Background and Aim: Metabolic reprogramming is characterized by dysregulated levels of metabolites and metabolic enzymes. Integrated metabolomic and transcriptomic data analysis can help to elucidate changes in the levels of metabolites and metabolic enzymes, screen the core metabolic pathways, and develop novel therapeutic strategies for cancer.Methods: Here, the metabolome of gastric cancer tissues was determined using liquid chromatography–mass spectrometry. The transcriptome data from The Cancer Genome Atlas dataset were integrated with the liquid chromatography–mass spectrometry data to identify the common dysregulated gastric cancer‐specific metabolic pathways. Additionally, the protein expression and clinical significance of key metabolic enzymes were examined using a gastric cancer tissue array.Results: Metabolomic analysis of 16 gastric cancer tissues revealed that among the 15 dysregulated metabolomic pathways, the aminoacyl‐tRNA biosynthesis pathway in the gastric tissues was markedly upregulated relative to that in the adjacent noncancerous tissues, which was consistent with the results of transcriptome analysis. Bioinformatic analysis revealed that among the key regulators in the aminoacyl‐tRNA biosynthesis pathway, the expression levels of threonyl‐tRNA synthetase (TARS) and phenylalanyl‐tRNA synthetase (FARSB) were correlated with tumor grade and poor survival, respectively. Additionally, gastric tissue array data analysis indicated that TARS and FARSB were upregulated in gastric cancer tissues and were correlated with poor prognosis and tumor metastasis.Conclusions: This study demonstrated that the aminoacyl‐tRNA biosynthesis pathway is upregulated in gastric cancer and both TARS and FARSB play key roles in the progression of gastric cancer. Additionally, a novel therapeutic strategy for gastric cancer was proposed that involves targeting the aminoacyl‐tRNA biosynthesis pathway.

In recent years, studies on the diagnostic accuracy of in-house real-time PCR (hRT-PCR) assay for the detection of Mycobacterium tuberculosis (Mtb) have been reported with unignorable discrepancies. To assess the overall accuracy of the hRT-PCR assay for Mtb diagnosis in different samples for individuals with active pulmonary and extra-pulmonary Mtb infection, a systematic review and meta-analysis were performed. The PUBMED, EMBASE, Web of Science, and Cochrane databases were searched up to June 2017 for eligible studies that estimated diagnostic sensitivity and specificity with the hRT-PCR assay in respiratory and non-respiratory samples in pulmonary and extra-pulmonary Mtb infection patients, with Mtb culture as the reference standard. Bivariate random effect models were used to provide pooled estimation of diagnostic accuracy. Further, subgroup and meta-regression analyses were performed to explore sources of heterogeneity. The risk of bias was assessed by the QUADAS-2 tool. Of the 3589 candidate studies, 18 eligible studies met our inclusion criteria. Compared to Mtb culture data, the pooled sensitivity and specificity were 0.96 and 0.92, respectively. The diagnostic odds ratio (DOR) was 192.96 (95% CI 68.46, 543.90), and the area under the summary ROC curve (AUC) was 0.9791. There was significant heterogeneity in sensitivity and specificity among the enrolled studies (p < 0.001). The studies with high-quality assessment and application of respiratory specimen were associated with better accuracy. In low-income/high-burden settings, our results suggested that the hRT-PCR assay could be a useful test for the diagnosis of TB with high sensitivity and specificity.

Background: One forth whole-world population is infected with Mycobacterium tuberculosis (Mtb), but 90% of them are asymptotic latent infection without any symptoms but positive result in IFN-γ release assay. There is lack of ideal strategy to distinguish active tuberculosis (TB) and latent tuberculosis infection (LTBI). Some scientist had focused on a set of cytokines as biomarkers besides interferon- gamma (IFN-γ) to distinguish active TB and LTBI, but with considerable variance of results. This meta-analysis aimed to evaluate the overall discriminative ability of potential immune molecules to distinguish active TB and LTBI. Methods: PubMed, the Cochrane Library, and Web of Science databases were searched to identify studies assessing diagnostic roles of cytokines for distinguishing active TB and LTBI published up to August 2018. The quality of enrolled studies was assessed using Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). The pooled diagnostic sensitivity and specificity of each cytokine was calculated by using Meta-DiSc software. Area under the summary receiver operating characteristic curve (AUC) was used to summarize the overall diagnostic performance of each biomarker. Results: Fourteen studies with 982 subjects met the inclusion criteria, including 526 active TB and 456 LTBI patients. Pooled sensitivity, specificity and AUC for discriminating between active TB and LTBI were analyzed for IL-2 (0.87, 0.61 and 0.9093), IP-10 (0.77, 0.73 and 0.8609), IL-5 (0.64, 0.75 and 0.8533), IL-13 (0.75, 0.71 and 0.8491), IFN-γ (0.67, 0.75 and 0.8031), IL-10 (0.68, 0.74 and 0.7957) and TNF-α (0.67, 0.64 and 0.7783). The heterogeneous subgroup analysis showed that cytokine detection assays, TB incidence, and stimulator with Mtb antigens are main influence factors for their diagnostic performance. Conclusions: The meta-analysis showed cytokine production could assist the distinction between active TB and LTBI, IL-2 with the highest overall accuracy. No single biomarker is likely to show sufficiently diagnostic performance due to limited sensitivity and specificity. Further prospective studies are needed to identify the optimal combination of biomarkers to enhanced diagnostic capacity in clinical practice.

Hypothalamic dysfunction is a key factor in depression; increasing evidence highlights neuroinflammation abnormalities as well as imbalances in neurotransmitters and the purinergic system in the pathophysiology of depression. However, little is known about the metabolomic changes in the hypothalamus of depressed patients with neuroinflammation. Herein, taking advantage of the well-established lipopolysaccharide (LPS)-induced depression mouse model, we measured metabolic changes in the hypothalamus using gas chromatography-mass spectrometry (GC-MS). Sucrose preference test (SPT), open field test (OFT), forced swimming test (FST), and tail suspension test (TST) were conducted to assess our depressive model. To better understand the metabolic disturbances occurring in the hypothalamus of depressed mice, multivariate statistics were applied to analyse the clinical significance of differentially expressed metabolites in the hypothalamus of mice with LPS-induced depression. Bioinformatic analysis was conducted to detect potential relationships among the changed metabolites. The data confirmed that mice with LPS-induced depression were good mimics of depression patients in some characteristic symptoms such as decreased sucrose intake and increased immobility. In our study, 27 differentially expressed metabolites were identified in the hypothalamus of mice with LPS-induced depression. Herein, seventeen of these metabolites decreased, whereas 10 metabolites increased. These molecular changes were closely related to perturbations in the amino acid and purine metabolisms. Our data indicate that dysfunction of amino acid and purine metabolisms is one of main characteristics of inflammation-mediated depression. These results provide new insights into the mechanisms underlying depression, which may shed some light on the role of the hypothalamus in the pathogenesis of inflammation-mediated depression.

Background: Depression is two-to-three times more frequent among women. The hypothalamus, a sexually dimorphic area, has been implicated in the pathophysiology of depression. Neuroinflammation-induced hypothalamic dysfunction underlies behaviors associated with depression. The lipopolysaccharide (LPS)-induced mouse model of depression has been well-validated in numerous laboratories, including our own, and is widely used to investigate the relationship between neuroinflammation and depression. However, the sex-specific differences in metabolic alterations underlying depression-associated hypothalamic neuroinflammation remain unknown. Methods: Here, we employed the LPS-induced mouse model of depression to investigate hypothalamic metabolic changes in both male and female mice using a metabolomics approach. Through bioinformatics analysis, we confirmed the molecular pathways and biological processes associated with the identified metabolites. Furthermore, we employed quantitative real-time PCR, enzyme-linked immunosorbent assay, western blotting, and pharmacological interventions to further elucidate the underlying mechanisms. Results: A total of 124 and 61 differential metabolites (DMs) were detected in male and female mice with depressive-like behavior, respectively, compared to their respective sex-matched control groups. Moreover, a comparison between female and male model mice identified 37 DMs. We capitalized on biochemical clustering and functional enrichment analyses to define the major metabolic changes in these DMs. More than 55% of the DMs clustered into lipids and lipid-like molecules, and an imbalance in lipids metabolism was presented in the hypothalamus. Furthermore, steroidogenic pathway was confirmed as a potential sex-specific pathway in the hypothalamus of female mice with depression. Pregnenolone, an upstream component of the steroid hormone biosynthesis pathway, was downregulated in female mice with depressive-like phenotypes but not in males and had considerable relevance to depressive-like behaviors in females. Moreover, exogenous pregnenolone infusion reversed depressive-like behaviors in female mice with depression. The 5α-reductase type I (SRD5A1), a steroidogenic hub enzyme involved in pregnenolone metabolism, was increased in the hypothalamus of female mice with depression. Its inhibition increased hypothalamic pregnenolone levels and ameliorated depressive-like behaviors in female mice with depression. Conclusions: Our study findings demonstrate a marked sexual dimorphism at the metabolic level in depression, particularly in hypothalamic steroidogenic metabolism, identifying a potential sex-specific pathway in female mice with depressive-like behaviors.

Objective: PD-L1, a target of immune checkpoint blockade, has been proven to take the role of an oncogene in most human tumors. However, the role of PD-L1 in human pan-cancers has not yet been fully investigated. Materials and methods: Pan-cancer analysis was conducted to analyze expression, genetic alterations, prognosis analysis, and immunological characteristics of PD-L1. Estimating the correlation between PD-L1 expression and survival involved using pooled odds ratios and hazard ratios with 95% CI. The Kaplan–Meier (K-M) technique, COX analysis, and receiver operating characteristic (ROC) curves were applied to the survival analysis. Additionally, we investigated the relationships between PD-L1 and microsatellite instability (MSI), tumor mutational burden (TMB), DNA methyltransferases (DNMTs), the associated genes of mismatch repair (MMR), and immune checkpoint biomarkers using Spearman's correlation analysis. Also, immunohistochemical analysis and qRT-PCR were employed in evaluating PD-L1’s protein and mRNA expression in pan-caner. Results: PD-L1 showed abnormal mRNA and protein expression in a variety of cancers and predicted prognosis in cancer patients. Furthermore, across a variety of cancer types, the aberrant PD-L1 expression was connected to the MSI, MMR, TMB, drug sensitivity, and tumor immune microenvironment (TIME). Moreover, PD-L1 was significantly correlated with infiltrating levels of immune cells (T cell CD8 + , neutrophil, and so on). Conclusion: Our study provides a better theoretical basis and guidance for the clinical treatment of PD-L1.