Integrated miRNA-mRNA Expression Profiles Revealing Key Molecules in Ovarian Cancer Based on Bioinformatics Analysis

Ovarian cancer is one of the leading causes of gynecological malignancy-related deaths. The underlying molecular development mechanism has however not been elucidated. In this study, we used bioinformatics to reveal critical molecular and biological processes associated with ovarian cancer. The microarray datasets of miRNA and mRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Besides, we performed target prediction of the identified differentially expressed miRNAs. The overlapped differentially expressed genes (DEGs) were obtained combined with miRNA targets predicted and the DEGs identified from the mRNA dataset. The Cytoscape software was used to design a regulatory network of miRNA-gene. Moreover, the overlapped DEGs in the network were subjected to enrichment analysis to explore the associated biological processes. The molecular protein-protein interaction (PPI) network was used to identify the key genes among the DEGs of prognostic value for ovarian cancer, and the genes were evaluated via Kaplan-Meier curve analysis. A total of 186 overlapped DEGs were identified. Through miRNA-gene network analysis, we found that miR-195-5p, miR-424-5p, and miR-497-5p highly exhibited targeted association with overlapped DEGs. The three miRNAs are critical in the regulatory network and act as tumor suppressors. The overlapped DEGs were mainly associated with protein metabolism, histogenesis, and development of the reproductive system and ocular tissues. The PPI network identified 10 vital genes that promote tumor progression. Survival analysis found that CEP55 and CCNE1 may be associated with the prognosis of ovarian cancer. These findings provide insights to understand the pathogenesis of ovarian cancer and suggest new candidate biomarkers for early screening of ovarian cancer.

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Integrated miRNA-mRNA expression profiles revealing key molecules in ovarian cancer based on bioinformatics analysis

10.21203/rs.3.rs-17869/v1 ◽

2020 ◽

Author(s):

Chao Li ◽

Xiaodan Zhu ◽

Linghua Zhang ◽

Miaoling Ou ◽

Zhantong Hong

Keyword(s):

Ovarian Cancer ◽

Mrna Expression ◽

Regulatory Network ◽

Expression Profiles ◽

Mirna Gene ◽

Differentially Expressed ◽

Biological Processes ◽

Ppi Network ◽

Transcription Activator ◽

Key Genes

Abstract Background: Ovarian cancer was one of the leading causes of death in gynecological malignancies, of which molecular mechanism hadn’t been elucidated clearly yet. Our research aimed to reveal the potential key molecular and biological processes of ovarian cancer by means of bioinformatics.Methods: The microarray sets of miRNA and mRNA expression profiles were downloaded from the GEO database. The target prediction was performed on the differentially expressed miRNAs identified and the overlapped differentially expressed genes (DEGs) were obtained combined with miRNA and mRNA datasets. The regulatory network of miRNA-gene was further constructed by cytoscape software. The overlapped DEGs in the network were analyzed to explore the biological processes involved by enrichment analysis. The molecular protein-protein interaction (PPI) network was used to identify key genes among the DEGs.Results: A total of 167 overlapped DEGs were identified. The miRNA-gene network analysis found that miR-29c-3p, miR-1271-5p, and miR-133b, existed the most extensive targeting relationship with overlapped DEGs, being three key miRNAs of the regulatory network, and played the role of tumor suppressor. The GO enrichment showed that the overlapped DEGs were mainly involved in process named extracellular related organization, embryonic organ development, postsynaptic specialization, collagen trimer and DNA−binding transcription activator et al. The KEGG pathway analysis showed that these DEGs were involved in protein digestion and absorption and relaxin signaling pathway. The PPI network identified 10 key genes, playing the role in promoting tumor.Conclusion: The methodology used and identification of key molecules in our study contributed to understanding the pathogenesis of ovarian cancer and providing new candidate biomarkers for early screening of ovarian cancer.

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LncRNA-mRNA Expression Profiles and Functional Networks Associated with Cognitive Impairment in Folate-deficient Mice

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210208110517 ◽

2021 ◽

Vol 24 ◽

Author(s):

Xiaojin Feng ◽

Fenfang Zhan ◽

Jialing Hu ◽

Fuzhou Hua ◽

Guohai Xu

Keyword(s):

Gene Expression ◽

Cognitive Impairment ◽

Mrna Expression ◽

Expression Profiles ◽

Differentially Expressed ◽

Functional Networks ◽

Ppi Network ◽

Hub Genes ◽

Cerna Network ◽

Deficient Mice

Background: Cognitive impairment is a common neurocognitive disorder that affects millions of worldwide people’s health,related tofolate deficiency. Objective: The present study aimed to investigate the lncRNA-mRNA functional networks associated with cognitive impairment in folate-deficient mice and elucidate their possible molecular mechanisms. Methods: We downloaded the gene expression profile (GSE148126) of lncRNAs and mRNAs from NCBI Gene Expression Omnibus (GEO) database. Four groups of mouse hippocampi were analyzed, including 4 months (4mo) and 18 months (18mo) of folic acid (FA) deficiency/supplementation. The differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were identified using gplots and heatmap packages. The functions of the DEmRNAs were evaluated using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The hub genes wereidentified by CytoHubba plugins of Cytoscape, and protein-protein interaction (PPI) network of deregulated mRNAs was performed using STRING database. Finally, lncRNA-mRNA co-expression and competitive endogenous RNA (ceRNA) network analyses were constructed. Results: In total, we screened 67 lncRNAs with 211 mRNAs, and 89 lncRNAs with 229 mRNAs were differentially expressed in 4mo_FAand 18mo_FA deficient mice, respectively. GO analyses indicated that DEmRNAs were highly related to terms involved in binding and biological regulation. KEGG pathway analyses demonstrated that these genes were significantly enriched for Renin secretion, Pancreatic secretion and AMPK signaling pathways in 18mo_FA deficiency group. Subsequently, the top 5 hub genes werescreened from the PPI network, which may be key genes with the progression of folate deficiency. Upon the lncRNA-mRNA co-expression network analysis, we identified the top 10 lncRNAs having the maximum number of connections with related mRNAs. Finally, a ceRNA network was constructed for DE lncRNAs and DEmRNAs, and several pivotal miRNAs were predicted. Conclusions: This study identified the lncRNA-mRNA expression profiles and functional networks associated with cognitive impairment in folate-deficient mice, which provided support for the possible mechanisms and therapy for this disease.

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Comprehensive bioinformatics analysis of mRNA expression profiles and identification of a miRNA–mRNA network associated with lupus nephritis

Lupus ◽

10.1177/0961203320925155 ◽

2020 ◽

Vol 29 (8) ◽

pp. 854-861

Author(s):

Jianbo Song ◽

Liqin Zhao ◽

Yuanping Li

Keyword(s):

Lupus Nephritis ◽

Mrna Expression ◽

Lupus Erythematosus ◽

Target Genes ◽

Expression Profiles ◽

Enrichment Analysis ◽

Gene Expression Omnibus ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Ppi Network

Objective Lupus nephritis (LN) is one of the serious complications of systemic lupus erythematosus. The aim of this study was to identify core genes and pathways involved in the pathogenesis of LN. Methods We screened differentially expressed genes (DEGs) in LN patients using mRNA expression profile data from the Gene Expression Omnibus. The functional and pathway enrichment analysis of DEGs was performed utilizing the Database for annotation, Visualization and Integrated Discovery. Target genes with differentially expressed miRNAs (DEMIs) were predicted using the miRTarBase database, and the intersection between these target genes and DEGs was selected to be studied further. Results In total, 107 common DEGs (CDEGs) were identified from the Tub_LN group and Glom_LN group, and 66 DEMIs were identified. Fifty-three hub genes and two significant modules were identified from the protein–protein interaction (PPI) network, and a miRNA–mRNA network was constructed. The CDEGs, module genes in the PPI network and genes intersecting with the CDEGs and target genes of DEMIs were all associated with the PI3K-Akt signalling pathway. Conclusion In summary, this study reveals some crucial genes and pathways potentially involving in the pathogenesis of LN. These findings provide a new insight for the research and treatment of LN.

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Transcriptome analysis reveals potential function of long non-coding RNAs in 20-hydroxyecdysone regulated autophagy in Bombyx mori

BMC Genomics ◽

10.1186/s12864-021-07692-1 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Huili Qiao ◽

Jingya Wang ◽

Yuanzhuo Wang ◽

Juanjuan Yang ◽

Bofan Wei ◽

...

Keyword(s):

Bombyx Mori ◽

Target Gene ◽

Fat Body ◽

Expression Profiles ◽

Functional Characterization ◽

Differentially Expressed ◽

Post Injection ◽

Biological Processes ◽

Potential Target ◽

Non Coding Rnas

Abstract Background 20-hydroxyecdysone (20E) plays important roles in insect molting and metamorphosis. 20E-induced autophagy has been detected during the larval–pupal transition in different insects. In Bombyx mori, autophagy is induced by 20E in the larval fat body. Long non-coding RNAs (lncRNAs) function in various biological processes in many organisms, including insects. Many lncRNAs have been reported to be potential for autophagy occurrence in mammals, but it has not been investigated in insects. Results RNA libraries from the fat body of B. mori dissected at 2 and 6 h post-injection with 20E were constructed and sequenced, and comprehensive analysis of lncRNAs and mRNAs was performed. A total of 1035 lncRNAs were identified, including 905 lincRNAs and 130 antisense lncRNAs. Compared with mRNAs, lncRNAs had longer transcript length and fewer exons. 132 lncRNAs were found differentially expressed at 2 h post injection, compared with 64 lncRNAs at 6 h post injection. Thirty differentially expressed lncRNAs were common at 2 and 6 h post-injection, and were hypothesized to be associated with the 20E response. Target gene analysis predicted 6493 lncRNA-mRNA cis pairs and 42,797 lncRNA-mRNA trans pairs. The expression profiles of LNC_000560 were highly consistent with its potential target genes, Atg4B, and RNAi of LNC_000560 significantly decreased the expression of LNC_000560 and Atg4B. These results indicated that LNC_000560 was potentially involved in the 20E-induced autophagy of the fat body by regulating Atg4B. Conclusions This study provides the genome-wide identification and functional characterization of lncRNAs associated with 20E-induced autophagy in the fat body of B. mori. LNC_000560 and its potential target gene were identified to be related to 20-regulated autophagy in B. mori. These results will be helpful for further studying the regulatory mechanisms of lncRNAs in autophagy and other biological processes in this insect model.

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Integrative Analysis of miRNA and mRNA Expression Profiles in Mammary Glands of Holstein Cows Artificially Infected with Staphylococcus aureus

Pathogens ◽

10.3390/pathogens10050506 ◽

2021 ◽

Vol 10 (5) ◽

pp. 506

Author(s):

Xiaolong Wang ◽

Yongliang Fan ◽

Yifan He ◽

Ziyin Han ◽

Zaicheng Gong ◽

...

Keyword(s):

Staphylococcus Aureus ◽

Mrna Expression ◽

Calcium Binding ◽

Expression Profiles ◽

Bovine Mastitis ◽

Mammary Glands ◽

Integrative Analysis ◽

Differentially Expressed ◽

Future Application ◽

Collagen Type Iv

Staphylococcus aureus- induced mastitis is one of the most intractable problems for the dairy industry, which causes loss of milk yield and early slaughter of cows worldwide. Few studies have used a comprehensive approach based on the integrative analysis of miRNA and mRNA expression profiles to explore molecular mechanism in bovine mastitis caused by S. aureus. In this study, S. aureus (A1, B1 and C1) and sterile phosphate buffered saline (PBS) (A2, B2 and C2) were introduced to different udder quarters of three individual cows, and transcriptome sequencing and microarrays were utilized to detected miRNA and gene expression in mammary glands from the challenged and control groups. A total of 77 differentially expressed microRNAs (DE miRNAs) and 1625 differentially expressed genes (DEGs) were identified. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that multiple DEGs were enriched in significant terms and pathways associated with immunity and inflammation. Integrative analysis between DE miRNAs and DEGs proved that miR-664b, miR-23b-3p, miR-331-5p, miR-19b and miR-2431-3p were potential factors regulating the expression levels of CD14 Molecule (CD14), G protein subunit gamma 2 (GNG2), interleukin 17A (IL17A), collagen type IV alpha 1 chain (COL4A1), microtubule associated protein RP/EB family member 2 (MAPRE2), member of RAS oncogene family (RAP1B), LDOC1 regulator of NFKB signaling (LDOC1), low-density lipoprotein receptor (LDLR) and S100 calcium binding protein A9 (S100A9) in bovine mastitis caused by S. aureus. These findings could enhance the understanding of the underlying immune response in bovine mammary glands against S. aureus infection and provide a useful foundation for future application of the miRNA–mRNA-based genetic regulatory network in the breeding cows resistant to S. aureus.

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Interrogation of brain miRNA and mRNA expression profiles reveals a molecular regulatory network that is perturbed by mutant huntingtin

Journal of Neurochemistry ◽

10.1111/j.1471-4159.2012.07925.x ◽

2012 ◽

Vol 123 (4) ◽

pp. 477-490 ◽

Cited By ~ 37

Author(s):

Jing Jin ◽

Yong Cheng ◽

Yongqing Zhang ◽

William Wood ◽

Qi Peng ◽

...

Keyword(s):

Mrna Expression ◽

Regulatory Network ◽

Expression Profiles ◽

Mutant Huntingtin

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Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.675197 ◽

2021 ◽

Vol 12 ◽

Author(s):

Min Zhou ◽

Shasha Hong ◽

Bingshu Li ◽

Cheng Liu ◽

Ming Hu ◽

...

Keyword(s):

Ovarian Cancer ◽

Dna Methylation ◽

Mrna Expression ◽

Risk Score ◽

Cox Regression ◽

Expression Profiles ◽

Figo Stage ◽

Prognostic Indicator ◽

Tissue Expression ◽

The Cancer Genome Atlas

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

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Genomic Expression Profiling of Colorectal Cancer in Silico

10.21203/rs.3.rs-310514/v1 ◽

2021 ◽

Author(s):

Feifei Liu ◽

Yu Wang ◽

Wenxue Li ◽

Diancheng Li ◽

Yuwei Xin ◽

...

Keyword(s):

Colorectal Cancer ◽

Survival Analysis ◽

Mrna Expression ◽

Differentially Expressed Genes ◽

Expression Profiles ◽

Functional Enrichment ◽

Differentially Expressed ◽

Analysis Tool ◽

Hub Genes ◽

Normal Tissues

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies of the digestive system; the progression and prognosis of which are affected by a complicated network of genes and pathways. The aim of this study was to identify potential hub genes associated with the progression and prognosis of colorectal cancer (CRC).Methods: We obtained gene expression profiles from GEO database to search differentially expressed genes (DEGs) between CRC tissues and normal tissue. Subsequently, we conducted a functional enrichment analysis, generated a protein–protein interaction (PPI) network to identify the hub genes, and analyzed the expression validation of the hub genes. Kaplan–Meier plotter survival analysis tool was performed to evaluate the prognostic value of hub genes expression in CRC patients.Results: A total of 370 samples, involving CRC and normal tissues were enrolled in this article. 283 differentially expressed genes (DEGs), including 62 upregulated genes and 221 downregulated genes between CRC and normal tissues were selected. We finally filtered out 6 hub genes, including INSL5, MTIM, GCG, SPP1, HSD11B2, and MAOB. In the database of TCGA-COAD, the mRNA expression of INSL5, MT1M, HSD11B2, MAOB in tumor is lower than that in normal; the mRNA expression of SPP1 in tumor is higher than that in normal. In the HPA database, the expression of INSL5, GCG, HSD11B2, MAOB in tumor is lower than that in normal tissues; the expression of SPP1 in the tumor is higher than that in normal tissues. Survival analysis revealed that INSL5, GCG, SPP1 and MT1M may serve as prognostic biomarkers in CRC. Conclusions: We screened out six hub genes to predict the occurrence and prognosis of patients with CRC using bioinformatics methods, which may provide new targets and ideas for diagnosis, prognosis and individualized treatment for CRC.

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Identifcation of The Ferroptosis-Related Gene Signature In Placenta of Patients With Early-Onset Preeclampsia

10.21203/rs.3.rs-618453/v1 ◽

2021 ◽

Author(s):

Nana Yang ◽

Qianghua Wang ◽

Biao Ding ◽

Yinging Gong ◽

Yue Wu ◽

...

Keyword(s):

Iron Homeostasis ◽

Molecular Mechanisms ◽

Late Onset ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Ppi Network ◽

Network Analyses

Abstract Background: The accumulation of ROS resulting from upregulated levels of oxidative stress is commonly implicated in preeclampsia (PE). Ferroptosis is a novel form of iron-dependent cell death instigated by lipid peroxidation likely plays important role in PE pathogenesis. This study aims to investigate expression profiles and functions of the ferroptosis-related genes (FRGs) in early- and late-onset preeclampsia.Methods: The gene expression data and clinical information were downloaded from GEO database. The “limma” R package was used for screening differentially expressed genes. GO(Gene Ontology), Kyoto Encyclopedia of Genes and Genomes(KEGG) and protein protein interaction (PPI) network analyses were conducted to investigate the bioinformatics functions and molecular interactions of significantly different FRGs. Quantitative real-time reverse transcriptase PCR was used to verify the expression of hub FRGs in PE.Results: A total number of 4,215 DEGs were identified between EOPE and preterm cases and 3,356 DEGs were found between EOPE and LOPE subtypes. 20 significantly different FRGs were identified in EOPE, while only 3 in LOPE. Functional enrichment analysis revealed that the differentially expressed FRGs was mainly involved in EOPE and enriched in hypoxia- and iron-related pathways, such as response to hypoxia, iron homeostasis and iron ion binding process. The PPI network analysis and verification by RT-qPCR resulted in the identification of the following six interesting FRGs: FTH1, HIF1A, FTL, IREB2, MAPK8 and PLIN2. Conclusions: EOPE and LOPE owned distinct underlying molecular mechanisms and ferroptosis may be mainly implicated in pathogenesis of EOPE. Further studies are necessary for deeper inquiry into placental ferroptosis and its role in the pathogenesis of EOPE.

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BCL2 and hsa-miR-181a-5p are potential biomarkers associated with papillary thyroid cancer based on bioinformatics analysis

World Journal of Surgical Oncology ◽

10.1186/s12957-019-1755-9 ◽

2019 ◽

Vol 17 (1) ◽

Cited By ~ 2

Author(s):

Cong Zhang ◽

Chunrui Bo ◽

Lunhua Guo ◽

Pingyang Yu ◽

Susheng Miao ◽

...

Keyword(s):

Gene Expression ◽

Thyroid Cancer ◽

Gene Regulatory Network ◽

Papillary Thyroid Cancer ◽

Regulatory Network ◽

Mirna Gene ◽

Cancer Type ◽

Papillary Thyroid ◽

Ppi Network ◽

Gene Regulatory

Abstract Background The morbidity of thyroid carcinoma has been rising worldwide and increasing faster than any other cancer type. The most common subtype with the best prognosis is papillary thyroid cancer (PTC); however, the exact molecular pathogenesis of PTC is still not completely understood. Methods In the current study, 3 gene expression datasets (GSE3678, GSE3467, and GSE33630) and 2 miRNA expression datasets (GSE113629 and GSE73182) of PTC were selected from the Gene Expression Omnibus (GEO) database and were further used to identify differentially expressed genes (DEGs) and deregulated miRNAs between normal thyroid tissue samples and PTC samples. Then, Gene Ontology (GO) and pathway enrichment analyses were conducted, and a protein-protein interaction (PPI) network was constructed to explore the potential mechanism of PTC carcinogenesis. The hub gene detection was performed using the CentiScaPe v2.0 plugin, and significant modules were discovered using the MCODE plugin for Cytoscape. In addition, a miRNA-gene regulatory network in PTC was constructed using common deregulated miRNAs and DEGs. Results A total of 263 common DEGs and 12 common deregulated miRNAs were identified. Then, 6 significant KEGG pathways (P < 0.05) and 82 significant GO terms were found to be enriched, indicating that PTC was closely related to amino acid metabolism, development, immune system, and endocrine system. In addition, by constructing a PPI network and miRNA-gene regulatory network, we found that hsa-miR-181a-5p regulated the most DEGs, while BCL2 was targeted by the most miRNAs. Conclusions The results of this study suggested that hsa-miR-181a-5p and BCL2 and their regulatory networks may play important roles in the pathogenesis of PTC.

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