scholarly journals Systematic and integrated analysis of tRNA-derived small RNAs reveals novel potential pathogenesis targets of sarcoidosis

2022 ◽  
Author(s):  
Min Zhao ◽  
Chang Tian ◽  
Xin Di ◽  
Xin Jin ◽  
Shan Cong ◽  
...  
Keyword(s):  
Visual Analysis ◽  
Expression Profiles ◽  
Target Prediction ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Deep Sequencing Technology ◽  
Non Coding Rnas ◽  
Polymerase Chain ◽  
Regulatory Functions

Abstract The pathogenesis of sarcoidosis, which involves several systems, is unclear, and its pathological type is non-caseating epithelioid granulomas. tRNA-derived small RNA (tsRNA) is a novel class of short non-coding RNAs with potential regulatory functions. However, whether tsRNA contributes to sarcoidosis pathogenesis remains unclear. Deep sequencing technology was used to identify alterations in tsRNA expression profiles between patients with sarcoidosis and healthy controls. A total of 360 tsRNAs were identified for exact matches. Among them, the expression of three tRNAs (tiRNA-Glu-TTC-001, tiRNA-Lys-CTT-003, and tRF-Ser-TGA-007) was markedly regulated in sarcoidosis and validated by quantitative real-time polymerase chain reaction. The expression of various tsRNAs was significantly correlated with age, the number of affected systems, and calcium levels in the blood. Additionally, target prediction and bioinformatics analyses revealed that these tsRNAs may play roles in chemokine, cAMP, cGMP-PKG, retrograde endorphin, and FoxO signalling pathways. The Cytoscape software was used for visual analysis to obtain 10 hub genes of each target tsRNA. Among the hub genes, APP, PRKACB, ARRB2, and NR5A1 finding may participate in the occurrence and development of sarcoidosis through immune inflammation. This study provides novel insights to explore tsRNA as a novel and efficacious pathogenic target of sarcoidosis.

scholarly journals Upregulation of 15 Antisense Long Non-Coding RNAs in Osteosarcoma

Genes ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1132
Author(s):  
Emel Rothzerg ◽  
Xuan Dung Ho ◽  
Jiake Xu ◽  
David Wood ◽  
Aare Märtson ◽  
...  
Keyword(s):  
Tumour Progression ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Osteosarcoma Cell ◽  
Osteoblast Cell Line ◽  
Antisense Lncrnas ◽  
Non Coding Rnas ◽  
Polymerase Chain ◽  
Multiple Levels

The human genome encodes thousands of natural antisense long noncoding RNAs (lncRNAs); they play the essential role in regulation of gene expression at multiple levels, including replication, transcription and translation. Dysregulation of antisense lncRNAs plays indispensable roles in numerous biological progress, such as tumour progression, metastasis and resistance to therapeutic agents. To date, there have been several studies analysing antisense lncRNAs expression profiles in cancer, but not enough to highlight the complexity of the disease. In this study, we investigated the expression patterns of antisense lncRNAs from osteosarcoma and healthy bone samples (24 tumour-16 bone samples) using RNA sequencing. We identified 15 antisense lncRNAs (RUSC1-AS1, TBX2-AS1, PTOV1-AS1, UBE2D3-AS1, ERCC8-AS1, ZMIZ1-AS1, RNF144A-AS1, RDH10-AS1, TRG-AS1, GSN-AS1, HMGA2-AS1, ZNF528-AS1, OTUD6B-AS1, COX10-AS1 and SLC16A1-AS1) that were upregulated in tumour samples compared to bone sample controls. Further, we performed real-time polymerase chain reaction (RT-qPCR) to validate the expressions of the antisense lncRNAs in 8 different osteosarcoma cell lines (SaOS-2, G-292, HOS, U2-OS, 143B, SJSA-1, MG-63, and MNNG/HOS) compared to hFOB (human osteoblast cell line). These differentially expressed IncRNAs can be considered biomarkers and potential therapeutic targets for osteosarcoma.

scholarly journals Integrated Analysis of Long Non-Coding RNA and mRNA Expression Profiles in Testes of Calves and Sexually Mature Wandong Bulls (Bos taurus)

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2006
Author(s):  
Hongyu Liu ◽  
Ibrar Muhammad Khan ◽  
Huiqun Yin ◽  
Xinqi Zhou ◽  
Muhammad Rizwan ◽  
...  
Keyword(s):  
Target Genes ◽  
Expression Profiles ◽  
Age Groups ◽  
Adherens Junction ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Rna Interaction ◽  
Long Non Coding Rna

The mRNAs and long non-coding RNAs axes are playing a vital role in the regulating of post-transcriptional gene expression. Thereby, elucidating the expression pattern of mRNAs and long non-coding RNAs underlying testis development is crucial. In this study, mRNA and long non-coding RNAs expression profiles were investigated in 3-month-old calves and 3-year-old mature bulls’ testes by total RNA sequencing. Additionally, during the gene level analysis, 21,250 mRNAs and 20,533 long non-coding RNAs were identified. As a result, 7908 long non-coding RNAs (p-adjust < 0.05) and 5122 mRNAs (p-adjust < 0.05) were significantly differentially expressed between the distinct age groups. In addition, gene ontology and biological pathway analyses revealed that the predicted target genes are enriched in the lysine degradation, cell cycle, propanoate metabolism, adherens junction and cell adhesion molecules pathways. Correspondingly, the RT-qPCR validation results showed a strong consistency with the sequencing data. The source genes for the mRNAs (CCDC83, DMRTC2, HSPA2, IQCG, PACRG, SPO11, EHHADH, SPP1, NSD2 and ACTN4) and the long non-coding RNAs (COX7A2, COX6B2, TRIM37, PRM2, INHBA, ERBB4, SDHA, ATP6VOA2, FGF9 and TCF21) were found to be actively associated with bull sexual maturity and spermatogenesis. This study provided a comprehensive catalog of long non-coding RNAs in the bovine testes and also offered useful resources for understanding the differences in sexual development caused by the changes in the mRNA and long non-coding RNA interaction expressions between the immature and mature stages.

scholarly journals Identification of RRM2 as a Potential Biomarker for the Diagnosis and Prognosis of Rheumatoid Arthritis

2021 ◽  
Author(s):  
Wu Biao ◽  
Yufeng Chen ◽  
Junlong Zhong ◽  
Shuping Zhong ◽  
Bin Wang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Mononuclear Cells ◽  
Expression Profiles ◽  
Clinical Samples ◽  
Healthy Controls ◽  
Rna Seq ◽  
Hub Genes ◽  
Healthy Human ◽  
Bioinformatics Analyses ◽  
Potential Biomarker

Abstract Background: Rheumatoid arthritis (RA) is a common autoimmune disease that can occur at any age. If treatment is delayed, RA can seriously affect the patients’ quality of life. However, there is no diagnostic criteria for RA and the positive predictive value of the current biomarkers is moderate. Objective: to identify RA-associated susceptibility genes and explore their potential as a novel biomarker for diagnosis and evaluation of the prognosis of RA.Methods: Peripheral blood mononuclear cells (PBMCs) were collected from healthy human donors and RA patients. RNA-seq analyses were performed to identify the differentially expressed genes (DEGs) between RA and control samples. The PBMCs-mRNA in DEGs were further subjected to enrichment analysis. Furthermore, the hub genes and key modules associated with RA were screened by bioinformatics analyses. Then, the expression of hub genes in RA were assessed in mRNA expression profiles. Next, real time-quantitative PCR (RT-qPCR) analyses were performed to further confirm the expression of the hub genes from the PBMCs that collected from 47 patients with RA and 40 healthy controls. Finally, we evaluated the clinical characters for the candidate mRNAs.Results: RNA-seq analyses revealed the expression of 178 mRNAs from PBMCs were disregulated between the healthy controls and the RA patients. Bioinformatics analyses revealed 10 hub mRNAs. The top 3 significant functional modules screened from PPI network functionally were involved in DNA replication origin binding, chemokine activity, etc. After validating the 10 hub mRNAs in GSE93272 dataset and clinical samples, we identified 3 candidate mRNAs, including ASPM, DTL and RRM2. Among which, RRM2 showed great capacity in discriminating between remissive RA and active RA. Significant correlations were observed between DTL and IL-8, TNF-α, between RRM2 and CDAI, DAS-28, tender joints and swollen joints, respectively. The AUC values of ASPM, DTL and RRM2 were 0.654, 0.995 and 0.990, respectively.Conclusion: We successfully identified multiple candidate mRNAs associated with RA. RRM2 showed high diagnosis efficiency with the AUC of 0.990 (sensitivity=100%, specificity=97.5%). And RRM2 severed as an additional biomarker for evaluating disease activity. The findings provided a novel candidate biomarker for diagnosis and evaluation of the prognosis of RA.

Integrated analysis profiles of long non–coding RNAs reveal potential biomarkers across brain regions in post–traumatic stress disorder

2020 ◽  
Author(s):  
Yaoyao Bian ◽  
Lili Yang ◽  
Zhongli Wang ◽  
Wen Li ◽  
Qing Wang ◽  
...  
Keyword(s):  
Post Traumatic Stress Disorder ◽  
Traumatic Stress ◽  
Stress Disorder ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Post Traumatic Stress ◽  
Post Traumatic ◽  
Non Coding Rnas ◽  
Potential Biomarkers

Abstract Background Post–traumatic stress disorder (PTSD) is characterized by impaired fear extinction, excessive anxiety and depression. However, underlying mechanisms, especially the function roles of long non–coding RNAs (lncRNAs) involved in PTSD is still unclear. We argued that the lncRNAs, co–expressed mRNAs, as well as the associated pathways, are altered and may thus serve as potential biomarkers and key pathways related to PTSD.Methods The gene expression profiles of GSE68077 was downloaded from the GEO database, and the differentially expressed lncRNAs and mRNAs were identified. Gene ontology (GO) and Kyto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis were performed. Subsequently, protein–protein interaction (PPI) network was analyzed, and module analysis of the differentially expressed mRNAs was performed with Cytoscape software. Finally, lncRNAs–mRNAs co–expression network was constructed and core pair lncRNAs involved in PTSD were mapped.Results A total of 45 differentially expressed lncRNAs and 726 differentially expressed mRNAs were obtained. Among of which, 17 lncRNAs and 86 mRNAs were inter–regulated, and most of the lncRNAs–mRNAs co–expression showed positive correlations. The lncRNAs–mRNAs co–expressed network suggested the potentially functional roles of lncRNAs, regulated mRNAs and related pathways in PTSD. By implication of the core pair network, lncRNA–NONMMUT010120.2 synergistically up–regulated Ppargc1a and down–regulated Cir1, Slc38a9, Atp6v0a2. Moreover, lncRNA–NONMMUT023440.2, NONMMUT034155.2, NONMMUT105407.1 and NONMMUT149972.1 were co–expressed with 10 co–expressed mRNAs, which indicated that lncRNAs involved in PTSD might work by regulating the co–expressed mRNAs.

scholarly journals An integrated analysis of the competing endogenous RNA network and co-expression network revealed seven hub long non-coding RNAs in osteoarthritis

2020 ◽  
Vol 9 (3) ◽  
pp. 90-98 ◽  
Author(s):  
Haitao Chen ◽  
Liaobin Chen
Keyword(s):  
Extracellular Matrix ◽  
Expression Profiles ◽  
Osteoclast Differentiation ◽  
Signalling Pathway ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Knee Cartilage ◽  
Competing Endogenous Rna ◽  
Cerna Network ◽  
Non Coding Rnas

Aims This study aimed to uncover the hub long non-coding RNAs (lncRNAs) differentially expressed in osteoarthritis (OA) cartilage using an integrated analysis of the competing endogenous RNA (ceRNA) network and co-expression network. Methods Expression profiles data of ten OA and ten normal tissues of human knee cartilage were obtained from the Gene Expression Omnibus (GEO) database (GSE114007). The differentially expressed messenger RNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified using the edgeR package. We integrated human microRNA (miRNA)-lncRNA/mRNA interactions with DElncRNA/DEmRNA expression profiles to construct a ceRNA network. Likewise, lncRNA and mRNA expression profiles were used to build a co-expression network with the WGCNA package. Potential hub lncRNAs were identified based on an integrated analysis of the ceRNA network and co-expression network. StarBase and Multi Experiment Matrix databases were used to verify the lncRNAs. Results We detected 1,212 DEmRNAs and 49 DElncRNAs in OA and normal knee cartilage. A total of 75 dysregulated lncRNA-miRNA interactions and 711 dysregulated miRNA-mRNA interactions were obtained in the ceRNA network, including ten DElncRNAs, 69 miRNAs, and 72 DEmRNAs. Similarly, 1,330 dysregulated lncRNA-mRNA interactions were used to construct the co-expression network, which included ten lncRNAs and 407 mRNAs. We finally identified seven hub lncRNAs, named MIR210HG, HCP5, LINC00313, LINC00654, LINC00839, TBC1D3P1-DHX40P1, and ISM1-AS1. Subsequent enrichment analysis elucidated that these lncRNAs regulated extracellular matrix organization and enriched in osteoclast differentiation, the FoxO signalling pathway, and the tumour necrosis factor (TNF) signalling pathway in the development of OA. Conclusion The integrated analysis of the ceRNA network and co-expression network identified seven hub lncRNAs associated with OA. These lncRNAs may regulate extracellular matrix changes and chondrocyte homeostasis in OA progress. Cite this article: Bone Joint Res. 2020;9(3):90–98.

scholarly journals Integrative analysis of miRNA-mRNA network in high altitude retinopathy by bioinformatics analysis

2021 ◽  
Author(s):  
Tong Su ◽  
Chufeng Gu ◽  
Deji Draga ◽  
Chuandi Zhou ◽  
Thashi Lhamo ◽  
...  
Keyword(s):  
High Altitude ◽  
Bioinformatics Analysis ◽  
Cell Model ◽  
Expression Profiles ◽  
Oxygen Deficiency ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Effective Prevention ◽  
Qrt Pcr

High-altitude retinopathy (HAR) is an ocular manifestation of acute oxygen deficiency at high altitudes. Although the pathophysiology of HAR has been revealed by many studies in recent years, the molecular mechanism is not yet clear. Our study aimed to systematically identify the genes and miRNA and explore the potential biomarkers associated with HAR by integrated bioinformatics analysis. The mRNA and miRNA expression profiles were obtained from the GEO database. We performed Gene Ontology (GO) functional annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Potential target gene analysis and miRNA-mRNA network analysis were also conducted. Quantitative RT-PCR (qRT-PCR) was used to validate the results of the bioinformatics analysis. Through a series of bioinformatics analyses and experiments, we selected 16 differentially expressed miRNAs (DE-miRNAs) and 157 differentially expressed genes (DEGs) related to AMS and constructed a miRNA-mRNA network containing 240 relationship pairs. The hub genes were filtered from the PPI network: IL7R, FOS, IL10, FCGR2A, DDX3X, CDK1, BCL11B and HNRNPH1, which were all downregulated in the AMS group. Then, 9 upregulated DE-miRNAs and 8 hub genes were verified by qRT-PCR in our hypoxia-induced HAR cell model. The expression of miR-3177-3p, miR-369-3p, miR-603, miR-495, miR-4791, miR-424-5p, FOS, IL10 and IL7R was consistent with our bioinformatics results. In conclusion, FOS, IL10, IL-7R and 7 DE-miRNAs may participate in the development of HAR. Our findings will contribute to the identification of biomarkers and promote the effective prevention and treatment of HAR in the future.

scholarly journals Identification of Key Genes and Pathways in SARS-CoV-2 Infection using Bioinformatics Analysis

2020 ◽  
Author(s):  
Zhe Wang ◽  
Chenhao Jiang ◽  
Xuxuan Zhang ◽  
Yingna Zhang ◽  
Yan Ren ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Expression Profiles ◽  
Severe Pneumonia ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Airway Epithelial ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Human Airway

Abstract Background: Coronavirus disease 2019 (COVID-19) is a disease that causes fatal disorders including severe pneumonia. Our study aimed to utilize bioinformatics method to analyze the expression profiling by high throughput sequencing in human bronchial organoids/primary human airway epithelial infected with SARS-CoV-2 to identify the potentially crucial genes and pathways associated with COVID-19.Methods: We analyzed microarray datasets GSE153970 and GSE150819 derived from the GEO database. Firstly, the Differentially expressed genes （DEGs） in human bronchial organoids/primary human airway epithelial infected with SARS-CoV-2. Next, the DEGs were used for GO and KEGG pathway enrichment analysis. Then, the PPI network was constructed and Cytoscape was used to find the key genes.Results: Gene expression profiles of GSE153970 and GSE150819, in all 12 samples were analyzed. A total of 145 DEGs and 5 hub genes were identified in SARS-CoV-2. Meanwhile, we found that the 145 genes are associated with immune responses and the top 5 hub genes including CXCL8, CXCL1, CXCL2, CCL20, and CSF2 were mainly related to leukocyte migration, endoplasmic reticulum lumen, receptor ligand activity. In addition, the results also showed that the hub genes were associated with Cytokine−cytokine receptor interaction, IL−17 signaling pathway, and Rheumatoid arthritis in SARS-CoV-2 infection.Conclusion: The five crucial genes consisting of CXCL8, CXCL1, CXCL2, CCL20, and CSF2 were considered as hub genes of SARS-CoV-2, which may be used as diagnostic biomarkers or molecular targets for the treatment of SARS-CoV-2. It is evidenced that bioinformatics analyses in SARS-CoV-2 can be useful for understanding the underlying molecular mechanism and exploring effective therapeutic targets.

scholarly journals Micro-Transcriptome Analysis Reveals Immune-Related MicroRNA Regulatory Networks of Paralichthys olivaceus Induced by Vibrio anguillarum Infection

2020 ◽  
Vol 21 (12) ◽  
pp. 4252
Author(s):  
Xianhui Ning ◽  
Li Sun
Keyword(s):  
Immune Response ◽  
Transcriptome Analysis ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Paralichthys Olivaceus ◽  
Vibrio Anguillarum ◽  
Japanese Flounder ◽  
Integrated Analysis ◽  
Immune Gene ◽  
Hub Genes

MicroRNAs (miRNAs) are non-coding regulatory RNAs that play a vital part in the host immune response to pathogen infection. Japanese flounder (Paralichthys olivaceus) is an important aquaculture fish species that has suffered from bacterial diseases, including that caused by Vibrio anguillarum infection. In a previous study, we examined the messenger RNA (mRNA) expression profiles of flounder during V. anguillarum infection and identified 26 hub genes in the flounder immune response. In this study, we performed the micro-transcriptome analysis of flounder spleen in response to V. anguillarum infection at 3 different time points. Approximately 277 million reads were obtained, from which 1218 miRNAs were identified, including 740 known miRNAs and 478 novel miRNAs. Among the miRNAs, 206 were differentially expressed miRNAs (DEmiRs), and 104 of the 206 DEmiRs are novel miRNAs identified for the first time. Most of the DEmiRs were strongly time-dependent. A total of 1355 putative target genes of the DEmiRs (named DETGs) were identified based on integrated analysis of miRNA-mRNA expressions. The DETGs were enriched in multiple functional categories associated with immunity. Thirteen key DEmiRs and 66 immune DETGs formed an intricate regulatory network constituting 106 pairs of miRNAs and DETGs that span five immune pathways. Furthermore, seven of the previously identified hub genes were found to be targeted by 73 DEmiRs, and together they formed interlinking regulatory networks. These results indicate that V. anguillarum infection induces complicated miRNA response with extensive influences on immune gene expression in Japanese flounder.

scholarly journals Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7408 ◽  
Author(s):  
Shucai Xie ◽  
Xili Jiang ◽  
Jianquan Zhang ◽  
Shaowei Xie ◽  
Yongyong Hua ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Hepatitis B ◽  
Pathway Analysis ◽  
Expression Profiles ◽  
Financial Burden ◽  
Disease Free Survival ◽  
Ppi Network ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Extracellular Region

Background Hepatocellular carcinoma (HCC) is a common malignant tumor affecting the digestive system and causes serious financial burden worldwide. Hepatitis B virus (HBV) is the main causative agent of HCC in China. The present study aimed to investigate the potential mechanisms underlying HBV-related HCC and to identify core biomarkers by integrated bioinformatics analyses. Methods In the present study, HBV-related HCC GSE19665, GSE55092, GSE94660 and GSE121248 expression profiles were downloaded from the Gene Expression Omnibus database. These databases contain data for 299 samples, including 145 HBV-related HCC tissues and 154 non-cancerous tissues (from patients with chronic hepatitis B). The differentially expressed genes (DEGs) from each dataset were integrated and analyzed using the RobustRankAggreg (RRA) method and R software, and the integrated DEGs were identified. Subsequently, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the DAVID online tool, and the protein–protein interaction (PPI) network was constructed using STRING and visualized using Cytoscape software. Finally, hub genes were identified, and the cBioPortal online platform was used to analyze the association between the expression of hub genes and prognosis in HCC. Results First, 341 DEGs (117 upregulated and 224 downregulated) were identified from the four datasets. Next, GO analysis showed that the upregulated genes were mainly involved in cell cycle, mitotic spindle, and adenosine triphosphate binding. The majority of the downregulated genes were involved in oxidation reduction, extracellular region, and electron carrier activity. Signaling pathway analysis showed that the integrated DEGs shared common pathways in retinol metabolism, drug metabolism, tryptophan metabolism, caffeine metabolism, and metabolism of xenobiotics by cytochrome P450. The integrated DEG PPI network complex comprised 288 nodes, and two important modules with high degree were detected using the MCODE plug-in. The top ten hub genes identified from the PPI network were SHCBP1, FOXM1, KIF4A, ANLN, KIF15, KIF18A, FANCI, NEK2, ECT2, and RAD51AP1. Finally, survival analysis revealed that patients with HCC showing altered ANLN and KIF18A expression profiles showed worse disease-free survival. Nonetheless, patients with FOXM1, NEK2, RAD51AP1, ANLN, and KIF18A alterations showed worse overall survival. Conclusions The present study identified key genes and pathways involved in HBV-related HCC, which improved our understanding of the mechanisms underlying the development and recurrence of HCC and identified candidate targets for the diagnosis and treatment of HBV-related HCC.

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