Identification of key microRNAs and hub genes in non‐small‐cell lung cancer using integrative bioinformatics and functional analyses

Background. Small-cell lung cancer (SCLC) is a major cause of carcinoma-related deaths worldwide. The aim of this study was to identify the key biomarkers and pathways in SCLC using biological analysis. Methods. Key genes involved in the development of SCLC were identified by downloading three datasets from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened using the GEO2R online analyzer; for the functional annotation and pathway enrichment analysis of genes, Funrich software was used. Construction of protein-to-protein interaction (PPI) networks was accomplished using the Search Tool for the Retrieval of Interacting Genes (STRING), and network visualization and module identification were performed using Cytoscape. Results. A total of 268 DEGs were ultimately obtained. The enriched functions and pathways of the upregulated DEGs included cell cycle, mitotic, and DNA replication, and the downregulated DEGs were enriched in epithelial-to-mesenchymal transition, serotonin degradation, and noradrenaline. Analysis of significant modules demonstrated that the upregulated genes are primarily concentrated in functions related to cell cycle and DNA replication. Kaplan-Meier analysis of hub genes revealed that they may promote the carcinogenesis and progression of SCLC. The result of ONCOMINE demonstrated that these 10 hub genes were significantly overexpressed in SCLC compared with normal samples. Conclusion. Identification of the molecular functions and signaling pathways of participating DEGs can deepen the current understanding of the molecular mechanisms of SCLC. The knowledge gained from this work may contribute to the development of treatment options and improve the prognosis of SCLC in the future.

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Analysis of the Interaction Network of Hub miRNAs-Hub Genes, Being Involved in Idiopathic Pulmonary Fibers and Its Emerging Role in Non-small Cell Lung Cancer

Frontiers in Genetics ◽

10.3389/fgene.2020.00302 ◽

2020 ◽

Vol 11 ◽

Cited By ~ 1

Author(s):

Dong Hu Yu ◽

Xiao-Lan Ruan ◽

Jing-Yu Huang ◽

Xiao-Ping Liu ◽

Hao-Li Ma ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Interaction Network ◽

Small Cell ◽

Small Cell Lung ◽

Hub Genes

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Identification of Hub Genes and Small Molecule Drugs Associated with Acquired Resistance to Gefitinib in Non-Small Cell Lung Cancer

Journal of Cancer ◽

10.7150/jca.56506 ◽

2021 ◽

Vol 12 (17) ◽

pp. 5286-5295

Author(s):

Guangda Li ◽

Yunfei Ma ◽

Mingwei Yu ◽

Xiaoxiao Li ◽

Xinjie Chen ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Small Molecule ◽

Cell Lung Cancer ◽

Acquired Resistance ◽

Small Cell ◽

Small Cell Lung ◽

Hub Genes ◽

Small Molecule Drugs

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Identification and validation of aberrant regulation of miR-445-3p /TTK , miR-140-5p/TTK and miR-133b/CDCA8 in small-cell lung cancer via bioinformatics analysis

10.21203/rs.3.rs-25687/v1 ◽

2020 ◽

Author(s):

Ming-bo Tang ◽

Xin-liang Gao ◽

Zhuo-yuan Xin ◽

Li-nan Fang ◽

Wei Liu

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Expression Profiles ◽

Small Cell ◽

Functional Enrichment ◽

Differentially Expressed ◽

Small Cell Lung ◽

Hub Genes ◽

Hub Gene

Abstract Background: Small-cell lung cancer (SCLC) remains the leading form of malignant lung cancer, but little bioinformation on SCLC is available. This study explored the molecular targets of SCLC by evaluating differentially expressed genes (DEGs) and differentially expressed microRNAs (miRNAs) (DEMs).Methods: Five mRNA expression profiles and two miRNAs expression profiles from Gene Expression Omnibus (GEO) were downloaded. R software was utilized to analyze the DEGs and DEMs between SCLC and normal samples. The DEGs were analyzed via functional enrichment analyses and were used to construct protein-protein interaction (PPI) networks. DEM targets were then predicted and intersected with the DEGs. Furthermore, the hub genes of SCLC in the overlapping DEGs were analyzed in Oncomine. Finally, the expression of DEM-hub gene pairs were verified in tissues by RT-qPCR and Western blotting.Results: In total, 236 common DEGs and 104 common DEMs were identified. Functional enrichment analysis showed the DEGs were primarily enriched in ‘cell cycle’, ‘DNA replication’ and ‘oocyte meiosis’. Twenty hub genes and five modules were identified from the PPI network. Furthermore, 6732 targeted genes of the DEMs were predicted. After intersecting with DEGs, 54 genes and 153 miRNA-mRNA pairs were eventually identified aberrant regulation in SCLC. MiR-445-3p/TTK, miR-140-5p/TTK and miR-133b/CDCA8 were identified as DEM-hub gene pairs. Oncomine analysis confirmed the overexpression of TTK and CDCA8 in SCLC. Further validation demonstrated that TTK and CDCA8 levels in SCLC tissue samples were markedly increased relative to normal controls, while miR-445-3p, miR-140-5p, and miR-133b levels were lower in SCLC samples than in controls.Conclusions: Our results revealed key miRNA-mRNA pairs associated with SCLC, providing new insights into potential disease targets.

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Comprehensive analysis of non-small-cell lung cancer microarray datasets identifies several prognostic biomarkers

Future Oncology ◽

10.2217/fon-2018-0824 ◽

2019 ◽

Vol 15 (27) ◽

pp. 3135-3148

Author(s):

Xiuxiu Qin ◽

Ruoshi Chen ◽

Rui Xiong ◽

Zimiao Tan ◽

Shanshan Gao ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Gene Expression Omnibus ◽

Small Cell ◽

Small Cell Lung ◽

Hub Genes ◽

Protein Protein Interaction ◽

Nsclc Patients ◽

Microarray Datasets

Aim: To find accurate and effective biomarkers for diagnosis of non-small-cell lung cancer (NSCLC) patients. Materials & methods: We downloaded microarray datasets GSE19188, GSE33532, GSE101929 and GSE102286 from the database of Gene Expression Omnibus. We screened out differentially expressed genes (DEGs) and miRNAs (DEMs) with GEO2R. We also performed analyses for the enrichment of DEGs’ and DEMs’ function and pathway by several tools including database for annotation, visualization and integrated discovery, protein–protein interaction and Kaplan–Meier-plotter. Results: Total 913 DEGs were screened out, among which ten hub genes were discovered. All the hub genes were linked to the worsening overall survival of the NSCLC patients. Besides, 98 DEMs were screened out. MiR-9 and miR-520e were the most significantly regulated miRNAs. Conclusion: Our results could provide potential targets for the diagnosis and treatment of NSCLC.

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Bioinformatics analysis reveals 6 key biomarkers associated with non-small-cell lung cancer

Journal of International Medical Research ◽

10.1177/0300060519887637 ◽

2019 ◽

Vol 48 (3) ◽

pp. 030006051988763 ◽

Cited By ~ 2

Author(s):

Bai Dai ◽

Li-qing Ren ◽

Xiao-yu Han ◽

Dong-jun Liu

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Target Genes ◽

Enrichment Analysis ◽

Small Cell ◽

Ppi Network ◽

Small Cell Lung ◽

Hub Genes ◽

Detection And Diagnosis

Objective Non-small-cell lung cancer (NSCLC) accounts for >85% of lung cancers, and its incidence is increasing. We explored expression differences between NSCLC and normal cells and predicted potential target sites for detection and diagnosis of NSCLC. Methods Three microarray datasets from the Gene Expression Omnibus database were analyzed using GEO2R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted. Then, the String database, Cytoscape, and MCODE plug-in were used to construct a protein–protein interaction (PPI) network and screen hub genes. Overall and disease-free survival of hub genes were analyzed using Kaplan-Meier curves, and the relationship between expression patterns of target genes and tumor grades were analyzed and validated. Gene set enrichment analysis and receiver operating characteristic curves were used to verify enrichment pathways and diagnostic performance of hub genes. Results In total, 293 differentially expressed genes were identified and mainly enriched in cell cycle, ECM–receptor interaction, and malaria. In the PPI network, 36 hub genes were identified, of which 6 were found to play significant roles in carcinogenesis of NSCLC: CDC20, ECT2, KIF20A, MKI67, TPX2, and TYMS. Conclusion The identified target genes can be used as biomarkers for the detection and diagnosis of NSCLC.

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Analysis of functional hub genes identifies CDC45 as an oncogene in non-small cell lung cancer - a short report

Cellular Oncology ◽

10.1007/s13402-019-00438-y ◽

2019 ◽

Vol 42 (4) ◽

pp. 571-578 ◽

Cited By ~ 5

Author(s):

Jianbing Huang ◽

Yuan Li ◽

Zhiliang Lu ◽

Yun Che ◽

Shouguo Sun ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Short Report ◽

Small Cell Lung ◽

Hub Genes

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Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Cancers ◽

10.3390/cancers12010037 ◽

2019 ◽

Vol 12 (1) ◽

pp. 37 ◽

Cited By ~ 12

Author(s):

Magdalena Niemira ◽

Francois Collin ◽

Anna Szalkowska ◽

Agnieszka Bielska ◽

Karolina Chwialkowska ◽

...

Keyword(s):

Lung Cancer ◽

Network Analysis ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Molecular Signature ◽

Normal Lung ◽

Specific Gene ◽

Small Cell Lung ◽

Hub Genes

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies consisting essentially of adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Although the diagnosis and treatment of ADC and SCC have been greatly improved in recent decades, there is still an urgent need to identify accurate transcriptome profile associated with the histological subtypes of NSCLC. The present study aims to identify the key dysregulated pathways and genes involved in the development of lung ADC and SCC and to relate them with the clinical traits. The transcriptional changes between tumour and normal lung tissues were investigated by RNA-seq. Gene ontology (GO), canonical pathways analysis with the prediction of upstream regulators, and weighted gene co-expression network analysis (WGCNA) to identify co-expressed modules and hub genes were used to explore the biological functions of the identified dysregulated genes. It was indicated that specific gene signatures differed significantly between ADC and SCC related to the distinct pathways. Of identified modules, four and two modules were the most related to clinical features in ADC and SCC, respectively. CTLA4, MZB1, NIP7, and BUB1B in ADC, as well as GNG11 and CCNB2 in SCC, are novel top hub genes in modules associated with tumour size, SUVmax, and recurrence-free survival. Our research provides a more effective understanding of the importance of biological pathways and the relationships between major genes in NSCLC in the perspective of searching for new molecular targets.

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