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Qi H, Xie YY, Yang XJ, Xia J, Liu K, Zhang FX, Peng WJ, Wen FY, Li BX, Zhang BW, Yao XY, Li BY, Meng HD, Shi ZM, Wang Y, Zhang L. Susceptibility gene identification and risk evaluation model construction by transcriptome-wide association analysis for salt sensitivity of blood pressure. BMC Genomics 2024; 25:612. [PMID: 38890564 PMCID: PMC11184770 DOI: 10.1186/s12864-024-10409-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 05/13/2024] [Indexed: 06/20/2024] Open
Abstract
BACKGROUND Salt sensitivity of blood pressure (SSBP) is an intermediate phenotype of hypertension and is a predictor of long-term cardiovascular events and death. However, the genetic structures of SSBP are uncertain, and it is difficult to precisely diagnose SSBP in population. So, we aimed to identify genes related to susceptibility to the SSBP, construct a risk evaluation model, and explore the potential functions of these genes. METHODS AND RESULTS A genome-wide association study of the systemic epidemiology of salt sensitivity (EpiSS) cohort was performed to obtain summary statistics for SSBP. Then, we conducted a transcriptome-wide association study (TWAS) of 12 tissues using FUSION software to predict the genes associated with SSBP and verified the genes with an mRNA microarray. The potential roles of the genes were explored. Risk evaluation models of SSBP were constructed based on the serial P value thresholds of polygenetic risk scores (PRSs), polygenic transcriptome risk scores (PTRSs) and their combinations of the identified genes and genetic variants from the TWAS. The TWAS revealed that 2605 genes were significantly associated with SSBP. Among these genes, 69 were differentially expressed according to the microarray analysis. The functional analysis showed that the genes identified in the TWAS were enriched in metabolic process pathways. The PRSs were correlated with PTRSs in the heart atrial appendage, adrenal gland, EBV-transformed lymphocytes, pituitary, artery coronary, artery tibial and whole blood. Multiple logistic regression models revealed that a PRS of P < 0.05 had the best predictive ability compared with other PRSs and PTRSs. The combinations of PRSs and PTRSs did not significantly increase the prediction accuracy of SSBP in the training and validation datasets. CONCLUSIONS Several known and novel susceptibility genes for SSBP were identified via multitissue TWAS analysis. The risk evaluation model constructed with the PRS of susceptibility genes showed better diagnostic performance than the transcript levels, which could be applied to screen for SSBP high-risk individuals.
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Affiliation(s)
- Han Qi
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
- Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Capital Medical University, Beijing, 100088, China
| | - Yun-Yi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xiao-Jun Yang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Juan Xia
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Feng-Xu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Wen-Juan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Fu-Yuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bing-Xiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Wen Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Xin-Yue Yao
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Bo-Ya Li
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China
| | - Hong-Dao Meng
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Zu-Min Shi
- Human Nutrition Department, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Yang Wang
- Department of Cardiovascular Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, No.10 Youanmenwai, Beijing, 100069, China.
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Peng W, Xie Y, Xia J, Qi H, Liu K, Li B, Zhang F, Wen F, Zhang L. Integrated analysis of the lncRNA-associated competing endogenous RNA network in salt sensitivity of blood pressure. Heliyon 2023; 9:e22466. [PMID: 38125519 PMCID: PMC10731005 DOI: 10.1016/j.heliyon.2023.e22466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 11/12/2023] [Accepted: 11/13/2023] [Indexed: 12/23/2023] Open
Abstract
Accumulating evidence showed that competing endogenous RNA (ceRNA) mechanism plays a pivotal role in salt sensitivity of blood pressure (SSBP). We constructed a ceRNA network based on SSBP-related differently expressed lncRNAs (2), mRNAs (73) and miRNAs (18). Bioinformatic analyses were utilized to analyze network and found network genes participate in biological pathways related to SSBP pathogenesis such as regulation of nitric oxide biosynthetic process (GO:0045,428) and cellular response to cytokine stimulus (GO:0071,345). Fourteen candidate ceRNA pathways were selected from network to perform qRT-PCR validation and found nine RNAs (KCNQ1OT1, SLC8A1-AS1, IL1B, BCL2L11, KCNJ15, CX3CR1, KLF2, hsa-miR-362-5p and hsa-miR-423-5p) differently expressed between salt-sensitive (SS) and salt-resistant (SR) groups (P < 0.05). Four ceRNA pathways were further validated by luciferase reporter assay and found KCNQ1OT1→hsa-miR-362-5p/hsa-miR-423-5p→IL1B pathways may influence the pathogenic mechanism of SS. Our findings suggested the ceRNA pathway and network may affect SS occurrence mainly through endothelial dysfunction and inflammatory activation.
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Affiliation(s)
- Wenjuan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
- Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China
| | - Yunyi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Juan Xia
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Han Qi
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Bingxiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Fengxu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Fuyuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, And Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
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Ma J, Chen X. Advances in pathogenesis and treatment of essential hypertension. Front Cardiovasc Med 2022; 9:1003852. [PMID: 36312252 PMCID: PMC9616110 DOI: 10.3389/fcvm.2022.1003852] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 09/26/2022] [Indexed: 11/23/2022] Open
Abstract
Hypertension is a significant risk factor for cardiovascular and cerebrovascular diseases and the leading cause of premature death worldwide. However, the pathogenesis of the hypertension, especially essential hypertension, is complex and requires in-depth studies. Recently, new findings about essential hypertension have emerged, and these may provide important theoretical bases and therapeutic tools to break through the existing bottleneck of essential hypertension. In this review, we demonstrated important advances in the different pathogenesis areas of essential hypertension, and highlighted new treatments proposed in these areas, hoping to provide insight for the prevention and treatment of the essential hypertension.
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Zhu H, Tan J, Wang Z, Wu Z, Zhou W, Zhang Z, Li M, Zhao Y. Bioinformatics analysis constructs potential ferroptosis-related ceRNA network involved in the formation of intracranial aneurysm. Front Cell Neurosci 2022; 16:1016682. [PMCID: PMC9612944 DOI: 10.3389/fncel.2022.1016682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 09/29/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundIntracranial aneurysm (IA) causes more than 80% of nontraumatic subarachnoid hemorrhages (SAHs). The mechanism of ferroptosis involved in IA formation remains unclear. The roles played by competitive endogenous RNA (ceRNA) regulation networks in many diseases are becoming clearer. The goal of this study was to understand more fully the ferroptosis-related ceRNA regulation network in IA.Materials and methodsTo identify differentially expressed genes (DEGs), differentially expressed miRNAs (DEMs), and differentially expressed lncRNAs (DELs) across IA and control samples, the GEO datasets GSE122897 and GSE66239 were downloaded and analyzed with the aid of R. Ferroptosis DEGs were discovered by exploring the DEGs of ferroptosis-related genes of the ferroptosis database. Potentially interacting miRNAs and lncRNAs were predicted using miRWalk and StarBase. Enrichment analysis was also performed. We utilized the STRING database and Cytoscape software to identify protein-protein interactions and networks. DAB-enhanced Prussian blue staining was used to detect iron in IA tissues.ResultsIron deposition was evident in IA tissue. In all, 30 ferroptosis DEGs, 5 key DEMs, and 17 key DELs were screened out for constructing a triple regulatory network. According to expression regulation of DELs, DEMs, and DEGs, a hub triple regulatory network was built. As the functions of lncRNAs are determined by their cellular location, PVT1-hsa-miR-4644-SLC39A14 ceRNA and DUXAP8-hsa-miR-378e/378f-SLC2A3 ceRNA networks were constructed.ConclusionCeRNA (PVT1-hsa-miR-4644-SLC39A14 and DUXAP8-hsa-miR-378e/378f-SLC2A3) overexpression networks associated with ferroptosis in IA were established.
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SNPs in lncRNA KCNQ1OT1 Modulate Its Expression and Confer Susceptibility to Salt Sensitivity of Blood Pressure in a Chinese Han Population. Nutrients 2022; 14:nu14193990. [PMID: 36235643 PMCID: PMC9571541 DOI: 10.3390/nu14193990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/15/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Long noncoding RNA (lncRNA) plays an important role in cardiovascular diseases, but the involvement of lncRNA in salt sensitivity of blood pressure (SSBP) is not well-known. We aimed to explore the association of sixteen single-nucleotide polymorphisms (SNPs) in five lncRNA genes (KCNQOT1, lnc-AGAP1-8:1, lnc-IGSF3-1:1, etc.) with their expression and susceptibility to SSBP. A two-stage association study was conducted among 2057 individuals. Quantified expression of the lncRNA was detected using real-time PCR. Genotyping was accomplished using the MassARRAY System. The expression quantitative tra2it loci test and the generalized linear model were utilized to explore the function of SNPs. One-sample Mendelian randomization was used to study the causal relationship between KCNQOT1 and SSBP. Significant effects were observed in KCNQ1OT1 expressions on the SSBP phenotype (p < 0.05). Rs10832417 and rs3782064 in KCNQ1OT1 may influence the secondary structure, miRNA binding, and expression of KCNQ1OT1. Rs10832417 and rs3782064 in KCNQ1OT1 were identified to be associated with one SSBP phenotype after multiple testing corrections and may be mediated by KCNQ1OT1. One-sample Mendelian randomization analyses showed a causal association between KCNQ1OT1 and SSBP. Our findings suggest that rs10832417 and rs3782064 might be associated with a lower risk of SSBP through influencing the KCNQ1OT1 secondary structure and miRNA binding, resulting in changes in KCNQ1OT1 expression.
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Association of Circulating Biomarkers of lnc-IGSF3-1:1, SCOC-AS1, and SLC8A1-AS1 with Salt Sensitivity of Blood Pressure in Chinese Population. J Cardiovasc Transl Res 2021; 15:906-917. [PMID: 34855149 DOI: 10.1007/s12265-021-10190-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/16/2021] [Indexed: 01/24/2023]
Abstract
Accumulating evidence suggested that long non-coding RNAs (lncRNAs) could play biological roles in cardiovascular diseases. We investigated whether lncRNAs can serve as biomarkers for salt sensitivity of blood pressure (SSBP). Participants were divided into salt-sensitive (SS) and salt-resistant (SR) ones by oral saline test. LncRNAs were tested by microarray (N = 20) and two-stage qRT-PCR (N = 89 and 228). We identified five differently expressed lncRNAs (lnc-IGSF3-1:1, SCOC-AS1, SLC8A1-AS1, KCNQ1OT1, and lnc-GNG-10-3:1) between SS and SR. In single-lncRNA analyses, lnc-IGSF3-1:1 displayed better diagnostic performance in hypertensive patients (AUC = 0.840), while SCOC-AS1 in normotensive (AUC = 0.810). In multi-lncRNA analyses, lnc-IGSF3-1:1 + SCOC-AS1 + SLC8A1-AS1 combination showed the best diagnostic performance in hypertensive (AUC = 0.853) and normotensive groups (AUC = 0.873). We constructed a lncRNA-mRNA-GO-KEGG-disease network by bioinformatic analysis; lnc-IGSF3-1:1 and SLC8A1-AS1 were identified as hub biomarkers. Our findings suggest that lnc-IGSF3-1:1, SCOC-AS1, and SLC8A1-AS1 may represent as genetic susceptible biomarkers for SSBP, and had different SS diagnostic performance in hypertensive patients and normotensive individuals.
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Xie Y, Liu Z, Liu K, Qi H, Peng W, Cao H, Liu X, Li B, Wen F, Zhang F, Zhang L. Candidate Gene Polymorphisms Influence the Susceptibility to Salt Sensitivity of Blood Pressure in a Han Chinese Population: Risk Factors as Mediators. Front Genet 2021; 12:675230. [PMID: 34671380 PMCID: PMC8521039 DOI: 10.3389/fgene.2021.675230] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 08/26/2021] [Indexed: 01/13/2023] Open
Abstract
Genome-wide association studies suggest that there is a significant genetic susceptibility to salt sensitivity of blood pressure (SSBP), but it still needs to be verified in varied and large sample populations. We attempted to verify the associations between single-nucleotide polymorphisms (SNPs) in candidate genes and SSBP and to estimate their interaction with potential risk factors. A total of 29 candidate SNPs were genotyped in the 2,057 northern Han Chinese population from the Systems Epidemiology Study on Salt Sensitivity. A modified Sullivan’s acute oral saline load and diuresis shrinkage test (MSAOSL-DST) was used to identify SSBP. A generalized linear model was conducted to analyze the association between SNPs and SSBP, and Bonferroni correction was used for multiple testing. Mediation analysis was utilized to explore the mediation effect of risk factors. Eleven SNPs in eight genes (PRKG1, CYBA, BCAT1, SLC8A1, AGTR1, SELE, CYP4A11, and VSNL1) were identified to be significantly associated with one or more SSBP phenotypes (P < 0.05). Four SNPs (PRKG1/rs1904694 and rs7897633, CYP4A11/rs1126742, and CYBA/rs4673) were still significantly associated after Bonferroni correction (P < 0.0007) adjusted for age, sex, fasting blood glucose, total cholesterol, salt-eating habit, physical activity, and hypertension. Stratified analysis showed that CYBA/rs4673 was significantly associated with SSBP in hypertensive subjects (P < 0.0015) and CYP4A11/rs1126742 was significantly associated with SSBP in normotensive subjects (P < 0.0015). Subjects carrying both CYBA/rs4673-AA and AGTR1/rs2638360-GG alleles have a higher genetic predisposition to salt sensitivity due to the potential gene co-expression interaction. Expression quantitative trait loci analysis (eQTL) suggested that the above positive four SNPs showed cis-eQTL effects on the gene expression levels. Mediation analysis suggested that several risk factors were mediators of the relation between SNP and SSBP. This study suggests that the genetic variants in eight genes might contribute to the susceptibility to SSBP, and other risk factors may be the mediators.
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Affiliation(s)
- Yunyi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Zheng Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Kuo Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Han Qi
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Wenjuan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Han Cao
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Xiaohui Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Bingxiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Fuyuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Fengxu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
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Deciphering the Mechanisms Behind Cardiovascular Disease: Long Noncoding RNAs as Key Molecular Signaling Hubs and Biomarkers of Atherosclerosis. J Cardiovasc Pharmacol 2021; 76:125-127. [PMID: 32569014 DOI: 10.1097/fjc.0000000000000863] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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Zhang L, Qi H, Liu Z, Peng WJ, Cao H, Guo CY, Sun YY, Pao C, Xiang YT. Construction of a ceRNA coregulatory network and screening of hub biomarkers for salt-sensitive hypertension. J Cell Mol Med 2020; 24:7254-7265. [PMID: 32410228 PMCID: PMC7379024 DOI: 10.1111/jcmm.15285] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Revised: 01/12/2020] [Accepted: 03/28/2020] [Indexed: 12/21/2022] Open
Abstract
Salt-sensitive hypertension (SSH) is an independent risk factor for cardiovascular disease. The regulation of long non-coding RNAs, mRNAs and competing endogenous RNAs (ceRNAs) in the pathogenesis of SSH is uncertain. An RNA microarray was performed to discover SSH-associated differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs), and 296 DElncRNAs and 44 DEmRNAs were identified, and 247 DElncRNAs and 44 DEmRNAs among these RNAs were included in the coexpression network. The coregulatory network included 23 ceRNA loops, and six hub RNAs (lnc-ILK-8:1, lnc-OTX1-7:1, lnc-RCAN1-6:1, GIMAP8, SUV420H1 and PIGV) were identified for further population validation. The ceRNA correlations among lnc-OTX1-7:1, hsa-miR-361-5p and GIMAP8 were confirmed in SSH and SRH patients. A larger-sample validation confirmed that GIMAP8, SUV420H1 and PIGV were differentially expressed between the SSH and SRH groups. In addition, SUV420H1 was included in the SSH screening model, and the area under the curve of the model was 0.720 (95% CI: 0.624-0.816). Our study explored the transcriptome profiles of SSH and constructed a ceRNA network to help elucidate the mechanism of SSH. In addition, SUV420H1 was identified as a hub element that participates in SSH transcriptional regulation and as a potential biomarker for the early diagnosis of SSH.
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Affiliation(s)
- Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Han Qi
- The National Clinical Research Center for Mental Disorders, Beijing Key Laboratory of Mental Disorders & the Advanced Innovation Center for Human Brain Protection, Beijing Anding Hospital, School of Mental Health, Capital Medical University, Beijing, China
| | - Zheng Liu
- Science Department, Peking University People's Hospital, Beijing, China
| | - Wen-Juan Peng
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Han Cao
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Chun-Yue Guo
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Yan-Yan Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Christine Pao
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yu-Tao Xiang
- Unit of Psychiatry, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao, China
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