Bioinformatic identification of key pathways, hub genes, and microbiota for therapeutic intervention in Helicobacter pylori infection.

The pathogenic mechanisms of Helicobacter pylori infection remain to be defined, and potential interventional microbiota are just beginning to be identified. In this study, gene-set enrichment analysis (GSEA) was used to integrate three H. pylori infection microarray data sets from the gene expression omnibus database and identified ten hallmark gene sets and 35 Kyoto encyclopedia of genes and genomes (KEGG) pathways that differed between healthy and Helicobacter pylori-infected individuals. Weighted gene co-expression network analysis (WGCNA) performed on two of the data sets identified three key gene coexpression modules. These modules contained 54 enriched KEGG pathways, 25 of which overlapped with the GSEA analysis, suggesting potentially important roles in H. pylori-infection. We selected 116 hub genes from the three key modules for in vitro validation at the transcriptional level using H. pylori Sydney Strain 1 and verified the upregulation of 80. WGCNA of the microbiomes based on 20 mucosal samples and a sequence read archive data set revealed four microbiota modules correlated with H. pylori infection. The negatively correlated modules contained 11 microbiome families. These findings provide new insight into the pathogenesis of H. pylori infection and systematically identify 25 key pathways, 80 upregulated hub genes, and 11 families of candidate interventional microbiota for further research.

Automatic origin prediction of liver metastases via hierarchical artificial-intelligence system trained on multiphasic CT data: a retrospective, multicentre study.

Background: Currently, the diagnostic testing for the primary origin of liver metastases (LMs) can be laborious, complicating clinical decision-making. Directly classifying the primary origin of LMs at computed tomography (CT) images has proven to be challenging, despite its potential to streamline the entire diagnostic workflow. Methods: We developed ALMSS, an artificial intelligence (AI)-based LMs screening system, to provide automated liver contrast-enhanced CT analysis for distinguishing LMs from hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), as well as subtyping primary origin of LMs as six organ systems. We processed a CECT dataset between January 1, 2013 and June 30, 2022 (n = 3105: 840 HCC, 354 ICC, and 1911 LMs) for training and internally testing ALMSS, and two additional cohorts (n = 622) for external validation of its diagnostic performance. The performance of radiologists with and without the assistance of ALMSS in diagnosing and subtyping LMs was assessed. Findings: ALMSS achieved average area under the curve (AUC) of 0.917 (95% confidence interval [CI]: 0.899-0.931) and 0.923 (95% [CI]: 0.905-0.937) for differentiating LMs, HCC and ICC on both the internal testing set and external testing set, outperformed that of two radiologists. Moreover, ALMSS yielded average AUC of 0.815 (95% [CI]: 0.794-0.836) and 0.818 (95% [CI]: 0.790-0.842) for predicting six primary origins on both two testing sets. Interestingly, ALMSS assigned origin diagnoses for LMs with pathological phenotypes beyond the training categories with average AUC of 0.761 (95% [CI]: 0.657-0.842), which verify the model's diagnostic expandability. Interpretation: Our study established an AI-based diagnostic system that effectively identifies and characterizes LMs directly from multiphasic CT images. Funding: National Natural Science Foundation of China, Guangdong Provincial Key Laboratory of Medical Image Processing.

Noninvasive evaluation of neutrophil extracellular traps signature predicts clinical outcomes and immunotherapy response in hepatocellular carcinoma.

Background: Neutrophil extracellular traps (NETs) have been shown to play a pivotal role in promoting metastasis and immune escape in hepatocellular carcinoma (HCC). Therefore, noninvasive tests to detect the formation of NETs in tumors can have significant implications for the treatment and prognoses of patients. Here, we sought to develop and validate a computed tomography (CT)-based radiomics model to predict the gene expression profiles that regulate the formation of NETs in HCC. Methods: This study included 1133 HCC patients from five retrospective cohorts. Based on the mRNA expression levels of 69 biomarkers correlated with NET formation, a 6-gene score (NETs score, NETS) was constructed in cohort 1 from TCIA database (n=52) and validated in cohort 2 (n=232) from ICGC database and cohort 3 (n=365) from TCGA database. And then based on the radiomics features of CT images, a radiomics signature (RNETS) was developed in cohort 1 to predict NETS status (high- or low-NETS). We further employed two cohorts from Nanfang Hospital (Guangzhou, China) to evaluate the predictive power of RNETS in predicting prognosis in cohort 4 (n=347) and the responses to PD-1 inhibitor of HCC patients in cohort 5 (n=137). Results: For NETS, in cohort 1, the area under the curve (AUC) values predicting 1, 2, and 3-year overall survival (OS) were 0.836, 0.879, and 0.902, respectively. The low-NETS was associated with better survival and higher levels of immune cell infiltration. The RNETS yielded an AUC value of 0.853 in distinguishing between high-NETS or low-NETS and patients with low-RNETS were associated with significantly longer survival time in cohort 1 (P<0.001). Notably, the RNETS was competent in predicting disease-free survival (DFS) and OS in cohort 4 (P<0.001). In cohort 5, the RNETS was found to be an independent risk factor for progression-free survival (PFS) (P<0.001). In addition, the objective response rate of HCC patients treated with PD-1 inhibitor was significantly higher in the low-RNETS group (27.8%) than in the high-RNETS group (10.8%). Conclusions: This study revealed that RNETS as a radiomics biomarker could effectively predict prognosis and immunotherapy response in HCC patients.

The Effects of Fatty Acids on Inflammatory Bowel Disease: A Two-Sample Mendelian Randomization Study.

Inflammatory Bowel Disease (IBD) is a severe relapsing inflammation of the gastrointestinal tract. The association between fatty acids (FAs) and IBD is controversial and it remains unclear whether there is a causal relationship between them. Mendelian randomization (MR) analysis was province/state for affiliations from the same country performed to clarify the causality. Eligible single nucleotide polymorphisms were selected as instrumental variables from six Genome-wide association studies, involving 114,999 individuals in UK Biobank. The summary-level data on IBD, including Crohn's disease (CD) and ulcerative colitis (UC), were obtained from the International Inflammatory Bowel Disease Genetics Consortium with 20,883 and 27,432 individuals involved. The primary inverse variance weighted (IVW) method as well as other supplementary analysis ones were adopted to evaluate the causal relationship between diverse FAs and IBD. The tests for heterogeneity and pleiotropy, and Leave-one-out analysis were adopted to verify the stability of the results. Omega-3 FA was found to have a causal effect on UC instead of CD. For each Standard Deviation increase in Omega-3 FA genetic levels, the risk of ulcerative colitis was found to be reduced by 39.9% by the IVW method (p = 1.766 × 10-4), by 57.8% by the MR Egger (p = 1.11 × 10-2), by 51.5% by the Weighted median estimator (p = 7.706 × 10-4), by 39% by the Maximum likelihood estimation (p = 3.262 × 10-4), and by 54.5% by the penalized weighted median estimator (p = 1.628 × 10-4). No causal relationship was found between other FAs (including total FA, saturated FA, polyunsaturated FA, monounsaturated FA and omega-6 FA) and IBD. The pleiotropic test and Leave-one-out analysis both proved the validity and reliability of these MR analyses. Omega-3 FA was observed to have a protective effect against UC, providing a new perspective on the investigation of the associations between FAs and IBD.

Gut Microbiota and Metabolites in Atrial Fibrillation Patients and Their Changes after Catheter Ablation.

The gut microbiota has been shown to be associated with multiple cardiovascular diseases, but there is little research on the gut microbiota and atrial fibrillation (AF); thus, how the gut microbiota and metabolites change in AF patients after catheter ablation is unclear. In this study, we used 16S rRNA high-throughput sequencing and nontargeted metabolomic detection to conduct horizontal and longitudinal analyses of the gut microbiota and metabolites of AF patients. Compared with a control group, species richness and diversity increased significantly in AF patients. Among them, opportunistic pathogenic bacteria, such as Klebsiella, Haemophilus, Streptococcus, and Enterococcus, were significantly increased, and symbiotic bacteria, such as Agathobacter and Butyrivibrio, were significantly reduced. After catheter ablation, intestinal symbiotic bacteria (Lactobacillus, Agathobacter, Lachnospira, etc.) were increased in most AF patients, while pathogenic bacteria (Ruminococcus, etc.) were reduced. Moreover, in AF patients, caffeine, which was negatively correlated with Klebsiella, was downregulated, and estradiol and ascorbic acid, which were positively correlated with Agathobacter, were also downregulated. After catheter ablation, citrulline, which was positively correlated with Ralstonia and Lactobacillus, was increased. Oleanolic acid, which was negatively correlated with Ralstonia was downregulated. In conclusion, our results not only show overall changes in the gut microbiota and metabolites in AF patients but also indicate their changes in the short term after catheter ablation. These data will provide novel possibilities for the future clinical diagnosis and treatment of AF. IMPORTANCE Gut microbiota and metabolites play a very important role in human health and can not only assess human health but also treat and prevent diseases. We analyzed the characteristics of the microbiota and metabolites in the human gut and found the effect of disease on gut microbiota and metabolites, which may be of important value in the pathogenesis of atrial fibrillation. At the same time, we also observed dynamic changes in gut microbiota and metabolites with the intervention of catheter ablation, which was not available in previous studies.

Correlation between spina bifida manifesta in fetal rats and c-Jun N-terminal kinase signaling.

Fetal rat models with neural tube defects were established by injection with retinoic acid at 10 days after conception. The immunofluorescence assay and western blot analysis showed that the number of caspase-3 positive cells in myeloid tissues for spina bifida manifesta was increased. There was also increased phosphorylation of c-Jun N-terminal kinase, a member of the mitogen activated protein kinase family. The c-Jun N-terminal kinase phosphorylation level was positively correlated with caspase-3 expression in myeloid tissues for spina bifida manifesta. Experimental findings indicate that abnormal apoptosis is involved in retinoic acid-induced dominant spina bifida formation in fetal rats, and may be associated with the c-Jun N-terminal kinase signal transduction pathway.