An Integrated Analysis of Prognostic and Immune Infiltrates for Hub Genes as Potential Survival Indicators in Patients with Lung Adenocarcinoma

doi:10.21203/rs.3.rs-900442/v1

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An Integrated Analysis of Prognostic and Immune Infiltrates for Hub Genes as Potential Survival Indicators in Patients with Lung Adenocarcinoma

https://doi.org/10.21203/rs.3.rs-900442/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 30 Mar, 2022

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Objective: Lung adenocarcinoma is one of the major subtypes of lung cancer. However, the prognosis of individuals with LUAD is still not promising. Therefore, this research aims to discover useful biomarkers to enhance the treatment and diagnosis of LUAD.

Methods: GEO2R was used to identify common up-regulated DEGs in the GSE32863, GSE40791 and GSE75037. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis. Metascape was also utilized to construct the PPI network, and the MCODE plug-in was employed to filter important subnetworks. The prognosis and expression levels of the hub genes were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The Timer database was utilized to confirm the correlation between immune cells infiltration and the expression levels of hub genes in LUAD tissues.

Results: This research discovered 307 common up-regulated DEGs, and gene ontology and pathway enrichment analysis indicated that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF and KNTC1. The seven hub genes were highly expressed in LUAD tissues and had a poor prognosis. AURKB, CCNB2, and CDC20 were inversely associated with B and CD4+ T cells. CDCA5, CDCA8, and CENPF have a substantially negative correlation with B Cell, but positive correlation with Neutrophil.

Conclusions: This research demonstrates that increased expression of seven hub genes is associated with worse prognosis for LUAD patients. Additionally, immune cells infiltrating LUAD tissues may serve as a regulating mechanism.

Oncology

Lung adenocarcinoma

Tumor microenvironment

Prognosis

Data mining

Lung cancer is the leading cause of tumors with a high rate of morbidity and mortality worldwide, and is mainly divided into non-small cell lung cancer and small cell lung cancer[1]. Among them, the proportion of lung adenocarcinoma (LUAD) in non-small cell lung cancer is approximately 55%[2]. In recent years, the incidence of LUAD has shown a significant upward trend, but the specific pathogenesis of LUAD still remains unclear[3]. In addition, despite the fact that there are many therapeutic options for patients with LUAD, including surgery, targeted therapy, and immunotherapy, the overall survival rate of patients is still not optimistic[4, 5]. As a result, it is critical to discover effective biomarkers that may be identified in the progression of LUAD and serve as therapeutic targets.

Numerous microarray datasets from various tumor samples have been collected in recent years due to the continual development of chip sequencing technology[6]. Bioinformatics techniques have been extensively utilized in cancer research to identify useful biomarkers using large amounts of microarray datasets[7, 8]. The purpose of this research is to analyze three microarray datasets related to LUAD from the GEO database, namely GSE32863, GSE40791, and GSE75037. GEO2R algorithms were used to identify differentially expressed genes (DEGs) between tumor and normal tissues. Following that, the common up-regulated genes were analyzed in Metascape for gene ontology and pathway enrichment[9, 10]. Additionally, the PPI network was constructed and the MCODE online plug-in was utilized to identify the significant subnetworks of the PPI network, both of which were done using Metascape[9]. Then, for further analysis, the subnetwork with the greatest number of hub genes was chosen. We verified the differences in the expression levels of key genes between tumor and normal samples using the GEPIA2 and UALCAN datasets[11, 12]. Then, the impact of candidate genes on the prognosis of LUAD patients was examined using the Kaplan-Meier Plotter database[13]. Finally, the Timer database was used to explore the correlation between hub genes and the infiltration of six different types of immune cells in LUAD tissues, including B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages, and dendritic cells[14]. We anticipate that this research will identify useful indicators for predicting patient prognosis and immune-related treatment targets.

Acquisition of data

The GEO database is an open and free database for storing microarrays and high-throughput sequencing data[15]. All GSE datasets analyzed were downloaded from the GEO database. Among them, GSE32863 was analyzed using the Illumina HumanWG-6 v3.0 expression beadchip, containing 58 LUAD tissues and 58 adjacent normal lung tissues. GSE40791 was analyzed using the Affymetrix Human Genome U133 Plus 2.0 Array, containing 94 LUAD samples and 100 normal samples. GSE75037 was profiled using the Illumina HumanWG-6 v3.0 expression beadchip, containing 83 tumor tissues and 83 adjacent non-tumor tissues.

Analysis of DEGs

In order to obtain DEGs needed in this study, we first analyzed the respective DGEs in the three GSE datasets. GEO2R was used as a tool to identify DEGs, and the parameters were set as P > 0.05 and |logFC| > 2 respectively in the screening process. In addition, the volcano map was used to visualize the DEGs in three different GSE datasets. Subsequently, we constructed the venn diagram to search the DEGs in the three GSE datasets that were up-regulated or down-regulated simultaneously. So far, we regarded the common up-regulate expression of DEGs as the main object of this study.

Analysis of gene ontology and pathway enrichment

Gene ontology (GO) has been widely used to analyze specific functions of genes. In the process of GO analysis, genes are classified into the following aspects: molecular function (MF), biological process (BP), and cellular component (CC) after annotating a given gene list, respectively. The purpose of pathway enrichment analysis is to use mature statistical methods to find significantly enriched pathways in the target gene list. Metascape is an online analysis website with the function of analysis of GO and biological pathway enrichment, and the results of analysis can be presented in a high-quality chart and sententious explanation[9]. Therefore, we submitted the set of common up-regulated DEGs obtained in the previous step to Metascape for online analysis. In order to obtain reasonable results of GO and pathway enrichment, the cutoff of P Value, min overlap and min enrichment were set as less than 0.01, 3 and 1.5, respectively.

Construction of the PPI network

Meanwhile, the PPI network can be constructed instantly using Metascape through showing the interaction network of all proteins based on the relevance and similarity of the submitted gene list. Then, subnetworks with obvious significance can be quickly selected from the overall PPI for subsequent research using the MCODE plug-in. At this point, we investigated the subnetwork with the largest number of genes and explored the characteristics of these hub genes.

Verification of the expression level of hub genes

GEPIA2 is a database that can analyze RNA sequencing expression data from 9736 tumor samples and 8587 normal samples from the TCGA and GTEx projects[11]. UALCAN is a comprehensive and interactive online database, which has the characteristic of quickly analyzing the TCGA database to validate the expression level of candidate genes according to different tumors[12]. Therefore, the GEPIA2 database was used to verify the differential expression of hub genes in the tumor tissues and adjacent tissues of LUAD patients. Then, we analyzed the expression level of hub genes in different stages in patients with LUAD using the UALCAN database. In addition, we further analyzed the co-expression correlation between hub genes and the expression level of hub genes in different cancers using GEPIA2.

Analysis of prognosis

The Kaplan-Meier Plotter is an online survival analysis database for 54, 000 genes in 21 malignant tumors[16]. Therefore, we analyzed the OS of hub genes to predict the specific prognostic of LUAD patients. In addition, we also analyzed the impact on prognosis of hub genes in different types of cancer using the GEPIA2 database.

Analysis of immune infiltration

TIMER is an online database for detecting the degree of immune cell infiltration in tumor tissues using RNA sequencing expression data in TCGA[14]. The database was used to explore the infiltration of six immune cells in specific tumor tissues, including B cells, CD4 + T cells, CD8 + T cells, neutrphils, macrophages and dendritic cells. Hence, we analyzed the correlation between the expression level of hub genes and the infiltrations of the six immune cells mentioned above in LUAD.

Identification of DEGs in LUAD

We screened a total of 307 up-regulated and 591 down-regulated DEGs according to the screening conditions in the GSE32863 dataset (Fig. 1A). A total of 2,298 DEGs were successfully identified in GSE40791 dataset, including 721 upregulated and 1250 down-regulated genes (Fig. 1B). In the GSE75037 dataset, 2550 DEGs involving 1122 up-regulated and 1428 down-regulated genes were found (Fig. 1C). Subsequently, we obtained 156 common up-regulated genes and 434 down-regulated genes in the above GSE datasets after the construction of the venn diagram (Fig. 1D-E).

Gene ontology functional and pathway enrichment analysis of DEGs

In this study, Metascape was used to perform gene ontology functional annotation and pathway enrichment analysis of the common up-regulated genes from the three datasets. Gene ontology was explored according to the following three categories, namely biological processes (BP), cellular components (CC) and molecular functions (MF)༎BP terms are most significantly enriched in the mitotic cell cycle process, spindle assembly, meiotic spindle assembly, collagen fibril organization, and double-strand break repair via break-induced replication (Fig. 2A, Table 1). As for the CC terms, midbody, apical plasma membrane, cell-cell junction, lateral plasma membrane and fibrillar collagen trimer are mostly enriched (Fig. 2B, Table 2). Besides, MF terms are most significantly enriched in cell adhesion molecule binding, kinase binding, extracellular matrix structural constituents, protein homodimerization activity and calcium ion binding (Fig. 2C, Table 3). In addition, the results of pathway enrichment analysis revealed that all up-regulated DEGs were mainly enriched in Cell Cycle, Cell Cycle Checkpoints, Integrin cell surface interactions, APC/C:Cdh1 mediated degradation of Cdc20 and other APC/C:Cdh1 targeted proteins in late mitosis/early G1 and SUMOylation of DNA replication proteins (Fig. 2D, Table 4).

Table 1

The analysis of biological processes enrichment
Term	Description	Count	LogP
GO:1903047	mitotic cell cycle process	23	-10.799
GO:0051225	spindle assembly	8	-5.971
GO:0090306	meiotic spindle assembly	3	-5.039
GO:0030199	collagen fibril organization	5	-4.645
GO:0000727	double-strand break repair via break-induced replication	3	-4.451
GO:0007096	regulation of exit from mitosis	3	-3.970
GO:0061484	hematopoietic stem cell homeostasis	3	-3.893
GO:0048871	multicellular organismal homeostasis	11	-3.860
GO:0098742	cell-cell adhesion via plasma-membrane adhesion molecules	8	-3.807
GO:0097435	supramolecular fiber organization	13	-3.559
GO:0007218	neuropeptide signaling pathway	5	-3.500
GO:0010035	response to inorganic substance	11	-3.428
GO:0015718	monocarboxylic acid transport	5	-3.408
GO:0009312	oligosaccharide biosynthetic process	3	-3.307
GO:0010951	negative regulation of endopeptidase activity	7	-3.275
GO:2000027	regulation of animal organ morphogenesis	5	-3.172
GO:0007169	transmembrane receptor protein tyrosine kinase signaling pathway	11	-3.099
GO:0045216	cell-cell junction organization	6	-3.037
GO:0030177	positive regulation of Wnt signaling pathway	5	-2.951
GO:0031669	cellular response to nutrient levels	6	-2.907

Table 2

The analysis of cellular components enrichment
Term	Description	Count	LogP
GO:0030496	midbody	9	-5.802
GO:0016324	apical plasma membrane	11	-5.142
GO:0005911	cell-cell junction	12	-4.671
GO:0016328	lateral plasma membrane	5	-4.645
GO:0005583	fibrillar collagen trimer	3	-4.451
GO:0031261	DNA replication preinitiation complex	3	-4.451
GO:0045120	pronucleus	3	-4.339
GO:0098687	chromosomal region	8	-3.127
GO:0045171	intercellular bridge	4	-3.091
GO:0048471	perinuclear region of cytoplasm	11	-2.553
GO:0016363	nuclear matrix	4	-2.465
GO:0035580	specific granule lumen	3	-2.304

Table 3

The analysis of molecular functions enrichment
Term	Description	Count	LogP
GO:0050839	cell adhesion molecule binding	14	-5.655
GO:0019900	kinase binding	15	-4.649
GO:0005201	extracellular matrix structural constituent	7	-4.293
GO:0042803	protein homodimerization activity	13	-3.950
GO:0005509	calcium ion binding	13	-3.748
GO:0008028	monocarboxylic acid transmembrane transporter activity	4	-3.516
GO:0008017	microtubule binding	7	-3.080
GO:0019842	vitamin binding	5	-2.831
GO:0017111	nucleoside-triphosphatase activity	10	-2.596
GO:0098632	cell-cell adhesion mediator activity	3	-2.594
GO:0004866	endopeptidase inhibitor activity	5	-2.475
GO:0016860	intramolecular oxidoreductase activity	3	-2.473
GO:0003682	chromatin binding	9	-2.325
GO:0005518	collagen binding	3	-2.175

Table 4

The analysis of significant pathway enrichment
Term	Description	Count	LogP
R-HSA-1640170	Cell Cycle	21	-9.543
R-HSA-69620	Cell Cycle Checkpoints	12	-7.004
R-HSA-216083	Integrin cell surface interactions	7	-6.318
R-HSA-174178	APC/C:Cdh1 mediated degradation of Cdc20 and other APC/C:Cdh1 targeted proteins in late mitosis/early G1	5	-4.237
R-HSA-4615885	SUMOylation of DNA replication proteins	4	-3.946
R-HSA-5173105	O-linked glycosylation	5	-3.408
R-HSA-421270	Cell-cell junction organization	4	-3.352
R-HSA-202733	Cell surface interactions at the vascular wall	5	-2.993
R-HSA-196854	Metabolism of vitamins and cofactors	5	-2.375
R-HSA-193648	NRAGE signals death through JNK	3	-2.365
R-HSA-983189	Kinesins	3	-2.365
R-HSA-69275	G2/M Transition	5	-2.319

PPI network construction and hub gene screening

Subsequently, the PPI network was successfully constructed by Metascape and each code represented the specific common up-regulated DEG in the network (Fig. 3A). Then, the molecular complex detection (MCODE) algorithm has been applied to identify densely connected network components. In the whole screening process, six clusters of MCODE with closely related functions were found and displayed in different colors, namely MCODE1, MCODE2, MCODE3, MCODE4, MCODE5, MCODE6 and MCODE7 (Fig. 3B). Among them, MCODE1 included AURKB, CCNB2, KNTC1, CENPF, CDCA8, CDCA5 and CDC20; MCODE2 included COL3A1, COL1A1, COL 5A2 and COL 10A1; MCODE3 included TUBB2B, MCM4, PRC1 and KIF20A; MCODE4 included EEF1A2, MCM2 and CDC45; MCODE5 included LCN2, MMP9 and TCN1; MCODE6 included AURKA, PTTG1 and UBE2C; MCODE7 included MUC20, GALNT6 and MUC16, respectively. Further, we conducted a deep analysis of MCODE1 with the largest number of genes to determine the clinical significance of each gene in LUAD.

Expression analysis

Based on the function provided by the GEPIA2 database, we first analyzed the expression level of genes included in MCODE1 between tumor tissues and normal tissues and found that the six genes, not including KNTC1, were significantly up-regulated in tumor tissues compared to normal samples (Fig. 4). Further, we used the UALCAN database to validate the expression levels of the seven genes in the different stages of patients with LUAD and identified that the expression levels of the above genes were significantly higher than those in normal tissues regardless of tumor stage (Fig. 5). Meanwhile, we explored the expression level of seven genes in different types of cancer and found that they were highly expressed in most cancers (Supplementary Fig. 1).

Survival analysis

So far, we analyzed the prognosis of seven genes in LUAD patients through the Kaplan-Meier plotter database showing that the higher the expression level of hub genes, the shorter the overall survival time of patients with LUAD (Fig. 6). The specific data is as follows: The HR of AURKA is 2.78, 95%CI is 2.2–3.51, and Logrank P is less than 1e-16; the HR of CCNB2 is 2.68, 95%CI is 2.05–3.49, Logrank P = 4.1e-14; the HR of CDC20 is 2.46, 95%CI is 1.92–3.15, Logrank P = 1.2e-13; the HR of CDCA5 is 2.32, 95%CI is 1.8–2.99, Logrank P = 2.5e-11; the HR of CDCA8 is 1.99, 95%CI is 1.58–2.51, Logrank P = 3.5e-09; the HR of CENPF is 1.66, 95%CI is 1.31–2.10, Logrank P = 2.1e-05; the HR of KNTC1 is 1.47, 95%CI was 1.16–1.86, Logrank P = 0.0015. Moreover, these genes were examined to explore the impact on patient survival in a range of malignancies and discovered that high expression of these genes were associated with a poor prognosis in the majority of cancers. However, it is noteworthy that the analysis results in LUAD and lung squamous cell carcinoma were inconclusive (Supplementary Fig. 2).

Analysis of immune infiltration

To further investigate the potential function of hub genes in LUAD patients, we focused on the infiltration of six types of immune cells in the tumor microenvironment of LUAD. Specifically, we used the TIMER database to validate whether the expression level of hub genes was related to the extent of infiltration of immune cells. The results of analysis were as follows (Fig. 7, Table 5): AURKB was significantly negative correlated with B Cell, CD4 + T Cell, Macrophage, and Dendritic Cell; CCNB2 was significantly negative correlated with B Cell, CD4 + T Cell, but was significantly positive correlated with Neutrophil; CDC20 was significantly negative correlated with B Cell, CD4 + T Cell and Macrophage; CDCA5, CDCA8 and CENPF were negative correlated with B Cell and are significantly positive correlated with Neutrophil, respectively; KNTC1 was significantly negative correlated with Macrophage and was significantly positive correlated with CD4 + T Cell, and Neutrophil.

Table 5

The analysis of correlation between immune cells infiltration and hub genes expression level in LUAD patients.
Symbol	Variable	Correlation	P Value
AURKB	B Cell	-0.173	< 0.001
AURKB	CD4 + T Cell	-0.104	< 0.05
AURKB	Macrophage	-0.146	< 0.01
AURKB	Dendritic Cell	-0.142	< 0.01
CCNB2	B Cell	-0.233	< 0.001
CCNB2	CD4 + T Cell	-0.151	< 0.001
CCNB2	Neutrophil	0.096	< 0.05
CDC20	B Cell	-0.199	< 0.001
CDC20	CD4 + T Cell	-0.116	< 0.05
CDC20	Macrophage	-0.107	< 0.05
CDCA5	B Cell	-0.152	< 0.001
CDCA5	Neutrophil	0.131	< 0.01
CDCA8	B Cell	-0.158	< 0.001
CDCA8	Neutrophil	0.131	< 0.01
CENPF	B Cell	-0.111	< 0.01
CENPF	Neutrophil	0.106	< 0.05
KNTC1	CD4 + T Cell	0.110	< 0.05
KNTC1	Macrophage	-0.090	< 0.05
KNTC1	Neutrophil	0.149	< 0.01

Nowadays, LUAD is one of the most important subtypes of non-small cell lung cancer, which still has the characteristics of high incidence and extreme mortality[17]. The main reason is lacking effective biomarkers that can accurately identify the diagnosis of LUAD patients at present[18]. Therefore, we conducted a comprehensive analysis of the datasets selected from the GEO database using bioinformatics methods to screen candidate genes for the diagnosis and prognosis of LUAD patients, and explored the correlation between the immune cells infiltration and the expression levels of these genes in the LUAD tissues in this study.

First, the three GSE datasets obtained from the GEO database were utilized to screen the DEGs, and a total of 156 common up-regulated genes and 434 common down-regulated genes were found. Subsequently, 156 common up-regulated genes were submitted to Metascape for the analysis of GO and pathway enrichment. Biological processes (BP), molecular functions (MF) and cellular components (CC) are fully included in this GO analysis. Subsequently, the PPI network was constructed based on common up-regulated genes using Metascape and the MCODE plug-in was used to screen out seven important subnetworks in the PPI network, namely MCODE 1, MCODE 2, MCODE 3, MCODE 4, MCODE 5, MCODE 6 and MCODE 7[19]. MCODE 1 contains the largest number of key genes, which are AURKB, CDC20, CDCA5, CDCA8, CENPF, KNTC1 and CCNB2, respectively. Next, GEPIA2 and UALCAN databases were used to analyze the expression levels of the above candidate genes in LUAD tissues and normal lung tissues and found that the expression levels of hub genes in tumor tissues were all significantly higher than in normal tissues, expect KNTC1. In addition, the analysis of the Kaplan-Meier Plotter database found that these key genes with high expression levels showed worse prognosis in patients with LUAD. Further, we explored the correlation of these candidate genes with the infiltration of six immune cells in patients with LUAD in order to look forward to providing new insight in tumor immunotherapy.

AURKB (Aurora Kinase B) is a protein coding gene that acts as a key regulator of mitosis[20]. Many existing studies have confirmed that AURKB is a crucial carcinogenic factor in different kinds of carcinoma. For example, AURKB was found to be expressed at higher levels in renal cell carcinoma tissues than in normal kidney tissues, suggesting that it may regulate renal cell carcinoma progression by modulating the intestinal immune network for IgA production and signaling pathways involving cytokine-cytokine receptor interactions[21]. Furthermore, AURKB was overexpressed in gastric cancer and was strongly linked to clinicopathologic features of the disease. Silenced AURKB may decrease the invasive and migratory capacities of gastric cancer cells by disrupting the VEGFA/Akt/mTOR and Wnt/-catenin/Myc pathways[22]. Additionally, AURKB activation was attributed to acquired resistance to EGFR TKIs, suggesting that AURKB might be a target in NSCLC patients who were advancing to anti-EGFR treatment but did not have resistance mutations[23].

CDC20 (Cell Division Cycle 20) appears to act as a regulatory protein interacting with several other proteins at multiple points in the cell cycle[24]. Min Shi et al. found that CDC20 played a crucial role in the development of hepatocellular carcinoma by governing PHD3 protein[25]. Besides, Yang Gao et al. found that targeting CDC20 radiosensitized colorectal cancer cells through mitochondrial-dependent apoptotic signaling[26]. Thus, Qin Zhang et al. found that CDC20 combined with CD44 or β-catenin could serve as an important indicator for the prognosis of patients with prostate cancer[27]. Further, Huan Deng et al. found that CDC20 was upregulated in LUSC at the mRNA and protein levels[28]. However, little research has been done on the effects of CDC20 on LUAD, and the mechanism of action remains unclear.

CDCA5 (Cell Division Cycle Associated 5) is another protein-coding gene involved in DNA repair[29]. CDCA5 promoted the progression of bladder cancer via dysregulating mitochondria-mediated apoptosis, cell cycle regulation, and activation of the PI3k/AKT/mTOR pathway[30]. Additionally, CDCA5 aided in the development of esophageal squamous cell carcinoma. and may be an interesting target for esophageal squamous cell carcinoma immunotherapy[31]. Moreover, CDCA5 phosphorylation and activation by mitogen-activated protein kinase were critical for human lung cancer[32].

CDCA8 (Cell Division Cycle Associated 8) is a component of the chromosomal passenger complex, which is required for mitosis and cell division to occur[33]. According to current literatures, increased CDCA8 expression in ovarian cancer tissues probably played a critical role in the development of ovarian cancer and it most likely functioned in carcinogenesis through the PLK1 pathway[34]. Besides, CDCA8 was involved in the construction of meiotic spindles and chromosomal segregation during human oocyte meiosis[35]. Meanwhile, CDCA8 overexpression accelerated the development of cutaneous melanoma and resulted in a worse prognosis[36]. Additionally, aurora kinase B-mediated phosphorylation and activation of CDCA8 played a major role in human lung cancer[37]. Moreover, miR-133b suppressed lung adenocarcinoma cell proliferation, motility, and invasion by targeting CDCA8[38].

CENPF is a gene that encodes a protein that is involved in the centromere-kinetochore complex association[39]. A current study indicated that overexpression of CENPF in breast cancer was associated with a poor prognosis and tumor bone metastases by controlling parathyroid hormone-related peptide (PTHrP) production via activating PI3K-AKT-mTORC1[40]. Besides, the HnRNPR-CCNB1/CENPF axis was involved in the proliferation and metastasis of gastric cancer[41]. Additionally, silencing CENPF substantially reduced LUAD cell tumor development in an experimental xenograft lung cancer model using naked mice armpits of the right forelimb. However, there was no sufficient studies on the mechanism of CENPF in LUAD[42].

KNTC1 encodes a protein participating in the processes that guarantee correct chromosomal segregation during cell division[43]. A recent study indicated that silencing KNTC1 with shRNA inhibited cell viability and caused apoptosis in esophageal squamous cell carcinoma[44]. Moreover, relevant bioinformatics publications demonstrated that KNTC1 was associated with a poor outcome in patients with hepatocellular carcinoma and cervical cancer[45, 46]. However, there has been no research on the mechanism of action of KNTC1 in patients with LUAD.

CCNB2 (Cyclin B2) is a member of the cyclin family, more precisely the B-type cyclins that can interact with p34cdc2, and was a critical component of the cell cycle regulation mechanism[47]. In vitro and in vivo, CCNB2 promoted the proliferation of triple-negative breast cancer cells[48]. As validated by a comprehensive bioinformatics study, CCNB2 was a promising therapeutic target for ovarian cancer[49]. Additionally, miR-335-5p targeting CCNB2 could disrupt the cell cycle and increase lung adenocarcinoma metastasis[50]. Moreover, CCNB2 had been discovered as a marker of immune checkpoint inhibitor (ICI) responsiveness in NSCLC and overexpression of CCNB2 was a poor prognostic indicator in Chinese patients with NSCLC[51, 52].

The findings of our research were acquired through data mining online database using bioinformatics techniques. The limitations of this study included the absence of specific in vitro or in vivo experiments to validate the significance of the selected hub genes in LUAD patients. Additionally, the results of our study may include partial bias owing to the issue of data quantity and quality. As a result, we will further verify experimental evidence of function for these potential genes as soon as possible in the future.

In conclusion, the seven candidate genes investigated in this study exhibited highly expression levels in patients with LUAD and predicted a poor prognosis. However, more experiments are essential to completely validate the significance and particular mechanism of these genes in LUAD patients.

LUAD

Lung adenocarcinoma; DEGs:Differentially expressed genes; GO:Gene ontology; PPI:Protein-protein interaction; MCODE:Molecular complex detection; MF:Molecular function; BP:Biological process; CC:Cellular component; GEO:Gene Expression Omnibus; OS:Overall survival; AURKB:Aurora Kinase B; CDC20:Cell Division Cycle 20; CDCA5:Cell Division Cycle Associated 5; CDCA8:Cell Division Cycle Associated 8; CCNB2:Cyclin B2; NSCLC:non-small cell lung cancer; ICI:immune checkpoint inhibitor.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Author details

¹Department of Thoracic Surgery, the Affiliated Cancer Hospital of Nanjing Medical University & Jiangsu Cancer Hospital & Jiangsu Institute of Cancer Research, Nanjing, 210000, China.²Department of Cardiothoracic Surgery, the Affiliated Huaian No. 1 People's Hospital of Nanjing Medical University, Huaian, 223300, China.³Department of Cardiothoracic Surgery, Jingling Hospital, Jingling School of Clinical Medicine, Nanjing Medical University, Nanjing, 210000, China.

Acknowledgements

We would like to express our gratitude to the team members for their contributions to this article, and then we will work diligently to do relevant research in the future.

Authors' contributions

Binhui Ren, Lin Xu, and Zhiyun Xu participated in the whole study design. The manuscript was drafted by Zhiyun Xu, Shi Wang and Zhijian Ren, revised by Zhiyun Xu and Xiang Gao. The authors read and approved the final manuscript.

Funding

The authors have no funding to report.

Availability of data and materials

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

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SupplementaryFigure1.pdf
Supplementary Fig. 1 The landscape of expression levels of hub genes among a variety of cancers using GEPIA2 database. The graphic demonstrated that the hub genes were highly expressed in the majority of cancers.
SupplementaryFigure2.pdf
Supplementary Fig. 2 The prognostic of hub genes in a variety of malignancies. The redr the square color, the higher the gene expression level, indicating a worse prognosis for patients.
SupplementaryTable1.docx

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published 30 Mar, 2022

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An Integrated Analysis of Prognostic and Immune Infiltrates for Hub Genes as Potential Survival Indicators in Patients with Lung Adenocarcinoma

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Methods

Acquisition of data

Analysis of DEGs

Analysis of gene ontology and pathway enrichment

Construction of the PPI network

Verification of the expression level of hub genes

Analysis of prognosis

Analysis of immune infiltration

Results

Identification of DEGs in LUAD

Gene ontology functional and pathway enrichment analysis of DEGs

PPI network construction and hub gene screening

Expression analysis

Survival analysis

Analysis of immune infiltration

Discussion

Conclusion

Abbreviations

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1