Open Access

Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis

  • Authors:
    • Nan Zhang
    • Shao‑Wei Zhang
  • View Affiliations

  • Published online on: August 6, 2019     https://doi.org/10.3892/ol.2019.10723
  • Pages: 3754-3768
  • Copyright: © Zhang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Lung cancer (LC), with its high morbidity and mortality rates, is one of the most widespread and malignant neoplasms. Mediastinal lymph node metastasis (MLNM) severely affects postoperative survival of patients with LC. Additionally, the molecular mechanisms of LC with MLNM (MM LC) remain not well understood. To identify the key biomarkers in its carcinogenesis and development, the datasets GSE23822 and GSE13213 were obtained from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization and Integrated Discovery was used to perform functional annotations of DEGs. Search Tool for the Retrieval of Interacting Genes and Cytoscape were utilized to obtain the protein‑protein interaction (PPI) network, and to analyze the most significant module. Subsequently, a Kaplan‑Meier plotter was used to analyze overall survival (OS). Additionally, one co‑expression network of the hub genes was obtained from cBioPortal. A total of 308 DEGs were identified in the two microarray datasets, which were mainly enriched during cellular processes, including the Gene Ontology terms ‘cell’, ‘catalytic activity’, ‘molecular function regulator’, ‘signal transducer activity’ and ‘binding’. The PPI network was composed of 315 edges and 167 nodes. Its significant module had 11 hub genes, and high expression of actin β, MYC, arginine vasopressin, vesicle associated membrane protein 2 and integrin subunit β1, and low expression of NOTCH1, synaptojanin 2 and intersectin 2 were significantly associated with poor OS. In summary, hub genes and DEGs presented in the present study may help identify underlying targets for diagnostic and therapeutic methods for MM LC.

Introduction

Lung cancer (LC) is one of the most malignant neoplasms, and increases in its morbidity and mortality rates have made it a main cause of human mortality. In the last 50 years, research has revealed a marked increase in the incidence and mortality rates of LC (1). The incidence and mortality rates of LC in men ranked first among all malignant tumor groups, and the incidence in women ranked second most common worldwide in 2018 (2). The onset of LC is insidious and it has a poor prognosis. Mediastinal lymph node metastasis (MLNM) during treatment is one of the most important factors affecting the postoperative survival of patients (3).

The MLNM process of LC includes the growth of a primary tumor, angiogenesis and exfoliation of tumor cells, which invade the tissue matrix, and make cancer cells survive in the blood circulation and amass into tiny tumor thrombi (4). Previous studies in China have reported that the 5-year survival rate of patients with LC without MLNM could be >60%, but was only 15–42% with MLNM (5,6). Numerous previous studies (711) have demonstrated that the pathophysiological process of the development of LC with MLNM (MM LC) is associated with the mutation and abnormal expression of genes, including C-X-C motif chemokine receptor 4 (CXCR4), vascular endothelial growth factor-C (VEGF-C), vascular endothelial growth factor receptor-3 (VEGFR-3), ADAM metallopeptidase domain (ADAM) and vascular endothelial growth factor-D (VEGF-D). In a previous study by Na et al (9) of 46 patients with LC, abnormal expression of CXCR4 in the nucleus was markedly associated with MLNM. In addition, multiple previous studies have suggested that co-expression of VEGFR-3 and VEGF-C (7), high expression levels of ADAM family members (8), downregulation of VEGF-D expression (10), and high expression levels of VEGF-C (11) may be involved in the MLNM of LC. These genes may be used as prognostic factors or targets for gene therapy. However, in individualized applications, the diagnostic or therapeutic value of a single gene remains uncertain. Due to the lack of timely detection, dynamic monitoring and effective control of the occurrence of MLNM, the poor survival rate of MM LC still cannot be effectively controlled. Therefore, novel signaling pathways and molecular targets should be investigated and screened to develop novel diagnostic and therapeutic methods.

Microarray technology allows simultaneous analysis of alterations in the expression levels of multiple genes to obtain gene sets that can predict MLNM in LC with high accuracy. Clinical application of these gene sets is expected to maximize the survival period and narrow the surgical range (whether lymph node resection is required) for the benefit of patients. Previously, a number of studies (1217) have performed bioinformatic analyses to investigate differentially expressed genes (DEGs) in various types of cancer, as well as their roles in different pathways, molecular functions and biological processes. Kikuchi et al (12) used microarray technology to obtain a set of genes which could predict MLNM and drug sensitivity. Li et al (13) applied the same method to identify the significant genes in the carcinogenesis and progression of hepatocellular carcinoma. Furthermore, multiple previous studies have also used microarray technology to obtain gene expression profiles to predict lymph node metastasis of other malignant tumors, including esophageal cancer (14), oral squamous cell carcinoma (15,16) and cervical cancer (17).

Therefore, the purpose of the present study was to download and analyze two expression profiling datasets of human samples from the Gene Expression Omnibus (GEO) database, and to identify DEGs between LC samples without MLNM (non-MM LC) and MM LC samples. Subsequently, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analysis were carried out. Protein-protein interaction (PPI) network analysis and co-expression network analyses were used to demonstrate the molecular pathogenesis underlying carcinogenesis and development of MM LC. Overall, 11 hub genes and 308 DEGs, which may be potential molecular targets or biomarkers for MM LC, were identified.

Materials and methods

Access to public data

The GEO (http://www.ncbi.nlm.nih.gov/geo) is an open platform for storing genetic data (18). In total, two expression profiling datasets [GSE23822 (19) (GPL6947 platform) and GSE13213 (20) (GPL6480 platform)] were obtained from GEO. The GSE23822 dataset contained four non-MM LC samples and four MM LC samples. Similarly, GSE13213 consisted of 22 non-MM LC samples and 22 MM LC samples.

Identification of DEGs using GEO2R

GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) is an interactive online tool for the identification of DEGs from GEO series (21). GEO2R was used to identify DEGs between MM LC and non-MM LC tissue samples. If one probe set did not have the homologous gene, or if one gene had numerous probe sets, the data were removed. The rules of statistical significance were that P≤0.01 and fold change ≥1.5.

Functional annotation of DEGs by KEGG and GO analysis

Database for Annotation, Visualization and Integrated Discovery (DAVID; version 6.8, http://david.ncifcrf.gov/home.jsp), is an online analysis tool suite with the functions of integrated discovery and annotation (22). GO (http://geneontology.org) is widely used in bioinformatics, and covers three aspects of biology; biological process (BP), cellular component (CC) and molecular function (MF) (23). KEGG (https://www.kegg.jp) is one of the most commonly used biological information databases in the world (24). OmicShare (http://www.omicshare.com/tools), an open data analysis platform, was used to perform GO analysis (25). To analyze the biological pathway information of DEGs, the DAVID online tool was implemented. P<0.05 was considered to indicate a statistically significant difference.

Construction of the PPI network and identification of the significant module

Search Tool for the Retrieval of Interacting Genes (http://string.embl.de/), an online open tool, was applied to construct the PPI network of DEGs, and Cytoscape was used to present the network (26). Cytoscape (version 3.6.1) is a free visualization software (27). A confidence score >0.4 was considered as the criterion of judgment. Subsequently, the Molecular Complex Detection (28) (MCODE; version 1.5.1; a plug-in of Cytoscape), was used to identify the most important module of the network map. The criteria for MCODE analysis were as follows: i) Degree cut-off=2; ii) MCODE scores >5; iii) max depth=100; iv) node score cut-off=0.2; and v) k-score=2 (29). Subsequently, following the KEGG and GO analysis using the DAVID database and OmicShare website, functional annotation for genes of these modules was performed.

Analysis and identification of hub genes

The degrees were set (degrees ≥10), and the hub genes were excavated. A co-expression network of these hub genes was obtained from cBioPortal (http://www.cbioportal.org) (30). Furthermore, the Biological Networks Gene Oncology tool (BiNGO; version 3.0.3) was used to analyze and visualize the CCs, BPs and MFs of the hub genes (31). The clustering analysis of hub genes was performed using OmicShare (https://www.omicshare.com/tools/Home/Soft/getsoft/type/index) (25). The mean value of amount of gene expression was defined as the cut-off value for the high or low expression level. Additionally, Kaplan-Meier Plotter (http://kmplot.com/analysis/index.php?p=background), an online analysis tool, was utilized to perform survival analysis for the hub genes. The Kaplan Meier plotter (32) is capable of assessing the effect of 54,000 genes on survival in 21 different types of cancer. The largest datasets include breast (n=6,234), ovarian (n=2,190), lung (n=3,452) and gastric (n=1,440) cancer. The miRNA subsystems include 11k samples from 20 different cancer types. Primary purpose of the tool is a meta-analysis based discovery and validation of survival biomarkers. The P-value was achieved by using a log-rank test. University of California Santa Cruz (UCSC) Xena (https://xena.ucsc.edu/welcome-to-ucsc-xena/) was used to securely analyze and visualize the hub genes in the scope of public genomic datasets. The expression profiles of actin β (ACTB) and integrin subunit β1 (ITGB1) were analyzed and visualized using the online database Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/).

Results

Screening of DEGs in MM LC samples

Following analysis of the datasets (GSE23822 and GSE13213) with GEO2R, the difference between MM LC and non-MM LC tissues was presented in volcano plots (Fig. 1A and B). The analyses of GSE23822 and GSE13213 identified 1,297 and 4,552 DEGs, respectively (Fig. 1C). The Venn diagram demonstrated that the commonality between the two datasets included 308 DEGs, including the most upregulated genes [Erb-b2 receptor tyrosine kinase 2 (ERBB2) and ITGB1] and the most downregulated genes [ACTB, RAB5B, member RAS oncogene family (RAB5B) and intersectin 2 (ITSN2)] (data not shown).

Functional annotation of DEGs by KEGG and GO analyses

The results of the GO analysis demonstrated that variations in the BP were primarily enriched in the GO terms ‘cellular process’, ‘metabolic process’, ‘response to stimulus’, ‘biological regulation’, ‘signaling’ and ‘localization’, ‘single-organism process’, ‘developmental process’, ‘cellular component organization or biogenesis’, and so on. Alterations in CC were mainly enriched in the GO terms ‘cell’, ‘membrane’, ‘extracellular region’ and ‘organelle part’. The variations in MF were enriched in the GO terms ‘binding’, ‘catalytic activity’, ‘molecular transducer activity’, ‘signal transducer activity’ and ‘molecular function regulator’ (Fig. 2). The enriched GO terms were ‘generation of precursor metabolites and energy’, ‘signal transduction’, ‘regulation of biological process’, ‘regulation of cellular process’, ‘cellular response to stimulus’, ‘cellular component organization or biogenesis’, ‘cellular component organization’, ‘anatomical structure morphogenesis’, ‘cell communication’, ‘signaling’, ‘single organism signaling’, ‘membrane organization’, ‘growth’, ‘cell morphogenesis’, ‘cellular component morphogenesis’, ‘immune system process’, ‘localization’, ‘transport’, ‘establishment of localization’, and ‘vesicle-mediated transport’ (Fig. 3A). KEGG analysis revealed that DEGs were enriched in ‘ErbB signaling pathway’, ‘microRNAs in cancer’, ‘endometrial cancer’, ‘Jak-STAT signaling pathway’, ‘non-small cell lung cancer’, ‘chronic myeloid leukemia’, ‘hypertrophic cardiomyopathy’ and ‘Hippo signaling pathway’ (Fig. 3B).

Construction of the PPI network and identification of the significant module

Construction of the PPI network and identification of the significant module were performed, and there were 315 edges and 167 nodes in the PPI network (Fig. 4). Furthermore, there were 26 edges and 11 nodes in the significant module (Fig. 5). Using DAVID, KEGG and GO analyses of DEGs involved in the significant module were performed. The results demonstrated that genes in the significant module were enriched in the following categories: ‘Cellular process’, ‘localization’, ‘signaling’, ‘cell’, ‘organelle’, ‘extracellular region’, ‘membrane’, ‘binding’, ‘molecular function regulator’ and ‘molecular transducer activity’ (Fig. 6). The enriched GO terms were ‘generation of precursor metabolites and energy’, ‘signal transduction’, ‘regulation of biological processes’, ‘regulation of cellular processes’, ‘cellular response to stimulus’, ‘cellular component organization or biogenesis’, ‘cellular component organization’, ‘anatomical structure morphogenesis’, and so on (Fig. 7A). The KEGG pathway analysis revealed that genes in the significant module were mainly enriched in ‘vasopressin-regulated water reabsorption’, ‘proteoglycans in cancer’, ‘thyroid hormone signaling pathway’, ‘phagosome’, ‘focal adhesion’, ‘microRNAs in cancer’, ‘bladder cancer’, ‘pathogenic Escherichia coli infection’, ‘endometrial cancer’ and ‘shigellosis’ (Fig. 7B).

Hub gene selection and analysis

Degrees ≥10 was considered as the criterion of judgment. A total of 11 genes were identified as hub genes using Cytoscape: ITGB1, MYC, ERBB2, NOTCH1, ACTB, RAB5B, arginine vasopressin (AVP), synaptojanin 2 (SYNJ2), ITSN2, SH3 domain containing GRB2 like 2 endophilin A1 and vesicle associated membrane protein 2 (VAMP2; Table I). A co-expression network of these significant genes was obtained using cBioPortal (Fig. 8). The BP, CC and MF analyses by BiNGO for these genes supported the results of the GO analysis (Figs. S1S3). The results of the BiNGO analysis demonstrated that variations in the BP were also mainly enriched for the ‘cellular process’ term (Fig. S1). Changes in CC were also enriched in cell, membrane (‘plasma membrane’ and ‘plasma membrane part’) and organelle terms (Fig. S2). Additionally, the variations in MF were enriched in binding (‘kinesin binding’, ‘nitric-oxide synthase binding’ and ‘Hsp90 protein binding’), ‘molecular transducer activity’ (Fig. S3).

Table I.

Summary of the functions of the 11 hub genes.

Table I.

Summary of the functions of the 11 hub genes.

No.Gene symbolFull nameFunction(Refs.)
1ITGB1Integrin subunit β1Diseases associated with ITGB1 include gallbladder cancer and breast cancer. Among its related pathways are ERK signaling and focal adhesion. Gene Ontology annotations related to this gene include protein heterodimerization activity and signaling receptor binding.(49)
2MYCV-myc avian myelocytomatosis viral oncogene homologActivates the transcription of growth-related genes. Binds to the VEGFA promoter, promoting VEGFA production and subsequent sprouting angiogenesis.(52)
3ERBB2Erb-b2 receptor tyrosine kinase 2Regulates outgrowth and stabilization of peripheral microtubules. Involved in the transcription of ribosomal RNA genes by RNA Pol I and enhances protein synthesis and cell growth.(53)
4NOTCH1Notch 1Affects the implementation of differentiation, proliferation and apoptotic programs. Involved in angiogenesis; negatively regulates endothelial cell proliferation and migration and angiogenic sprouting.(54)
5ACTBActin βAmong its related pathways are ERK signaling and cytoskeleton remodeling regulation of actin cytoskeleton by ρ GTPases.(55)
6RAB5BRAB5B, member RAS oncogene familyProtein transport. Probably involved in vesicular traffic.(56)
7AVPArginine vasopressinVasopressin has a direct antidiuretic action on the kidney, it also causes vasoconstriction of the peripheral vessels.(57)
8SYNJ2Synaptojanin 2Inositol 5-phosphatase which may be involved in distinct membrane trafficking and signal transduction pathways. May mediate the inhibitory effect of Rac1 on endocytosis.(58)
9ITSN2Intersectin 2May regulate the formation of CCPs. Seems to be involved in CCPs maturation including invagination or budding.(59)
10SH3GL2SH3 domain containing GRB2 like 2, endophilin A1Implicated in synaptic vesicle endocytosis. Cooperates with SH3GL2 to mediate brain derived neurotrophic factor-neurotrophic receptor tyrosine kinase 2 early endocytic trafficking and signaling from early endosomes.(60)
11VAMP2Vesicle associated membrane protein 2Involved in the targeting and/or fusion of transport vesicles to their target membrane. Modulates the gating characteristics of the delayed rectifier voltage-dependent potassium channel potassium voltage-gated channel subfamily B member 1.(61)

[i] CCPs, clathrin-coated vesicles; VEGFA, vascular endothelial growth factor A.

Hierarchical clustering revealed that the hub genes could differentiate the MM LC samples from the non-MM LC samples (Fig. 9). Subsequently, a Kaplan-Meier plotter was used to perform overall survival (OS) analysis (Figs. 10 and 11). The samples for OS analysis, derived from the Kaplan-Meier plotter, were different from those used in the analysis of DEGs. Patients with LC with genomic alterations in high expression of ITGB1 (Fig. 10A), high expression of MYC (Fig. 10B), low expression of NOTCH1 (Fig. 10D), high expression of ACTB (Fig. 10E), high expression of AVP (Fig. 11B), low expression of SYNJ2 (Fig. 11C), low expression of ITSN2 (Fig. 11D) and high expression of VAMP2, exhibited poorer OS. However, via the GEPIA, the expression of ACTB (Fig. 10F), ERBB2 (Fig. 10C) RAB5B (Fig. 11A) and SH3GL2 (Fig. 11E), were not associated with OS. In the UCSC Xena analysis, hierarchical clustering revealed that these hub genes could differentiate the patients with LC from the normal patients (Fig. 12A). Among the hub genes, ACTB and ITGB1 had the highest score of 6.415, suggesting that they may serve important roles in the occurrence or development of MM LC. Using the database of the Kaplan-Meier plotter, the present study identified that high expression of ACTB was associated with poor OS in patients with LE (P<0.001). Additionally, high expression of ITGB1 was associated with worse OS (P=0.024). The expression profiles of ACTB and ITGB1 in human tissues were visualized using GEPIA. The present study revealed that ACTB and ITGB1 exhibited lower expression levels in lung squamous cell carcinoma compared with the matched normal samples, but no statistically significant difference was identified (P>0.05; Fig. 12B and C).

Discussion

LC causes the highest cancer-associated mortality rate in China and the majority of countries worldwide. The most common pathological type of LC is non-small-cell LC, which accounts for 80–85% of LC cases (33). Optimization of LC treatment strategies depends on accurate pathological staging and International Association for the Study of Lung Cancer pathological TNM staging (34). In clinical practice, patients with stages 0, I, II and IIIa may benefit from surgery. For patients with MLNM, neoadjuvant chemotherapy could prolong their postoperative survival (35). For patients without surgical indications, the definite diagnosis of mediastinal lymph node staging could effectively reduce the irradiation area of the radiation target area, which may reduce the incidence of radioactive lung injury. Therefore, if MLNM can clearly defined prior to treatment, it is of great significance to develop appropriate treatment schemes to improve the prognosis of patients.

Currently, there are numerous clinical diagnostic methods for MLNM of LC, including computed tomography (CT), positron emission tomography (PET) and PET-CT. CT is the most widely used diagnostic method for MLNM. A previous study demonstrated that imaging methods continue to have limitations in the evaluation of MLNM, and some patients cannot be clearly diagnosed or are misdiagnosed (36). Diagnosis of MLNM using CT is mainly based on the size of lymph nodes. The larger the lymph nodes, the higher the metastasis rate. The average size of the short diameter of normal lymph nodes depends on the region in which they are located. McLoud et al (37) evaluated 443 lymph nodes in 143 patients using CT. The sensitivity to lymph nodes of various regions was 17–78%, and the specificity was 72–94%. The previous study demonstrated that there were some differences in the sensitivity and specificity of diagnosis using CT to evaluate mediastinal lymph nodes in different subgroups (31). In addition, PET is superior to CT in the diagnosis of MLNM, since PET considers not only lymph node size, but also lymph node metabolism information. A meta-analysis by Toloza et al (38) of PET examination of 1,045 patients in 18 studies manifested that PET was more accurate than CT, and the total sensitivity and specificity were 0.84 (95% CI, 0.78–0.89) and 0.89 (95% CI, 0.83–0.93), respectively. However, the diagnostic method of PET has relatively high rates of false negatives and false positives (39). Furthermore, PET-CT effectively combines the technical advantages of CT and PET, which could significantly improve the accuracy of preoperative diagnosis of MLNM in LC. However, several previous studies have reported that PET-CT has high specificity and low sensitivity (40,41). The sensitivity of PET-CT in preoperative assessment of MLNM of lung adenocarcinoma is too low, and further surgical staging is required for patients with LC without MLNM detected by PET-CT.

To overcome the limitations of imaging evaluation of MLNM, previous studies have attempted to identify molecular biomarkers of MM LC (710). During the past decades, bioinformatics technology has been generally used to screen potential genetic targets of diseases, which assisted the authentication of DEGs and underlying pathways associated with the occurrence and recurrence of diseases.

Following analysis of the two microarray datasets in the present study, DEGs between non-MM LC and MM LC were identified. A total of 308 DEGs were contained in the two datasets simultaneously. From the KEGG and GO analyses, the interactions of the DEGs were explored. The DEGs were mainly enriched in the GO terms ‘cellular process’, ‘signaling’, ‘cell’, ‘organelle’, ‘binding’, ‘molecular transducer activity’ and ‘molecular function regulator’. Among the hub genes, ACTB and ITGB1 exhibited the highest score of 6.415 via the MCODE analysis, suggesting that they may serve important roles in the occurrence or development of MM LC.

Actin β, encoded by the ACTB gene, is widely present in non-muscle cells in the form of a ball or fiber, and participates in the construction of the cytoskeleton and cell movement. As a downstream regulatory protein, actin β has the function of maintaining normal cell migration, growth, differentiation and signal transduction (42). Therefore, it may also be involved in the occurrence mechanism of vascular remodeling. Numerous previous studies (4345) have demonstrated a close association between ACTB and the occurrence of tumors. Lim et al (43) reported that the mutation of ACTB may cause pilocytic astrocytoma in their clinical experience. Furthermore, the fusions of ACTB and glioma-associated oncogene homolog 1 (GLI1) were regarded as a specific genetic abnormality, which could result in a distinctive type of actin-positive, perivascular myoid tumors, known as ‘pericytoma with the t (7;12) translocation’ (44). Furthermore, Castro et al (45) reported that the extremely unusual translocation t (7;12) may lead to the gene fusion of ACTB and GLI1, which may induce an infrequent gastric tumor derived from the pyloric wall of the stomach. The results of the present study revealed that the expression levels of ACTB in MM LC were downregulated; therefore, the production of actin β was reduced, which may lead to abnormal growth, differentiation and exfoliation of LC cells. The detached cancer cells first enter the mediastinal lymph nodes to form a cancer embolus. According to the Kaplan-Meier survival analysis, patients with low expression levels of ACTB had a good prognosis (P<0.05). However, the expression levels of ACTB had no significant effects on the prognosis based on the survival analysis of GEPIA (P>0.05). The influence of ACTB expression on the prognosis was undefined; therefore, more data are required to verify the suggested effect.

ITGB1 is a member of the integrin family of proteins. Integrin family proteins are involved in the regulation of cell adhesion and recognition processes, including hemostasis, embryogenesis, immune response, tissue repair and tumor cell metastasis (46). Yan et al (47) reported that the expression levels of ITGB1 were associated with OS and metastasis in patients with aggressive breast cancer. Wang et al (48) reported that linc-ITGB1 promoted the invasion and migration of gallbladder cancer cells by activating epithelial-mesenchymal transition, and knockout of ITGB1 significantly inhibited the metastasis and invasion of gallbladder cancer cells. Klahan et al (49) knocked out ITGB1 in breast cancer cells and revealed that calcium influx decreased, resulting in a significant decrease in the invasion and metastasis of triple-negative breast cancer cells. Wang et al (50) reported that ITGB1 serves important roles in the occurrence and metastasis of LC. The findings of Qin et al (51) suggested that microRNA-134 suppresses migration and invasion of non-small cell LC by targeting ITGB1. The present study demonstrated that the expression levels of ITGB1 were upregulated in MM LC. According to the OS analysis, patients with high expression levels of ITGB1 had a poor prognosis. The reason for this may be that high ITGB1 expression induces the occurrence of MLNM, which could invade the systemic organs along the lymphatic duct, causing systemic organ failure and a shorter lifespan. Based on the aforementioned results, the alterations in the expression levels of ITGB1 may be a molecular mechanism for stimulating the metastasis and invasion of LC cells to the mediastinal lymph node. However, currently, studies regarding IGTB1 are rare, so more efforts should be made in the future.

There are some limitations of the present study. First, the results of the present study are based on bioinformatics analysis only. Therefore, they require laboratory work to be verified using a large set of samples of patients with LC. Currently, it is difficult to obtain the ethical approval documents and informed consent. In the next stage of research, ethical approval and informed consent will be obtained to perform verification of the results of the present study in humans and animals.

In conclusion, the present study aimed to identify DEGs which may be involved in the occurrence or development of LC. Finally, 308 DEGs and 11 hub genes were identified by comparisons between MM LC and non-MM LC samples, which could be used as diagnostic and therapeutic biomarkers for MM LC. The present study provided novel insight for the diagnosis and treatment of MM LC. The results suggested that data mining and integration could be a promising tool to predict biomarkers of malignant tumors. However, the present study is only a preliminary report, and the number of samples in the present study was limited. Since cancer biomarkers only have meaning if they are integrated with clinical data, further experiments should be conducted to confirm the conclusions of the present study.

Supplementary Material

Supporting Data

Acknowledgements

Not applicable.

Funding

No funding was received.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

NZ performed the experiment, and was a major contributor in writing and submitting the manuscript. SWZ made substantial contributions to the conception and design of the study, as well as the acquisition, analysis and interpretation of the data, and also designed the draft of the research process. NZ was involved in critically revising the manuscript for intellectual content. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Patient consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Glossary

Abbreviations

Abbreviations:

ACTB

actin β

ADAM

ADAM metallopeptidase domain

AVP

arginine vasopressin

BiNGO

Biological Networks Gene Oncology tool

BP

biological processes

CC

cellular components

CXCR4

C-X-C motif chemokine receptor 4

DEGs

differentially expressed genes

ERBB2

Erb-b2 receptor tyrosine kinase 2

GEO

Gene Expression Omnibus

GEPIA

Gene Expression Profiling Interactive Analysis

GO

Gene Ontology

ITGB1

integrin subunit β1

ITSN2

intersectin 2

KEGG

Kyoto Encyclopedia of Genes and Genomes

LC

lung cancer

MCODE

Molecular Complex Detection

MF

molecular function

MLNM

mediastinal lymph node metastasis

MM LC

lung cancer with mediastinal lymph node metastasis

non-MM LC

lung cancer samples without mediastinal lymph node metastasis

OS

overall survival

PET

positron emission tomography

PPI

protein-protein interaction

RAB5B

RAB5B, member RAS oncogene family

SYNJ2

synaptojanin 2

VEGF-C

vascular endothelial growth factor-C

VEGF-D

vascular endothelial growth factor-D

VEGFR-3

vascular endothelial growth factor receptor-3

VAMP2

vesicle associated membrane protein 2

References

1 

Lopez-Pastorini A, Riedel R, Koryllos A, Beckers F, Ludwig C and Stoelben E: The impact of preoperative elevated serum C-reactive protein on postoperative morbidity and mortality after anatomic resection for lung cancer. Lung Cancer. 109:68–73. 2017. View Article : Google Scholar : PubMed/NCBI

2 

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA and Jemal A: Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 68:394–424. 2018. View Article : Google Scholar : PubMed/NCBI

3 

Isaka M, Kojima H, Takahashi S, Omae K and Ohde Y: Risk factors for local recurrence after lobectomy and lymph node dissection in patients with non-small cell lung cancer: Implications for adjuvant therapy. Lung Cancer. 115:28–33. 2018. View Article : Google Scholar : PubMed/NCBI

4 

Yeh YC, Kadota K, Nitadori J, Sima CS, Rizk NP, Jones DR, Travis WD and Adusumilli PS: International association for the study of lung cancer/American thoracic society/European respiratory society classification predicts occult lymph node metastasis in clinically mediastinal node-negative lung adenocarcinoma. Eur J Cardiothorac Surg. 49:e9–e15. 2016. View Article : Google Scholar : PubMed/NCBI

5 

Wang S, Zhou W, Zhang H, Zhao M and Chen X: Analysis of predictive factors for postoperative survival for non small cell lung carcinoma patients with unexpected mediastinal lymph nodes metastasis. Thorac Cardiovasc Surg. 62:126–132. 2014. View Article : Google Scholar : PubMed/NCBI

6 

Ma K, Chang D, He B, Gong M, Tian F, Hu X, Ji Z and Wang T: Radical systematic mediastinal lymphadenectomy versus mediastinal lymph node sampling in patients with clinical stage IA and pathological stage T1 non-small cell lung cancer. J Cancer Res Clin Oncol. 134:1289–1295. 2008. View Article : Google Scholar : PubMed/NCBI

7 

Saintigny P, Kambouchner M, Ly M, Gomes N, Sainte-Catherine O, Vassy R, Czernichow S, Letoumelin P, Breau JL, Bernaudin JF and Kraemer M: Vascular endothelial growth factor-C and its receptor VEGFR-3 in non-small-cell lung cancer: Concurrent expression in cancer cells from primary tumour and metastatic lymph node. Lung Cancer. 58:205–213. 2007. View Article : Google Scholar : PubMed/NCBI

8 

Ohtsuka T, Shiomi T, Shimoda M, Kodama T, Amour A, Murphy G, Ohuchi E, Kobayashi K and Okada Y: ADAM28 is overexpressed in human non-small cell lung carcinomas and correlates with cell proliferation and lymph node metastasis. Int J Cancer. 118:263–273. 2006. View Article : Google Scholar : PubMed/NCBI

9 

Na IK, Scheibenbogen C, Adam C, Stroux A, Ghadjar P, Thiel E, Keilholz U and Coupland SE: Nuclear expression of CXCR4 in tumor cells of non-small cell lung cancer is correlated with lymph node metastasis. Hum Pathol. 39:1751–1755. 2008. View Article : Google Scholar : PubMed/NCBI

10 

Maekawa S, Iwasaki A, Shirakusa T, Enatsu S, Kawakami T and Kuroki M and Kuroki M: Correlation between lymph node metastasis and the expression of VEGF-C, VEGF-D and VEGFR-3 in T1 lung adenocarcinoma. Anticancer Res. 27:3735–3741. 2007.PubMed/NCBI

11 

Li J, Li BL, Zhang HQ, Xu SF, Liu ZD, Yue WT and Han Y: Relationship between vascular endothelial growth factor C expression level and lymph node metastasis in non small cell lung cancer. Zhonghua Yi Xue Za Zhi. 88:2982–2985. 2008.PubMed/NCBI

12 

Kikuchi T, Daigo Y, Katagiri T, Tsunoda T, Okada K, Kakiuchi S, Zembutsu H, Furukawa Y, Kawamura M, Kobayashi K, et al: Expression profiles of non-small cell lung cancers on cDNA microarrays: Identification of genes for prediction of lymph-node metastasis and sensitivity to anti-cancer drugs. Oncogene. 22:2192–2205. 2003. View Article : Google Scholar : PubMed/NCBI

13 

Li L, Lei Q, Zhang S, Kong L and Qin B: Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis. Oncol Rep. 38:2607–2618. 2017. View Article : Google Scholar : PubMed/NCBI

14 

Kan T, Shimada Y, Sato F, Ito T, Kondo K, Watanabe G, Maeda M, Yamasaki S, Meltzer SJ and Imamura M: Prediction of lymph node metastasis with use of artificial neural networks based on gene expression profiles in esophageal squamous cell carcinoma. Ann Surg Oncol. 11:1070–1078. 2004. View Article : Google Scholar : PubMed/NCBI

15 

O'Donnell RK, Kupferman M, Wei SJ, Singhal S, Weber R, O'Malley B, Cheng Y, Putt M, Feldman M, Ziober B and Muschel RJ: Gene expression signature predicts lymphatic metastasis in squamous cell carcinoma of the oral cavity. Oncogene. 24:1244–1251. 2005. View Article : Google Scholar : PubMed/NCBI

16 

Nguyen ST, Hasegawa S, Tsuda H, Tomioka H, Ushijima M, Noda M, Omura K and Miki Y: Identification of a predictive gene expression signature of cervical lymph node metastasis in oral squamous cell carcinoma. Cancer Sci. 98:740–746. 2007. View Article : Google Scholar : PubMed/NCBI

17 

Kim TJ, Choi JJ, Kim WY, Choi CH, Lee JW, Bae DS, Son DS, Kim J, Park BK, Ahn G, et al: Gene expression profiling for the prediction of lymph node metastasis in patients with cervical cancer. Cancer Sci. 99:31–38. 2008.PubMed/NCBI

18 

Edgar R, Domrachev M and Lash AE: Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30:207–210. 2002. View Article : Google Scholar : PubMed/NCBI

19 

Wright CM, Savarimuthu Francis SM, Tan ME, Martins MU, Winterford C, Davidson MR, Duhig EE, Clarke BE, Hayward NK, Yang IA, et al: MS4A1 dysregulation in asbestos-related lung squamous cell carcinoma is due to CD20 stromal lymphocyte expression. PLoS One. 7:e349432012. View Article : Google Scholar : PubMed/NCBI

20 

Tomida S, Takeuchi T, Shimada Y, Arima C, Matsuo K, Mitsudomi T, Yatabe Y and Takahashi T: Relapse-related molecular signature in lung adenocarcinomas identifies patients with dismal prognosis. J Clin Oncol. 27:2793–2799. 2009. View Article : Google Scholar : PubMed/NCBI

21 

Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets - update. Nucleic Acids Res. 41(Database issue): D991–D995. 2013.PubMed/NCBI

22 

Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC and Lempicki RA: The DAVID gene functional classification tool: A novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8:R1832007. View Article : Google Scholar : PubMed/NCBI

23 

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al: Gene ontology: Tool for the unification of biology. The gene ontology consortium. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI

24 

Kanehisa M: The KEGG database. Novartis Found Symp. 247:91–101. 2002. View Article : Google Scholar : PubMed/NCBI

25 

Ni M, Liu X, Wu J, Zhang D, Tian J, Wang T, Liu S, Meng Z, Wang K, Duan X, et al: Identification of candidate biomarkers correlated with the pathogenesis and prognosis of non-small cell lung cancer via integrated bioinformatics analysis. Front Genet. 9:4692018. View Article : Google Scholar : PubMed/NCBI

26 

Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43(Database issue): D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI

27 

Smoot ME, Ono K, Ruscheinski J, Wang PL and Ideker T: Cytoscape 2.8: New features for data integration and network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar : PubMed/NCBI

28 

Huang HM, Jiang X, Hao ML, Shan MJ, Qiu Y, Hu GF, Wang Q, Yu ZQ, Meng LB and Zou YY: Identification of biomarkers in macrophages of atherosclerosis by microarray analysis. Lipids Health Dis. 18:1072019. View Article : Google Scholar : PubMed/NCBI

29 

Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI

30 

Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al: The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2:401–404. 2012. View Article : Google Scholar : PubMed/NCBI

31 

Maere S, Heymans K and Kuiper M: BiNGO: A cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 21:3448–3449. 2005. View Article : Google Scholar : PubMed/NCBI

32 

Nagy Á, Lánczky A, Menyhárt O and Győrffy B: Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep. 8:92272018. View Article : Google Scholar : PubMed/NCBI

33 

Siegel R, Ma J, Zou Z and Jemal A: Cancer statistics, 2014. CA Cancer J Clin. 64:9–29. 2014. View Article : Google Scholar : PubMed/NCBI

34 

Detterbeck FC, Boffa DJ and Tanoue LT: The new lung cancer staging system. Chest. 136:260–271. 2009. View Article : Google Scholar : PubMed/NCBI

35 

Suntharalingam M, Paulus R, Edelman MJ, Krasna M, Burrows W, Gore E, Wilson LD and Choy H: Radiation therapy oncology group protocol 02-29: A phase II trial of neoadjuvant therapy with concurrent chemotherapy and full-dose radiation therapy followed by surgical resection and consolidative therapy for locally advanced non-small cell carcinoma of the lung. Int J Radiat Oncol Biol Phys. 84:456–463. 2012. View Article : Google Scholar : PubMed/NCBI

36 

Shim SS, Lee KS, Kim BT, Chung MJ, Lee EJ, Han J, Choi JY, Kwon OJ, Shim YM and Kim S: Non-small cell lung cancer: Prospective comparison of integrated FDG PET/CT and CT alone for preoperative staging. Radiology. 236:1011–1019. 2005. View Article : Google Scholar : PubMed/NCBI

37 

McLoud TC, Bourgouin PM, Greenberg RW, Kosiuk JP, Templeton PA, Shepard JA, Moore EH, Wain JC, Mathisen DJ and Grillo HC: Bronchogenic carcinoma: Analysis of staging in the mediastinum with CT by correlative lymph node mapping and sampling. Radiology. 182:319–323. 1992. View Article : Google Scholar : PubMed/NCBI

38 

Toloza EM, Harpole L and McCrory DC: Noninvasive staging of non-small cell lung cancer: A review of the current evidence. Chest 123(1 Suppl). 137S–146S. 2003. View Article : Google Scholar

39 

Takamochi K, Yoshida J, Murakami K, Niho S, Ishii G, Nishimura M, Nishiwaki Y, Suzuki K and Nagai K: Pitfalls in lymph node staging with positron emission tomography in non-small cell lung cancer patients. Lung Cancer. 47:235–242. 2005. View Article : Google Scholar : PubMed/NCBI

40 

Billé A, Pelosi E, Skanjeti A, Arena V, Errico L, Borasio P, Mancini M and Ardissone F: Preoperative intrathoracic lymph node staging in patients with non-small-cell lung cancer: Accuracy of integrated positron emission tomography and computed tomography. Eur J Cardiothorac Surg. 36:440–445. 2009. View Article : Google Scholar : PubMed/NCBI

41 

Billè A, Okiror L, Skanjeti A, Errico L, Arena V, Penna D, Ardissone F and Pelosi E: Evaluation of integrated positron emission tomography and computed tomography accuracy in detecting lymph node metastasis in patients with adenocarcinoma vs. squamous cell carcinoma. Eur J Cardiothorac Surg. 43:574–579. 2013. View Article : Google Scholar : PubMed/NCBI

42 

Pavlyk I, Leu NA, Vedula P, Kurosaka S and Kashina A: Rapid and dynamic arginylation of the leading edge β-actin is required for cell migration. Traffic. 19:263–272. 2018. View Article : Google Scholar : PubMed/NCBI

43 

Lim YH, Burke AB, Roberts MS, Collins MT and Choate KA: Multilineage ACTB mutation in a patient with fibro-osseous maxillary lesion and pilocytic astrocytoma. Am J Med Genet A. 176:2037–2040. 2018. View Article : Google Scholar : PubMed/NCBI

44 

Antonescu CR, Agaram NP, Sung YS, Zhang L, Swanson D and Dickson BC: A distinct malignant epithelioid neoplasm with GLI1 gene rearrangements, frequent S100 protein expression, and metastatic potential: Expanding the spectrum of pathologic entities with ACTB/MALAT1/PTCH1-GLI1 fusions. Am J Surg Pathol. 42:553–560. 2018. View Article : Google Scholar : PubMed/NCBI

45 

Castro E, Cortes-Santiago N, Ferguson LM, Rao PH, Venkatramani R and López-Terrada D: Translocation t(7;12) as the sole chromosomal abnormality resulting in ACTB-GLI1 fusion in pediatric gastric pericytoma. Hum Pathol. 53:137–141. 2016. View Article : Google Scholar : PubMed/NCBI

46 

Huang R and Rofstad EK: Integrins as therapeutic targets in the organ-specific metastasis of human malignant melanoma. J Exp Clin Cancer Res. 37:922018. View Article : Google Scholar : PubMed/NCBI

47 

Yan M, Zhang L, Li G, Xiao S, Dai J and Cen X: Long noncoding RNA linc-ITGB1 promotes cell migration and invasion in human breast cancer. Biotechnol Appl Biochem. 64:5–13. 2017. View Article : Google Scholar : PubMed/NCBI

48 

Wang L, Zhang Y, Lv W, Lu J, Mu J, Liu Y and Dong P: Long non-coding RNA Linc-ITGB1 knockdown inhibits cell migration and invasion in GBC-SD/M and GBC-SD gallbladder cancer cell lines. Chem Biol Drug Des. 86:1064–1071. 2015. View Article : Google Scholar : PubMed/NCBI

49 

Klahan S, Huang WC, Chang CM, Wong HS, Huang CC, Wu MS, Lin YC, Lu HF, Hou MF and Chang WC: Gene expression profiling combined with functional analysis identify integrin beta1 (ITGB1) as a potential prognosis biomarker in triple negative breast cancer. Pharmacol Res. 104:31–37. 2016. View Article : Google Scholar : PubMed/NCBI

50 

Wang XM, Li J, Yan MX, Liu L, Jia DS, Geng Q, Lin HC, He XH, Li JJ and Yao M: Integrative analyses identify osteopontin, LAMB3 and ITGB1 as critical pro-metastatic genes for lung cancer. PLoS One. 8:e557142013. View Article : Google Scholar : PubMed/NCBI

51 

Qin Q, Wei F, Zhang J and Li B: miR-134 suppresses the migration and invasion of nonsmall cell lung cancer by targeting ITGB1. Oncol Rep. 37:823–830. 2017. View Article : Google Scholar : PubMed/NCBI

52 

Demin DE, Bogolyubova AV, Zlenko DV, Uvarova AN, Deikin AV, Putlyaeva LV, Belousov PV, Mitkin NA, Korneev KV, Sviryaeva EN, et al: The novel short isoform of securin stimulates the expression of cyclin D3 and angiogenesis factors VEGFA and FGF2, but does not affect the expression of MYC transcription factor. Mol Biol (Mosk). 52:508–518. 2018. View Article : Google Scholar : PubMed/NCBI

53 

Zaoui K, Benseddik K, Daou P, Salaün D and Badache A: ErbB2 receptor controls microtubule capture by recruiting ACF7 to the plasma membrane of migrating cells. Proc Natl Acad Sci USA. 107:18517–18522. 2010. View Article : Google Scholar : PubMed/NCBI

54 

Huang CC, Kuo HM, Wu PC, Cheng SH, Chang TT, Chang YC, Kung ML, Wu DC, Chuang JH and Tai MH: Soluble delta-like 1 homolog (DLK1) stimulates angiogenesis through Notch1/Akt/eNOS signaling in endothelial cells. Angiogenesis. 21:299–312. 2018. View Article : Google Scholar : PubMed/NCBI

55 

Joassard OR, Amirouche A, Gallot YS, Desgeorges MM, Castells J, Durieux AC, Berthon P and Freyssenet DG: Regulation of Akt-mTOR, ubiquitin-proteasome and autophagy-lysosome pathways in response to formoterol administration in rat skeletal muscle. Int J Biochem Cell Biol. 45:2444–2455. 2013. View Article : Google Scholar : PubMed/NCBI

56 

Inoue J, Ninomiya M, Umetsu T, Nakamura T, Kogure T, Kakazu E, Iwata T, Takai S, Sano A, Fukuda M, et al: Small interfering RNA screening for the small GTPase rab proteins identifies Rab5B as a major regulator of hepatitis B virus production. J Virol. 93:e006212019. View Article : Google Scholar : PubMed/NCBI

57 

Yamada K, Nakayama M, Miura Y, Nakano H, Mimura N and Yoshida S: Role of AVP in the regulation of vascular tonus and blood pressure in patients with chronic renal failure. Regul Pept. 45:91–95. 1993. View Article : Google Scholar : PubMed/NCBI

58 

Du Q, Guo X, Zhang X, Zhou W, Liu Z, Wang J, Zhang T, Mao Z, Luo J, Jin T and Liu C: SYNJ2 variant rs9365723 is associated with colorectal cancer risk in Chinese Han population. Int J Biol Markers. 31:e138–e143. 2016. View Article : Google Scholar : PubMed/NCBI

59 

Nakatsu F, Perera RM, Lucast L, Zoncu R, Domin J, Gertler FB, Toomre D and De Camilli P: The inositol 5-phosphatase SHIP2 regulates endocytic clathrin-coated pit dynamics. J Cell Biol. 190:307–315. 2010. View Article : Google Scholar : PubMed/NCBI

60 

Dasgupta S, Jang JS, Shao C, Mukhopadhyay ND, Sokhi UK, Das SK, Brait M, Talbot C, Yung RC, Begum S, et al: SH3GL2 is frequently deleted in non-small cell lung cancer and downregulates tumor growth by modulating EGFR signaling. J Mol Med (Berl). 91:381–393. 2013. View Article : Google Scholar : PubMed/NCBI

61 

Caceres PS, Mendez M and Ortiz PA: Vesicle-associated membrane protein 2 (VAMP2) but not VAMP3 mediates cAMP-stimulated trafficking of the renal Na+-K+-2Cl- co-transporter NKCC2 in thick ascending limbs. J Biol Chem. 289:23951–23962. 2014. View Article : Google Scholar : PubMed/NCBI

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October-2019
Volume 18 Issue 4

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Spandidos Publications style
Zhang N and Zhang N: Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis. Oncol Lett 18: 3754-3768, 2019
APA
Zhang, N., & Zhang, N. (2019). Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis. Oncology Letters, 18, 3754-3768. https://doi.org/10.3892/ol.2019.10723
MLA
Zhang, N., Zhang, S."Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis". Oncology Letters 18.4 (2019): 3754-3768.
Chicago
Zhang, N., Zhang, S."Identification of differentially expressed genes between primary lung cancer and lymph node metastasis via bioinformatic analysis". Oncology Letters 18, no. 4 (2019): 3754-3768. https://doi.org/10.3892/ol.2019.10723