Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

doi:10.21203/rs.3.rs-580657/v1

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Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

https://doi.org/10.21203/rs.3.rs-580657/v1

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Background

Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment.

Methods

Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Finally, Kaplan-Meier plotter were performed to analyze the prognosis efficacy of hub genes.

Results

74 up-regulated and 238 down-regulated DEGs were identified. As for the up-regulated DEGs, KEGG analysis results revealed they were mainly enrolled in protein digestion and absorption. However, the down-regulated DEGs were primarily enriched in cell adhesion molecules. Subsequently, 9 hub genes: KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, were identified and showed higher expression in both LUAD and other cancers. Finally, all these hub genes were found significantly related to the prognosis of LUAD (p < 0.05).

Conclusions

Our results screened out the hub genes and pathways that were related to the development and prognosis of LUAD, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of LUAD.

Oncology

LUAD

bioinformatics

DEGs

hub genes

prognosis

Lung cancer is the leading cause of cancer morbidity and mortality in the world, with 2.1 million new cases and 1.8 million deaths predicted in 2018 [1]. Although the great advance in surgical and molecular target therapies, the prognosis of lung cancer is still dismal [2]. According to histopathology characteristics, lung cancer can be divided into two main types: non-small cell lung cancer (NSLC) and small cell lung cancer (SCLC), with NSLC being accounted for 85% of lung cancer with lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC) and large cell carcinoma (LCC) three main subtypes [3]. LUAD has been ranked the first cancer mortality in china [4],and receiving an operation remains a primary method, unfortunately the 5-year survival rate is around 10–15% [5]. Lacking sensitive and effective early diagnosis biomarkers and tending to metastasis and rapid dissemination are deemed to contribute to the poor prognosis. Therefore, looking for reliable biomarkers or drug targets is of great importance for improving the diagnosis and prognosis of LUAD.

High-throughput screening technique which is based on RNA sequencing or microarray gene expression profiles has been widely used for gene detection and diseases analysis. Academic can rapidly and precisely get the knowledge of key genes associated with serious diseases like cancer via a large number of biological information data mining [6]. For instance, Huang et al. used integrated bioinformatics analysis of RNA sequencing data and superenhancer catalogs to identify superenhancer-associated circRNAs and further found that loss of superenhancer-regulated circNfix promoted cardiac regenerative repair and functional recovery [7]. As for lung cancer, compound CA-5f was considered as a novel late-stage autophagy inhibitor with potent anti-tumor effect against NSLC via bioinformatics technique [8]. The discovery and the lucubration of these key genes obtained through bioinformatics analysis contribute to the discovery of effective biomarkers, which can be applied to the diagnosis, treatment and prognosis of tumors.

The aim of our study was to perform multiple analyses towards key genes of LUAD through integrated bioinformatics methods. First of all, we downloaded three microarray datasets containing mRNA expression data from Gene Expression Omnibus (GEO) and the differentially expressed genes (DEGs) were screened out via GEO2R online tool. Next, by virtue of Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), functional annotation and enrichment analysis about these DEGs were performed, and then we established protein-protein interaction (PPI) network and obtained the hub genes in LUAD. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Kaplan-Meier survival analysis were carried out to provide support for the targeted therapy and prognosis of LUAD.

Acquisition and processing of GEO data. 3 LUAD datasets were obtained from GEO database (available online: http://www.ncbi.nlm.nih.gov/geo) [9], including the gene expression profiles GSE118370 (expression profiling by array, 6 LUAD samples, 6 normal samples), GSE32863 (expression profiling by array, 58 LUAD samples, 58 normal samples) and GSE43458 (expression profiling by array, 80 LUAD samples, 30 normal samples). The GSE118370 dataset (Submission date: Jan 23, 2019) was based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array, Affymetrix, Inc). The GSE32863 dataset (Submission date: Mar 21, 2012) was based on the GPL6884 platform (Illumina HumanWG-6 v3.0 expression beadchip). The dataset GSE43458 (Submission date: Aug 06, 2013) was based on the GPL6244 platform ([HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version]). DEGs were screened and identified via GEO2R online tools (available online: https://www.ncbi.nlm.nih.gov/geo/geo2r/) with the cut-off |log2FC|≥1 and adj. p < 0.05. The numbers of DEGs we got from GSE118370, GSE32863 and GSE43458 were 3302, 1584 and 1025, respectively. DEGs co-occurred in all 3 datasets were integrated together and showed in a Venn diagram (available online: http://bioinformatics.psb.ugent. be/webtools/Venn/).

GO and KEGG Pathway enrichment analyses and visualization. Functional annotations and pathway enrichment analyses on DEGs were carried out through Enricher website (available online: https://amp.pharm.mssm.edu/Enrichr) [10], which included the visual results of GO and KEGG enrichment analyses. GO can be used to study gene-related biological process (BP), cellular component (CC) and molecular function (MF), while KEGG can simulate and analyze the pathways that DEGs were enriched in.

PPI network and acquisition of hub genes. PPI network can explain the possible mechanisms of the genes involved in intracellular processing. So the STRING (available online: https://stringdb.org/, Version11.0) [11], an online tool was first applied to establish a PPI network of DEGs, which was then imported into Cytoscape software (version 3.6.1) [12]. In addition, the plug-in CytoHubba in Cytoscape was used to search and analyze the hub genes that were generated through Maximal Clique Centrality (MCC) score.

Expression verification of hub genes in LUAD and other cancers. We utilized Gene Expression Profiling Interactive Analysis (GEPIA, available online: http://gepia.cancer-pku.cn/) [13] and the human protein atlas database (HPA v19) [14] to confirm the expression level of these hub genes in LUAD from mRNA and protein level, respectively. Oncomine website (available online: http://www.oncomine.org) [15] was used to the analysis of hub genes expression differences between tumor and normal tissues in common human cancers, whose thresholds were set as follows: p-value: 0.01; fold change: 2; gene rank: 10%; analysis type: cancer vs. normal analysis; data type: mRNA. And for each gene, a comparison by cancer vs. normal analysis was performed.

Prognosis analyses of hub genes. The Kaplan-Meier plotter (available online: http://kmplot.com/) [16], a online database for survival analysis, was used to analyze the prognosis value of the hub genes. The hazard ratio (HR) with 95% confidence intervals and logrank P value were calculated and displayed on the diagram.

Identification and screening of DEGs. Three microarray datasets: GSE118370, GSE32863 and GSE43458 were obtained from GEO database, including 144 samples from LUAD patients and 94 samples of normal lung tissues. The differentially expressed genes (DEGs) were identified by online GEO2R analysis tool with the cut-off criterion of |log2FC|≥1 and adj. p < 0.05, and got 3302, 1584 and 1025 DEGs from each dataset, respectively. Afterwards, a Venn diagram (Fig. 1) was used to get an intersection of DEGs from the 3 datasets above, there were 74 up-regulated genes and 238 down-regulated genes (Table 1) in the LUAD samples compared to the normal samples.

Table 1

Differentially Expressed Genes
Up- or Down-regulated	Number	Differentially Expressed Genes
Up-regulated	74	GDF15 TPX2 C15orf48 IGF2BP3 COL1A1 ADGRF1 METTL7B ANKRD22 SLC50A1 TMPRSS4 SPP1 CLDN10 PLPP2 TIMP1 MMP12 HABP2 UBE2T SLC2A1 EPCAM ECT2 MMP11 FAM83A CDH3 CRABP2 CEACAM5 FUT3 LAD1 TNFRSF21 TFAP2A SULF1 GCNT3 COL11A1 FUT2 KIAA0101 AGR2 SPINK1 S100P HMGB3 DSP GOLM1 PROM2 MUC16 CST1 SERINC2 ETV4 LGSN PYCR1 CDCA7 SFN CDC20 COL3A1 ASPM OCIAD2 CD24 TCN1 SLC7A5 IL37 NQO1 PSAT1 SPDEF TOP2A GJB2 THBS2 SMPDL3B PCP4 AIM2 COL10A1 LCN2 CTHRC1 FBXO32 ATP10B CENPF MMP9
Down-regulated	238	RTKN2 FRAS1 MSRB3 AQP9 NOSTRIN PAPSS2 A2M HIGD1B ANXA3 TEK GRK5 VGLL3 C10orf67 GHR MS4A2 ZNF385B SLCO2A1 LAMP3 BCHE STARD13 ID1 PTRF SPTBN1 TSPAN7 LRRK2 AFAP1L1 ACADL AHNAK FHL1 KLF4 PLSCR4 NDRG2 TCF21 GLDN NEDD4L LDLR SFTPD OLR1 GLIPR2 AGER HOXA5 CD93 MSR1 WIF1 RAMP3 CLIC5 NPNT MME LAMA3 ICAM2 PDK4 GBP4 EFEMP1 EMCN ADARB1 LMO7 STARD8 LRRC32 SASH1 ESAM SCGB1A1 CA4 TACC1 FMO3 LYVE1 GIMAP4 P3H2 PHACTR2 FEZ1 PDE5A CPA3 TBX3 PDZD2 LHFP ADIRF HBB HEY1 NTNG1 GNG11 RHOJ SRPX TSPAN18 ZNF106 SLC39A8 SFTPC MYL9 TGFBR3 LPL CAT PTPRM NDRG4 SOSTDC1 SBSPON GPC3 ADCY4 FPR2 PGM5 LMCD1 FOSB MARCO GSTM5 DCN EPAS1 SLIT2 CX3CR1 FZD4 PCOLCE2 CYBRD1 C2orf40 AOX1 HSD17B6 GKN2 CYP4B1 SMAD6 AFF3 HSPB8 SLC6A4 TMEM204 GIMAP8 CDH13 ACVRL1 TIMP3 DACH1 EML1 RERG HBEGF CRYAB CD36 ETS2 PTPRB ADGRL2 PPARG AQP4 MT1M TNNC1 IL1RL1 MCEMP1 CPB2 KLF6 EMP1 ABCA8 SLC19A3 AOC3 TMEM47 ZBTB16 EDNRB SLIT3 FAM167A MYH10 STX11 MYH11 DUSP1 RGCC VWF ABI3BP ARRB1 VSIG4 CYYR1 MS4A15 HPGD C7 S100A8 HEG1 IL6 CPED1 PRICKLE2 ANGPT1 SEMA6A TMEM100 DUOX1 FERMT2 CD34 KIAA1324L DPT MFAP4 PECAM1 PRG4 MAOB B3GALNT1 CDO1 SEPP1 SDPR EPB41L2 ADAMTS1 CLEC14A ANOS1 C8B STXBP6 S1PR1 GIMAP7 LIMCH1 SPOCK2 RNF144B LDB2 CALCRL RECK CAV1 CLEC1A JAM2 SPARCL1 CA2 ANKRD29 ADH1A METTL7A ITM2A FGD5 FAM189A2 LRRN3 LGI3 IL7R FMO2 SOCS2 ACTG2 TMOD1 FGFBP2 ARHGEF6 IL33 PLLP HHIP PGC ADH1B NEDD9 FABP4 FCN3 PDLIM3 FAM107A GPM6B SEMA5A RAMP2 VIPR1 PLAC8 ANKRD1 MAMDC2 CLDN18 CDH5 OLFML1 COL6A6 FBLN5

GO analysis and KEGG pathway analysis of DEGs. To evaluate the function of DEGs, we performed functional annotations and pathway enrichment analyses via GO and KEGG. According to the result of KEGG, up-regulated DEGs emerged a high enrichment in the pathways of protein digestion and absorption, ECM-receptor interaction, glycosphingolipid biosynthesis and so on (Fig. 2A; Table 2). GO analysis showed that up-regulated DEGs mainly participated in the biological processes (BP) of the extracellular matrix organization, extracellular matrix disassembly, collagen fibril organization and so on (Fig. 2B). The analysis of cellular component (CC) emerged that these genes mainly encoded the components of endoplasmic reticulum lumen (Fig. 2C). The molecular function (MF) included platelet-derived growth factor binding, protease binding, fucosyltransferase activity and so on (Fig. 2D). As for the down-regulated DEGs, they were mainly enriched in cell adhesion molecules, tyrosine metabolism, PPAR signaling pathway, drug metabolism and so on from the analyses of KEGG (Fig. 3A; Table 3). GO analysis showed that down-regulated DEGs mainly participated in the BP of the regulation of angiogenesis, sprouting angiogenesis, positive regulation of cell differentiation and so on (Fig. 3B). In addition, the results of CC explicated the hub genes were mainly components of plasma membrane (Fig. 3C) and MF included amyloid-beta binding, low density lipoprotein particle binding and so on (Fig. 3D).

Table 2

KEGG Analysis of the Up-regulated DEGs
Term	P-value	Odds Ratio	Combined Score
Protein digestion and absorption	0.000	13.18	105.02
ECM-receptor interaction	0.003	10.62	60.05
Glycosphingolipid biosynthesis	0.012	12.84	56.74
Relaxin signaling pathway	0.012	6.59	28.90
Vitamin B6 metabolism	0.022	54.58	208.31
Central carbon metabolism in cancer	0.024	8.76	32.60
Focal adhesion	0.038	4.25	13.96
Ubiquinone and other terpenoid-quinone biosynthesis	0.040	27.28	87.84
PI3K-Akt signaling pathway	0.042	3.20	10.11
IL-17 signaling pathway	0.047	6.05	18.57

Table 3

KEGG Analysis of the Down-regulated DEGs
Term	P-value	Odds Ratio	Combined Score
Cell adhesion molecules (CAMs)	0.000	5.67	55.01
Tyrosine metabolism	0.000	13.66	131.93
PPAR signaling pathway	0.000	7.49	62.08
Drug metabolism	0.000	5.90	47.63
Complement and coagulation cascades	0.000	6.98	55.33
Vascular smooth muscle contraction	0.001	4.76	32.72
Bile secretion	0.002	6.31	40.32
Leukocyte transendothelial migration	0.002	4.80	29.27
Malaria	0.003	7.49	44.24
Hematopoietic cell lineage	0.006	4.59	23.42

Analysis of PPI network and identification of hub genes. Exploring the protein-protein interaction in organisms can help to study molecular mechanisms of some diseases from a perspective of system. We built a PPI network about up-regulated DEGs via STRING website (Fig. 4A), then the interaction data were imported into Cytoscape, and then CytoHubba plug-in was applied to confirm 9 hub genes according to the scores of MCC. The results were shown in Fig. 4B. They were KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, and their full names and functions were shown in Table 4.

Table 4

The Full Name and Functions of Hub Genes
Rank	Name	Full name	Function
1	KIAA0101	PCLAF(PCNA clamp associated factor)	KIAA0101 can prevent doxorubicin-induced apoptosis by inhibiting p53 activation
2	CDCA7	Cell division cycle associated 7	Overexpression of this gene is found to enhance the transformation of lymphoblastoid cells
3	TOP2A	DNA topoisomerase II alpha	TOP2A is involved in processes such as chromosome condensation, chromatid separation, and the relief of torsional stress.
4	CDC20	Cell division cycle 20	CDC20 is a regulatory protein interacting with several other proteins at multiple points in the cell cycle.
5	ASPM	Assembly factor for spindle microtubules	ASPM is essential for normal mitotic spindle function in embryonic neuroblasts.
6	TPX2	TPX2 microtubule nucleation factor	TPX2 can bind to and increases the activity of a protein kinase, an enzyme which phosphorylates a protein.
7	CENPF	Centromere protein F	CENPF is a component of the nuclear matrix during the G2 phase of interphase
8	UBE2T	Ubiquitin-conjugating enzyme E2T	The protein encoded by UBE2T catalyzes the covalent attachment of ubiquitin to protein substrates.
9	ECT2	Epithelial cell transforming 2	The expression of this gene is elevated with the onset of DNA synthesis and remains elevated during G2 and M phases.

Expression analysis of hub genes in multiple databases. GEPIA and Oncomine databases were used to validate the mRNA expression level of 9 hub genes in LUAD and other common cancers, respectively. These hub genes were all significantly overexpressed in LUAD compared to normal tissue (Fig. 5). In addition, a summary of hub genes in multiple tumors indicated that hub genes were significantly overexpressed in lung cancer (Fig. 6). Furthermore, we detected the protein expression level of hub genes between lung cancer and normal tissues, which were agreed with the mRNA expression results that most genes were overexpressed in lung cancer tissue (Fig. 7).

Prognosis analyses of hub genes. We further used the Kaplan-Meier plotter to analyze the prognostic value of these 9 hub genes in lung cancer patients. It showed that a higher expression of KIAA0101 (HR = 1.71, logrank P = 1.8e-16), CDCA7 (HR = 1.47, logrank P = 4.2e-06), TOP2A (HR = 1.65, logrank P = 1.5e-14), CDC20 (HR = 1.82, logrank P < 1E-16), ASPM (HR = 1.77, logrank P < 1E-16), TPX2 (HR = 1.86, logrank P < 1E-16), CENPF (HR = 1.71, logrank P = 1.1e-16), UBE2T (HR = 2.23, logrank P < 1E-16) and ECT2 (HR = 1.32, logrank P = 2.2e-05) were associated with a poor prognosis in lung cancer patients.

Lung cancer is a leading cause of cancer-related deaths globally and cancer survival largely depends on the stage at diagnosis, unfortunately, 40–60% of lung cancer patients are not diagnosed until advanced stages [17]. Therefore, effective biomarkers and therapeutic targets are in need. Integrated bioinformatics analysis, which focuses on screening of DEGs, discovering hub genes of network-based and doing survival analysis, which has been diffusely used to recognize latent biomarkers related to cancer diagnosis, treatment, and prognosis estimation.

In this study, DEGs in LUAD based on the GEO expression profiles of GSE118370 (6 LUAD samples, 6 normal samples), GSE32863 (58 LUAD samples, 58 normal samples) and GSE43458 (80 LUAD samples, 30 normal samples) were identified by bioinformatics analysis. 312 DEGs were identified including 74 up-regulated and 238 down-regulated genes. Furthermore, through Enricher online web tool, we visualized the outcomes derived from Gene Ontology and KEGG pathway enrichment analysis. As for the biological processes, these up-regulated DEGs were enriched in the extracellular matrix organization, extracellular matrix disassembly, collagen fibril organization and so on. As for the cellular component, it showed that these genes mainly encoded the components of endoplasmic reticulum lumen. And the molecular function included platelet-derived growth factor binding, protease binding, fucosyltransferase activity and so on. According to the result of KEGG, up-regulated DEGs emerged a high enrichment in the pathways of protein digestion and absorption, ECM-receptor interaction, glycosphingolipid biosynthesis and so on. As for the down-regulated DEGs, they were mainly enriched in cell adhesion molecules, tyrosine metabolism, PPAR signaling pathway, drug metabolism and so on from the analyses of KEGG. GO analysis showed that down-regulated DEGs mainly participated in the biological processes of the regulation of angiogenesis, sprouting angiogenesis, positive regulation of cell differentiation and so on. In addition, the results of cellular component explicated the hub genes were mainly components of plasma membrane and molecular function included amyloid-beta binding, low density lipoprotein particle binding and so on. Angiogenesis is closely related to the occurrence and progression of cancers [18] and the formation of extracellular matrix is also associated with tumor metastasis and invasion [19, 20]. According to KEGG pathway enrichment analysis, tyrosine metabolism was found to be significant in LUAD. Li et al. revealed that activation of tyrosine metabolism in CD13⁺ cancer stem cells may drive relapse in hepatocellular carcinoma by means of generating nuclear acetyl-CoA to acetylate and stabilize Foxd3, and allowing CD13⁺ cancer stem cells to sustain quiescence and resistance to chemotherapeutic agents [21]. Nitration of protein tyrosine has proved to be involved in a variety of biological processes, including signal transduction, protein degradation, energy metabolism, mitochondrial dysfunction, enzyme inactivation, immunogenic response, cell apoptosis and cell death, and plays an important role in the occurrence and metastasis of lung cancer [22]. Therefore, the signaling pathway of tyrosine metabolism was expected to be a potential drug therapy target for LUAD.

Next, DEGs PPI network was constructed via the STRING online database and Cytoscape software. By virtue of “CytoHubba” plug-in, the top 9 hub genes, KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2 were identified. Using GEPIA and Oncomine validation, the mRNA expression of these 9 hub genes in LUAD samples was higher than normal lung samples, HPA database data also displayed that the protein level of hub genes was consistent with mRNA level that most genes were overexpressed in lung cancer tissue. Furthermore, to inquire prognostic biomarkers of LUAD, we analyzed the influence of hub genes expression level on survival of lung cancer patients and found that high level gene expression of these all hub genes were related to lung cancer patients’ poor overall survival. Therefore, these all 9 genes may be functional in lung cancer’s occurrence and development.

KIAA0101 encoded protein is a cell-cycle regulated oncoprotein that can regulate DNA synthesis, maintenance of DNA methylation, and DNA-damage bypass, through its interaction with the human sliding clamp PCNA [23, 24]. It has been verified that KIAA0101 overexpressed in various solid tumors including lung cancer [24–28]. Kim et al. found that PAF (PCLAF/KIAA0101) that was highly expressed in LUAD and associated with poor prognosis, could drive cell quiescence exit to promote lung tumorigenesis by remodeling the DREAM complex [27]. Another study also confirmed that overexpression of KIAA0101 could promote the progression of NSCLC and KIAA0101 knockdown induced G1 phase cell cycle arrest and inhibited NSCLC cell proliferation and migration [29]. And the interaction of KIAA0101 and UbcH10 could also regulate NSCLC cell proliferation by disrupting the function of the spindle assembly checkpoint [30].

Cell division cycle associated 7, CDCA7, was identified as a c-Myc responsive gene, and behaved as a direct c-Myc target gene. Overexpression of CDCA7 was found to enhance the transformation of lymphoblastoid cells, and it complements a transformation-defective Myc Box II mutant, suggesting its involvement in c-Myc-mediated cell transformation [31]. In quite a number of tumors, such as hepatocellular carcinoma [32], colorectal cancer [33], lymphoma [34], breast cancer [35], CDCA7 was all reported up-regulated and might be a potential prognostic factor and therapeutic target. Wang et al. have found that CDCA7 could promote lung adenocarcinoma proliferation via regulating the cell cycle and silencing CDCA7 inhibited cell proliferation in LUAD through G1 phase arrest and induction of apoptosis, which implied that CDCA7 might be identified as a potential therapeutic target for new biomarkers and LUAD [36].

Topoisomerase II (TOP2) has been clarified to have crucial functions, including DNA replication, transcription and chromosome segregation, and more and more active anticancer drugs targeted it [37]. TOP2 contains two types of isozymes: TOP2A and topoisomerase II beta (TOP2B) [38], TOP2A is the only enzyme able to cleave and re-ligate the double-strand backbone of DNA, which is indispensable for DNA replication, transcription, and repair [39, 40]. Ejlertsen et al. [39] showed that TOP2A was a direct molecular target of anthracyclines that can improve the sensitivity of anthracycline-containing chemotherapy in high-risk breast cancer patients. In malignant peripheral-nerve sheath tumor, TOP2A was the most overexpressed gene compared with benign neurofibromas [41]. High expression of TOP2A was found to be correlated to worse overall survival (OS) in all non-small-cell lung cancer and lung adenocarcinoma patients, but not in lung squamous cell carcinoma patients [42, 43]. It has also been reported that miRNA’s being associated with TOP2A plays an important role in lung cancer, for example, down-regulation of miRNA-144-3p whose potential target was TOP2A, was highly enriched in various key pathways like the protein digestion and absorption and the thyroid hormone signaling pathways in non-small cell lung cancer from the comprehensive meta-analysis [44].

CDC20, which is called Fizzy, contains seven WD40 repeats that are necessary for mediating protein-protein interactions [45]. Mounting evidence has revealed that CDC20 plays an oncogenic role in human tumorigenesis. Overexpression of CDC20 was observed in a variety of human tumors. For example, in pancreatic cancer over-expression of CDC20 was detected in pancreatic tumor tissues compared with normal adjacent tissues from pancreatic cancer patients [46]. Besides, Yuan et al. reported that the mRNA and protein levels of CDC20 were significantly higher in breast cancer cells and high-grade primary breast cancer tissues [47]. In lung cancer, multiple studies have indicated that CDC20 is highly expressed, and could be a potential prognostic marker in human NSCLC [48]. However, the exact molecular mechanism of CDC20-mediated lung tumorigenesis is still elusive and needs to be further explored.

ASPM is a kind of protein involved in multiple cellular or developmental processes, such as neurogenesis and brain growth. It is widely reported that ASPM is expressed in multiple tumor tissues and involved in the development and progression of several cancers including hepatocellular carcinomas, gastric cancer, pancreatic ductal adenocarcinomas, and lung cancer [49–52]. Previous study indicated that ASPM was involved in the development and progression of lung adenocarcinoma and was associated with poor prognosis [53].

TPX2, which is also known as DIL2 or p100, uses two flexibly linked elements ('ridge' and 'wedge') in a novel interaction mode to simultaneously bind across longitudinal and lateral tubulin interfaces [54, 55]. In ovarian cancer, it can promote the proliferation and migration of human ovarian cancer cells by regulating PLK1 expression [56]. Except for these function, in various cancers can TPX2 also control bladder cancer cell’s proliferation and invasion via TPX2-p53-GLIPR1 regulatory circuitry [57], regulate the PI3K/AKT signaling pathway to facilitate hepatocellular carcinoma [58], interactive with miRNA such as miR-485-3p [59], miR-361-5p [60], miR-335-5p [61], miR-216b [62] and so on. Zhou et al. have verified that TPX2 can activate the epithelial-mesenchymal transition process and promote both the expression and activities of matrix metalloproteinase (MMP)2 and MMP9 in non-small cell lung cancer (NSCLC), which means TPX2 promotes the metastasis and malignant progression of NSCLC and could thus serve as a marker of poor prognosis in NSCLC [63].

CENPF,which contains 3,210 amino acids and has 367 kDa in full-length molecular weight, has been proven to be highly expressed in lung adenocarcinoma. Furthermore, the expression of CENPF and ERβ2/5 (Estrogen receptors beta2/5) in LUAD patients have been shown to be correlated to TNM staging, providing a basis for exploring the interactions between CENPF and ERβ2/5. Rattner et al. demonstrated that CENPF is involved in mitosis and tumor proliferation [64]. CENPF is directly associated with disease outcomes after undergoing gene amplification [65]. In prostate cancer, CENPF has been shown to predict survival and tumor metastasis [66].

UBE2T whose full name is ubiquitin-conjugating enzyme E2T plays a significant role in carcinogenesis [67]. It has been proved that UBE2T promotes the development and progression of numerous types of cancer, including gastric cancer [68], breast cancer [69], hepatocellular cancer [70, 71] and also lung cancer [72]. A study showed that UBE2T promoted proliferation, migration, invasion, and radiation resistance in vitro and in vivo by accelerating the G2/M transition and inhibiting apoptosis and in mechanism, UBE2T promotes epithelial-mesenchymal transition via ubiquitination-mediated FOXO1 degradation and Wnt/β-catenin signaling pathway activation in NSCLC [72, 73]. Other bioinformatics analyses also found that UBE2T could have a potential efficacy in the predictability of early stage NSCLC [74, 75].

ECT2, epithelial cell transforming 2, is a guanine nucleotide exchange factor and transforming protein that is related to Rho-specific exchange factors and yeast cell cycle regulators. The expression of this gene is elevated with the onset of DNA synthesis and remains elevated during G2 and M phases [76]. Multiple important studies have detected the relationship between ECT2 and lung cancer. Justilien et al. demonstrated that nuclear ECT2 GEF activity was required for Kras-Trp53 lung tumorigenesis in vivo and that ECT2-mediated transformation requires ECT2-dependent rDNA transcription [76]. Furthermore, Nuclear PKCι-ECT2-Rac1 and ribosome biogenesis mignt be a novel axis in lung tumorigenesis [77], through extracellular matrix dynamics and focal adhesion signaling, ECT2 could promote lung adenocarcinoma progression [78].

In summary, our study indicated that KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2 might be involved in LUAD tumorigenesis and progression. Multiple database analysis and survival analysis demonstrated that these 9 hub genes may regard as a latent prognostic biomarker and the overexpression of these 9 hub genes might lead to reduced overall survival in lung cancer patients and the mechanism and their mutual regulation network were worthy of further research and experiments. However, in our present study, only bioinformatic analysis was performed, and the role of identified hub genes in LUAD in vivo and in vitro should be further verified. Anyway, all of our analysis may provide some useful direction into the potential biomarkers and prognosis evaluation of LUAD.

LUAD, lung adenocarcinoma; GEO, Gene Expression Omnibus; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, The Kyoto Encyclopedia of Genes and Genomics; PPI, The protein-protein interaction; STRING, Search Tool for the Retrieval of Interacting Genes; GEPIA, Gene Expression Profiling Interactive Analysis; HPA, the human protein atlas; NSLC, non-small cell lung cancer; SCLC, small cell lung cancer; LUSC, lung squamous cell carcinoma; LCC, large cell carcinoma; BP, biological process; MF, molecular function; CC, cellular component; HR, hazard ratio.

Acknowledgements

Not applicable.

Funding

This study was supported by grants provided by the Teaching Reform Research Project of Medical School, Wuhan University（No.2020004）and Open Experimental Project of Wuhan University (2020).

Availability of data and materials

All data generated or analyzed during this study are included in this published article.

Authors' contributions

All authors contributed to the study concept and design, as well as the interpretation of the data. MZ designed and managed the whole research; ZMW analyzed the data and wrote the main manuscript text. All authors read and approved the final version of the 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.

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Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

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