The use of mushrooms as functional foods and in the treatment of diseases has a long history. Inonotus obliquus is a mushroom belonging to the Hymenochaetaceae family and has possible anticancer, antiviral, and hypoglycemic properties. Chemical analysis of this mushroom has allowed the identification of various constituents such as melanins, phenolic compounds, and lanostane-type triterpenoids. A plethora of findings have highlighted the potential molecular mechanisms of actions of this mushroom such as its ability to scavenge reactive oxygen species, inhibit the growth of tumors, decrease inflammation and insulin resistance in type 2 diabetes, and stimulate the immune system. This review summarizes the relevant findings with reference to the therapeutic potential of this mushroom in countering the progression of cancers, diabetes mellitus, and antiviral activities, while highlighting its possible molecular mechanisms of action. The possible role of this mushroom as a therapeutic agent in addressing the pathogenesis of diabetes and cancer has also been suggested.

Background Urothelial bladder cancer (BLCA) is one of the most common internal malignancies worldwide with poor prognosis. This study aims to explore effective prognostic biomarkers and construct a prognostic risk score model for patients with BLCA. Methods Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the pathological stage of BLCA based on the RNA-Seq data retrieved from The Cancer Genome Atlas database. Prognostic biomarkers screened by Cox proportional hazard regression model and random forest were used to construct a risk score model that can predict the prognosis of patients with BLCA. The GSE13507 dataset was used as the independent testing dataset to test the performance of the risk score model in predicting the prognosis of patients with BLCA. Results WGCNA identified seven co-expression modules, in which the brown module consisted of 77 genes was most significantly correlated with the pathological stage of BLCA. Cox proportional hazard regression model and random forest identified TPST1 and P3H4 as prognostic biomarkers. Elevated TPST1 and P3H4 expressions were associated with the high pathological stage and worse survival. The risk score model based on the expression level of TPST1 and P3H4 outperformed pathological stage indicators and previously proposed prognostic models. Conclusion The gene co-expression network-based study could provide additional insight into the tumorigenesis and progression of BLCA, and our proposed risk score model may aid physicians in the assessment of the prognosis of patients with BLCA. Electronic supplementary material The online version of this article (10.1186/s41065-019-0100-1) contains supplementary material, which is available to authorized users.

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