scholarly journals Bioinformatic Analysis Combined With Experimental Validation Reveals Novel Hub Genes and Pathways Associated With Focal Segmental Glomerulosclerosis

2022 ◽  
Vol 8 ◽  
Author(s):  
Yan-Pei Hou ◽  
Tian-Tian Diao ◽  
Zhi-Hui Xu ◽  
Xin-Yue Mao ◽  
Chang Wang ◽  
...  
Keyword(s):  
Focal Segmental Glomerulosclerosis ◽  
Experimental Validation ◽  
Therapeutic Targets ◽  
Bioinformatic Analysis ◽  
Pcr Analysis ◽  
Diagnostic Efficiency ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Qrt Pcr ◽  
Segmental Glomerulosclerosis

Background: Focal segmental glomerulosclerosis (FSGS) is a type of nephrotic syndrome leading to end-stage renal disease, and this study aimed to explore the hub genes and pathways associated with FSGS to identify potential diagnostic and therapeutic targets.Methods: We downloaded the microarray datasets GSE121233 and GSE129973 from the Gene Expression Omnibus (GEO) database. The datasets comprise 25 FSGS samples and 25 normal samples. The differential expression genes (DEGs) were identified using the R package “limma”. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the database for Annotation, Visualization and Integrated Discovery (DAVID) to identify the pathways and functional annotation of the DEGs. The protein–protein interaction (PPI) was constructed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. The hub genes of the DEGs were then evaluated using the cytoHubba plugin of Cytoscape. The expression of the hub genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) using the FSGS rat model, and receiver operating characteristic (ROC) curve analysis was performed to validate the accuracy of these hub genes.Results: A total of 45 DEGs including 18 upregulated and 27 downregulated DEGs, were identified in the two GSE datasets (GSE121233 and GSE129973). Among them, five hub genes with a high degree of connectivity were selected. From the PPI network, of the top five hub genes, FN1 was upregulated, while ALB, EGF, TTR, and KNG1 were downregulated. The qRT-PCR analysis of FSGS rats confirmed that the expression of FN1 was upregulated and that of EGF and TTR was downregulated. The ROC analysis indicated that FN1, EGF, and TTR showed considerable diagnostic efficiency for FSGS.Conclusion: Three novel FSGS-specific genes were identified through bioinformatic analysis combined with experimental validation, which may promote our understanding of the molecular underpinning of FSGS and provide potential therapeutic targets for the clinical management.

scholarly journals Bioinformatics Analysis Reveals Biomarkers With Prognostic Benefits in Diffuse Type Gastric Cancer

2020 ◽  
Author(s):  
Sheng Li ◽  
Chao Yu ◽  
Yuanguang Cheng ◽  
Fangchao Du ◽  
Gang Wen
Keyword(s):  
Gastric Cancer ◽  
Pcr Analysis ◽  
Diffuse Type ◽  
Hub Genes ◽  
Screening Criteria ◽  
Clinical Prognosis ◽  
Qrt Pcr ◽  
Ppi Networks ◽  
Immunohistochemistry Analysis ◽  
Kaplan Meier

Abstract BackgroundGastric cancer (GC) is one of the most common malignancies in digestive system, among which the differentiation of diffuse type GC is relatively poor, the probability of distant metastasis and lymph node metastasis is relatively high, and the clinical prognosis is relatively poor. The purpose of this study is to explore potential signaling pathways and key biomarkers that drive the development of diffuse type GC. Methods Using the “limma” package in R to screen Differentially expressed genes. Screening hub genes by PPI analysis. Immunohistochemistry analysis and qRT-PCR analysis was carried out to detect genes expression. Using Kaplan-Meier Plotter database analyzed the prognostic roles of hub genes.ResultsA total of 355 DEGs consisting of 293 diffuse type DEGs and 62 intestinal type DEGs were selected according to screening criteria, 3 hub genes were chosen from diffuse type DEGs according to the degree of connectivity by using protein-protein interaction (PPI) networks and Cytoscape software including AGT, CXCL12 and ADRB2. Immunohistochemistry analysis and qRT-PCR results showed that the expression of three genes was related to the different GC lauren types. The Kaplan Meier analysis showed that the expression values of these three genes were related to prognosis of diffuse type GC. ConclusionsAGT, CXCL12 and ADRB2 might contribute to the progression of diffuse type GC, which could have potential as biomarkers or therapeutic targets for diffuse type GC.

scholarly journals Circulating exosomal lncRNAs in patients with chronic coronary syndromes

2021 ◽  
Author(s):  
Meili Zheng ◽  
Ruijuan Han ◽  
Wen Yuan ◽  
Hongjie Chi ◽  
Yeping Zhang ◽  
...  
Keyword(s):  
Operating Characteristic ◽  
Case Control Study ◽  
Diagnostic Value ◽  
Pcr Analysis ◽  
Roc Curve Analysis ◽  
Coronary Syndrome ◽  
Qrt Pcr ◽  
Coronary Syndromes ◽  
European Society Of Cardiology ◽  
Control Study

IntroductionThe concept of chronic coronary syndrome (CCS) was first presented at the European Society of Cardiology Meeting in 2019. However, the roles of exosomal lncRNAs in CCS remain largely unclear.Material and methodsA case-control study was performed with a total of 218 participants (137 males and 81 females), including 15 CCS patients and 15 controls for sequencing profiles, 20 CCS patients and 20 controls for the first validation, and 100 CCS patients and 48 controls for the second validation. Exosomes were isolated from the plasma of CCS patients and controls, and exosomal lncRNAs were identified by sequencing profiles and verified twice by qRT-PCR analysis. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of exosomal lncRNAs for CCS patients.ResultsA total of 152 significantly differentially expressed lncRNAs with over two-fold changes were detected in plasma exosomes of CCS patients, including 90 upregulated and 62 downregulated lncRNAs. Importantly, 6 upregulated lncRNAs with the top fold changes were selected for validations. Exosomal lncRNAs ENST00000424615.2 and ENST00000560769.1 were significantly elevated in CCS patients in both validations compared with controls. The areas under the ROC of lncRNAs ENST00000424615.2 and ENST00000560769.1 were 0.654 and 0.722, respectively. Additionally, exosomal lncRNA ENST00000560769.1 was significantly higher in the CCS patients with more diseased vessels (p = 0.028).ConclusionsExosomal lncRNA ENST00000424615.2 and ENST00000560769.1 were identified as novel diagnosis biomarkers for patients with CCS. Moreover, exosomal lncRNA ENST00000560769.1 was significantly higher in the CCS patients with more diseased vessels, and might be associated with a poor prognosis.

scholarly journals Identification and verification of HCAR3 and INSL5 as new potential therapeutic targets of colorectal cancer

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xuan Yang ◽  
Wangao Wei ◽  
Shisheng Tan ◽  
Linrui Guo ◽  
Song Qiao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Tumor Tissue ◽  
Therapeutic Targets ◽  
The Cancer Genome Atlas ◽  
Pcr Analysis ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Real Time Quantitative Pcr

Abstract Background Colorectal cancer (CRC) is one of the most common cancers of the gastrointestinal tract and ranks third in cancer-related deaths worldwide. This study was conducted to identify novel biomarkers related to the pathogenesis of CRC based upon a bioinformatics analysis, and further verify the biomarkers in clinical tumor samples and CRC cell lines. Methods A series of bioinformatics analyses were performed using datasets from NCBI-GEO and constructed a protein–protein interaction (PPI) network. This analysis enabled the identification of Hub genes, for which the mRNA expression and overall survival of CRC patients data distribution was explored in The Cancer Genome Atlas (TCGA) colon cancer and rectal cancer (COADREAD) database. Furthermore, the differential expression of HCAR3 and INLS5 was validated in clinical tumor samples by Real-time quantitative PCR analysis, western blotting analysis, and immunohistochemistry analysis. Finally, CRC cells over-expressing INSL5 were constructed and used for CCK8, cell cycle, and cell apoptosis validation assays in vitro. Results A total of 286 differentially expressed genes (DEGs) were screened, including 64 genes with increased expression and 143 genes with decreased expression in 2 CRC database, from which 10 key genes were identified: CXCL1, HCAR3, CXCL6, CXCL8, CXCL2, CXCL5, PPY, SST, INSL5, and NPY1R. Among these genes, HCAR3 and INSL5 had not previously been explored and were further verified in vitro. Conclusions HCAR3 expression was higher in CRC tissues and associated with better overall survival of CRC patients. INSL5 expression in normal tissue was higher than that in tumor tissue and its high expression was associated with a better prognosis for CRC. The overexpression of INSL5 significantly inhibited the proliferation and promoted the shearing of PARP of CRC cells. This integrated bioinformatics study presented 10 key hub genes associated with CRC. HCAR3 and INSL5 were expressed in tumor tissue and these were associated with poor survival and warrant further studies as potential therapeutic targets.

scholarly journals Identification Candidate Diagnostic Biomarkers Between Minimal Change Disease and Focal Segmental Glomerulosclerosis by Bioinformatics Analysis

2021 ◽  
Author(s):  
Yayu Li ◽  
Yuanyuan Du ◽  
Xue Jiang
Keyword(s):  
Differential Diagnosis ◽  
Differential Expression ◽  
Focal Segmental Glomerulosclerosis ◽  
Minimal Change Disease ◽  
Bioinformatics Analysis ◽  
Minimal Change ◽  
Hub Genes ◽  
U6 Snrna ◽  
Segmental Glomerulosclerosis ◽  
Healthy Control

Abstract Background: Minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) are common causes of nephrotic syndrome which have similar clinical as well as histologic magnification and hard to differentiate. This study aimed to identify novel biomarkers to distinguish FSGS and MCD through bioinformatics analysis and elucidate the possible molecular mechanism. Material and Methods: Based on the microarray datasets GSE104948 and GSE108113 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between FSGS vs healthy control, MCD vs healthy control were identified, and further defined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Hub genes were checked by protein-protein interaction networks. Results: A total of and 358 and 368 genes were identified in FSGS and MCD compared with healthy controls, among them, there were156 overlapping DEGs. GO analysis showed the DEGs in these two diseases were simultaneously enriched in mRNA splicing, RNA polymerase II transcription, mRNA export, insulin stimulus, integrin-mediated signaling pathway, viral process and phagocytosis. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with Spliceosome among FSGS and MCD. The top 10 hub genes analysis discovered that most of hub genes were same between two disease, while among these genes, CD2 cytoplasmic tail binding protein 2 (CD2BP2), U6 snRNA-associated Sm-like protein (LSM8) and Small nuclear ribonucleoprotein polypeptides B (SNRPB) only differential expression in FSGS and Splicing factor 3A, subunit 3 (SF3A3) only differential expression in MCD, which may be used for differential diagnosis of these two diseases in the future. Conclusions: We identified key genes and mainly pathway associated with FSGS and MCD. Our results provide a set of potential genes used for differential diagnosis of these two diseases.

scholarly journals Identification of critical pathways and potential therapeutic targets in poorly differentiated duodenal papilla adenocarcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yuanxiang Lu ◽  
Wensen Li ◽  
Ge Liu ◽  
Yongbo Yang ◽  
Erwei Xiao ◽  
...  
Keyword(s):  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Therapeutic Targets ◽  
Differentially Expressed ◽  
Clinical Samples ◽  
Rna Seq ◽  
Duodenal Papilla ◽  
Ppi Network ◽  
Hub Genes ◽  
Qrt Pcr

Abstract Background Duodenal papilla carcinoma (DPC) is a rare malignancy of the gastrointestinal tract with high recurrence rate, and the pathogenesis of this highly malignant neoplasm is yet to be fully elucidated. This study aims to identify key genes to further understand the biology and pathogenesis underlying the molecular alterations driving DPC, which could be potential diagnostic or therapeutic targets. Methods Tumor samples of three DPC patients were collected and integrating RNA-seq analysis of tumor tissues and matched normal tissues were performed to discover differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were carried out to understand the potential bio-functions of the DPC differentially expressed genes (DEGs). Protein–protein interaction (PPI) network was constructed for functional modules analysis and identification of hub genes. qRT-PCR of clinical samples was conducted to validate the expression level of the hub genes. Results A total of 110 DEGs were identified from our RNA-seq data, GO and KEGG analyses showed that the DEGs were mainly enriched in multiple cancer-related functions and pathways, such as cell proliferation, IL-17signaling pathway, Jak-STAT signaling pathway, PPAR signaling pathway. The PPI network screened out five hub genes including IL-6, LCN2, FABP4, LEP and MMP1, which were identified as core genes in the network and the expression value were validated by qRT-PCR. The hub genes identified in this work were suggested to be potential therapeutic targets of DPC. Discussion The current study may provide new insight into the exploration of DPC pathogenesis and the screened hub genes may serve as potential diagnostic indicator and novel therapeutic target.

scholarly journals Identification of Key Genes with Clinical Outcome in Benign Meningioma Using Bioinformatic Analysis

2020 ◽  
Author(s):  
Ming Cao ◽  
Chen Shen ◽  
Jie Zhu ◽  
YuHai Wang
Keyword(s):  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Microarray Dataset ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Benign Meningioma ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Key Genes

Abstract Background: Meningioma is the second most common type of brain neoplasms.However,the underlying molecular mechanisms are still not clear,and the main treatment is mainly surgery plus radiotherapy. Material and method: To explore the key genes in benign meningioma,we downloaded microarray dataset GSE43290 from Gene Expression Omnibus(GEO) database.The differential genes (DEGs) between benign meningioma and normal meninges were identified by GEO2R.The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway were performed by the Database for Annotation,Visualization and Integrated Discovery (DAVID).The protein-protein interaction (PPI) network and module analysis were performed and visualized by the Search Tool for the Retrieval of Interacting Gene database (STRING) and Cytoscape.The hub genes were evaluated by the Cytohubba and further explored by MCODE plugin of Cytoscape and Enrichr.The relationship between hub genes and clinical factors were further explored by GSE16581 through R software. Result: A total of 358 DEGs were identified,including 15 upregulated genes and 343 downregulated genes.The main enriched functions were extracellular matrix organization、inflammatory response、cell adhesion、extracellular space and integrin binding.The main KEGG pathways were Malaria and focal adhesion.Among these DEGs,5 overlapping genes(CXCL8、AGT、CXCL2、CXCL12、CXCR4) were selected as hub genes.CXCL2 and CXCL8 were correlated with age and tumor recurrence,which could be clinical therapeutic targets. Conclusion: This study indicates the key genes in benign meningioma which may help us understand the molecular mechanisms and provide the candidate therapeutic targets.

scholarly journals Identification Candidate Diagnostic Biomarkers Between Minimal Change Disease and Focal Segmental Glomerulosclerosis by Bioinformatics Analysis

2020 ◽  
Author(s):  
Xue Jiang ◽  
Laite Chen ◽  
Yuanyuan Du ◽  
Yayu Li
Keyword(s):  
Differential Diagnosis ◽  
Differential Expression ◽  
Focal Segmental Glomerulosclerosis ◽  
Minimal Change Disease ◽  
Bioinformatics Analysis ◽  
Minimal Change ◽  
Hub Genes ◽  
U6 Snrna ◽  
Segmental Glomerulosclerosis ◽  
Healthy Control

Abstract Background: Minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS) are common causes of nephrotic syndrome which have similar clinical as well as histologic magnification and hard to differentiate. This study aimed to identify novel biomarkers to distinguish FSGS and MCD through bioinformatics analysis and elucidate the possible molecular mechanism.Methods: Based on the microarray datasets GSE104948 and GSE108113 downloaded from the Gene Expression Omnibus database, the differentially expressed genes (DEGs) between FSGS vs healthy control , MCD vs healthy control were identified, and further defined by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Hub genes were checked by protein-protein interaction networks.Results: A total of and 358 and 368 genes were identified in FSGS and MCD compared with healthy controls, among them, there were156 overlapping DEGs. GO analysis showed the DEGs in these two diseases were simultaneously enriched in mRNA splicing, RNA polymerase II transcription, mRNA export, insulin stimulus, integrin-mediated signaling pathway, viral process and phagocytosis. Module analysis showed that genes in the top 1 significant module of the PPI network were mainly associated with Spliceosome among FSGS and MCD. The top 10 hub genes analysis discovered that Most of hub genes were same between two disease, while among these genes, CD2 cytoplasmic tail binding protein 2 (CD2BP2), U6 snRNA-associated Sm-like protein (LSM8) and Small nuclear ribonucleoprotein polypeptides B (SNRPB) only differential expression in FSGS and Splicing factor 3A, subunit 3 (SF3A3) only differential expression in MCD, which may be used for differential diagnosis of these two diseases in the future.Conclusions: We identified key genes and mainly pathway associated with FSGS and MCD. Our results provide a set of potential genes used for differential diagnosis of these two diseases.

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