Identification of Key Genes and Pathways in Gefitinib-Resistant Lung Adenocarcinoma using Bioinformatics Analysis.
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ABSTRACT:
SUBMITTER: Mao K
PROVIDER: S-EPMC8202261 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma (LUAD) via bioinformatics analysis, and investigate potential therapeutic targets.AimTo determine reliable prognostic biomarkers for early diagnosis and treatment of LUAD.MethodsTo identify potential therapeutic targets for LUAD, two microarray datasets derived from the Gene Expression Omnibus (GEO) database were analyzed, GSE3116959 and GSE118370. Differentially expressed genes (DEGs) in LUAD and normal tissues were identified using the GEO2R tool. The Hiplot database was then used to generate a volcanic map of the DEGs. Weighted gene co-expression network analysis was conducted to cluster the genes in GSE116959 and GSE118370 into different modules, and identify immune genes shared between them. A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database, then the CytoNCA and CytoHubba components of Cytoscape software were used to visualize the genes. Hub genes with high scores and co-expression were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform enrichment analysis of these genes. The diagnostic and prognostic values of the hub genes were calculated using receiver operating characteristic curves and Kaplan-Meier survival analysis, and gene-set enrichment analysis was conducted. The University of Alabama at Birmingham Cancer data analysis portal was used to analyze relationships between the hub genes and normal specimens, as well as their expression during tumor progression. Lastly, validation of protein expression was conducted on the identified hub genes via the Human Protein Atlas database.ResultsThree hub genes with high connectivity were identified; cellular retinoic acid binding protein 2 (CRABP2), matrix metallopeptidase 12 (MMP12), and DNA topoisomerase II alpha (TOP2A). High expression of these genes was associated with a poor LUAD prognosis, and the genes exhibited high diagnostic value.ConclusionExpression levels of CRABP2, MMP12, and TOP2A in LUAD were higher than those in normal lung tissue. This observation has diagnostic value, and is linked to poor LUAD prognosis. These genes may be biomarkers and therapeutic targets in LUAD, but further research is warranted to investigate their usefulness in these respects.
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Project description:Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a high degree of malignancy that is difficult to diagnose and treat. The present study integrated PDAC cohort profile datasets to identify key candidate genes and pathways involved in the pathogenesis of the disease. The expression profiles of GSE28735 included 45 PDCA and matching pairs of adjacent non-tumor tissue. Differentially expressed genes (DEGs) were sorted and candidate genes and pathway enrichment were analyzed. A DEG-associated protein-protein interaction (PPI) network was constructed. A total of 424 DEGs were identified in PDAC, including 159 upregulated genes and 265 downregulated genes. Gene Ontology analysis results indicated that upregulated DEGs were significantly enriched in biological process, molecular function and cellular component categories. Kyoto Encyclopedia of Genes and Genomes pathway analysis demonstrated that the upregulated DEGs were enriched in 'pancreatic secretion', 'protein digestion' and 'absorption'. Downregulated DEGs were enriched in 'ECM-receptor interaction', 'focal adhesion' and 'PI3K/AKT' signaling pathways. The PPI network revealed that these genes were involved in significant pathways, including 'ECM organization' signaling pathways (Hippo signaling pathway, TGF-β signaling pathway, Hedgehog signaling pathway and Wnt signaling pathway), 'serine-type peptidase activity' signaling pathway (PI3K-Akt signaling pathway, TNF-α signaling pathway and Wnt signaling pathway) and 'extracellular region' signaling pathways (RTP signaling pathway, G protein-coupled receptor signaling pathway and RAS-RAF-MAPK signaling pathway). The identification of these candidate genes and pathways sheds light on the etiology and molecular mechanisms of PDAC and may guide the development of novel therapies for pancreatic cancer.
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Project description:Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, comprising around 40% of all lung cancer. Until now, the pathogenesis of LUAD has not been fully elucidated. In the current study, we comprehensively analyzed the dysregulated genes in lung adenocarcinoma by mining public datasets. Two sets of gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. The dysregulated genes were identified by using the GEO2R online tool, and analyzed by R packages, Cytoscape software, STRING, and GPEIA online tools. A total of 275 common dysregulated genes were identified in two independent datasets, including 54 common up-regulated and 221 common down-regulated genes in LUAD. Gene Ontology (GO) enrichment analysis showed that these dysregulated genes were significantly enriched in 258 biological processes (BPs), 27 cellular components (CCs), and 21 molecular functions (MFs). Furthermore, protein-protein interaction (PPI) network analysis showed that PECAM1, ENG, KLF4, CDH5, and VWF were key genes. Survival analysis indicated that the low expression of ENG was associated with poor overall survival (OS) of LUAD patients. The low expression of PECAM1 was associated with poor OS and recurrence-free survival of LUAD patients. The cox regression model developed based on age, tumor stage, ENG, PECAM1 could effectively predict 5-year survival of LUAD patients. This study revealed some key genes, BPs, CCs, and MFs involved in LUAD, which would provide new insights into understanding the pathogenesis of LUAD. In addition, ENG and PECAM1 might serve as promising prognostic markers in LUAD.
| S-EPMC10454774 | biostudies-literature
Project description:Esophageal adenocarcinoma (EAC) is the predominant pathological subtype of esophageal cancer in Europe and the USA. The present bioinformatics study analyzed a high-throughput sequencing dataset, GSE94869, to determine differentially expressed genes (DEGs) in order to identify key genes, biological processes and pathways associated with EAC. Functional enrichment analysis was performed using the Database for Annotation Visualization and Integrated Discovery. The co-expression network of the DEGs was established using Weighted Gene Co-Expression Network Analysis and visualized using Cytoscape. A Kaplan-Meier analysis based on The Cancer Genome Atlas (TCGA) database was used to identify prognosis-associated genes. Univariate and multivariate Cox proportional hazard models were used to identify genes with a prognostic value regarding relapse-free survival (RFS), while validation of the differential expression of prognosis-associated genes was performed using a box plot based on data from TCGA and another microarray dataset, GSE26886. A total of 130 DEGs, comprising 82 upregulated and 48 downregulated genes, were identified. The upregulated DEGs were significantly associated with extracellular matrix organization, disassembly, and the phosphoinositide-3 kinase/AKT, Rap1 and Ras signaling pathways, while the downregulated genes were associated with the Wnt signalling pathway. Subsequently, two co-expression modules were established and 20 hub genes were identified. The blue module was associated with the Rap1 signaling pathway, while the turquoise module was associated with the Ras and Rap1 signaling pathways. Among them, methyltransferase like 7B (METTL7B) was associated with RFS. Furthermore, the overexpression of METTL7B in EAC was successfully validated using data from TCGA and GSE26886. The present study identified key genes and provides potential biomarkers for the diagnosis and treatment of EAC.
| S-EPMC6781836 | biostudies-literature
Project description:BackgroundIdentification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis.MethodsIn this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues.ResultsA total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues.ConclusionsThe hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.
| S-EPMC8812315 | biostudies-literature
Project description:Esophageal adenocarcinoma (EAC) is one of the most common subtypes of esophageal cancer, and is associated with a low 5-year survival rate. The present study aimed to identify key genes and pathways associated with EAC using bioinformatics analysis. The gene expression profiles of GSE92396, which includes 12 EAC samples and 9 normal esophageal samples, were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the EAC and normal samples were identified using the limma package in R language. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the identified DEGs were conducted using the online analysis tool, the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. Finally, module analysis was conducted for the PPI network using the MCODE plug-in in Cytoscape. Of the 386 DEGs identified, the 150 upregulated genes were mainly enriched in the KEGG pathways of complement and coagulation cascades, maturity onset diabetes of the young and protein digestion and absorption; and the 236 downregulated genes were mainly enriched in amoebiasis, retinol metabolism and drug metabolism-cytochrome P450. Based on information from the STRING database, a PPI network comprising of 369 nodes and 534 edges was constructed in Cytoscape. The top 10 hub nodes with the highest degrees were determined as interleukin-8, involucrin, tissue inhibitor of metalloproteinase 1, fibronectin 1, serpin family E member 1, serpin family A member 1, cystic fibrosis transmembrane conductance regulator, secreted phosphoprotein 1, collagen type I alpha 1 chain and angiotensinogen. A total of 6 modules were detected from the PPI network that satisfied the criteria of MCODE score >4 and number of nodes >4. KEGG pathways enriched for the module DEGs were mainly within arachidonic acid metabolism, complement and coagulation cascades and rheumatoid arthritis. In conclusion, identification of these key genes and pathways may improve understanding of the mechanisms underlying the development of EAC, and may be used as diagnostic and therapeutic targets in EAC.
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Project description:Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in the head and neck. The aim of the current study was to identify the key pathways and genes involved in NPC through bioinformatics analysis and to identify potential molecular mechanisms underlying NPC proliferation and progression. Three gene expression profiles (GSE12452, GSE34573 and GSE64634) were downloaded from the Gene Expression Omnibus database. A total of 76 samples were analyzed, of which 59 were NPC samples and 17 were normal samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were subsequently conducted. The protein-protein interaction (PPI) network of the differentially expressed genes (DEGs) was constructed using Cytoscape software. Analysis of GSE12452, GSE34573 and GSE64634 datasets identified 1,301 (553 upregulated and 748 downregulated), 1,232 (348 upregulated and 884 downregulated) and 1,218 (555 upregulated and 663 downregulated) DEGs, respectively. Using Venn diagram analysis, 268 DEGs (59 upregulated and 209 downregulated) that intersected all three datasets, were selected for further analysis. The results of GO analysis revealed that upregulated DEGs were significantly enriched in biological processes, including 'cell adhesion', 'cell division', 'mitosis' and 'mitotic cell cycle'. The downregulated DEGs were mainly enriched in 'microtubule-based movement', 'cilium movement', 'cilium axoneme assembly' and 'epithelial cell differentiation'. The KEGG pathway analysis results revealed that the upregulated DEGs were highly associated with several pathways, including 'extracellular matrix-receptor interaction', 'human papillomavirus infection', 'arrhythmogenic right ventricular cardiomyopathy' and 'focal adhesion', whereas the downregulated DEGs were enriched in 'metabolic pathways', 'Huntington's disease', 'fluid shear stress and atherosclerosis' and 'chemical carcinogenesis'. On the basis of the PPI network of the DEGs, the following top 10 hub genes were identified: Dynein axonemal light intermediate chain 1, dynein axonemal intermediate chain 2, calmodulin 1, coiled-coil domain containing 114, dynein axonemal heavy chain 5, radial spoke head 9 homolog, radial spoke head component 4A, NDC80 kinetochore complex component, thymidylate synthetase and coiled-coil domain containing 39. In conclusion, by performing a comprehensive bioinformatics analysis of DEGs, putative targets that could be used to elucidate the molecular mechanisms underlying NPC were identified.
| S-EPMC6447945 | biostudies-literature
Project description:BackgroundAtherosclerosis (AS) is a chronic inflammatory disease that might induce severe cardiovascular events, such as myocardial infarction and cerebral infarction. These risk factors in the pathogenesis of AS remain uncertain and further research is needed. This study aims to explore the potential molecular mechanisms of AS by bioinformatics analyses.MethodsGSE100927 gene expression profiles, including 69 AS samples and 35 healthy controls, were downloaded from Gene Expression Omnibus database and indenfied for key genes and pathways in AS.ResultsA total of 443 differentially expressed genes (DEGs) between control and AS were identified, including 323 down-regulated genes and 120 up-regulated genes. The Gene ontology terms enriched by the up-regulated DEGs were associated with the regulation of leukocyte activation, endocytic vesicle, and cytokine binding, while the down-regulated DEGs were associated with negative regulation of cell growth, extracellular matrix, and G protein-coupled receptor binding. KEGG pathway analysis showed that the up-regulated DEGs were enriched in Osteoclast differentiation and Phagosome, while the down-regulated DEGs were enriched in vascular smooth muscle contraction and cGMP-PKG signaling pathway. Using the modular analysis of Cytoscape, we identified 3 modules mainly involved in Leishmaniasis and Osteoclast differentiation. The GSEA analysis showed the up-regulated gene sets were enriched in the ribosome, ascorbated metabolism, and propanoate metabolism. The LASSO Cox regression analysis showed the top 3 genes were TNF, CX3CR1, and COL1R1. Finally, we found these immune cells were conferred significantly higher infiltrating density in the AS group.ConclusionsOur data showed the pathway of Osteoclast differentiation and Leishmaniasis was involved in the AS process and we developed a three-gene model base on the prognosis of AS. These findings clarified the gene regulatory network of AS and may provide a novel target for AS therapy.
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Project description:The aim of the present study is to identify novel promising marks and targets of diagnosis, therapy and prognosis for patients with vestibular schwannoma at the molecular level. The gene expression profiles of GSE54934, GSE39645 and GSE56597 datasets were obtained respectively from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified by comparing between gene expression profiles of the vestibular schwannoma tissues and normal tissues. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) network analysis were performed. The function and pathway enrichment analysis were performed for DEGs with DAVID. Reverse transcription-quantitative PCR were conducted to confirm the expression of BCL2, AGT, IL6 and ITGA2 in human Schwann cells and vestibular schwannoma cells. A total of 4,025, 1,1291 and 1,513 DEGs were identified from GSE54934, GSE56597 and GSE39645 datasets, respectively. GO and KEGG analysis showed that the mutual upregulated genes were mainly enriched in cell division, mitotic nuclear division, and transition of mitotic cell cycle, whilst mutual downregulated genes were enriched in chemical synaptic transmission, neurotransmitter transport, and synaptic vesicle membrane. Subsequently, 20 genes, including BCL2, AGT, IL6 and ITGA2 were selected as hub genes with high degrees after PPI network analysis. The significant differential expression of those genes were detected among vestibular schwannoma tissues compared with normal nerve tissues. In conclusion, BCL2, AGT, IL6 and ITGA2 are significantly higher expressed in vestibular schwannoma tissues compared with human Schwann tissues. The DEGs identified in the present study provide novel targets for the diagnosis and treatment of vestibular schwannoma.
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Project description:Endometrial cancer (EC) is one of the most common gynecological cancer types worldwide. However, to the best of our knowledge, its underlying mechanisms remain unknown. The current study downloaded three mRNA and microRNA (miRNA) datasets of EC and normal tissue samples, GSE17025, GSE63678 and GSE35794, from the Gene Expression Omnibus to identify differentially expressed genes (DEGs) and miRNAs (DEMs) in EC tumor tissues. The DEGs and DEMs were then validated using data from The Cancer Genome Atlas and subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. STRING and Cytoscape were used to construct a protein-protein interaction network and the prognostic effects of the hub genes were analyzed. Finally, miRecords was used to predict DEM targets and an miRNA-gene network was constructed. A total of 160 DEGs were identified, of which 51 genes were highly expressed and 100 DEGs were discovered from the PPI network. Three overlapping genes between the DEGs and the DEM targets, BIRC5, CENPF and HJURP, were associated with significantly worse overall survival of patients with EC. A number of DEGs were enriched in cell cycle, human T-lymphotropic virus infection and cancer-associated pathways. A total of 20 DEMs and 29 miRNA gene pairs were identified. In conclusion, the identified DEGs, DEMs and pathways in EC may provide new insights into understanding the underlying molecular mechanisms that facilitate EC tumorigenesis and progression.
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