Prostate cancer (PCa) patients commonly present with osteoblastic‐type bone metastasis. Exosomes derived from tumor cells possess biological significance and can mediate intercellular communication in the tumor microenvironment. Long noncoding RNA (lncRNA) nuclear‐enriched abundant transcript 1 (NEAT1) is also implicated in the stability in tumorigenesis and the development of PCa, but the underlying mechanism remains elusive. Hence, the current study set out to investigate the physiological mechanisms by which exosomes‐encapsulated NEAT1 affects the progression of PCa. First, after isolation, we found PCa cell‐derived exosomes induced the osteogenic differentiation of human bone marrow‐derived mesenchymal stem cells (hBMSCs). Besides, NEAT1 in PCa cells could be transferred into hBMSCs via exosomes. Further gain‐ and loss‐of‐function experimentation revealed that NEAT1 acted as a competing endogenous RNA (ceRNA) of microRNA (miR)‐205‐5p to upregulate the runt‐related transcription factor 2 (RUNX2) levels. Moreover, NEAT1 could promote the RUNX2 expression via the splicing factor proline‐ and glutamine‐rich (SFPQ)/polypyrimidine tract‐binding protein 2 (PTBP2) axis. Functional assays uncovered that NEAT1 shuttled by PCa‐exosomes facilitated the activity of alkaline phosphatase (ALP) and mineralization of extracellular matrix, and continuously upregulated the levels of RUNX2, ALP, alpha‐1 type 1 collagen, and osteocalcin by regulating RUNX2, to induce the osteogenic differentiation of hBMSCs. Furthermore, in vivo experimentation confirmed that upregulated NEAT1 induced osteogenesis. Collectively, our findings indicated that PCa‐derived exosomes‐loaded NEAT1 upregulated RUNX2 to facilitate the osteogenesis of hBMSCs by competitively binding to miR‐205‐5p via the SFPQ/PTBP2 axis, therefore providing a potential therapeutic target to treat osteogenesis of hBMSCs in PCa. PCa cells secrete exosomes containing NEAT1, and NEAT1 exerts effects on osteogenic differentiation of hBMSCs in PCa. NEAT1 shuttled by PCa‐derived exosomes could be transferred into hBMSCs, where NEAT1 exerted inductive properties in osteogenic differentiation of hBMSCs through the upregulation of RUNX2 by competitively binding to miR‐205‐5p and regulating SFPQ/PTBP2 in vitro and in vivo.

Sunitinib is a first-line targeted therapy for patients with renal cell carcinoma (RCC), but resistance represents a significant obstacle to the treatment of advanced and metastatic RCC. Metabolic reprogramming is a characteristic of RCC, and changes in metabolic processes might contribute to resistance to sunitinib. In this study, we identified methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), a mitochondrial enzyme involved in one-carbon metabolism, as a critical mediator of sunitinib resistance in RCC. MTHFD2 was elevated in sunitinib-resistant RCC cells, and loss of MTHDF2 conferred sensitivity to sunitinib. In patients, MTHFD2 was highly expressed in RCC and was associated with poor outcomes. Mechanistically, MTHFD2 stimulated UDP-N-acetylglucosamine (UDP-GlcNAc) biosynthesis and promoted cMYC O-GlcNAcylation by driving the folate cycle. O-GlcNAcylation enhanced cMYC stability and promoted MTHFD2 and cyclin D1 transcription. Targeting MTHFD2 or cyclin D1 sensitized tumor cells to sunitinib in vitro and in vivo. Consistently, development of a peptide drug capable of efficiently degrading MTHFD2 enabled reversal of sunitinib resistance in RCC. These findings identify a noncanonical metabolic function of MTHFD2 in cell signaling and response to therapy and reveal the interplay between one-carbon metabolism and sunitinib resistance in RCC. Targeting MTHFD2 could be an effective approach to overcome sunitinib resistance. Significance: MTHFD2 regulates cMYC O-GlcNAcylation to promote sunitinib resistance in renal cell carcinoma, highlighting the important role of one-carbon metabolism in sunitinib resistance and proposing therapeutic strategies to improve patient outcomes.

Background: Disulfidptosis is a novel form of programmed cell death induced by high SLC7A11 expression under glucose starvation conditions, unlike other known forms of cell death. However, the roles of disulfidptosis in cancers have yet to be comprehensively well-studied, particularly in ccRCC. Methods: The expression profiles and somatic mutation of DGs from the TCGA database were investigated. Two DGs clusters were identified by unsupervised consensus clustering analysis, and a disulfidptosis-related prognostic signature (DR score) was constructed. Furthermore, the predictive capacity of the DR score in prognosis was validated by several clinical cohorts. We also developed a nomogram based on the DR score and clinical features. Then, we investigated the differences in the clinicopathological information, TMB, tumor immune landscapes, and biological characteristics between the high- and low-risk groups. We evaluated whether the DR score is a robust tool for predicting immunotherapy response by the TIDE algorithm, immune checkpoint genes, submap analysis, and CheckMate immunotherapy cohort. Results: We identified two DGs clusters with significant differences in prognosis, tumor immune landscapes, and clinical features. The DR score has been demonstrated as an independent risk factor by several clinical cohorts. The high-risk group patients had a more complicated tumor immune microenvironment and suffered from more tumor immune evasion in immunotherapy. Moreover, patients in the low-risk group had better prognosis and response to immunotherapy, particularly in anti-PD1 and anti-CTLA-4 inhibitors, which were verified in the CheckMate immunotherapy cohort. Conclusion: The DR score can accurately predict the prognosis and immunotherapy response and assist clinicians in providing a personalized treatment regime for ccRCC patients.

Introduction: Bladder cancer (BC) is a significant carcinoma of the urinary system that has a high incidence of morbidity and death owing to the challenges in accurately identifying people with early-stage BC and the lack of effective treatment options for those with advanced BC. Thus, there is a need to define new markers of prognosis and prediction.Methods: In this study, we have performed a comprehensive proteomics experiment by label-free quantitative proteomics to compare the proteome changes in the serum of normal people and bladder cancer patients—the successful quantification of 2064 Quantifiable proteins in total. A quantitative analysis was conducted to determine the extent of changes in protein species' relative intensity and reproducibility. There were 43 upregulated proteins and 36 downregulated proteins discovered in non-muscle invasive bladder cancer and normal individuals. Sixty-four of these proteins were elevated, and 51 were downregulated in muscle-invasive and non-muscle-invasive bladder cancer, respectively. Functional roles of differentially expressed proteins were annotated using Gene Ontology (GO) and Clusters of Orthologous Groups of Proteins (COG). To analyze the functions and pathways enriched by differentially expressed proteins, GO enrichment analysis, protein domain analysis, and KEGG pathway analysis were performed. The proteome differences were examined and visualized using radar plots, heat maps, bubble plots, and Venn diagrams.Results: As a result of combining the Venn diagram with protein-protein interactions (PPIs), Chloride intracellular channel 1 (CLIC1) was identified as the primary protein. Using the Gene Set Cancer Analysis (GSCA) website, the influence of CLIC1 on immune infiltration was analyzed. A negative correlation between CD8 naive and CLIC1 levels was found. For validation, immunohistochemical (IHC), qPCR, and western blotting (WB) were performed.Further, we found that CLIC1 was associated with a poor prognosis of bladder cancer in survival analysis.Discussion: Our research screened CLIC1 as a tumor-promoting protein in bladder cancer for the first time using serum mass spectrometry. And CLIC1 associated with tumor stage, and immune infiltrate. The prognostic biomarker and therapeutic target CLIC1 may be new for bladder cancer patients.

Background: As a novel type of the prevalent post-transcriptional modifications, N7-methylguanosine (m7G) modification is essential in the tumorigenesis, progression, and invasion of many cancers, including bladder cancer (BCa). However, the integrated roles of m7G-related lncRNAs in BCa remain undiscovered. This study aims to develop a prognostic model based on the m7G-related lncRNAs and explore its predictive value of the prognosis and anti-cancer treatment sensitivity. Methods: We obtained RNA-seq data and corresponding clinicopathological information from the TCGA database and collected m7G-related genes from previous studies and GSEA. Based on LASSO and Cox regression analysis, we developed a m7G prognostic model. The Kaplan–Meier (K-M) survival analysis and ROC curves were performed to evaluate the predictive power of the model. Gene set enrichment analysis (GSEA) was conducted to explore the molecular mechanisms behind apparent discrepancies between the low- and high-risk groups. We also investigated immune cell infiltration, TIDE score, TMB, the sensitivity of common chemotherapy drugs, and the response to immunotherapy between the two risk groups. Finally, we validated the expression levels of these ten m7G-related lncRNAs in BCa cell lines by qRT-PCR. Results: We developed a m7G prognostic model (risk score) composed of 10 m7G-related lncRNAs that are significantly associated with the OS of BCa patients. The K-M survival curves revealed that the high-risk group patients had significantly worse OS than those in the low-risk group. The Cox regression analysis confirmed that the risk score was a significant independent prognostic factor for BCa patients. We found that the high-risk group had higher the immune scores and immune cell infiltration. Furthermore, the results of the sensitivity of common anti-BCa drugs showed that the high-risk group was more sensitive to neoadjuvant cisplatin-based chemotherapy and anti-PD1 immunotherapy. Finally, qRT-PCR revealed that AC006058.1, AC073133.2, LINC00677, and LINC01338 were significantly downregulated in BCa cell lines, while the expression levels of AC124312.2 and AL158209.1 were significantly upregulated in BCa cell lines compared with normal cell lines. Conclusion: The m7G prognostic model can be applied to accurately predict the prognosis and provide robust directions for clinicians to develop better individual-based and precise treatment strategies for BCa patients.

Background: As a new form of regulated cell death, cuproptosis differs profoundly from apoptosis, ferroptosis, pyroptosis, and necroptosis. The correlation between cuproptosis and long non-coding RNAs (lncRNAs) has been increasingly studied recently. In this study, a novel cuproptosis-related lncRNA prognostic signature was developed to investigate biochemical recurrence (BCR) and tumor immune landscape in prostate cancer (PCa).Methods and Materials: The transcriptome data and clinicopathologic information of PCa patients were downloaded from The Cancer Genome Atlas (TCGA). Pearson’s correlation analysis was applied to identify lncRNAs associated with cuproptosis. Based on Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis, we developed a cuproptosis-related lncRNA prognostic model (risk score) to predict the BCR of PCa patients. Additionally, we also constructed a nomogram with the risk score and clinicopathologic features. The biological function, tumor mutation burden (TMB), immune cell infiltration, expression levels of immune checkpoint genes, and anti-cancer drug sensitivity were investigated.Results: We constructed and validated the cuproptosis-related lncRNA signature prognostic model (risk score) by six crlncRNAs. All patients were divided into the low- and high-risk groups based on the median risk score. The Kaplan–Meier (KM) survival analysis revealed that the high-risk group had shorter BCR-free survival (BCRFS). The risk score has been proven to be an independent prognostic factor of BCR in PCa patients. In addition, a nomogram of risk scores and clinicopathologic features was established and demonstrated an excellent predictive capability of BCR. The ROC curves further validated that this nomogram had higher accuracy of predicting the BCR compared to other clinicopathologic features. We also found that the high-risk group had higher TMB levels and more infiltrated immune cells. Furthermore, patients with high TMB in the high-risk group were inclined to have the shortest BCRFS. Finally, patients in the high-risk group were more susceptible to docetaxel, gefitinib, methotrexate, paclitaxel, and vinblastine.Conclusion: The novel crlncRNA signature prognostic model shows a greatly prognostic prediction value of BCR for PCa patients, extends our thought on the association of cuproptosis and PCa, and provides novel insights into individual-based treatment strategies for PCa.