Integrative analysis of genetics, epigenetics and RNA expression data reveal three susceptibility loci for smoking behavior in Chinese Han population.

Despite numerous studies demonstrate that genetics and epigenetics factors play important roles on smoking behavior, our understanding of their functional relevance and coordinated regulation remains largely unknown. Here we present a multiomics study on smoking behavior for Chinese smoker population with the goal of not only identifying smoking-associated functional variants but also deciphering the pathogenesis and mechanism underlying smoking behavior in this under-studied ethnic population. After whole-genome sequencing analysis of 1329 Chinese Han male samples in discovery phase and OpenArray analysis of 3744 samples in replication phase, we discovered that three novel variants located near FOXP1 (rs7635815), and between DGCR6 and PRODH (rs796774020), and in ARVCF (rs148582811) were significantly associated with smoking behavior. Subsequently cis-mQTL and cis-eQTL analysis indicated that these variants correlated significantly with the differential methylation regions (DMRs) or differential expressed genes (DEGs) located in the regions where these variants present. Finally, our in silico multiomics analysis revealed several hub genes, like DRD2, PTPRD, FOXP1, COMT, CTNNAP2, to be synergistic regulated each other in the etiology of smoking.

Nutrition Status Plays a Partial Mediation Role in the Relationship between Number of Teeth and Frailty: A Cross-Sectional Multicenter Study.

INTRODUCTION: Although the relationship between the number of teeth and frailty has been extensively studied, the mediating role of nutrition status in the association between the number of teeth and frailty remains to be clarified. METHODS: A number of 6,664 participants lived in the communities of West China were analyzed in our study. Physical frailty was determined based on the phenotype established by Fried. Nutrition status was evaluated using the Mini Nutrition Assessment-Short Form (MNA-SF) scale. Multiple linear regression was employed to evaluate the direct relationships between the number of teeth, nutrition, and frailty. Mediation models and structural equation model (SEM) pathway analysis were used to test the mediating role of nutrition status in the relationship between the number of teeth and frailty. RESULTS: Among the 6,664 participants aged over 50 years old, the prevalence of frailty was 6.2%. Multiple linear regression analysis showed a significant total relationship between the number of teeth (ß = -0.359, 95% CI: -0.473 to -0.244, p < 0.001) and frailty. After adjusting for MNA-SF scores, the relationship between the number of teeth and frailty remained significant (ß = -0.327, 95% CI: -0.443 to -0.211, p < 0.001), indicating a partial mediating effect of nutrition. Mediation analysis verified that nutrition partially mediated the relationship between the number of teeth and frailty (indirect effect estimate = -0.0121, bootstrap 95% CI: -0.0151 to -0.0092; direct effect estimate = -0.0874, bootstrap 95% CI: -0.1086 to -0.0678) in the fully adjusted model. This mediating effect occurred through influencing weight loss, low level of physical activity, and debility. SEM framework pathway analysis confirmed the association between the number of teeth, nutrition, and frailty. CONCLUSIONS: Our findings demonstrated that frailty was correlated with the number of teeth and poorer nutritional status, with nutrition partially mediating the correlation between the number of teeth and frailty. Our results supported early nutritional evaluation and intervention in oral health to decrease the risk of frailty.

Alcohol drinking, DNA methylation and psychiatric disorders: A multi-omics Mendelian randomization study to investigate causal pathways.

BACKGROUND AND AIMS: Whether alcohol-related DNA methylation has a causal effect on psychiatric disorders has not been investigated. Furthermore, a comprehensive investigation into the causal relationship and underlying mechanisms linking alcohol consumption and psychiatric disorders has been lacking. This study aimed to evaluate the causal effect of general alcohol intake and pathological drinking behaviors on psychiatric disorders, alcohol-associated DNA methylation on gene expression and psychiatric disorders, and gene expression on psychiatric disorders. DESIGN: Two-sample design Mendelian randomization (MR) analysis. Various sensitivity and validation analyses, including colocalization analysis, were conducted to test the robustness of the results. SETTING: Genome-wide association study (GWAS) data mainly from GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), Genetics of DNA Methylation Consortium (GoDMC) and Psychiatric Genomics Consortium (PGC) with European ancestry. PARTICIPANTS: The GWAS summary data on general alcohol intake (drinks per week, n = 941 280), pathological drinking behaviors (including alcohol use disorder [AUD, n = 313 959] and problematic alcohol use [PAU, n = 435 563]) and psychiatric disorders (including schizophrenia, major depressive disorder and bipolar disorder, n = 51 710-500 199) were included. Alcohol-related DNA methylation CpG sites (n = 9643) and mQTL data from blood (n = 27 750) and brain (n = 1160), BrainMeta v2 and GTEx V8 eQTL summary data (n = 73-2865) were also included. MEASUREMENTS: Genetic variants were selected as instrumental variables for exposures, including drinks per week, AUD, PAU, alcohol-related DNA methylation CpG sites (mQTL) and genes selected (eQTL). FINDINGS: Pathological drinking behaviors were associated with an increased risk of psychiatric disorders after removing outliers or controlling for alcohol consumption. MR analysis identified 10 alcohol-related CpG sites with colocalization evidence that were causally associated with psychiatric disorders (P = 1.65 × 10-4-7.52 × 10-22). Furthermore, the expression of genes (RERE, PTK6, GATAD2B, COG8, PDF and GAS5) mapped to these CpG sites in the brain, led by the cortex, were significantly associated with psychiatric disorders (P = 1.19 × 10-2-3.51 × 10-7). CONCLUSIONS: Pathological drinking behavior and alcohol-related DNA methylation appear to have a causal effect on psychiatric disorders. The expression of genes regulated by the alcohol-related DNA methylation sites may underpin this association.

CRISPR/Cas9 based gene editing of Frizzled class receptor 6 (FZD6) reveals its role in depressive symptoms through disrupting Wnt/ß-catenin signaling pathway.

INTRODUCTION: As one of the common psychiatric diseases, depression poses serious threats to human health. Although many genes have been nominated for depression, few of them were investigated in details at the molecular level. OBJECTIVES: To demonstrate Frizzled class receptor 6 (FZD6) functions in depression through disrupting Wnt/ß-catenin signal pathway. METHODS: The FZD6 edited cell line and mouse model were generated by using CRISPR/Cas9 technique. The expression of key genes and proteins in Wnt/ß-catenin pathway was determined by qRT-PCR and Western blotting, respectively. Animal behavioral tests, including open field test (OFT), elevated plus maze test (EPM), forced swimming test (FST), tail suspension test (TST), and sucrose preference test (SPT), were employed to determine anxiety- and depressive-like behaviors. Immunofluorescent staining was used to assess cell proliferation in the hippocampus of mouse brain. RESULTS: Among patients with depression, FZD6, one of the receptors of Wnt ligand, was significantly decreased. In CRISPR/Cas9-based FZD6 knockdown cells, we showed that FZD6 plays a significant role in regulating expression of genes involved in Wnt/ß-catenin pathway. Subsequently behavioral studies on Fzd6 knockdown mice (with a 5-nucleotide deletion; Fzd6-Δ5) revealed significant changes in depressive symptoms, including increased immobility duration in FST, less preference of sucrose in SPT, reduction of distance traveled in OFT, and decreased time spent in open arms in EPM. Immunofluorescent staining showed decreased cell proliferation in the hippocampus of Fzd6-Δ5 mice with reduced number of Ki67+ and PCNA+ cells. Moreover, decreased Gsk3ß mRNA expression, phosphorylated GSK3ß, and cytoplasmic ß-catenin in the hippocampus of Fzd6-Δ5 mice provided further evidence supporting the role of Fzd6 in depression. CONCLUSION: Together, above findings proved the significant role of FZD6 in depression through its effect on hippocampal cell proliferation and its ability to regulate canonical Wnt/ß-catenin pathway.

Single nucleotide polymorphisms rs148582811 regulates its host gene ARVCF expression to affect nicotine-associated hippocampus-dependent memory.

Although numerous susceptibility loci are nominated for nicotine dependence (ND), no report showed any association of ARVCF with ND. Through genome-wide sequencing analysis, we first identified genetic variants associated nominally with ND and then replicated them in an independent sample. Of the six replicated variants, rs148582811 in ARVCF located in the enhancer-associated marker peak is attractive. The effective-median-based Mendelian randomization analysis indicated that ARVCF is a causal gene for ND. RNA-seq analysis detected decreased ARVCF expression in smokers compared to nonsmokers. Luciferase reporter assays indicated that rs148582811 and its located DNA fragment allele-specifically regulated ARVCF expression. Immunoprecipitation analysis revealed that transcription factor X-ray repair cross-complementing protein 5 (XRCC5) bound to the DNA fragment containing rs148582811 and allele-specifically regulated ARVCF expression at the mRNA and protein levels. With the Arvcf knockout mouse model, we showed that Arvcf deletion not only impairs hippocampus-dependent learning and memory, but also alleviated nicotine-induced memory deficits.

Determination of genetic correlation between tobacco smoking and coronary artery disease.

Backgrounds: Tobacco smoking is an important risk factor for coronary artery disease (CAD), but the genetic mechanisms linking smoking to CAD remain largely unknown. Methods: We analyzed summary data from the genome-wide association study (GWAS) of the UK Biobank for CAD, plasma lipid concentrations (n = 184,305), and smoking (n = 337,030) using different biostatistical methods, which included LD score regression and Mendelian randomization (MR). Results: We identified SNPs shared by CAD and at least one smoking behavior, the genes where these SNPs are located were found to be significantly enriched in the processes related to lipoprotein metabolic, chylomicron-mediated lipid transport, lipid digestion, mobilization, and transport. The MR analysis revealed a positive correlation between smoking cessation and decreased risk for CAD when smoking cessation was considered as exposure (p = 0.001), and a negative correlation between the increased risk for CAD and smoking cessation when CAD was considered as exposure (p = 2.95E-08). This analysis further indicated that genetic liability for smoking cessation increased the risk of CAD. Conclusion: These findings inform the concomitant conditions of CAD and smoking and support the idea that genetic liabilities for smoking behaviors are strongly associated with the risk of CAD.

Heme metabolism mediates the effects of smoking on gut microbiome.

INTRODUCTION: The number of smokers worldwide increased greatly during the past decades and reached 1.14 billion in 2019, becoming a leading risk factor for human health. Tobacco smoking has wide effects on human genetics, epigenetics, transcriptome, and gut microbiome. Although many studies have revealed effects of smoking on host transcriptome, research on the relationship among smoking, host gene expression, and the gut microbiome is limited. METHODS: We first explored transcriptome and metagenome profile differences between smokers and non-smokers. To evaluate the relationship between host gene expression and gut microbiome, we then applied bi-directional mediation analysis to infer causal relationships between smoking, gene expression, and gut microbes. RESULTS: Metagenome and transcriptome analyses revealed 71 differential species and 324 differential expressed genes between smokers and non-smokers. With smoking as an exposure variable, we identified 272 significant causal relationships between gene expression and gut microbes, among which there were 247 genes that mediate the effect of smoking on gut microbes. Pathway-based enrichment analysis showed that these genes were significantly enriched in heme metabolic pathway, which mainly mediated the changes of Bacteroides finegoldii and Lachnospiraceae bacterium 9_1_43BFAA. Additionally, by performing metabolome data analysis in the Integrated Human Microbiome project (iHMP) database, we verified the correlation between the intermediate products of the heme metabolism pathway (porphobilinogen, bilirubin, and biliverdin) and gut microbiome. CONCLUSIONS: By investigating the bi-directional interaction between smoking-related host gene expression and gut microbes, this study provided evidence for the mediation of smoking on gut microbes through co-involvement or interaction of heme metabolism. IMPLICATIONS: By comparing the metagenome and transcriptome sequencing profiles between 34 smokers and 33 age- and gender-matched non-smokers, we are the first to reveal causal relationships among tobacco smoking, host gene expression and gut microbes. These findings offer insight into how smoking affects gut microbes through host gene expression and metabolism, which highlights the importance of heme metabolism in modulating the effects of smoking on gut microbiome.

An HBV susceptibility variant of KNG1 modulates the therapeutic effects of interferons α and λ1 in HBV infection by promoting MAVS lysosomal degradation.

BACKGROUND: Hepatitis B virus (HBV) infection is one of the main causes of hepatocellular carcinoma (HCC). The relationship between HBV infection and the host genome as well as their underlying mechanisms remain largely unknown. METHODS: In this study, we performed a whole-genome exon sequencing analysis of 300 sib-pairs of Chinese HBV-infected families with the goal of identifying variants and genes involved in HBV infection. A site-direct mutant plasmid was used to investigate the function of SNP rs76438938 in KNG1. The functional and mechanical studies of KNG1 were conducted with in vitro liver cell lines and a hydrodynamic injection model in vivo. The impact of KNG1 on HBV infection therapy was determined in hepatocytes treated with IFN-α/λ1. FINDINGS: Our whole-exon association study of 300 families with hepatitis B infection found that SNP rs76438938 in KNG1 significantly increased the risk for HBV infection, and the rs76438938-T allele was found to promote HBV replication by increasing the stability of KNG1 mRNA. By competitively binding HSP90A with MAVS, KNG1 can inhibit the expression of types I and III IFNs by promoting MAVS lysosomal degradation. Such suppression of IFN expression and promotion of HBV replication by Kng1 were further demonstrated with an animal model in vivo. Lastly, we showed that the rs76438938-C allele can improve the therapeutic effect of IFN-α and -λ1 in HBV infection. INTERPRETATION: This study identified a SNP, rs76438938, in a newly discovered host gene, KNG1, for its involvement in HBV infection and treatment effect through modulating the cellular antiviral process. FUNDING: This study was supported in part by the Independent Task of State Key Laboratory for Diagnosis and Treatment of Infectious Diseases of the First Affiliated Hospital of Zhejiang University, the China Precision Medicine Initiative (2016YFC0906300), and the Research Center for Air Pollution and Health of Zhejiang University.

Landscapes of gut microbiome and bile acid signatures and their interaction in HBV-associated acute-on-chronic liver failure.

Introduction: Submassive hepatic necrosis (SMHN, defined as necrosis of 15-90% of the entire liver on explant) is a likely characteristic pathological feature of ACLF in patients with hepatitis B cirrhosis. We aimed to comprehensively explore microbiome and bile acids patterns across enterhepatic circulation and build well-performing machine learning models to predict SMHN status. Methods: Based on the presence or absence of SMHN, 17 patients with HBV-related end-stage liver disease who received liver transplantation were eligible for inclusion. Serum, portal venous blood, and stool samples were collected for comparing differences of BA spectra and gut microbiome and their interactions. We adopted the random forest algorithm with recursive feature elimination (RF-RFE) to predict SMHN status. Results: By comparing total BA spectrum between SMHN (-) and SMHN (+) patients, significant changes were detected only in fecal (P = 0.015). Compared with the SMHN (+) group, the SMHN (-) group showed that UDCA, 7-KLCA, 3-DHCA, 7-KDCA, ISOLCA and α-MCA in feces, r-MCA, 7-KLCA and 7-KDCA in serum, γ-MCA and 7-KLCA in portal vein were enriched, and TUDCA in feces was depleted. PCoA analysis showed significantly distinct overall microbial composition in two groups (P = 0.026). Co-abundance analysis showed that bacterial species formed strong and broad relationships with BAs. Among them, Parabacteroides distasonis had the highest node degree. We further identified a combinatorial marker panel with a high AUC of 0.92. Discussion: Our study demonstrated the changes and interactions of intestinal microbiome and BAs during enterohepatic circulation in ACLF patients with SMHN. In addition, we identified a combinatorial marker panel as non-invasive biomarkers to distinguish the SMHN status with high AUC.

CAR-T cell therapy-related cytokine release syndrome and therapeutic response is modulated by the gut microbiome in hematologic malignancies.

Immunotherapy utilizing chimeric antigen receptor T cell (CAR-T) therapy holds promise for hematologic malignancies, however, response rates and associated immune-related adverse effects widely vary among patients. Here we show, by comparing diversity and composition of the gut microbiome during different CAR-T therapeutic phases in the clinical trial ChiCTR1800017404, that the gut flora characteristically differs among patients and according to treatment stages, and might also reflect patient response to therapy in relapsed/refractory multiple myeloma (MM; n = 43), acute lympholastic leukemia (ALL; n = 23) and non-Hodgkin lymphoma (NHL; n = 12). We observe significant temporal differences in diversity and abundance of Bifidobacterium, Prevotella, Sutterella, and Collinsella between MM patients in complete remission (n = 24) and those in partial remission (n = 11). Furthermore, we find that patients with severe cytokine release syndrome present with higher abundance of Bifidobacterium, Leuconostoc, Stenotrophomonas, and Staphylococcus, which is reproducible in an independent cohort of 38 MM patients. This study has important implications for understanding the biological role of the microbiome in CAR-T treatment responsiveness of hematologic malignancy patients, and may guide therapeutic intervention to increase efficacy. The success rate of CAR-T cell therapy is high in blood cancers, yet individual patient characteristics might reduce therapeutic benefit. Here we show that therapeutic response in MM, ALL and NHL, and occurrence of severe cytokine release syndrome in multiple myeloma are associated with specific gut microbiome alterations.

Investigation of the genetic effect of 56 tobacco-smoking susceptibility genes on DNA methylation and RNA expression in human brain.

Although various susceptibility genes have been revealed to influence tobacco smoking, the underlying regulatory mechanisms between genetic variants and smoking are poorly understood. In this study, we investigated cis-expression quantitative trait loci (cis-eQTLs) and methylation quantitative trait loci (mQTLs) for 56 candidate smoking-linked genes using the BrainCloud cohort samples. An eQTL was revealed to significantly affect EGLN2 expression in the European sample and two mQTLs were respectively detected in CpG sites in NRXN1 and CYP2A7. Interestingly, we found for the first time that the minor allele of the single nucleotide polymorphism (SNP) rs3745277 located in CYP2A7P1 (downstream of CYP2B6) significantly decreased methylation at the CpG site for CYP2A7 (cg25427638; P = 5.31 × 10-7), reduced expression of CYP2B6 (P = 0.03), and lowered the percentage of smokers (8.8% vs. 42.3%; Odds Ratio (OR) = 0.14, 95% Confidence Interval (CI): 0.02-0.62; P = 4.47 × 10-3) in a dominant way for the same cohort sample. Taken together, our findings resulted from analyzing genetic variation, DNA methylation, mRNA expression, and smoking status together using the same participants revealed a regulatory mechanism linking mQTLs to the smoking phenotype. Moreover, we demonstrated the presence of different regulatory effects of low-frequency and common variants on mRNA expression and DNA methylation.

Identification of a Novel Functional Non-synonymous Single Nucleotide Polymorphism in Frizzled Class Receptor 6 Gene for Involvement in Depressive Symptoms.

Although numerous susceptibility loci for depression have been identified in recent years, their biological function and molecular mechanism remain largely unknown. By using an exome-wide association study for depressive symptoms assessed by the Center for Epidemiological Studies Depression (CES-D) score, we discovered a novel missense single nucleotide polymorphism (SNP), rs61753730 (Q152E), located in the fourth exon of the frizzled class receptor 6 gene (FZD6), which is a potential causal variant and is significantly associated with the CES-D score. Computer-based in silico analysis revealed that the protein configuration and stability, as well as the secondary structure of FZD6 differed greatly between the wild-type (WT) and Q152E mutant. We further found that rs61753730 significantly affected the luciferase activity and expression of FZD6 in an allele-specific way. Finally, we generated Fzd6-knockin (Fzd6-KI) mice with rs61753730 mutation using the CRISPR/Cas9 genome editing system and found that these mice presented greater immobility in the forced swimming test, less preference for sucrose in the sucrose preference test, as well as decreased center entries, center time, and distance traveled in the open filed test compared with WT mice after exposed to chronic social defeat stress. These results indicate the involvement of rs61753730 in depression. Taken together, our findings demonstrate that SNP rs61753730 is a novel functional variant and plays an important role in depressive symptoms.

A self-healing and self-adhesive chitosan based ion-conducting hydrogel sensor by ultrafast polymerization.

Recently, owing to the wide applications in electronic skin and human activity monitoring, flexible hydrogel strain sensors have attracted great attention. And the better preparation with more efficient is always common aspiration. In this work, acrylamide (AM) was in situ polymerized in chitosan (CS) matrix to prepare hydrogels (PAM@CS). Inspired by the adhesion of natural mussels, plant polyphenol tannic acid (TA) was introduced into the system, Fe3+ was also introduced as redox agent to perform an ultrafast polymerization, and the composite hydrogel PAM@CS/TA-Fe can be prepared at 60 °C within 1 min. The hydrogels are ion conductive and show good sensing performance in detecting major and subtle body motions. Benefiting from the multiple dynamic noncovalent bonds, the PAM@CS/TA-Fe hydrogels also show excellent adhesion performance and good self-healing property, which would expand their application range in wearable and flexible electronic equipment.

Genome-wide methylation and expression analyses reveal the epigenetic landscape of immune-related diseases for tobacco smoking.

BACKGROUND: Smoking is a major causal risk factor for lung cancer, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and is the main preventable cause of deaths in the world. The components of cigarette smoke are involved in immune and inflammatory processes, which may increase the prevalence of cigarette smoke-related diseases. However, the underlying molecular mechanisms linking smoking and diseases have not been well explored. This study was aimed to depict a global map of DNA methylation and gene expression changes induced by tobacco smoking and to explore the molecular mechanisms between smoking and human diseases through whole-genome bisulfite sequencing (WGBS) and RNA-sequencing (RNA-seq). RESULTS: We performed WGBS on 72 samples (36 smokers and 36 nonsmokers) and RNA-seq on 75 samples (38 smokers and 37 nonsmokers), and cytokine immunoassay on plasma from 22 males (9 smokers and 13 nonsmokers) who were recruited from the city of Jincheng in China. By comparing the data of the two groups, we discovered a genome-wide methylation landscape of differentially methylated regions (DMRs) associated with smoking. Functional enrichment analyses revealed that both smoking-related hyper-DMR genes (DMGs) and hypo-DMGs were related to synapse-related pathways, whereas the hypo-DMGs were specifically related to cancer and addiction. The differentially expressed genes (DEGs) revealed by RNA-seq analysis were significantly enriched in the "immunosuppression" pathway. Correlation analysis of DMRs with their corresponding gene expression showed that genes affected by tobacco smoking were mostly related to immune system diseases. Finally, by comparing cytokine concentrations between smokers and nonsmokers, we found that vascular endothelial growth factor (VEGF) was significantly upregulated in smokers. CONCLUSIONS: In sum, we found that smoking-induced DMRs have different distribution patterns in hypermethylated and hypomethylated areas between smokers and nonsmokers. We further identified and verified smoking-related DMGs and DEGs through multi-omics integration analysis of DNA methylome and transcriptome data. These findings provide us a comprehensive genomic map of the molecular changes induced by smoking which would enhance our understanding of the harms of smoking and its relationship with diseases.

Integrative Analysis of Epigenome and Transcriptome Data Reveals Aberrantly Methylated Promoters and Enhancers in Hepatocellular Carcinoma.

DNA methylation is a key transcription regulator, whose aberration was ubiquitous and important in most cancers including hepatocellular carcinoma (HCC). Whole-genome bisulfite sequencing (WGBS) was conducted for comparison of DNA methylation in tumor and adjacent tissues from 33 HCC patients, accompanying RNA-seq to determine differentially methylated region-associated, differentially expressed genes (DMR-DEGs), which were independently replicated in the TCGA-LIHC cohort and experimentally validated via 5-aza-2-deoxycytidine (5-azadC) demethylation. A total of 9,867,700 CpG sites showed significantly differential methylation in HCC. Integrations of mRNA-seq, histone ChIP-seq, and WGBS data identified 611 high-confidence DMR-DEGs. Enrichment analysis demonstrated activation of multiple molecular pathways related to cell cycle and DNA repair, accompanying repression of several critical metabolism pathways such as tyrosine and monocarboxylic acid metabolism. In TCGA-LIHC, we replicated about 53% of identified DMR-DEGs and highlighted the prognostic significance of combinations of methylation and expression of nine DMR-DEGs, which were more efficient prognostic biomarkers than considering either type of data alone. Finally, we validated 22/23 (95.7%) DMR-DEGs in 5-azadC-treated LO2 and/or HepG2 cells. In conclusion, integration of epigenome and transcriptome data depicted activation of multiple pivotal cell cycle-related pathways and repression of several metabolic pathways triggered by aberrant DNA methylation of promoters and enhancers in HCC.

Correlation Between Prognostic Biomarker SLC1A5 and Immune Infiltrates in Various Types of Cancers Including Hepatocellular Carcinoma.

BACKGROUND: Solute carrier family 1 member 5 (SLC1A5) is a major glutamine transporter and plays a key role in tumor growth. The main objectives of this study were to visualize the prognostic landscape of SLC1A5 in multiple cancers and determine the relations between SLC1A5 expression and tumor immunity. METHODS: SLC1A5 expression and its effect on tumor prognosis were analyzed using multiple online tools Oncomine, Gene Expression Profiling Interactive Analysis, PrognoScan, and Kaplan-Meier plotter with their own datasets as well as the data from The Cancer Genome Atlas. The correlations between SLC1A5 and tumor immune infiltrates were determined via TIMER. RESULTS: SLC1A5 expression was significantly higher in several types of cancers, including hepatocellular carcinoma (HCC), compared with corresponding normal tissues. High SLC1A5 expression correlated with poor overall survival and with disease-free survival related to alcohol consumption. Moreover, SLC1A5 expression correlated positively with the numbers of tumor-infiltrating B cells, CD4+ T and CD8+ T cells, macrophages, neutrophils, and dendritic cells in HCC and in lower-grade glioma (LGG). Also, SLC1A5 expression showed strong correlations with diverse immune marker sets in HCC and LGG, indicating its role in regulating tumor immunity. CONCLUSIONS: SLC1A5 represents a useful prognostic biomarker in multiple cancers, and its expression correlates highly with tumor immune-cell infiltration, especially in HCC and LGG.

Landscape analysis of lncRNAs shows that DDX11-AS1 promotes cell-cycle progression in liver cancer through the PARP1/p53 axis.

Although long non-coding RNAs (lncRNAs) play important roles in tumorigenesis, the underlying mechanisms are unclear. Transcriptomic analysis of 33 hepatocellular carcinoma (HCC) samples revealed that the most enriched pathway for differentially expressed genes was related to the cell cycle process, where DDX11-AS1 is the most significant lncRNA. Upregulation of DDX11-AS1 expression through demethylation was significantly associated with a poor prognosis. Further mechanistic studies revealed that DDX11-AS1 promoted the growth of HCC by interacting with PARP1 through attenuating its binding to p53, leading to downregulated expression of p53 for inhibiting the transcription of downstream genes such as p21. Knockdown of DDX11-AS1 expression in xenograft mice using anti-DDX11-AS1 oligonucleotide suppressed liver tumor proliferation. These findings indicate that DDX11-AS1 plays a role in the development of liver cancer by affecting the cell cycle.

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods.

BACKGROUND: Major depressive disorder (MDD) is a global health challenge that impacts the quality of patients' lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes. MATERIALS AND METHODS: We collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package "MetaOmics" was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection. RESULTS: Our analysis revealed six differentially expressed genes (AKR1C3, ARG1, KLRB1, MAFG, TPST1, and WWC3) with a false discovery rate (FDR) < 0.05 between MDD patients and control subjects. We then evaluated the diagnostic ability of these genes individually. With single gene prediction, we achieved a corresponding area under the curve (AUC) value of 0.63 ± 0.04, 0.67 ± 0.07, 0.70 ± 0.11, 0.64 ± 0.08, 0.68 ± 0.07, and 0.62 ± 0.09, respectively, for these genes. Next, we constructed the classifiers of SVM, RF, kNN, and NB with an AUC of 0.84 ± 0.09, 0.81 ± 0.10, 0.73 ± 0.11, and 0.83 ± 0.09, respectively, in validation datasets, suggesting that the SVM classifier might be superior for constructing an MDD diagnostic model. The final SVM classifier including 70 feature genes was capable of distinguishing MDD samples from healthy controls and yielded an AUC of 0.78 in an independent dataset. CONCLUSION: This study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.