scholarly journals Identification of Key Genes, miRNAs and miRNAs-mRNA Regulatory Pathways for Chemotherapy Resistance in Ovarian Cancer

2021 ◽  
Author(s):  
Wenwen Wang
Keyword(s):  
Ovarian Cancer ◽  
Target Genes ◽  
Chemotherapy Resistance ◽  
Differentially Expressed ◽  
Platinum Resistance ◽  
Hub Genes ◽  
Regulatory Pathways ◽  
Qrt Pcr ◽  
Platinum Resistant ◽  
Key Genes

Abstract Background: Chemotherapy resistance, especially platinum resistance, is the main cause of poor prognosis of ovarian cancer. It is of great urgency to find molecular markers and mechanism related to platinum resistance in ovarian cancer.Methods: One mRNA dataset (GSE28739) and one miRNA dataset (GSE25202) were acquired from Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) between platinum-resistant and platinum-sensitive ovarian cancer patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed using the DAVID to present the most visibly enriched pathways. Protein–protein interaction (PPI) of these DEGs was constructed based on the information of the STRING database. Hub genes related to platinum resistance were visualized by Cytoscape software. Then, we chose seven interested hub genes to further validate using qRT-PCR in A2780 ovarian cancer cell lines. And, at last, the TF-miRNA-target genes regulatory network was predicted and constructed using miRNet software.Results: A total of 63 upregulated DEGs, 124 downregulated DEGs, 4 upregulated miRNAs and 6 downregulated miRNAs were identified. From the PPI network, the top 10 hub genes were identified, which were associated with platinum resistance. Our further qRT-PCR showed that seven hub genes (BUB1, KIF2C, NUP43, NDC80, NUF2, CCNB2 and CENPN) were differentially expressed in platinum-resistant ovarian cancer cells. Furthermore, the upstream transcription factors (TF) for upregulated DE-miRNAs were SMAD4, NFKB1, SMAD3, TP53 and HNF4A. Three overlapping downstream target genes (KIF2C, STAT3 and BUB1) were identified by miRNet, which was regulated by hsa-miR-494.Conclusions: The TF-miRNA–mRNA regulatory pairs, that is TF (SMAD4, NFKB1 and SMAD3)-miR-494-target genes (KIF2C, STAT3 and BUB1), were established. In conclusion, the present study is of great significance to find the key genes of platinum resistance in ovarian cancer. Further study is needed to identify the mechanism of these genes in ovarian cancer.

scholarly journals Identification of keygenes, miRNAs and miRNA-mRNA regulatory pathways for chemotherapy resistance in ovarian cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12353
Author(s):  
Wenwen Wang ◽  
Wenwen Zhang ◽  
Yuanjing Hu
Keyword(s):  
Ovarian Cancer ◽  
Target Genes ◽  
Chemotherapy Resistance ◽  
Differentially Expressed ◽  
Platinum Resistance ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Regulatory Pathways ◽  
Qrt Pcr ◽  
Platinum Resistant

Background Chemotherapy resistance, especially platinum resistance, is the main cause of poor prognosis of ovarian cancer. It is of great urgency to find molecular markers and mechanism related to platinum resistance in ovarian cancer. Methods One mRNA dataset (GSE28739) and one miRNA dataset (GSE25202) were acquired from Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) and differentially expressed miRNAs (DE-miRNAs) between platinum-resistant and platinum-sensitive ovarian cancer patients. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs were performed using the DAVID to present the most visibly enriched pathways. Protein–protein interaction (PPI) of these DEGs was constructed based on the information of the STRING database. Hub genes related to platinum resistance were visualized by Cytoscape software. Then, we chose seven interested hub genes to further validate using qRT-PCR in A2780 ovarian cancer cell lines. And, at last, the TF-miRNA-target genes regulatory network was predicted and constructed using miRNet software. Results A total of 63 upregulated DEGs, 124 downregulated DEGs, four upregulated miRNAs and six downregulated miRNAs were identified. From the PPI network, the top 10 hub genes were identified, which were associated with platinum resistance. Our further qRT-PCR showed that seven hub genes (BUB1, KIF2C, NUP43, NDC80, NUF2, CCNB2 and CENPN) were differentially expressed in platinum-resistant ovarian cancer cells. Furthermore, the upstream transcription factors (TF) for upregulated DE-miRNAs were SMAD4, NFKB1, SMAD3, TP53 and HNF4A. Three overlapping downstream target genes (KIF2C, STAT3 and BUB1) were identified by miRNet, which was regulated by hsa-miR-494. Conclusions The TF-miRNA–mRNA regulatory pairs, that is TF (SMAD4, NFKB1 and SMAD3)-miR-494-target genes (KIF2C, STAT3 and BUB1), were established. In conclusion, the present study is of great significance to find the key genes of platinum resistance in ovarian cancer. Further study is needed to identify the mechanism of these genes in ovarian cancer.

A loss of microRNA expression as a characterization of synchronous peritoneal secondary localizations of epithelial ovarian cancer as compared to primary tumors.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 11037-11037
Author(s):  
Delia Mezzanzanica ◽  
Loris De Cecco ◽  
Daniela Califano ◽  
Simona Losito ◽  
Marina Bagnoli ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Epithelial Ovarian Cancer ◽  
Target Genes ◽  
Ectopic Expression ◽  
Differentially Expressed ◽  
Platinum Resistance ◽  
Primary Tumors ◽  
Gynecological Malignancy ◽  
Primary Surgery

11037 Background: Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy and one of the most challenging areas of cancer research being a highly heterogeneous disease difficult to diagnose and treat. EOC has a peculiar dissemination process due to the sloughing-off of cells from primary tumors and their spread throughout the peritoneal cavity. A better characterization of the mechanism involved in tumor spreading might help in design new therapeutic intervention. Methods: Forty-four couples of chemo naïf primary tumors and synchronous secondary peritoneal localizations, obtained at primary surgery from MITO2 clinical trial, have been profiled for microRNA (miRNA) expression on an Agilent Platform. Total RNA was extracted from formalin-fixed paraffin embedded tissues. An independent validation set of samples with similar characteristics, has been collected at INT Milan. Results: By class comparison analysis, imposing a false discovery rate <10%,45 miRNAs were identified as differentially expressed: 32 down-modulated and 13 up-modulated in secondary localizations compared to primary tumors. Among the miRNAs down-modulated in the secondary localizations we detected most of the miRNA belonging to the Xq27.3 cluster, whose low expression we previously described to be associated with EOC early relapse, and a number of miRNAs related to epithelial/mesenchimal transition (EMT) whose modulation could be related to dissemination of the disease and response to drug treatment. In particularly loss of has-miR-506 resulted associated to platinum resistance since its ectopic expression in EOC cell lines increased their sensitivity to the drug. Furthermore preliminary data indicated that has-miR-506 regulated N-cadherin linking its modulation to EMT. Conclusions: To our knowledge, the present study is the first attempt to characterize a miRNA signature differentially expressed between EOC primary tumors and synchronous secondary peritoneal localizations. The validation of the miRNA profile as well as of target genes might help in elucidating EOC dissemination mechanisms and in defining possible new therapeutic targets.

scholarly journals Biomarkers of platinum resistance in ovarian cancer: what can we use to improve treatment

2018 ◽  
Vol 25 (5) ◽  
pp. R303-R318 ◽  
Author(s):  
Belinda van Zyl ◽  
Denise Tang ◽  
Nikola A Bowden
Keyword(s):  
Ovarian Cancer ◽  
Survival Rates ◽  
Chemotherapy Resistance ◽  
Initial Response ◽  
Disease Recurrence ◽  
Future Research ◽  
Platinum Resistance ◽  
Platinum Resistant ◽  
New Treatments ◽  
Platinum Chemotherapy

Ovarian cancer has poor survival rates due to a combination of diagnosis at advanced disease stages and disease recurrence as a result of platinum chemotherapy resistance. High-grade serous ovarian cancer (HGSOC), the most common ovarian cancer subtype, is conventionally treated with surgery and paclitaxel/carboplatin combination chemotherapy. Initial response rates are 60–80%, but eventually the majority of patients become platinum-resistant with subsequent relapses. Extensive research on individual biomarkers of platinum resistance has revealed many potential targets for the development new treatments. While this is ongoing, there are also epigenetic, DNA repair, genome and immune changes characterised in platinum-resistant HGSOC that can be targeted with current therapies. This review discusses biomarkers of platinum chemotherapy resistance in ovarian cancer with a focus on biomarkers that are targetable with alternative treatment combinations to those currently used. After decades of research focused on elucidating the biological cause of platinum resistance, future research needs to focus on using this knowledge to overcome resistance for patients with ovarian cancer.

scholarly journals INPP1 is differentially expressed in platinum-resistant ovarian cancers.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Ovarian Cancer ◽  
Cell Lines ◽  
Gynecological Cancer ◽  
The United States ◽  
Differentially Expressed ◽  
Platinum Resistance ◽  
Platinum Drugs ◽  
Cancer Death ◽  
Primary Tumors ◽  
Platinum Resistant

Ovarian cancer is the most common reason for a gynecological cancer death in the developed world and fifth leading cause of cancer death in women in the United States (1, 2). Chemotherapy includes the use of platinum drugs (3) and resistance to platinum drugs is a serious problem for women diagnosed with ovarian cancer (4, 5, 6). We found, using two published datasets (7, 8) that INPP1 was one of the genes most differentially expressed when comparing the transcriptomes of platinum-resistant and platinum-sensitive tumors and cell lines but that the pattern of differential expression was opposite in cell lines versus that in primary tumors from patients. Manipulation of INPP1 expression should be assessed for its ability to reverse platinum resistance.

scholarly journals Bioinformatics analysis of mRNA and miRNA microarray to identify the key miRNA-mRNA pairs in cisplatin-resistant ovarian cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bai Xue ◽  
Shupeng Li ◽  
Xianyu Jin ◽  
Lifeng Liu
Keyword(s):  
Ovarian Cancer ◽  
Cell Lines ◽  
Protein Interaction ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Differentially Expressed ◽  
Mirna Sequence ◽  
Drug Resistant ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background Ovarian cancer (OC) is a gynecological malignancy with the highest mortality rate. Cisplatin (DDP) based chemotherapy is a standard strategy for ovarian cancer. Despite good response rates for initial chemotherapy, almost 80% of the patients treated with DDP based chemotherapy will experience recurrence due to drug-resistant, which will ultimately result in fatality. The aim of the present study was to examine the pathogenesis and potential molecular markers of cisplatin-resistant OC by studying the differential expression of mRNAs and miRNAs between cisplatin resistant OC cell lines and normal cell lines. Methods Two mRNA datasets (GSE58470 and GSE45553) and two miRNA sequence datasets (GSE58469 and GSE148251) were downloaded from the Gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were screened by the NetworkAnalyst. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. The protein-protein interaction network was constructed using STRING and Cytoscape software to identify the molecular mechanisms of key signaling pathways and cellular activities. FunRich and MiRNATip databases were used to identify the target genes of the DEMs. Results A total of 380 DEGs, and 5 DEMs were identified. Protein–protein interaction (PPI) network of DEGs containing 379 nodes and 1049 edges was constructed, and 4 key modules and 24 hub genes related to cisplatin-resistant OC were screened. Two hundred ninety-nine target genes of the 5 DEMs were found out. Subsequently, one of these 299 target genes (UBB) belonging to the hub genes of GSE58470 and GSE45553 was identified by MCODE and CytoHubba,which was regulated by one miRNA (mir-454). Conclusions One miRNA–mRNA regulatory pairs (mir-454-UBB) was established. Taken together, our study provided evidence concerning the alteration genes involved in cisplatin-resistant OC, which will help to unravel the mechanisms underlying drug resistant.

Identification of MicroRNA-mRNA Regulatory Networks in Periodontitis by Bioinformatics Analysis

2021 ◽  
Author(s):  
Xiaoli Gao ◽  
Dong Zhao ◽  
Zuomin Wang ◽  
Zheng Zhang ◽  
Jing Han
Keyword(s):  
Gene Expression ◽  
Periodontal Disease ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Kegg Pathway ◽  
Microarray Dataset ◽  
Hub Genes ◽  
Regulatory Pathways ◽  
Key Genes

Abstract Background: Periodontitis is a complex infectious disease with various causes and contributing factors. In recent years, microRNAs (miRNAs) have been commonly accepted as having key regulatory functions in periodontal disease. The aim of this study was to identify miRNAs and hub genes involved in periodontal disease pathogenesis using a miRNA-mRNA interaction network.Methods: The GSE54710 miRNA microarray dataset and the gene expression microarray dataset GSE16134 were downloaded from the Gene Expression Omnibus database. The differentially expressed miRNAs (DEMis) and mRNAs (DEMs) were screened using P <0.05 and |log FC| ≥1. Potential upstream transcription factors and downstream target genes of candidate DEMis were predicted using the FunRich and miRNet programs, respectively. Subsequently, DEMs were uploaded to the STRING database, a protein-protein interaction (PPI) network was established, and the cytoHubba plugin was used to screen out key hub mRNAs. The key genes in the miRNA-mRNA regulatory network were extracted by intersecting the target genes of candidate DEMis and DEMs. Cytoscape software was used to visualise the interaction between miRNAs and mRNAs and to predict the hub genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to analyse the key genes in the regulatory network.Results: Ten DEMis and 161 DEMs were filtered out, from which we constructed a miRNA-mRNA network consisting of six miRNAs and 32 mRNAs. KEGG pathway analysis showed that mRNAs in the regulatory network were mainly involved in the IL-17 signalling pathway. Hsa-miR-203/CXCL8, hsa-miR-203/BTG2, and hsa-miR-203/DNAJB9 were identified as four potential regulatory pathways for periodontitis. Conclusion: In this study, a potential miRNA–mRNA regulatory network was first constructed and four regulatory pathways were identified for periodontitis to help clarify the aetiology of the disease and provide potential therapeutic targets.

scholarly journals Construction of miRNA-mRNA regulatory network and prognostic signature in endometrial cancer

2020 ◽  
Author(s):  
Jinhui Liu ◽  
Rui Sun ◽  
Sipei Nie ◽  
Jing Yang ◽  
Siyue Li ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Regulatory Network ◽  
Target Genes ◽  
Immune Cell ◽  
Cox Proportional Hazards ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Prognostic Signature ◽  
Hub Genes ◽  
Qrt Pcr

Abstract Background: Many studies have well supported the close relationship between miRNA and endometrial cancer (EC). This bioinformatic study, compared with other similar studies, confirmed a new miRNA-mRNA regulatory network to investigate the miRNA-mRNA regulatory network and the prognostic biomarkers in EC. Methods: We downloaded RNA-seq and miRNA-seq data of endometrial cancer from the TCGA database, and then we used EdegR package to screen differentially expressed miRNAs and mRNAs (DE-miRNAs and DE-mRNAs). The differentially expressed genes (DEGs) were identified and their functions were predicted using the functional and pathway enrichment analysis. Protein–protein interaction (PPI) network was established using STRING database, and the hub genes were verified by Gene Expression Profiling Interactive Analysis (GEPIA). Then, we constructed a regulatory network of EC-associated miRNAs and hub genes by Cytoscape, and determined the expression of unexplored miRNAs in EC tissues and normal adjacent tissues by quantitative Real-Time PCR (qRT-PCR). A prognostic signature model and a predictive nomogram were constructed. Finally, we explored the association between the prognostic model and the immune cell infiltration. Results: 11531 DE-mRNAs and 236 DE-miRNAs, as well as 275 and 118 candidate DEGs for upregulated and downregulated DE-miRNAs were screened out. These DEGs were significantly concentrated in FOXO signaling pathway, cell cycle and Focal adhesion. Among the 20 hub genes identified, 17 exhibited significantly different expression compared with normal tissues. The miRNA-mRNA network included 5 downregulated and 13 upregulated DE-miRNAs . qRT-PCR proved that the expression levels of miRNA-18a-5p, miRNA-18b-5p, miRNA-449c-5p and miRNA-1224-5p and their target genes, NR3C1, CTGF, MYC, and TNS1 were consistent with our predictions. Univariate and multivariate Cox proportional hazards regression analyses of the hub genes revealed that NR3C1, EZH2, and GATA4 showed a significant prognostic value. We identified the three-gene signature as an independent prognostic indicator for EC ( p =0.022，HR=1.321, 95% CI: 1.041-1.675) and these genes were closely related to eight types of immune infiltration cells. Conclusion: Our study revealed the mechanisms of the carcinogenesis and progression of EC.

scholarly journals Hub microRNAs and genes in the development of atrial fibrillation identified by weighted gene co-expression network analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qiang Qu ◽  
Jin-Yu Sun ◽  
Zhen-Ye Zhang ◽  
Yue Su ◽  
Shan-Shan Li ◽  
...  
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Expression Patterns ◽  
Mirna Gene ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Interaction Patterns ◽  
Hub Genes ◽  
Key Genes

AbstractCo-expression network may contribute to better understanding molecular interaction patterns underlying cellular processes. To explore microRNAs (miRNAs) expression patterns correlated with AF, we performed weighted gene co-expression network analysis (WGCNA) based on the dataset GSE28954. Thereafter, we predicted target genes using experimentally verified databases (ENOCRI, miRTarBase, and Tarbase), and overlapped genes with differentially expressed genes (DEGs) from GSE79768 were identified as key genes. Integrated analysis of association between hub miRNAs and key genes was conducted to screen hub genes. In general, we identified 3 differentially expressed miRNAs (DEMs) and 320 DEGs, predominantly enriched in inflammation-related functional items. Two significant modules (red and blue) and hub miRNAs (hsa-miR-146b-5p and hsa-miR-378a-5p), which highly correlated with AF-related phenotype, were detected by WGCNA. By overlapping the DEGs and predicted target genes, 38 genes were screened out. Finally, 9 genes (i.e. ATP13A3, BMP2, CXCL1, GABPA, LIF, MAP3K8, NPY1R, S100A12, SLC16A2) located at the core region in the miRNA-gene interaction network were identified as hub genes. In conclusion, our study identified 2 hub miRNAs and 9 hub genes, which may improve the understanding of molecular mechanisms and help to reveal potential therapeutic targets against AF.

scholarly journals Microarray analysis reveals an inflammatory transcriptomic signature in peripheral blood for sciatica

BMC Neurology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yi Wang ◽  
Guogang Dai ◽  
Ling Jiang ◽  
Shichuan Liao ◽  
Jiao Xia
Keyword(s):  
Functional Analysis ◽  
Network Analysis ◽  
Peripheral Blood ◽  
Differentially Expressed ◽  
Healthy Controls ◽  
Toll Like Receptor ◽  
Qrt Pcr ◽  
Transcriptomic Signature ◽  
Transcriptional Changes ◽  
Key Genes

Abstract Background Although the pathology of sciatica has been studied extensively, the transcriptional changes in the peripheral blood caused by sciatica have not been characterized. This study aimed to characterize the peripheral blood transcriptomic signature for sciatica. Methods We used a microarray to identify differentially expressed genes in the peripheral blood of patients with sciatica compared with that of healthy controls, performed a functional analysis to reveal the peripheral blood transcriptomic signature for sciatica, and conducted a network analysis to identify key genes that contribute to the observed transcriptional changes. The expression levels of these key genes were assessed by qRT-PCR. Results We found that 153 genes were differentially expressed in the peripheral blood of patients with sciatica compared with that of healthy controls, and 131 and 22 of these were upregulated and downregulated, respectively. A functional analysis revealed that these differentially expressed genes (DEGs) were strongly enriched for the inflammatory response or immunity. The network analysis revealed that a group of genes, most of which are related to the inflammatory response, played a key role in the dysregulation of these DEGs. These key genes are Toll-like receptor 4, matrix metallopeptidase 9, myeloperoxidase, cathelicidin antimicrobial peptide, resistin and Toll-like receptor 5, and a qRT-PCR analysis validated the higher transcript levels of these key genes in the peripheral blood of patients with sciatica than in that of healthy controls. Conclusion We revealed inflammatory characteristics that serve as a peripheral blood transcriptomic signature for sciatica and identified genes that are essential for mRNA dysregulation in the peripheral blood of patients with sciatica.

scholarly journals SENP1-mediated deSUMOylation of JAK2 regulates its kinase activity and platinum drug resistance

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Jing Li ◽  
Ruiqin Wu ◽  
Mingo M. H. Yung ◽  
Jing Sun ◽  
Zhuqing Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Drug Resistance ◽  
Kinase Activity ◽  
Regulatory Mechanism ◽  
Platinum Resistance ◽  
Platinum Drug ◽  
Clinical Prognosis ◽  
Platinum Resistant ◽  
Poor Clinical Prognosis ◽  
Stat Pathway

AbstractThe JAK2/STAT pathway is hyperactivated in many cancers, and such hyperactivation is associated with a poor clinical prognosis and drug resistance. The mechanism regulating JAK2 activity is complex. Although translocation of JAK2 between nucleus and cytoplasm is an important regulatory mechanism, how JAK2 translocation is regulated and what is the physiological function of this translocation remain largely unknown. Here, we found that protease SENP1 directly interacts with and deSUMOylates JAK2, and the deSUMOylation of JAK2 leads to its accumulation at cytoplasm, where JAK2 is activated. Significantly, this novel SENP1/JAK2 axis is activated in platinum-resistant ovarian cancer in a manner dependent on a transcription factor RUNX2 and activated RUNX2/SENP1/JAK2 is critical for platinum-resistance in ovarian cancer. To explore the application of anti-SENP1/JAK2 for treatment of platinum-resistant ovarian cancer, we found SENP1 deficiency or treatment by SENP1 inhibitor Momordin Ic significantly overcomes platinum-resistance of ovarian cancer. Thus, this study not only identifies a novel mechanism regulating JAK2 activity, but also provides with a potential approach to treat platinum-resistant ovarian cancer by targeting SENP1/JAK2 pathway.

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