scholarly journals Four Key Genes are Biomarkers Associated with Immunity in Neuroglioma

2020 ◽  
Author(s):  
Xin Yang ◽  
Jia-Qi Hao ◽  
Yu Zhang ◽  
Jia-Ying Shi ◽  
Xiao-Lin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Intracranial Tumor ◽  
Ppi Networks ◽  
Key Genes ◽  
The Relationship ◽  
Geo Database ◽  
Differential Genes

Abstract Background: Glioma is the most common intracranial tumor, with glioblastoma being the most malignant. However, its treatment is very few, and targeted therapy is an important breakthrough in treatment. Methods: Numerous genes are differentially expressed during the progression of glioma, some of which may play a key role. To find key genes, we analyzed three multi-sample microarrays (GSE4290, GSE54004, and GSE29796) in the GEO database to obtain intersection differential genes among them. We entered all DEGs into the STRING database and characterized the protein interactions of these DEGs as visual PPI networks by Cytoscape software. Also, we used the GEPIA2 and CGGAdatabase to predict the relationship between key genes and the prognosis of glioma patients.Results: A total of 222 up-regulated genes and 127 down-regulated genes were identified. Four genes(FN1, LAMB1, FAM20C, and COL6A1) were significantly negatively correlated with malignant glioma survival. Expression levels of four genes increased with the glioma grade. All gene expression is more common in IDH wild glioma and are enriched in the Mesenchymal subtype(AUC>0.8). In addition,they can be defined as hazard factors for glioma. We found that these genes were co-expressed and jointly involved in the infiltration of immune cells in tumors. Conclusion: In conclusion, FN1, LAMB1, FAM20C, and COL6A1 is associated with poor prognosis in glioma patients. These genes might be clinical targets of glioma immunotherapy.

scholarly journals HLF is a Potential Prognostic Biomarker in Head and Neck Squamous Cell Carcinoma Based on Bioinformatic Analysis and Experimental Validation

2021 ◽  
Author(s):  
Wei Fang ◽  
Di Wan ◽  
Jun Chen ◽  
Weiqun Ma ◽  
Zhen Luo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Culture ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Poor Prognosis ◽  
Prognostic Biomarker ◽  
Neck Squamous Cell Carcinoma ◽  
Rt Pcr ◽  
The Relationship

Abstract BackgroundHead and neck squamous cell carcinoma (HNSCC) is one of the most frequent cancers worldwide, with an increasing incidence. However, the underlying molecular mechanisms of HNSCC are poorly understood.MethodIn this work, 5 original datasets (GSE23558, GSE13601, GSE30784, GSE9844, GSE78060) of Head and neck squamous cell carcinoma (HNSCC) were selected from Gene Expression Omnibus (GEO) database. To identify differentially expressed genes (DEGs) in HNSCC and adjacent tissues. The common DEGs were acquired by Venn diagram. The sensitivity and specificity of HLF were determined by Receiver operating characteristic curves (ROC). Then, In order to further confirm the relationship between HLF and HNSCC patient’s prognosis, the expression and survival analysis of HLF was performed by Gene Expression Profiling Interactive Analysis (GEPIA), Cell culture, reverse transcription polymerase chain reaction (RT-PCR), western blotting and immunohistochemical staining.ResultsSeventeen DEGs were screened from five sets of HNSCC functional gene expression series in GEO datasets. The low expression of HLF was indicated might be correlated with poor prognosis of HNSCC patients based on the bioinformatics analysis. According to the results of Cell culture, RT-PCR, western blotting, immunohistochemical staining, it was confirmed that the low level of HLF expression correlated with poor prognosis of HNSCC patients.ConclusionThe study effectively revealed useful information about the relationship of the low level of HLF expression and HNSCC. In summary, we identified HLF as a potential prognostic biomarker and therapeutic target for HNSCC.

scholarly journals The diagnostic and prognostic value of H2AFY in hepatocellular carcinoma

2020 ◽  
Author(s):  
xuyang ma ◽  
Ying Ding ◽  
Li Zeng
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Clinical Characteristics ◽  
Cox Regression ◽  
Normal Liver ◽  
Diagnostic Value ◽  
The Relationship ◽  
Diagnostic And Prognostic Value

Abstract Background: The potential correlation between H2AFY (also known as MacroH2A1) and the clinical characteristics of hepatocellular carcinoma (HCC) patients was analysed through gene expression profiles and clinical data in The Cancer Genome Atlas (TCGA) database, and the diagnostic and prognostic value of H2AFY in HCC was discussed. Methods: The gene expression data of HCC and the corresponding clinical characteristics of HCC patients were downloaded from the TCGA database. The differences in H2AFY in normal liver tissues and HCC were analysed. The relationship between H2AFY and clinical characteristics was analysed by Wilcoxon signed-rank test, logistic regression and Kruskal-Wallis test. The Kaplan-Meier method and the Cox regression method were used to analyse the relationship between overall survival and clinical characteristics of the patients. An ROC curve was used to predict the diagnostic value of H2AFY in HCC. Gene set enrichment analysis (GSEA) was used to analyse the pathway enrichment of H2AFY. Result: Compared with normal liver tissues, H2AFY was significantly highly expressed in HCC. H2AFY was positively correlated with the age, clinical stage, G stage (grade) and T stage (tumor stage) of liver cancer patients. Higher H2AFY expression predicted a poor prognosis in HCC patients. Cox regression analysis suggested that H2AFY was an independent risk factor for the prognosis of HCC patients. The ROC curve suggested that H2AFY had certain diagnostic value in HCC. GSEA suggested that H2AFY was correlated with lipid metabolism and a variety of tumour pathways. Conclusion: Our study showed that H2AFY was significantly overexpressed in HCC. H2AFY may be a potential diagnostic and prognostic marker for HCC, and high expression of H2AFY predicts a poor prognosis in patients with HCC.

Genomic DNA methylation analysis revealed that BLNK is a key potential gene in the regulation of autophagy-related thyroid cancer progression

Genome ◽  
2021 ◽  
Author(s):  
Shengchi Zhang ◽  
Yongzhe Zheng ◽  
Guimin Zhang ◽  
Peng Lin ◽  
Wei Wang
Keyword(s):  
Dna Methylation ◽  
Thyroid Cancer ◽  
Cancer Progression ◽  
Protein Interactions ◽  
Methylation Status ◽  
Expression Change ◽  
Prognostic Analysis ◽  
Key Genes ◽  
New Perspective ◽  
The Relationship

The purpose of this study was to explore the relationship between autophagy and DNA methylation, and to identify key genes for autophagy-regulated thyroid cancer progression. We divided patients with thyroid cancer into high-autophagy score (AS) group and low-AS group based on their AS values. The results found that AS was associated with the distant metastasis of thyroid cancer, and adversely affected prognosis. Then, we screened 359 differently expressed genes (DEGs) with DNA methylation status consistent with gene expression change. Functional classification analysis demonstrated that the 359 DEGs consistent with DNA methylation status were significantly involved in adhesion, migration and differentiation of immune cells. To further screen the key genes in the autophagy-related thyroid cancer progression, we constructed a protein-protein interactions (PPI) network and performed prognostic analysis. B cell linker (BLNK) was identified as the key potential gene affecting autophagy-related thyroid cancer progression. Finally, we verified that BLNK promoted the proliferation of thyroid cancer cells, and BLNK expression was regulated by DNA methylation. Our research provides a new perspective for exploring the relationship between autophagy and DNA methylation during the progression of thyroid cancer, and provides a new target for the treatment of metastatic thyroid cancer.

scholarly journals mRNAsi Index: Machine Learning in Mining Lung Adenocarcinoma Stem Cell Biomarkers

Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 257 ◽  
Author(s):  
Yitong Zhang ◽  
Joseph Ta-Chien Tseng ◽  
I-Chia Lien ◽  
Fenglan Li ◽  
Wei Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cell ◽  
Lung Adenocarcinoma ◽  
Tumor Necrosis Factor Receptor ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Clinical Stages ◽  
Key Genes ◽  
Geo Database

Cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, lead to therapeutic resistance in lung cancer. In this study, we aimed to investigate the expressions of stem cell-related genes in lung adenocarcinoma (LUAD). The stemness index based on mRNA expression (mRNAsi) was utilized to analyze LUAD cases in the Cancer Genome Atlas (TCGA). First, mRNAsi was analyzed with differential expressions, survival analysis, clinical stages, and gender in LUADs. Then, the weighted gene co-expression network analysis was performed to discover modules of stemness and key genes. The interplay among the key genes was explored at the transcription and protein levels. The enrichment analysis was performed to annotate the function and pathways of the key genes. The expression levels of key genes were validated in a pan-cancer scale. The pathological stage associated gene expression level and survival probability were also validated. The Gene Expression Omnibus (GEO) database was additionally used for validation. The mRNAsi was significantly upregulated in cancer cases. In general, the mRNAsi score increases according to clinical stages and differs in gender significantly. Lower mRNAsi groups had a better overall survival in major LUADs, within five years. The distinguished modules and key genes were selected according to the correlations to the mRNAsi. Thirteen key genes (CCNB1, BUB1, BUB1B, CDC20, PLK1, TTK, CDC45, ESPL1, CCNA2, MCM6, ORC1, MCM2, and CHEK1) were enriched from the cell cycle Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, relating to cell proliferation Gene Ontology (GO) terms, as well. Eight of the thirteen genes have been reported to be associated with the CSC characteristics. However, all of them have been previously ignored in LUADs. Their expression increased according to the pathological stages of LUAD, and these genes were clearly upregulated in pan-cancers. In the GEO database, only the tumor necrosis factor receptor associated factor-interacting protein (TRAIP) from the blue module was matched with the stemness microarray data. These key genes were found to have strong correlations as a whole, and could be used as therapeutic targets in the treatment of LUAD, by inhibiting the stemness features.

scholarly journals Identification of Key Genes and miRNA-mRNA Regulatory Pathways in Bronchopulmonary Dysplasia in Preterm Infants by Bioinformatics Methods

2020 ◽  
Author(s):  
Tong Sun ◽  
Haiyang Yu ◽  
Jianhua Fu
Keyword(s):  
Gene Expression ◽  
Preterm Infants ◽  
Bronchopulmonary Dysplasia ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Regulatory Pathways ◽  
Ppi Networks ◽  
Key Genes

Abstract Background: Bronchopulmonary dysplasia (BPD) remains a severe respiratory complication of preterm infants in neonatal intensive care units (NICUs). However, its pathogenesis has been unclear. Bioinformatics analysis, which can help us explore genetic alternations and recognize latent diagnostic biomarkers, has recently promoted the comprehension of the molecular mechanisms underlying disease occurrence and development. Methods: In this study, we identified key genes and miRNA-mRNA regulatory networks in BPD in preterm infants to elucidate the pathogenesis of BPD. We downloaded and analyzed miRNA and gene expression microarray datasets from the Gene Expression Omnibus database (GEO). Differentially expressed miRNA (DEMs) and differentially expressed genes (DEGs) were obtained through NetworkAnalyst. We performed pathway enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery program (DAVID), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then we used the STRING to establish protein–protein interactions and the Cytoscape tool to establish miRNA–mRNA regulatory networks. Results: We identified 19 significant DEMs and 140 and 33 significantly upregulated and downregulated DEGs, respectively. Functional enrichment analysis indicated that significant DEGs were associated with the antigen processing and presentation, and B-cell receptor signaling pathways in BPD. Key DEGs, such as CD19, CD79B, MS4A1, and FCGR2B were selected as hub genes in PPI networks. Conclusions: In this study, we screened out 19 DEMs that might play important roles in the regulatory networks of BPD. Higher expression of miRNAs such as miR-15b-5p, hsa-miR-32-5p, miR-3613-3p, and miR-33a-5p and lower expression of miRNAs such as miR-3960, miR-425-5p, and miR-3202 might be correlated with the process of BPD.

scholarly journals Identification of Key Prognostic Biomarker and Its Correlation with Immune Infiltrates in Pancreatic Ductal Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Hong Luan ◽  
Chuang Zhang ◽  
Tuo Zhang ◽  
Ye He ◽  
Yanna Su ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Poor Prognosis ◽  
Immune Therapy ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Immune Checkpoints ◽  
Hub Genes ◽  
Immune Infiltration ◽  
The Relationship

Pancreatic ductal adenocarcinoma (PDAC) is an extremely malignant tumor. The immune profile of PDAC and the immunologic milieu of its tumor microenvironment (TME) are unique; however, the mechanism of how the TME engineers the carcinogenesis of PDAC is not fully understood. This study is aimed at better understanding the relationship between the immune infiltration of the TME and gene expression and identifying potential prognostic and immunotherapeutic biomarkers for PDAC. Analysis of data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases identified differentially expressed genes (DEGs), including 159 upregulated and 53 downregulated genes. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes enrichment were performed and showed that the DEGs were mainly enriched for the PI3K-Akt signaling pathway and extracellular matrix organization. We used the cytoHubba plugin of Cytoscape to screen out the most significant ten hub genes by four different models (Degree, MCC, DMNC, and MNC). The expression and clinical relevance of these ten hub genes were validated using Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas, respectively. High expression of nine of the hub genes was positively correlated with poor prognosis. Finally, the relationship between these hub genes and tumor immunity was analyzed using the Tumor Immune Estimation Resource. We found that the expression of SPARC, COL6A3, and FBN1 correlated positively with infiltration levels of six immune cells in the tumors. In addition, these three genes had a strong coexpression relationship with the immune checkpoints. In conclusion, our results suggest that nine upregulated biomarkers are related to poor prognosis in PDAC and may serve as potential prognostic biomarkers for PDAC therapy. Furthermore, SPARC, COL6A3, and FBN1 play an important role in tumor-related immune infiltration and may be ideal targets for immune therapy against PDAC.

FYN and CD247: key Genes for Septic Shock Based on Bioinformatics and Meta-Analysis

2021 ◽  
Vol 24 ◽  
Author(s):  
Yue Jiang ◽  
Qian Miao ◽  
Lin Hu ◽  
Tingyan Zhou ◽  
Yingchun Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Survival Analysis ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Key Genes ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background: Septic shock is sepsis accompanied by hemodynamic instability and high clinical mortality. Material and Methods: GSE95233, GSE57065, GSE131761 gene-expression profiles of healthy control subjects and septic shock patients were downloaded from the Gene-Expression Omnibus (GEO) database, and differences of expression profiles and their intersection were analysed using GEO2R. Function and pathway enrichment analysis was performed on common differentially expressed genes (DEG), and key genes for septic shock were screened using a protein-protein interaction network created with STRING. Also, data from the GEO database were used for survival analysis for key genes, and a meta-analysis was used to explore expression trends of core genes. Finally, high-throughput sequencing using the blood of a murine sepsis model was performed to analyse the expression of CD247 and FYN in mice. Results: A total of 539 DEGs were obtained (p < 0.05). Gene ontology analysis showed that key genes were enriched in functions, such as immune response and T cell activity, and DEGs were enriched in signal pathways, such as T cell receptors. FYN and CD247 are in the centre of the protein-protein interaction network, and survival analysis found that they are positively correlated with survival from sepsis. Further, meta-analysis results showed that FYN could be useful for the prognosis of patients, and CD247 might distinguish between sepsis and systemic inflammatory response syndrome patients. Finally, RNA sequencing using a mouse septic shock model showed low expression of CD247 and FYN in this model. Conclusion: FYN and CD247 are expected to become new biomarkers of septic shock.

scholarly journals Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yu Wang ◽  
Sibo Hu ◽  
Xianguang Bai ◽  
Ke Zhang ◽  
Ruixue Yu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tissue Samples ◽  
Screening Criteria ◽  
Hub Gene ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Limma Package ◽  
Key Genes ◽  
Upregulated Genes ◽  
Geo Database

Background. The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach. Methods. We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣ log 2   fold   change   FC ∣ > 2 . Results. A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape. Conclusions. We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.

scholarly journals Resilience and evolvability of protein-protein interaction networks

2020 ◽  
Author(s):  
Brennan Klein ◽  
Ludvig Holmér ◽  
Keith M. Smith ◽  
Mackenzie M. Johnson ◽  
Anshuman Swain ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Preferential Attachment ◽  
Network Resilience ◽  
Biological Noise ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ppi Networks

AbstractProtein-protein interaction (PPI) networks represent complex intra-cellular protein interactions, and the presence or absence of such interactions can lead to biological changes in an organism. Recent network-based approaches have shown that a phenotype’s PPI network’s resilience to environmental perturbations is related to its placement in the tree of life; though we still do not know how or why certain intra-cellular factors can bring about this resilience. One such factor is gene expression, which controls the simultaneous presence of proteins for allowed extant interactions and the possibility of novel associations. Here, we explore the influence of gene expression and network properties on a PPI network’s resilience, focusing especially on ribosomal proteins—vital molecular-complexes involved in protein synthesis, which have been extensively and reliably mapped in many species. Using publicly-available data of ribosomal PPIs for E. coli, S.cerevisae, and H. sapiens, we compute changes in network resilience as new nodes (proteins) are added to the networks under three node addition mechanisms—random, degree-based, and gene-expression-based attachments. By calculating the resilience of the resulting networks, we estimate the effectiveness of these node addition mechanisms. We demonstrate that adding nodes with gene-expression-based preferential attachment (as opposed to random or degree-based) preserves and can increase the original resilience of PPI network. This holds in all three species regardless of their distributions of gene expressions or their network community structure. These findings introduce a general notion of prospective resilience, which highlights the key role of network structures in understanding the evolvability of phenotypic traits.1Author SummaryProteins in organismal cells are present at different levels of concentration and interact with other proteins to provide specific functional roles. Accumulating lists of all of these interactions, complex networks of protein interactions become apparent. This allows us to begin asking whether there are network-level mechanisms at play guiding the evolution of biological systems. Here, using this network perspective, we address two important themes in evolutionary biology (i) How are biological systems able to successfully incorporate novelty? (ii) What is the evolutionary role of biological noise in evolutionary novelty? We consider novelty to be the introduction of a new protein, represented as a new “node”, into a network. We simulate incorporation of novel proteins into Protein-Protein Interaction (PPI) networks in different ways and analyse how the resilience of the PPI network alters. We find that novel interactions guided by gene expression (indicative of concentration levels of proteins) creates a more resilient network than either uniformly random interactions or interactions guided solely by the network structure (preferential attachment). Moreover, simulated biological noise in the gene expression increases network resilience. We suggest that biological noise induces novel structure in the PPI network which has the effect of making it more resilient.

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