PHF8-GLUL axis in lipid deposition and tumor growth of clear cell renal cell carcinoma

The role of metabolic reprogramming in kidney cancer

Metabolic reprogramming is a cellular process in which cells modify their metabolic patterns to meet energy requirements, promote proliferation, and enhance resistance to external stressors. This process also introduces new functionalities to the cells. The 'Warburg effect' is a well-studied example of metabolic reprogramming observed during tumorigenesis. Recent studies have shown that kidney cells undergo various forms of metabolic reprogramming following injury. Moreover, metabolic reprogramming plays a crucial role in the progression, prognosis, and treatment of kidney cancer. This review offers a comprehensive examination of renal cancer, metabolic reprogramming, and its implications in kidney cancer. It also discusses recent advancements in the diagnosis and treatment of renal cancer.

Acetylation- and ubiquitination-regulated SFMBT2 acts as a tumor suppressor in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (RCC) is the most common kidney tumor. The analysis from medical database showed that Scm-like with four MBT domains protein 2 (SFMBT2) was decreased in advanced clear cell RCC cases, and its downregulation was associated with the poor prognosis. This study aims to investigate the role of SFMBT2 in clear cell RCC.

METHODS

The expression of SFMBT2 in clear cell RCC specimens were determined by immunohistochemistry staining and western blot. The overexpression and knockdown of SFMBT2 was realized by infection of lentivirus loaded with SFMBT2 coding sequence or silencing fragment in 786-O and 769-P cells, and its effects on proliferation and metastasis were assessed by MTT, colony formation, flow cytometry, wound healing, transwell assay, xenograft and metastasis experiments in nude mice. The interaction of SFMBT2 with histone deacetylase 3 (HDAC3) and seven in absentia homolog 1 (SIAH1) was confirmed by co-immunoprecipitation.

RESULTS

In our study, SFMBT2 exhibited lower expression in clear cell RCC specimens with advanced stages than those with early stages. Overexpression of SFMBT2 inhibited the growth and metastasis of clear cell RCC cells, 786-O and 769-P, in vitro and in vivo, and its silencing displayed opposites effects. HDAC3 led to deacetylation of SFMBT2, and the HDAC3 inhibitor-induced acetylation prevented SFMBT2 from SIAH1-mediated ubiquitination modification and proteasome degradation. K687 in SFMBT2 protein molecule may be the key site for acetylation and ubiquitination.

CONCLUSIONS

SFMBT2 exerted an anti-tumor role in clear cell RCC cells, and HDAC3-mediated deacetylation promoted SIAH1-controlled ubiquitination of SFMBT2. SFMBT2 may be considered as a novel clinical diagnostic marker and/or therapeutic target of clear cell RCC, and crosstalk between its post-translational modifications may provide novel insights for agent development.

P2RY13 is a prognostic biomarker and associated with immune infiltrates in renal clear cell carcinoma: A comprehensive bioinformatic study

Background and Aims

Clear cell renal cell carcinoma (ccRCC) is a common and aggressive form of cancer with a high incidence globally. This study aimed to investigate the role of P2RY13 in the progression of ccRCC and elucidate its mechanism of action.

Methods

Gene Expression Omnibus and The Cancer Genome Atlas databases were used to extract gene expression profiles of ccRCC. These profiles were annotated and visualized by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses, as well as Gene Set Enrichment Analysis (GSEA). The STRING database was used to establish a protein-protein interaction network and to analyze the functional similarity. The GEPIA2 database was used to predict survival associated with hub genes. Meanwhile, the TIMER2.0 database was used to assess immune cell infiltration and its link with the hub genes. Immunohistochemistry (IHC) was used to determine the difference between ccRCC and adjacent normal tissue.

Results

We identified 272 differentially expressed genes (DEGs). GO and KEGG analyses suggested that DEGs were primarily involved in lymphocyte activation, inflammatory response, immunological effector mechanism pathways. By cytohubba, the 20 highest-scoring hub genes were screened to identify critical genes in the protein-protein interaction network linked with ccRCC. Resting dendritic cells, CD8 T cells, and activated mast cells all showed a significant positive correlation with these hub genes. Moreover, a higher immune score was associated with increased prognostic risk scores, which in turn correlated with a poorer prognosis. IHC revealed that P2RY13 was expressed at higher levels in ccRCC compared to para-cancer tissues.

Conclusion

Identifying the DEGs will aid in the understanding of the causes and molecular mechanisms involved in ccRCC. P2RY13 may play a pivotal role in the progression and prognosis of ccRCC, potentially driving carcinogenesis though immune system mechanisms.