Background. Cancer is a major threat to human health worldwide. Although recent innovations and advances in early detection and effective therapies such as targeted drugs and immune checkpoint inhibitors have saved more lives of cancer patients and improved their quality of life, our knowledge about cancer remains largely unknown. CCNA2 belongs to the cell cyclin family and has been demonstrated to be a tumorigenic gene in multiple solid tumor types. The aim of the present study was to make a comprehensive analysis on the role of CCNA2 at a pancancer level. Methods. Multidatabases were collected to evaluate the different expression, prognostic value, DNA methylation, tumor mutation burden, microsatellite instability, mismatch repair, tumor immune microenvironment, and drug sensitivity of CCNA2 across pancancer. IHC was utilized to validate the expression and prognostic value of CCNA2 in ccRCC patients from SMMU cohort. Results. CCNA2 was differentially expressed in most cancer types vs. normal tissues. CCNA2 may significantly influence the prognosis of multiple cancer types, especially clear cell renal cell carcinoma (ccRCC). CCNA2 was also frequently mutated in most cancer types. Notably, CCNA2 was significantly correlated with immune cell infiltration and immune checkpoint inhibitory genes. In addition, CCNA2 was also strongly related to drug resistance. Conclusion. CCNA2 may prove to be a new biomarker for prognostic prediction, tumor immunity assessment, and drug susceptibility evaluation in pancancer level, especially in ccRCC.

Background: Pyroptosis 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.Methods: First, 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.Results: Two 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.Conclusion: Two 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.

KRT8 upregulation promotes tumor metastasis and is predictive of a poor prognosis in clear cell renal cell carcinoma KRT8 upregulation promotes tumor metastasis and is predictive of a poor prognosis in clear cell renal cell carcinoma

Reactive oxygen species (ROS)‐mediated sonodynamic therapy (SDT) holds increasing potential in treating deep‐seated tumor owing to the high tissue‐penetration depth. However, the inevitable accumulation of sonosensitizers in normal tissues not only make it difficult to realize the in situ SDT, but also induces sonodynamic effects in normal tissues. Herein, this work reports the passivation and selective activation strategies for the sonodynamic and near‐infrared (NIR) imaging performances of an intelligent antitumor theranostic platform termed Cu‐IR783 nanoparticles (NPs). Owing to the ruptured coordination bond between IR783 with Cu ions by responding to tumor microenvironment (TME), the selective activation of IR783 only occurred in tumor tissues to achieve the visualized in‐situ SDT. The tumor‐specific released Cu ions not only realized the cascade amplification of ROS generation through Cu⁺‐mediated Fenton‐like reaction, but also triggered cuproptosis through Cu⁺‐induced DLAT oligomerization and mitochondrial dysfunction. More importantly, the immunosuppressive TME can be reversed by the greatly enhanced ROS levels and high‐efficiency cuproptosis, ultimately inducing immunogenic cell death that promotes robust systemic immune responses for the eradication of primary tumors and suppression of distant tumors. This work provides a distinct paradigm of the integration of SDT, CDT, and cuproptosis in a controlled manner to achieve visualized in‐situ antitumor 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.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma and is associated with poor prognosis and notorious for its immune dysfunction characteristic. SNRPA1 is a spliceosome component responsible for processing pre-mRNA into mRNA, while the biological effect of SNRPA1 in ccRCC remains elusive. The aim of this study was to decipher the effect of SNRPA1 on clinical effect and tumor immunity for ccRCC patients. Multi-databases were collected to evaluate the different expression, prognostic value, DNA methylation, tumor immune microenvironment, and drug sensitivity of SNRPA1 on ccRCC. IHC was utilized to validate the expression and prognostic value of SNRPA1 in ccRCC patients from the SMMU cohort. The knockout expression of SNRPA by sgRNA plasmid inhibited the cell proliferation, migration, and metastasis ability and significantly increased the sensitivity of sunitinib treatment. In addition, we explored the role of SNRPA1 in pan-cancer level. The results indicated that SNRPA1 was differentially expressed in most cancer types. SNRPA1 may significantly influence the prognosis of multiple cancer types, especially in ccRCC patients. Notably, SNRPA1 was significantly correlated with immune cell infiltration and immune checkpoint inhibitory genes. In addition, the aggressive and immune inhibitory effects shown in SNRPA1 overexpression and the effect of SNRPA1 on ccRCC cell line invasion, metastasis, and drug sensitivity in vitro were observed. Moreover, SNRPA1 was related to Myc, MTORC, G2M, E2F, and DNA repair pathways in various cancer types. In all, SNRPA1 may prove to be a new biomarker for prognostic prediction, effect tumor immunity, and drug susceptibility in ccRCC.