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Abstract Background Emerging evidence suggests that Rho GTPases play a crucial role in tumorigenesis and metastasis, but their involvement in the tumor microenvironment (TME) and prognosis of hepatocellular carcinoma (HCC) is not well understood. Methods We aim to develop a tumor prognosis prediction system called the Rho GTPases-related gene score (RGPRG score) using Rho GTPase signaling genes and further bioinformatic analyses. Results Our work found that HCC patients with a high RGPRG score had significantly worse survival and increased immunosuppressive cell fractions compared to those with a low RGPRG score. Single-cell cohort analysis revealed an immune-active TME in patients with a low RGPRG score, with strengthened communication from T/NK cells to other cells through MIF signaling networks. Targeting these alterations in TME, the patients with high RGPRG score have worse immunotherapeutic outcomes and decreased survival time in the immunotherapy cohort. Moreover, the RGPRG score was found to be correlated with survival in 27 other cancers. In vitro experiments confirmed that knockdown of the key Rho GTPase-signaling biomarker SFN significantly inhibited HCC cell proliferation, invasion, and migration. Conclusions This study provides new insight into the TME features and clinical use of Rho GTPase gene pattern at the bulk-seq and single-cell level, which may contribute to guiding personalized treatment and improving clinical outcome in HCC.

Abstract Background Glioblastoma multiforme (GBM) is one of the most malignant tumors in brain with high morbidity and mortality. Mitophagy plays a significant role in carcinogenesis, metastasis, and invasion. In our study, we aim to construct a mitophagy-related risk model to predict prognosis in GBM. Methods RNA-seq data combined with clinical information were downloaded from TCGA. The 4-gene risk model and nomograph was then constructed and validated in external cohort. Evaluation of immune infiltration, functional enrichment and tumor microenvironment (TME) were then performed. Result A mitophagy-related risk model was established and patients in TCGA and CGGA were classified into low-risk and high-risk groups. In both cohorts, patients in low-risk group had improved survival, while high-risk group had poor prognosis. Also, the risk model was identified as an independent factor for predicting overall survival via Cox regression. Furthermore, a prognostic nomogram including mitophagy signatures was established with excellent predictive performance. In addition, the risk model was closely associated with regulation of immune infiltration as well as TME. Conclusion In conclusion, our study constructed a mitophagy-related risk model, which can be utilized for the clinical prognostic prediction in GBM.

IntroductionCrohn’s disease is characterized of dysregulated inflammatory and immune reactions. The role of the NOD-like receptor family, pyrin domain-containing 3 (NLRP3) inflammasome in Crohn’s disease remains largely unknown.MethodsThe microarray-based transcriptomic data and corresponding clinical information of GSE100833 and GSE16879 were obtained from the Gene Expression Omnibus (GEO) database. Identification of in the NLRP3 inflammasome-related genes and construction of LASSO regression model. Immune landscape analysis was evaluated with ssGSEA. Classification of Crohn’s-disease samples based on NLRP3 inflammasome-related genes with ConsensusClusterPlus. Functional enrichment analysis, gene set variation analysis (GSVA) and drug-gene interaction network.ResultsThe expressions of NLRP3 inflammasome-related genes were increased in diseased tissues, and higher expressions of NLRP3 inflammasome-related genes were correlated with generally enhanced immune cell infiltration, immune-related pathways and human leukocyte antigen (HLA)-gene expressions. The gene-based signature showed well performance in the diagnosis of Crohn’s disease. Moreover, consensus clustering identified two Crohn’s disease clusters based on NLRP3 inflammasome-related genes, and cluster 2 was with higher expressions of the genes. Cluster 2 demonstrated upregulated activities of immune environment in Crohn’s disease. Furthermore, four key hub genes were identified and potential drugs were explored for the treatment of Crohn’s disease.ConclusionsOur findings indicate that NLRP3 inflammasome and its related genes could regulate immune cells and responses, as well as involve in the pathogenesis of Crohn’s disease from transcriptomic aspects. These findings provide in silico insights into the diagnosis and treatment of Crohn’s disease and might assist in the clinical decision-making process.

ObjectiveEnergy metabolism plays a crucial role in the improvement of heart dysfunction as well as the development of heart failure (HF). The current study is designed to identify energy metabolism-related diagnostic biomarkers for predicting the risk of HF due to myocardial infarction.MethodsTranscriptome sequencing data of HF patients and non-heart failure (NF) people (GSE66360 and GSE59867) were obtained from gene expression omnibus (GEO) database. Energy metabolism-related differentially expressed genes (DEGs) were screened between HF and NF samples. The subtyping consistency analysis was performed to enable the samples to be grouped. The immune infiltration level among subtypes was assessed by single sample gene set enrichment analysis (ssGSEA). Random forest algorithm (RF) and support vector machine (SVM) were applied to identify diagnostic biomarkers, and the receiver operating characteristic curves (ROC) was plotted to validate the accuracy. Predictive nomogram was constructed and validated based on the result of the RF. Drug screening and gene-miRNA network were analyzed to predict the energy metabolism-related drugs and potential molecular mechanism.ResultsA total of 22 energy metabolism-related DEGs were identified between HF and NF patients. The clustering analysis showed that HF patients could be classified into two subtypes based on the energy metabolism-related genes, and functional analyses demonstrated that the identified DEGs among two clusters were mainly involved in immune response regulating signaling pathway and lipid and atherosclerosis. ssGSEA analysis revealed that there were significant differences in the infiltration levels of immune cells between two subtypes of HF patients. Random-forest and support vector machine algorithm eventually identified ten diagnostic markers (MEF2D, RXRA, PPARA, FOXO1, PPARD, PPP3CB, MAPK14, CREB1, MEF2A, PRMT1) for risk prediction of HF patients, and the proposed nomogram resulted in good predictive performance (GSE66360, AUC = 0.91; GSE59867, AUC = 0.84) and the clinical usefulness in HF patients. More importantly, 10 drugs and 15 miRNA were predicted as drug target and hub miRNA that associated with energy metabolism-related genes, providing further information on clinical HF treatment.ConclusionThis study identified ten energy metabolism-related diagnostic markers using random forest algorithm, which may help optimize risk stratification and clinical treatment in HF patients.

Necrotizing enterocolitis (NEC) is one of the most prevalent neonatal gastrointestinal disorders. Despite ongoing breakthroughs in its treatment and prevention, the incidence and mortality associated with NEC remain high. New therapeutic approaches, such as breast milk composition administration, stem cell therapy, immunotherapy, and fecal microbiota transplantation (FMT) have recently evolved the prevention and the treatment of NEC. This study investigated the most recent advances in NEC therapeutic approaches and discussed their applicability to bring new insight to NEC treatment.

Background: Emerging data have suggested colorectal cancer (CRC) often coexists with cardiovascular diseases, but whether cardiovascular risk factors play a role in CRC remains unclear. We performed a systematic review and meta-analysis to better illustrate the associations between cardiovascular risk factors and CRC. Methods: We searched EMBASE, MEDLINE and Web of Science databases from inception up to June 14, 2020. Prospective cohort studies were included if they evaluated the association between at least one of cardiovascular risk factors and CRC incidence, containing sufficient data to obtain relative risk (RR) and 95% confidence interval (CI). We performed separate meta-analyses for each cardiovascular risk factor using random-effect model. PROSPERO registration number: CRD42020175537. Findings: Data from 84 studies, reporting 52, 348, 827 individuals and 384, 973 incident cases were included in the analysis. Overall, the risk of CRC was 1.31(95% CI, 1.21–1.42) for obesity, 1.14 (95% CI, 1.09–1.20) for per 5 kg/m2 increase in body mass index, 1.18 (95% CI, 1.14–1.23) for former smoker, 1.20 (95% CI, 1.11–1.30) for current smoker, 1.25 (95% CI, 1.16–1.35) for diabetes, 1.07 (95% CI, 1.02–1.12) for hypertension. The summary RRs of CRC for the highest versus lowest quartiles of total cholesterol, triglyceride, low-density lipoprotein were 1.12 (95% CI, 1.03–1.22), 1.18 (95% CI, 1.04–1.35), 0.85 (95% CI, 0.62–1.17) respectively and the pooled RR for the lowest versus highest quartile of high-density lipoprotein was 1.14 (95% CI, 1.02–1.28). Interpretation: Unfavorable cardiovascular risk factors are associated with increased risk of CRC, which may provide novel insight into the screening strategies of CRC in patient with these risk factors.

Background: Acute myocardial infarction (AMI) is a common cardiovascular disease that has a high mortality. Pyroptosis is a programmed cell death mediated by inflammasome. It remains to be clarified on the expression pattern and risk predictive role of pyroptosis-related genes in AMI. Methods: The gene expression data were extracted from the Gene Expression Omnibus (GEO), and pyroptosis-related genes were obtained from published articles. Pyroptosis-related differential expressed genes were selected between normal and AMI samples and then we explored their immune infiltration level using CIBERSORT. Univariate Cox and LASSO regression were applied to establish a classifier based on pyroptosis-related genes. ROC analysis was utilized to evaluate the classifier. Results: In this study, we obtained 20 pyroptosis-related genes which showed differential expression in AMI and normal samples. Among the differential expressed genes, GZMB was significantly positively associated with activated NK cells (R = 0.71, p < 0.01), while NLRP3 exhibited a negative correlation with resting NK cells (R = –0.66, p < 0.01). 9 genes (NLRP9, GSDMD, CASP8, AIM2, GPX4, NOD1, NOD2, SCAF11, GSDME) were eventually identified as a predictive risk classifier for AMI patients. With the classifier, patients at high and low risk could be discriminated. Further external validation showed the high accuracy of the classifier (AUC = 0.75). Conclusions: Pyroptosis-related genes are closely related to immune infiltration in AMI, and a 9-gene classifier has good performance in predicting the risk of AMI with high accuracy, which could provide a new way for targeted treatment in AMI.