Screening feature genes of lung carcinoma with DNA microarray analysis.
ABSTRACT: Lung carcinoma is the most common and aggressive malignant tumor with poor clinical outcome. Identification of new marker of lung cancer is essential for the diagnosis and prognosis of the disease. To identify differentially expressed genes (DEGs) and find associated pathways that may function as targets of lung cancer. Gene expression profiling of GSE40791 were downloaded from GEO (Gene Expression Omnibus), including 100 normal specimens and 94 lung cancer samples. The DEGs were screened out by LIMMA package in R language. Besides, novel genes associated with lung cancer were identified by co-expression analysis. Then, GO enrichment and transcription binding site analysis were performed on these DEGs, and novel genes were predicted using DAVID. Finally, PPI network was constructed by String software in order to get the hub codes involved in cancer carcinoma. A total of 541 DEGs were filtered out between normal samples and patients with lung carcinoma, including 155 up-regulated genes and 386 down-regulated genes. Additionally, nine novel genes, CA4, CDC20, CHRDL1, DLGAP5, EMCN, GPM6A, NUSAP1, S1PR1 and TCF21, were figured out. The transcription biding site analysis showed that these genes were regulated by LHX3, HNF3B, CDP, HFH1, FOXO4, STAT, SOX5, MEF2, FOXO3 and SRY. Hub codes as BUB1B, MAD2L and TOP2A may play as target genes in lung carcinoma in the result of PPI network analysis. Newly predicted genes and hub codes can perform as target genes for diagnose and clinical therapy of lung cancer.
Project description:Background: Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC. Methods: The microarray datasets of GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 from Gene Expression Omnibus database were downloaded. The differential expressed genes (DEGs) between NSCLC and normal samples were identified by limma package. The construction of protein-protein interaction (PPI) network, module analysis and enrichment analysis were performed using bioinformatics tools. The expression and prognostic values of hub genes were validated by GEPIA database and real-time quantitative PCR. Based on these DEGs, the candidate small molecules for NSCLC were identified by the CMap database. Results: A total of 408 overlapping DEGs including 109 up-regulated and 296 down-regulated genes were identified; 300 nodes and 1283 interactions were obtained from the PPI network. The most significant biological process and pathway enrichment of DEGs were response to wounding and cell adhesion molecules, respectively. Six DEGs (PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5) which significantly up-regulated in NSCLC tissues, were selected as hub genes according to the results of module analysis. The GEPIA database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. Additionally, CMap predicted the 20 most significant small molecules as potential therapeutic drugs for NSCLC. DL-thiorphan was the most promising small molecule to reverse the NSCLC gene expression. Conclusions: Based on the gene expression profiles of 696 NSCLC samples and 237 normal samples, we first revealed that PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5 could act as the promising novel diagnostic and therapeutic targets for NSCLC. Our work will contribute to clarifying the molecular mechanisms of NSCLC initiation and progression.
Project description:BACKGROUND:Large-cell neuroendocrine carcinoma of the lung (LCNEC) and small-cell lung carcinoma (SCLC) are neuroendocrine neoplasms. However, the underlying mechanisms of common and distinct genetic characteristics between LCNEC and SCLC are currently unclear. Herein, protein expression profiles and possible interactions with miRNAs were provided by integrated bioinformatics analysis, in order to explore core genes associated with tumorigenesis and prognosis in SCLC and LCNEC. METHODS:GSE1037 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in LCNEC and SCLC, as compared with normal lung tissues, were selected using the GEO2R online analyzer and Venn diagram software. Gene ontology (GO) analysis was performed using Database for Annotation, Visualization and Integrated Discovery. The biological pathway analysis was performed using the FunRich database. Subsequently, a protein-protein interaction (PPI) network of DEGs was generated using Search Tool for the Retrieval of Interacting Genes and displayed via Cytoscape software. The PPI network was analyzed by the Molecular Complex Detection app from Cytoscape, and 16 upregulated hub genes were selected. The Oncomine database was used to detect expression patterns of hub genes for validation. Furthermore, the biological pathways of these 16 hub genes were re-analyzed, and potential interactions between these genes and miRNAs were explored via FunRich. RESULTS:A total of 384 DEGs were identified. A Venn diagram determined 88 common DEGs. The PPI network was constructed with 48 nodes and 221 protein pairs. Among them, 16 hub genes were extracted, 14 of which were upregulated in SCLC samples, as compared with normal lung specimens, and 10 were correlated with the cell cycle pathway. Furthermore, 57 target miRNAs for 8 hub genes were identified, among which 31 miRNAs were correlated with the progression of carcinoma, drug-resistance, radio-sensitivity, or autophagy in lung cancer. CONCLUSION:This study provided effective biomarkers and novel therapeutic targets for diagnosis and prognosis of SCLC and LCNEC.
Project description:Lung cancer is the leading cause of cancer-related mortality worldwide. Despite progress in the treatment of non-small-cell lung cancer, there are limited treatment options for lung squamous cell carcinoma (LUSC), compared with lung adenocarcinoma. The present study investigated the disease mechanism of LUSC in order to identify key candidate genes for diagnosis and therapy. A total of three gene expression profiles (GSE19188, GSE21933 and GSE74706) were analyzed using GEO2R to identify common differentially expressed genes (DEGs). The DEGs were then investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. A protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes/Proteins, and visualized using Cytoscape software. The expression levels of the hub genes identified using CytoHubba were validated using the University of California, Santa Cruz (UCSC) database and the Human Protein Atlas. A Kaplan-Meier curve and Gene Expression Profiling Interactive Analysis were then employed to evaluate the associated prognosis and clinical pathological stage of the hub genes. Furthermore, non-coding RNA regulatory networks were constructed using the Gene-Cloud Biotechnology information website. A total of 359 common DEGs (155 upregulated and 204 downregulated) were identified, which were predominantly enriched in 'mitotic nuclear division', 'cell division', 'cell cycle' and 'p53 signaling pathway'. The PPI network consisted of 257 nodes and 2,772 edges, and the most significant module consisted of 66 upregulated genes. A total of 19 hub genes exhibited elevated RNA levels, and 10 hub genes had elevated protein levels compared with normal lung tissues. The upregulation of five hub genes (CCNB1, CEP55, FOXM1, MKI67 and TYMS; defined in Table I) were significantly associated with poor overall survival and unfavorable clinical pathological stages. Various ncRNAs, such as C1orf220, LINC01561 and MGC39584, may also play important roles in hub-gene regulation. In conclusion, the present study provides further understanding of the pathogenesis of LUSC, and reveals CCNB1, CEP55, FOXM1, MKI67 and TYMS as potential biomarkers or therapeutic targets.
Project description:Background:Lung squamous cell carcinoma (LUSC) is a major subtype of lung cancer with limited therapeutic options and poor clinical prognosis. Methods:Three datasets (GSE19188, GSE33532 and GSE33479) were obtained from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between LUSC and normal tissues were identified by GEO2R, and functional analysis was employed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. Protein-protein interaction (PPI) and hub genes were identified via the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Hub genes were further validated in The Cancer Genome Atlas (TCGA) database. Subsequently, survival analysis was performed using the Kapla-Meier curve and Cox progression analysis. Based on univariate and multivariate Cox progression analysis, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic value of the model. Results:A total of 116 up-regulated genes and 84 down-regulated genes were identified. These DEGs were mainly enriched in the two pathways: cell cycle and p53 signaling way. According to the degree of protein nodes in the PPI network, 10 hub genes were identified. The mRNA expression levels of the 10 hub genes in LUSC were also significantly up-regulated in the TCGA database. Furthermore, a novel seven-gene signature (FLRT3, PPP2R2C, MMP3, MMP12, CAPN8, FILIP1 and SPP1) from the DEGs was constructed and acted as a significant and independent prognostic signature for LUSC. Conclusions:The 10 hub genes might be tightly correlated with LUSC progression. The seven-gene signature might be an independent biomarker with a significant predictive value in LUSC overall survival.
Project description:Lung cancer is a common malignancy worldwide. The aim of the present study was to investigate differentially expressed genes (DEGs) between non-small-cell lung cancer (NSCLC) and normal lung tissue, and to reveal the potential molecular mechanism underlying NSCLC. The Gene Expression Omnibus database was used to obtain three gene expression profiles (GSE18842, GSE30219 and GSE33532). DEGs were obtained by GEO2R. Gene Ontology and pathway enrichment analyses were performed for DEGs in the Database for Annotation, Visualization and Integrated Discovery. A protein-protein interaction (PPI) network of DEGs was constructed and analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins database and Cytoscape software. A survival analysis was performed and protein expression levels of DEGs in human NSCLC were analyzed in order to determine clinical significance. A total of 764 DEGs were identified, consisting of 428 upregulated and 336 downregulated genes in NSCLC tissues compared with normal lung tissues, which were enriched in the 'cell cycle', 'cell adhesion molecules', 'p53 signaling pathway', 'DNA replication' and 'tight junction'. A PPI network of DEGs consisting of 51 nodes and 192 edges was constructed. The top 10 genes were identified as hub genes from the PPI network. High expression of 4 of the 10 hub genes was associated with worse overall survival rate in patients with NSCLC, including CDK1, PLK1, RAD51 and RFC4. In conclusion, the present study aids in improving the current understanding of aberrant gene expression between NSCLC tissues and normal lung tissues underlying tumorgenesis in NSCLC. Identified hub genes can be used as a tumor marker for diagnosis and prognosis or as a drug therapy target in NSCLC.
Project description:BACKGROUND: To explore the molecular mechanisms of the anti-cancer effect of curcumin in human lung squamous cell carcinoma (LSQCC) SK-MES-1 cells. METHODS: Cell viability was determined using MTT assay. Ribonucleic acid sequencing was performed to measure expression levels of transcripts in LSQCC cells treated with 15??mol/L curcumin (treatment groups) or an equal amount of dimethylsulfoxide (control). Cuffdiff software was used to identify differentially expressed genes (DEGs) in treatment groups, followed by enrichment analysis of DEGs using the Database for Annotation, Visualization and Integration Discovery. The protein-protein interaction (PPI) networks for up and downregulated DEGs were constructed by Cytoscape software using Search Tool for the Retrieval of Interacting Genes data to identify hub nodes. RESULTS: Curcumin significantly reduced cell viability in LSQCC cells. In total, 380 DEGs including 154?upregulated and 126 downregulated genes were found in the treatment groups. The upregulated genes were enriched in base excision repair (BER, such as PCNA, POLL, and MUTYH) and Janus kinase-signal transducer and activator of transcription (JAT-STAT) signaling pathways (such as AKT1 and STAT5A), while the downregulated genes were enriched in nine pathways, including the vascular endothelial growth factor (VEGF) signaling pathway (such as PTK2, VEGFA, MAPK1, and MAPK14) and mitogen-activated protein kinase (MAPK) signaling pathway (ARRB2, MAPK1, MAPK14, and NFKB1). PCNA and AKT1 were the hub nodes in the PPI network of upregulated genes while MAPK1, MAPK14, VEGFA, and NFKB1 were the hub nodes in the PPI network of downregulated genes. CONCLUSIONS: Curcumin might exert anti-cancer effects on LSQCC via regulating BER, JAT-STAT, VEGF, and MAPK signaling pathways.
Project description:Smoking is considered the major risk factor for lung cancer, but only a small portion of female lung adenocarcinoma patients are associated with smoking. Thus, identifying crucial genes and pathways related to nonsmoking female lung cancer patients is of great importance. Gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases. The R software packages were applied to screen the differentially expressed genes (DEGs). GO term enrichment and KEGG pathway analyses were carried out using DAVID tools. The protein-protein interaction (PPI) network was constructed by Cytoscape software. In total, 487 downregulated and 199 upregulated DEGs were identified. The down-regulated DEGs were mainly enriched for behavior and response to wounding, and the upregulated DEGs were significantly enriched for multicellular organismal metabolic process and cell division. The KEGG pathway analysis revealed that the downregulated DEGs were significantly enriched for cell adhesion molecules and neuroactive ligand-receptor interaction, while the upregulated DEGs were mainly enriched for cell cycle and the p53 signaling pathway. The top 10 hub genes and top 3 gene interaction modules were selected from the PPI network. Of the ten hub genes, a high expression of five genes was related to a poor OS in female lung cancer patients who never smoked, including IL6, CXCR2, FPR2, PPBP and HBA1. However, a low expression of GNG11, LRRK2, CDH5, CAV1 and SELE was associated with a worse OS for the female lung cancer patients who never smoked. In conclusion, our study provides novel insight for a better understanding of the pathogenesis of nonsmoking female lung cancer, and these identified DEGs may serve as biomarkers for diagnostics and treatment.
Project description:BACKGROUND:Lung cancer is the most common cause of cancer-related death among all human cancers and the five-year survival rates are only 23%. The precise molecular mechanisms of non-small cell lung cancer (NSCLC) are still unknown. The aim of this study was to identify and validate the key genes with prognostic value in lung tumorigenesis. METHODS:Four GEO datasets were obtained from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (DEGs) were selected for Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology enrichment analysis. Protein-protein interaction (PPI) networks were constructed using the STRING database and visualized by Cytoscape software and Molecular Complex Detection (MCODE) were utilized to PPI network to pick out meaningful DEGs. Hub genes, filtered from the CytoHubba, were validated using the Gene Expression Profiling Interactive Analysis database. The expressions and prognostic values of hub genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier plotter. Finally, quantitative PCR and the Oncomine database were used to verify the differences in the expression of hub genes in lung cancer cells and tissues. RESULTS:A total of 121 DEGs (49 upregulated and 72 downregulated) were identified from four datasets. The PPI network was established with 121 nodes and 588 protein pairs. Finally, AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 were selected by Cytohubba, and they all correlated with worse overall survival (OS) in NSCLC. CONCLUSION:The results showed that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC. KEY POINTS:Our results indicated that AURKA, KIAA0101, CDC20, MKI67, CHEK1, HJURP, and OIP5 may be critical genes in the development and prognosis of NSCLC. Our methods showed a new way to explore the key genes in cancer development.
Project description:Small cell lung cancer (SCLC) is a carcinoma of the lungs with strong invasion, poor prognosis and resistant to multiple chemotherapeutic drugs. It has posed severe challenges for the effective treatment of lung cancer. Therefore, searching for genes related to the development and prognosis of SCLC and uncovering their underlying molecular mechanisms are urgent problems to be resolved. This study is aimed at exploring the potential pathogenic and prognostic crucial genes and key pathways of SCLC via bioinformatic analysis of public datasets. Firstly, 117 SCLC samples and 51 normal lung samples were collected and analyzed from three gene expression datasets. Then, 102 up-regulated and 106 down-regulated differentially expressed genes (DEGs) were observed. And then, functional annotation and pathway enrichment analyzes of DEGs was performed utilizing the FunRich. The protein-protein interaction (PPI) network of the DEGs was constructed through the STRING website, visualized by Cytoscape. Finally, the expression levels of eight hub genes were confirmed in Oncomine database and human samples from SCLC patients. It showed that CDC20, BUB1, TOP2A, RRM2, CCNA2, UBE2C, MAD2L1, and BUB1B were upregulated in SCLC tissues compared to paired adjacent non-cancerous tissues. These suggested that eight hub genes might be viewed as new biomarkers for prognosis of SCLC or to guide individualized medication for the therapy of SCLC.
Project description:With the rapid breakthrough of electrochemical treatment of tumors, electric field (EF)-sensitive genes, previously rarely exploited, have become an emerging field recently. Here, we reported our work for the identification of EF-sensitive genes in lung cancer cells. The gene expression profile (GSE33845), in which the human lung cancer CL1-0 cells were treated with a direct current electric field (dcEF) (300?mV/mm) for 2?h, was retrieved from GEO database. Differentially expressed genes (DEGs) were acquired, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) and protein-protein interaction (PPI) analysis. Hub genes were acquired and analyzed by various tools including the Human Protein Atlas, Kaplan-Meier analysis, Cytoscape, FunRich, Oncomine and cBioPortal. Subsequently, three-dimensional protein models of hub genes were modeled by Modeller 9.20 and Rosetta 3.9. Finally, a 100?ns molecular dynamics simulation for each hub protein was performed with GROMACS 2018.2. A total of 257 DEGs were acquired and analyzed by GO, KEGG and PPI. Then, 10 hub genes were obtained, and the signal pathway analysis showed that two inflammatory pathways were activated: the FoxO signaling pathway and the AGE-RAGE signaling pathway. The molecular dynamic analysis including RMSD and the radius of gyration hinted that the 3D structures of hub proteins were built. Overall, our work identified EF-sensitive genes in lung cancer cells and identified that the inflammatory state of tumor cells may be involved in the feedback mechanism of lung cancer cells in response to electric field stimulation. In addition, qualified three-dimensional protein models of hub genes were also constructed, which will be helpful in understanding the complex effects of dcEF on human lung cancer CL1-0 cells.