Deep learning-based advances and applications for single-cell RNA-sequencing data analysis.

The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The application of deep learning to scRNA-seq data analysis is rapidly evolving and can overcome the unique challenges in upstream (quality control and normalization) and downstream (cell-, gene- and pathway-level) analysis of scRNA-seq data. In the present study, recent advances and applications of deep learning-based methods, together with specific tools for scRNA-seq data analysis, were summarized. Moreover, the future perspectives and challenges of deep-learning techniques regarding the appropriate analysis and interpretation of scRNA-seq data were investigated. The present study aimed to provide evidence supporting the biomedical application of deep learning-based tools and may aid biologists and bioinformaticians in navigating this exciting and fast-moving area.

Clinical and radiographic outcomes of lateral sinus floor elevation with simultaneous hydrophilic implants placement: A retrospective study of 2-5 years.

AIMS: To investigate the clinical and radiographic outcomes of a chemically modified sandblasted large-grit acid-etched implant (hydrophilic) in lateral sinus floor elevation (LSFE), compared with a conventional one (hydrophobic). MATERIALS AND METHODS: A retrospective study design was adopted. Patients who received LSFE with simultaneous implant placement were recruited. According to different types of implant surfaces, patients were divided into two groups (the hydrophilic group and the hydrophobic group). Implant survival rate (SR), endo-sinus bone stability on the radiographs, mean probing depths, percentage of bleeding on probing, marginal bone loss, and patient satisfaction were evaluated. RESULTS: A total of 106 patients with 180 implants (hydrophilic:101, hydrophobic:79) in 119 maxillary sinuses were included. The follow-up period ranged from 2 to 5 years. Three hydrophobic implants and one hydrophilic implant in four different patients failed. The SR of the hydrophilic group was higher than that of the hydrophobic group but without a significant difference (p > .05). The change and change rate of endo-sinus bone height (ΔESBH and RΔESBH) and bone volume (ΔESBV and RΔESBV) in the hydrophilic group were less than those in the hydrophobic group, with a significant difference at 6 months after implantation. No other significant difference was found between the two groups. CONCLUSION: Within the limitations of this study, both hydrophilic and hydrophobic implants were suitable for LSFE with predictable clinical outcomes. Meanwhile, hydrophilic implants could contribute to the grafted endo-sinus bone stability during healing time.

Computational principles and practice for decoding immune contexture in the tumor microenvironment.

Tumor-infiltrating immune cells (TIICs) have been recognized as crucial components of the tumor microenvironment (TME) and induced both beneficial and adverse consequences for tumorigenesis as well as outcome and therapy (particularly immunotherapy). Computer-aided investigation of immune cell components in the TME has become a promising avenue to better understand the interplay between the immune system and tumors. In this study, we presented an overview of data sources, computational methods and software tools, as well as their application in inferring the composition of tumor-infiltrating immune cells in the TME. In parallel, we explored the future perspectives and challenges that may be faced with more accurate quantitative infiltration of immune cells in the future. Together, our study provides a little guide for scientists in the field of clinical and experimental immunology to look for dedicated resources and more competent tools for accelerating the unraveling of tumor-immune interactions with the implication in precision immunotherapy.

Machine learning-based integrative analysis of methylome and transcriptome identifies novel prognostic DNA methylation signature in uveal melanoma.

Uveal melanoma (UVM) is the most common primary intraocular human malignancy with a high mortality rate. Aberrant DNA methylation has rapidly emerged as a diagnostic and prognostic signature in many cancers. However, such DNA methylation signature available in UVM remains limited. In this study, we performed a genome-wide integrative analysis of methylome and transcriptome and identified 40 methylation-driven prognostic genes (MDPGs) associated with the tumorigenesis and progression of UVM. Then, we proposed a machine-learning-based discovery and validation strategy to identify a DNA methylation-driven signature (10MeSig) composing of 10 MDPGs (AZGP1, BAI1, CCDC74A, FUT3, PLCD1, S100A4, SCN8A, SEMA3B, SLC25A38 and SLC44A3), which stratified 80 patients of the discovery cohort into two risk subtypes with significantly different overall survival (HR = 29, 95% CI: 6.7-126, P < 0.001). The 10MeSig was validated subsequently in an independent cohort with 57 patients and yielded a similar prognostic value (HR = 2.1, 95% CI: 1.2-3.7, P = 0.006). Multivariable Cox regression analysis showed that the 10MeSig is an independent predictive factor for the survival of patients with UVM. With a prospective validation study, this 10MeSig will improve clinical decisions and provide new insights into the pathogenesis of UVM.

Mechanistically derived patient-level framework for precision medicine identifies a personalized immune prognostic signature in high-grade serous ovarian cancer.

An accurate prognosis assessment for cancer patients could aid in guiding clinical decision-making. Reliance on traditional clinical features alone in a complex clinical environment is challenging and unsatisfactory in the era of precision medicine; thus, reliable prognostic biomarkers are urgently required to improve a patient staging system. In this study, we proposed a patient-level computational framework from mechanistic and translational perspectives to establish a personalized prognostic signature (named PLPPS) in high-grade serous ovarian carcinoma (HGSOC). The PLPPS composed of 68 immune genes achieved accurate prognostic risk stratification for 1190 patients in the meta-training cohort and was rigorously validated in multiple cross-platform independent cohorts comprising 792 HGSOC patients. Furthermore, the PLPPS was shown to be the better prognostic factor compared with clinical parameters in the univariate analysis and retained a significant independent association with prognosis after adjusting for clinical parameters in the multivariate analysis. In benchmark comparisons, the performance of PLPPS (hazard ratio (HR), 1.371; concordance index (C-index), 0.604 and area under the curve (AUC), 0.637) is comparable to or better than other published gene signatures (HR, 0.972 to 1.340; C-index, 0.495 to 0.592 and AUC, 0.48-0.624). With further validation in prospective clinical trials, we hope that the PLPPS might become a promising genomic tool to guide personalized management and decision-making of HGSOC in clinical practice.

Computational recognition of lncRNA signature of tumor-infiltrating B lymphocytes with potential implications in prognosis and immunotherapy of bladder cancer.

Long noncoding RNAs (lncRNAs) have been associated with cancer immunity regulation and the tumor microenvironment (TME). However, functions of lncRNAs of tumor-infiltrating B lymphocytes (TIL-Bs) and their clinical significance have not yet been fully elucidated. In the present study, a machine learning-based computational framework is presented for the identification of lncRNA signature of TIL-Bs (named 'TILBlncSig') through integrative analysis of immune, lncRNA and clinical profiles. The TILBlncSig comprising eight lncRNAs (TNRC6C-AS1, WASIR2, GUSBP11, OGFRP1, AC090515.2, PART1, MAFG-DT and LINC01184) was identified from the list of 141 B-cell-specific lncRNAs. The TILBlncSig was capable of distinguishing worse compared with improved survival outcomes across different independent patient datasets and was also independent of other clinical covariates. Functional characterization of TILBlncSig revealed it to be an indicator of infiltration of mononuclear immune cells (i.e. natural killer cells, B-cells and mast cells), and it was associated with hallmarks of cancer, as well as immunosuppressive phenotype. Furthermore, the TILBlncSig revealed predictive value for the survival outcome and immunotherapy response of patients with anti-programmed death-1 (PD-1) therapy and added significant predictive power to current immune checkpoint gene markers. The present study has highlighted the value of the TILBlncSig as an indicator of immune cell infiltration in the TME from a noncoding RNA perspective and strengthened the potential application of lncRNAs as predictive biomarkers of immunotherapy response, which warrants further investigation.

Self-assembly flexible SERS imprinted membrane based on Ag nanocubes for selective detection of microcystin-LR.

Silver nanocubes monolayer-modified polydimethylsiloxane (Ag NC/PDMS) flexible SERS substrates have been prepared by a three-phase interface self-assembly procedure. The combination of this method with membrane technology brings nanoparticles in close proximity, densely, and regularly arranged in monolayers over a large area, leading to excellent SERS properties. Considering the complexity of practical detection, molecular imprinted polymers (MIPs) were anchored on the surface of SERS substrate and applied to selective detection of microcystin-LR (MC-LR). It is worth mentioning that the SERS imprinted membranes (AP-MIMs) were still clearly detected at a concentration of 0.1 µg·L-1 of MC-LR in drinking water, and the detection limit was as low as 0.0067 µg·L-1. The substrate exhibited excellent uniformity with a relative standard deviation (RSD) of 6.1%. In the presence of interference molecules, AP-MIMs exhibited excellent selectivity for MC-LR. Furthermore, in the spiking and recovery tests of practical lake water samples, the method showed excellent recoveries ranging from 96.47 to 105.31%. It has been demonstrated that the prepared AP-MIMs can be applied to sensitive and specific detection of trace amounts of MC-LR in drinking water.

A blood-based miRNA signature for early non-invasive diagnosis of preeclampsia.

BACKGROUND: Preeclampsia (PE) is a multisystemic maternal syndrome with substantial maternal and fetal morbidity and mortality. Currently, there is no clinically viable non-invasive biomarker assay for early detection, thus limiting the effective prevention and therapeutic strategies for PE. METHODS: We conducted a discovery-training-validation three-phase retrospective and prospective study with cross-platform and multicenter cohorts. The initial biomarkers were discovered and verified in tissue specimens by small RNA sequencing and qRT-PCR. A miRNA signature (miR2PE-score) was developed using Firth's bias-reduced logistic regression analysis and subsequently validated in two independent multinational retrospective cohorts and two prospective plasma cohorts. RESULTS: We initially identified five PE-associated differentially expressed miRNAs from miRNA sequencing data and subsequently validated two miRNAs (miR-196b-5p and miR-584-5p) as robust biomarkers by association analysis with clinical characteristics and qRT-PCR in tissue specimens in the discovery phase. Using Firth's bias-reduced logistic regression analysis, we developed the miR2PE-score for the early detection of PE. The miR2PE-score showed a high diagnostic performance with an area under the receiver operating characteristic curve (AUROC) of 0.920, 0.848, 0.864, and 0.812 in training, internal, and two external validation cross-platform and multicenter cohorts, respectively. Finally, we demonstrated the non-invasive diagnostic performance of the miR2PE-score in two prospective plasma cohorts with AUROC of 0.933 and 0.787. Furthermore, the miR2PE-score revealed superior performance in non-invasive diagnosis compared with previously published miRNA biomarkers. CONCLUSIONS: We developed and validated a novel and robust blood-based miRNA signature, which may serve as a promising clinically applicable non-invasive tool for the early detection of PE.

Computational identification of mutator-derived lncRNA signatures of genome instability for improving the clinical outcome of cancers: a case study in breast cancer.

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.

Genomic instability-derived plasma extracellular vesicle-microRNA signature as a minimally invasive predictor of risk and unfavorable prognosis in breast cancer.

BACKGROUND: Breast cancer (BC) is the most frequently diagnosed cancer and the leading cause of cancer-associated deaths in women. Recent studies have indicated that microRNA (miRNA) regulation in genomic instability (GI) is associated with disease risk and clinical outcome. Herein, we aimed to identify the GI-derived miRNA signature in extracellular vesicles (EVs) as a minimally invasive biomarker for early diagnosis and prognostic risk stratification. EXPERIMENTAL DESIGN: Integrative analysis of miRNA expression and somatic mutation profiles was performed to identify GI-associated miRNAs. Then, we constructed a discovery and validation study with multicenter prospective cohorts. The GI-derived miRNA signature (miGISig) was developed in the TCGA discovery cohort (n = 261), and was subsequently independently validated in internal TCGA validation (n = 261) and GSE22220 (n = 210) cohorts for prognosis prediction, and in GSE73002 (n = 3966), GSE41922 (n = 54), and in-house clinical exosome (n = 30) cohorts for diagnostic performance. RESULTS: We identified a GI-derived three miRNA signature (MIR421, MIR128-1 and MIR128-2) in the serum extracellular vesicles of BC patients, which was significantly associated with poor prognosis in all the cohorts tested and remained as an independent prognostic factor using multivariate analyses. When integrated with the clinical characteristics, the composite miRNA-clinical prognostic indicator showed improved prognostic performance. The miGISig also showed high accuracy in differentiating BC from healthy controls with the area under the receiver operating characteristics curve (ROC) with 0.915, 0.794 and 0.772 in GSE73002, GSE41922 and TCGA cohorts, respectively. Furthermore, circulating EVs from BC patients in the in-house cohort harbored elevated levels of miGISig, with effective diagnostic accuracy. CONCLUSIONS: We report a novel GI-derived three miRNA signature in EVs, as an excellent minimally invasive biomarker for the early diagnosis and unfavorable prognosis in BC.

Light-induced degradation of rhodamine B by tellurium quantum dots.

Tellurium quantum dots (Te QDs) were prepared using bulk tellurium as the precursor. Te QDs can be a highly active photocatalyst for boosting the photocatalytic degradation of rhodamine B (RhB) under visible light irradiation. The morphology and composition of Te QDs were characterized by transmission electron microscopy (TEM), X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS). The results showed that in the presence of H2O2, the photocatalytic efficiency of Te QDs on RhB could achieve a good degradation effect within a very short time (30 min). The effects of initial dye concentration, pH value, light intensity, catalyst dosage and H2O2 concentration on dye degradation were successively studied. The effects of inorganic ions (NO3-, Cl-, SO42-, Ca2+, Mg2+ and Fe3+) on photocatalytic degradation were also discussed. Experimental results of free radical capture showed that OH• and O2•- played important roles in photocatalytic degradation. More importantly, Te QDs efficiency still remained above 85% after four cycles of use, indicating good stability, recyclability and utility. This work may inspire further design of other semiconductor QDs for highly efficient dye degradation.

Study on the efficient removal of azo dyes by heterogeneous photo-Fenton process with 3D flower-like layered double hydroxide.

As organic dyes are the main pollutants in water pollution, seeking effective removal solutions is urgent for humans and the environment. A novel environmentally friendly three-dimensional CoFe-LDHs (3D CoFe-LDHs) catalyst was synthesized by one-step hydrothermal method. Scanning electron microscopy, energy dispersive spectroscopy, Fourier transform infrared spectra, X-ray diffraction, X-ray photoelectron spectroscopy, Brunauer-Emmett-Teller technique as well as UV-Vis diffuse reflectance spectra were used to characterize the prepared samples. The experimental results revealed that 3D CoFe-LDHs exhibited a rapid decolorization of methyl orange and Rhodamine B by heterogeneous photo-Fenton process after reaching the adsorption equilibrium, and the final decolorization efficiency reached 91.18% and 93.56%, respectively. On the contrary, the decolorizing effect of 3D CoFe-LDHs on neutral blue was relatively weak. The initial concentrations of azo dyes, pH and H2O2 concentration affected the decolorization of dyes and the catalyst maintained excellent reusability and stability after reuse over five cycles. The quenching experiments found that •OH, •O2 - and h+ were the main active substances and reaction mechanisms were further proposed. The study suggests that the synergistic effect of photocatalysis and Fenton oxidation process significantly improved the removal of azo dyes and the synthesized catalyst had potentially promising applications for difficult-to-biodegrade organic pollutants in wastewater.

Integrative analysis from multi-centre studies identifies a function-derived personalized multi-gene signature of outcome in colorectal cancer.

Colorectal cancer (CRC) is highly heterogeneous leading to variable prognosis and treatment responses. Therefore, it is necessary to explore novel personalized and reproducible prognostic signatures to aid clinical decision-making. The present study combined large-scale gene expression profiles and clinical data of 1828 patients with CRC from multi-centre studies and identified a personalized gene prognostic signature consisting of 46 unique genes (called function-derived personalized gene signature [FunPGS]) from an integrated statistics and function-derived perspective. In the meta-training and multiple independent validation cohorts, the FunPGS effectively discriminated patients with CRC with significantly different prognosis at the individual level and remained as an independent factor upon adjusting for clinical covariates in multivariate analysis. Furthermore, the FunPGS demonstrated superior performance for risk stratification with respect to other recently reported signatures and clinical factors. The complementary value of the molecular signature and clinical factors was further explored, and it was observed that the composite signature called IMCPS greatly improved the predictive performance of survival estimation relative to molecular signatures or clinical factors alone. With further prospective validation in clinical trials, the FunPGS may become a promising and powerful personalized prognostic tool for stratifying patients with CRC in order to achieve an optimal systemic therapy.

A novel lncRNA-focus expression signature for survival prediction in endometrial carcinoma.

BACKGROUND: Endometrial cancer (UCEC) is a complex malignant tumor characterized by both genetic level and clinical trial. Patients with UCEC exhibit the similar clinical features, however, they have distinct outcomes due to molecular heterogeneity. The aim of this study was to access the prognostic value of long non-coding RNAs (lncRNAs) in UCEC patients and to identify potential lncRNA signature for predicting patients' survival and improving patient-tailored treatment. METHODS: We performed a comprehensive genome-wide analysis of lncRNA expression profiles and clinical data in a large cohort of 301 UCEC patients. UCEC patients were randomly divided into the discovery cohort (n = 150) and validation cohort (n = 151). A novel lncRNA-focus expression signature was identified in the discovery cohort, and independently accessed in the validation cohort. Additionally, the lncRNA signature was evaluated by multivariable Cox regression and stratification analysis as well as functional enrichment analysis. RESULTS: We detected a novel lncRNA-focus expression signature (LFES) consisting of 11 lncRNAs that were associated with survival based on risk scoring strategy in UCEC. The risk score based on the LFES was able to separate patients of discovery cohort into high-risk and low-risk groups with significantly different overall survival and progression-free survival, and has been successfully confirmed in the validation cohort. Furthermore, the LFES is an independent prognostic predictor of survival and demonstrates superior prognostic performance compared with the clinical covariates for predicting 5-year survival (AUC = 0.887). Functional analysis has linked the expression of prognostic lncRNAs to well-known tumor suppressor or ontogenetic pathways in endometrial carcinogenesis. CONCLUSIONS: Our study revealed a novel 11-lncRNA signature to predict survival of UCEC patient. This lncRNA signature may be a valuable and alternative marker for risk evaluation to aid patient-tailored treatment and improve the outcome of patients with UCEC.

Comparison of the photoconversion of 1-chloronaphthalene and 2,3-dichlornaphthalene in water.

In this work, the photoconversion of 1-chloronaphthalene (CN-1) and 2,3-dichlornaphthalene (CN-10) under the simulated sunlight had been studied. The results showed that the photoconversion of CN-1 and CN-10 obeyed the first-order kinetics model. NO2 -, NO3 -, Fe3+ and Fe2+ extensively present in natural water can accelerate CN-1 photoconversion via generating ·OH, which may induce indirect photooxidation of CN-1. The photoproducts were treated by the derivatization method and analyzed by GC-MS after being irradiated for 6 h. Ten products were characterized for CN-1 and CN-10, and there were six common products. On this basis, the photoconversion pathways of CN-10 and CN-1 were proposed, and both of them have a similar conversion mechanism.

Discovery and validation of immune-associated long non-coding RNA biomarkers associated with clinically molecular subtype and prognosis in diffuse large B cell lymphoma.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is an aggressive and complex disease characterized by wide clinical, phenotypic and molecular heterogeneities. The expression pattern and clinical implication of long non-coding RNAs (lncRNAs) between germinal center B-cell-like (GCB) and activated B-cell-like (ABC) subtypes in DLBCL remain unclear. This study aims to determine whether lncRNA can serve as predictive biomarkers for subtype classification and prognosis in DLBCL. METHODS: Genome-wide comparative analysis of lncRNA expression profiles were performed in a large number of DLBCL patients from Gene Expression Omnibus (GEO), including GSE31312 cohort (N = 426), GSE10846 (N = 350) cohort and GSE4475 cohort (N = 129). Novel lncRNA biomarkers associated with clinically molecular subtype and prognosis were identified in the discovery cohort using differential expression analyses and weighted voting algorithm. The predictive value of the lncRNA signature was then assessed in two independent cohorts. The functional implication of lncRNA signature was also analyzed by integrative analysis of lncRNA and mRNA. RESULTS: Seventeen of the 156 differentially expressed lncRNAs between GCB and ABC subtypes were identified as candidate biomarkers and integrated into form a lncRNA-based signature (termed SubSigLnc-17) which was able to discriminate between GCB and ABC subtypes with AUC of 0.974, specificity of 89.6% and sensitivity of 92.5%. Furthermore, subgroups of patients characterized by the SubSigLnc-17 demonstrated significantly different clinical outcome. The reproducible predictive power of SubSigLnc-17 in subtype classification and prognosis was successfully validated in the internal validation cohort and another two independent patient cohorts. Integrative analysis of lncRNA-mRNA suggested that these candidate lncRNA biomarkers were mainly related to immune-associated processes, such as T cell activation, leukocyte activation, lymphocyte activation and Chemokine signaling pathway. CONCLUSIONS: Our study uncovered differentiated lncRNA expression pattern between GCB and ABC DLBCL and identified a 17-lncRNA signature for subtype classification and prognosis prediction. With further prospective validation, our study will improve the understanding of underlying molecular heterogeneities in DLBCL and provide candidate lncRNA biomarkers in DLBCL classification and prognosis.

Photoconversion of 2-Chloronaphthalene in Water.

The photoconversion of 2-chloronaphthalene (CN-2) in water in a simulated sunlight system was investigated. The photoconversion efficiency, photoproducts and mechanisms were inspected, and the effects of inorganic ions (NO3-, NO2-) and fulvic acid (FA) were discussed. The results showed that CN-2 could be transformed in water under the irradiation. NO3- and NO2- promoted the photoconversion of CN-2 owing to ·OH generated by the photolysis of NO3- and NO2-; FA at a lower concentration promoted the photoconversion, but it had an inhibition effect at a higher concentration. It was demonstrated that the acidic conditions promoted the photoconversion of CN-2 by the active groups such as superoxide radical anion, hydrogen peroxide and hydroxyl radical produced in the system. Eight photoproducts of CN-2 were characterized by the GC-MS method and the possible photoconversion mechanisms were proposed accordingly.

Study on the dominant mechanism of direct hole oxidation for the photodegradation of tetracycline.

Antibiotic contamination has a significant negative impact on China, one of the largest producers and consumers of antibiotics worldwide. In this study, a three-dimensional flower-like structure of CoFe-LDHs was used to efficiently degrade tetracycline (TC) in a system triggered by peroxymonosulfate (PMS) and exposed to visible light. After exploring the effects of different metal ratios, catalyst dosage, initial TC concentrations, and pH, the optimal reaction conditions were determined. In comparison to pure CoFe-LDHs, the TC elimination rate was dramatically increased by the addition of the PMS. The strong environmental resistance, excellent stability and reusability, and universal flexibility were shown. The quenching experiments and electron spin resonance detection showed that the creation of reactive oxygen species was facilitated by the synergistic transmission of electrons between the active bimetallic components. Further, photogenerated holes was the dominant oxidizing species, which contributed more to the degradation of TC. The potential degradation pathways and intermediate toxicity of TC were suggested. This work offers a new method dominated by photogenerated holes for efficiently removing TC effluent.

The causal effect of oxidative stress on the risk of glaucoma.

Glaucoma is a complex multifactorial disease. Oxidative stress has been implicated in its pathogenesis. However, establishing a causal relationship between oxidative stress and glaucoma is challenging due to confounding and reverse causality. In this study, we performed bidirectional two-sample Mendelian randomization (MR) analyses based on genetic instrumental variables as proxies for 11 biomarkers of oxidative stress injury to investigate the causal relationship between oxidative stress and glaucoma. Eight significant associations were identified. Increased circulating levels of catalase (OR = 0.915, 95 % CI: 0.848-0.987, P = 0.022), retinol (OR = 0.481, 95 % CI: 0.248-0.932, P = 0.044) and superoxide dismutase (OR = 0.779, 95 % CI: 0. 616-0.986, P = 0.038) are associated with a decreased risk of glaucoma, whereas an increased myeloperoxidase level (OR = 2.145, 95 % CI: 1.119-4.111, P = 0.029) is associated with an increased risk of glaucoma. Glaucoma was causally associated with lower levels of total bilirubin (OR = 0.961, 95 % CI: 0.927-0.997, P = 0.039), glutathione peroxidase (OR = 0. 934, 95 % CI: 0.890-0.981, P = 0.006), paraoxonase (OR = 0.883, 95 % CI: 0.810-0.963, P = 0.005) and albumin (OR = 0.988, 95 % CI: 0.978-0.998, P = 0.014). The bidirectional MR analysis revealed a causal relationship between oxidative stress and glaucoma. These findings provide a greater understanding of the underlying mechanisms of glaucomatous neurodegeneration and imply a potential therapeutic approach for glaucoma through targeting oxidative stress pathways.

Effect of the gut microbiome in glaucoma risk from the causal perspective.

OBJECTIVE: Evidence from observational studies has reported possible associations between the gut microbiome (GM) and glaucoma. However, the causal effect of GM on glaucoma risk remains to be determined. METHODS AND ANALYSIS: We conducted two-sample bidirectional Mendelian randomisation (MR) analyses to explore the causal association between GM and glaucoma. Genome-wide association study summary statistics of 196 GM taxa (n=18 340) and glaucoma (18 902 cases and 358 375 controls) were obtained from MiBioGen and FinnGen Consortium. Inverse variance weighted, MR-Egger, weighted median, weighted mode, Mendelian Randomisation Pleiotropy Residual Sum and Outlier, MR-Egger intercept and Cochran's Q statistical analyses were used to supplement MR results and sensitivity analysis. An independent cohort from the Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol (MRC-IEU) Consortium (1715 cases and 359 479 controls) was used to validate causal effects. RESULTS: Results of the MR analysis suggested that the family Oxalobacteraceae (OR 0.900, 95% CI 0.843 to 0.961, p=0.002) and the genus Eggerthella (OR 0.881, 95% CI 0.811 to 0.957, p=0.003) had a negative effect on glaucoma, whereas the genus Bilophila (OR 1.202, 95% CI 1.074 to 1.346, p=0.001), LachnospiraceaeUCG010 (OR 1.256, 95% CI 1.109 to 1.423, p=0.0003) and Ruminiclostridium 9 (OR 1.258, 95% CI 1.083 to 1.461, p=0.003) had a positive effect on glaucoma. Among these, the positive causal effect of LachnospiraceaeUCG010 (OR 1.002, 95% CI 1.000 to 1.004, p=0.033) on glaucoma was replicated in an independent cohort. CONCLUSION: This MR analysis from large population studies demonstrated the causal effect of GM on glaucoma risk and supported the role of GM in influencing glaucoma susceptibility.