID) website, whereas gene set enrichment analysis (GSEA) was performed with the R package phenoTest. The survival analysis was determined with the R package survivALL. Analyzing the expression data of more than 2500 primary BC, we ascertained that GPER levels are associated with pro-migratory and metastatic genes belonging to cell adhesion molecules (CAMs), extracellular matrix (ECM)-receptor interaction, and focal adhesion (FA) signaling pathways. Thereafter, evaluating the disease-free interval (DFI) in ER-negative BC patients, we found that the subjects expressing high GPER levels exhibited a shorter DFI in respect to those exhibiting low GPER levels. Overall, our results may pave the way to further dissect the network triggered by GPER in the breast malignancies lacking ER toward a better assessment of its prognostic significance and the action elicited in mediating the aggressive features of the aforementioned BC subtype.

scholarly journals Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Go Enrichment ◽  
Jakob Disease

AbstractSporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. Pathway and GO enrichment analyses of DEGs were performed. Furthermore, the protein-protein interaction (PPI) network was predicted using the IntAct Molecular Interaction Database and visualized with Cytoscape software. In addition, hub genes and important modules were selected based on the network. Finally, we constructed target genes - miRNA regulatory network and target genes - TF regulatory network. Hub genes were validated. A total of 891 DEGs 448 of these DEGs presented significant up regulated, and the remaining 443 down regulated were obtained. Pathway enrichment analysis indicated that up regulated genes were mainly linked with glutamine degradation/glutamate biosynthesis, while the down regulated genes were involved in melatonin degradation. GO enrichment analyses indicated that up regulated genes were mainly linked with chemical synaptic transmission, while the down regulated genes were involved in regulation of immune system process. hub and target genes were selected from the PPI network, modules, and target genes - miRNA regulatory network and target genes - TF regulatory network namely YWHAZ, GABARAPL1, EZR, CEBPA, HSPB8, TUBB2A and CDK14. The current study sheds light on the molecular mechanisms of sCJD and may provide molecular targets and diagnostic biomarkers for sCJD.

scholarly journals UBE2T Contributes to the Prognosis of Esophageal Squamous Cell Carcinoma

2021 ◽  
Vol 27 ◽  
Author(s):  
Xiaoyuan Wang ◽  
Yang Liu ◽  
Xue Leng ◽  
Kui Cao ◽  
Wentao Sun ◽  
...  
Keyword(s):  
High Risk ◽  
Squamous Cell ◽  
Kegg Pathway ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Gene Signatures ◽  
Pathway Enrichment

Background: The ubiquitin-conjugating enzyme E2 T (UBE2T) has been shown to contribute to several types of cancer. However, no publication has reported its implication in esophageal squamous cell cancer (ESCC).Methods: We explored several public databases, including The Cancer Genome Atlas (TCGA), Oncomine, and gene expression Omnibus (GEO). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and gene set enrichment analysis (GSEA) were adopted to explore involved signaling pathways. We used R software to develop prognostic gene signatures with the LASSO and stepwise Cox regression analysis, separately. Immunohistochemistry staining was performed to detect UBE2T in 90 ESCC patients, followed by survival analysis. We also used an R package pRRophetic to evaluate chemotherapy sensitivity for the TCGA–ESCC cohort.Results: We found significantly increased UBE2T transcript levels and DNA copy numbers in ESCC tissues. UBE2T was associated with the p53 signaling pathway, cell cycle, Fanconi anemia pathway, and DNA replication, as indicated by Go, KEGG pathway enrichment analysis. These pathways were also upregulated in ESCC. The prognostic signatures with UBE2T-associated genes could stratify ESCC patients into low- and high-risk groups with significantly different overall survival in the TCGA–ESCC cohort. We also validated the association of UBE2T with unfavorable survival in 90 ESCC patients recruited for this study. Moreover, we found that the low-risk group was significantly more sensitive to chemotherapy than the high-risk group.Conclusions: UBE2T is involved in the development of ESCC, and gene signatures derived from UBE2T-associated genes are predictive of prognosis in ESCC.

scholarly journals Quantitative Phosphoproteomic Comparison of Lens Proteins in Highly Myopic Cataract and Age-Related Cataract

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Shaohua Zhang ◽  
Keke Zhang ◽  
Wenwen He ◽  
Yi Lu ◽  
Xiangjia Zhu
Keyword(s):  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Phosphoglycerate Kinase ◽  
Enrichment Analysis ◽  
Glutathione Metabolism ◽  
Pathway Enrichment Analysis ◽  
Phosphorylation Sites ◽  
Pathway Enrichment ◽  
Age Related ◽  
Go Enrichment

Purpose. To investigate and compare the lens phosphoproteomes in patients with highly myopic cataract (HMC) or age-related cataract (ARC). Methods. In this study, we undertook a comparative phosphoproteome analysis of the lenses from patients with HMC or ARC. Intact lenses from ARC and HMC patients were separated into the cortex and nucleus. After protein digestion, the phosphopeptides were quantitatively analyzed with TiO2 enrichment and liquid chromatography-mass spectrometry. The potential functions of different phosphopeptides were assessed by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Results. In total, 522 phosphorylation sites in 164 phosphoproteins were identified. The number of phosphorylation sites was significantly higher in the cortex than in the nucleus, in both ARC and HMC lenses. The differentially phosphorylated peptides in the lens cortex and nucleus in HMC eyes were significantly involved in the glutathione metabolism pathway. The KEGG pathway enrichment analysis indicated that the differences in phosphosignaling mediators between the ARC and HMC lenses were associated with glycolysis and the level of phosphorylated phosphoglycerate kinase 1 was lower in HMC lenses than in ARC lenses. Conclusions. We provide an overview of the differential phosphoproteomes of HMC and ARC lenses that can be used to clarify the molecular mechanisms underlying their different phenotypes.

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