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Abstract

Previous studies have suggested potential signature genes for lung cancer, however, due to factors such as sequencing platform, control, data selection and filtration conditions, the results of lung cancer-related gene expression analysis are quite different. Here, we performed a meta-analysis on existing lung cancer gene expression results to identify Meta-signature genes without noise. In this study, functional enrichment, protein-protein interaction network, the DAVID, String, TfactS, and transcription factor binding were performed based on the gene expression profiles of lung adenocarcinoma and non-small cell lung cancer deposited in the GEO database. As a result, a total of 574 differentially expressed genes (DEGs) affecting the pathogenesis of lung cancer were identified (207 up-regulated expression and 367 down-regulated expression in lung cancer tissues). A total of 5,093 interactions existed among the 507 (88.3%) proteins, and 10 Meta-signatures were identified: AURKA, CCNB1, KIF11, CCNA2, TOP2A, CENPF, KIF2C, TPX2, HMMR, and MAD2L1. The potential biological functions of Meta-signature DEGs were revealed. In summary, this study identified key genes involved in the process of lung cancer. Our results would help the developing of novel biomarkers for lung cancer.

Keywords

Lung cancer DEG PPI-interaction biomarkers AURKA CCNB1

1. Introduction

Lung cancer, which accounts for the most percentage of morbidity and mortality from cancer, injures people all over the world [1, 2, 3, 4, 5]. It is reported that only in 2018, there were about 2.1 million incidences and 1.8 million deaths [6, 7]. In theory, lung cancer can be classified into two categories, which are small cell lung cancer (SCLC) and non-small cell lung carcinoma (NSCLC) [7]. Worse still, lung cancer is usually diagnosed at a later stage of the disease, which limits its possibility for treatment [8]. Although many efforts have been done for lung cancer, such as surgical therapy, chemotherapy, and molecular targeting therapy, the prognosis of lung cancer and the 5-year survival rate are still very low [9].

Lung cancer is diagnosed at the molecular level, so it is needed to figure out the biological mechanisms of it to find out effective therapies [10]. It is reported that lung cancer contains various cell clones, corresponding to their only molecular signature, respectively [10, 11]. The therapies of lung cancer are different among patients because of the presence of genetic variations in the patient’s tumor [12].

In this study, we intended to achieve the following two goals: one is to provide reliable genetic information related to lung cancer; the other is to analyze the biological functions of differentially expressed genes in the pathogenesis of lung cancer, and provide a theoretical basis for the use of related genes as a biomarker to prevent and treat lung cancer.

Figure 1.

Analysis process for this study.

2. Materials and methods

2.1 Identification of lung cancer gene expression data sets and differentially expressed DEGs

NCBI’s GEO database (https://www.ncbi.nlm.nih. gov/geo/) was used to search for lung cancer gene expression data with “Lung cancer” and “Homo sapiens” as keywords. By analyzing the search results carefully, the lung cancer sequencing samples and control samples used in the above studies were filtered and analyzed, and the information of gene expression was extracted. The data was grouped according to treatment and control. The average expression level of the genes was calculated, and the $P$ value of the difference between the gene expression levels was calculated by t test (selected paired or unpaired $t$ -test according to the number of treatment and control samples). The differentially expressed genes (DEGs) in each record were determined based on “foldchange $\geqslant$ 1.2” and “ $P$ value $<$ 0.05”. The Venn map was drawn to determine the similarities and differences of DEGs between the data sets. Extract differentially expressed genes that The up-regulation of Meta-signature DEGs and down-regulation of Meta-signature DEGs were determined by shown in at least 3 data records.

2.2 Functional enrichment analysis of differential expression of meta-signature DEGs in lung cancer

The up-and-down DEGs were mapped to the DAVID database [13]. The Gene Ontology (GO) [14] annotation and KEGG Pathway [15, 16] were further analyzed. The filter condition was that the $P$ value was less than 0.05. The top 5 terms enriched in GO biological process were extracted.

2.3 Protein-protein interaction network construction and analysis for Meta-signature DEGs in lung cancer

The lung cancer differential expression Meta- signature DEGs were mapped to the String database (https://string-db.org/) [17], and the protein-protein interaction relationship between related genes was mapped using Cytoscape software [18]. The top 10 genes with the most protein-protein interactions in the selected modules were analyzed, and their relationship with lung adenocarcinoma was analyzed.

2.4 Transcription factor analysis of differentially expressed Meta-signature DEGs in lung cancer

The lung cancer differential expression Meta- signature DEGs were submitted to the Tfacts database (http://www.tfacts.org/) [19, 20]. The $p$ -value, q-value, E-value, and FDR were used to predict transcription factors regulating the expression of meta-miRNA target genes. Only four transcription factors with less than 0.05 were considered reliable transcription factors. The transcription factors of differentially expressed Meta-signature DEGs in lung cancer were separately counted, and the common and unique transcription factors between Meta-signature DEGs were compared.

3. Results

In this study, the existing lung cancer-related GEO data (GSE18842, GSE32863, GSE75037, and GSE81089) was applied to explore important genes affecting the pathogenesis of lung cancer, determine the relevant biomarkers for lung cancer, and explore the possible mechanisms of genes involved in lung cancer [21, 22, 23, 24]. The key up- and down-regulation genes of lung cancer tissues compared with the control were determined by meta-analysis [25, 26]. The functional enrichment, protein-protein interaction network and transcription factor binding analysis of these DEGs were further analyzed. The work flow for the analysis was shown in Fig. 1.

First, the DEGs was determined by comparing the existing lung cancer gene expression results from multiple datasets. Functional enrichment analysis, protein-protein interaction network analysis, and transcription factor binding analysis of up and down DEGs were used to investigate the possible mechanisms of these genes affecting the pathogenesis of lung cancer and to study their biological functions.

3.1 Identification of differentially expressed genes related to lung cancer

Based on “foldchange $\geqslant$ 1.2” and “ $P$ value $<$ 0.05”, up and down DEGS were identified in lung cancer samples compared with control samples. The four lung cancer gene expression data sets differed greatly in the number and composition of DEGs (Fig. 2). In all four data sets, a total of 6222 up-regulated genes and 3621 down-regulated genes were identified. In the up-regulated expression gene sets, only 12 (0.19%) genes were significantly up-regulated in all four data sets, while in the down-regulated expression gene sets, only 49 (1.35%) were significantly down-regulated in all four data sets. Therefore, there are large differences in the number and composition of DEGs among the four data sets. In the subsequent analysis, we selected genes differentially expressed in at least three gene expression data sets as Meta-signature genes, and obtained 207 up-regulated expressions of DEGs and 367 down-regulated expressions of DEGs.

Figure 2.

Distribution of differentially expressed genes in four lung cancer datasets. A. The lung cancer dataset focuses on the up-regulation of the number of genes expressed. A total of 6222 up-regulated genes were obtained, and only 12 genes were up-regulated in all four data sets. Select genes that are up-regulated in at least 3 data as Meta-signature genes, totaling 207. B. The lung cancer dataset down-regulates the quantitative statistics of expressed genes. A total of 3621 genes down-regulated were obtained, and only 49 genes were down-regulated in all four data sets. Genes up-regulated in at least 3 data were selected as Meta-signature genes, for a total of 367.

3.2 Functional enrichment analysis results of differential expression Meta-signature DEGs

GO enrichment analysis was performed on up and down DEGs. The results showed that the up-regulated expression of Meta-signature genes was mainly involved in mitotic nuclear division (GO: 0007067), cell division (GO: 0051301), sister chromatid condensation (GO: 0007062), chromosome separation (GO: 0007059), G1/S transition of mitotic cell cycle (GO: 000082) and other biological processes (Fig. 3A). While down-regulated expression of Meta-signature genes were mainly involved in angiogenesis (GO: 0001525), leukocyte migration (GO: 0050900), cell adhesion (GO: 0007155), immune response (GO: 0006955), vasculogenesis (GO: 0001570 $\sim$ ) and other biological processes (Fig. 3B).

Table 1
Basic information on lung cancer gene expression data sets

Dataset	Tissue		Sample number		Plantform	Reference
	Case	Control	Case	Control
GSE18842	NSCLC tumor samples	Paired nontumor samples	46	45	GPL570 Affymetrix Human Genome U133 Plus 2.0 Array	Sanchez et al., 2011
GSE32863	Lung adenocarcinoma	Non-tumor lung samples	58	58	GPL6884 Illumina HumanWG-6 v3.0 expression beadchip	Selamat et al., 2012
GSE75037	Lung adenocarcinomas	Non-malignant adjacent tissue	83	83	GPL6884 Illumina HumanWG-6 v3.0 expression beadchip	Girard et al., 2016
GSE81089	Non-small cell lung cancer	Normal organs	199	19	GPL16791 Illumina HiSeq 2500 (Homo sapiens)	Mezheyeuski et al., 2018

Figure 3.

Up- and down-regulation of differentially expressed genes in GO biological processes and KEGG metabolic pathways enrichment results. A. Up-regulation of differentially expressed genes GO biological process enrichment results. B. Down-regulation of differential expression target gene GO biological process enrichment results. C. Up-regulation of differentially expressed genes KEGG metabolic pathway enrichment results. D. Down-regulation of differentially expressed genes KEGG metabolic pathway enrichment results.

The results of KEGG enrichment showed that the up-regulated expression of Meta-signature genes were mainly enriched in the cell cycle (hsa04110), p53 (mutated) signaling pathway (hsa04115), oocyte meiosis (hsa04114), progesterone-mediated oocyte maturation and ECM-receptor interaction (hsa04512) and other metabolic pathways (Fig. 3C). While down-regulated expression of the meta-signature genes were mainly enriched in malaria (hsa05144), Hematopoietic cell lineage (hsa05150), rheumatoid arthritis (hsa05323), cell adhesion molecules (hsa04514) and other metabolic pathways (Fig. 3D).

3.3 Protein-protein interaction network analysis for Meta-signature Genes

The interaction between the corresponding proteins of Meta-signature Genes was analyzed using String database and Cytoscape software. As shown in Fig. 4, a total of 5,093 interactions were present among 507 (88.3%) of the total 574 proteins.

Furthermore, the number of hub genes in the above protein-protein interaction network was calculated and the genes corresponding to the top 10 proteins were extracted (Table 2). The results showed that the strongest hub genes were AURKA, CCNB1, KIF11, CCNA2, TOP2A in the protein-protein interaction network.

Table 2
Key role genes for lung cancer pathogenesis identified by PPI analysis

Gene symbol	Ensembl_gene_id	Gene annotation	# adjacency genes
AURKA	ENSP00000216911	Aurora kinase A	96
CCNB1	ENSP00000256442	Cyclin B1	96
KIF11	ENSP00000260731	Kinesin family member 11	96
CCNA2	ENSP00000274026	Cyclin A2	95
TOP2A	ENSP00000411532	DNA topoisomerase II alpha	95
CENPF	ENSP00000355922	Centromere protein F	94
KIF2C	ENSP00000361298	Kinesin family member 2C	94
TPX2	ENSP00000300403	TPX2 microtubule nucleation factor	94
HMMR	ENSP00000377492	HMMR antisense RNA 1	93
MAD2L1	ENSP00000296509	Mitotic arrest deficient 2 like 1	93

Figure 4.

Protein-protein interaction networks of lung cancer meta-signature genes. The node of the yellow-filled rectangle in the figure represented the 10 proteins with the largest number of effector proteins, and the related genes may have a greater impact on the pathogenesis of lung cancer.

3.4 Transcriptional factor analysis of Meta-signature DEGs

Furthermore, the transcription factor genes corresponding to DEGs were analyzed using the TfactS database, and the similarity and differences of transcription factor genes were compared corresponding to up- and down-regulation of DEGs. As shown in Fig. 5A, in the up-regulated DEGs, a total of 73 transcription factor genes formed 178 interactions with 49 genes, while in the down-regulation of DEGs, a total of 85 transcription factor genes formed 212 interactions with 62 genes. A total of 111 transcription factor genes regulated a total differentially expressed genes, and 47 transcription factor genes were shared by two types of target genes, accounting for 42.3% of all transcription factor genes.

Figure 5.

Transcription factor analysis of differentially expressed genes up- and down-regulated expressed genes. A. Up-and-down adjustment of differentially expressed genes and comparison of unique transcription factors. The red background represents the number of transcription factors that up-regulate differentially expressed genes. Blue background represents the number of transcription factors that down-regulate differentially expressed genes. B. Transcription factor gene regulates the quantitative statistics of differentially expressed genes. C. The 10 transcription factor genes that regulate the largest number of genes.

As shown in Fig. 5B, a higher proportion of transcription factor genes only regulated a single target gene. By statistically adjusting the number of target genes, it was found that the regulatory factors of DEGs such as SP1, CTNNB1, MYC, CEBPA, and NFKB1 were strong (Fig. 5C).

4. Discussion

In this study, through meta and protein-protein interaction network analysis, ten important genes related to lung cancer incidence were identified. They were AURKA, CCNB1, KIF11, CCNA2, TOP2A, CENPF, KIF2C, TPX2, HMMR and MAD2L1. Among them, KIF11, belonging to the Kinesin-5 family, is related to kinds of mitotic microtubule functions, such as microtubule crosslinking, antiparallel microtubule sliding [27, 28]. Moreover, it has been found in cell proliferation and cancers including bladder cancer and pancreatic cancer [29, 30]. Thus, it may be used as a biomarker for chemotherapy. TOP2A, as a key nuclear protein, can control DNA topology [31, 32]. The overexpression of it happened in kinds of human cancers, including cervical carcinoma, breast cancer, and prostate cancer [33, 34]. KIF2C, as a member of the Kinesin family, affects spindle assembly and chromosome segregation during mitosis [35, 36]. The higher level of KIF2C is related to human cancer including gastric cancer, colorectal cancer, and breast cancer [37, 38]. TPX2, as a microtubule-related protein, has been reported in many studies, which are related to kinds of cancers including lung cancer, cervical cancer. HMMR has been shown to play an important role in the neoplastic progression. Moreover, the over-expression of it is associated with many human cancers and it can be used as an attractive target for inhibiting glioblastoma. MAD2L1 is an important mediator in the chromosomal control pathway [39]. Hyperexpression of MAD2L1 is correlated with poor prognosis of tumor patients [40]. The reports about the rest genes are few.

Interestingly, there was a down regulation in angiogenesis consistent across all data sets. The angiogenic process that is responsible for the support of tumor progression and metastasis represents one of the main hallmarks of cancer. Our hypothesis on why angiogenic factors would be downregulated in the context of lung cancer is that the downregulation of key angiogenic players could contribute to the control of extra thoracic invasion of cancer cells in human adenocarcinoma with predominant lepidic growth, which is consistent with previous report [41].

Additionally, it can be concluded that by statistically adjusting the number of target genes, it can be seen that the regulatory factors of differentially expressed genes such as SP1, CTNNB1, MYC, CEBPA and NFKB1 were stronger. SP1, as one of the members of the family of Sp/Kruppel – like factors, can be used as a transcription factor in various cancers and works as a transcriptional activator of target genes. By managing the transcription of various housekeeping genes, SP1 functions in kinds of biological processes, such as metabolism, cell cycle and cell death. Until now, it has been found that over 12,000 SP1 binding sites exist in human genes. SP1 play a key role in providing methods for cancer by regulating the functions of countless cellular genes [40]. CTNNB1 was reported to have a relationship with clonogenic cell growth, differentiation, and apoptosis. Previous studies showed that CTNNB1 is related to the oncogenesis of many human cancers, such as colorectal cancer and breast cancer. Also, it played a key role in tumor growth and metastasis. MYC belongs to the family of cellular oncogenes and functions as a pleiotropic transcription factors in various cellular processes. It has been studied that about 55% MYC is up-regulated. By many mechanisms such as gene duplications, somatic mutations, the stability of MYC can be increased [42]. MYC, as a frequently amplified proto-oncogene in human cancers, can adjust immune checkpoint genes and has been implicated in immune evasion by cancer cells. Meanwhile, MYC can be used as a potential target for new therapeutic strategies [42]. CEBPA, as a member of the leucine zipper family of transcription factors, can serve as a prognostic marker for various cancers. The family expresses in lots of tissues and cell types and controls many cellular processes, such as cellular proliferation and differentiation, inflammation and metabolism. NFKB1, as one of the members of the transcription factor family, is a possible gene for cancer susceptibility. Inappropriate activation NFKB1 is related to the development of various diseases and tumors. The pro-inflammatory NFKB1 plays an important role in inflammatory, while lung inflammation is the major pathogenetic feature for lung cancer.

5. Conclusions

In conclusion, in this study, a total of ten meta-signature genes in lung cancer and 10 transcription factors affecting the pathogenesis of lung cancer were identified. These 10 Meta-signature genes can be used as biomarkers for targeted prevention and treatment of lung cancer.

Footnotes

Acknowledgments

This study was supported by the innovative grant of the Department of Oncology, Tianjin Medical University General Hospital.

Conflict of interest

The authors declare that they have no competing interests.

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Transcriptome analysis reveals key signature genes involved in the oncogenesis of lung cancer

Abstract

Keywords

1. Introduction

2.1 Identification of lung cancer gene expression data sets and differentially expressed DEGs

2.2 Functional enrichment analysis of differential expression of meta-signature DEGs in lung cancer

2.3 Protein-protein interaction network construction and analysis for Meta-signature DEGs in lung cancer

2.4 Transcription factor analysis of differentially expressed Meta-signature DEGs in lung cancer

3. Results

3.1 Identification of differentially expressed genes related to lung cancer

Table 1 Basic information on lung cancer gene expression data sets

Table 2 Key role genes for lung cancer pathogenesis identified by PPI analysis

5. Conclusions

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Basic information on lung cancer gene expression data sets

Table 2
Key role genes for lung cancer pathogenesis identified by PPI analysis