Genome-Wide Association Study of Obstructive Sleep Apnea and Objective Sleep-related Traits Identifies Novel Risk Loci in Han Chinese Individuals.

Rationale: Previous genetic studies of obstructive sleep apnea (OSA) have limitations in terms of precise case definition, integrated quantitative traits, and interpretation of genetic functions; thus, the heritability of OSA remains poorly explained. Objectives: To identify novel genetic variants associated with OSA and objective sleep-related traits and to explore their functional roles. Methods: A genome-wide association study was performed in 20,590 Han Chinese individuals (5,438 OSA and 15,152 control samples). Human samples and point mutation knockin mice were used for follow-up investigation of gene functions. Measurements and Main Results: Two characteristic study-wide significant loci (P < 2.63 × 10-9) for OSA were identified: the PACRG intronic variant rs6455893 on 6q26 (odds ratio [OR] = 1.62; 95% confidence interval [CI], 1.39-1.89; P = 6.98 × 10-10) and the missense variant rs3746804 (p.Pro267Leu) in the riboflavin transporter SLC52A3 on 20p13 (OR = 0.83; 95% CI, 0.79-0.88; P = 7.57 × 10-10). In addition, 18 genome-wide significant loci associated with quantitative OSA and objective sleep-related traits were identified, 5 of which exceeded the study-wide significance threshold. Rs3746804 was associated with elevated serum riboflavin concentrations, and the corresponding mutation in mice increased riboflavin concentrations, suggesting that this variant may facilitate riboflavin uptake and riboflavin-dependent physiological activity. Conclusions: We identified several novel genome-wide significant loci associated with OSA and objective sleep-related traits. Our findings provide insight into the genetic architecture of OSA and suggest that SLC52A3 might be a therapeutic target, whereas riboflavin might be a therapeutic agent.

Association of serum phthalates exposure with incident type 2 diabetes risk in Chinese population: A nested case-control study.

Prospective epidemiological evidence was lacking on the association of phthalates (PAEs) exposure with incident type 2 diabetes mellitus (T2DM) risk. In present nested case-control study, we identified 1006 T2DM cases and matched 1006 controls based on Dongfeng-Tongji cohort study, and 6 PAEs were detected in baseline serum. The conditional logistic regression model, Bayesian kernel machine regression (BKMR) model and Quantile-based g-computation were applied to evaluate the associations of determined PAEs, either as individuals or as a mixture, with incident T2DM risk. Subgroup analysis was conducted to identify the potential sensitive population of PAEs effects on T2DM. After multiple adjustment, no statistically significant association was observed between single or mixture of PAEs and incident T2DM risk in the whole population. However, serum levels of Di-n-butyl phthalate (DnBP) [OR= 2.06; 95% CI: (1.11-3.96)], Σdibutyl phthalate (ΣDBP) [OR= 1.96; 95% CI: (1.06-3.76)], and Σlow-molecular- weight phthalate (ΣLMW) [OR= 2.27; 95% CI: (1.17-4.57)] were significantly associated with T2DM in current drinker group. Moreover, significant potential interactions were observed among Di-iso-butyl phthalate (DiBP), DnBP, Butyl-benzyl phthalate (BBP), ΣDBP, and ΣLMW with drinking status on T2DM risk (P for interaction = 0.036, 0.005, 0.049. 0.010, and 0.005). We did not find significant associations between serum PAEs levels and T2DM in the whole population. However, current alcohol drinkers expose to higher levels of DnBP, ΣDBP, and ΣLMW had higher risk of T2DM.

Development of a four-gene prognostic model for clear cell renal cell carcinoma based on transcriptome analysis.

This study aimed to develop a prognostic model for clear cell renal cell carcinoma (ccRCC) based on transcriptome analysis. We screened Gene Expression Omnibus (GEO) database and the Cancer Genome Atlas (TCGA) database for gene expression data and clinical characteristics of ccRCC. After differentially expression analysis, we identified 533 key genes of the development of ccRCC. Next, a weighted gene co-expression network analysis (WGCNA) was executed to investigate the association between differentially expressed genes and clinical characteristics. Then, based on protein-protein interaction (PPI) network, least absolute shrinkage and selection operator (LASSO) regression and Cox regression, a four-gene (COL4A5, ABCB1, NR3C2 and PLG) prognostic model has been constructed in TCGA training cohort. Finally, we examined the predictive power of this model with survival analysis and receiver operating characteristic (ROC) curve both in training cohort and in validation cohorts. And we found this model was significantly correlated with infiltrating immune cells. The four-gene prognosis model could be a potential prediction tool with high accuracy and reliability for ccRCC patients.

CENPA promotes clear cell renal cell carcinoma progression and metastasis via Wnt/ß-catenin signaling pathway.

Clear cell renal cell carcinoma (ccRCC) is the most common malignant tumor of the kidney. New and reliable biomarkers are in urgent need for ccRCC diagnosis and prognosis. The CENP family is overexpressed in many types of cancers, but its functions in ccRCC have not been fully clarified. In this paper, we found that several CENP family members were highly expressed in ccRCC tissues. Also, CENPA expression level was related to clinicopathological grade and prognosis by weighted gene co-expression network analysis (WGCNA). CENPA served as a representative CENP family member as a ccRCC biomarker. Further in vitro experiments verified that overexpression of CENPA promoted ccRCC proliferation and metastasis by accelerating the cell cycle and activating the Wnt/ß-catenin signaling pathway. The elevated ß-catenin led by CENPA overexpression translocated to nucleus for downstream effect. Functional recovery experiment confirmed that Wnt/ß-catenin pathway was essential for ccRCC progression and metastasis. Developing selective drugs targeting CENPA may be a promising direction for cancer treatment.

Long noncoding RNA SNHG12 indicates the prognosis of prostate cancer and accelerates tumorigenesis via sponging miR-133b.

Prostate cancer (PCa) is the second leading cause of death among American men. Increasing evidence has shown that long noncoding RNAs (lncRNAs) play important roles in tumorigenesis of PCa. In this study, we explored the biological functions of small nucleolar RNA host gene 12 (SNHG12) and investigated the interaction between miR-133b and SNHG12 in the progression of PCa. Data was downloaded from The Cancer Genome Atlas and Human Cancer Metastasis Database, and clinicopathological characteristics were analyzed with relapse-free survival rate. We detected SNHG12 expression level in PCa cells and tissues, and then analyzed its clinical significance, which revealed that SNHG12 has the potent to predict prognosis of PCa. Bioinformatic analysis revealed that SNHG12 was closely related to the progression of PCa and could target candidate microRNA (miR-133b). After transfecting SNHG12 silencing plasmid and miR-133b mimic/sponge, biological function assays were conducted and results illustrated that SNHG12 associated with miR-133b exerted biological effects on cancer cell growth, migration, and invasion. Direct interactions between miR-133b and SNHG12 have been found and SNHG12 acts as an oncogene to promote tumorigenesis of PCa by sponging tumor suppressor gene miR-133b.

Association of TNF-α-308(G/A) and -238(G/A) polymorphisms with non-traumatic osteonecrosis of the femoral head risks: a meta-analysis.

PURPOSE: The association between TNF-α-308(G/A) and -238(G/A) polymorphisms and the susceptibility of non-traumatic osteonecrosis of the femoral head (NONFH) was investigated in many studies with conflicting results. We aimed to conduct a meta-analysis to evaluate the relationship between them comprehensively. METHODS: Relevant literatures published in PubMed, Web of Science, Embase, Cochrane library databases, China National Knowledge Infrastructure (CNKI), WANFANG Data, and China Science and Technology Journal Database (CSTJ) updated to January 30, 2018, were reviewed by two investigators independently. Odds ratios (ORs) and its 95% confidence intervals (95% CIs) were calculated by a fixed-effect model based on the indistinctive heterogeneity. RESULTS: For TNF-α-308(G/A) polymorphism, we recruited five studies including 432 NONFH patients and 760 controls and a statistically significant association was identified in Asians in four modes consisting of alleles mode (OR = 0.648, 95% CI 0.475-0.885), homozygote mode (OR = 0.330, 95% CI 0.136-0.802), dominant mode (OR = 0.344, 95% CI 0.143-0.827), and recessive mode (OR = 0.674, 95% CI 0.468-0.971), but no significant association was observed in Caucasians. For TNF-α-238(G/A) polymorphism, three eligible studies including 275 cases and 610 controls were evaluated and there was a significant association in alleles mode (OR = 0.270, 95% CI 0.4148-0.490) as well as recessive mode (OR = 0.254, 95% CI 0.138-0.468). CONCLUSION: This meta-analysis shows that TNF-α-308(G/A) and -238(G/A) polymorphisms are associated with the susceptibility of NONFH, while the significant association for 308(G/A) is mainly observed in Asians.

A novel model for relation prediction in knowledge graphs exploiting semantic and structural feature integration.

Relation prediction is a critical task in knowledge graph completion and associated downstream tasks that rely on knowledge representation. Previous studies indicate that both structural features and semantic information are meaningful for predicting missing relations in knowledge graphs. This has led to the development of two types of methods: structure-based methods and semantics-based methods. Since these two approaches represent two distinct learning paradigms, it is difficult to fully utilize both sets of features within a single learning model, especially deep features. As a result, existing studies usually focus on only one type of feature. This leads to an insufficient representation of knowledge in current methods and makes them prone to overlooking certain patterns when predicting missing relations. In this study, we introduce a novel model, RP-ISS, which combines deep semantic and structural features for relation prediction. The RP-ISS model utilizes a two-part architecture, with the first component being a RoBERTa module that is responsible for extracting semantic features from entity nodes. The second part of the system employs an edge-based relational message-passing network designed to capture and interpret structural information within the data. To alleviate the computational burden of the message-passing network on the RoBERTa module during the sampling process, RP-ISS introduces a node embedding memory bank, which updates asynchronously to circumvent excessive computation. The model was assessed on three publicly accessible datasets (WN18RR, WN18, and FB15k-237), and the results revealed that RP-ISS surpasses all baseline methods across all evaluation metrics. Moreover, RP-ISS showcases robust performance in graph inductive learning.

Clinical value of molecular subtypes identification based on anoikis-related lncRNAs in castration-resistant prostate cancer.

BACKGROUND: Anoikis is a distinctive type of apoptosis. It is involved in tumor progression and metastasis. But its function in castration-resistant prostate cancer (CRPC) remains veiled. We aimed to develop a prognostic indicator based on anoikis-related long non-coding RNAs (arlncRNAs) and to investigate their biological function in CRPC. MATERIAL AND METHOD: Differentially expressed anoikis-related genes were extracted from two CRPC datasets, GSE51873, and GSE78201. Four lncRNAs associated with the anoikis-related genes were selected. A risk model based on these lncRNAs was developed and validated in The Cancer Genome Atlas (TCGA) and the Memorial Sloan-Kettering Cancer Center (MSKCC) prostate cancer cohorts. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, immune infiltration, immune checkpoints expression, and drug susceptibility were performed based on the model. To identify the biofunction of anoikis-related lncRNA, CCK-8 assays, colony formation assays, and flow cytometry were used. RESULT: Twenty-nine anoikis-related genes were differentially expressed in the CRPC datasets. And 36 prognostic arlncRNAs were selected for the LASSO Cox analysis. Patients were subsequently classified into two subtypes by constructing an anoikis-related lncRNA based prognostic index (ARPI). The accuracy of this index was validated. KEGG enrichment analysis revealed that the high-ARPI group was enriched in cancer-related and immune-related pathways. Immune infiltration analysis has indicated a positive association between high-ARPI groups and increased immune infiltration. Fulvestrant, OSI-027, Lapatinib, Dabrafenib, and Palbociclib were identified as potential sensitive drugs for high-ARPI patients. In vitro experiments exhibited that silencing LINC01138 dampened the proliferation, migration and enzalutamide resistance in CRPC. Furthermore, it stimulated apoptosis and inhibited the eithelial-mesenchymal transition process. CONCLUSION: Four arlncRNAs were identified and a risk model was established to predict the prognosis of patients with prostate cancer. Immune infiltration and drug susceptibility analysis revealed a potential therapeutic strategy for patients with castration-resistant prostate cancer.

Arsenic exposure at environmentally relevant levels induced metabolic toxicity in development mice: Mechanistic insights from integrated transcriptome and metabolome.

Emerging evidence has linked arsenic exposure and metabolic homeostasis, but the mechanism is incompletely understood, especially at relatively low concentrations. In this study, we used a mouse model to evaluate the health impacts and metabolic toxicity of arsenic exposure in drinking water at environmentally relevant levels (0.25 and 1.0 ppm). Our results indicated that arsenic damaged intestinal barrier and induced arsenic accumulation, oxidative stress, and pathological changes in the liver and illum. Interestingly, arsenic increased the hepatic triglyceride (TG) and total cholesterol (TC), while reduced serum TG and TC levels. The liver transcriptome found that arsenic exposure caused transcriptome perturbation and promoted hepatic lipid accumulation by regulating the exogenous fatty acids degradation and apolipoproteins related genes. The serum metabolomics identified 74 and 88 differential metabolites in 0.25 and 1.0 ppm, respectively. The KEGG disease and subcellular location analysis indicated that arsenic induced liver and intestinal diseases, and the mitochondrion might be the target organelle for arsenic-induced toxicity. Co-enrichment of transcriptome and metabolome identified 24 metabolites and 9 genes as metabolic toxicity biomarkers. Moreover, 40 male (20 nonalcoholic fatty liver disease (NAFLD) cases and 20 healthy controls) was further selected to validate our findings. Importantly, the significantly changed L-palmitoylcarnitine, 3-hydroxybutyric acid, 2-hydroxycaproic acid and 6 genes of Hadha, Acadl, Aldh3a2, Cpt1a, Cpt2, and Acox1 were found in the NAFLD cases. The results from integrated multi-omics and chemical-protein network analysis indicated that L-palmitoylcarnitine played a critical role in metabolic toxicity by regulating mitochondrial fatty acids ß-oxidation genes (Cpt1a, Cpt2). In conclusion, these findings provided new clues for the metabolic toxicity of arsenic exposure at environmentally relevant levels, which involved in the late-life NAFLD development. Our results also contribute to understanding the human responses and phenotypic changes to this hazardous material exposure in the environment.

Gut microbiota deficiency aggravates arsenic-induced toxicity by affecting bioaccumulation and biotransformation in C57BL/6J mice.

Gut microbiome can influence the arsenic metabolism in mammals. Confusingly, gut microbiome was found to both mitigate and exacerbate arsenic toxicity. In this study, the role of gut microbiota in arsenic bioaccumulation, biotransformation, and organ toxicity in C57BL/6J mice was investigated. Gut microbiota deficiency model was established by antibiotics (Ab) cocktail AVNM. Conventional and gut microbiota deficiency mice were exposed to NaAsO2 for 4 weeks. Comparing with Ab-treated mice, the total arsenic (tAs) in the tissues was significantly reduced in conventional mice, which was opposed to the results of those in feces. Interestingly, dimethyl arsenite (DMA) was the most abundant metabolite in the feces of Ab-treated mice, while arsenic acid (AsV) had the highest proportion in the feces of conventional mice with approximately 16-fold than that in Ab-treated mice, indicating the critical role of gut microbiota in metabolizing arsenious acid (AsIII) to AsV. Additionally, the liver and kidney in Ab-treated mice showed more severe pathological changes and apoptosis. The significant increased level of ionized calcium-binding adapter molecule 1 (IBA-1) was also found in the brains of Ab-treated mice. Our results indicated that gut microbiota protected the host from arsenic-induced toxicity in liver, kidney, and brain by reducing the arsenic accumulation.

Associations of arsenic exposure and arsenic metabolism with the risk of non-alcoholic fatty liver disease.

Growing evidences supported that arsenic exposure contributes to non-alcoholic fatty liver disease (NAFLD) risk, but findings were still inconsistent. Additionally, once absorbed, arsenic is methylated into monomethyl and dimethyl arsenicals. However, no studies investigated the association of arsenic metabolism with NAFLD. Our objectives were to evaluate the associations of arsenic exposure and arsenic metabolism with NAFLD prevalence. We conducted a case-control study with 1790 participants derived from Dongfeng-Tongji cohort and measured arsenic species (arsenite, arsenate, monomethylarsonate [MMA], dimethylarsinate [DMA], and arsenobetaine) in urine. Arsenic exposure (∑As) was defined as the sum of inorganic arsenic (iAs), MMA, and DMA. Arsenic metabolism was evaluated as the proportions of inorganic-related species (iAs%, MMA%, and DMA%) and methylation efficiency ratios (primary methylation index [PMI], secondary methylation index [SMI]). NAFLD was diagnosed by liver ultrasound. Logistic regression was used to evaluate the associations. The median of ∑As was 13.24 µg/g creatinine. The ∑As showed positive and nonlinear association with moderate/severe NAFLD (OR: per log-SD = 1.33, 95% CI: [1.03,1.71]; Pfor nonlinearity = 0.021). The iAs% (OR: per SD = 1.16, 95% CI: [1.03,1.30]) and SMI (OR: per log-SD = 1.16, 95% CI: [1.03,1.31]) showed positive while MMA% (OR: per SD = 0.80, 95% CI: [0.70,0.91]) and PMI (OR: per log-SD = 0.86, 95% CI: [0.77,0.96]) showed inverse associations with NAFLD. Moreover, the ORs (95% CI) of NAFLD for each 5% increase in iAs% was 1.36 (1.17,1.58) when MMA% decreased and 1.07 (1.01,1.13) when DMA% decreased; and for each 5% increase in MMA%, it was 0.74 (0.63,0.86) and 0.79 (0.69,0.91) when iAs% and DMA% decreased, respectively. The results suggest that inorganic arsenic exposure is positively associated with NAFLD risk and arsenic methylation efficiency plays a role in the NAFLD. The findings provide clues to explore potential interventions for the prevention of NAFLD. Prospective studies are needed to validate our findings.

Integrative Analysis and Experimental Validation of Competing Endogenous RNAs in Obstructive Sleep Apnea.

BACKGROUND: Obstructive sleep apnea (OSA) is highly prevalent yet underdiagnosed. This study aimed to develop a predictive signature, as well as investigate competing endogenous RNAs (ceRNAs) and their potential functions in OSA. METHODS: The GSE135917, GSE38792, and GSE75097 datasets were collected from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Weighted gene correlation network analysis (WGCNA) and differential expression analysis were used to identify OSA-specific mRNAs. Machine learning methods were applied to establish a prediction signature for OSA. Furthermore, several online tools were used to establish the lncRNA-mediated ceRNAs in OSA. The hub ceRNAs were screened using the cytoHubba and validated by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Correlations between ceRNAs and the immune microenvironment of OSA were also investigated. RESULTS: Two gene co-expression modules closely related to OSA and 30 OSA-specific mRNAs were obtained. They were significantly enriched in the antigen presentation and lipoprotein metabolic process categories. A signature that consisted of five mRNAs was established, which showed a good diagnostic performance in both independent datasets. A total of twelve lncRNA-mediated ceRNA regulatory pathways in OSA were proposed and validated, including three mRNAs, five miRNAs, and three lncRNAs. Of note, we found that upregulation of lncRNAs in ceRNAs could lead to activation of the nuclear factor kappa B (NF-κB) pathway. In addition, mRNAs in the ceRNAs were closely correlated to the increased infiltration level of effector memory of CD4 T cells and CD56bright natural killer cells in OSA. CONCLUSIONS: In conclusion, our research opens new possibilities for diagnosis of OSA. The newly discovered lncRNA-mediated ceRNA networks and their links to inflammation and immunity may provide potential research spots for future studies.

circPTPN12 promotes the progression and sunitinib resistance of renal cancer via hnRNPM/IL-6/STAT3 pathway.

Renal cell carcinoma (RCC) is characterized by the difficulties in early diagnosis and the propensity to metastases. For advanced RCC, sunitinib targeted therapy is the clinically recommended first-line drug and the major challenge of sunitinib treatment is adaptive resistance. Therefore, it is imperative to research the mechanisms underlying sunitinib resistance. In this study, we discovered that circPTPN12 was highly expressed in RCC tissues and was associated with poorer clinical outcomes. circPTPN12 could promote the proliferation, migration, invasion, and sunitinib resistance of RCC cells. Mechanistically, circPTPN12 was found to form a complex with hnRNPM, which was involved in the regulation of mRNA processing. The combination with circPTPN12 enhanced the ability of hnRNPM to maintain the stability of IL-6 mRNA and further activated the STAT3 signaling pathway. The study revealed that circPTPN12/hnRNPM/IL-6/STAT3 axis promoted RCC progression and sunitinib resistance, which might be a promising therapeutic target for relieving sunitinib resistance in RCC.

Adenoid lymphocyte heterogeneity in pediatric adenoid hypertrophy and obstructive sleep apnea.

Introduction: Adenoid hypertrophy is the main cause of obstructive sleep apnea in children. Previous studies have suggested that pathogenic infections and local immune system disorders in the adenoids are associated with adenoid hypertrophy. The abnormalities in the number and function of various lymphocyte subsets in the adenoids may play a role in this association. However, changes in the proportion of lymphocyte subsets in hypertrophic adenoids remain unclear. Methods: To identify patterns of lymphocyte subsets in hypertrophic adenoids, we used multicolor flow cytometry to analyze the lymphocyte subset composition in two groups of children: the mild to moderate hypertrophy group (n = 10) and the severe hypertrophy group (n = 5). Results: A significant increase in naïve lymphocytes and a decrease in effector lymphocytes were found in severe hypertrophic adenoids. Discussion: This finding suggests that abnormal lymphocyte differentiation or migration may contribute to the development of adenoid hypertrophy. Our study provides valuable insights and clues into the immunological mechanism underlying adenoid hypertrophy.

AIM2 promotes renal cell carcinoma progression and sunitinib resistance through FOXO3a-ACSL4 axis-regulated ferroptosis.

Renal cell carcinoma (RCC) is a serious threat to people's health due to its rapid progression, and patients easily develop resistance to targeted therapy. The absent in melanoma 2 (AIM2) is a receptor protein that has recently been proposed to play an important role in various diseases. In this study, AIM2 was identified as a new biomarker of RCC and promoted RCC progression and sunitinib resistance in an inflammasome-independent manner. Mechanistically, AIM2 promoted FOXO3a phosphorylation and proteasome degradation, thereby reducing its transcriptional effect on ACSL4 and inhibiting ferroptosis. In summary, AIM2 promoted RCC progression and sunitinib resistance through FOXO3a-ACSL4 axis-regulated ferroptosis, which could provide new ideas and therapeutic targets for RCC diagnosis and treatment.

circRARS synergises with IGF2BP3 to regulate RNA methylation recognition to promote tumour progression in renal cell carcinoma.

As the most prominent RNA modification, N6-methyladenosine (m6 A) participates in the regulation of tumour initiation and progression. Circular RNAs (circRNAs) also play crucial roles in ubiquitous life processes. Whether circRNAs are required for m6 A regulation in renal cell carcinoma (RCC) remains unclear. Meta-analysis and bioinformatics identified that IGF2BP3 was upregulated in RCC and indicated a worse prognosis. IGF2BP3 significantly promoted RCC progression in vitro and in vivo. Mechanistically, circRARS bound to KH1-KH2 domains of IGF2BP3 to enhance m6 A modification recognition. A 12-nt sequence (GUCUUCCAGCAA) was proven to be the IGF2BP3-binding site of circRARS. Additionally, CAPN15, CD44, HMGA2, TNRC6A and ZMIZ2 were screened to be the target genes regulated by the IGF2BP3/circRARS complex in an m6 A-dependent manner. Stabiliser proteins, including HuR, Matrin3 and pAbPC1, were recruited by circRARS, thereby increasing the mRNA stability of the forementioned five target genes. Consequently, the IGF2BP3/circRARS complex facilitated the lipid accumulation of RCC cells and promoted sunitinib resistance via target genes. circRARS synergised with IGF2BP3 to facilitate m6 A recognition, thereby promoting RCC progression. Thus, IGF2BP3 could be a potential biomarker for RCC diagnosis and prognosis and a therapeutic target.

Fatty Acid Oxidation Mediated by Malonyl-CoA Decarboxylase Represses Renal Cell Carcinoma Progression.

Fatty acid metabolism reprogramming is a prominent feature of clear cell renal cell carcinoma (ccRCC). Increased lipid storage supports ccRCC progression, highlighting the importance of understanding the molecular mechanisms driving altered fatty acid synthesis in tumors. Here, we identified that malonyl-CoA decarboxylase (MLYCD), a key regulator of fatty acid anabolism, was downregulated in ccRCC, and low expression correlated with poor prognosis in patients. Restoring MLYCD expression in ccRCC cells decreased the content of malonyl CoA, which blocked de novo fatty acid synthesis and promoted fatty acid translocation into mitochondria for oxidation. Inhibition of lipid droplet accumulation induced by MLYCD-mediated fatty acid oxidation disrupted endoplasmic reticulum and mitochondrial homeostasis, increased reactive oxygen species levels, and induced ferroptosis. Moreover, overexpressing MLYCD reduced tumor growth and reversed resistance to sunitinib in vitro and in vivo. Mechanistically, HIF2α inhibited MLYCD translation by upregulating expression of eIF4G3 microexons. Together, this study demonstrates that fatty acid catabolism mediated by MLYCD disrupts lipid homeostasis to repress ccRCC progression. Activating MLYCD-mediated fatty acid metabolism could be a promising therapeutic strategy for treating ccRCC. SIGNIFICANCE: MLYCD deficiency facilitates fatty acid synthesis and lipid droplet accumulation to drive progression of renal cell carcinoma, indicating inducing MYLCD as a potential approach to reprogram fatty acid metabolism in kidney cancer.

Urinary arsenic metabolism, genetic susceptibility, and their interaction on type 2 diabetes.

Growing studies investigated the association of arsenic metabolism with type 2 diabetes (T2D), however, the epidemiological evidence is inconsistent. In addition, the interaction of arsenic metabolism-related genetic risk score (GRS)-arsenic on T2D risk was unclear. The present study aimed to evaluate the association of arsenic metabolism efficiency [inorganic arsenic (iAs)%, monomethylarsonic acid (MMA)%, and dimethylarsinic acid (DMA%)] with T2D risk. Moreover, the relationship of GRS and arsenic metabolism efficiency and the interaction of GRS-arsenic on T2D were investigated. Age- and sex-matched new-onset diabetes case-control study derived from the Dongfeng-Tongji cohort was conducted and 996 pairs participants were included in this study. The leave-one-out approach was used to evaluate the association of arsenic metabolism efficiency with T2D risk. The GRS and weight GRS (wGRS) were calculated based on 79 candidate SNPs. We estimated the relationship of GRS with arsenic metabolism efficiency by linear regression model. The interaction of GRS-arsenic on T2D was assessed by adding a multiplicative interaction term (GRS × arsenic) in the logistic regression models. Urinary iAs% was positively associated with T2D risk, and the OR (95% CI) was 1.06 (1.01, 1.12). MMA% and PMI were negatively associated with T2D risk, and the ORs (95% CI) were 0.87 (0.78, 0.97) and 0.64 (0.47, 0.86), respectively. Urinary DMA, As3+, and As5+ were positively associated with T2D risk. Similar relationships were found between arsenic metabolites and levels of FPG and HbA1c. Moreover, arsenic metabolism-related GRS/wGRS was positively associated with MMA% but negatively associated with DMA%. Genetic predisposition to arsenic metabolism modified the association of inorganic arsenic with T2D risk (Pinteraction = 0.033). Taken together, lower arsenic primary metabolism efficiency (higher iAs% and lower MMA%) may increase T2D risk. Genetic predisposition to arsenic metabolism was associated with arsenic metabolism efficiency, and might modify the association of inorganic arsenic with T2D risk.

FKBP5 genetic variants are associated with respiratory- and sleep-related parameters in Chinese patients with obstructive sleep apnea.

Introduction: Obstructive sleep apnea (OSA) has been associated with psychiatric disorders, especially depression and posttraumatic stress disorder (PTSD). FKBP5 genetic variants have been previously reported to confer the risk of depression and PTSD. This study aimed to investigate the association of single nucleotide polymorphisms (SNPs) in the FKBP5 gene with OSA and OSA-related quantitative traits. Methods: Four SNPs within the FKBP5 gene (rs1360780, rs3800373, rs9296158, rs9470080) were genotyped in 5773 participants with anthropometric and polysomnography data. Linear regression and logistic regression analyses were performed to evaluate the relationship between FKBP5 SNPs and OSA-related traits. Binary logistic regression was used to assess the effect of SNPs on OSA susceptibility. Interacting genes of SNPs were assessed based on the 3DSNP database, and expression quantitative trait loci (eQTL) analysis for SNPs was adopted to examine the correlation of SNPs with gene expression. Gene expression analyses in human brains were performed with the aid of Brain Atlas. Results: In moderate-to-severe OSA patients, all four SNPs were positively associated with AHIREM, and rs9296158 showed the strongest association (ß = 1.724, p = 0.001). Further stratified analyses showed that in men with moderate OSA, rs1360780, rs3800373 and rs9470080 were positively associated with wake time (p = 0.0267, p = 0.0254 and p = 0.0043, respectively). Rs1360780 and rs3800373 were 28 and 29.4%more likely to rate a higher ordered MAI category (OR (95% CI) = 1.280 (1.042 - 1.575), p = 0.019; OR (95% CI) = 1.294 (1.052 - 1.592), p = 0.015, respectively). Rs9296158 and rs9470080 increased the risk of low sleep efficiency by 25.7 and 28.1% (OR (95% CI) = 1.257 (1.003 - 1.575), p = 0.047; OR (95% CI) = 1.281 (1.026-1.6), p = 0.029, respectively). Integrated analysis of eQTL and gene expression patterns revealed that four SNPs may exert their effects by regulating FKBP5, TULP1, and ARMC12. Conclusion: Single nucleotide polymorphisms in the FKBP5 gene were associated with sleep respiratory events in moderate-to-severe OSA patients during REM sleep and associated with sleep architecture variables in men with moderate OSA. FKBP5 variants may be a potential predisposing factor for sleep disorders, especially in REM sleep.

Comprehensive Analysis of N6-Methyladenosine Regulators in the Subcluster Classification and Drug Candidates Prediction of Severe Obstructive Sleep Apnea.

Background: Obstructive sleep apnea (OSA) is the most common type of sleep apnea that impacts the development or progression of many other disorders. Abnormal expression of N6-methyladenosine (m6A) RNA modification regulators have been found relating to a variety of human diseases. However, it is not yet known if m6A regulators are involved in the occurrence and development of OSA. Herein, we aim to explore the impact of m6A modification in severe OSA. Methods: We detected the differentially expressed m6A regulators in severe OSA microarray dataset GSE135917. The least absolute shrinkage and selection operator (LASSO) and support vector machines (SVM) were used to identify the severe OSA-related m6A regulators. Receiver operating characteristic (ROC) curves were performed to screen and verify the diagnostic markers. Consensus clustering algorithm was used to identify m6A patterns. And then, we explored the character of immune microenvironment, molecular functionals, protein-protein interaction networks and miRNA-TF coregulatory networks for each subcluster. Finally, the Connectivity Map (CMap) tools were used to tailor customized treatment strategies for different severe OSA subclusters. An independent dataset GSE38792 was used for validation. Results: We found that HNRNPA2B1, KIAA1429, ALKBH5, YTHDF2, FMR1, IGF2BP1 and IGF2BP3 were dysregulated in severe OSA patients. Among them, IGF2BP3 has a high diagnostic value in both independent datasets. Furthermore, severe OSA patients can be accurately classified into three m6A patterns (subcluster1, subcluster2, subcluster3). The immune response in subcluster3 was more active because it has high M0 Macrophages and M2 Macrophages infiltration and up-regulated human leukocyte antigens (HLAs) expression. Functional analysis showed that representative genes for each subcluster in severe OSA were assigned to histone methyltransferase, ATP synthesis coupled electron transport, virus replication, RNA catabolic, multiple neurodegeneration diseases pathway, et al. Moreover, our finding demonstrated cyclooxygenase inhibitors, several of adrenergic receptor antagonists and histamine receptor antagonists might have a therapeutic effect on severe OSA. Conclusion: Our study presents an overview of the expression pattern and crucial role of m6A regulators in severe OSA, which may provide critical insights for future research and help guide appropriate prevention and treatment options.