Identification of key pathways and genes in nasopharyngeal carcinoma using bioinformatics analysis

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Presentation of potential genes and deleterious variants associated with non-syndromic hearing loss: a computational approach

Non-syndromic hearing loss (NSHL) is a common hereditary disorder. Both clinical and genetic heterogeneity has created many obstacles to understanding the causes of NSHL. The present study has attempted to ravel the genetic aetiology in NSHL progression and to screen out potential target genes using computational approaches. The reported NSHL target genes (2009-2020) have been studied by analyzing different biochemical and signaling pathways, interpretation of their functional association network, and discovery of important regulatory interactions with three previously established miRNAs in the human inner ear as well as in NSHL such as miR-183, miR-182, and miR-96. This study has identified SMAD4 and SNAI2 as the most putative target genes of NSHL. But pathogenic and deleterious non-synonymous single nucleotide polymorphisms discovered within SMAD4 is anticipated to have an impact on NSHL progression. Additionally, the identified deleterious variants in the functional domains of SMAD4 added a supportive clue for further study. Thus, the identified deleterious variant i.e., rs377767367 (G491V) in SMAD4 needs further clinical validation. The present outcomes would provide insights into the genetics of NSHL progression.

Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers

Systemic Sclerosis (SSc) is an autoimmune disease associated with changes in the skin's structure in which the immune system attacks the body. A recent meta-analysis has reported a high incidence of cancer prognosis including lung cancer (LC), leukemia (LK), and lymphoma (LP) in patients with SSc as comorbidity but its underlying mechanistic details are yet to be revealed. To address this research gap, bioinformatics methodologies were developed to explore the comorbidity interactions between a pair of diseases. Firstly, appropriate gene expression datasets from different repositories on SSc and its comorbidities were collected. Then the interconnection between SSc and its cancer comorbidities was identified by applying the developed pipelines. The pipeline was designed as a generic workflow to demonstrate a premise comorbid condition that integrate regarding gene expression data, tissue/organ meta-data, Gene Ontology (GO), Molecular pathways, and other online resources, and analyze them with Gene Set Enrichment Analysis (GSEA), Pathway enrichment and Semantic Similarity (SS). The pipeline was implemented in R and can be accessed through our Github repository: https://github.com/hiddenntreasure/comorbidity. Our result suggests that SSc and its cancer comorbidities share differentially expressed genes, functional terms (gene ontology), and pathways. The findings have led to a better understanding of disease pathways and our developed methodologies may be applied to any set of diseases for finding any association between them. This research may be used by physicians, researchers, biologists, and others.

RNA-binding protein YBX1 promotes cell proliferation and invasiveness of nasopharyngeal carcinoma cells via binding to AURKA mRNA

Background: RNA-binding proteins (RBPs) play essential roles in post-transcriptional control of gene expression. Dysregulation of RBPs is intensively implicated in development and progression of human diseases, including cancers. However, the roles of RBPs in nasopharyngeal carcinoma (NPC), which is a distinct subtype of head and neck cancer, remain elusive. Methods: NPC-related RBPs were explored by analyzing GEO database and high-throughput proteomic data obtained from crosslinking immunoprecipitation. The expression levels of Y box binding protein 1 (YBX1) protein in NPC samples were measured by immunohistochemistry (IHC) staining. The association of YBX1 protein levels with prognosis of NPC patients was analyzed by Kaplan-Meier Plotter. The expression levels of YBX1 in NPC cells were inhibited by RNA interference. Cell growth was measured by CCK-8 assay. Cell mobility and invasiveness were measured by transwell assays. Tumorigenicity was measured by using a xenograft tumor assay. The expression levels of mRNAs or proteins were determined by qPCR or western blot assays, respectively. The mRNAs binding to YBX1 were determined by RNA immunoprecipitation (RIP) and qPCR. The effect of YBX1 on mRNA translation was measured by luciferase reporter assay. Results: In the present study, we demonstrated a differentially expressed RBPs profile between NPC and its normal counterpart. Among these aberrantly expressed RBPs, YBX1 was overexpressed in NPC. We found that YBX1 is mainly localized in the cytoplasm of NPC cells. Loss of YBX1 led to reduced cell proliferation, migration and invasiveness in vitro, and reduced tumorigenicity in vivo. Overexpression of YBX1 associates with high expression of cell cycle G2/M checkpoint modulators. In addition, YBX1 promotes AURKA protein expression by directly binding to its mRNA. Loss of YBX1 leads to reduction of AURKA protein level. Forced expression of AURKA rescues cell proliferation and invasiveness in YBX1-silenced NPC cell. Conclusions: The current study indicated that YBX1 promotes NPC cell proliferation and invasiveness through enhancing protein synthesis of AURKA.

Screening and bioinformatical analysis of differentially expressed genes in nasopharyngeal carcinoma

Objective: To identify differentially expressed genes via bioinformatical analysis for nasopharyngeal carcinoma (NPC) and explore potential biomarkers for NPC.

Methods: We downloaded the NPC gene expression datasets (GSE40290, GSE53819) and obtained differentially expressed genes (DEGs) via GEO2R. Functional analysis of DEGs was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. In order to explore the interaction of DEGs and screen the core genes, we established protein-protein interaction (PPI) network. Then the expression level, prognostic and diagnostic analysis of the core genes in NPC were performed to reveal their potential effects on NPC. Furthermore, we obtained the transcription factors (TF) and microRNAs of core genes to construct the coregulatory network.

Results: We obtained 124 up-regulated genes and 190 down-regulated genes in total. These genes were found to be related to signal transduction, extracellular matrix organization and cell adhesion based on GO analysis. KEGG analysis revealed that the NF-kappa B (NF-κB) signaling pathway, pathways in cancer were mainly enriched signaling pathways. 25 core genes were obtained by constructing PPI network. Then the high expression of 10 core genes in NPC were verified via GEPIA, Oncomine databases and laboratory experiments. The TF-microRNA coregulatory network of the 10 core genes was built. Survival and diagnostic analysis indicated that SPP1 had negative influence on the prognosis of NPC patients based on two datasets and nine up-regulated core genes (FN1, MMP1, MMP3, PLAU, PLAUR, SERPINE1, SPP1, COL8A1, COL10A1) might be diagnostic markers for NPC.

Conclusions: Core genes of NPC were screened out by bioinformatical analysis in the present study and these genes may serve as prognostic and diagnostic biomarkers for NPC.

Enhanced expression of queuine tRNA-ribosyltransferase 1 (QTRT1) predicts poor prognosis in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most frequently diagnosed type of lung cancer with high percentage of tumor relapse and metastasis. The correlation between queuine tRNA-ribosyltransferase 1 (QTRT1) expression and LUAD remains largely unknown. In this study, we aim to investigate the potential role of QTRT1 expression in the prognosis of LUAD.

Methods

We abstracted data from The Cancer Genome Atlas (TCGA) and four independent Gene Expression Omnibus (GEO) datasets. In total, 1,012 LUAD samples and 112 normal tissue samples were selected. The relationship between QTRT1 expression, methylation, and clinical features in LUAD were determined, and bioinformatics analyses were also performed.

Results

The expression of QTRT1 was higher in LUAD patients. A marked downregulation in QTRT1 methylation in LUAD was also found. Low QTRT1 expression was associated with longer overall survival across the GEO and TCGA datasets (P=0.0033, 0.0022, respectively). Furthermore, QTRT1 expression was significantly correlated with 'axoneme assembly', 'androgen response', and 'epithelial mesenchymal transition', as determined by Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) term enrichment analysis.

Conclusions

QTRT1 was highly expressed in LUAD, and enhanced expression of QTRT1 might therefore serve as a biomarker for poor prognosis in LUAD. The result of bioinformatic analyses might present a new insight for investigating the pathogenesis of LUAD.