Bioinformatic analysis reveals CCNA2 as Lung Adenocarcinoma hub gene and is associated with cell proliferation and epithelial- mesenchymal transition

doi:10.21203/rs.3.rs-3229666/v1

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Bioinformatic analysis reveals CCNA2 as Lung Adenocarcinoma hub gene and is associated with cell proliferation and epithelial- mesenchymal transition

https://doi.org/10.21203/rs.3.rs-3229666/v1

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Lung adenocarcinoma (LUAD) is one of the malignant tumors worldwide. The purpose of this study is to identify the hub genes related to LUAD using bioinformatics analysis. Three gene expression datasets (GSE27262, GSE74706, GSE75037) were obtained from GEO database. Identification of DEGs was performed using GEO2R and constructed protein-protein interaction network using STRING and cytoscape. GO, KEGG and GSEA analysis were carried out through R package “clusterprofiler”. GEPIA was used to verify the survival of hub genes, UALCAN database and HPA database were used to determine protein expression, complemented by IHC staining results. Lastly, CCK-8 and colony formation assays were used to verify PC9 cell growth ability and we used western blot, Transwell and scratch wound assays for validation of PC9 cells migration and EMT markers. We identified 107 up-regulated genes and 344 down-regulated genes. Thirty hub genes were identified and the 12 hub genes with a rank of degrees > 30 are as follow: CCNA2, AURKA, HMMR, EZH2, TTK, TPX2, CCNB1, ANLN, ECT2, CEP55, MAD2L1 and PBK. CCNA2 as one of the genes having the highest node degree of 35 was chosen for subsequent analysis. Our findings indicated that high expression of CCNA2 had the highest enrichment effects on cell cycle and was associated with poor prognosis. CCK-8 and colony formation analysis further verified that CCNA2 contributed to the progression of LUAD and the protein expression was significantly elevated in tumor tissues compared to the normal lung tissues. In addition, high expression of CCNA2 is related to the EMT process and may play a role in EMT-induced metastasis in LUAD. Briefly, CCNA2 was identified as one of the hub genes and can promote lung cancer cell growth, migration and invasion via epithelial-mesenchymal transition. Moreover, could avail as a biomarker or therapeutic target to judge the prognosis of LUAD.

Biological sciences/Cancer/Lung cancer

Biological sciences/Biological techniques/Bioinformatics

Lung adenocarcinoma

bioinformatics

differentially expressed genes

hub genes

CCNA2

Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer death, with an estimated 2.2 million new cancer cases and 1.8 million deaths in 2020 ^[1]. Non-small cell lung cancer (NSCLC) accounts for 85% of primary lung cancer cases and could be divided into lung adenocarcinoma and lung squamous cell carcinoma (LUSC) according to their histopathological type ^[2]. LUAD is the most common subtypes and also a heterogeneous disease characterized by high rates of genetic mutation ^[3]. Despite the substantial advance in combined therapies, the long-term survival is still poor with an overall survival of less than 5 years ^[4]. The lack of sensitive and specific early biomarkers, insufficient knowledge about molecular mechanism of LUAD progression have limited the development of novel treatment strategies. Patients are often diagnosed at an advanced stage, and there is also a high possibility of drug resistance and metastasis which contribute to high mortality of this disease. Therefore, it is necessary to explore biomarkers that can be detected during the early stages of the disease.

Gene expression profiling is a powerful method widely used in the research fields to explore the mechanism of tumorigenesis. High-throughput microarray technologies have been employed to screen the differentially expressed genes (DEGs) between normal and tumor samples ^[5,^6]. One of the key advantages of microarray technology is that it can simultaneously and comprehensively detect a tremendous number of genes, moreover possessing the ability to explore the gene regulation pattern and molecular mechanisms taking part in oncogenesis and progression of LUAD. Proposed innovative algorithm of probe signal detection and analysis also further improved the accuracy and reliability of microarray results ^[7,^8].

In the present study, three original LUAD datasets (GSE27262, GSE74706, GSE75037) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained by comparing gene expression between LUAD samples and normal samples. Subsequently, protein-protein interaction (PPI), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) network analyses were conducted in order to explore the key modules and hub genes involved. Among the 12 hub genes, we chose to further evaluate the role of CCNA2 in LUAD. We undergo Gene Set Enrichment Analysis (GSEA) to find out the most enrichment pathway in high expression CCNA2 lung cancer and utilized dbEMT2.0 database to examine the link between CCNA2 and epithelial-mesenchymal transition (EMT) process. Furthermore, various laboratory techniques were used to verify our bioinformatics analysis results (Fig. 1). Our experiment’s finding clarified the potential mechanism and relationship between the progression of LUAD and CCNA2.

Microarray data. GEO database (https://www.ncbi.nlm.nih.gov/geo/) is a public functional genomics data repository supporting MIAME-compliant data submission. It archives and freely distributes microarray, next-generation sequencing, and other forms of high-throughput functional genomics data submitted by the research community. GSE27262 was based on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array), and it included 25 matched pairs of LUAD samples and normal samples. GSE74706 was based on GPL13497 (Agilent-026652 Whole Human Genome Microarray 4x44K v2), and it included 10 LUAD samples and 18 normal samples. GSE75037 was based on GPL6884 (Illumina HumanWG-6 v3.0 expression beadchip), and it included 83 matched pairs of LUAD samples and normal samples. All of the data can be viewed online.

Identification of DEGs. GEO2R is an online web tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) used for comparing two or more groups of samples to determine DEGs. The cutoff criteria were set to adjust P-value <0.05 and ∣log 2FC ∣>1.5 and probe sets without exact gene symbols were excluded. Then, the results of difference analysis from the three datasets were intersected, the shared genes were used for the following analysis. Numbers of up- and downregulated genes from different databases and counts of crossed genes were shown using a Venn diagram.

Protein-Protein Interaction (PPI) Network Construction and Hub Genes Verification. In order to detect potential associations among those DEGs, we construct the PPI network using Search Tool for the Retrieval of Interacting Genes (STRING) database (https://string-db.org/), a minimum required interaction score of 0.4 was considered significant. Subsequently, we used Cytoscape version 3.8.2 software to visualize the molecular interaction network of DEGs. In addition, we used Cytoscape plug-ins Molecular Complex Detection (MCODE) to search for candidate modules with the degree cutoff value of 2, node score cutoff value of 0.2, k-core value of 2, and maximum depth of 100, respectively. Modules with a score greater than 5 were screened out and analyzed using another Cytoscape plug-ins cytoHubba to screen out hub genes with scores greater than 10.

GO functional enrichment and KEGG pathway analysis. GO functional enrichment and KEGG pathway analysis were conducted using R package “clusterprofiler” version 4.0.5. GO term or KEGG pathways with P-value <0.05 were considered statistically significant and visualized by “ggplot2” and “enrichplot”

Survival Analysis. Gene Expression Profiling Interaction Analysis (GEPIA) database (http://gepia.cancer-pku.cn/) is a free online visualization platform that provides key interactive and customizable functions including survival analysis. The top 12 hub genes were entered into GEPIA database to examine the relationship between these genes and the survival months of LUAD patients. The logrank P value and hazard ratio (HR) with 95% confidence intervals were presented on the plot. P <0.05 was considered statistically significant.

Co-expression analysis between CCNA2, HMMR and AURKA. The cBioPortal for Cancer Genomics (https://www.cbioportal.org/) is an open-source platform that allows for visualization, exploration and analysis of cancer genomics data sets. We used cBioPortal co-expression section to verify and visualize the correlation between CCNA2, HMMR and AURKA.

Protein Expression of CCNA2, HMMR and AURKA. The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) (https://ualcan.path.uab.edu/) is a comprehensive, user-friendly and convenient free online platform used to analyze various cancer data. We used UALCAN to conduct and visualize protein expression analysis of CCNA2, HMMR and AURKA, which UALCAN obtained the data from Clinical Proteomic Tumor Analysis Consortium(CPTAC). CPTAC is a centralized repository containing clinical data and proteomic sequence datasets of different types of cancers. In addition, immunohistochemistry of CCNA2, HMMR and AURKA between LUAD and normal tissues was determined using the Human Protein Atlas database v.21 (HPA, https://www.proteinatlas.org/). HPA is a useful and powerful database which is dedicated to providing tissue and cellular distribution information of a variety of human proteins. Furthermore, the IHC-based protein expression pattern is commonly used to help researchers in detecting the relative location and abundance of proteins [⁹].

Analysis of Tumor-Infiltration Immune Cells. To examine the correlation between expression of CCNA2, HMMR and six different types of tumor-infiltrated immune cells (B cells, CD8⁺T cells, CD4⁺T cells, macrophages, neutrophils and dendritic cells), we chose to use Tumor Immune Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer/) database to analyze and visualize our results. TIMER is a public resource for systematical analysis of immune infiltrates across diverse cancers.

Heatmap analysis of EMT-related genes. Large number of samples from TCGA database were downloaded and combined with the Epithelial-Mesenchymal Transition Gene Database (dbEMT2.0, http://dbemt.bioinfo-minzhao.org/) to analyze the relationship of CCNA2 and these EMT-related genes. Heatmap analysis was conducted and visualized using R package “pheatmap”.

Immunohistochemical staining and scoring method. Paraffin-embedded specimens of resected lung adenocarcinoma tissues obtained from surgical treatment were prepared by pathological department of Ruijin hospital, Shanghai, China, and the immunostaining was carried out by Boerfu Biotechnology company (Wuhan, China). Brown staining was considered positive and the protein expression levels were independently scored by two pathologists. Initially, four degrees of proportional score for positive staining were assigned for both tumor cells and peritumoral cells (1, ≤25% positive cells; 2, 25–50% positive cells; 3, 50–75% positive cells; and 4, >75% positive cells). Thereafter, four degrees of intensity score were evaluated according to the intensity of staining (1, very weak; 2, weak; 3, intermediate; and 4, strong). The proportion and intensity scores were then added to obtain a total score ranging from 2 to 8. A total score ≥5 was considered as high expression and a score ≤4 was considered as low expression. The scoring criteria were based on the assessment reported by Meng et.al and Kawai et.al [¹⁰,¹¹].

Ethics approval and consent to participate. The informed consent of all patients has been obtained before the operation, and the procedures for organizing collection have been approved by the Ethics Committee of Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China. All procedures comply with the guidelines and ethical principles outlined in the Helsinki Declaration.

Cell culture and transfection. The human NSCLC cell lines PC9 were kindly provided by Shanghai Cancer Institute, cultured in RPMI 1640 (Shanghai BasalMedia Technologies Co, Shanghai, China) supplemented with 10% fetal bovine serum (FBS) (Lonsera, Uruguay), 100U/ml penicillin and 100µg/ml streptomycin, and were cultured at 37°C in a humidified incubator of 5% CO₂. For shRNA-mediated knockdown, specific short hairpin RNAs (shRNA) targeting CCNA2 (shRNA-CCNA2-1, target sequence: 5’-GCTGACCCATACCTCAAGTAT-3’, shRNA-CCNA2-2, target sequence: 5’-CCTTAGGGAAATGGAGGTTAA-3’, shRNA-CCNA2-3, target sequence: 5’-CCTCTTGATTATCCAATGGAT-3’ and shRNA-CCNA2-4, target sequence: 5’-GCCTGAATCATTAATACGAAA-3’) and corresponding negative control (pLKO.1-copGFP-PURO-NC) were purchased from TsingkeBiotechnologyCo, Shanghai. 293T cells were seeded into 6-well plates and transfected with 3ug plasmid mixture (shRNA plasmid + psPAX2 + pMD2.G) and 9ul PEI transfection reagent. Following 5h of transfection, the transfection reagent in the wells were replaced with fresh DMEM (Shanghai BasalMedia Technologies Co, Shanghai, China) supplemented with 10% FBS and antibiotics. The cells were then cultured for 2 days before transfecting PC9 cells.

Cell proliferation and Colony formation assays. Stably transfected PC9 cells were inoculated into 96-well plate at a density of 3000 cells per well for the determination of cell proliferation. After incubation for 0, 1, 2 and 3 days, Cell Counting Kit-8 (CCK-8) (Bimake, Texas, America, cat no. B34302) solution was seeded to each well based on the manufacture protocol. The optical density of each hole was measured under 450nm. For cell colony formation assays, PC9 and shCCNA2-PC9 cells were incubated in 6-well plates at a density of 1000 cells per well. A weeks later, the cells were stained with crystal violet (0.2%) for 30 min and the colony numbers were counted.

qPCR Analysis. Total RNA was extracted from PC9 and shCCNA2-PC9 cells using the TRIzol reagent (Invitrogen, America) following the manufacturer’s instructions. The total RNA was then further quantified using NanoDrop (Thermo Fisher Scientific, Massachusetts, America). cDNA synthesis was performed using Hifair® Ⅱ 1st Strand cDNA Synthesis Kit (Yeasen, Shanghai, China) and resulting complementary DNA was used for real-time qPCR (Light Cycler® 480II) using SYBR Green (Yeasen, Shanghai, China) and primers specific for CCNA2 (forward: 5’- GGATGGTAGTTTTGAGTCACCAC-3’, reverse: 5’- CACGAGGATAGCTCTCATACTGT-3’). Primers were synthesized by TsingkeBiotechnologyCo, Shanghai.

Western Blot Analysis. Whole cells lysates were prepared using SDS buffer containing protease and phosphatase inhibitors. After determining protein concentration using Protein Quantification Kit (BCA Assay), equal amounts of protein samples were fully electrophoresed with SDS-PAGE and transferred onto PVDF membranes. The associated primary antibodies that we used were as following: Cyclin A2 (proteintech, Chicago, America, cat no. 18202-1-AP), E-cadherin (Cell Signaling Technology, Danvers, America, cat no. 3195), Vimentin (Cell Signaling Technology, Danvers, America, cat no. 5741), HSP90 (Abclonal, Wuhan, China, cat no. A5006).

Migration and Wound Healing assays. The migration ability of cells was measured by Transwell chambers (Corning costar, Corning, America, cat no. 3422). Medium with 20% FBS was added to the lower chamber as a chemical attractant. About 8x10⁵ of transfected cells were incubated with 200µl serum-free medium in the upper chamber, and then incubated with 5% CO₂ at 37°C. After 24h, the cells were fixed with polyformaldehyde fixing solution (Servicebio, Wuhan, China, cat no: G1101) and stained with crystal violet dyeing solution (Servicebio, Wuhan, China, cat no: G1014). For wound healing assays, cells were seeded in 6-well plates and a 200µl sterile micropipette tip was used to make a scratch wound. Wound healing was observed after 24h standardized culture and photographed using phase-contrast microscopy.

Identification of DEGs between LUAD and normal samples. In this study, three datasets (GSE27262, GSE74706, GSE75037) were chosen from GEO database, containing 118 tumor samples and 126 normal samples which were analyzed by GEO2R, detailed information mentioned before and shown in Supplementary Table 1. Of these, a total of 451 genes were identified, including 107 up-regulated genes and 344 down-regulated genes (Fig. 2a-b).

PPI network and hub genes selection. All DEGs were loaded into the STRING database to illustrate potential associations between DEGs and identify the most significant genes of PPI network. Subsequently, data were downloaded and mapped into Cytoscape (Fig. 2c). This network consists of 383 nodes and 1535 edges, we filtered out genes that were not connected to any other genes and obtained 30 hub genes with MCODE score of 28.483 and degree ≥ 10 (Fig. 2d). The 30 hub genes were shown in Supplementary Table 2 and the 12 hub genes with rank of degrees > 30 are as follow: Cyclin A2 (CCNA2), Aurora Kinase A (AURKA), Hyaluronan mediated motility receptor (HMMR), Enhancer of zeste 2 polycomb repressive complex 2 (EZH2), TTK protein kinase (TTK), TPX2 microtubule nucleation factor (TPX2), Cyclin B1 (CCNB1), Anillin actin binding protein (ANLN), Epithelial cell transforming 2 (ECT2), Centrosomal Protein 55 (CEP55), Mitotic Arrest Deficient 2 like 1 (MAD2L1) and PBZ binding kinase (PBK) (Table 1). In addition, we also presented one other significant module with MCODE score > 5 (Fig. 2e).

GO and KEGG of the hub genes. Figure 2findicates that the 30 hub genes were most enriched in the following BP terms: ‘Mitotic nuclear division’, ‘Nuclear division’, ‘Organelle fusion’, ‘Microtubule cytoskeleton organization involved in mitosis’ and ‘Chromosome segregation’. They were also most enriched in the following CC terms: ‘Spindle’, ‘Chromosomal region’, ‘Midbody’, ‘Chromosome, centromeric region’ and ‘Spindle pole’. In addition, they were most enriched in the following MF terms: ‘Microtubule binding’, ‘Tubulin binding’, ‘Microtubule motor activity’, ‘Protein serine/Threonine/Tyrosine kinase activity’ and ‘Motor activity’ (Supplementary Table 3). We also found that they were mainly involved in ‘Cell cycle’, ‘Progesterone-mediated oocyte maturation’ and ‘Cellular senescence’ (Figure 2g, Supplementary Table 4).

Overall survival (OS). Furthermore, we analyzed the potential relation between the expression levels of the top 12 hub genes and the OS of LUAD patients. The results showed that high expression of the 12 hub genes could cause poor OS in LUAD patients (Fig. 3a). We decided to choose the top 4 genes for further analysis which is CCNA2, AURKA, HMMR and EZH2 to have a better understanding about the correlation between these genes, protein expression and their relationship with different types of tumor-infiltrated immune cells, however, EZH2 was ruled out due to insignificant of OS P-value.

Correlation between CCNA2, HMMR and AURKA. We used cBioportal to visualize the results of correlation between the 3 hub genes and found that these genes were positively correlated with one another in LUAD (Fig. 3b). Both Pearson correlation score and Spearman correlation score were also shown in the plot. P-value <0.05 was considered as the cut-off criterion with significant difference. The Pearson correlation score for CCNA2 vs AURKA, CCNA2 vs HMMR and HMMR vs AURKA were 0.75, 0.74 and 0.64, the Spearman correlation score were 0.83, 0.88 and 0.81 respectively.

Protein expression of CCNA2, HMMR and AURKA. In order to determine the protein expression of the 3 hub genes, we conducted protein expression analysis using UALCAN. Based on the boxplot, protein levels of CCNA2, HMMR and AURKA were higher in tumor tissues compared with normal tissues. Furthermore, high expressions of the 3 genes were also observed in the immunohistochemistry of LUAD tissues obtained from HPA database (Fig. 3c). AURKA will then be ruled out in the following analysis due to insignificant P-value. The correlations between protein expression of CCNA2, HMMR and Age, Gender, Tumor stage, Tumor grade were presented in Supplementary Figure 1. In addition, our IHC staining results also further verified the consistency of CCNA2 protein expression in human lung cancer tissues and the results obtained from the databases demonstrated above. Our mean CCNA2 IHC score in the tumor and peritumor is 6.05 and 4.05 respectively. In summary, the IHC score of CCNA2 protein was significantly higher in the tumor tissues compared to the normal adjacent tissues (P<0.0001) (Fig. 3d-e). Table 2 shows details regarding patients clinicopathological factors.

Correlation of CCNA2, HMMR with tumor infiltrating immune cells. We investigate correlation between CCNA2, HMMR expression and tumor immune infiltrates via TIMER database. Neither CCNA2 (r = -0.013, P = 7.7E-01) or HMMR (r = -0.006, P = 8.97E-01) is not correlated with tumor purity, but strongly correlated with CD8⁺T cells (r = 0.112, P = 1.36E-02; r = 0.103, P = 2.33E-02) and neutrophils (r = 0.172, P = 1.44E-04; r = 0.152, P = 8.14E-04). Moreover, CCNA2 was also slightly correlated with macrophages (r = 0.007, P = 8.86E-01) and dendritic cells (r = 0.021, P = 6.36E-01) while HMMR was slightly correlated with dendritic cells (r = 0.029, P = 5.21E-01) (Fig. 3f).

GSEA analysis of CCNA2 and HMMR. GSEA analysis was measured between low and high CCNA2 and HMMR expression datasets. The screening criteria depend on the p.adjust value, and the top five are selected for visual display. Our findings showed that the high expression of CCNA2 and HMMR both had the highest enrichment effects on cell cycle, the other enriched pathways are DNA replication, oocyte meiosis, spliceosome and lupus erythematosus. (Fig. 4a)

CCNA2 accelerates PC9 cells growth, migration and regulates EMT. To disclose the outcomes of CCNA2 in LUAD progression, the shRNA of CCNA2 was transfected into cells. As demonstrated in Figure 4b, CCK-8 results indicated that knockdown of CCNA2 slows the proliferation ability of PC9 cells. Consistently, colony formation assay indicated that low expression levels of CCNA2 noticeably inhibited the clonogenic ability (Fig. 4c). Epithelial mesenchymal transition (EMT) is a reversible process which drive metastasis in multiple cancer types by transdifferentiating epithelial cells to mesenchymal cells [¹²,¹³]. The transition is characterized by the increase of mesenchymal markers (such as Vimentin, N-cadherin) expression and the decrease expression of epithelial markers (such as E-cadherin). Firstly, EMT-related genes obtained from the dbEMT2.0 database were separated into EMT-related + oncogenic, EMT-related + suppressive genes and only EMT-related genes as indicated by the dbEMT2.0 database. Expression of CCNA2 and these EMT-related genes were then jointly investigated. The results of the heatmaps (Fig. 5a-c) demonstrated that high expression of CCNA2 were mostly positively co-related to the genes in EMT-related + oncogenic genes (Fig. 5a) and only EMT-related genes group (Fig. 5c), which also further implies that CCNA2 is related to the EMT process. Subsequently, we assessed the expression of EMT-markers mentioned before in PC9 and PC9-shCCNA2 cells. By using western blotting, we observed elevated expression of E-cadherin and a decreased expression of Vimentin in PC9-shCCNA2 cells. (Fig. 5d) To further verify the occurrence of EMT, we also performed Transwell assays and wound healing assay to evaluate the migration and invasion ability of cells. The results showed that knocking down expression of CCNA2 in PC9 cells significantly reduced cells migration and invasion (Fig. 5e-f). All these results indicated that CCNA2 plays a role in LUAD cell growth, migration and may contribute to the EMT-induced metastasis in LUAD.

Despite the emergence and improvement in LUAD treatment such as targeted therapy and immunotherapy, the overall survival of LUAD patients has been significantly improved compared to traditional chemotherapy treatment [¹⁴,¹⁵]. However, we are still far-flung in finding an effective way to completely cure LUAD due to its intricate pathogenesis and unobvious symptoms in the early stage of disease which increases the difficulties in early detection. On the other hand, the oncogenic pathway and molecular mechanism of LUAD are incompletely understood.

To investigate this underlying mechanism, three LUAD gene expression datasets were downloaded from the GEO database. Our analysis selected 451 DEGs, including 107 up-regulated genes and 344 down-regulated genes. By constructing the PPI network, we identified the 12 top degree hub genes: CCNA2, AURKA, HMMR, EZH2, TTK, TPX2, CCNB1, ANLN, ECT2, CEP55, MAD2L1, PBK and chose the four genes with the highest node degree at 35 and 34 for subsequent analysis. From our perspective, these hub genes would be possible candidates as novel indicators of LUAD in near future. We finally determined CCNA2 to be potential core genes of LUAD.

CCNA2, cyclin A2 is widely known as a cell cycle regulatory protein that function as regulatory subunits of the cyclin-dependent kinases (CDK) to control the transitions between mitotic and S-phase in the somatic cell [¹⁶]. The up-regulation of CCNA2 was found in various malignancies and may act as a potential biomarker in predicting prognosis [¹⁷-^18[i]19]. In lung cancer, CCNA2 also to be found as one of the pivotal key genes and its expression were found to be increase with tumorigenesis and poor prognosis [²⁰,²¹]. Moreover, progression of LUAD could be suppressed through downregulating CCNA2-CDK2 in a dose-dependent manner by using Tanshinone IIA (TSA) [²²]. Through RNA network analysis, Huang’s and Du’s studies have successfully constructed LINC00665-miR-let-7b-5p-CCNA2 and DNAH17-AS1-miR-877-5p-CCNA2 as potential ceRNA network of LUAD, which is also related to the progression of disease [²³,²⁴]. These facts explained that overexpression of CCNA2 in LUAD did involve in the regulation of cell cycle and cell proliferation, which is consistent with our results. Subsequent in vitro experiments further verify that knockdown of CCNA2 inhibits PC9 cell growth and migration.

Researchers have been proposing hypotheses, trying to explain the link between CCNA2 and cancer progression [²⁵]. The process of EMT stood out to our interest as its occurrence is one of the molecular mechanisms involved in cancer cell metastasis [²⁶]. Including lung cancer, EMT could lead cancer to an advanced stage by interacting with TAF15/TBP complex and this process could be impeded by Transcriptional intermediary factor 1 γ (TIFγ) [²⁷]. Another literature also discovered that quaking 5 (QKI-5), a protein which is involved in RNA translation, transcription and non-coding RNA processing could inhibits TGF‐β‐induced EMT in LUAD [²⁸]. Similar with our findings, knockdown of CCNA2 expression induced a significant reduction of EMT progression in bladder cancer. The authors also verified that miR-381-3p was the upstream of CCNA2 and could down-regulate CCNA2 expression [²⁹].

These results indicate that ability of cell invasion could be enhanced through EMT in LUAD and may be affected by the expression of CCNA2. However, the correlation between CCNA2, EMT in LUAD have rarely been probed. Of further interest, we examined the relationship between CCNA2 and EMT in LUAD. Heatmap results indicate that high expression of CCNA2 plays an oncogenic role in LUAD and is related to the process of EMT. Subsequently, in vitro experiments further verify that knockdown of CCNA2 could induce EMT-MET transition.

In summary, the outcomes from the present study identified CCNA2 as one of LUAD pivotal hub genes via bioinformatics methods. Overexpression of CCNA2 could promote cell growth and may prompt metastasis through modulating EMT process. We believe that these findings may provide additional direction and guidance for future studies. However, more experimental studies are required as the molecular mechanisms of LUAD are highly complex and still not been fully clarified.

Authors’ contribution.

Study and design: JT, SS, WG, XW, HL; Materials and data collections: JT, SS; Data analysis: YH, WM; Manuscript writing: JT, SS; Revised, reviewed and final approved of manuscript: All authors.

Competing Interests.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding.

This study was supported by National Natural Science Foundation of China (82072557), National Key Research and Development Program of China (2021YFC2500900), Shanghai Municipal Education Commission- Gaofeng Clinical Medicine Grant (20172005, the 2nd round of disbursement), program of Shanghai Academic Research Leader from Science and Technology Commission of Shanghai Municipality (20XD1402300), Novel Interdisciplinary Research Project from Shanghai Municipal Health Commission (2022JC023), Interdisciplinary Program of Shanghai Jiao Tong University (YG2023ZD04), and Shanghai Sailing Program (21YF1427100).

Data Availability. All data used in this study are included in this published article.

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Table 1: Top 12 hub genes with rank of degrees > 30.

Genes	Full names	Degree
CCNA2	Cyclin A2	35
AURKA	Aurora kinase A	35
HMMR	Hyaluronan mediated motility recepter	34
EZH2	Enhancer of zeste 2 polycomb repressive complex 2	34
TTK	TTK protein kinase	33
TPX2	TPX2 microtubule nucleation factor	33
CCNB1	Cyclin B1	33
ANLN	Anillin actin binding protein	32
ECT2	Epithelial cell transforming 2	32
CEP55	Centrosomal protein 55	31
MAD2L1	Mitotic arrest deficient 2 like 1	31
PBK	PBZ binding kinase	31

Table 2: Clinicopathological characteristics of Lung Adenocarcinoma patients (n=40)

Clinicopathological variables

Patients (n=40), n (%)

CCNA2 expression in tumor tissue

High

Low

36 (90.0)

4 (10.0)

CCNA2 expression in peritumor tissue

High

Low

18 (45.0)

22 (55.0)

Gender

Male

Female

17 (42.5)

23 (57.5)

Age

≤ 60

> 60

24 (60.0)

16 (40.0)

Stage

Tis

IA1

IA2

IA3

IIB

IIIA

IIIB

6 (15.0)

5 (12.5)

6 (15.0)

7 (17.5)

3 (7.5)

6 (15.0)

1 (2.5)

No competing interests reported.

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Bioinformatic analysis reveals CCNA2 as Lung Adenocarcinoma hub gene and is associated with cell proliferation and epithelial- mesenchymal transition

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