RNA modification Regulators' Co-Expression Score (RMRCoeS) predicts biochemical recurrence and therapy response in prostate cancer: A multi-omics and experimental validation study.

BACKGROUND: Owing to the heterogeneity of prostate cancer (PCa), the clinical indicators traditionally fall short of meeting the requirements for personalized medicine. The realm of RNA modification has emerged as an increasingly relevant domain, shedding light on its pivotal role in tumor heterogeneity. However, the specific contributions of RNA modification regulators within the context of PCa remain largely unexplored. METHODS: In this study, we undertook a literature review to summarize the common 8 types of RNA modifications (ac4c, AI, APA, m1A, m5c, m6A, m7G, Ψ) encompassing a total of 84 regulators. Moreover, we integrated multi-center cohorts with Ridge regression to develop the Regulators' Co-Expression Score (RMRCoeS). Then we assessed the role of RMRCoeS in several clinical aspects such as biochemical recurrence (BCR), responses to chemotherapy, androgen receptor signaling inhibitor (ARSI) therapy and immunotherapy in PCa. Finally, we validated the cancer-promoting performance of five hub genes through immunohistochemistry and in vitro assays. RESULTS: Within the mutation landscape of RNA modification regulators, we observed a relatively low overall mutation rate. Remarkably, RMRCoeS, comprising 81 RNA modification regulators, exhibited a notable capability for accurately predicting the prognosis and therapeutic responses in PCa patients subjected to BCR, chemotherapy, ARSI therapy, and immunotherapy. A high RMRCoeS was indicative of a poor prognosis and unfavorable therapy responses. Functional enrichment analysis unveiled that RMRCoeS may exert its influence on PCa progression through various metabolic pathways. Furthermore, a higher RMRCoeS showed a positive correlation with elevated CNV mutations. Lastly, we validated the oncogene effects of CPSF4, WBSCR22, RPUSD3, TRMT61A, and NSUN5-five hub regulators-within the context of PCa. CONCLUSION: The function of different RNA modifications is interconnected. Comprising eight distinct RNA modifications' regulators, RMRCoeS exhibits multifaceted roles in various aspects of PCa, including disease progression, prognosis, and responses to multiple therapies. Furthermore, we provide the initial validation of the oncogene effect associated with WBSCR22, RPUSD3, TRMT61A and NSUN5 in PCa. Our findings offer novel insights into the significance of RNA modifications in PCa personalized medicine.

A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma.

Integrating genomics and histology for cancer prognosis demonstrates promise. Here, we develop a multi-classifier system integrating a lncRNA-based classifier, a deep learning whole-slide-image-based classifier, and a clinicopathological classifier to accurately predict post-surgery localized (stage I-III) papillary renal cell carcinoma (pRCC) recurrence. The multi-classifier system demonstrates significantly higher predictive accuracy for recurrence-free survival (RFS) compared to the three single classifiers alone in the training set and in both validation sets (C-index 0.831-0.858 vs. 0.642-0.777, p < 0.05). The RFS in our multi-classifier-defined high-risk stage I/II and grade 1/2 groups is significantly worse than in the low-risk stage III and grade 3/4 groups (p < 0.05). Our multi-classifier system is a practical and reliable predictor for recurrence of localized pRCC after surgery that can be used with the current staging system to more accurately predict disease course and inform strategies for individualized adjuvant therapy.

ACOX2 Serves as a Favorable Indicator Related to Lipid Metabolism and Oxidative Stress for Biochemical Recurrence in Prostate Cancer.

Given the heterogeneity of tumors, there is an urgent need for accurate prognostic parameters in prostate cancer (PCa) patients. Lipid metabolism (LM) reprogramming and oxidative stress (OS) play a vital role in the progression of PCa. In this work, we identified five LM-OS-related genes (including ACOX2, PPRAGC1A, PTGS1, PTGS2, and HAO1) associated with the biochemical recurrence (BCR) of PCa. Subsequently, a prognostic signature was established based on these five genes. Kaplan-Meier survival estimates, receiver operating characteristic curves, and relationship analysis between risk score and clinical characters were applied to measure the robustness of the signature in an external cohort. A nomogram of risk score combined with clinical characteristics was constructed for clinical application. Functional enrichment analysis suggested that the underlying mechanism related to the signature included the calcium signaling, lipid transport, and cell cycle signaling pathways. Furthermore, WEE1 inhibitor was identified as a potential agent related to the cell cycle for high-risk patients. The mRNA expression and the prognostic value of the five genes were determined, and ACOX2 was identified as the key gene related to the prognostic signature. The protein expression of ACOX2 was measured in a prostate tissue microarray through an immunohistochemistry assay, confirming the bioinformatics results. By constructing the ACOX2-overexpressing PCa cell lines PC-3 and 22Rv1, the biological function of PCa cells was investigated. The cell viability, colony formation, migration, and invasion ability of PCa cell lines overexpressing ACOX2 were hindered. Decreased cellular lipid content and elevated cellular ROS content were observed in ACOX2-overexpressing PCa cell lines with reduced G2/M phases. In conclusion, this work presents the first prognostic signature specifically focused on LM-OS for PCa. ACOX2 could serve as a favorable indicator for the BCR in PCa. Further experiments are required to identify the potential underlying mechanism.

A Unique Approach: Biomimetic Graphdiyne-Based Nanoplatform to Treat Prostate Cancer by Combining Cuproptosis and Enhanced Chemodynamic Therapy.

Purpose: Current treatment approaches for Prostate cancer (PCa) often come with debilitating side effects and limited therapeutic outcomes. There is urgent need for an alternative effective and safe treatment for PCa. Methods: We developed a nanoplatform to target prostate cancer cells based on graphdiyne (GDY) and a copper-based metal-organic framework (GDY-CuMOF), that carries the chemotherapy drug doxorubicin (DOX) for cancer treatment. Moreover, to provide GDY-CuMOF@DOX with homotypic targeting capability, we coated the PCa cell membrane (DU145 cell membrane, DCM) onto the surface of GDY-CuMOF@DOX, thus obtaining a biomimetic nanoplatform (DCM@GDY-CuMOF@DOX). The nanoplatform was characterized by using transmission electron microscope, atomic force microscope, X-ray diffraction, etc. Drug release behavior, antitumor effects in vivo and in vitro, and biosafety of the nanoplatform were evaluated. Results: We found that GDY-CuMOF exhibited a remarkable capability to load DOX mainly through π-conjugation and pore adsorption, and it responsively released DOX and generated Cu+ in the presence of glutathione (GSH). In vivo experiments demonstrated that this nanoplatform exhibits remarkable cell-killing efficiency by generating lethal reactive oxygen species (ROS) and mediating cuproptosis. In addition, DCM@GDY-CuMOF@DOX effectively suppresses tumor growth in vivo without causing any apparent side effects. Conclusion: The constructed DCM@GDY-CuMOF@DOX nanoplatform integrates tumor targeting, drug-responsive release and combination with cuproptosis and chemodynamic therapy, offering insights for further biomedical research on efficient PCa treatment.

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma.

BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-cell subtype, which are related to poor disease stage and immunotherapy response in various cancers. METHODS: 10 machine-learning algorithms and their combinations were applied in this work. A stable Tstr-related score (TCs) was constructed to predict the outcomes and PD-1 blockade treatment response in ccRCC patients. A nomogram based on TCs for personalized prediction of patient prognosis was constructed. Functional enrichment analysis and TimiGP algorithm were used to explore the underlying role of Tstr in ccRCC. The key TCs-related gene was identified by comprehensive analysis, and the bioinformatics results were verified by immunohistochemistry using a tissue microarray. RESULTS: A robust TCs was constructed and validated in four independent cohorts. TCs accurately predicted the prognosis and PD-1 blockade treatment response in ccRCC patients. The novel nomogram was able to precisely predict the outcomes of ccRCC patients. The underlying biological process of Tstr was related to acute inflammatory response and acute-phase response. Mast cells were identified to be involved in the role of Tstr as a protective factor in ccRCC. TNFS13B was shown to be the key TCs-related gene, which was an independent predictor of unfavorable prognosis. The protein expression analysis of TNFSF13B was consistent with the mRNA analysis results. High expression of TNFSF13B was associated with poor response to PD-1 blockade treatment. CONCLUSIONS: This study provides a Tstr cell-related score for predicting outcomes and PD-1 blockade therapy response in ccRCC. Tstr cells may exert their pro-tumoral role in ccRCC, acting against mast cells, in the acute inflammatory tumor microenvironment. TNFSF13B could serve as a key biomarker related to TCs.

Arginase 2 is a Diagnostic and Prognostic Marker for Prostate Cancer and Is Associated with Metabolism.

Objective: Metabolism, a basic need and biochemical process for cell survival and proliferation, is closely connected with the pathogenesis and progression of prostate cancer. Methods: A four-gene signature construct that includes CKM (CKM), CD38, Enoyl Coenzyme A(EHHADH), and Arginase 2(ARG2) was created by bioinformatics. Finally, hub genes were validated by IHC and in vitro experiments. Results: The results showed the AUCs of the logistic regression and neural networks diagnostic model for the diagnosis of two subtypes were 0.920 and 0.936, respectively. The risk score demonstrated by univariable and multivariable Cox analysis is an independent predictive component of the prognostic signature for DFS. According to immunohistochemical analyses, ARG2 and CD38 expression levels were considerably under-expressed, but CKM and EHHADH expression levels were significantly overexpressed. Furthermore, The expression of ARG2 was significantly down-regulated in the late Gleason score. Finally, we found that ARG2 is lowly expressed in prostate cancer cells. Furthermore, based on the effect of ARG2 on the malignant phenotype of PCa in vitro, we also found that ARG2 may be a tumor suppressor that plays an important role in inhibiting proliferation, migration, and invasion. Conclusions: These findings suggest that ARG2 has been tentatively identified as a new target for research into how PCa develops in metabolism and for the development of innovative targeted treatments.

Identification of a 9-gene signature to enhance biochemical recurrence prediction in primary prostate cancer: A benchmarking study using ten machine learning methods and twelve patient cohorts.

Prostate cancer (PCa) is a prevalent malignancy among men worldwide, and biochemical recurrence (BCR) after radical prostatectomy (RP) is a critical turning point commonly used to guide the development of treatment strategies for primary PCa. However, the clinical parameters currently in use are inadequate for precise risk stratification and informing treatment choice. To address this issue, we conducted a study that collected transcriptomic data and clinical information from 1662 primary PCa patients across 12 multicenter cohorts globally. We leveraged 101 algorithm combinations that consisted of 10 machine learning methods to develop and validate a 9-gene signature, named BCR SCR, for predicting the risk of BCR after RP. Our results demonstrated that BCR SCR generally outperformed 102 published prognostic signatures. We further established the clinical significance of these nine genes in PCa progression at the protein level through immunohistochemistry on Tissue Microarray (TMA). Moreover, our data showed that patients with higher BCR SCR tended to have higher rates of BCR and distant metastasis after radical radiotherapy. Through drug target prediction analysis, we identified nine potential therapeutic agents for patients with high BCR SCR. In conclusion, the newly developed BCR SCR has significant translational potential in accurately stratifying the risk of patients who undergo RP, monitoring treatment courses, and developing new therapies for the disease.

FKBP10 promotes clear cell renal cell carcinoma progression and regulates sensitivity to the HIF2α blockade by facilitating LDHA phosphorylation.

Renal cell carcinoma (RCC) is one of the three major malignant tumors of the urinary system and originates from proximal tubular epithelial cells. Clear cell renal cell carcinoma (ccRCC) accounts for approximately 80% of RCC cases and is recognized as a metabolic disease driven by genetic mutations and epigenetic alterations. Through bioinformatic analysis, we found that FK506 binding protein 10 (FKBP10) may play an essential role in hypoxia and glycolysis pathways in ccRCC progression. Functionally, FKBP10 promotes the proliferation and metastasis of ccRCC in vivo and in vitro depending on its peptidyl-prolyl cis-trans isomerase (PPIase) domains. Mechanistically, FKBP10 binds directly to lactate dehydrogenase A (LDHA) through its C-terminal region, the key regulator of glycolysis, and enhances the LDHA-Y10 phosphorylation, which results in a hyperactive Warburg effect and the accumulation of histone lactylation. Moreover, HIFα negatively regulates the expression of FKBP10, and inhibition of FKBP10 enhances the antitumor effect of the HIF2α inhibitor PT2385. Therefore, our study demonstrates that FKBP10 promotes clear cell renal cell carcinoma progression and regulates sensitivity to HIF2α blockade by facilitating LDHA phosphorylation, which may be exploited for anticancer therapy.

Lamin B1: a novel biomarker in adult and pediatric adrenocortical carcinoma.

Adrenocortical carcinoma (ACC) is a malignancy with a poor prognosis and high mortality rate. A high tumor mutational burden (TMB) has been found to be associated with poor prognosis in ACC. Thus, exploring ACC biomarkers based on TMB holds significant importance for patient risk stratification. In our research, we utilized weighted gene coexpression network analysis and an assay for transposase-accessible chromatin with high-throughput sequencing to identify genes associated with TMB. Through the comprehensive analysis of various public datasets, Lamin B1 (LMNB1) was identified as a biomarker associated with a high TMB and low chromatin accessibility. Immunohistochemical staining demonstrated high expression of LMNB1 in ACC compared to noncancerous tissues. Functional enrichment analyses revealed that the function of LMNB1 is associated with cell proliferation and division. Furthermore, cell assays suggested that LMNB1 promotes tumor proliferation and invasion. In addition, mutation analysis suggested that the high expression of LMNB1 is associated with TP53 mutations. Additionally, LMNB1 was highly expressed in the vast majority of solid tumors across cancers. In our immune analysis, we discovered that the high expression of LMNB1 might suppress the infiltration of CD8+ T cells in the ACC microenvironment. In summary, LMNB1 is a predictive factor for the poor prognosis of adult and pediatric ACC. Its high expression in ACC is positively associated with high TMB and lower chromatin accessibility, and it promotes ACC cell proliferation and invasion. Therefore, LMNB1 holds promise as a novel biomarker and potential therapeutic target for ACC.

Silencing HOXC13 exerts anti-prostate cancer effects by inducing DNA damage and activating cGAS/STING/IRF3 pathway.

BACKGROUND: Advanced prostate cancer (PCa) will develop into castration-resistant prostate cancer (CRPC) and lead to poor prognosis. As the primary subtype of CRPC, CRPC-AR accounts for the major induction of PCa heterogeneity. CRPC-AR is mainly driven by 25 transcription factors (TFs), which we speculate may be the key factors driving PCa toward CRPC. Therefore, it is necessary to clarify the key regulator and its molecular mechanism mediating PCa progression. METHODS: Firstly, we downloaded transcriptomic data and clinical information from TCGA-PRAD. The characteristic gene cluster was identified by PPI clustering, GO enrichment, co-expression correlation and clinical feature analyses for 25 TFs. Then, the effects of 25 TFs expression on prognosis of PCa patients was analyzed using univariate Cox regression, and the target gene was identified. The expression properties of the target gene in PCa tissues were verified using tissue microarray. Meanwhile, the related mechanistic pathway of the target gene was mined based on its function. Next, the target gene was silenced by small interfering RNAs (siRNAs) for cellular function and mechanistic pathway validation. Finally, CIBERSORT algorithm was used to analyze the infiltration levels of 22 immune cells in PCa patients with low and high expression of target gene, and validated by assaying the expression of related immunomodulatory factor. RESULTS: We found that HOX family existed independently in 25 TFs, among which HOXC10, HOXC12 and HOXC13 had unique clinical features and the PCa patients with high HOXC13 expression had the worst prognosis. In addition, HOXC13 was highly expressed in tumor tissues and correlated with Gleason score and pathological grade. In vitro experiments demonstrated that silencing HOXC13 inhibited 22RV1 and DU145 cell function by inducing cellular DNA damage and activating cGAS/STING/IRF3 pathway. Immune infiltration analysis revealed that high HOXC13 expression suppressed infiltration of γδ T cells and plasma cells and recruited M2 macrophages. Consistent with these results, silencing HOXC13 up-regulated the transcriptional expression of IFN-ß, CCL2, CCL5 and CXCL10. CONCLUSION: HOXC13 regulates PCa progression by mediating the DNA damage-induced cGAS/STING/IRF3 pathway and remodels TIME through regulation of the transcription of the immune factors IFN-ß, CCL2, CCL5 and CXCL10.