Idiopathic Rapid Eye Movement Sleep Behavior Disorder (iRBD) Shares Similar Fecal Short-Chain Fatty Acid Alterations with Multiple System Atrophy (MSA) and Parkinson's Disease (PD).

BACKGROUND: Idiopathic rapid eye movement sleep behavior disorder (iRBD) is considered as a prodromal stage of synucleinopathies. Fecal short-chain fatty acid (SCFA) changes in iRBD and the relationships with synucleinopathies have never been investigated. OBJECTIVES: To investigate fecal SCFA changes among iRBD, multiple system atrophy (MSA), and Parkinson's disease (PD), and evaluate their relationships. METHODS: Fecal SCFAs and gut microbiota were measured in 29 iRBD, 42 MSA, 40 PD, and 35 normal controls (NC) using gas chromatography-mass spectrometry and 16S rRNA gene sequencing. RESULTS: Compared with NC, fecal SCFA levels (propionic, acetic, and butyric acid) were lower in iRBD, MSA, and PD. Combinations of these SCFAs could differentiate NC from iRBD (AUC 0.809), MSA (AUC 0.794), and PD (AUC 0.701). Decreased fecal SCFAs were associated with the common reducing SCFA-producing gut microbiota in iRBD, MSA, and PD. CONCLUSIONS: iRBD shares similar fecal SCFA alterations with MSA and PD, and the combination of these SCFAs might be a potential synucleinopathies-related biomarker. © 2024 International Parkinson and Movement Disorder Society.

Predicting the Prognosis of Multiple System Atrophy Using Cluster and Principal Component Analysis.

BACKGROUND: Multiple system atrophy (MSA) is an intractable neurodegenerative disorder with poorly understanding of prognostic factors. OBJECTIVE: The purpose of this retrospective longitudinal study was to explore the main predictors of survival of MSA patients with new clinical subtypes based on cluster analysis. METHODS: A total of 153 Chinese MSA patients were recruited in our study. The basic demographic data and motor and nonmotor symptoms were assessed. Cluster and principal component analysis (PCA) were used to eliminate collinearity and search for new clinical subtypes. The multivariable Cox regression was used to find factors associated with survival in MSA patients. RESULTS: The median survival time from symptom onset to death (estimated using data from all patients by Kaplan-Meier analysis) was 6.3 (95% CI = 6.1-6.7) years. The survival model showed that a shorter survival time was associated with motor principal component (PC)1 (HR = 1.71, 95% CI: 1.26-2.30, p < 0.001) and nonmotor PC3 (HR = 1.68, 95% CI: 1.31-2.10, p < 0.001) through PCA. Four clusters were identified: Cluster 1 (mild), Cluster 2 (mood disorder-dominant), Cluster 3 (axial symptoms and cognitive impairment-dominant), and Cluster 4 (autonomic failure-dominant). Multivariate Cox regression indicated that Cluster 3 (HR = 4.15, 95% CI: 1.73-9.90, p = 0.001) and Cluster 4 (HR = 4.18, 95% CI: 1.73-10.1, p = 0.002) were independently associated with shorter survival time. CONCLUSION: More serious motor symptoms, axial symptoms such as falls and dysphagia, orthostatic hypotension, and cognitive impairment were associated with poor survival in MSA via PCA and cluster analysis.

Cell-Cell Communication Alterations via Intercellular Signaling Pathways in Substantia Nigra of Parkinson's Disease.

Parkinson's disease (PD) is a neurodegenerative movement disorder characterized with dopaminergic neuron (DaN) loss within the substantia nigra (SN). Despite bulk studies focusing on intracellular mechanisms of PD inside DaNs, few studies have explored the pathogeneses outside DaNs, or between DaNs and other cells. Here, we set out to probe the implication of intercellular communication involving DaNs in the pathogeneses of PD at a systemic level with bioinformatics methods. We harvested three online published single-cell/single-nucleus transcriptomic sequencing (sc/snRNA-seq) datasets of human SN (GSE126838, GSE140231, and GSE157783) from the Gene Expression Omnibus (GEO) database, and integrated them with one of the latest integration algorithms called Harmony. We then applied CellChat, the latest cell-cell communication analytic algorithm, to our integrated dataset. We first found that the overall communication quantity was decreased while the overall communication strength was enhanced in PD sample compared with control sample. We then focused on the intercellular communication where DaNs are involved, and found that the communications between DaNs and other cell types via certain signaling pathways were selectively altered in PD, including some growth factors, neurotrophic factors, chemokines, etc. pathways. Our bioinformatics analysis showed that the alteration in intercellular communications involving DaNs might be a previously underestimated aspect of PD pathogeneses with novel translational potential.

Clinical characteristics, outcome and prognostic factors in critically ill patients with lupus nephritis.

OBJECTIVES: To describe the clinical characteristics and outcome of patients with lupus nephritis (LN) in intensive care unit (ICU), identify prognostic factors and construct a predictive model of in-ICU survival. METHODS: A total of 505 ICU admissions of lupus patients were screened and LN patients confirmed by renal biopsy were enrolled. Clinical characteristics and outcome of patients in ICU were collected. A logistic regression analysis was performed to identify independent prognostic factors and a nomogram was plotted to construct a predictive model. RESULTS: A total of 70 patients with LN were enrolled. The median age of the patients was 28.5 years, and the median course of LN was two months. Renal pathology classes indicated that 38 patients were class IV, 11 were class IV+V, and 10 were class III. The most common primary cause of ICU admission was infection in 40 patients, followed by LN in 11 patients. Forty-one patients died in ICU. The multivariate analyses revealed that lactic acid (OR 1.682 [2.130-17.944], p=0.001), gamma-glutamyl transpeptidase (OR 1.057 [1.009-1.107], p=0.020), APACHE II (OR 3.852 [1.176-12.618], p=0.026), vasopressor (OR 10.571 [1.615-69.199], p=0.014) and platelet count (OR 0.967 [0.941-0.993], p=0.013) were independently associated with ICU survival of critical LN patients. A predictive model was constructed and validated. CONCLUSIONS: This study is the first to elucidate the features and identify prognostic factors in critically ill patients with LN. These findings could help clinicians to early identify high-risk patients of mortality, which consequently may reduce the mortality of critically ill patients with LN.

m5C-Related Signatures for Predicting Prognosis in Cutaneous Melanoma with Machine Learning.

BACKGROUND: Cutaneous melanoma (CM) is one of the most life-threatening primary skin cancers and is prone to distant metastases. A widespread presence of posttranscriptional modification of RNA, 5-methylcytosine (m5C), has been observed in human cancers. However, the potential mechanism of the tumorigenesis and prognosis in CM by dysregulated m5C-related regulators is obscure. METHODS: We use comprehensive bioinformatics analyses to explore the expression of m5C regulators in CM, the prognostic implications of the m5C regulators, the frequency of the copy number variant (CNV), and somatic mutations in m5C regulators. Additionally, the CM patients were divided into three clusters for better predicting clinical features and outcomes via consensus clustering of m5C regulators. Then, the risk score was established via Lasso Cox regression analysis. Next, the prognosis value and clinical characteristics of m5C-related signatures were further explored. Then, machine learning was used to recognize the outstanding m5C regulators to risk score. Finally, the expression level and clinical value of USUN6 were analyzed via the tissue microarray (TMA) cohort. RESULTS: We found that m5C regulators were dysregulated in CM, with a high frequency of somatic mutations and CNV alterations of the m5C regulatory gene in CM. Furthermore, 16 m5C-related proteins interacted with each other frequently, and we divided CM patients into three clusters to better predicting clinical features and outcomes. Then, five m5C regulators were selected as a risk score based on the LASSO model. The XGBoost algorithm recognized that NOP2 and NSUN6 were the most significant risk score contributors. Immunohistochemistry has verified that low expression of USUN6 was closely correlated with CM progression. CONCLUSION: The m5C-related signatures can be used as new prognostic biomarkers and therapeutic targets for CM, and NSUN6 might play a vital role in tumorigenesis and malignant progression.

Development of a Novel Simple Model to Predict Mortality in Patients With Systemic Lupus Erythematosus Admitted to the Intensive Care Unit.

Background: Patients with systemic lupus erythematosus (SLE) may sometimes require admission to the intensive care unit (ICU), and the outcome is poor. The aim of this study was to explore the clinical features of patients with SLE in the ICU, identify prognostic factors, and develop and evaluate a prognostic model to predict in-ICU mortality of patients with SLE. Patients and Methods: This was a single center retrospective study in a tertiary medical institution in China. A total of 480 SLE patients with 505 ICU admissions from 2010 to 2019 were screened, and 391 patients were enrolled. The clinical feature and outcomes of the patients were analyzed. According to the random number table, patients were divided into two mutually exclusively groups named derivation (n = 293) and validation (n = 98). Prognostic factors were identified by a Cox model with Markov Chain Monte Carlo simulation and evaluated by latent analysis. The risk score was developed based on the derivation group and evaluated using the validation group. Results: Among the 391 patients, 348 (89.0%) patients were females. The median age of patients was 34 years, and the median course of SLE was 6 months. The median APACHE II and SLEDAI were 17 and 10, respectively. The average in-ICU mortality was 53.4% (95% CI, 48.5-58.4%). A total of 186 patients were admitted to the ICU due to infection. Pneumonia (320/391, 81.8%) was the most common clinical manifestation, followed by renal disease (246/391, 62.9%). Nine prognostic factors were identified. The model had C statistic of 0.912 (95% CI, 0.889-0.948) and 0.807 (95% CI 0.703-0.889), with predictive range of 5.2-98.3% and 6.3-94.7% for the derivation and validation groups, respectively. Based on distribution of the risk score, 25.3, 49.5, and 25.2% of patients were stratified into the high, average, and low-risk groups, with corresponding in-ICU mortality of 0.937, 0.593, and 0.118, respectively. Conclusion: Nine prognostic factors including age, white blood cell count, alanine transaminase, uric acid, intracranial infection, shock, intracranial hemorrhage, respiratory failure, and cyclosporin A/tacrolimus usage were identified. A prognostic model was developed and evaluated to predict in-ICU mortality of patients with SLE. These findings may help clinicians to prognostically stratify patients into different risk groups of in-ICU mortality, and provide patients with intensive and targeted management.

RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown. METHODS: The differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis. RESULTS: A total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed. CONCLUSION: Our study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

Pharmacotherapy for Behçet's Disease and the Risk of Malignancy.

Background: Behçet's disease (BD) is associated with an increased risk of cancer. Few reports have been published on the relationship between drug exposure and the risk of cancer in patients with BD. Herein, we explored the relationship between pharmacologic interventions for BD and the risk of cancer. Methods: we carried out a retrospective nested case-control study in a cohort of BD patients from attending our institution. Among 1,148 patients, 22 cancer patients were individually 1:2 matched to 44 cancer-free controls. The following biochemical indicators were evaluated: routine blood analysis, liver and kidney function tests, inflammatory indexes, blood gas analysis, blood electrolyte and previous pharmacologic interventions to manage BD including systemic glucocorticoids, methotrexate, cyclosporine-A, azathioprine, cyclophosphamide (CYC), and thalidomide, which are considered the primary medicines used for the management of BD. Results: Among the 22 BD patients with cancers, myelodysplastic syndrome (MDS) (22.72%) was the most common type. Furthermore, CYC administration was significantly higher in BD patients with cancer compared with the cancer-free matched control group. Further, we observed that complement 4 (C4) (odds ratio [OR] = 0.0001, 95% confidence interval [CI]: 0.001-0.065) and hemoglobin (Hb) (OR = 0.891, 95% CI: 0.795-0.998) levels were independent protective factors for predicting cancer risk in BD patients on multivariate analyses. Conclusion: Our study revealed that CYC was associated with a high risk of cancer in BD patients. Furthermore, C4 and Hb are independent protective factors for oncogenesis in BD patients. These findings may provide references and suggestions for clinicians to select appropriate treatments and for the early recognition of high-risk patients to reduce cancer incidence in BD patients.

m6A Regulator-Associated Modification Patterns and Immune Infiltration of the Tumor Microenvironment in Hepatocarcinoma.

Background: Immunotherapy elicits durable responses in many tumors. Nevertheless, the positive response to immunotherapy always depends on the dynamic interactions between the tumor cells and infiltrating lymphocytes in the tumor microenvironment (TME). Currently, the application of immunotherapy in hepatocellular carcinoma (HCC) has achieved limited success. The ectopic modification of N6-methyladenosine (m6A) is a common feature in multiple tumors. However, the relationship between m6A modification with HCC clinical features, prognosis, immune cell infiltration, and immunotherapy efficacy remains unclear. Materials and Methods: Here, we comprehensively evaluated m6A modification clusters based on 22 m6A regulators and systematically explored the relationship between m6A modification with tumor progression, prognosis, and immune cell infiltration characteristics. The m6Ascore was calculated by principal component analysis to quantify the m6A modifications of individual patients. Key regulators involved in immunoregulation in HCC were identified using immunohistochemistry and immunofluorescence. Results: Three distinct m6A modification clusters were identified. The m6A clusters were significantly associated with clinical features, prognosis, and immune cell infiltration. The three clusters were highly consistent with the three tumor immune phenotypes, i.e., immune-excluded, immune-inflamed, and immune-desert. Comprehensive bioinformatics analysis revealed that high m6Ascore was closely associated with tumor progression, poor prognosis, and immunotherapy non-response. m6A regulators were dysregulated in HCC tissues. Hence, they play a role as predictors of poor prognosis. Tissue microarray demonstrated that overexpressed YTHDF1 was associated with low CD3+ and CD8+ T cell infiltration in HCC. Conclusion: Our findings demonstrate that m6A modification patterns play a crucial role in the tumor immune microenvironment and the prognosis of HCC. High YTHDF1 expression is closely associated with low CD3+ and CD8+ T cell infiltration in HCC.

DJ-1 in neurodegenerative diseases: Pathogenesis and clinical application.

Neurodegenerative diseases (NDs) are one of the major health threats to human characterized by selective and progressive neuronal loss. The mechanisms of NDs are still not fully understood. The study of genetic defects and disease-related proteins offers us a window into the mystery of it, and the extension of knowledge indicates that different NDs share similar features, mechanisms, and even genetic or protein abnormalities. Among these findings, PARK7 and its production DJ-1 protein, which was initially found implicated in PD, have also been found altered in other NDs. PARK7 mutations, altered expression and posttranslational modification (PTM) cause DJ-1 abnormalities, which in turn lead to downstream mechanisms shared by most NDs, such as mitochondrial dysfunction, oxidative stress, protein aggregation, autophagy defects, and so on. The knowledge of DJ-1 derived from PD researches might apply to other NDs in both basic research and clinical application, and might yield novel insights into and alternative approaches for dealing with NDs.

Dysregulated m6A-Related Regulators Are Associated With Tumor Metastasis and Poor Prognosis in Osteosarcoma.

Background: Osteosarcoma (OS) is the most common primary bone tumor. The disease has a poor prognosis due to the delay in the diagnosis and the development of metastasis. N6-Methyladenosine (m6A)-related regulators play an essential role in various tumors. In this study, a comprehensive analysis was conducted to elucidate the relationship between the expression profiles of m6A-related molecules and the clinical outcome of OS patients. Materials and Methods: Public genome datasets and a tissue microarray (TMA) cohort were used to analyze the mRNA and protein expression levels of m6A regulators. Next, immunofluorescence (IF) analysis was used to determine the subcellular localization of m6A-related molecules. Kaplan-Meier and Cox regression analyses were performed to confirm the prognostic value of m6A-related molecules in OS. A comprehensive bioinformatic analysis was conducted to identify the potential molecular mechanisms mediated by m6A modification in OS. Results: We found that m6A-related regulator expression was dysregulated in OS tissues, especially in metastatic tumor tissues. Low expression of METTL3, METTL14, and YTHDF2 and high expression of KIAA1429 and HNRNPA2B1 were significantly associated with poor prognosis in the TMA cohort. Simultaneously, the genome meta-cohort analysis revealed that low expression of FTO and METTL14 and high expression of METTL3, HNRNPA2B1, and YTHDF3 were associated with poor prognosis in OS. Cox regression analysis showed that HNRNPA2B1 might be an independent risk factor for OS. Bioinformatic analysis indicated that m6A regulators might be involved in OS progression through humoral immune response and cell cycle pathways. Conclusion: M6A-related regulators are frequently dysregulated and correlate with metastasis and prognosis in OS. M6A-related regulators may serve as novel therapeutic targets and prognostic biomarkers for OS.

Beneficial effects of PGC-1α in the substantia nigra of a mouse model of MPTP-induced dopaminergic neurotoxicity.

BACKGROUND: Mitochondrial dysfunction and oxidative stress are closely associated with the pathogenesis of Parkinson's disease. Peroxisome proliferator-activated receptor γ coactivator 1 alpha (PGC-1α) is thought to play multiple roles in the regulation of mitochondrial biogenesis and cellular energy metabolism. We recently reported that altering PGC-1α gene expression modulates mitochondrial functions in N-methyl-4-phenylpyridinium ion (MPP+) treated human SH-SY5Y neuroblastoma cells, possibly via the regulation of Estrogen-related receptor α (ERRα), nuclear respiratory factor 1 (NRF-1), nuclear respiratory factor 2 (NRF-2) and peroxisome proliferator-activated receptor γ (PPARγ) expression. In the present study, we aimed to further investigate the potential beneficial effects of PGC-1α in the substantia nigra of 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) treated C57BL mice. METHODS: The overexpression or knockdown of the PGC-1α gene in the mouse model of dopaminergic neurotoxicity was performed using a stereotactic injection of lentivirus in MPTP-treated male C57BL/6 mice. Mice were randomly assigned to one of 6 groups (n=24 per group): normal saline (NS) intraperitoneal injection (i.p.) (con); MPTP i.p. (M); solvent of the lentivirus striatal injection (lentivirus control) + MPTP i.p. (LVcon+M); lentivirus striatal injection + MPTP i.p. (LV+M); LV-PGC-1α striatum injection + MPTP i.p. (LVPGC+M); and LV-PGC-1α-siRNA striatal injection + MPTP i.p. (LVsiRNA+M). Intraperitoneal injections of MPTP/NS were conducted two weeks after lentivirus injection. RESULTS: We found significant improvement in motor behavior and increases in tyrosine hydroxylase expression in the substantia nigra (SN) in the brains of mice in the LVPGC+M group. The opposite tendency was observed in those in the LVsiRNA+M group. The expression of superoxide dismutase (SOD) in the SN region was also consistent with the changes in PGC-1α expression. Electron microscopy showed an increasing trend in the mitochondrial density in the LVPGC+M group and a decreasing trend in the M and LVsiRNA+M groups compared to that in the controls. CONCLUSIONS: Our results indicated that PGC-1α rescues the effects of MPTP-induced mitochondrial dysfunction in C57BL mice.