Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

Evidence of Omics, Immune Infiltration, and Pharmacogenomics for BATF in a Pan-Cancer Cohort

Background: Cytotoxic CD8+ T-cell exhaustion is the major barrier for immunotherapy in tumors. Recent studies have reported that the basic leucine zipper activating transcription factor–like transcription factor (BATF) is responsible for countering cytotoxic CD8+ T-cell exhaustion. Nevertheless, the expression and roles of BATF in tumors have been poorly explored.

Methods: In the present study, we conducted a multi-omics analysis, including gene expression, methylation status, DNA alterations, pharmacogenomics, and survival status based on data from the Cancer Genome Atlas (TCGA) database to discern expression patterns and prognostic roles of BATF in tumors. We also explored potential roles of BATF in a pan-cancer cohort by performing immune infiltration, Gene Ontology (GO) enrichment, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In vitro assay was also performed to explore roles of BATF in tumor cells.

Results: We found that BATF was aberrantly upregulated in 27 types of tumors with respect to the corresponding normal tissues. Abnormal BATF expression in tumors predicted survival times of patients in a tissue-dependent manner. The results of GO, immune infiltration, and KEGG analysis revealed that increased BATF expression in tumors participated in modulating immune cell infiltration via immune-related pathways. BATF expression could also predict immunotherapeutic and chemotherapy responses in cancers. Moreover, knockdown of BATF suppresses tumor cell viability.

Conclusion: Our present study reports the vital roles of BATF in tumors and provides a theoretical basis for targeting BATF therapy.

Clinical and Epidemiological Study of Intracranial Tumors in Children and Identification of Diagnostic Biomarkers for the Most Common Tumor Subtype and Their Relationship with the Immune Microenvironment Through Bioinformatics Analysis

Brain tumors are the second most common pediatric malignancy and have poor prognosis. Understanding the pathogenesis of tumors at the molecular level is essential for clinical treatment. We conducted a retrospective study on the epidemiology of brain tumors in children based on clinical data obtained from a neurosurgical center. After identifying the most prevalent tumor subtype, we identified new potential diagnostic biomarkers through bioinformatics analysis of the public database. All children (0-15 years) with brain tumors diagnosed histopathologically between 2010 and 2020 at the Department of Neurosurgery, Xijing Hospital, were reviewed retrospectively for age distribution, sex predilection, native location, tumor location, symptoms, and histological grade, and identified the most common tumor subtypes. Two datasets (GSE44971 and GSE44684) were downloaded from the Gene Expression Omnibus database, whereas the GSE44971 dataset was used to screen the differentially expressed genes between normal and tumor samples. Gene ontology, disease ontology, and gene set enrichment analysis enrichment analyses were performed to investigate the underlying mechanisms of differentially expressed genes in the tumor. Combined with methylation data in the GSE44684 dataset, we further analyzed the correlation between methylation and gene expression levels. Two algorithms, LASSO and SVM-RFE, were used to select the hub genes of the tumor. The diagnostic value of the hub genes was assessed using the receiver operating characteristic (ROC) curve. Finally, we further evaluated the relationship between the hub gene and the tumor microenvironment and immune gene sets. Overall, 650 children from 18 provinces in China were included in this study. The male-to-female ratio was 1.41:1, and the number of patients reached a peak in the 10-15-year-old group (41.4%).The most common symptoms we encountered in our institute were headache and dizziness 250 (28.2%), and nausea and vomiting 228 (25.7%). The predominant location is supratentorial, with a supratentorial to infratentorial ratio of 1.74:1. Low-grade tumors (WHO I/II) constituted 60.9% of all cases and were predominant in every age group. According to basic classification, the most common tumor subtype is pilocytic astrocytoma (PA). A total of 3264 differentially expressed genes were identified in the GSE44971 dataset, which are mainly involved in the process of neural signal transduction, immunity, and some diseases. Correlation analysis indicated that the expression of 45 differentially expressed genes was negatively correlated with promoter DNA methylation. Next, we acquired five hub genes (NCKAP1L, GPR37L1, CSPG4, PPFIA4, and C8orf46) from the 45 differentially expressed genes by intersecting the LASSO and SVM-RFE models. The ROC analysis revealed that the five hub genes had good diagnostic value for patients with PA (AUC > 0.99). Furthermore, the expression of NCKAP1L was negatively correlated with immune, stromal, and estimated scores, and positively correlated with immune gene sets. This study, based on the data analysis of intracranial tumors in children in a single center over the past 10 years, reflected the clinical and epidemiological characteristics of intracranial tumors in children in Northwest China to a certain extent. PA is considered the most common subtype of intracranial tumors in children. Through bioinformatics analysis, we suggested that NCKAP1L, GPR37L1, CSPG4, PPFIA4, and C8orf46 are potential biomarkers for the diagnosis of PA.

Prognostic Role of the Ubiquitin Proteasome System in Clear Cell Renal Cell Carcinoma: A Bioinformatic Perspective

Background: Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor of the urinary system. The ubiquitin proteasome system (UPS) plays an important role in the generation, metabolism and survival of tumor. We are aimed to make a comprehensive exploration of the UPS's role in ccRCC with bioinformatic tools, which may contribute to the understanding of UPS in ccRCC, and give insight for further research. Methods: The UPS-related genes (UPSs) were collected by an integrative approach. The expression and clinical data were downloaded from TCGA database. R soft was used to perform the differentially expressed UPSs analysis, functional enrichment analysis. We also estimated prognostic value of each UPS with the help of GEPIA database. Two predicting models were constructed with the differentially expressed UPSs and prognosis-related genes, respectively. The correlations of risk score with clinical characteristics were also evaluated. Data of GSE29609 cohort were obtained from GEO database to validate the prognostic models. Results: We finally identified 91 differentially expressed UPSs, 48 prognosis related genes among them, and constructed a prognostic model with 18 UPSs successfully, the AUC was 0.760. With the help of GEPIA, we found 391 prognosis-related UPSs, accounting for 57.84% of all UPSs. Another prognostic model was constructed with 28 prognosis-related genes of them, and with a better AUC of 0.825. Additionally, our models can also stratify patients into high and low risk groups accurately in GSE29609 cohort. Similar prognostic values of our models were observed in the validated GSE29609 cohort. Conclusions: UPS is dysregulated in ccRCC. UPS related genes have significant prognostic value in ccRCC. Models constructed with UPSs are effective and applicable. An abnormal ubiquitin proteasome system should play an important role in ccRCC and be worthy of further study.