Prognosis, Immune Microenvironment Infiltration and Immunotherapy Response in Clear Cell Renal Cell Carcinoma Based on Cuproptosis-related Immune Checkpoint Gene Signature.

Background: Immune checkpoint genes (ICGs), which are the cornerstone of immunotherapy, influence the incidence and progression of clear cell renal cell carcinoma (ccRCC). It is important to note that there is not much data in the literature to determine how cuproptosis and antitumor immunity are related. Methods: On the basis of The Cancer Genome Atlas ccRCC dataset (n=526), cuproptosis-related ICGs (CICGs) were used to identify distinct molecular subtypes. Using the Cox regression method, a risk signature was constructed and externally validated using the ICGC (n=91) and primary ccRCC subgroups of GSE22541 (n=24). The molecular and immune characteristics and efficacy of immunotherapy in the subgroups defined by the risk score were investigated. Four risk CICGs were verified through in vitro experiment. Results: We identified two unique molecular subgroups with substantial prognostic differences based on 17 CICGs. The two subtypes clearly differ in terms of the tumor immune microenvironment (TME). A predictive risk signature (CD276, HLA-E, LGALS9, and TNFRSF18) was created and externally confirmed, and their expressions were validated by realtime PCR. The multivariate Cox regression analysis demonstrated that this signature could independently predict survival. Thus, a credible nomogram incorporating the signature, age, stage, and grade was constructed, and discrimination was confirmed using the C-index, calibration curve, and decision curve analyses. The underlying implications for immune checkpoint inhibitors, the landscape of the TME, and single-cell level localization are depicted. In addition, its accuracy in forecasting actual immunotherapeutic results has been proven (CheckMate025 and TCGA-SKCM cohorts). The sensitivity of the two risk groups to various drug-targeted therapy methods was analyzed. Conclusions: The data provided here provide the groundwork for creating customized therapeutic options for individuals with ccRCC. The findings also suggested that researching the cuproptosis-based pathway might improve ccRCC patient better prognosis, development of anti-tumor immunity, and therapeutic strategies for immunotherapy.

Long noncoding RNA MIAT regulates TP53 ubiquitination and expedites prostate adenocarcinoma progression by recruiting TBL1X.

Despite recent advances in cancer immunotherapy, their efficacy for treating patients with prostate adenocarcinoma (PRAD) is low due to complex immune evasion mechanisms. However, the function of long non-coding RNA (lncRNAs) in immune evasion has not been fully clarified. This study aimed to expound the role of myocardial infarction-associated transcript (MIAT), a lncRNA significantly upregulated in three PRAD-associated datasets, in immune evasion and try to reveal the potential mechanism. MIAT was highly expressed in PRAD tissues and predicted poor prognosis, and suppression of MIAT inhibited the malignant biological behavior of PRAD cells. Moreover, the depletion of MIAT promoted the immune response of CD8+ T cells and hampered the immune evasion of PRAD cells. In addition, MIAT downregulated TP53 protein expression by recruiting transducin beta-like protein 1X (TBL1X) for ubiquitination modification. Silencing of TP53 or overexpression of TBL1X was enough to abate the tumor suppressive effects of MIAT knockdown in vitro and in vivo. Our results provide evidence for a novel regulation mechanism of CD8+ T cells in PRAD and MIAT may serve as a potential therapeutic target in PRAD.

Development and Validation of a Nomogram for Predicting Blood Pressure Change Failure in Patients with Pheochromocytoma and Concomitant Hypertension after Adrenalectomy.

(1) Background: Pheochromocytoma is a common cause of secondary hypertension, which is considered curable; nevertheless, some patients still suffer from hypertension after adrenalectomy. Therefore, we developed and validated a nomogram for predicting blood pressure change failure in patients with pheochromocytoma and concomitant hypertension after adrenalectomy. (2) Methods: The development cohort of this study consisted of 259 patients with pheochromocytoma who underwent adrenalectomy at our center between 1 January 2007 and 31 December 2018. Each patient's clinicopathologic data were recorded. LASSO (the least absolute shrinkage and selection operator) regression was used to reduce and select the features of the data. Furthermore, we used multivariate logistic regression analysis to develop the prediction model. An independent cohort of 110 consecutive patients from 1 January 2019 to 31 December 2021 was used for validation. The performance of this nomogram was assessed with regard to discrimination, calibration, and clinical usefulness. (3) Results: 40.9% and 46.4% of patients experienced blood pressure change failure in the development and validation cohorts of this study, respectively. We found that older patients with a longer duration of hypertension and concomitant cardiovascular events were more likely to suffer from blood pressure change failure. In the validation cohort, the model manifested great discrimination with an AUROC (area under the receiver operating characteristic) of 0.996 (p < 0.001) and good calibration (unreliability test, p = 0.359). Decision curve analysis demonstrated that the model was clinically useful. (4) Conclusions: This study presented a reliable nomogram that facilitated individualized preoperative prediction of blood pressure change failure after adrenalectomy in patients with pheochromocytoma, which may help decision-making in perioperative treatment and follow-up strategies.

Establishment of Novel Prostate Cancer Risk Subtypes and A Twelve-Gene Prognostic Model.

Prostate cancer (PCa) is the most common malignancy among men worldwide. However, its complex heterogeneity makes treatment challenging. In this study, we aimed to identify PCa subtypes and a gene signature associated with PCa prognosis. In particular, nine PCa-related pathways were evaluated in patients with PCa by a single-sample gene set enrichment analysis (ssGSEA) and an unsupervised clustering analysis (i.e., consensus clustering). We identified three subtypes with differences in prognosis (Risk_H, Risk_M, and Risk_L). Differences in the proliferation status, frequencies of known subtypes, tumor purity, immune cell composition, and genomic and transcriptomic profiles among the three subtypes were explored based on The Cancer Genome Atlas database. Our results clearly revealed that the Risk_H subtype was associated with the worst prognosis. By a weighted correlation network analysis of genes related to the Risk_H subtype and least absolute shrinkage and selection operator, we developed a 12-gene risk-predicting model. We further validated its accuracy using three public datasets. Effective drugs for high-risk patients identified using the model were predicted. The novel PCa subtypes and prognostic model developed in this study may improve clinical decision-making.

Establishment of Novel DNA Methylation-Based Prostate Cancer Subtypes and a Risk-Predicting Eight-Gene Signature.

Prostate cancer (PCa) is the most common malignant tumor affecting males worldwide. The substantial heterogeneity in PCa presents a major challenge with respect to molecular analyses, patient stratification, and treatment. Least absolute shrinkage and selection operator was used to select eight risk-CpG sites. Using an unsupervised clustering analysis, called consensus clustering, we found that patients with PCa could be divided into two subtypes (Methylation_H and Methylation_L) based on the DNA methylation status at these CpG sites. Differences in the epigenome, genome, transcriptome, disease status, immune cell composition, and function between the identified subtypes were explored using The Cancer Genome Atlas database. This analysis clearly revealed the risk characteristics of the Methylation_H subtype. Using a weighted correlation network analysis to select risk-related genes and least absolute shrinkage and selection operator, we constructed a prediction signature for prognosis based on the subtype classification. We further validated its effectiveness using four public datasets. The two novel PCa subtypes and risk predictive signature developed in this study may be effective indicators of prognosis.