Cardiac valve replacement is an effective method to treat valvular heart disease. Artificial valves used routinely in clinic still have defects. In our study, we explored a novel method to modify the performance of Decellularized Heart Valve (DHV) scaffold. The decellularized porcine aortic valve was prepared using sequential hydrophile and lipophile solubilization method. The sericin was extracted from silk fibroin-deficient silkworm cocoon by lithium bromide method. First, DHV was immersed in sericin solution to produce the sericin–DHV composite scaffold. Then, we modified the DHV by making a Polydopamine (PDA) coating on the DHV first and then binding the sericin. The physical properties and biological compatibility of our composite scaffold were assessed in vitro and in vivo. Sericin were successfully prepared, combined to DHV and improved its biocompatibility. PDA coating further promoted the combination of sericin on DHV and improved the physical properties of scaffolds. The decay rate of our modified valve scaffold was decreased in vivo and it showed good compatibility with blood. In conclusion, our modification improved the physical properties and biocompatibility of the valve scaffold. The combination of PDA and sericin promoted the recellularization of decellularized valves, showing great potential to be a novel artificial valve.

Background. The tumor immune microenvironment is vital to kidney renal clear cell carcinoma (KIRC) progression, and immunotherapies have been shown to be effective in the management of KIRC. However, the prognostic genes associated with the tumor immune microenvironment in KIRC have not been fully identified. We obtained the KIRC RNA sequencing data and the clinical characteristics from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. We screened the gene modules associated with the tumor immune microenvironment based on the ESTIMATE algorithm and weighted gene coexpression network analysis (WGCNA). Univariate Cox analysis and the LASSO method were used to construct a prognostic model. Receiver Operating Characteristic (ROC) curve analysis was performed to evaluate the accuracy of our risk model. TIMER and Single‐Sample Gene Set Enrichment Analysis (ssGSEA) were used to explore the correlation between prognostic genes and immune cell infiltration. Results. Fifty‐four genes in modules were significantly associated with the overall survival (OS) time of patients with KIRC. Furthermore, 12 hub genes were selected to construct the prognostic model. The prognostic model showed superior accuracy in both TCGA and ICGC cohorts using ROC curve analysis. Systematic analysis of immune cell infiltration revealed that nine genes were significantly correlated with levels of tumor‐infiltrating immune cells. Conclusions. Our findings indicated that the tumor immune microenvironment was an important determinant of KIRC outcomes and revealed potential biomarkers for predicting patient OS and for targeted immunotherapies.