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The coronavirus disease 2019 (COVID-19) has been spreading worldwide. Severe cases quickly progressed with unfavorable outcomes. We aim to investigate the clinical features of COVID-19 and identify the risk factors associated with its progression. Data of confirmed SARS-CoV-2-infected patients and healthy participants were collected. Thirty-seven healthy people and 79 confirmed patients, which include 48 severe patients and 31 mild patients, were recruited. COVID-19 patients presented with dysregulated immune response (decreased T, B, and NK cells and increased inflammatory cytokines). Also, they were found to have increased levels of white blood cell, neutrophil count, and D-dimer in severe cases. Moreover, lymphocyte, CD4+ T cell, CD8+ T cell, NK cell, and B cell counts were lower in the severe group. Multivariate logistic regression analysis showed that CD4+ cell count, neutrophil-to-lymphocyte ratio (NLR) and D-dimer were risk factors for severe cases. Both CT score and clinical pulmonary infection score (CPIS) were associated with disease severity. The receiver operating characteristic (ROC) curve analysis has shown that all these parameters and scores had quite a high predictive value. Immune dysfunction plays critical roles in disease progression. Early and constant surveillance of complete blood cell count, T lymphocyte subsets, coagulation function, CT scan and CPIS was recommended for early screening of severe cases.

for genetic studies of POAG. All case control studies investigating the association between singlenucleotide polymorphisms (SNPs) and POAG risk were included. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated by fixed-or random-effect model. RESULTS. This meta-analysis included 108 case control studies involving 35,389 POAG patients and 51,742 controls. The pooled results showed a significant association between 20 SNPs in 12 genes (148Asp/Glu in APE1 gene; rs449647 in APOE gene; rs1052990 and rs4236601 in CAV1/CAV2 gene; rs1799750 in MMP gene; c.603T3A (Met98Lys) in OPTN gene; rs7081455 in PLXDC2 gene; rs1279683 in SLC23A2 gene; 372 T/C in TIMP1 gene; rs1927911, rs2149356, rs4986791, rs7037117, and rs10759930 in TLR4 gene; rs4656461 in TMCO1 gene; 399Arg/Gln in XRCC1 gene; and rs540782, rs547984, and rs693421 in ZP4 gene) with POAG. CONCLUSIONS. Based on the current meta-analysis, we indicate 20 SNPs in 12 genes (APE1, APOE, CAV1/CAV2, MMP, OPTN, PLXDC2, SLC23A2, TIMP1, TLR4, TMCO1, XRCC1, ZP4) as predictive risk factors for POAG. More studies with large sample sizes and various ethnicities are warranted in the future to provide more powerful evidence.

Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms. Results Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan–Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures. Conclusions We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.