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Abstract Background Metabolic reprogramming is a hallmark of various cancers. Targeting metabolic processes is a very attractive treatment for cancer. Renal cell carcinoma (RCC) is a type of metabolic disease, and the lipidomic profile of RCC is significantly altered compared with that of healthy tissue. However, the molecular mechanism underlying lipid metabolism regulation in RCC is not clear. Methods The XF long-chain fatty acid oxidative stress test kits were used to assess the dependence on long-chain fatty acids and mitochondrial function after knockdown TRIM21 in RCC cells. The effect of TRIM21 on the lipid content in RCC cells was determined by metabolomics analysis, Oil Red O staining, and cellular Nile red staining. qRT-PCR and western blot were used to explore the relationship between TRIM21 and lipogenesis, and then the key molecule sterol regulatory element binding transcription factor 1 (SREBF1) was identified to interact with TRIM21 by immunoprecipitation, which was also identified in an orthotopic model. Subsequently, the relevance and clinical significance of TRIM21 and SREBF1 were analyzed by The Cancer Genome Atlas (TCGA) database, and 239 tissues were collected from RCC patients. Results TRIM21 silencing attenuated the dependence of RCC cells on fatty acids, and enhanced lipid accumulation in RCC cells. TRIM21 overexpression significantly decreased lipid contents by decreasing the expression of lipogenic enzymes via ubiquitination-mediated degradation of SREBF1. SREBF1 is critical for TRIM21-mediated lipogenesis inhibition in vitro and in vivo. Moreover, TRIM21 expression is negatively correlated with SREBF1 expression, and TRIM21-SREBF1 is a reliable combinational biomarker for RCC prognosis. Conclusion The findings from this study reveal a novel pathway through which TRIM21 inhibits the lipid metabolism process of RCC and shed light on the development of targeted metabolic treatment and prognosis diagnosis of RCC.

Abstract Enhancer of zeste homolog 2 (EZH2), as a main component of Polycomb Repressive Complex 2, catalyzes histone H3K27me3 to silence its target gene expression. EZH2 upregulation results in cancer development and poor prognosis of cancer patients. Post-translational modifications (PTMs) are important biological events in cancer progression. PTMs regulate protein conformation and diversity functions. Recently, mounting studies have demonstrated that EZH2 stability, histone methyltransferase activity, localization, and binding partners can be regulated by PTMs, including phosphorylation, O-GlcNAcylation, acetylation, methylation and ubiquitination. However, the detailed molecular mechanisms of the EZH2-PTMs and whether other types of PTMs occur in EZH2 remain largely unclear. This review presents an overview of different roles of EZH2 modification and EZH2-PTMs crosstalk during tumorigenesis and cancer metastasis. We also discussed the therapeutic potential of targeting EZH2 modifications for cancer therapy.

Abstract Background Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. The work of Chinese medicine is subject to the two factors of the inheritance and development of clinical experience of famous Chinese medicine practitioners and the difficulty in improving the service capacity of basic Chinese medicine practitioners. Heterogeneous information networks (HINs) are a kind of graphical model for integrating and modeling real-world information. Through HINs, we can integrate and model the large-scale heterogeneous TCM data into structured graph data and use this as a basis for analysis. Methods Mining categorizations from TCM data is an important task for precision medicine. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network. Results The results from experiments on the Pharmacopoeia of the People’s Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas. Conclusions We use heterogeneous information networks to model TCM data and propose a TCM-HIN. Combining the heterogeneous graph with the probability graph, we proposed the TCM-Clus algorithm, which combines clustering with ranking and classifies traditional Chinese medicine prescriptions. The results of the categorizations can help Chinese medicine practitioners to make clinical decision.