Background: Accumulating evidence support the hypothesis that long noncoding RNAs (lncRNAs) are involved in several physiological and pathological conditions, including cancer. Here, we investigated the potential role of lncRNAs in bladder cancer. Methods: We first looked at available datasets retrieved from the TCGA database and discovered that the lncRNA KTN 1 antisense RNA 1 (KTN1-AS1) was significantly up-regulated in several cancer types including bladder cancer, but was decreased in some other tumors. Therefore, we focused our attention on KTN1-AS1. Using both in vitro and in vivo systems that allowed the modulation of KTN1-AS1 and expression of other relevant proteins, we investigated in-depth the role of KTN1-AS1 in bladder cancer (and the mechanism behind). We further investigated the potential KTN1-AS1-interacting proteins using RNA immunoprecipitation, and explored the KTN1-AS1-related epigenetic landscape (with a particular emphasis on acetylation) using chromatin immunoprecipitation (ChIP) assays. Results: KTN1-AS1 silencing inhibited the proliferation, invasion, and migration of bladder cancer cells, while KTN1-AS1 overexpression had the obvious opposite effects. Mechanistically, KTN1-AS1 promoted the recruitment of EP300, a histone acetyltransferase that enriched acetylation of histone H3 at lysine 27 (H3K27Ac) in the KTN1 promoter region. This epigenetic modulation contributed to the up-regulation of KTN1, which affected bladder cancer growth and progression via the regulation of Rho GTPase (RAC1, RHOA, and CDC42)-mediated signaling. Conclusion: Overall, our data support the idea that the lncRNA KTN1-AS1 promotes bladder cancer tumorigenesis via modulation of the KTN1/Rho GTPase axis and is a promising new therapeutic target for the treatment of bladder cancer.

The development of immunotherapy has changed the treatment landscape of advanced kidney renal clear cell carcinoma (KIRC), offering patients more treatment options. Cuproptosis, a novel cell death mode dependent on copper ions and mitochondrial respiration has not yet been studied in KIRC. We assembled a comprehensive cohort of The Cancer Genome Atlas (TCGA)-KIRC and GSE29609, performed cluster analysis for typing twice using seven cuproptosis-promoting genes (CPGs) as a starting point, and assessed the differences in biological and clinicopathological characteristics between different subtypes. Furthermore, we explored the tumor immune infiltration landscape in KIRC using ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) and the potential molecular mechanisms of cuproptosis in KIRC using enrichment analysis. We constructed a cuproptosis score (CUS) using the Boruta algorithm combined with principal component analysis. We evaluated the impact of CUS on prognosis, targeted therapy, and immunotherapy in patients with KIRC using survival analysis, the predictions from the Cancer Immunome Atlas database, and targeted drug susceptibility analysis. We found that patients with high CUS levels show poor prognosis and efficacy against all four immune checkpoint inhibitors, and their immunosuppression may depend on TGFB1. However, the high-CUS group showed higher sensitivity to sunitinib, axitinib, and elesclomol. Sunitinib monotherapy may reverse the poor prognosis and result in higher progression free survival. Then, we identified two potential CPGs and verified their differential expression between the KIRC and the normal samples. Finally, we explored the effect of the key gene FDX1 on the proliferation of KIRC cells and confirmed the presence of cuproptosis in KIRC cells. We developed a targeted therapy and immunotherapy strategy for advanced KIRC based on CUS. Our findings provide new insights into the relationship among cuproptosis, metabolism, and immunity in KIRC.

Background Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with high incidence and poor prognosis. Although immunotherapy has brought significant survival benefits to advanced UCEC patients, traditional evaluation indicators cannot accurately identify all potential beneficiaries of immunotherapy. Consequently, it is necessary to construct a new scoring system to predict patient prognosis and responsiveness of immunotherapy. Methods CIBERSORT combined with weighted gene co-expression network analysis (WGCNA), non-negative matrix factorization (NMF), and random forest algorithms to screen the module associated with CD8+ T cells, and key genes related to prognosis were selected out by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to develop the novel immune risk score (NIRS). Kaplan–Meier (K-M) analysis was used to compare the difference of survival between high- and low- NIRS groups. We  also explored the correlations between NIRS, immune infiltration and immunotherapy, and three external validation sets were used to verify the predictive performance of NIRS. Furthermore, clinical subgroup analysis, mutation analysis, differential expression of immune checkpoints, and drug sensitivity analysis were performed to generate individualized treatments for patients with different risk scores. Finally, gene set variation analysis (GSVA) was conducted to explore the biological functions of NIRS, and qRT-PCR was applied to verify the differential expressions of three trait genes at cellular and tissue levels. Results Among the modules clustered by WGCNA, the magenta module was most positively associated with CD8+ T cells. Three genes (CTSW, CD3D and CD48) were selected to construct NIRS after multiple screening procedures. NIRS was confirmed as an independent prognostic factor of UCEC, and patients with high NIRS had significantly worse prognosis compared to those with low NIRS. The high NIRS group showed lower levels of infiltrated immune cells, gene mutations, and expression of multiple immune checkpoints, indicating reduced sensitivity to immunotherapy. Three module genes were identified as protective factors positively correlated with the level of CD8+ T cells. Conclusions In this study, we constructed NIRS as a novel predictive signature of UCEC. NIRS not only differentiates patients with distinct prognoses and immune responsiveness, but also guides their therapeutic regimens.