Abstract The m6A modification is a methylation of the adenosine N6 position, is a kind of reversible post-transcriptional modification of RNA. Moreover, this process has been shown to be associated with multiple tumor progression. Prostate cancer is the second most common cancer in men after lung cancer, and is closely related to m6A. However, previous studies were often limited to a few m6A related genes, and failed to fully understand the relationship between prostate cancer and m6A.Therefore, we used the CIBERSORT and ESTIMATE algorithms to define three m6A clusters based on the expression of m6A-related genes in 1099 PC patients. Subsequently, we identified three different m6A gene clusters based on the overlap of differentially expressed genes (DEGs) within the m6A clusters. In addition, by principal component analysis (PCA) was performed to calculate the m6A scores. The results showed that patients with high m6A scores had longer survival time and those with low scores had shorter survival time. Furthermore, the m6A score was negatively correlated with the tumor mutation burden (TMB) value of PC. Patients with higher m6A scores showed clinical benefit and advantage of immunotherapy, indicating that the m6A score is an accurate and valid predictor to assess the effect of immunotherapy. Overall, our study presents a new method for reference that can provide guidance for current immunotherapy and predict patient prognosis to help physicians make judgments about patient disease and treatment modalities, and can guide current research on immunotherapy strategies for PC.
Abstract Long non-coding RNA (lncRNA) plays an important role in tumor progression. Numerous studies show that lncRNA is strongly associated with prostate cancer progression. Our study confirmed that lncRNA HCG18 was highly expressed in prostate cancer (PC) and correlated with tumor progression in databases and cell lines. Western blot, RNA Pull-down, dual luciferase assay and rescue assays verified the correlation among lncRNA HCG18, miR-512-3p and hexokinase-2(HK-2). In general, the results showed that lncRNA HCG18 accelerated cell proliferation, migration, and invasion of PC via up-regulating HK-2 through sponging miR-512-3p, which provided a new direction for the diagnosis and treatment of PC.
Introduction Immunogenic cell death (ICD) is a form of regulated cell death that activates an adaptive immune response in an immunocompetent host and is particularly sensitive to antigens from tumor cells. Kidney clear cell carcinoma (KIRC) is an immunogenic tumor with extensive tumor heterogeneity. However, no reliable predictive biomarkers have been identified to reflect the immune microenvironment and therapeutic response of KIRC. Methods Therefore, we used the CIBERSORT and ESTIMATE algorithms to define three ICD clusters based on the expression of ICD-related genes in 661 KIRC patients. Subsequently, we identified three different ICD gene clusters based on the overlap of differentially expressed genes (DEGs) within the ICD clusters. In addition, principal component analysis (PCA) was performed to calculate the ICD scores. Results The results showed that patients with reduced ICD scores had a poorer prognosis and reduced transcript levels of immune checkpoint genes regulated with T cell differentiation. Furthermore, the ICD score was negatively correlated with the tumor mutation burden (TMB) value of KICD. patients with higher ICD scores showed clinical benefits and advantages of immunotherapy, indicating that the ICD score is an accurate and valid predictor to assess the effect of immunotherapy. Discussion Overall, our study presents a comprehensive KICD immune-related ICD landscape that can provide guidance for current immunotherapy and predict patient prognosis to help physicians make judgments about the patient’s disease and treatment modalities, and can guide current research on immunotherapy strategies for KICD.
This study aims to evaluate the predictive value of the renal resistive index (RRI) and β2-microglobulin (β2-MG) for acute kidney injury (AKI) in urosepsis patients and to develop a clinical prediction model for AKI risk.
Abstract Prostate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in males. Studies showed that genetic factors are responsible for the occurrences of both diseases. However, the genetic association between them is still unclear. Gene Expression Omnibus (GEO) database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were utilized to find pathways DEGs enriched. The STRING database can provide a protein–protein interaction (PPI) network, and find hub genes in PPI network. R software was used to analyze the clinical value of hub genes in PCa. Finally, the function of these hub genes was tested in different databases, clinical samples, and PCa cells. Fifteen up-regulated and forty-five down-regulated genes were found from GEO database. Seven hub genes were found in PPI network. The expression and clinical value of hub genes were analyzed by The Cancer Genome Atlas (TCGA) data. Except CXCR4 , all hub genes expressed differently between tumor and normal samples. Exclude CXCR4 , other hub genes have diagnostic value in predicting PCa and their mutations can cause PCa. The expression of CSRP1 , MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect disease-free survival (DFS). Same results reflected in different databases. The expression and function of MYC , MYL9 , and SNAI2 , were validated in clinical samples and PCa cells. In conclusion, seven hub genes among sixty DEGs may be achievable targets for predicting which BPH patients may later develop PCa and they can influence the progression of PCa.
Abstract Prostate cancer (PCa) and benign prostate hyperplasia (BPH) are commonly encountered diseases in males. Studies showed that genetic factors are responsible for the occurrences of both diseases. However, the genetic association between them is still unclear. Gene Expression Omnibus (GEO) database can help determine the differentially expressed genes (DEGs) between BPH and PCa. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were utilized to find pathways DEGs enriched. The STRING database can provide a protein–protein interaction (PPI) network, and find hub genes in PPI network. GEPIA can be used to analyze expression and survival data for hub genes. R software was used to progress regression analysis. Finally, the results were tested in other databases, clinical samples and PCa cells. Fifteen up-regulated and forty-five down-regulated genes were found from GEO database. Seven hub genes were found in PPI network. The hub gene expression was tested on The Cancer Genome Atlas (TCGA) data. Except CXCR4, all hub genes expressed differently between tumor and normal samples. Exclude CXCR4, other hub genes have diagnostic value in predicting PCa and their mutations can cause PCa. The expression of CSRP1, MYL9 and SNAI2 changed in different tumor stage. CSRP1 and MYH11 could affect disease-free survival (DFS). Same results reflected in different databases. The expression and function of MYC, MYL9, and SNAI2, were validated in clinical samples and PCa cells. In conclusion, seven hub genes among sixty DEGs can be achievable targets for predicting which BPH patients may later develop PCa.
Background Prostate cancer (PCa) is a common malignancy occurring in men. As both an endocrine and gonadal organ, prostate is closely correlated with androgen. So, androgen deprivation therapy (ADT) is effective for treating PCa. However, patients will develop castration-resistant prostate cancer (CRPC) stage after ADT. Many other treatments for CRPC exist, including chemotherapy. Vinblastine, a chemotherapeutic drug, is used to treat CRPC. However, patients will develop resistance to vinblastine. Genetic alterations have been speculated to play a critical role in CRPC resistance to vinblastine; however, its mechanism remains unclear. Methods Various databases, such as Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Chinese Prostate Cancer Genome and Epigenome Atlas (CPGEA), were used to collect the RNA-sequence data of PCa and CRPC patients and vinblastine-resistant PCa cells. Using online tools, Metascape and TIMER, the pathways and immune infiltration associated with vinblastine resistance-related genes in PCa were analyzed. The function of these genes was verified in clinical samples and CRPC cells. Results Using GSE81277 dataset, we collected the RNA-sequence data of vinblastine sensitive and resistant LNCaP cells and found nine genes ( CDC20 , LRRFIP1 , CCNB1 , GPSM2 , AURKA , EBLN2 , CCDC150 , CENPA and TROAP ) that correlated with vinblastine resistance. Furthermore, CCNB1 , GPSM2 and AURKA were differently expressed between normal prostate and PCa tissues, even influencing PCa progression. The GSE35988 dataset revealed that CCNB1 and AURKA were upregulated in PCa and CRPC samples. Various genes were also found to affect the survival status of PCa patients based on TCGA. These genes were also related to immune cell infiltration. Finally, we verified the function of CCNB1 and AURKA and observed that they were upregulated in PCa and CRPC clinical samples and increased the sensitivity of CRPC cells to vinblastine. Conclusion CCNB1 and AURKA are central to CRPC resistance to vinblastine and affect PCa progression.
Background: Type 2 diabetes mellitus (T2DM) significantly contributes to sepsis, with patients suffering from both conditions exhibiting greater severity and higher mortality rates compared to those with sepsis alone. Elderly individuals in the intensive care unit (ICU) are particularly prone to these comorbidities. A nomogram prediction model was developed to accurately assess prognosis and guide treatment for elderly patients with sepsis and T2DM. Methods: Data from 1489 patients with sepsis and T2DM in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database were analyzed and categorized into 28-days survival ( n = 1156) and 28-days death groups ( n = 333). The dataset’s clinical characteristics were employed to create a nomogram predicting 28-days mortality in elderly ICU patients with sepsis and T2DM. The least absolute shrinkage and selection operator (LASSO) regression identified candidate predictors, followed by a multivariate logistic regression analysis incorporating variables with p < .05 into the final model. A nomogram was then constructed using these significant risk predictors. The model’s discriminatory power was evaluated through a receiver operating curve (ROC) and the area under the curve (AUC). Additionally, model performance was assessed using a calibration plot and the Hosmer-Lemeshow goodness-of-fit test (HL test), and clinical utility was examined via decision curve analysis (DCA). Results: Risk factors incorporated into the nomogram included age, ICU length of stay, mean blood pressure (MBP), metastatic solid tumor, Sequential Organ Failure Assessment (SOFA) score, Logistic Organ Dysfunction System (LODS) score, blood urea nitrogen (BUN), and vasopressor use. The predictive model demonstrated robust discrimination, with an AUC of 0.802 (95% CI 0.768–0.835) in the training dataset and 0.753 (95% CI 0.697–0.809) in the validation set. Calibration was confirmed with the HL test ( p > .05), and DCA indicated clinical usefulness. Conclusion: This new nomogram serves as a practical tool for predicting 28-days mortality among elderly ICU patients with sepsis and T2DM. Optimizing treatment strategies based on this model could enhance 28-days survival rates for these patients.
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