Background. We conducted a systematic review and meta-analysis of existing literature to evaluate the different outcomes of microRNAs (miRNAs) in diabetic nephropathy (DN), including urinary albumin excretion rates, urinary albumin creatinine rates, glomerular filtration rate, HbAc1, and creatinine.Methods. Electronic databases including PUBMED, MEDLINE, and EMBASE were searched for eligible publications to July 2018. The following comparisons between treatment groups were included: normal group versus DN group; control group versus micro/macroalbuminuria group.Results. Twelve eligible studies that included 2500 participants were finally recruited in this meta-analysis. Fifteen miRNAs (miRNA-21, miRNA-181b, miRNA-194, miRNA-30, miRNA-215, and others) were upregulated whereas seven miRNAs (miRNA-26a, miRNA-126, miRNA-424, miRNA-574-3p, miR-223, miR-155, and miR-192) were downregulated in the DN group compared with control groups. The miR-133b, miR-342, miR-30, miR-192, miR-194, and miR-215 were significantly correlated in urinary albumin excretion rates (r=0.33, 95% CI= 0.26-0.39). miR-192, miR-217, miR-15b, miR-34a, and miR-636 were correlated with urinary albumin creatinine rates (r=0.69; 95% CI=0.12-0.92), while miR-133b, miR-345, miR-33, miR-326, miR-574-3p, miR-126, miR-217, miR-15b, miR-34a, and miR-636 were significantly correlated with HbAc1 (r =0.23, 95% CI = 0.15-0.31). There were twelve miRNAs that were closely related to the glomerular filtration rate (r=0.28, 95% CI =0.21-0.34). Creatinine (r=0.33, 95% CI = 0.22-0.40) was significantly different between normal and DN groups.Conclusions. The meta-analysis acquired the correlations between miRNAs and outcomes including UAER, UACR, eGFR, HbAc1, and creatinine in DN. It suggested that miRNAs may participate in the pathogenesis of DN process.

Hepatic immune tolerance might contribute to the development of therapeutic resistance to immunotherapy. However, addressing this issue is challenging since the efficacy of immunotherapy in the context of liver metastasis (LM) remains poorly studied. Here, we aimed to establish an LM common immune feature (LMCIF) score to quantify the characteristics of LM immunotolerance across cancer types for assisting clinical disease management. Large-scale clinical data were collected to identify the prognosis of LM. Multi-omics datasets of metastatic cancers with LM special immune-related pathways (LMSIPs) from the Molecular Signatures Database (MSigDB)were used to obtain an LMCIF cluster. Based on differential expression genes (DEGs), a novel LMCIF score for the LMCIF cluster was constructed. In addition, multi-omics, and immunohistochemistry (IHC) data from the public and in-house cohorts were used to explore the features of LM, and LMCIF score. Patients with LM had a worse prognosis and significantly lower infiltration of immune cells than patients with metastasis to other organs when analyzed with combined clinical and RNA sequencing data. After extracting the LMCIF cluster from 373 samples by utilizing 29 LMSIPs and validating them in a microarray cohort, an LMCIF score was established to confirm the role of the immunosuppressive environment as a contributor to the poor prognosis of LM across cancer types. Moreover, this LMCIF score could be used to predict the immune response of cancer patients undergoing immunotherapy. Finally, we identified that the majority of the 31 LMCIF genes exhibited a negative correlation with TME cells in LM patients, one of them, KRT19, which possessed the strongest positive correlation with LMCIF score, was confirmed to have an immunosuppressive effect through IHC analysis. Our results suggest that LM across cancer types share similar immunological profiles that induce immunotolerance and escape from immune monitoring. The novel LMCIF score represents a common liver metastasis immune cluster for predicting immunotherapy response, the results of which might benefit clinical disease management.