Identification of Core Genes and Pathways in Medulloblastoma by Integrated Bioinformatics Analysis

Regulation of microglia inflammation and oligodendrocyte demyelination by Engeletin via the TLR4/RRP9/NF-κB pathway after spinal cord injury

Microglia polarization is crucial for neuroinflammatory response after spinal cord injury (SCI). Small molecule compounds and hub genes play an important role in regulating microglia polarization, reducing neuroinflammatory response and oligodendrocyte demyelination after SCI. In this study, suitable data sets were used to screen hub genes, and Western blot and Immunofluorescence (IF) experiments were used to confirm the expressions of proteins related to SDAD1, RRP9 and NF-κB pathways under LPS/SCI conditions. Engeletin (ENG) reduced microglia polarization and inflammation in vivo and in vitro via the SDAD1, RRP9 or NF-κB signaling pathways. In addition, ENG binds to the membrane receptor Toll-like receptor 4 (TLR4) through small molecule-protein docking. COIP experiment and protein-protein docking revealed protein-protein interaction (PPI) between RRP9 and SDAD1. By gene knock-down (KD) / overexpression (OE) and Western blot experiments, RRP9 and SDAD1 can regulate inflammatory response through NF-κB signaling and ribosome biogenesis pathway. Western blot analysis showed that CU increased the expression of SDAD1, RRP9 and NF-κB pathway related proteins through TLR1/2, while C34 decreased the expression of SDAD1 and RRP9 proteins through TLR4. These results suggest that ENG can reduce inflammation through TLR4/RRP9(SDAD1)/NF-κB signaling pathway. In addition, we demonstrated that oligodendrocyte apoptosis and demyelination could be influenced by the regulation of microglia and tissue inflammation. In conclusion, this study found the gene Rrp9/Sdad1 and the small molecule compound ENG, which control the inflammatory response of microglia, and further explored the related mechanism of oligodendrocyte demyelination, which has important theoretical significance.

Bioinformatics-based diagnosis and evaluation of several pivotal genes and pathways associated with immune infiltration at different time points in spinal cord injury

Spinal Cord Injury (SCI) is a devastating neurological event. To assess the degree of spinal cord damage and classify the injury, it is recommended to use the 2019 version of the AIS standard. The severity of trauma was evaluated using the Trauma Severity Score, and various classification systems have been proposed for injuries at different parts and segments of the spine. Understanding the regulated signaling pathways and immune processes following SCI can lead to a better understanding of SCI-induced biomarkers and their underlying mechanisms. In this study, two gene expression datasets (GSE464 and GSE45006) from the Gene Expression Omnibus database were utilized. Differential gene expression and co-expression network analysis were performed, revealing 370 shared genes in the 3-day group and 111 shared genes in the 14-day group after SCI. The study used functional enrichment analysis methods such as Gene Set Enrichment Analysis, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes. The ssGSEA method was used to assess the levels and composition of immune infiltration in both the sham (control) and SCI groups. The single-cell transcriptomics dataset GSE182803 was analyzed to identify genes associated with immune marker cells. Four key genes (Ptgs2, Fn1, Ccl2, and Icam1) were identified in the 3-day group, while only one gene (Cyp51) was identified in the 14-day group after SCI. The findings offer significant insights into the immune-related genes and signaling pathways involved in secondary SCI at different time points and hold potential for the development of intervention strategies for acute and chronic post-SCI.

Identification and validation of a metabolism-related gene signature for predicting the prognosis of paediatric medulloblastoma

Medulloblastoma (MB) is a malignant brain tumour that is highly common in children and has a tendency to spread to the brain and spinal cord. MB is thought to be a metabolically driven brain tumour. Understanding tumour cell metabolic patterns and characteristics can provide a promising foundation for understanding MB pathogenesis and developing treatments. Here, by analysing RNA-seq data of MB samples from the Gene Expression Omnibus (GEO) database, 12 differentially expressed metabolic-related genes (DE-MRGs) were chosen for the construction of a predictive risk score model for MB. This model demonstrated outstanding accuracy in predicting the outcomes of MB patients and served as a standalone predictor. An evaluation of functional enrichment revealed that the risk score showed enrichment in pathways related to cancer promotion and the immune response. In addition, a high risk score was an independent poor prognostic factor for MB in patients with different ages, sexes, metastasis stages and subgroups (SHH and Group 4). Consistently, the metabolic enzyme ornithine decarboxylase (ODC1) was upregulated in MB patients with poor survival time. Inhibition of ODC1 in primary and metastatic MB cell lines decreased cell proliferation, migration and invasion but increased immune infiltration. This study could aid in identifying metabolic targets for MB as well as optimizing risk stratification systems and individual treatment plans for MB patients via the use of a metabolism-related gene prognostic risk score signature.

The Predictive Competing Endogenous RNA Regulatory Networks and Potential Prognostic and Immunological Roles of Cyclin A2 in Pan-Cancer Analysis

Although accumulating evidence has verified the relationship between CCNA2 and cancers, no pan-cancer analysis about the function and the upstream molecular mechanism of CCNA2 is available. For the first time, we analyzed potential oncogenic roles of CCNA2 in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of CCNA2 is widespread in almost all cancer types, and it is related to poor prognosis and advanced pathological stages in most cases. Moreover, we conducted upstream miRNAs and lncRNAs of CCNA2 to establish upstream regulatory networks in kidney renal clear cell carcinoma (LINC00997/miR-27b-3p/CCNA2), liver hepatocellular carcinoma (SNHG16, GUSBP11, FGD5-AS1, LINC00630, CD27-AS1, LINC00997/miR-22-3p/CCNA2, miR-29b-3p/CCNA2, miR-29c-3p/CCNA2, and miR-204-5p/CCNA2), and lung adenocarcinoma (miRNA-218-5p/CCNA2 and miR-204-5p/CCNA2) by expression analysis, survival analysis, and correlation analysis. The CCNA2 expression is positively correlated with Th2 cell infiltration and negatively correlated with CD4+ central memory and effector memory T-cell infiltration in different cancer types. Furthermore, CCNA2 is positively associated with expressions of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1, and TIGIT) in most cancer types. Our first CCNA2 pan-cancer study contributes to understanding the prognostic and immunological roles and potential upstream molecular mechanisms of CCNA2 in different cancers.

Identification of Potential Key Genes and Molecular Mechanisms of Medulloblastoma Based on Integrated Bioinformatics Approach

Background

Medulloblastoma (MB) is the most occurring brain cancer that mostly happens in childhood age. This cancer starts in the cerebellum part of the brain. This study is designed to screen novel and significant biomarkers, which may perform as potential prognostic biomarkers and therapeutic targets in MB.

Methods

A total of 103 MB-related samples from three gene expression profiles of GSE22139, GSE37418, and GSE86574 were downloaded from the Gene Expression Omnibus (GEO). Applying the limma package, all three datasets were analyzed, and 1065 mutual DEGs were identified including 408 overexpressed and 657 underexpressed with the minimum cut-off criteria of ∣log fold change | >1 and P < 0.05. The Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and WikiPathways enrichment analyses were executed to discover the internal functions of the mutual DEGs. The outcomes of enrichment analysis showed that the common DEGs were significantly connected with MB progression and development. The Search Tool for Retrieval of Interacting Genes (STRING) database was used to construct the interaction network, and the network was displayed using the Cytoscape tool and applying connectivity and stress value methods of cytoHubba plugin 35 hub genes were identified from the whole network.

Results

Four key clusters were identified using the PEWCC 1.0 method. Additionally, the survival analysis of hub genes was brought out based on clinical information of 612 MB patients. This bioinformatics analysis may help to define the pathogenesis and originate new treatments for MB.

Identification of ITPR1 as a Hub Gene of Group 3 Medulloblastoma and Coregulated Genes with Potential Prognostic Values

The Group 3 Medulloblastoma (Grp3-MB) is an aggressive molecular subtype with a high incidence of metastasis and deaths. In this study, were used an RNA sequencing data (RNA-Seq) from a Brazilian cohort of MBs to identify hub genes associated with the metastatic risk. Data validation were performed by using multiple large datasets from MBs (GSE85217, GSE37418, and EGAS00001001953). DESeq2 package in R software was used to identify the differentially expressed genes (DEGs) in our RNA-Seq data. The DEGs data were accessed to construct the modules/graphs of co-expression and to identify hub genes through Cytoscape platform. The coregulated genes were enriched by the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the protein-protein interaction (PPI) network was visualized by Cytoscape. The Kaplan-Meier plotter and ROC curves were used to validate the diagnostic and prognostic values of specific biomarkers identified through this model. We identified that inositol 1,4,5-trisphosphate receptor type 1 (ITPR1) as a downregulated hub gene, with a high diagnostic accuracy to Grp3-MBs and associated with tumor metastasis. In addition, we identified genes significantly correlated with ITPR1 that were associated with metastasis in Grp3-MB (ATP1A2, MTTL7A, and RGL1) and worst overall survival in MBs (ANTXR1 and RGL1). Our findings suggest that the ITPR1 hub gene is potentially involved in the metastatic process for Grp3-MB. Our data also provide evidence of targets that may serve as prognostic predictors and/or regulators for the metastatic process that maybe explored for further research of individualized therapy to Grp3-MBs.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.

Methods

Two groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.

Results

We identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.

Conclusion

The findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

Identification of Regeneration and Hub Genes and Pathways at Different Time Points after Spinal Cord Injury

Spinal cord injury (SCI) is a neurological injury that can cause neuronal loss around the lesion site and leads to locomotive and sensory deficits. However, the underlying molecular mechanisms remain unclear. This study aimed to verify differential gene time-course expression in SCI and provide new insights for gene-level studies. We downloaded two rat expression profiles (GSE464 and GSE45006) from the Gene Expression Omnibus database, including 1 day, 3 days, 7 days, and 14 days post-SCI, along with thoracic spinal cord data for analysis. At each time point, gene integration was performed using "batch normalization." The raw data were standardized, and differentially expressed genes at the different time points versus the control were analyzed by Gene Ontology enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes pathway analysis, and gene set enrichment analysis. A protein-protein interaction network was then built and visualized. In addition, ten hub genes were identified at each time point. Among them, Gnb5, Gng8, Agt, Gnai1, and Psap lack correlation studies in SCI and deserve further investigation. Finally, we screened and analyzed genes for tissue repair, reconstruction, and regeneration and found that Anxa1, Snap25, and Spp1 were closely related to repair and regeneration after SCI. In conclusion, hub genes, signaling pathways, and regeneration genes involved in secondary SCI were identified in our study. These results may be useful for understanding SCI-related biological processes and the development of targeted intervention strategies.