Bioinformatics analysis of gene expression profiles to diagnose crucial and novel genes in glioblastoma multiform

IER5L is a Prognostic Biomarker in Pan-Cancer Analysis and Correlates with Immune Infiltration and Immune Molecules in Non-Small Cell Lung Cancer

Purpose

Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases. Immediate early response 5 like (IER5L) plays crucial roles in progression and prognosis for several tumors, but its role in NSCLC remains unclear.

Patients and Methods

Gene expression and mutation profiles, DNA methylation data, and clinical information for cancers were downloaded from multiple databases. Relative expression, prognostic value, and correlation with disease progression of IER5L were analyzed in multiple cancers, including NSCLC. Upstream mechanisms were explored using a transcriptional network. Functional enrichment analysis, protein-protein interaction network, and gene set enrichment analysis were applied to study downstream mechanisms. Correlations of IER5L with immune infiltration, immune molecules, methylation status, and tumor mutation burden (TMB) were analyzed using R language. Finally, quantitative polymerase chain reaction (qPCR) and single-cell RNA sequencing (scRNA seq) analysis were performed to validate IER5L expression in NSCLC.

Results

Pan-cancer analysis displayed that IER5L expression was upregulated in multiple cancers and was associated with disease prognosis and progression, including NSCLC, which was validated using qPCR. scRNA seq analysis showed that multiple cells had increased IER5L expression. An EGR1-hsa-miR-8075-IER5L network was constructed for NSCLC. A total of 191 DEGs were identified between the two IER5L groups, which were significantly enriched in biological process of action potential, sodium ion transport, and regulation of membrane potential. Increased IER5L expression was primarily enriched in cell cycle, NOTCH signaling pathway, and oxidative phosphorylation pathway, and was correlated with increased regulatory T cells and neutrophils, elevated levels of immune molecules, and higher TMB.

Conclusion

Our findings show that increased IER5L expression was correlated with progression and prognosis in multiple cancers as well as with immune infiltration and immune molecules in NSCLC. Thus, IER5L is a prognostic biomarker in multiple cancers and may correlate with immunotherapeutic response in NSCLC.

YY1 regulates the proliferation and invasion of triple-negative breast cancer via activating PLAUR

It is well-established that breast cancer is a highly prevalent malignancy among women, emphasizing the need to investigate mechanisms underlying its pathogenesis and metastasis. In this study, the Gene Expression Omnibus (GEO) database was utilized to conduct differential expression analysis in breast cancer and adjacent tissues. Upregulated genes were selected for prognostic analysis of breast cancer. The expression of urokinase plasminogen activator receptor (uPAR), also known as PLAUR, was assessed using RT-qPCR and western blot. Immunofluorescence staining was employed to determine PLAUR localization. Various cellular processes were analyzed, including proliferation, migration, invasion, apoptosis, and cell cycle. Bioinformatics analysis was used to predict transcription factors of PLAUR, which were subsequently validated in a double luciferase reporter gene experiment. Rescue experiments confirmed the impact of PLAUR on the proliferation, apoptosis, and migration of MDA-MB-231 cells. Furthermore, the effects of PLAUR were evaluated in an orthotopic tumor transplantation and lung metastasis nude mouse model. Our findings substantiated the critical involvement of PLAUR in the progression of triple-negative breast cancer (TNBC) in vitro and among TNBC patients with a poor prognosis. Additionally, we demonstrated Yin Yang-1 (YY1) as a notable transcriptional regulator of PLAUR, whose activation could transcriptionally enhance the proliferation and invasion capabilities of TNBC cells. We also identified the downstream mechanism of PLAUR associated with PLAU, focal adhesion kinase (FAK), and AKT. Overall, these findings offer a novel perspective on PLAUR as a potential therapeutic target for TNBC.

Identification of potential crucial genes in monocytes for atherosclerosis using bioinformatics analysis

Objective

To use bioinformatics tools to screen for gene biomarkers from monocytes, which play an important role in the pathogenesis of atherosclerosis.

Methods

Two expression profiling datasets (GSE27034 and GSE10195) were obtained from the Gene Expression Omnibus dataset and the differentially expressed genes (DEGs) between atherosclerotic human peripheral blood mononuclear cells (PBMC) samples and control subjects were screened using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted for the DEGs. STRING and MCODE plug-in of Cytoscape were used for constructing a protein–protein interaction network and analysing hub genes.

Results

The two datasets had 237 DEGs in common between non-atherosclerotic- and atherosclerotic PBMC samples. Functional annotation demonstrated that these DEGs were mainly enriched in protein binding, positive regulation of transcription from RNA polymerase II promoter, nucleus and viral carcinogenesis. Five hub genes, FBXL4, UBOX5, KBTBD6, FZR1 and FBXO2, were identified.

Conclusion

This present bioinformatics analysis identified that the FBXL4, UBOX5, KBTBD6 and FBXO21 genes might play vital roles in the pathogenesis of atherosclerosis. These four genes might represent new biomarkers for the diagnosis and treatment of atherosclerosis.

Identification of key modules and hub genes in glioblastoma multiforme based on co-expression network analysis

Glioblastoma multiforme (GBM) is the most malignant primary tumour in the central nervous system, but the molecular mechanisms underlying its pathogenesis remain unclear. In this study, data set GSE50161 was used to construct a co‐expression network for weighted gene co‐expression network analysis. Two modules (dubbed brown and turquoise) were found to have the strongest correlation with GBM. Functional enrichment analysis indicated that the brown module was involved in the cell cycle, DNA replication, and pyrimidine metabolism. The turquoise module was primarily related to circadian rhythm entrainment, glutamatergic synapses, and axonal guidance. Hub genes were screened by survival analysis using The Cancer Genome Atlas and Human Protein Atlas databases and further tested using the GSE4290 and Gene Expression Profiling Interactive Analysis databases. The eight hub genes (NUSAP1, SHCBP1, KNL1, SULT4A1, SLC12A5, NUF2, NAPB, and GARNL3) were verified at both the transcriptional and translational levels, and these gene expression levels were significant based on the World Health Organization classification system. These hub genes may be potential biomarkers and therapeutic targets for the accurate diagnosis and management of GBM.

Pathological Grade-Associated Transcriptome Profiling of lncRNAs and mRNAs in Gliomas

The aim of the present study was to explore the expression profiles of lncRNAs and mRNAs in glioma patients and to elucidate any potential relationship between lncRNAs and mRNAs in glioma. High-throughput transcriptome sequencing of mRNAs and lncRNAs from six normal tissues and 16 glioma tissues (grade II, six cases; grade III, four cases; and grade IV, six cases) was performed. Series test of cluster (STC) analysis was used to screen significant trending models associated with glioma. Gene co-expression networks were constructed for the differentially expressed lncRNAs and mRNAs, and gene-ontology (GO) and pathway-enrichment analyses were further performed. Quantitative real-time PCR was performed to validate the five most differentially expressed lncRNAs and mRNAs. After filtering the raw sequencing data, we found 578 lncRNAs and 3,216 mRNAs that were significantly dysregulated in glioma (fold change ≥ 2, p < 0.05). Twenty model profiles of lncRNA and 10 model profiles of mRNA were summarized, and three patterns of lncRNAs and two patterns of mRNAs were of clinical significance. Three gene co-expression networks between mRNAs and lncRNAs were built to clarify the relationship between lncRNAs and mRNAs in glioma. GO and pathway analyses indicated that the differentially expressed lncRNAs and mRNAs were enriched in several biological processes and signaling pathways associated with tumorigenesis. Both lncRNAs and mRNAs exhibited dynamic differential expression profiles that indicated their potential roles in different degrees of glioma malignancy. A series of bioinformatics analyses indicated that most of these lncRNAs and mRNAs are involved in important biological processes and pathways associated with the pathogenesis of glioma. These results provide potential directions and valuable resources for future investigations via the comprehensive integration of these lncRNAs and mRNAs.

Identification of CFTR as a novel key gene in chromophobe renal cell carcinoma through bioinformatics analysis

Chromophobe renal cell carcinoma (chRCC), the third most common histological subtype of RCC, comprises 5–7% of all RCC cases. The aim of the present study was to identify potential biomarkers for chRCC and to examine the underlying mechanisms. A total of 4 profile datasets were downloaded from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs were performed with the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed to predict hub genes. Hub gene expression within chRCC across multiple datasets, as well as overall survival, were investigated by utilizing the Oncomine platform and UALCAN dataset, separately. A total of 266 DEGs (88 upregulated genes and 168 downregulated genes) were identified from 4 profile datasets. Integrating the results from the PPI network, Oncomine platform and survival analysis, CFTR was screened as a key factor in the prognosis of chRCC. GO and KEGG analysis revealed that 266 DEGs were mainly enriched in 17 terms and 9 pathways. The present study identified key genes and potential molecular mechanisms underlying the development of chRCC, and CFTR may be a potential prognostic biomarker and novel therapeutic target for chRCC.

Identification of EGFR as a Novel Key Gene in Clear Cell Renal Cell Carcinoma (ccRCC) through Bioinformatics Analysis and Meta-Analysis

Clear cell renal cell carcinoma (ccRCC) was the most aggressive histological type of renal cell carcinoma (RCC) and accounted for 70-80% of cases of all RCC. The aim of this study was to identify the potential biomarker in ccRCC and explore their underlying mechanisms. Four profile datasets were downloaded from the GEO database to identify DEGs. GO and KEGG analysis of DEGs were performed by DAVID. A protein-protein interaction (PPI) network was constructed to predict hub genes. The hub gene expression within ccRCC across multiple datasets and the overall survival analysis were investigated utilizing the Oncomine Platform and UALCAN dataset, separately. A meta-analysis was performed to explore the relationship between the hub genes: EGFR and ccRCC. 127 DEGs (55 upregulated genes and 72 downregulated genes) were identified from four profile datasets. Integrating the result from PPI network, Oncomine Platform, and survival analysis, EGFR, FLT1, and EDN1 were screened as key factors in the prognosis of ccRCC. GO and KEGG analysis revealed that 127 DEGs were mainly enriched in 21 terms and 4 pathways. The meta-analysis showed that there was a significant difference of EGFR expression between ccRCC tissues and normal tissues, and the expression of EGFR in patients with metastasis was higher. This study identified 3 importance genes (EGFR, FLT1, and EDN1) in ccRCC, and EGFR may be a potential prognostic biomarker and novel therapeutic target for ccRCC, especially patients with metastasis.