Exosomal hsa-miR-21-5p is a biomarker for breast cancer diagnosis

Purpose Breast cancer (BC) is characterized by concealed onset, delayed diagnosis, and high fatality rates making it particularly dangerous to patients’ health. The purpose of this study was to use comprehensive bioinformatics analysis and experimental verification to find a new biomarker for BC diagnosis. Methods We comprehensively analyzed microRNA (miRNA) and mRNA expression profiles from the Gene Expression Omnibus (GEO) and screened out differentially-expressed (DE) miRNAs and mRNAs. We used the miRNet website to predict potential DE-miRNA target genes. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID), we performed Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on overlapping potential target genes and DE-mRNAs. The protein-protein interaction (PPI) network was then established. The miRNA-mRNA regulatory network was constructed using Cytoscape and the analysis results were visualized. We verified the expression of the most up-regulated DE-miRNA using reverse transcription and a quantitative polymerase chain reaction in BC tissue. The diagnostic value of the most up-regulated DE-miRNA was further explored across three levels: plasma-derived exosomes, cells, and cell exosomes. Results Our comprehensive bioinformatics analysis and experimental results showed that hsa-miR-21-5p was significantly up-regulated in BC tissue, cells, and exosomes. Our results also revealed that tumor-derived hsa-miR-21-5p could be packaged in exosomes and released into peripheral blood. Additionally, when evaluating the diagnostic value of plasma exosomal hsa-miR-21-5p, we found that it was significantly up-regulated in BC patients. Receiver operating characteristic (ROC) analysis also confirmed that hsa-miR-21-5p could effectively distinguish healthy people from BC patients. The sensitivity and specificity were 86.7% and 93.3%, respectively. Conclusion This study’s results showed that plasma exosomal hsa-miR-21-5p could be used as a biomarker for BC diagnosis.

Download Full-text

Identification of Hub Genes in Pancreatic Ductal Adenocarci-noma Using Bioinformatics Analysis

Iranian Journal of Public Health ◽

10.18502/ijph.v50i11.7578 ◽

2021 ◽

Author(s):

Congcong Wang ◽

Jianping Guo ◽

Xiaoyang Zhao ◽

Jia Jia ◽

Wenting Xu ◽

...

Keyword(s):

Bioinformatics Analysis ◽

Expression Profiles ◽

Gene Expression Omnibus ◽

Receptor Interaction ◽

Analysis Tool ◽

Hub Genes ◽

Protein Protein Interaction ◽

Kegg Analysis ◽

Prognostic Assessment ◽

Scheme Selection

Background: To address the biomarkers that correlated with the prognosis of patients with PDCA using bioinformatics analysis. Methods: The raw data of genes were obtained from the Gene Expression Omnibus. We screened differently expressed genes (DEGs) by Rstudio. Database for Annotation,Visualization and Intergrated Discovery was used to investigate their biological function by Gene Ontology(GO) and Kyoto Encyclopedia of Genes (KEGG) analysis. Protein-protein interaction of these DEGs were analyzed based on the Search Tool for the Retrieval of Interacting Genes database (STRING) and visualized by Cytoscape. Genes calculated by CytoHubba with degree >10 were identified as hub genes. Then, the identified hub genes were verified by UALCAN online analysis tool to evaluate the prognostic value in PDCA. Results: Three expression profiles (GSE15471, GSE16515 and GSE32676) were downloaded from GEO database. The three sets of DEGs exhibited an intersection consisting of 223 genes (214 upregulated DEGs and 9 downregulated DEGs). GO analysis showed that the 223 DEGs were significantly enriched in extracellular exosome, plasma membrane and extracellular space. ECM-receptor interaction, PI3K-Akt signaling pathway and Focal adhesion were the most significantly enriched pathway according to KEGG analysis. By combining the results of Cytohubba, 30 hub genes with a high degree of connectivity were picked out. Finally, we candidated 3 biomarkers by UALCAN online survival analysis, including CEP55, ANLN and PRC1. Conclusion: we identified CEP55, ANLN and PRC1 may be the potential biomarkers and therapeutic targets of PDCA, which used for prognostic assessment and scheme selection.

Download Full-text

Identification of Novel Breast Cancer Genes based on Gene Expression Profiles and PPI Data

Current Proteomics ◽

10.2174/1570164616666190126111354 ◽

2019 ◽

Vol 16 (5) ◽

pp. 415-426

Author(s):

Cheng-Wen Yang ◽

Huan-Huan Cao ◽

Yu Guo ◽

Yuan-Ming Feng ◽

Ning Zhang

Keyword(s):

Breast Cancer ◽

Expression Profiles ◽

Endocrine Resistance ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Breast Cancer Diagnosis ◽

Cancer Genes ◽

Protein Protein Interaction ◽

Go Enrichment ◽

Go Enrichment Analysis

Background:Breast cancer is one of the most common malignancies, and a threat to female health all over the world. However, the molecular mechanism of breast cancer has not been fully discovered yet.Objective:It is crucial to identify breast cancer-related genes, which could provide new biomarker for breast cancer diagnosis as well as potential treatment targets.Methods:Here we used the minimum redundancy-maximum relevance (mRMR) method to select significant genes, then mapped the transcripts of the genes on the Protein-Protein Interaction (PPI) network and traced the shortest path between each pair of two proteins.Results:As a result, we identified 24 breast cancer-related genes whose betweenness were over 700. The GO enrichment analysis indicated that the transcription and oxygen level are very important in breast cancer. And the pathway analysis indicated that most of these 24 genes are enriched in prostate cancer, endocrine resistance, and pathways in cancer.Conclusion:We hope these 24 genes might be useful for diagnosis, prognosis and treatment for breast cancer.

Download Full-text

Identification and characterization of critical genes associated with tamoxifen resistance in breast cancer

PeerJ ◽

10.7717/peerj.10468 ◽

2020 ◽

Vol 8 ◽

pp. e10468

Author(s):

Kai Zhang ◽

Kuikui Jiang ◽

Ruoxi Hong ◽

Fei Xu ◽

Wen Xia ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Target Genes ◽

Expression Profiles ◽

Tamoxifen Resistance ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Ppi Network ◽

Hub Genes ◽

Mcf 7

Background Tamoxifen resistance in breast cancer is an unsolved problem in clinical practice. The aim of this study was to determine the potential mechanisms of tamoxifen resistance through bioinformatics analysis. Methods Gene expression profiles of tamoxifen-resistant MCF-7/TR and MCF-7 cells were acquired from the Gene Expression Omnibus dataset GSE26459, and differentially expressed genes (DEGs) were detected with R software. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using Database for Annotation, Visualization and Integrated Discovery. A protein–protein interaction (PPI) network was generated, and we analyzed hub genes in the network with the Search Tool for the Retrieval of Interacting Genes database. Finally, we used siRNAs to silence the target genes and conducted the MTS assay. Results We identified 865 DEGs, 399 of which were upregulated. GO analysis indicated that most genes are related to telomere organization, extracellular exosomes, and binding-related items for protein heterodimerization. PPI network construction revealed that the top 10 hub genes—ACLY, HSPD1, PFAS, GART, TXN, HSPH1, HSPE1, IRAS, TRAP1, and ATIC—might be associated with tamoxifen resistance. Consistently, RT-qPCR analysis indicated that the expression of these 10 genes was increased in MCF-7/TR cells comparing with MCF-7 cells. Four hub genes (TXN, HSPD1, HSPH1 and ATIC) were related to overall survival in patients who accepted tamoxifen. In addition, knockdown of HSPH1 by siRNA may lead to reduced growth of MCF-7/TR cell with a trend close to significance (P = 0.07), indicating that upregulation of HSPH1 may play a role in tamoxifen resistance. Conclusion This study revealed a number of critical hub genes that might serve as therapeutic targets in breast cancer resistant to tamoxifen and provided potential directions for uncovering the mechanisms of tamoxifen resistance.

Download Full-text

Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis

BioMed Research International ◽

10.1155/2020/2379280 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Zhengqing Zhu ◽

Lei Zhong ◽

Ronghang Li ◽

Yuzhe Liu ◽

Xiangrun Chen ◽

...

Keyword(s):

Gene Expression ◽

Bioinformatics Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Interaction Network ◽

Gene Expression Omnibus ◽

Protein Protein Interaction ◽

Polymerase Chain ◽

Upregulated Genes ◽

Protein Protein Interaction Network

Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold‐change∣>1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.

Download Full-text

miR-432 is a novel biomarker for PCNSL and is associated with cell adhesion: An integrated bioinformatics analysis

10.21203/rs.3.rs-16455/v1 ◽

2020 ◽

Author(s):

Qiang Ma

Keyword(s):

Cell Adhesion ◽

Operating Characteristic ◽

Target Genes ◽

Bioinformatics Analysis ◽

Central Nervous System Lymphoma ◽

Gene Expression Omnibus ◽

Serum Samples ◽

Protein Protein Interaction ◽

Candidate Mirnas ◽

Ppi Networks

Abstract Background: Primary central nervous system lymphoma (PCNSL), a rare form of the non-Hodgkin's lymphoma (NHL), usually has a poor prognosis, and molecular pathogenesis of PCNSL has not been fully elucidated. Here, potential miRNA biomarkers were investigated in patients with PCNSL using an integrated bioinformatics analysis. Methods: Expression profile arrays (GSE122011, GSE139031, and GSE25297) were obtained from the Gene Expression Omnibus (GEO). Free-scale miRNA co-expression networks were constructed with 27 PCNSL patients from GSE122011 by the weighted gene co-expression network analysis (WGCNA) in order to identify candidate biomarkers. Subsequently, miRNA-miRNA networks were visualized with the Cytoscape. Expression of candidate miRNAs was assessed in serum samples from GSE139031, including 42 PCNSL patients and 77 non-cancer individuals, and the sensitivity and the specificity were assessed by the receiver operating characteristic (ROC) curve. From GSE25297, differentially expressed genes (DEGs) from the PCNSL tissues (n = 7) and the normal lymph nodes (n = 7) were compared, target genes of candidate miRNAs were downloaded from TargetScan database, and target genes that were also down-regulated in GSE25297 were used to construct the protein-protein interaction (PPI) networks and for the gene ontology (GO) analysis. Results: miRNAs were clustered into two groups with 8 modules in 27 patients with PCNSL. One group consisted of the yellow and the turquoise modules, and the second group consisted of the other six modules. In the miRNA-miRNA network, the highest nodes were observed between miR-432 and miR-330-3p, which were from the yellow and the turquoise modules, and only miR-432 was closely associated with both the yellow (0.977, P = 2.88E -18 ) and the turquoise modules (0.525, P = 0.005). Additionally, patients with PCNSL had higher serum miR-432 expression compared with that in the non-cancer controls in GSE139031, and miR-432 has a higher accuracy for discriminating between PCNSL and non-cancer samples (AUC: 0.77; 95% CI: 0.6923 to 0.8550). For target genes of miR-432, RASGRF , DGKG , SMIM22 , SPOCD1 , NRCAM , CNTN2 , PTPRD , POTED , IGSF3 , SLC24A2 , CTNND2 , AIF1L , TMEM229A , GLDN , and MOBP were down-regulated in the PCNSL tissues. Among them, CTNND2 , GLDN , NRCAM , and PTPRD were associated with cell adhesion. Conclusion: Up-regulated miR-432 expression is a novel biomarker for patients with PCNSL and may be associated with cell adhesion.

Download Full-text

Comprehensive analysis of IgA nephropathy expression profiles: identification of potential biomarkers and therapeutic agents

BMC Nephrology ◽

10.1186/s12882-021-02356-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Alieh Gholaminejad ◽

Yousof Gheisari ◽

Sedigheh Jalali ◽

Amir Roointan

Keyword(s):

Iga Nephropathy ◽

Regulatory Network ◽

Target Genes ◽

Expression Profiles ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Integrative Approach ◽

Potential Factors ◽

Ngf Signaling ◽

Underlying Mechanisms

Abstract Background IgA nephropathy (IgAN) is a kidney disease recognized by the presence of IgA antibody depositions in kidneys. The underlying mechanisms of this complicated disease are remained to be explored and still, there is an urgent need for the discovery of noninvasive biomarkers for its diagnosis. In this investigation, an integrative approach was applied to mRNA and miRNA expression profiles in PBMCs to discover a gene signature and novel potential targets/biomarkers in IgAN. Methods Datasets were selected from gene expression omnibus database. After quality control checking, two datasets were analyzed by Limma to identify differentially expressed genes/miRNAs (DEGs and DEmiRs). Following identification of DEmiR-target genes and data integration, intersecting mRNAs were subjected to different bioinformatic analyses. The intersecting mRNAs, DEmiRs, related transcription factors (from TRRUST database), and long-non coding RNAs (from LncTarD database) were used for the construction of a multilayer regulatory network via Cytoscape. Result “GSE25590” (miRNA) and “GSE73953” (mRNA) datasets were analyzed and after integration, 628 intersecting mRNAs were identified. The mRNAs were mainly associated with “Innate immune system”, “Apoptosis”, as well as “NGF signaling” pathways. A multilayer regulatory network was constructed and several hub-DEGs (Tp53, STAT3, Jun, etc.), DEmiRs (miR-124, let-7b, etc.), TFs (NF-kB, etc.), and lncRNAs (HOTAIR, etc.) were introduced as potential factors in the pathogenesis of IgAN. Conclusion Integration of two different expression datasets and construction of a multilayer regulatory network not only provided a deeper insight into the pathogenesis of IgAN, but also introduced several key molecules as potential therapeutic target/non-invasive biomarkers.

Download Full-text

Differential Expression of mRNAs in Peripheral Blood Related to Prodrome and Progression of Alzheimer’s Disease

BioMed Research International ◽

10.1155/2020/4505720 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Weishuang Xue ◽

Jinwei Li ◽

Kailei Fu ◽

Weiyu Teng

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Peripheral Blood ◽

Bioinformatics Analysis ◽

Gene Expression Omnibus ◽

Venn Diagram ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Ppi Networks

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that affects the quality of life of elderly individuals, while the pathogenesis of AD is still unclear. Based on the bioinformatics analysis of differentially expressed genes (DEGs) in peripheral blood samples, we investigated genes related to mild cognitive impairment (MCI), AD, and late-stage AD that might be used for predicting the conversions. Methods. We obtained the DEGs in MCI, AD, and advanced AD patients from the Gene Expression Omnibus (GEO) database. A Venn diagram was used to identify the intersecting genes. Gene Ontology (GO) and Kyoto Gene and Genomic Encyclopedia (KEGG) were used to analyze the functions and pathways of the intersecting genes. Protein-protein interaction (PPI) networks were constructed to visualize the network of the proteins coded by the related genes. Hub genes were selected based on the PPI network. Results. Bioinformatics analysis indicated that there were 61 DEGs in both the MCI and AD groups and 27 the same DEGs among the three groups. Using GO and KEGG analyses, we found that these genes were related to the function of mitochondria and ribosome. Hub genes were determined by bioinformatics software based on the PPI network. Conclusions. Mitochondrial and ribosomal dysfunction in peripheral blood may be early signs in AD patients and related to the disease progression. The identified hub genes may provide the possibility for predicting AD progression or be the possible targets for treatments.

Download Full-text

Identification of MicroRNA as Sepsis Biomarker Based on miRNAs Regulatory Network Analysis

BioMed Research International ◽

10.1155/2014/594350 ◽

2014 ◽

Vol 2014 ◽

pp. 1-12 ◽

Cited By ~ 14

Author(s):

Jie Huang ◽

Zhandong Sun ◽

Wenying Yan ◽

Yujie Zhu ◽

Yuxin Lin ◽

...

Keyword(s):

Regulatory Network ◽

Expression Profiles ◽

Delayed Diagnosis ◽

Interaction Network ◽

High Sensitivity ◽

Regulate Gene Expression ◽

Protein Protein Interaction ◽

Small Noncoding Rnas ◽

Mirna Expression Profiles ◽

Pathways Analysis

Sepsis is regarded as arising from an unusual systemic response to infection but the physiopathology of sepsis remains elusive. At present, sepsis is still a fatal condition with delayed diagnosis and a poor outcome. Many biomarkers have been reported in clinical application for patients with sepsis, and claimed to improve the diagnosis and treatment. Because of the difficulty in the interpreting of clinical features of sepsis, some biomarkers do not show high sensitivity and specificity. MicroRNAs (miRNAs) are small noncoding RNAs which pair the sites in mRNAs to regulate gene expression in eukaryotes. They play a key role in inflammatory response, and have been validated to be potential sepsis biomarker recently. In the present work, we apply a miRNA regulatory network based method to identify novel microRNA biomarkers associated with the early diagnosis of sepsis. By analyzing the miRNA expression profiles and the miRNA regulatory network, we obtained novel miRNAs associated with sepsis. Pathways analysis, disease ontology analysis, and protein-protein interaction network (PIN) analysis, as well as ROC curve, were exploited to testify the reliability of the predicted miRNAs. We finally identified 8 novel miRNAs which have the potential to be sepsis biomarkers.

Download Full-text

Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02124-6 ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Xiao-hong Mao ◽

Qiang Ye ◽

Guo-bing Zhang ◽

Jin-ying Jiang ◽

Hong-ying Zhao ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Diagnosis ◽

High Sensitivity ◽

Breast Cancer Diagnosis ◽

Diagnostic Value ◽

Methylation Pattern ◽

Clinical Value ◽

Roc Curve Analysis ◽

Differentially Methylated Genes ◽

Sensitivity Specificity

Abstract Background Aberrant DNA methylation is significantly associated with breast cancer. Methods In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. Results In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994–1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976–1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. Conclusions Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer.

Download Full-text

Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

Download Full-text