Risk analysis of enfortumab vedotin: A real-world approach based on the FAERS database.

Purpose: To analyze the risk of enfortumab vedotin (EV), a targeted therapy for advanced bladder cancer, using real-world data from the U.S. Food and Drug Administration's Federal Adverse Event Reporting System (FAERS). Methods: A retrospective pharmacovigilance analysis was conducted using FAERS data from Q1 2020 to Q1 2024. Adverse drug events (ADEs) related to EV were identified and categorized according to the System Organ Classes (SOCs) and specific events. Statistical methods, such as the proportional reporting ratio, reporting odds ratio (ROR), Bayesian confidence propagation neural network, and empirical Bayesian geometric mean were used to detect safety signals. Results: Of the 7,449,181 FAERS case reports, 1,617 EV-related ADEs were identified, including 101 preferred terms and 22 SOCs. The key SOCs included skin and subcutaneous tissue, metabolic, and nutritional disorders. Rare ADEs, such as lichenoid keratosis (n = 4; ROR 26.89), small intestinal perforation (n = 3; ROR 24.51), pigmentation disorder (n = 9; ROR 18.16), and cholangitis (n = 8; ROR 17.48), showed significant disproportionality. Conclusion: While most findings aligned with the existing data, new signs such as lichenoid keratosis and small intestinal perforation were identified. Further studies are necessary to validate these findings and emphasize the need for the clinical monitoring of EV-related ADEs.

Prognostic Significance and Immune Landscape of an Efferocytosis-Related Gene Signature in Bladder Cancer.

Bladder cancer poses a significant global health challenge, underscoring the imperative for precise prognostic instruments to advance patient care. Against the backdrop of efferocytosis's increasingly recognized role in cancer, this research endeavors to develop and authenticate a prognostic signature intricately linked to efferocytosis in bladder cancer. LASSO-COX regression analysis crafted an efferocytosis-related genes risk prognostic model, followed by the construction of a column chart. External validation sets confirmed the predictive accuracy of both the model and chart. Clinical, tumor microenvironment, drug sensitivity, and immunotherapy analyses were employed to comprehensively assess efferocytosis-related scores. The expression of TGFB3 key genes was validated via RT-PCR and western blotting. Further validation included Transwell, Wound healing, Colony formation, and EDU assays. We formulated and validated an efferocytosis-related genes risk model in bladder cancer, comprising 13 core genes. The risk model demonstrated autonomous prognostic significance in both univariate and multivariate Cox analyses. Following the multivariate analysis, we devised a nomogram. Moreover, by utilizing individual risk scores derived from this risk model, we successfully stratified patients into two discernible risk cohorts, unveiling noteworthy variances in immune infiltration profiles and responsiveness to immunotherapy. Notably, the model's key gene TGFB3 was validated through comprehensive experimental investigations, including Transwell assays for migration and invasion and Wound healing assays for motility on the T24 and BIU cell lines. This study has furnished innovative perspectives on an efferocytosis-related prognostic signature, elucidating the prognosis and immune milieu intricacies in patients with bladder cancer.

A noncanonical E3 ubiquitin ligase RNF41-mediated MYO1C stability promotes prostate cancer metastasis by inducing actin remodeling.

Prostate cancer bone metastasis is a predominant cause of death for prostate cancer (PCa) patients. However, the underlying mechanisms are poorly understood. Here, we report that high levels of RNF41 are associated with metastatic human prostate cancer. RNF41 silencing inhibits prostate cancer cell growth, cell migration and invasion in vitro and in vivo. Mechanistically, we identify that RNF41 induces K27- and K63-linked noncanonical polyubiquitination of MYO1C to enhance its stability and induce actin remodeling, which promotes PCa bone metastasis. RNF41 was significantly upregulated in metastatic prostate cancer tissues and positively associated with MYO1C expression. Furthermore, we show in intraarterial injected-bone metastasis xenograft model that targeting MYO1C stability by inhibition of RNF41 markedly suppressed PCa bone metastasis. Collectively, our findings identify RNF41 is an important regulator of prostate cancer cell growth and metastasis and targeting RNF41/MYO1C could be a valuable strategy to ameliorate prostate cancer progression and metastasis.

Machine Learning-based Nomograms for Predicting Clinical Stages of Initial Prostate Cancer: A Multicenter Retrospective Study.

OBJECTIVE: To construct and externally validate machine learning-based nomograms for predicting progression stages of initial prostate cancer (PCa) using biomarkers and clinicopathologic features. METHODS: Three hundred sixty-two inpatients diagnosed with PCa at the First Affiliated Hospital were randomly assigned to training and testing sets in a 3:7 ratio, while 136 PCa patients from People's Hospital formed the external validation set. Imaging and clinicopathologic information were collected. Optimal features distinguishing advanced prostate cancer (APC) and metastatic PCa (mPCa) were identified through logistic regression (LR). ML algorithms were employed to build and compare ML models. The best-performing algorithm established models for PCa progression stage. Models performance was evaluated using metrics, ROC curves, calibration, and decision curve analysis (DCA) in training, testing, and external validation sets. RESULTS: Following LR analyses, PSA (P = .001), maximum tumor diameter (P = .026), Gleason score (P <.001), and RNF41 (P <.001) were optimal features for predicting APC, while ALP (P <.001), PSA (P <.001), and GS score (P = .024) were for mPCa. Among ML models, the LR models exhibited superior performance. Consequently, the LR algorithm was used for the APC-risk-nomogram and mPCa-risk-nomogram construction, with AUC values of 0.848, 0.814, 0.810, and 0.940, 0.913, 0.910, in the training, testing, and external validation sets, respectively. Calibration and DCA curves affirmed nomograms' consistency and net benefits for clinical decision-making. CONCLUSION: In summary, ML-based APC-risk-nomogram and mPCa-risk-nomogram exhibit outstanding predictive performance for PCa progression stages. These nomograms can assist clinicians in finely categorizing newly diagnosed PCa patients, facilitating personalized treatment plans and prognosis assessment.

Exploring necrosis-associated mitochondrial gene signatures: revealing their role in prognosis and immunotherapy of renal clear cell carcinoma.

Mitochondrial dysfunction and necrotic apoptosis, pivotal in therapeutic strategies for multiple diseases, lack comprehensive understanding in the context of renal clear cell carcinoma (ccRCC). This study explores their potential as valuable tools for ccRCC prediction, prevention, and personalized medical care. Transcriptomic and clinical datasets were acquired from the Cancer Genome Atlas (TCGA) repository. Mitochondrial and necrosis-associated gene sets were sourced from MitoCarta3.0 and the KEGG Pathway databases, respectively. Six necrosis-related mitochondrial genes (nc-MTGs) with prognostic significance were analyzed and screened, and a prognostic model was constructed. The accuracy of the model was verified using external data (E-MTAB-1980). TISCH was used to explore nc-MTGs at the cellular level. Finally, the expression level of BH3 interacting domain death agonist (BID) in ccRCC cell line was detected by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR), and the effect of BID down-regulation on tumor cell migration was verified by transwell assays and wound-healing experiments. We established and validated a prognostic model for clear cell renal cell carcinoma (ccRCC) utilizing six necrosis-related mitochondrial genes (nc-MTGs), affirming its efficacy in evaluating tumor progression. RT-PCR results showed that BID expression was up-regulated in ccRCC tissues compared with controls and exhibited oncogenic effects. In vitro cell function experiments showed that BID may be an important factor affecting the migration of ccRCC. Our study is the first to elucidate the biological functions and prognostic significance of mitochondrial molecules related to necroptosis, providing a new way to evaluate mitochondrial therapeutics in patients with ccRCC.

Machine learning-based CT radiomics enhances bladder cancer staging predictions: A comparative study of clinical, radiomics, and combined models.

BACKGROUND: Predicting the accurate preoperative staging of bladder cancer (BLCA), which markedly affects treatment decisions and patient outcomes, using traditional clinical parameters is challenging. Nevertheless, emerging studies in radiomics, especially machine learning-based computed tomography (CT) image-based radiomics, hold promise in improving stage prediction accuracy in various tumors. However, the comparative performance and clinical utility of models for BLCA are under investigation. PURPOSE: We aimed to investigate the application value of machine learning-based CT radiomics in preoperative staging prediction by comparing the performance of clinical, radiomics, and clinical-radiomics combined models. METHODS: A retrospective cohort of 105 patients with initial BLCA was randomized into training (70%) and testing (30%) cohorts. Radiomics features were extracted from CT images using the optimal feature filter, followed by the application of the least absolute shrinkage and selection operator algorithm for optimum feature selection. Furthermore, machine learning algorithms were used to establish a radiomics model within the training cohort. Independent risk factors for muscle-invasive BLCA (MIBC) obtained by multivariate logistic regression (LR) analysis were separately used to construct a clinical model. For a clinical-radiomics fusion model, radiomics features were combined with clinical parameters. Performance was evaluated based on receiver operating characteristic curves, calibration curves, decision curve analysis (DCA), and standard performance metrics. RESULTS: Patients exhibited a significantly higher age (p = 0.029), larger tumor size (p = 0.01), and an increased neutrophil-to-lymphocyte ratio (NLR; p = 0.045) in the MIBC group than in the NMIBC group. LR analysis revealed age (p = 0.026), tumor size (p = 0.007), and NLR (p = 0.019) as significant predictors for constructing the clinical model. In the testing cohort, the radiomics model, which used an Support Vector Machine classifier, achieved the highest area under the curve (AUC) value of 0.857. The clinical-radiomics model outperformed the remaining two models, with AUC values of 0.958 and 0.893 in the training and testing cohorts, respectively. DeLong's test indicated significant differences between the three models. Calibration curves showed good agreement, and DCA confirmed the superior clinical utility of the clinical-radiomics model. CONCLUSIONS: Machine learning-based CT radiomics combined with clinical parameters was a promising approach in staging BLCA accurately, which outperformed the individual models. Integrating radiomics features with clinical information holds the potential to improve personalized treatment planning and patient outcomes in BLCA.

Development of a propionate metabolism-related gene-based molecular subtypes and scoring system for predicting prognosis in bladder cancer.

PURPOSE: Bladder cancer (BLCA) is a prevalent malignancy. Dysregulated propionate metabolism, a key cancer factor, suggests a potential target for treating metastatic cancer. However, a complete understanding of the link between propionate metabolism-related genes (PMRGs) and bladder cancer is lacking. METHODS: From the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we gathered BLCA patient data, which was classified into distinct subgroups using non-negative matrix factorization (NMF). Survival and pathway analyses were conducted between these clusters. The PMRGs model, created through univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses, was assessed for prognostic significance using Kaplan-Meier and receiver operating characteristic (ROC) curves. A comprehensive evaluation included clinical, tumor microenvironment (TME), drug sensitivity, and immunotherapy analyses. Finally, the expression of HSD17B1 essential genes was confirmed via quantitative real-time polymerase chain reaction (qRT-PCR), with further validation through Transwell, wound healing, colony-formation, and EDU assays. RESULTS: We discovered two distinct subcategories (CA and CB) within BLCA using NMF analysis, with CA demonstrating significantly better overall survival compared to CB. Additionally, six PMRGs emerged as critical factors associated with propionate metabolism and prognosis. Kaplan-Meier analysis revealed that high-risk PMRGs were correlated with a poorer prognosis in BLCA patients. Moreover, significant differences were observed between the two groups in terms of infiltrated immune cells, immune checkpoint expression, TME scores, and drug sensitivity. Notably, we found that suppressing HSD17B1 gene expression inhibited the invasion of bladder cancer cells. CONCLUSION: Our study proposes molecular subtypes and a PMRG-based score as promising prognostic indicators in BLCA. Additionally, cellular experiments underscore the pivotal role of HSD17B1 in bladder cancer metastasis and invasion, suggesting its potential as a novel therapeutic target.

The enigmatic interplay of immune cells and abnormal spermatozoa through Mendelian randomization.

PURPOSE: Abnormal spermatozoa significantly impact reproductive health, affecting fertility rates, potentially prolonging conception time, and increasing the risk of miscarriages. This study employs Mendelian randomization to explore their potential link with immune cells, aiming to reveal their potential causal association and wider implications for reproductive health. METHODS: We conducted forward and reverse Mendelian randomization analyses to explore the potential causal connection between 731 immune cell signatures and abnormal spermatozoa. Using publicly available genetic data, we investigated various immune signatures such as median fluorescence intensities (MFI), relative cell (RC), absolute cell (AC), and morphological parameters (MP). Robustness was ensured through comprehensive sensitivity analyses assessing consistency, heterogeneity, and potential horizontal pleiotropy. The MR study produced a statistically significant p-value of .0000684, Bonferroni-corrected for the 731 exposures. RESULTS: The Mendelian randomization analysis revealed strong indications of a reciprocal relationship between immune cell pathways and sperm integrity. When examining immune cell exposure, a potential causal link with abnormal sperm was observed in 35 different types of immune cells. Conversely, the reverse Mendelian randomization results indicated that abnormal sperm might causally affect 39 types of immune cells. These outcomes suggest a potential mutual influence between alterations in immune cell functionality and the quality of spermatozoa. CONCLUSION: This study highlights the close link between immune responses and sperm development, suggesting implications for reproductive health and immune therapies. Further research may offer crucial insights into male fertility and immune disorders.

Trends in suicide mortality among prostate cancer survivors in the United States, 1975-2019.

BACKGROUND: Suicide was an important cause of death in prostate cancer. This study intended to investigate trends in suicide mortality among prostate cancer (PCa) survivors from 1975 to 2019 in the United States. METHOD: We identified PCa survivors from the Surveillance, Epidemiology, and End Results (SEER) program from January 1975 to December 2019. Standardized mortality rate (SMR) was calculated d to assess the relative risk of suicide in PCa survivors compared with the general men population. Poisson regression model was performed to test for trend of SMRs. The cumulative mortality rate of suicide was calculated to assess the clinical burden of suicide mortality. RESULTS: 7108 (0.2%) cases were death from suicide cause, and 2,308,923(65.04%%) cases recorded as dying from non-suicidal causes. Overall, a slightly higher suicide mortality rate among PCa survivors was observed compared with general male population (SMR: 1.15, 95%CI: 1.09-1.2). The suicide mortality rate declined significantly relative to the general population by the calendar year of diagnosis, from an SMR of 1.74(95%CI: 1.17-2.51) in 1975-1979 to 0.99(0.89-1.1) in 2015-2019 (Ptrend < 0.001). PCa survivors with aged over 84 years, black and other races, registered in registrations (including Utah, New Mexico, and Hawaii) failed to observe a decrease in suicide mortality (Ptrend > 0.05). The cumulative suicide mortality during 1975-1994 was distinctly higher than in 1995-2019(P < 0.001). CONCLUSION: The trend in suicide mortality declined significantly from 1975 to 2019 among PCa survivors compared with the general male population in the United States. Notably, part of PCa survivors had no improvement in suicide mortality, and additional studies in the future were needed to explore it.

Causal relationship between prostate cancer and 12 types of cancers: multivariable and bidirectional Mendelian randomization analyses.

BACKGROUND: Previous observational studies have shown an association between certain cancers and the subsequent risk of prostate cancer (PCa). However, the causal relationship between these cancers and PCa is still unclear. This study aimed to investigate the causal relationship between 12 common cancers and the risk of PCa. METHODS: We employed genome-wide association studies (GWAS) to perform forward and reverse Mendelian randomization (MR) within two-sample frameworks. Furthermore, we conducted multivariable MR analyses to investigate the relationships between different types of cancer. In addition, multiple sensitivity analysis methods were employed to assess the robustness of our findings. RESULTS: Our univariable MR analysis showed that genetically predicted hematological cancer was associated with a reduced risk of PCa (OR: 0.911, 95% CI 0.89-0.922, P = 0.03). Furthermore, MR analysis demonstrates that genetically predicted occurrence of thyroid gland and endocrine gland cancer also raised the risk of PCa (all P < 0.05). Multivariable analysis showed that thyroid gland cancer exhibited a higher incidence of PCa (OR: 1.12, 95% CI: 1.08-1.16, P = 0.008). In the reverse MR analysis, we found no significant inverse causal associations between PCa and 12 types of cancers. CONCLUSION: In summary, this study provided insights into the causal relationships between various types of cancer and PCa. Hematological cancer was suggested to associate with a lower risk of PCa, while thyroid gland cancer and endocrine gland cancer might increase the risk. These findings contribute to the understanding of genetic factors related to PCa and its potential associations with other cancers.

Design, synthesis, and biological activity evaluation of new tankyrase-2 directed inhibitors.

A new series of flavonoids and quinolone derivatives were designed, synthesized and, evaluated for their biological activity. Among them, compound 14e showed better inhibition potency against TNKS2 in comparison with G007-LK, one of the most potent preclinical stage TNKS inhibitor. Molecular docking results showed that 14e occupied both the adenosine and nicotinamide pockets and formed a hydrogen bond with Met1054 of TNKS2. This study provides a lead for the design and discovery of potent and selective TNKS2 inhibitors.

CPT1A mediates chemoresistance in human hypopharyngeal squamous cell carcinoma via ATG16L1-dependent cellular autophagy.

Hypopharyngeal squamous cell carcinoma (HSCC) is a highly aggressive malignancy that constitutes approximately 95% of all hypopharyngeal carcinomas, and it carries a poor prognosis. The primary factor influencing the efficacy of anti-cancer drugs for this type of carcinoma is chemoresistance. Carnitine palmitoyltransferase 1A (CPT1A) has been associated with tumor progression in various cancers, including breast, gastric, lung, and prostate cancer. The inhibition or depletion of CPT1A can lead to apoptosis, curbing cancer cell proliferation and chemoresistance. However, the role of CPT1A in HSCC is not yet fully understood. In this study, we discovered that CPT1A is highly expressed in HSCC and is associated with an advanced T-stage and a poor 5-year survival rate among patients. Furthermore, the overexpression of CPT1A contributes to HSCC chemoresistance. Mechanistically, CPT1A can interact with the autophagy-related protein ATG16L1 and stimulate the succinylation of ATG16L1, which in turn drives autophagosome formation and autophagy. We also found that treatment with 3-methyladenine (3-MA) can reduce cisplatin resistance in HSCC cells that overexpress CPT1A. Our findings also showed that a CPT1A inhibitor significantly enhances cisplatin sensitivity both in vitro and in vivo. This study is the first to suggest that CPT1A has a regulatory role in autophagy and is linked to poor prognosis in HSCC patients. It presents novel insights into the roles of CPT1A in tumorigenesis and proposes that CPT1A could be a potential therapeutic target for HSCC treatment.

Deciphering the immunological and prognostic features of bladder cancer through platinum-resistance-related genes analysis and identifying potential therapeutic target P4HB.

Objectives: To identify the molecular subtypes and develop a scoring system for the tumor immune microenvironment (TIME) and prognostic features of bladder cancer (BLCA) based on the platinum-resistance-related (PRR) genes analysis while identifying P4HB as a potential therapeutic target. Methods: In this study, we analyzed gene expression data and clinical information of 594 BLCA samples. We used unsupervised clustering to identify molecular subtypes based on the expression levels of PRR genes. Functional and pathway enrichment analyses were performed to understand the biological activities of these subtypes. We also assessed the TIME and developed a prognostic signature and scoring system. Moreover, we analyzed the efficacy of immune checkpoint inhibitors. Then we conducted real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) experiments to detect the expression level of prolyl 4-hydroxylase subunit beta (P4HB) in BLCA cell lines. Transfection of small interference ribonucleic acid (siRNA) was performed in 5637 and EJ cells to knock down P4HB, and the impact of P4HB on cellular functions was evaluated through wound-healing and transwell assays. Finally, siRNA transfection of P4HB was performed in the cisplatin-resistant T24 cell to assess its impact on the sensitivity of BLCA to platinum-based chemotherapy drugs. Results: In a cohort of 594 BLCA samples (TCGA-BLCA, n=406; GSE13507, n=188), 846 PRR-associated genes were identified by intersecting BLCA expression data from TCGA and GEO databases with the PRR genes from the HGSOC-Platinum database. Univariate Cox regression analysis revealed 264 PRR genes linked to BLCA prognosis. We identified three molecular subtypes (Cluster A-C) and the PRR scoring system based on PRR genes. Cluster C exhibited a better prognosis and lower immune cell infiltration compared to the other Clusters A and B. The high PRR score group was significantly associated with an immunosuppressive tumor microenvironment, poor clinical-pathological features, and a poor prognosis. Furthermore, the high PRR group showed higher expression of immune checkpoint molecules and a poorer response to immune checkpoint inhibitors than the low PRR group. The key PRR gene P4HB was highly expressed in BLCA cell lines, and cellular functional experiments in vitro indicate that P4HB may be an important factor influencing BLCA migration and invasion. Conclusion: Our study demonstrates that the PRR signatures are significantly associated with clinical-pathological features, the TIME, and prognostic features. The key PRR gene, P4HB, s a biomarker for the individualized treatment of BLCA patients.

Prognostic and tumor microenvironmental feature of clear cell renal cell carcinoma revealed by m6A and lactylation modification-related genes.

Background: Both lactylation and m6A modification have important implications for the development of clear cell renal cell carcinoma (ccRCC), and we aimed to use crosstalk genes of both to reveal the prognostic and immunological features of ccRCC. Methods: Our first step was to look for lactylation-related genes that differed between normal and tumor tissues, and then by correlation analysis, we found the genes associated with M6A. Following that, ccRCC subtypes will be identified and risk models will be constructed to compare the prognosis and tumor microenvironment among different subgroups. A nomogram was constructed to predict the prognosis of ccRCC, and in vitro, experiments were conducted to validate the expression and function of key genes. Results: We screened 100 crosstalk genes and identified 2 ccRCC subtypes. A total of 11 prognostic genes were screened for building a risk model. we observed higher immune scores, elevated tumor mutational burden, and microsatellite instability scores in the high-risk group. Therefore, individuals classified as high-risk would derive greater benefits from immunotherapy. The nomogram's ability to predict overall survival with a 1-year AUC of 0.863 demonstrates its significant practical utility. In addition, HIBCH was identified as a potential therapeutic target and its expression and function were verified by in vitro experiments. Conclusion: In addition to developing a precise prognostic nomogram for patients with ccRCC, our study also discovered the potential of HIBCH as a biomarker for the disease.

Value of the application of computed tomography-based radiomics for preoperative prediction of unfavorable pathology in initial bladder cancer.

OBJECTIVES: To construct and validate unfavorable pathology (UFP) prediction models for patients with the first diagnosis of bladder cancer (initial BLCA) and to compare the comprehensive predictive performance of these models. MATERIALS AND METHODS: A total of 105 patients with initial BLCA were included and randomly enrolled into the training and testing cohorts in a 7:3 ratio. The clinical model was constructed using independent UFP-risk factors determined by multivariate logistic regression (LR) analysis in the training cohort. Radiomics features were extracted from manually segmented regions of interest in computed tomography (CT) images. The optimal CT-based radiomics features to predict UFP were determined by the optimal feature filter and the least absolute shrinkage and selection operator algorithm. The radiomics model consist with the optimal features was constructed by the best of the six machine learning filters. The clinic-radiomics model combined the clinical and radiomics models via LR. The area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive value, calibration curve and decision curve analysis were used to evaluate the predictive performance of the models. RESULTS: Patients in the UFP group had a significantly older age (69.61 vs. 63.93 years, p = 0.034), lager tumor size (45.7% vs. 11.1%, p = 0.002) and higher neutrophil to lymphocyte ratio (NLR; 2.76 vs. 2.33, p = 0.017) than favorable pathologic group in the training cohort. Tumor size (OR, 6.02; 95% CI, 1.50-24.10; p = 0.011) and NLR (OR, 1.50; 95% CI, 1.05-2.16; p = 0.026) were identified as independent predictive factors for UFP, and the clinical model was constructed using these factors. The LR classifier with the best AUC (0.817, the testing cohorts) was used to construct the radiomics model based on the optimal radiomics features. Finally, the clinic-radiomics model was developed by combining the clinical and radiomics models using LR. After comparison, the clinic-radiomics model had the best performance in comprehensive predictive efficacy (accuracy = 0.750, AUC = 0.817, the testing cohorts) and clinical net benefit among UFP-prediction models, while the clinical model (accuracy = 0.625, AUC = 0.742, the testing cohorts) was the worst. CONCLUSION: Our study demonstrates that the clinic-radiomics model exhibits the best predictive efficacy and clinical net benefit for predicting UFP in initial BLCA compared with the clinical and radiomics model. The integration of radiomics features significantly improves the comprehensive performance of the clinical model.

Based on disulfidptosis, revealing the prognostic and immunological characteristics of renal cell carcinoma with tumor thrombus of vena cava and identifying potential therapeutic target AJAP1.

BACKGROUND: Patients with clear cell renal cell carcinoma (ccRCC) with venous tumor thrombus have a poor prognosis, high surgical risk, and lack of targeted therapeutic agents. METHODS: Genes with consistent differential expression trends in tumor tissues and VTT groups were first screened, and then differential genes associated with disulfidptosis were found by correlation analysis. Subsequently, identifying ccRCC subtypes and constructing risk models to compare the differences in prognosis and the tumor microenvironment in different subgroups. Finally, constructing a nomogram to predict the prognosis of ccRCC and validate key gene expression levels in cells and tissues. RESULTS: We screened 35 differential genes related to disulfidptosis and identified 4 ccRCC subtypes. Risk models were constructed based on the 13 genes, and the high-risk group had a higher abundance of immune cell infiltration, tumor mutational load, and microsatellite instability scores, predicting high sensitivity to immunotherapy. The 1-year AUC = 0.869 for predicting OS by nomogram has a high application value. The expression level of the key gene AJAP1 was low in both tumor cell lines and cancer tissues. CONCLUSIONS: Our study not only constructed an accurate prognostic nomogram for ccRCC patients but also identified an AJAP1 biomarker as a potential biomarker for the disease.

Targeting histone demethylases as a potential cancer therapy (Review).

Post­translational modifications of histones by histone demethylases have an important role in the regulation of gene transcription and are implicated in cancers. Recently, the family of lysine (K)­specific demethylase (KDM) proteins, referring to histone demethylases that dynamically regulate histone methylation, were indicated to be involved in various pathways related to cancer development. To date, numerous studies have been conducted to explore the effects of KDMs on cancer growth, metastasis and drug resistance, and a majority of KDMs have been indicated to be oncogenes in both leukemia and solid tumors. In addition, certain KDM inhibitors have been developed and have become the subject of clinical trials to explore their safety and efficacy in cancer therapy. However, most of them focus on hematopoietic malignancy. This review summarizes the effects of KDMs on tumor growth, drug resistance and the current status of KDM inhibitors in clinical trials.