Identification of Candidate Genetic Markers and a Novel 4-genes Diagnostic Model in Osteoarthritis through Integrating Multiple Microarray Data

Background: Osteoarthritis (OA) is a joint disease that leads to a high disability rate and a low quality of life. With the development of modern molecular biology techniques, some key genes and diagnostic markers have been reported. However, the etiology and pathogenesis of OA are still unknown. Objective: To develop a gene signature in OA. Method: In this study, five microarray data sets were integrated to conduct a comprehensive network and pathway analysis of the biological functions of OA related genes, which can provide valuable information and further explore the etiology and pathogenesis of OA. Results and Discussion: Differential expression analysis identified 180 genes with significantly expressed expression in OA. Functional enrichment analysis showed that the up-regulated genes were associated with rheumatoid arthritis (p < 0.01). Down-regulated genes regulate the biological processes of negative regulation of kinase activity and some signaling pathways such as MAPK signaling pathway (p < 0.001) and IL-17 signaling pathway (p < 0.001). In addition, the OA specific protein-protein interaction (PPI) network was constructed based on the differentially expressed genes. The analysis of network topological attributes showed that differentially upregulated VEGFA, MYC, ATF3 and JUN genes were hub genes of the network, which may influence the occurrence and development of OA through regulating cell cycle or apoptosis, and were potential biomarkers of OA. Finally, the support vector machine (SVM) method was used to establish the diagnosis model of OA, which not only had excellent predictive power in internal and external data sets (AUC > 0.9), but also had high predictive performance in different chip platforms (AUC > 0.9) and also had effective ability in blood samples (AUC > 0.8). Conclusion: The 4-genes diagnostic model may be of great help to the early diagnosis and prediction of OA.

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Identification of Neuropeptides as Potential Crosstalks Linking Down Syndrome and Periodontitis Revealed by Transcriptomic Analyses

Disease Markers ◽

10.1155/2021/7331821 ◽

2021 ◽

Vol 2021 ◽

pp. 1-18

Author(s):

Yue Chen ◽

Xiaofei Yu ◽

Jia Kong

Keyword(s):

Down Syndrome ◽

Signaling Pathway ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Mapk Signaling ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Future Research ◽

Biological Processes

Background. This bioinformatics study was aimed to investigate the relationship between periodontitis (PD) and Down Syndrome (DS) regarding potential crosstalk genes, related neuropeptides, and biological processes. Methods. Data for PD (GSE23586, GSE10334 and GSE16134) and DS (GSE35665) were downloaded from NCBI Gene Expression Omnibus (GEO). Following normalization and merging of PD data, differential expression analysis was performed ( p value < 0.05 and ∣ log FC ∣ ≥ 0.5 ). The common deregulated genes between PD and DS were considered as crosstalk genes. The significantly differentially expressed genes were used to construct the coexpression network and to further identify coexpression gene modules. To acquire the significant modules, the significant expression level of genes in the module was used to analyze the enrichment of genes in each module. Neuropeptides were assessed from NeuroPedia database. Neuropeptide genes and crosstalk genes were merged and mapped into PPI network, and the correlation coefficient (Spearman) was determined for the crosstalk genes. Results. 138 crosstalk genes were predicted. According to the functional enrichment analysis, these genes significantly regulated different biological processes and pathways. In enrichment analysis, the significant module of DS was pink module, and turquoise module was significant in PD. Four common crosstalk genes were acquired, i.e., CD19, FCRL5, FCRLA, and HLA-DOB. In the complex network, INS and IGF2 interacted with CASP3 and TP53, which commonly regulated the MAPK signaling pathway. Moreover, the results showed that TP53 interacted with IGF2 and INS inducing the dysregulation of PI3K-Akt signaling pathway. UBL was positively correlated with crosstalk genes in both diseases. LEP was revealed to be both a neuropeptide and crosstalk gene and was positively correlated with other crosstalk genes. Conclusion. Different crosstalk genes, related neuropeptides, and biological pathways and processes were revealed between PD and DS, which can serve as a theoretical basis for future research.

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Analysis of Crucial Genes and Pathways Associated with Spared Nerve Injury-Induced Neuropathic Pain

Neural Plasticity ◽

10.1155/2020/8822001 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Dong Mao ◽

Huang Zhai ◽

Gang Zhao ◽

Jingyi Mi ◽

Yongjun Rui

Keyword(s):

Transcription Factor ◽

Neuropathic Pain ◽

Signaling Pathway ◽

Nerve Injury ◽

Microarray Data ◽

Mapk Signaling ◽

Mapk Signaling Pathway ◽

Functional Enrichment ◽

Spared Nerve Injury ◽

Ppi Network

Purpose. The study was aimed at elucidating the molecular mechanism underlying neuropathic pain induced by spared nerve injury (SNI). Methods. The microarray data of GSE30691 were downloaded from the Gene Expression Omnibus database, including sciatic nerve lesion samples at 3, 7, 21, and 40 days after SNI and sham control samples at 3, 7, and 21 days. Differential analysis along with Mfuzz clustering analysis was performed to screen crucial clusters and cluster genes. Subsequently, comprehensive bioinformatic analyses were performed, including functional enrichment analysis, protein-protein interaction (PPI) network and module analysis, and transcription factor- (TF-) gene and miRNA-target interaction predictions. Moreover, the screened differentially expressed genes (DEGs) were corroborated using two other microarray datasets. Results. Three clusters with different change trends over time after SNI were obtained. Protein kinase CAMP-activated catalytic subunit beta (Prkacb), complement C3 (C3), and activating transcription factor 3 (Atf3) were hub nodes in the PPI network, and fibroblast growth factor 9 (Fgf9) was found to interact with more TFs. Prkacb and Fgf9 were significantly enriched in the MAPK signaling pathway. Moreover, rno-miR-3583-5p was targeted by Fgf9, and rno-miR-1912-3p was targeted by neuregulin 1 (Nrg1). Key genes like Nrg1 and Fgf9 in cluster 1, Timp1 in cluster 2, and Atf3 and C3 in cluster 3 were screened out after corroborating microarray data with other microarray data. Conclusions. Key pathways like the MAPK signaling pathway and crucial genes like Prkacb, Nrg1, Fgf9, Timp1, C3, and Atf3 may contribute to SNI-induced neuropathic pain development in rats.

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Identification of blood-derived candidate gene markers and a new 7-gene diagnostic model for multiple sclerosis

Biological Research ◽

10.1186/s40659-021-00334-6 ◽

2021 ◽

Vol 54 (1) ◽

Author(s):

Xin Chen ◽

Huiqing Hou ◽

Huimin Qiao ◽

Haolong Fan ◽

Tianyi Zhao ◽

...

Keyword(s):

Multiple Sclerosis ◽

Immune Response ◽

Signaling Pathway ◽

Innate Immune Response ◽

Peripheral Blood ◽

Innate Immune ◽

Functional Enrichment ◽

Support Vector ◽

Diagnostic Model ◽

Gene Markers

Abstract Background Multiple sclerosis (MS) is a central nervous system disease with a high disability rate. Modern molecular biology techniques have identified a number of key genes and diagnostic markers to MS, but the etiology and pathogenesis of MS remain unknown. Results In this study, the integration of three peripheral blood mononuclear cell (PBMC) microarray datasets and one peripheral blood T cells microarray dataset allowed comprehensive network and pathway analyses of the biological functions of MS-related genes. Differential expression analysis identified 78 significantly aberrantly expressed genes in MS, and further functional enrichment analysis showed that these genes were associated with innate immune response-activating signal transduction (p = 0.0017), neutrophil mediated immunity (p = 0.002), positive regulation of innate immune response (p = 0.004), IL-17 signaling pathway (p < 0.035) and other immune-related signaling pathways. In addition, a network of MS-specific protein–protein interactions (PPI) was constructed based on differential genes. Subsequent analysis of network topology properties identified the up-regulated CXCR4, ITGAM, ACTB, RHOA, RPS27A, UBA52, and RPL8 genes as the hub genes of the network, and they were also potential biomarkers of MS through Rap1 signaling pathway or leukocyte transendothelial migration. RT-qPCR results demonstrated that CXCR4 was obviously up-regulated, while ACTB, RHOA, and ITGAM were down-regulated in MS patient PBMC in comparison with normal samples. Finally, support vector machine was employed to establish a diagnostic model of MS with a high prediction performance in internal and external datasets (mean AUC = 0.97) and in different chip platform datasets (AUC = (0.93). Conclusion This study provides new understanding for the etiology/pathogenesis of MS, facilitating an early identification and prediction of MS.

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Identification of a novel 4-gene diagnostic model for atrial fibrillation risk based on integrated analysis across independent data sets

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210121103304 ◽

2021 ◽

Vol 24 ◽

Author(s):

Pei Zhang ◽

Qiang Miao ◽

Xiao Wang ◽

Yong Zhang ◽

Yinglong Hou

Keyword(s):

Atrial Fibrillation ◽

Functional Enrichment ◽

Support Vector ◽

Integrated Analysis ◽

Diagnostic Model ◽

Data Sets ◽

Specific Expression ◽

Data Set ◽

Ppi Networks ◽

Focal Expression

Background: Atrial fibrillation (AF) is the most common persistent arrhythmia and an important factor leading to cardiovascular morbidity and mortality. Several key genes and diagnostic markers have been discovered with the development of advanced modern molecular biology techniques, but the etiology and pathogenesis of AF remained unknown. Methods: In this study, three chip-seq data sets and a RNA-seq data set were integrated as a comprehensive network for pathway analysis of the biological functions of related genes in AF, hoping to provide a better understanding on the etiology and pathogenesis of AF. Results: Differential co-expression analysis identified 360 genes with specific expression in AF, and functional enrichment analysis further revealed that these genes were significantly correlated with focal expression (p <0.01), autophagy (p <0.01), and thyroid cancer. In addition, Af-specific protein-protein interaction (PPI) networks were constructed based on AF-specific expression genes. Network topology analysis identified PLEKHA7, YWHAQ, PPP1CB, WDR1, AKT1, IGF1R, CANX, MAPK1, SRPK2 and SRSF10 genes as hub genes of the networks, and they were considered as potential biomarkers of AF because they were found to participate in the development of AF through Oocyte meiosis and focal expression. Finally, a diagnostic model for AF established with support vector machine (SVM), demonstrated excellent predictive performance in internal and external data sets (AUC>0.9) and in different platform data sets (mean AUC>0.75). Conclusion: Finally, a diagnostic model for AF established, thus showing its potential in the early identification and prediction of AF.

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Identification of Chronic Hypersensitivity Pneumonitis Biomarkers with Machine Learning and Differential Co-expression Analysis

Current Gene Therapy ◽

10.2174/1566523220666201208093325 ◽

2020 ◽

Vol 20 ◽

Author(s):

Hongwei Zhang ◽

Steven Wang ◽

Tao Huang

Keyword(s):

Feature Selection ◽

Expression Analysis ◽

Hypersensitivity Pneumonitis ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Great Promise ◽

Support Vector ◽

Svm Classifier ◽

Clinical Tool ◽

Chronic Hypersensitivity Pneumonitis

Aims: We would like to identify the biomarkers for chronic hypersensitivity pneumonitis (CHP) and facilitate the precise gene therapy of CHP. Background: Chronic hypersensitivity pneumonitis (CHP) is an interstitial lung disease caused by hypersensitive reactions to inhaled antigens. Clinically, the tasks of differentiating between CHP and other interstitial lungs diseases, especially idiopathic pulmonary fibrosis (IPF), were challenging. Objective: In this study, we analyzed the public available gene expression profile of 82 CHP patients, 103 IPF patients, and 103 control samples to identify the CHP biomarkers. Method: The CHP biomarkers were selected with advanced feature selection methods: Monte Carlo Feature Selection (MCFS) and Incremental Feature Selection (IFS). A Support Vector Machine (SVM) classifier was built. Then, we analyzed these CHP biomarkers through functional enrichment analysis and differential co-expression analysis. Result: There were 674 identified CHP biomarkers. The co-expression network of these biomarkers in CHP included more negative regulations and the network structure of CHP was quite different from the network of IPF and control. Conclusion: The SVM classifier may serve as an important clinical tool to address the challenging task of differentiating between CHP and IPF. Many of the biomarker genes on the differential co-expression network showed great promise in revealing the underlying mechanisms of CHP.

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Exploring the Potential of Capsaicin Against Cancer Metastasis Based on TGF-β Signaling Modulation Through Module-based Network Pharmacology Approach

Current Drug Discovery Technologies ◽

10.2174/1570163816666190515104041 ◽

2020 ◽

Vol 17 (5) ◽

pp. 647-660 ◽

Cited By ~ 1

Author(s):

Shivananda Kandagalla ◽

Sharath Belenahalli Shekarappa ◽

Gollapalli Pavan ◽

Umme Hani ◽

Manjunatha Hanumanthappa

Keyword(s):

Amino Acid ◽

Amino Acid Composition ◽

Signaling Pathway ◽

Acid Composition ◽

Protein Interaction ◽

Cancer Metastasis ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Mode Analysis ◽

Network Pharmacology

Background: Capsaicin is an active alkaloid /principal component of red pepper responsible for the pungency of chili pepper. Capsaicin by changing the intracellular redox homeostasis regulate a variety of signaling pathways ultimately producing a divergent cellular outcome. Several reports showed the potential of capsaicin against cancer metastasis, however unexplored molecular mechanism is still an active part of the research. Several growth factors have a critical role during cancer metastasis among them TGF- β signaling play a vital role. Methods: The present study aimed at analyzing capsaicin modulation of TGF-β signaling using network pharmacology approach. The chemical and protein interaction data of capsaicin was curated and abstracted using STITCH4.0, PubChem and ChEMBL database. Further, the compiled data set was subjected to the pathway and functional enrichment analysis using Protein Analysis THrough Evolutionary Relationship (PANTHER) and, Database for Annotation, Visualization, and Integrated Discovery (DAVID) database. Meanwhile, the pattern of amino acid composition across the capsaicin targets was analyzed using the EMBOSS Pepstat tool. Capsaicin targets involved in TGF- β were identified and their Protein-Protein Interaction (PPI) network constructed using STRING v10 and Cytoscape (v 3.2.1). From the above-constructed network, the clusters were mined using the MCODE clustering algorithm and finally binding affinity of capsaicin with its targets involved in TGF-β signaling pathway was analyzed using Autodock Vina. Results: The analysis explored capsaicin targets and, their associated functional and pathway annotations. Besides, the analysis also provides a detailed distinct pattern of amino acid composition across the capsaicin targets. The capsaicin targets described as MAPK14, JUN, SMAD3, MAPK3, MAPK1 and MYC involved in TGF-β signaling pathway through pathway enrichment analysis. The binding mode analysis of capsaicin with its targets has shown high affinity with MAPK3, MAPK1, JUN and MYC. Conclusion: The study explores the potential of capsaicin as a potent modulator of TGF-β signaling pathway during cancer metastasis and proposes new methodology and mechanism of action of capsaicin against TGF- β signaling pathway.

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Integrative analyses of biomarkers and pathways for heart failure

10.21203/rs.3.rs-1031511/v1 ◽

2021 ◽

Author(s):

Shaowei Fan ◽

Yuanhui Hu

Keyword(s):

Heart Failure ◽

Extracellular Matrix ◽

Microarray Data ◽

Enrichment Analysis ◽

Signal Pathway ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Pathway Enrichment Analysis ◽

Ppi Networks ◽

Key Genes

Abstract Background: Heart failure (HF) is the most common potential cause of death, causing a huge health and economic burden all over the world. So far, some impressive progress has been made in the study of pathogenesis. However, the underlying molecular mechanisms leading to this disease remain to be fully elucidated. Methods: The microarray data sets of GSE76701, GSE21610 and GSE8331 were retrieved from the gene expression comprehensive database (GEO). After merging all microarray data and adjusting batch effects, differentially expressed genes (DEG) were determined. Functional enrichment analysis was performed based on Gene Ontology (GO) resources, Kyoto Encyclopedia of Genes and Genomes (KEGG) resources, gene set enrichment analysis (GSEA), response pathway database and Disease Ontology (DO). Protein protein interaction (PPI) network was constructed using string database. Combined with the above important bioinformatics information, the potential key genes were selected. The comparative toxicological genomics database (CTD) is used to explore the interaction between potential key genes and HF. Results: We identified 38 patients with heart failure and 16 normal controls. There were 315 DEGs among HF samples, including 278 up-regulated genes and 37 down-regulated genes. Pathway enrichment analysis showed that most DEGs were significantly enriched in BMP signal pathway, transmembrane receptor protein serine / threonine kinase signal pathway, extracellular matrix, basement membrane, glycosaminoglycan binding, sulfur compound binding and so on. Similarly, GSEA enrichment analysis showed that DEGs were mainly enriched in extracellular matrix and extracellular matrix related proteins. BBS9, CHRD, BMP4, MYH6, NPPA and CCL5 are central genes in PPI networks and modules. Conclusions: the enrichment pathway of DEGs and go ontology may reveal the molecular mechanism of HF. Among them, target genes EIF1AY, RPS4Y1, USP9Y, KDM5D, DDX3Y, NPPA, HBB, TSIX, LOC28556 and XIST are expected to become new targets for heart failure. Our findings provide potential biomarkers or therapeutic targets for the further study of heart failure and contribute to the development of advanced prediction, diagnosis and treatment strategies.

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The Mechanism Study of Xiao-Xian-Xiong Decoction in the Treatment of Atherosclerosis with Network Pharmacology

10.21203/rs.3.rs-73640/v1 ◽

2020 ◽

Author(s):

Liucheng Xiao ◽

Zonghuan Li ◽

Chongyuan Fan ◽

Chenggong Zhu ◽

Xingyu Ma ◽

...

Keyword(s):

Cell Proliferation ◽

Signaling Pathway ◽

Mapk Pathway ◽

Enrichment Analysis ◽

Foam Cells ◽

Functional Enrichment ◽

Network Pharmacology ◽

Mechanism Study ◽

Ppi Networks

Abstract Background: Xiao-Xian-Xiong decoction is a useful formula in the treatment of atherosclerosis in traditional Chinese medicine. In this study, we aimed to investigate the function of Xiao-Xian-Xiong decoction in the treatment of atherosclerosis. Methods: In this study, we conducted the method of network pharmacology and molecular docking to discover the mechanism of Xiao-Xian-Xiong decoction against atherosclerosis. Then, we validated the function of Xiao-Xian-Xiong decoction in atherosclerosis in vitro. We investigated the function and mechanism of Xiao-Xian-Xiong decoction in RAW264.7 macrophage-derived foam cells.Results: We identified 213 targets of Xiao-Xian-Xiong decoction and 331 targets of atherosclerosis. The PPI networks of Xiao-Xian-Xiong decoction and atherosclerosis were constructed. Furthermore, the two PPI networks were merged and the core PPI network was obtained. Then, functional enrichment analysis was conducted with GO and KEGG signaling pathway analysis. KEGG analysis indicated Xiao-Xian-Xiong decoction was correlated with ubiquitin mediated proteolysis pathway, PI3K-AKT pathway, MAPK pathway, Notch signaling pathway, and TGF-β signaling pathway. At last, we validated the function of Xiao-Xian-Xiong decoction with atherosclerosis in vitro. Xiao-Xian-Xiong decoction reduced lipid accumulation and promoted the outflow of cholesterol in RAW264.7-derived foam cells. Xiao-Xian-Xiong decoction increased the expression of ABCA1 and ABCG1 protein in foam cells. ABCA1 and ABCG1 were related with regulation of the inflammatory pathway and cell proliferation in atherosclerosis.Conclusions: Combined the mechanism of available treatments of atherosclerosis, we inferred Xiao-Xian-Xiong decoction could alleviate atherosclerosis by inhibiting inflammatory response and cell proliferation.

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Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

PeerJ ◽

10.7717/peerj.8831 ◽

2020 ◽

Vol 8 ◽

pp. e8831 ◽

Cited By ~ 1

Author(s):

Xiaojiao Guan ◽

Yao Yao ◽

Guangyao Bao ◽

Yue Wang ◽

Aimeng Zhang ◽

...

Keyword(s):

Gene Expression ◽

Esophageal Cancer ◽

Esophageal Carcinoma ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Diagnostic Model ◽

Link Type ◽

Key Genes

Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

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Integrated lncRNA and mRNA Transcriptome Analyses in the Ovary of Cynoglossus semilaevis Reveal Genes and Pathways Potentially Involved in Reproduction

Frontiers in Genetics ◽

10.3389/fgene.2021.671729 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yani Dong ◽

Likang Lyu ◽

Daiqiang Zhang ◽

Jing Li ◽

Haishen Wen ◽

...

Keyword(s):

Signaling Pathways ◽

Target Genes ◽

Enrichment Analysis ◽

Mapk Signaling ◽

Cynoglossus Semilaevis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Biological Processes ◽

Tongue Sole ◽

Oocyte Meiosis

Long non-coding RNAs (lncRNAs) have been reported to be involved in multiple biological processes. However, the roles of lncRNAs in the reproduction of half-smooth tongue sole (Cynoglossus semilaevis) are unclear, especially in the molecular regulatory mechanism driving ovarian development and ovulation. Thus, to explore the mRNA and lncRNA mechanisms regulating reproduction, we collected tongue sole ovaries in three stages for RNA sequencing. In stage IV vs. V, we identified 312 differentially expressed (DE) mRNAs and 58 DE lncRNAs. In stage V vs. VI, we identified 1,059 DE mRNAs and 187 DE lncRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DE mRNAs were enriched in ECM-receptor interaction, oocyte meiosis and steroid hormone biosynthesis pathways. Furthermore, we carried out gene set enrichment analysis (GSEA) to identify potential reproduction related-pathways additionally, such as fatty metabolism and retinol metabolism. Based on enrichment analysis, DE mRNAs with a potential role in reproduction were selected and classified into six categories, including signal transduction, cell growth and death, immune response, metabolism, transport and catabolism, and cell junction. The interactions of DE lncRNAs and mRNAs were predicted according to antisense, cis-, and trans-regulatory mechanisms. We constructed a competing endogenous RNA (ceRNA) network. Several lncRNAs were predicted to regulate genes related to reproduction including cyp17a1, cyp19a1, mmp14, pgr, and hsd17b1. The functional enrichment analysis of these target genes of lncRNAs revealed that they were involved in several signaling pathways, such as the TGF-beta, Wnt signaling, and MAPK signaling pathways and reproduction related-pathways such as the progesterone-mediated oocyte maturation, oocyte meiosis, and GnRH signaling pathway. RT-qPCR analysis showed that two lncRNAs (XR_522278.2 and XR_522171.2) were mainly expressed in the ovary. Dual-fluorescence in situ hybridization experiments showed that both XR_522278.2 and XR_522171.2 colocalized with their target genes cyp17a1 and cyp19a1, respectively, in the follicular cell layer. The results further demonstrated that lncRNAs might be involved in the biological processes by modulating gene expression. Taken together, this study provides lncRNA profiles in the ovary of tongue sole and further insight into the role of lncRNA involvement in regulating reproduction in tongue sole.

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