Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis

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Abstract

Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80–85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC.

Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein–protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset.

Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.

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Most cited references 64

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miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients.

András Lánczky, Ádám Nagy, Giulia Bottai … (2016)

The proper validation of prognostic biomarkers is an important clinical issue in breast cancer research. MicroRNAs (miRNAs) have emerged as a new class of promising breast cancer biomarkers. In the present work, we developed an integrated online bioinformatic tool to validate the prognostic relevance of miRNAs in breast cancer.

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Interleukin-6: from basic science to medicine--40 years in immunology.

Tadamitsu Kishimoto (2005)

This essay summarizes my 40 years of research in immunology. As a young physician, I encountered a patient with Waldenström's macroglobulinemia, and this inspired me to study the structure of IgM. I began to ask how antibody responses are regulated. In the late 1960s, the essential role of T cells in antibody production had been reported. In search of molecules mediating T cell helper function, I discovered activities in the culture supernatant of T cells that induced proliferation and differentiation of B cells. This led to my life's work: studying one of those factors, interleukin-6 (IL-6). To my surprise, IL-6 turned out to play additional roles, including myeloma growth factor and hepatocyte-stimulating factor activities. More importantly, it was involved in a number of diseases, such as rheumatoid arthritis and Castleman's disease. I feel exceptionally fortunate that my work not only revealed the framework of cytokine signaling, including identification of the IL-6 receptor, gp130, NF-IL6, STAT3, and SOCS-1, but also led to the development of a new therapy for chronic inflammatory diseases.

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Lung Cancer Statistics.

Lindsey A Torre, Rebecca L Siegel, Ahmedin Jemal (2016)

Lung cancer is the leading cause of cancer death among both men and women in the United States. It is also the leading cause of cancer death among men and the second leading cause of cancer death among women worldwide. Lung cancer rates and trends vary substantially by sex, age, race/ethnicity, socioeconomic status, and geography because of differences in historical smoking patterns. Lung cancer mortality rates in the United States are highest among males, blacks, people of lower socioeconomic status, and in the mid-South (e.g., Kentucky, Mississippi, Arkansas, and Tennessee). Globally, rates are highest in countries where smoking uptake began earliest, such as those in North America and Europe. Although rates are now decreasing in most of these countries (e.g., United States, United Kingdom, Australia), especially in men, they are increasing in countries where smoking uptake occurred later. Low- and middle-income countries now account for more than 50% of lung cancer deaths each year. This chapter reviews lung cancer incidence and mortality patterns in the United States and globally.

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Author and article information

Contributors

Xinkui Liu: URI : http://loop.frontiersin.org/people/568093/overview

Jiarui Wu: URI : http://loop.frontiersin.org/people/555960/overview

Journal

Journal ID (nlm-ta): Front Genet

Journal ID (iso-abbrev): Front Genet

Journal ID (publisher-id): Front. Genet.

Title: Frontiers in Genetics

Publisher: Frontiers Media S.A.

ISSN (Electronic): 1664-8021

Publication date (Electronic): 12 October 2018

Publication date Collection: 2018

Volume: 9

Electronic Location Identifier: 469

Affiliations

[1] ¹Department of Clinical Chinese Pharmacy, School of Chinese Materia Medica, Beijing University of Chinese Medicine , Beijing, China

[2] ²Evidence Based Medicine Center, School of Basic Medical Sciences, Lanzhou University , Lanzhou, China

[3] ³Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province , Lanzhou, China

[4] ⁴Beijing Institute of Traditional Chinese Medicine, Beijing University of Chinese Medicine , Beijing, China

Author notes

Edited by: Yi Zhao, Institute of Computing Technology (CAS), China

Reviewed by: Yonghua Wang, Northwest A&F University, China; Wiejian Bei, Guangdong Pharmaceutical University, China; Jia-bo Wang, 302 Military Hospital of China, China

*Correspondence: Jiarui Wu, exogamy@ 123456163.com

^†These authors have contributed equally to this work

This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Genetics

Article

DOI: 10.3389/fgene.2018.00469

PMC ID: 6194157

PubMed ID: 30369945

SO-VID: 4ee67b43-a218-4947-85ec-923b6a8d499f

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

History

Date received : 24 July 2018

Date accepted : 24 September 2018

Page count

Figures: 10, Tables: 2, Equations: 0, References: 80, Pages: 14, Words: 0

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