BackgroundPyroptosis is essential for tumorigenesis and progression of neoplasm. However, the heterogeneity of pyroptosis and its relationship with the tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC) remain unclear. The purpose of the present study was to identify pyroptosis-related subtypes and construct a prognosis prediction model based on pyroptosis signatures.MethodsFirst, heterogenous pyroptosis subgroups were explored based on 33 pyroptosis-related genes and ccRCC samples from TCGA, and the model established by LASSO regression was verified by the ICGC database. Then, the clinical significance, functional status, immune infiltration, cell–cell communication, genomic alteration, and drug sensitivity of different subgroups were further analyzed. Finally, the LASSO-Cox algorithm was applied to narrow down the candidate genes to develop a robust and concise prognostic model.ResultsTwo heterogenous pyroptosis subgroups were identified: pyroptosis-low immunity-low C1 subtype and pyroptosis-high immunity-high C2 subtype. Compared with C1, C2 was associated with a higher clinical stage or grade and a worse prognosis. More immune cell infiltration was observed in C2 than that in C1, while the response rate in the C2 subgroup was lower than that in the C1 subgroup. Pyroptosis-related genes were mainly expressed in myeloid cells, and T cells and epithelial cells might influence other cell clusters via the pyroptosis-related pathway. In addition, C1 was characterized by MTOR and ATM mutation, while the characteristics of C2 were alterations in SPEN and ROS1 mutation. Finally, a robust and promising pyroptosis-related prediction model for ccRCC was constructed and validated.ConclusionTwo heterogeneous pyroptosis subtypes were identified and compared in multiple omics levels, and five pyroptosis-related signatures were applied to establish a prognosis prediction model. Our findings may help better understand the role of pyroptosis in ccRCC progression and provide a new perspective in the management of ccRCC patients.

The epigenetic modification of tumorigenesis and progression in neoplasm has been demonstrated in recent studies. Nevertheless, the underlying association of N7-methylguanosine (m7G) regulation with molecular heterogeneity and tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC) remains unknown. We explored the expression profiles and genetic variation features of m7G regulators and identified their correlations with patient outcomes in pan-cancer. Three distinct m7G modification patterns, including MGCS1, MGCS2, and MGCS3, were further determined and systematically characterized via multi-omics data in ccRCC. Compared with the other two subtypes, patients in MGCS3 exhibited a lower clinical stage/grade and better prognosis. MGCS1 showed the lowest enrichment of metabolic activities. MGCS2 was characterized by the suppression of immunity. We then established and validated a scoring tool named m7Sig, which could predict the prognosis of ccRCC patients. This study revealed that m7G modification played a vital role in the formation of the tumor microenvironment in ccRCC. Evaluating the m7G modification landscape helps us to raise awareness and strengthen the understanding of ccRCC’s characterization and, furthermore, to guide future clinical decision making.

DEAD-box protein 39 (DDX39) has been demonstrated to be a tumorigenic gene in multiple tumor types, but its role in the progression and immune microenvironment of clear cell renal cell cancer (ccRCC) remains unclear. The aim of the present study was to investigate the role of DDX39 in the ccRCC tumor progression, immune microenvironment and efficacy of immune checkpoint therapy. The DDX39 expression level was first detected in tumors in the public data and then verified in ccRCC samples from Changzheng Hospital. The prognostic value of DDX39 expression was assessed in the Cancer Genome Atlas (TCGA) and ccRCC patients from Changhai Hospital. The role of DDX39 in promoting ccRCC was analyzed by bioinformatic analysis and in vitro experiments. The association between DDX39 expression and immune cell infiltration and immune inhibitory markers was analyzed, and its value in predicting the immune checkpoint therapy efficacy in ccRCC were evaluated in the public database. DDX39 expression was elevated in Oncomine, GEO and TCGA ccRCC databases, as well as in Changzheng ccRCC samples. In TCGA ccRCC patients, increased DDX39 expression predicted worse overall survival (OS) ( p <0.0001) and progression-free interval (PFI) ( p <0.0001), and was shown as an independent predictive factor for OS ( p =0.002). These findings were consistent with those from Changhai ccRCC patients. In addition, GO and GSEA analysis identified DDX39 as a pro-ccRCC gene. In vitro experiments confirmed the role of DDX39 in promoting ccRCC cell. Finally, DDX39 was found to be positively correlated with a variety of immune inhibitory markers, and could predict the adverse efficacy of immune checkpoint therapy in TIDE analysis. In conclusion, Increased DDX39 in ccRCC patients predicted worse clinical prognosis, promoted ccRCC cell proliferation, migration and invasion, and also predicted adverse efficacy of immune checkpoint therapy.

Rationale. Patients with clear cell renal cell cancer (ccRCC) may have completely different treatment choices and prognoses due to the wide range of heterogeneity of the disease. However, there is a lack of effective models for risk stratification, treatment decision-making, and prognostic prediction of renal cancer patients. The aim of the present study was to establish a model to stratify ccRCC patients in terms of prognostic prediction and drug selection based on multiomics data analysis. Methods. This study was based on the multiomics data (including mRNA, lncRNA, miRNA, methylation, and WES) of 258 ccRCC patients from TCGA database. Firstly, we screened the feature values that had impact on the prognosis and obtained two subtypes. Then, we used 10 algorithms to achieve multiomics clustering and conducted pseudotiming analysis to further validate the robustness of our clustering method, based on which the two subtypes of ccRCC patients were further subtyped. Meanwhile, the immune infiltration was compared between the two subtypes, and drug sensitivity and potential drugs were analyzed. Furthermore, to analyze the heterogeneity of patients at the multiomics level, biological functions between two subtypes were compared. Finally, Boruta and PCA methods were used for dimensionality reduction and cluster analysis to construct a renal cancer risk model based on mRNA expression. Results. A prognosis predicting model of ccRCC was established by dividing patients into the high- and low-risk groups. It was found that overall survival (OS) and progression-free interval (PFI) were significantly different between the two groups ( p < 0.01 ). The area under the OS time-dependent ROC curve for 1, 3, 5, and 10 years in the training set was 0.75, 0.72, 0.71, and 0.68, respectively. Conclusion. The model could precisely predict the prognosis of ccRCC patients and may have implications for drug selection for ccRCC patients.