Higher plasma aldosterone concentrations in patients with aortic diseases and hypertension: a retrospective observational study.

BACKGROUND: Aortic diseases remain a highly perilous macrovascular condition. The relationship between circulating aldosterone and aortic diseases is rarely explored, thus we investigated the difference in plasma aldosterone concentration (PAC) between patients with and without aortic disease in hypertensive people. METHODS: We analyzed 926 patients with hypertension, ranging in age from 18 to 89 years, who had their PAC measured from the hospital's electronic database. The case group and control group were defined based on inclusion and exclusion criteria. The analysis included general information, clinical data, biochemical data, and medical imaging examination results as covariates. To further evaluate the difference in PAC between primary hypertension patients with aortic disease and those without, we used multivariate logistic regression analysis and also employed propensity score matching to minimize the influence of confounding factors. RESULTS: In total, 394 participants were included in the analysis, with 66 individuals diagnosed with aortic diseases and 328 in the control group. The participants were predominantly male (64.5%) and over the age of 50 (68.5%), with an average PAC of 19.95 ng/dL. After controlling for confounding factors, the results showed hypertension patients with aortic disease were more likely to have high PAC levels than those without aortic disease (OR = 1.138, 95% CI [1.062 to 1.238]). Subgroup analysis revealed consistent relationship between PAC and primary hypertensive patients with aortic disease across the different stratification variables. Additionally, hypertensive patients with aortic disease still have a risk of higher PAC levels than those without aortic disease, even after propensity score matching. CONCLUSIONS: The results of this study suggest that primary hypertensive patients with aortic diseases have elevated levels of PAC, but the causal relationship between PAC and aortic disease requires further study.

Sex-specific differences in the association between steps per day and all-cause mortality among a cohort of adult patients from the United States with congestive heart failure.

BACKGROUND: There is a lack of understanding of how daily step counts differentially affect the risk of all-cause mortality in adult with congestive heart failure (CHF) by sex in the United States (US). OBJECTIVES: To explore the relationship between daily step counts and all-cause mortality in patients with CHF by sex. METHODS: This is a cohort analysis from the National Health and Nutrition Examination Survey from 2005 to 2006. Multiple Cox hazard regression was performed to explore the association of step counts and all-cause mortality in patients with CHF by sex. RESULTS: In this study, 363 unweighted samples were enrolled from NHANES 2005-2006, representing about 8.4 million of the US population. Further, 46.28% were women, and the average age was 46 years. Patients with CHF in the more than 5581 steps/day group (HR, 0.31 [95% CI, 0.16-0.58]) had a significantly reduced risk of all-cause mortality compared with the patients in the less 5581 steps/day group after accounting for all covariates. In men, after accounting for all the covariates, there was a significant difference in more than 5581 steps/day group (HR, 0.33 [95% CI, 0.14-0.76]) on all-cause mortality in men with CHF compared with men in the less than 5581 steps/day group. CONCLUSIONS: Step count is associated with all-cause mortality in patients with CHF. Taking 5581 daily steps was associated with a decreased risk of all-cause mortality in patients with CHF.

Molecular Subtypes and Machine Learning-Based Predictive Models for Intracranial Aneurysm Rupture.

BACKGROUND: The determination of biological mechanisms and biomarkers related to intracranial aneurysm (IA) rupture is of utmost significance for the development of effective preventive and therapeutic strategies in the clinical field. METHODS: GSE122897 and GSE13353 datasets were downloaded from Gene Expression Omnibus. Data extracted from GSE122897 were used for analyzing differential gene expression, and consensus clustering was performed to identify stable molecular subtypes. Clinical characteristics were compared between subgroups, and fast gene set enrichment analysis and weighted gene coexpression network analysis were performed. Hub genes were identified via least absolute shrinkage and selection operator analysis. Predictive models were constructed based on hub genes using the Light Gradient Boosting Machine, eXtreme Gradient Boosting, and logistic regression algorithm. Immune cell infiltration in IA samples was analyzed using Microenvironment Cell Population counter, CIBERSORT, and xCell algorithm. The correlation between hub genes and immune cells was analyzed. The predictive model and immune cell infiltration were validated using data from the GSE13353 dataset. RESULTS: A total of 43 IA samples were classified into 2 subgroups based on gene expression profiles. Subgroup I had a higher risk of rupture, while 70% of subgroup II remained unruptured. In subgroup I, specific genes were associated with inflammation and immunity, and weighted gene coexpression network analysis revealed that the black module genes were linked to IA rupture. We identified 4 hub genes (spermine synthase, macrophage receptor with collagenous structure, zymogen granule protein 16B, and LIM and calponin-homology domains 1), which constructed predictive models with good diagnostic performance in differentiating between ruptured and unruptured IA samples. Monocytic lineage was found to be a significant factor in IA rupture, and the 4 hub genes were linked to monocytic lineage (P < 0.05). CONCLUSIONS: We reveal a new molecular subtype that can reflect the actual pathological state of IA rupture, and our predictive models constructed by machine learning algorithms can efficiently predict IA rupture.

Diabetes mellitus induces a novel inflammatory network involving cancer progression: Insights from bioinformatic analysis and in vitro validation.

Background: Patients with diabetes mellitus (DM) have a higher incidence of malignant tumors than people without diabetes, but the underlying molecular mechanisms are still unclear. Methods: To investigate the link between DM and cancer, we screened publicly available databases for diabetes and cancer-related genes (DCRGs) and constructed a diabetes-based cancer-associated inflammation network (DCIN). We integrated seven DCRGs into the DCIN and analyzed their role in different tumors from various perspectives. We also investigated drug sensitivity and single-cell sequencing data in colon adenocarcinoma as an example. In addition, we performed in vitro experiments to verify the expression of DCRGs and the arachidonic acid metabolic pathway. Results: Seven identified DCRGs, including PPARG, MMP9, CTNNB1, TNF, TGFB1, PTGS2, and HIF1A, were integrated to construct a DCIN. The bioinformatics analysis showed that the expression of the seven DCRGs in different tumors was significantly different, which had varied effects on diverse perspectives. Single-cell sequencing analyzed in colon cancer showed that the activity of the DCRGs was highest in Macrophage and the lowest in B cells among all cell types in adenoma and carcinoma tissue. In vitro experiments showed that the DCRGs verified by western bolt and PEG2 verified by ELISA were all highly expressed in COAD epithelial cells stimulated by high glucose. Conclusion: This study, for the first time, constructed a DCIN, which provides novel insights into the underlying mechanism of how DM increases tumor occurrence and development. Although further research is required, our results offer clues for new potential therapeutic strategies to prevent and treat malignant tumors.

Novel insight into the underlying dysregulation mechanisms of immune cell-to-cell communication by analyzing multitissue single-cell atlas of two COVID-19 patients.

How does SARS-CoV-2 cause lung microenvironment disturbance and inflammatory storm is still obscure. We here performed the single-cell transcriptome sequencing from lung, blood, and bone marrow of two dead COVID-19 patients and detected the cellular communication among them. Our results demonstrated that SARS-CoV-2 infection increase the frequency of cellular communication between alveolar type I cells (AT1) or alveolar type II cells (AT2) and myeloid cells triggering immune activation and inflammation microenvironment and then induce the disorder of fibroblasts, club, and ciliated cells, which may cause increased pulmonary fibrosis and mucus accumulation. Further study showed that the increase of T cells in the lungs may be mainly recruited by myeloid cells through ligands/receptors (e.g., ANXA1/FPR1, C5AR1/RPS19, and CCL5/CCR1). Interestingly, we also found that certain ligands/receptors (e.g., ANXA1/FPR1, CD74/COPA, CXCLs/CXCRs, ALOX5/ALOX5AP, CCL5/CCR1) are significantly activated and shared among lungs, blood and bone marrow of COVID-19 patients, implying that the dysregulation of ligands/receptors may lead to immune cell's activation, migration, and the inflammatory storm in different tissues of COVID-19 patients. Collectively, our study revealed a possible mechanism by which the disorder of cell communication caused by SARS-CoV-2 infection results in the lung inflammatory microenvironment and systemic immune responses across tissues in COVID-19 patients.

Identification and Analysis of Hub Genes and Immune Cells Associated with the Formation of Acute Aortic Dissection.

Background: Acute type A aortic dissection (AAD) is a catastrophic disease with high mortality, but the pathogenesis has not been fully elucidated. This study is aimed at identifying hub genes and immune cells associated with the pathogenesis of AAD. Methods: The datasets were downloaded from Gene Expression Omnibus (GEO). Gene Set Enrichment Analysis (GSEA), gene set variation analysis (GSVA), and differential analysis were performed. The differentially expressed genes (DEGs) were intersected with specific genes collected from MSigDB. The gene function and pathway enrichment analysis were also performed on intersecting genes. The key modules were selected by weighted gene coexpression network analysis (WGCNA). Hub genes were identified by least absolute shrinkage and selection operator (LASSO) analysis and were verified in the metadataset. The immune cell infiltration was analyzed by CIBERSORT, and the relationship between hub genes and immune cells was performed by Pearson's correlation analysis. The single-cell RNA sequencing (scRNA-seq) dataset was used to verify the differences in DNA damage and repair signaling pathways and hub genes in different cell types. Results: The results of GSEA and GSVA indicated that DNA damage and repair processes were activated in the occurrence of AAD. The gene function and pathway enrichment analysis on differentially expressed DNA damage- and repair-related genes showed that these genes were mainly involved in the regulation of the cell cycle process, cellular response to DNA damage stimulus, response to wounding, p53 signaling pathway, and cellular senescence. Three key modules were identified by WGCNA. Five genes were screened as hub genes, including CDK2, EIF4A1, GLRX, NNMT, and SLCO2A1. Naive B cells and Gamma delta T cells (γδ T cells) were decreased in AAD, but monocytes and M0 macrophages were increased. scRNA-seq analysis included that DNA damage and repair processes were activated in smooth muscle cells (SMCs), tissue stem cells, and monocytes in the aortic wall of patients with AAD. Conclusions: Our results suggested that DNA damage- and repair-related genes may be involved in the occurrence of AAD by regulating many biological processes. The hub genes and immune cells reported in this study also increase the understanding of AAD.

Characteristics of lymphocyte subset alterations in COVID-19 patients with different levels of disease severity.

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a respiratory disorder caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which had rapidly spread all over the world and caused public health emergencies in the past two years. Although the diagnosis and treatment for COVID-19 have been well defined, the immune cell characteristics and the key lymphocytes subset alterations in COVID-19 patients have not been thoroughly investigated. METHODS: The levels of immune cells including T cells, B cells, and natural killer (NK) cells in 548 hospitalized COVID-19 patients, and 30 types of lymphocyte subsets in 125 hospitalized COVID-19 patients admitted to Wuhan Huoshenshan Hospital of China were measured using flow cytometry. The relationship between lymphocytes subsets with the cytokine interleukin-6 (IL-6) and the characteristics of lymphocyte subsets in single-cell RNA sequencing (scRNA-seq) data obtained from peripheral blood mononuclear cells (PBMCs) were also analysed in COVID-19 patients. RESULTS: In this study, we found that patients with critical COVID-19 infection exhibited an overall decline in lymphocytes including CD4+ T cells, CD8+ T cells, total T cells, B cells, and NK cells compared to mild and severe patients. However, the number of lymphocyte subsets, such as CD21low CD38low B cells, effector T4 cells, and PD1+ depleted T8 cells, was moderately increased in critical COVID-19 patients compared to mild cases. Notably, except for effector memory T4 cells, plasma blasts and Tregs, the number of all lymphocyte subsets was markedly decreased in COVID-19 patients with IL-6 levels over 30-fold higher than those in healthy cases. Moreover, scRNA-seq data showed obvious differences in the distribution and numbers of lymphocyte subsets between COVID-19 patients and healthy persons, and subsets-specific marker genes of lymphocyte subsets including CD4, CD19, CCR7, and IL7R, were markedly decreased in COVID-19 patients compared with those in healthy cases. CONCLUSION: A comprehensive decrease in immune cell and lymphocyte subsets in critical COVID-19 patients, and peripheral lymphocyte subset alterations showed a clear association with clinical characteristics.

A Prediction Model of the Incidence of Nonalcoholic Fatty Liver Disease With Visceral Fatty Obesity: A General Population-Based Study.

Objective: This study aimed to distinguish the risk variables of nonalcoholic fatty liver disease (NAFLD) and to construct a prediction model of NAFLD in visceral fat obesity in Japanese adults. Methods: This study is a historical cohort study that included 1,516 individuals with visceral obesity. All individuals were randomly divided into training group and validation group at 70% (n = 1,061) and 30% (n = 455), respectively. The LASSO method and multivariate regression analysis were performed for selecting risk factors in the training group. Then, overlapping features were selected to screen the effective and suitable risk variables for NAFLD with visceral fatty obesity, and a nomogram incorporating the selected risk factors in the training group was constructed. Then, we used the C-index, calibration plot, decision curve analysis, and cumulative hazard analysis to test the discrimination, calibration, and clinical meaning of the nomogram. At last, internal validation was used in the validation group. Results: We contract a nomogram and validated it using easily available and cost-effective parameters to predict the incidence of NAFLD in participants with visceral fatty obesity, including ALT, HbA1c, body weight, FPG, and TG. In training cohort, the area under the ROC was 0.863, with 95% CI: 0.84-0.885. In validation cohort, C-index was 0.887, with 95%CI: 0.857-0.888. The decision curve analysis showed that the model's prediction is more effective. Decision curve analysis of the training cohort and validation cohort showed that the predictive model was more effective in predicting the risk of NAFLD in Japanese patients with visceral fatty obesity. To help researchers and clinicians better use the nomogram, our online version can be accessed at https://xy2yyjzyxk.shinyapps.io/NAFLD/. Conclusions: Most patients with visceral fatty obesity have a risk of NALFD, but some will not develop into it. The presented nomogram can accurately identify these patients at high risk.

Identification of Hub Genes Associated with the Pathogenesis of Intracranial Aneurysm via Integrated Bioinformatics Analysis.

BACKGROUND: At present, the pathogenesis of intracranial aneurysms (IA) remains unclear, which significantly hinders the development of novel strategies for the clinical treatment. In this study, bioinformatics methods were used to identify the potential hub genes and pathways associated with the pathogenesis of IA. METHODS: The gene expression datasets of patients with intracranial aneurysm were downloaded from the Gene Expression Database (GEO), and the different data sets were integrated by the robust rank aggregation (RRA) method to identify the differentially expressed genes between patients with intracranial aneurysm and the controls. The functional enrichment analyses of the significant differentially expressed genes (DEGs) were performed and the protein-protein interaction (PPI) network was constructed; thereafter, the hub genes were screened by cytoHubba plug-in of Cytoscape, and finally sequencing dataset GSE122897 was used to verify the hub genes. RESULTS: The GSE15629, GSE75436, GSE26969, and GSE6551 expression profiles have been included in this study, including 34 intracranial aneurysm samples and 26 control samples. The four datasets obtained 136 significant DEGs (45 up-regulated, 91 down-regulated). Enrichment analysis showed that the extracellular matrix structural constituent and the ECM-receptor interaction were closely related to the occurrence of IA. It was finally determined that eight hub genes associated with the development of IA, including VCAN, COL1A1, COL11A1, COL5A1, COL5A2, POSTN, THBS2, and CDH2. CONCLUSION: The discovery of potential hub genes and pathways could enhance the understanding of the molecular mechanisms associated with the development of IA. These hub genes may be potential therapeutic targets for the management and new biomarker for the diagnosis of IA.

Evolution of COVID-19 in patients with autoimmune rheumatic diseases.

The characteristics of COVID-19 patients with autoimmune rheumatic diseases (AIRD) have rarely been reported. Patients with AIRD have suppressed immune defense function, which may increase their susceptibility to COVID-19. However, the immunosuppressive agents AIRD patients routinely used may be beneficial for protecting the cytokine storm caused by SARS-CoV-2. In this retrospective study, we included all confirmed cases in Huoshenshan Hospital from February 4 to April 9. Data were extracted from electronic medical records and were analyzed for clinical and laboratory features using SPSS (version 25.0). Of 3059 patients, 21 had the comorbidities with systematic lupus erythematosus (SLE) and/or rheumatoid arthritis (RA), including 5 with SLE, 15 with RA, and 1 with Rhupus. The proportion was 57.1% for severe cases, 61.9% for either severe or critical cases, and 4.8% for critical cases. The main manifestations, ARDS and ICU admission rate, as well as the mortality and length of hospital stay of COVID-19 in AIRD patients were similar to COVID-19 patients in the general population. Our preliminary experience shows that patients with AIRD tend to have a higher risk of SARS-CoV-2 infection, and may be at risk for a severe but less likely critical disease course. Further investigation is needed to understand the immunological features of these diseases.

Dynamic changes in anti-SARS-CoV-2 antibodies during SARS-CoV-2 infection and recovery from COVID-19.

Deciphering the dynamic changes in antibodies against SARS-CoV-2 is essential for understanding the immune response in COVID-19 patients. Here we analyze the laboratory findings of 1,850 patients to describe the dynamic changes of the total antibody, spike protein (S)-, receptor-binding domain (RBD)-, and nucleoprotein (N)-specific immunoglobulin M (IgM) and G (IgG) levels during SARS-CoV-2 infection and recovery. The generation of S-, RBD-, and N-specific IgG occurs one week later in patients with severe/critical COVID-19 compared to patients with mild/moderate disease, while S- and RBD-specific IgG levels are 1.5-fold higher in severe/critical patients during hospitalization. The RBD-specific IgG levels are 4-fold higher in older patients than in younger patients during hospitalization. In addition, the S- and RBD-specific IgG levels are 2-fold higher in the recovered patients who are SARS-CoV-2 RNA negative than those who are RNA positive. Lower S-, RBD-, and N-specific IgG levels are associated with a lower lymphocyte percentage, higher neutrophil percentage, and a longer duration of viral shedding. Patients with low antibody levels on discharge might thereby have a high chance of being tested positive for SARS-CoV-2 RNA after recovery. Our study provides important information for COVID-19 diagnosis, treatment, and vaccine development.

Long noncoding RNA expression in peripheral blood mononuclear cells and suicide risk in Chinese patients with major depressive disorder.

BACKGROUND: WHO stated that nearly one million people commit suicide every year worldly, and 40% of the suicide completer suffered from depression. The primary aim of this study was to explore the association between long noncoding RNAs (lncRNAs) expression in peripheral blood mononuclear cells (PBMCs) and suicide risk of patients with major depressive disorder (MDD). METHODS: Using Human LncRNA 3.0 microarray profiling which includes 30,586 human lncRNAs and RT-PCR, six down-regulated lncRNAs were identified differentially expressed in MDD patients. According to suicidal ideation and suicidal attempt, the suicide risk of MDD patients was classified into suicidal ideation versus no suicidal ideation groups, and past attempt versus no past attempt groups, respectively. The expression of six lncRNAs in MDD patients and controls were examined by RT-PCR. RESULTS: The expression of six lncRNAs had significant differences between no suicidal ideation, suicidal ideation, and controls; corresponding lncRNAs associated with suicidal attempt had remarkable differences between no past attempt, past attempt, and controls. Additionally, only the expression of lncRNAs in suicidal ideation group and past attempt group markedly declined compared with controls. CONCLUSIONS: This study indicated that the expression of six down-regulated lncRNAs had a negative association with suicide risk in MDD patients, and the expression of lncRNAs in PBMCs could have the potential to help clinician judge the suicide risk of MDD patients to provide timely treatment and prevent suicide.

Long noncoding RNAs: New evidence for overlapped pathogenesis between major depressive disorder and generalized anxiety disorder.

BACKGROUND: About half of patients with major depressive disorder (MDD) have clinically meaningful levels of anxiety. Greater severity of depressive illness and functional impairment has been reported in patients with high levels of anxiety accompanying depression. The pathogenesis for the comorbidity was still unsure. AIM: This study aimed to determine whether there would be molecular link for overlapped pathogenesis between MDD and anxiety disorder. MATERIALS AND METHODS: Using long noncoding RNA (lncRNA) microarray profiling and reverse transcription polymerase chain reaction, six downregulated lncRNAs and three upregulated lncRNAs had been identified to be the potential biomarkers for MDD and generalized anxiety disorder (GAD), respectively. Then, the lncRNAs were cross-checked in forty MDD patients, forty GAD patients, and forty normal controls. RESULTS: Compared with normal controls, six downregulated MDD lncRNAs also had a significantly lower expression in GAD (P < 0.01), and there was no significant difference between GAD and MDD (P > 0.05). In addition, three upregulated GAD lncRNAs had no different expression in MDD (P > 0.05), but there was remarkable difference between MDD and GAD (P < 0.01). CONCLUSIONS: These results indicated that lncRNAs in peripheral blood mononuclear cells could be potential molecular link between MDD and GAD, which added new evidence to the overlapped pathogenesis and suggested that anxious depression could be a valid diagnostic subtype of MDD.

Can lncRNAs be indicators for the diagnosis of early onset or acute schizophrenia and distinguish major depressive disorder and generalized anxiety disorder?-A cross validation analysis.

Depression and anxiety are apparent symptoms in the early onset or acute phase of schizophrenia (SZ), which complicate timely diagnosis and treatment. It is imperative to seek an indicator to distinguish schizophrenia from depressive and anxiety disorders. Using lncRNA microarray profiling and RT-PCR, three up-regulated lncRNAs in SZ, six down-regulated lncRNAs in major depressive disorder (MDD), and three up-regulated lncRNAs in generalized anxiety disorder (GAD) had been identified as potential biomarkers. All the lncRNAs were, then, cross-validated in 40 SZ patients, 40 MDD patients, 40 GAD patients, and 40 normal controls. Compared with controls, three up-regulated SZ lncRNAs had a significantly down-regulated expression in GAD, and no remarkable differences existed between MDD and the controls. Additionally, the six down-regulated MDD lncRNAs were expressed in an opposite fashion in SZ, and the expression of the three up-regulated GAD lncRNAs were significantly different between SZ and GAD. These results indicate that the expression patterns of the three up-regulated SZ lncRNAs could not be completely replicated in MDD and GAD, and vice versa. Thus, these three SZ lncRNAs seem to be established as potential indicators for diagnosis of schizophrenia and distinguishing it from MDD and GAD. © 2017 Wiley Periodicals, Inc.

Long noncoding RNA as an indicator differentiating schizophrenia from major depressive disorder and generalized anxiety disorder in nonpsychiatric hospital.

AIM: Depression and anxiety are common symptoms for schizophrenia (SZ) in the early onset. This study aimed to determine whether long noncoding RNAs (lncRNAs) can be indicators for diagnosing SZ in nonpsychiatric hospitals. MATERIALS & METHODS: Three upregulated SZ lncRNAs, six downregulated major depressive disorder (MDD) lncRNAs and three upregulated generalized anxiety disorder (GAD) lncRNAs were cross-validated in 45 SZ patients, 48 MDD patients, 52 GAD patients and 40 controls by reverse transcription-PCR. RESULTS: Three SZ lncRNAs were significantly downregulated in GAD patients. The expression of the six MDD lncRNAs showed an opposite trend in SZ patients, and the three GAD lncRNAs also showed significant differences between SZ and GAD patients. CONCLUSION: The three upregulated SZ lncRNAs are not entirely replicated in MDD and GAD patients and could be potential indicators for distinguishing SZ from MDD and GAD in nonpsychiatric hospital.

Aberrant Expression of Long Non-Coding RNAs in Schizophrenia Patients.

BACKGROUND Dysfunction of long non-coding RNAs (lncRNAs) has been demonstrated to be involved in psychiatric diseases. However, the expression patterns and functions of the regulatory lncRNAs in schizophrenia (SZ) patients have rarely been systematically reported. MATERIAL AND METHODS The lncRNAs in peripheral blood mononuclear cells (PBMCs) were screened and compared between the SZ patients and demographically-matched healthy controls using microarray analysis, and then were validated by quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) method. Three verified significantly dysregulated lncRNAs of PBMCs were selected and then measured in SZ patients before and after the antipsychotic treatment. SZ symptomatology improvement was measured by Positive And Negative Syndrome Scale (PANSS) scores. RESULTS One hundred and twenty-five lncRNAs were significantly differentially expressed in SZ patients compared with healthy controls, of which 62 were up-regulated and 63 were down-regulated. Concurrent with the significant decrease of the PANSS scores of patients after the treatment, the PBMC levels of lncRNA NONHSAT089447 and NONHSAT041499 were strikingly decreased (P<0.05). Down-regulation of PBMC expression of NONHSAT041499 was significantly correlated to the improvement of positive and activity symptoms of patients (r=-0.444 and -0.423, respectively, P<0.05, accounting for 16.9% and 15.1%, respectively), and was also significantly associated with better outcomes (odds ratio 2.325 for positive symptom and 12.340 for activity symptom). CONCLUSIONS LncRNA NONHSAT089447 and NONHSAT041499 might be involved in the pathogenesis and development of SZ, and the PBMC level of NONHSAT041499 is significantly associated with the treatment outcomes of SZ.

A preliminary analysis of microRNA-21 expression alteration after antipsychotic treatment in patients with schizophrenia.

Schizophrenia is a severe and debilitating psychiatric disorder of unknown etiology, and its diagnosis is essentially based on clinical symptoms. Despite growing evidence on the relation of altered expression of miRNAs and schizophrenia, most patients with schizophrenia usually had an extensive antipsychotic treatment history before miRNA expression profile analysis, and the pharmacological effects on miRNA expression are largely unknown. To overcome these impediments, miRNA microarray analysis was performed in peripheral blood mononuclear cells (PBMCs) obtained from patients with schizophrenia who were not on antipsychotic medication and healthy controls. Then, using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR), we verified the top 10 miRNAs with the highest fold-change values from microarray analysis in 82 patients with schizophrenia and 43 healthy controls, and nine miRNAs demonstrated significant differences in expression levels. Finally, we compared these nine miRNA profiles before and after antipsychotic treatment. Our results revealed that serum miR-21 expression decreased strikingly in patients after antipsychotic treatment. The change of miR-21 expression was negatively correlated with improvement of positive, general psychopathology, and aggressiveness symptoms. This study preliminarily analyzed the possible changes in circulating miRNAs expression in response to antipsychotic medication for schizophrenia, and the molecular mechanisms of this needs to be further explored.

hsa_circRNA_103636: potential novel diagnostic and therapeutic biomarker in Major depressive disorder.

AIM: This study aimed to determine whether circular RNA (circRNA) molecules in peripheral blood mononuclear cells (PBMCs) could be used as novel non-invasive biomarkers for major depressive disorder (MDD). MATERIALS & METHODS: Differentially expressed circRNAs were screened using an Arraystar Human CircRNA Array (which includes 13,617 human circRNAs) and qRT-PCR. Thirty MDD patients were randomly selected to retest the circRNA levels after 4-week and 8-week antidepressant regimens. RESULTS: Four differentially expressed circRNAs were identified between MDD patients and controls, and only down-regulated hsa_circRNA_103636 was significantly altered after the 8-week treatment in MDD patients. CONCLUSION: These results suggest that altered expression of hsa_circRNA_103636 in PBMCs is a potential novel biomarker for the diagnosis and treatment of MDD.