Integrative Analysis of a Novel Eleven-Small Nucleolar RNA Prognostic Signature in Patients With Lower Grade Glioma

HSPA4 regulated glioma progression via activation of AKT signaling pathway

Glioma is still an incurable disease with high invasiveness. Heat shock 70 kDa protein 4 (HSPA4) is a member of the HSP110 family, and is associated with the development and progression of various cancers. In the current study, we assessed the expression of HSPA4 in clinical samples, and found that HSPA4 was up-regulated in glioma tissues and correlated with tumor recurrence and grade. Survival analyses demonstrated that glioma patients with high HSPA4 expression had lower overall survival and disease-free survival times. In vitro knockdown of HSPA4 inhibited glioma cell proliferation, mediated cell cycle arrest at G2 phase and apoptosis, and reduced the migration ability. In vivo, the growth of HSPA4-knockdown xenografts was markedly suppressed compared to the tumors formed by HSPA4-positive control cells. Additionally, Gene set enrichment analyses disclosed that HSPA4 was associated with the PI3K/Akt signaling pathway. The regulatory effect of the AKT activator SC79 on cell proliferation and apoptosis was suppressed by HSPA4 knockdown, indicating that HSPA4 is capable of promoting glioma development. In summary, these data showed that HSPA4 is likely to play a pivotal role in the progression of glioma, and consequently may be a promising therapeutic target for glioma therapy.

SNORD17-mediated KAT6B mRNA 2'-O-methylation regulates vasculogenic mimicry in glioblastoma cells

Glioblastoma (GBM) is a primary tumor in the intracranial compartment. Vasculogenic mimicry (VM) is a process in which a pipeline of tumor cells that provide blood support to carcinogenic cells is formed, and studying VM could provide a new strategy for clinical targeted treatment of GBM. In the present study, we found that SNORD17 and ZNF384 were significantly upregulated and promoted VM in GBM, whereas KAT6B was downregulated and inhibited VM in GBM. RTL-P assays were performed to verify the 2'-O-methylation of KAT6B by SNORD17; IP assays were used to detect the acetylation of ZNF384 by KAT6B. In addition, the binding of ZNF384 to the promoter regions of VEGFR2 and VE-cadherin promoted transcription, as validated by chromatin immunoprecipitation and luciferase reporter assays. And finally, knockdown of SNORD17 and ZNF384 combined with KAT6B overexpression effectively reduced the xenograft tumor size, prolonged the survival time of nude mice and reduced the number of VM channels. This study reveals a novel mechanism of the SNORD17/KAT6B/ZNF384 axis in modulating VM development in GBM that may provide a new goal for the comprehensive treatment of GBM.

Identification of ferroptosis-related genes as potential diagnostic biomarkers for diabetic nephropathy based on bioinformatics

Objective: This study investigated to probe ferroptosis-related diagnostic biomarkers and underlying molecular mechanisms in Diabetic nephropathy (DN). Methods: GSE30122 and GSE1009 from GEO database were used as training and verification sets, respectively, to screen differentially expressed ferroptosis-related genes (FRGs). These genes were further analyzed using GO, KEGG, and GSEA methods, and screened with PPI, LASSO, and SVM-RFE to identify ferroptosis-related diagnostic biomarkers for DN. A diagnostic model was established using the Glm function and verified with ROC curve. The relationship between these biomarkers and immune cell was analyzed, and qRT-PCR and Western blot were used to detect the expression of these biomarkers in kidney tissues and identify the effect of TP53 on DN development. Results: Fifty one differentially expressed FRGs were enriched in bioprocesses such as p53 signaling pathway, oxidative stress and chemical stress response, and mTOR signaling pathway. TP53, RB1, NF2, RRM2, PRDX1, and CDC25A were identified as ferroptosis-related diagnostic biomarkers for DN. TP53 showed the most differential expression. ROC analysis showed that AUC values of TP53, RB1, NF2, RRM2, PRDX1, and CDC25A were 0.751, 0.705, 0.725, 0.882, 0.691, and 0.675, respectively. The AUC value of DN diagnosis model was 0.939 in training set and 1.000 in verification set. qRT-PCR results confirmed significant differences in these six biomarkers between DN and normal kidney tissue (p < 0.05), and correlation analysis showed that five biomarkers were significantly correlated with infiltrating immune cells (p < 0.05). Furthermore, western blots showed that TP53 promotes apoptosis through PI3K-AKT signaling in DN. Conclusion: TP53, RB1, NF2, RRM2, PRDX1, and CDC25A have potential as diagnostic biomarkers for DN. The diagnostic model containing the above six biomarkers performs well in the diagnosis of DN. Five of the six biomarkers are strongly associated with several infiltrating immune cells. TP53 may play an essential role in the development of DN.

Establishment and validation of a ubiquitination-related gene signature associated with prognosis in pancreatic duct adenocarcinoma

Background

Patients with pancreatic duct adenocarcinoma (PDAC) have varied prognoses that depend on numerous variables. However, additional research is required to uncover the latent impact of ubiquitination-related genes (URGs) on determining PDAC patients' prognoses.

Methods

The URGs clusters were discovered via consensus clustering, and the prognostic differentially expressed genes (DEGs) across clusters were utilized to develop a signature using a least absolute shrinkage and selection operator (LASSO) regression analysis of data from TCGA-PAAD. Verification analyses were conducted across TCGA-PAAD, GSE57495 and ICGC-PACA-AU to show the robustness of the signature. RT-qPCR was used to verify the expression of risk genes. Lastly, we formulated a nomogram to improve the clinical efficacy of our predictive tool.

Results

The URGs signature, comprised of three genes, was developed and was shown to be highly correlated with the prognoses of PAAD patients. The nomogram was established by combining the URGs signature with clinicopathological characteristics. We discovered that the URGs signature was remarkably superior than other individual predictors (age, grade, T stage, et al). Also, the immune microenvironment analysis indicated that ESTIMATEscore, ImmuneScores, and StromalScores were elevated in the low-risk group. The immune cells that infiltrated the tissues were different between the two groups, as did the expression of immune-related genes.

Conclusion

The URGs signature could act as the biomarker of prognosis and selecting appropriate therapeutic drugs for PDAC patients.

Integrative analysis of a novel 5 methylated snoRNA genes prognostic signature in patients with glioma

Aim: To explore the prognostic value of methylated snoRNA genes in glioma and construct a prognostic risk signature. Materials & methods: We retrieved clinical information and 450K methylation data from The Cancer Genome Atlas and obtained five methylated snoRNA genes. Then we established a risk signature and verified the effect of SNORA71B on glioma cells with functional assays. Results: A risk signature containing five methylated snoRNA genes was constructed and demonstrated to be an independent predictor of glioma prognosis. Silencing SNORA71B restrained the proliferation, migration and invasion of glioma cells and reduced the expression of mesenchymal and cell cycle marker proteins. Conclusion: This study constructed a methylated snoRNA gene risk signature, which may provide a reference for glioma patients' prognosis assessment.

Comprehensive analysis of molecular mechanism and a novel prognostic signature based on small nuclear RNA biomarkers in gastric cancer patients

Small nuclear RNAs (snRNAs) are rarely reported in cancer. This study is based on The Cancer Genome Atlas genome-wide data set to explore the prognostic value and molecular mechanism of snRNAs in gastric cancer (GC). Gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were used to explore the molecular mechanism of snRNAs. A total of 351 patients were included in the survival analysis, and 14 prognostic snRNAs were identified using multivariate survival analysis. We constructed a prognostic signature containing nine snRNAs, which can signally classify patients into high- and low-risk phenotypes (adjusted P < 0.0001, hazard ratio = 2.671, 95% confidence interval = 1.850–3.858). Combining the molecular mechanisms obtained by the three functional enrichment approaches, we concluded that this prognostic signature snRNAs participated in classical tumor-related signaling pathways, including Notch, PI3K, toll-like receptor, etc.; cell adhesion; cell cycle; cell proliferation; and other biological processes that affect the biological phenotype of cancer cells. We also found significant downregulation of the abundance of immune cell infiltrates and immune microenvironment scores for high-risk phenotypes of GC patients. In conclusion, this study has identified 14 prognostic snRNAs signally associated with GC overall survival and also constructed a novel prognostic signature containing nine prognostic snRNAs.

Identification of a novel snoRNA expression signature associated with overall survival in patients with lung adenocarcinoma: A comprehensive analysis based on RNA sequencing dataset

Since multiple studies have reported that small nucleolar RNAs (snoRNAs) can be serve as prognostic biomarkers for cancers, however, the prognostic values of snoRNAs in lung adenocarcinoma (LUAD) remain unclear. Therefore, the main work of this study is to identify the prognostic snoRNAs of LUAD and conduct a comprehensive analysis. The Cancer Genome Atlas LUAD cohort whole-genome RNA-sequencing dataset is included in this study, prognostic analysis and multiple bioinformatics approaches are used for comprehensive analysis and identification of prognostic snoRNAs. There were seven LUAD prognostic snoRNAs were screened in current study. We also constructed a novel expression signature containing five LUAD prognostic snoRNAs (snoU109, SNORA5A, SNORA70, SNORD104 and U3). Survival analysis of this expression signature reveals that LUAD patients with high risk score was significantly related to an unfavourable overall survival (adjusted P = 0.01, adjusted hazard ratio = 1.476, 95% confidence interval = 1.096-1.987). Functional analysis indicated that LUAD patients with different risk score phenotypes had significant differences in cell cycle, apoptosis, integrin, transforming growth factor beta, ErbB, nuclear factor kappa B, mitogen-activated protein kinase, phosphatidylinositol-3-kinase and toll like receptor signaling pathway. Immune microenvironment analysis also indicated that there were significant differences in immune microenvironment scores among LUAD patients with different risk score. In conclusion, this study identified an novel expression signature containing five LUAD prognostic snoRNAs, which may be serve as an independent prognostic indicator for LUAD patients.