Construction of a competitive endogenous RNA network and analysis of potential regulatory axis targets in glioblastoma

Abstract Background Glioblastoma is the most common primary malignant brain tumor. Due to the limited understanding of its pathogenesis, the prognosis of glioblastoma is poor. The purpose of this study is to explore potential ceRNA network chains and biomarkers in glioblastoma through integrated bioinformatics analysis. Methods Transcriptome expression data from The Cancer Genome Atlas database and Gene Expression Omnibus were analyzed to identify differentially expressed genes between glioblastoma tissue and normal tissue. The potential biological pathways associated with the differentially expressed genes were explored using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, and a protein-protein interaction network was established using the STRING database and Cytoscape. Survival analysis using Gene Expression Profiling Interactive Analysis was based on the Kaplan-Meier curve method. The ceRNA network chain was established using the intersection method to align data from four databases (miRTarBase, miRcode, TargetScan, and lncBace2.0), and expression differences and correlations were verified by using quantitative reverse-transcription polymerase chain reaction analysis and determining the Pearson correlation coefficient. Results A total of 2842 DEmRNAs, 2577 DElncRNAs, and 309 DEmiRNAs were dysregulated in glioblastoma. The final ceRNA network consisted of six specific lncRNAs, four miRNAs, and four mRNAs. Among them, four DEmRNAs and one DElncRNA were correlated with overall survival (p < 0.05). We found that C1S was significantly correlated with overall survival (p = 0.015) and could therefore be used as a biomarker for glioblastoma. Conclusions Four ceRNA networks were established that may influence the occurrence and development of glioblastoma. Among them, the MIR155HG/has-miR-129-5p/C1S axis may be a potential marker and therapeutic target. In particular, C1S has not yet been reported in glioblastoma studies. These findings clarify the role of the ceRNA regulatory network in glioblastoma and lay a foundation for further research.

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Construction of a competitive endogenous RNA network and analysis of potential regulatory axis targets in glioblastoma

Cancer Cell International ◽

10.1186/s12935-021-01789-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Kai Yu ◽

Huan Yang ◽

Qiao-li Lv ◽

Li-chong Wang ◽

Zi-long Tan ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Differentially Expressed Genes ◽

Pearson Correlation ◽

Differentially Expressed ◽

Migration And Invasion ◽

Glioblastoma Cells ◽

Network Chain ◽

Cerna Network ◽

Potential Marker

Abstract Background Glioblastoma is the most common primary malignant brain tumor. Because of the limited understanding of its pathogenesis, the prognosis of glioblastoma remains poor. This study was conducted to explore potential competing endogenous RNA (ceRNA) network chains and biomarkers in glioblastoma by performing integrated bioinformatics analysis. Methods Transcriptome expression data from The Cancer Genome Atlas database and Gene Expression Omnibus were analyzed to identify differentially expressed genes between glioblastoma and normal tissues. Biological pathways potentially associated with the differentially expressed genes were explored by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, and a protein-protein interaction network was established using the STRING database and Cytoscape. Survival analysis using Gene Expression Profiling Interactive Analysis was based on the Kaplan–Meier curve method. A ceRNA network chain was established using the intersection method to align data from four databases (miRTarBase, miRcode, TargetScan, and lncBace2.0), and expression differences and correlations were verified by quantitative reverse-transcription polymerase chain reaction analysis and by determining the Pearson correlation coefficient. Additionally, an MTS assay and the wound-healing and transwell assays were performed to evaluate the effects of complement C1s (C1S) on the viability and migration and invasion abilities of glioblastoma cells, respectively. Results We detected 2842 differentially expressed (DE) mRNAs, 2577 DE long non-coding RNAs (lncRNAs), and 309 DE microRNAs (miRNAs) that were dysregulated in glioblastoma. The final ceRNA network consisted of six specific lncRNAs, four miRNAs, and four mRNAs. Among them, four DE mRNAs and one DE lncRNA were correlated with overall survival (p < 0.05). C1S was significantly correlated with overall survival (p= 0.015). In functional assays, knockdown of C1S inhibited the proliferation and invasion of glioblastoma cell lines. Conclusions We established four ceRNA networks that may influence the occurrence and development of glioblastoma. Among them, the MIR155HG/has-miR-129-5p/C1S axis is a potential marker and therapeutic target for glioblastoma. Knockdown of C1S inhibited the proliferation, migration, and invasion of glioblastoma cells. These findings clarify the role of the ceRNA regulatory network in glioblastoma and provide a foundation for further research.

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Construction of a competitive endogenous RNA network and analysis of potential regulatory axis targets in glioblastoma

10.21203/rs.3.rs-78209/v2 ◽

2021 ◽

Author(s):

Kai Yu ◽

Huan Yang ◽

Qiao-li Lv ◽

Li-chong Wang ◽

Zi-long Tan ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Differentially Expressed Genes ◽

Pearson Correlation ◽

Differentially Expressed ◽

Migration And Invasion ◽

Glioblastoma Cells ◽

Network Chain ◽

Cerna Network ◽

Potential Marker

Abstract Background: Glioblastoma is the most common primary malignant brain tumor. Because of the limited understanding of its pathogenesis, the prognosis of glioblastoma remains poor. This study was conducted to explore potential competing endogenous RNA (ceRNA) network chains and biomarkers in glioblastoma by performing integrated bioinformatics analysis.Methods: Transcriptome expression data from The Cancer Genome Atlas database and Gene Expression Omnibus were analyzed to identify differentially expressed genes between glioblastoma and normal tissues. Biological pathways potentially associated with the differentially expressed genes were explored by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, and a protein-protein interaction network was established using the STRING database and Cytoscape. Survival analysis using Gene Expression Profiling Interactive Analysis was based on the Kaplan–Meier curve method. A ceRNA network chain was established using the intersection method to align data from four databases (miRTarBase, miRcode, TargetScan, and lncBace2.0), and expression differences and correlations were verified by quantitative reverse-transcription polymerase chain reaction analysis and by determining the Pearson correlation coefficient. Additionally, an MTS assay and the wound-healing and transwell assays were performed to evaluate the effects of complement C1s (C1S) on the viability and migration and invasion abilities of glioblastoma cells, respectively.Results: We detected 2842 differentially expressed (DE) mRNAs, 2577 DE long non-coding RNAs (lncRNAs), and 309 DE microRNAs (miRNAs) that were dysregulated in glioblastoma. The final ceRNA network consisted of six specific lncRNAs, four miRNAs, and four mRNAs. Among them, four DE mRNAs and one DE lncRNA were correlated with overall survival (p < 0.05). C1S was significantly correlated with overall survival (p = 0.015). In functional assays, knockdown of C1S inhibited the proliferation and invasion of glioblastoma cell lines.Conclusions: We established four ceRNA networks that may influence the occurrence and development of glioblastoma. Among them, the MIR155HG/has-miR-129-5p/C1S axis is a potential marker and therapeutic target for glioblastoma. Knockdown of C1S inhibited the proliferation, migration, and invasion of glioblastoma cells. These findings clarify the role of the ceRNA regulatory network in glioblastoma and provide a foundation for further research.

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Identification of Key Genes and Pathways in Pancreatic Cancer Gene Expression Profile by Integrative Analysis

Genes ◽

10.3390/genes10080612 ◽

2019 ◽

Vol 10 (8) ◽

pp. 612 ◽

Cited By ~ 5

Author(s):

Wenzong Lu ◽

Ning Li ◽

Fuyuan Liao

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Differentially Expressed Genes ◽

Topoisomerase Ii ◽

Leucine Zipper ◽

Interaction Network ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Hub Genes ◽

Topoisomerase Ii Alpha

Background: Pancreatic cancer is one of the malignant tumors that threaten human health. Methods: The gene expression profiles of GSE15471, GSE19650, GSE32676 and GSE71989 were downloaded from the gene expression omnibus database including pancreatic cancer and normal samples. The differentially expressed genes between the two types of samples were identified with the Limma package using R language. The gene ontology functional and pathway enrichment analyses of differentially-expressed genes were performed by the DAVID software followed by the construction of a protein–protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in cytoscape software, and the reliability and survival analysis of hub genes was carried out in The Cancer Genome Atlas gene expression data. Results: The 138 differentially expressed genes were significantly enriched in biological processes including cell migration, cell adhesion and several pathways, mainly associated with extracellular matrix-receptor interaction and focal adhesion pathway in pancreatic cancer. The top hub genes, namely thrombospondin 1, DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were identified from the protein–protein interaction network. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas. DNA topoisomerase II alpha, syndecan 1, maternal embryonic leucine zipper kinase and proto-oncogene receptor tyrosine kinase Met were significantly linked with poor survival in pancreatic adenocarcinoma. Conclusions: These hub genes may be used as potential targets for pancreatic cancer diagnosis and treatment.

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PO-046 Gene expression profiling of peripheral blood mononuclear cells in young male trampoline athletes

Exercise Biochemistry Review ◽

10.14428/ebr.v1i3.10113 ◽

2018 ◽

Vol 1 (3) ◽

Author(s):

Li Gao ◽

Yong Jie Yang ◽

En Qi Li ◽

Jia Ning Mao

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Bone Health ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Interaction Network ◽

Differentially Expressed ◽

Signal Pathways ◽

Healthy Controls ◽

Mineral Density

Objective Evidence indicates that physical activity influence bone health. However, the molecular mechanisms mediating the beneficial adaptations to exercise are not well understood. The purpose of this study was to examine the differentially expressed genes in PBMC between athletes and healthy controls, and to analyze the important functional genes and signal pathways that cause increased bone mineral density in athletes, in order to further reveal the molecular mechanisms of exercise promoting bone health. Methods Five professional trampoline athletes and five age-matched untrained college students participated in this study. Used the human expression Microarray V4.0 expression profiling chip to detect differentially expressed genes in the two groups, and performed KEGG Pathway analysis and application of STRING database to construct protein interaction Network; Real-Time PCR technology was used to verify the expression of some differential genes. Results Compared with healthy controls, there were significant improvement in lumbar spine bone mineral density, and 236 up-regulated as well as 265 down-regulated in serum samples of athletes. The differentially expressed genes involved 28 signal pathways, such as cell adhesion molecules. Protein interaction network showed that MYC was at the core node position. Real-time PCR results showed that the expression levels of CD40 and ITGα6 genes in the athletes were up-regulated compared with the healthy controls, the detection results were consistent with that of the gene chip. Conclusions The findings highlight that long-term high-intensity trampoline training could induce transcriptional changes in PBMC of the athletes. These data suggest that gene expression fingerprints can serve as a powerful research tool to design novel strategies for monitoring exercise. The findings of the study also provide support for the notion that PBMC could be used as a substitute to study exercise training that affects bone health.

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Application of a Deep Matrix Factorization Model on Integrated Gene Expression Data

Current Bioinformatics ◽

10.2174/1574893614666191017094331 ◽

2020 ◽

Vol 15 (4) ◽

pp. 359-367

Author(s):

Yong-Jing Hao ◽

Mi-Xiao Hou ◽

Ying-Lian Gao ◽

Jin-Xing Liu ◽

Xiang-Zhen Kong

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Gene Expression Data ◽

Matrix Factorization ◽

Coefficient Matrix ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Complex Data ◽

Expression Data ◽

Factorization Model

Background: Non-negative Matrix Factorization (NMF) has been extensively used in gene expression data. However, most NMF-based methods have single-layer structures, which may achieve poor performance for complex data. Deep learning, with its carefully designed hierarchical structure, has shown significant advantages in learning data features. Objective: In bioinformatics, on the one hand, to discover differentially expressed genes in gene expression data; on the other hand, to obtain higher sample clustering results. It can provide the reference value for the prevention and treatment of cancer. Method: In this paper, we apply a deep NMF method called Deep Semi-NMF on the integrated gene expression data. In each layer, the coefficient matrix is directly decomposed into the basic and coefficient matrix of the next layer. We apply this factorization model on The Cancer Genome Atlas (TCGA) genomic data. Results: The experimental results demonstrate the superiority of Deep Semi-NMF method in identifying differentially expressed genes and clustering samples. Conclusion: The Deep Semi-NMF model decomposes a matrix into multiple matrices and multiplies them to form a matrix. It can also improve the clustering performance of samples while digging out more accurate key genes for disease treatment.

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The Transcriptome of Immunomodulator-Resistant Multiple Myeloma

Blood ◽

10.1182/blood-2019-123407 ◽

2019 ◽

Vol 134 (Supplement_1) ◽

pp. 1772-1772

Author(s):

Moritz Binder ◽

S. Vincent Rajkumar ◽

Martha Q. Lacy ◽

Jessica L. Haug ◽

Angela Dispenzieri ◽

...

Keyword(s):

Gene Expression ◽

Multiple Myeloma ◽

Overall Survival ◽

Differentially Expressed Genes ◽

Research Funding ◽

Differentially Expressed ◽

Resistant Disease ◽

Kaplan Meier ◽

Gene Coding ◽

Cellular Pathways

Introduction: While the molecular target of immunomodulators such as pomalidomide (POM) and lenalidomide (LEN) has been identified, the mechanisms underlying therapeutic resistance remain incompletely understood. The uniformly emerging resistance to therapy over time in the absence of identifiable cereblon pathway mutations in the majority of patients raises questions about alternative mechanisms including aberrant gene expression. Methods: We performed gene expression profiling using an Affymetrix GeneChip Human Genome U133 Plus 2.0 microarray on CD138+ bone marrow cells from patients with relapsed / refractory (RRMM) and newly diagnosed (NDMM) multiple myeloma prior to initiating treatment. Patients were treated on two phase II clinical trial protocols (MC0789: POM ± dexamethasone in RRMM; MC0884: LEN ± dexamethasone in NDMM) between 2007 and 2012. We categorized patients based on their IMWG response as non-responders (SD) and responders (VGPR+). We selected 15 responders and 15 non-responders from MC0789 (n = 30) and compared overall survival, gene expression patterns, and involved cellular pathways between the two groups. We selected 5 responders and 5 non-responders from MC0884 (n = 10) for targeted validation of differentially expressed candidate genes. After data quality control and normalization of gene expression values, differential gene expression was estimated using limma. Statistical significance was adjusted for multiple testing in the discovery set using a false discovery rate-based approach for genome-wide experiments (q-value). We used Gene Ontology and PANTHER pathway analysis for functional annotation of differentially expressed genes. Overall survival estimates were calculated using the Kaplan-Meier method. Computation and visualization were performed in R. Results: Median age at treatment initiation on MC0789 was 65 years (40 - 82), 65% of the patients were male. Pomalidomide resistance was associated with an increase in mortality (median overall survival 1.6 versus 6.4 years, p = 0.009, Kaplan-Meier plot). There were 1076 differentially regulated genes between responders and non-responders (521 up- and 555 down-regulated, q < 0.050 for all genes, volcano plot). Expression of CRBN was 1.5-fold down-regulated in non-responders (q = 0.005). Supervised hierarchical clustering of the top 500 differentially expressed genes demonstrated distinct patterns in pomalidomide-resistant disease (heatmap). Gene ontology analysis revealed protein synthesis as one of the most enriched biological processes (bar graph). Pathway analysis showed a 6-fold enrichment (FDR = 0.007) of the ubiquitin proteasome pathway in pomalidomide-resistant disease. Differentially expressed genes involved key protein degradation pathways, epigenetic modifiers, and transcription factors. Targeted validation in MC0884 revealed 13 common genes with at least 1.5-fold differential expression (5 up- and 8 down-regulated), 12 of which have previously been implicated in the regulation of apoptosis, tumor glucose metabolism, Rho and Wnt signaling, miRNA-driven resistance to chemotherapy, and ubiquitin-dependent protein degradation (Table and Sankey diagram). The most up-regulated gene in non-responders was MYRIP, a gene coding for a vesicle trafficking protein associated with platinum resistance and suppression of pro-apoptotic BCL-2 family members in solid malignancies. The most down-regulated gene was FRZB, a gene coding for a negative regulator of Wnt signaling, previously implicated in the progression of monoclonal gammopathy of undetermined significance to multiple myeloma. Conclusions: Overall survival of patients with pomalidomide-resistant RRMM remains poor. Pomalidomide resistance was associated with differential gene expression in several potentially targetable cellular pathways beyond the known drug target cereblon. Targeted validation of candidate genes revealed common cellular pathways in immunomodulator-resistant disease. Elucidating the exact molecular mechanisms underlying immunomodulator resistance is of considerable interest for biomarker development and the identification of novel therapeutic targets and warrants further exploration. Figure Disclosures Lacy: Celgene: Research Funding. Dispenzieri:Celgene: Research Funding; Alnylam: Research Funding; Intellia: Consultancy; Janssen: Consultancy; Pfizer: Research Funding; Akcea: Consultancy; Takeda: Research Funding. Kumar:Takeda: Research Funding; Celgene: Consultancy, Research Funding; Janssen: Consultancy, Research Funding.

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Comprehensive analysis of ceRNA networks reveals prognostic lncRNAs related to immune infiltration in colorectal cancer

BMC Cancer ◽

10.1186/s12885-021-07995-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jingyi Chen ◽

Yuxuan Song ◽

Mei Li ◽

Yu Zhang ◽

Tingru Lin ◽

...

Keyword(s):

Gene Expression ◽

Colorectal Cancer ◽

Target Genes ◽

Immune Cell ◽

Pearson Correlation ◽

Enrichment Analysis ◽

Cell Types ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cerna Network

Abstract Background Competing endogenous RNA (ceRNA) represents a class of RNAs (e.g., long noncoding RNAs [lncRNAs]) with microRNA (miRNA) binding sites, which can competitively bind miRNA and inhibit its regulation of target genes. Increasing evidence has underscored the involvement of dysregulated ceRNA networks in the occurrence and progression of colorectal cancer (CRC). The purpose of this study was to construct a ceRNA network related to the prognosis of CRC and further explore the potential mechanisms that affect this prognosis. Methods RNA-Seq and miRNA-Seq data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed lncRNAs (DElncRNAs), microRNAs (DEmiRNAs), and mRNAs (DEmRNAs), and a prognosis-related ceRNA network was constructed based on DElncRNA survival analysis. Subsequently, pathway enrichment, Pearson correlation, and Gene Set Enrichment Analysis (GSEA) were performed to determine the function of the genes in the ceRNA network. Gene Expression Profiling Interactive Analysis (GEPIA) and immunohistochemistry (IHC) were also used to validate differential gene expression. Finally, the correlation between lncRNA and immune cell infiltration in the tumor microenvironment was evaluated based on the CIBERSORT algorithm. Results A prognostic ceRNA network was constructed with eleven key survival-related DElncRNAs (MIR4435-2HG, NKILA, AFAP1-AS1, ELFN1-AS1, AC005520.2, AC245884.8, AL354836.1, AL355987.4, AL591845.1, LINC02038, and AC104823.1), 54 DEmiRNAs, and 308 DEmRNAs. The MIR4435-2HG- and ELFN1-AS1-associated ceRNA subnetworks affected and regulated the expression of the COL5A2, LOX, OSBPL3, PLAU, VCAN, SRM, and E2F1 target genes and were found to be related to prognosis and tumor-infiltrating immune cell types. Conclusions MIR4435-2HG and ELFN1-AS1 are associated with prognosis and tumor-infiltrating immune cell types and could represent potential prognostic biomarkers or therapeutic targets in colorectal carcinoma.

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A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

Cancer Cell International ◽

10.1186/s12935-021-01823-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xin Xu ◽

Yida Lu ◽

Youliang Wu ◽

Mingliang Wang ◽

Xiaodong Wang ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Overall Survival ◽

Risk Score ◽

Cox Regression ◽

External Validation ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Survival Prediction ◽

Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

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Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.811585 ◽

2022 ◽

Vol 9 ◽

Author(s):

Shumei Zhang ◽

Haoran Jiang ◽

Bo Gao ◽

Wen Yang ◽

Guohua Wang

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Gene Interaction ◽

Diagnostic Markers ◽

Differentially Expressed ◽

Breast Cancer Patients ◽

Gene Interaction Network

Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women’s health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers.Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data.Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p < 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively).Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.

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The Genetics Analysis of Molecular Pathogenesis for Alzheimer's Disease

European Neurology ◽

10.1159/000509799 ◽

2020 ◽

Vol 83 (5) ◽

pp. 458-467

Author(s):

Guanchuan Lin ◽

Kaiyuan Ji ◽

Shiyu Li ◽

Wenli Ma ◽

Xinghua Pan

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Brain Regions ◽

Molecular Pathogenesis ◽

Differentially Expressed ◽

Control Group ◽

Membrane Structures

Introduction: The molecular pathogenesis of Alzheimer’s disease (AD) is still not clear, and the relationship between gene expression profile for different brain regions has not been studied. Objective: Bioinformatic analysis at the genetic level has become the best way for the pathogenesis research of AD, which can analyze the abovementioned relationship. Methods: In this study, the datasets of AD were obtained from the Gene Expression Omnibus (GEO), and Qlucore Omics Explorer (QOE) software was used to screen differentially expressed genes of GSE36980 and GSE9770 and verify gene expression of GSE63060. The Gene Ontology (GO) function enrichment analysis of these selected genes was conducted by Database for Annotation, Visualization, and Integrated Discovery (DAVID), and then the gene/protein interaction network was established by STRING to find the related proteins. R language was used for drafting maps and plots. Results: There were 20 differentially expressed genes related to AD selected from GSE36980 (p = 6.2e−6, q = 2.9422e−4) and GSE9770 (p = 3.3e−4, q = 0.016606). Their expression levels of the AD group were lower than those in the control group and varied among different brain regions. Cellular morphogenesis and establishment or maintenance of cell polarity were enriched, and LRRTM1 and RASAL1 were identified by the integration network. Moreover, the analysis of GSE63060 verified the expression level of LRRTM1 and RASAL1 in Alzheimer’s patients, which was much lower than that in normal people aged >65 years. Conclusions: The pathogenesis of AD at molecular levels may link to cell membrane structures and signal transduction; hence, a list of 20 genes, including LRRTM1 and RASAL1,potentially are important for the discovery of treatment target or molecular marker of AD.

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