CDK1 and HSP90AA1 appears as novel regulatory gene in Non-Small Cell Lung Cancer: A Bioinformatics Approach

Lung cancer is one of the most invasive cancer affecting over a million of population. Non-small cell lung cancer constitutes up to 85% of all lung cancer cases. Therefore, it is important to identify prognostic biomarkers of NSCLC for therapeutic purpose. The complex behaviour of the NSCLC gene-regulatory network interaction is investigated using a network theoretical approach. We used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, GSE31210 and meta analyse them to find differentially expressed genes (DEGs), construct protein-protein interaction (PPI) network, analysed its topological properties, significant modules using network analyser with MCODE, construct a PPI-MCODE network using the genes of the significant modules. We used topological properties such as Maximal Clique Centrality (MCC) and bottleneck from the PPI-MCODE network. We compare them with hub genes (those with highest degrees) to find key regulator (KR) gene. This result is also validated by finding of common genes among top twenty hub genes, genes with highest betweenness, closeness centrality and eigenvector values. It was found that two genes, CDK1 and HSP90AA1 were common in PPI-MCODE combined analysis, and it was also found that CDK1, HSP90AA1 and HSPA8 were common among hub and bottle neck properties and suggesting significant regulatory role of CDK1 in non-small cell lung cancer. After validation, the common genes among top twenty hubs and centrality values like Betweenness Centrality, Closeness Centrality and eigen vector properties, CDK1 again appeared as the common gene. Our study as a summary suggested CDK1 as key regulator gene in complex NSCLC network interaction using network theoretical approach and described the complex topological properties of the network.

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

Journal of Cellular Biochemistry ◽

10.1002/jcb.29489 ◽

2019 ◽

Vol 121 (3) ◽

pp. 2690-2703 ◽

Cited By ~ 2

Author(s):

Feifeng Song ◽

Zixue Xuan ◽

Xiuli Yang ◽

Xiaolan Ye ◽

Zongfu Pan ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Small Cell Lung ◽

Hub Genes ◽

Functional Analyses

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The Efficacy and Safety of Anlotinib Combined With PD-1 Antibody for Third-Line or Further-Line Treatment of Patients With Advanced Non-Small-Cell Lung Cancer

Frontiers in Oncology ◽

10.3389/fonc.2020.619010 ◽

2021 ◽

Vol 10 ◽

Cited By ~ 1

Author(s):

Chongya Zhai ◽

Xiaoling Zhang ◽

Lulu Ren ◽

Liangkun You ◽

Qin Pan ◽

...

Keyword(s):

Lung Cancer ◽

Adverse Events ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Line Treatment ◽

Efficacy And Safety ◽

Small Cell Lung ◽

The Common ◽

Third Line

BackgroundBoth anlotinib and programmed death 1 (PD-1) monoclonal antibody (mAb) have been approved for the third line treatment of metastatic non-small cell lung cancer (NSCLC). However, the combination of these two standard therapies has not been investigated in third-line or further-line treatment of patients with advanced NSCLC.MethodsWe reviewed 22 patients with NSCLC who received anlotinib combined with PD-1 mAb therapy from July 2018 to October 2019 at Sir Run Run Shaw Hospital. Based on the baseline characteristics, PD-L1 expression and EGFR mutation status, we retrospectively analyzed the efficacy and safety of this combination therapy by RESIST 1.1 and CTCAE 5.0.ResultsThe combination treatment of anlotinib and PD-1 mAb in 22 NSCLC patients gained a median PFS of 6.8 months and a median OS of 17.3 months. The disease control rate (DCR) was 90.9%, and the objective response rate (ORR) was 36.4%, where 1 (4.6%) patient achieved complete response (CR) and 7 (31.8%) patients achieved partial response (PR). The median time to response was 3.9 months, and the median duration of the response was 6.8 months. The common grades 1–2 adverse events were fatigue 10/22 (45.5%), decreased appetite 9/22 (40.9%), hypertension 10/22 (45.5%); the common grades 3–4 adverse events were hypertension 2/22 (9.1%) and mouth ulceration 2/22 (9.1%).ConclusionAnlotinib combined with PD-1 mAb showed promising efficacy in third-line or further-line treatment of NSCLC, and its adverse effects is tolerable.

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Survival-based bioinformatics analysis to identify hub genes and key pathways in non-small cell lung cancer

Translational Cancer Research ◽

10.21037/tcr.2019.06.35 ◽

2019 ◽

Vol 8 (4) ◽

pp. 1188-1198

Author(s):

Chunliang Liu ◽

Yu Chen ◽

Yuqi Deng ◽

Yu Dong ◽

Jixuan Jiang ◽

...

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Bioinformatics Analysis ◽

Small Cell ◽

Small Cell Lung ◽

Hub Genes ◽

Key Pathways

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Identification of Key Biomarkers and Pathways in Small-Cell Lung Cancer Using Biological Analysis

BioMed Research International ◽

10.1155/2021/5953386 ◽

2021 ◽

Vol 2021 ◽

pp. 1-21

Author(s):

Huanqing Liu ◽

Tingting Li ◽

Xunda Ye ◽

Jun Lyu

Keyword(s):

Lung Cancer ◽

Cell Cycle ◽

Dna Replication ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Small Cell Lung ◽

Hub Genes ◽

Biological Analysis ◽

Ppi Networks

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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