NUF2 Is a Potential Immunological and Prognostic Marker for Non-Small-Cell Lung Cancer

Li, Xia; Zhang, Lianlian; Yi, Zhongquan; Zhou, Jing; Song, Wenchun; Zhao, Panwen; Wu, Jixiang; Song, Jianxiang; Ni, Qinggan

doi:https://doi.org/10.1155/2022/1161931

Journal of Immunology Research

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Abstract Background Results Discussion Methods Data Availability Disclosure Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

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Exploration of Novel Potential Biomarkers for Cancer Immunotherapy

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Research Article | Open Access

Volume 2022 | Article ID 1161931 | https://doi.org/10.1155/2022/1161931

NUF2 Is a Potential Immunological and Prognostic Marker for Non-Small-Cell Lung Cancer

Xia Li,^1,2Lianlian Zhang,³Zhongquan Yi,²Jing Zhou,¹Wenchun Song,¹Panwen Zhao,²Jixiang Wu,⁴Jianxiang Song,⁴and Qinggan Ni⁵

Academic Editor: Fu Wang

Received17 Feb 2022

Accepted09 Apr 2022

Published12 May 2022

Abstract

Background. Globally, non-small-cell lung cancer (NSCLC) is one of the most prevalent tumors. Various studies have investigated its etiology, but the molecular mechanism of NSCLC has not been elucidated. Methods. The GSE19804, GSE118370, GSE19188, GSE27262, and GSE33532 microarray datasets were obtained from the Gene Expression Omnibus (GEO) database for the identification of genes involved in NSCLC development as well as progression. Then, the identified differentially expressed genes (DEGs) were subjected to functional enrichment analyses. The protein-protein interaction (PPI) network was built after which module analysis was conducted via the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape. There were 562 DEGs: 98 downregulated genes and 464 upregulated. These DEGs were established to be enriched in p53 signaling pathway, transendothelial leukocyte migration, cell adhesion molecules, contractions of vascular smooth muscles, coagulation and complement cascades, and axon guidance. Assessment of tumor immunity was performed to determine the roles of hub genes. Results. There were 562 dysregulated genes, while 12 genes were hub genes. NUF2 was established to be a candidate immunotherapeutic target with potential clinical implications. The 12 hub genes were highly enriched in the p53 signaling pathway, the cell cycle, progesterone-associated oocyte maturation, cellular senescence, and oocyte meiosis. Survival analysis showed that NUF2 is associated with NSCLC occurrence, invasion, and recurrence. Conclusion. The NUF2 gene discovered in this study helps us clarify the pathomechanisms of NSCLC occurrence as well as progression and provides a potential diagnostic and therapeutic target for NSCLC.

1. Background

Due to the increase in personal stress, lifestyle changes, the decline in environmental quality, exposure to secondhand tobacco smoke, and a series of other reasons, the incidence of tumors is high, and NSCLC is a very prevalent tumor type [1]. The NSCLC-associated mortality rate is among the highest among all malignancies, and its 5-year survival rate is low, relative to that of other tumors [2]. The development of NSCLC is a great burden to patients and their families. Thus, is it important to determine how to reduce the incidence of NSCLC. First, maintaining a healthy lifestyle is important, and second, high-quality and precise treatment methods are essential. The development and progression of NSCLC are linked to various factors, including genetic aberrations and immune infiltration [3]. Despite extensive studies on the pathomechanisms of its occurrence and progression, the clinical etiology of NSCLC is unclear [4]. Through bioinformatics analysis tools and major database data, we can efficiently search for a target to combat tumors and achieve early detection and prompt intervention in the early stages of tumors to avoid further development of tumors [5].

The histological forms of NSCLC are lung adenocarcinoma (LUAD), large cell carcinoma, and lung squamous cell carcinoma (LUSC). Its development is multistep, with abnormal gene expression as the main feature; this aberrant gene expression leads to phenotypic cell transformation [6–8]. Genetic changes within the genome have been evaluated by ribonucleic acid sequencing (RNA-Seq) [9]. Compared to traditional methods, systematic and comprehensive studies of interactions between differentially enriched pathways and protein-coding genes can precisely establish the carcinogenic effects of changes that occur in the course of NSCLC progression and development. Thus, the analysis of RNA-Seq data using bioinformatics tools can help us understand the pathomechanisms and identify important tumor biomarkers [10]. RNA-Seq is important for identification of key genes that play important roles in disease progression that may help clarify gene expression variations that occur in the course of NSCLC progression. To date, the principal driving force for carcinogenesis is still unclear, which limits the development of NSCLC-targeted therapy [11–14]. Thus, elucidation of NSCLC pathogenesis is still a major challenge, with various key genes yet to be established.

Current microarray technologies and biotin morphology analysis have begun to approach this scope of coverage in almost all tumors. Their applications in screening key gene changes have helped us identify the carcinogenesis-related functions of DEGs and the pathways that are activated in the development of NSCLC [15]. Nevertheless, the true positive rate in independent microarray analysis is not very high, so there are often false positives or false negatives. Therefore, to decrease the false positive rate, we chose five gene sets (GSE19804 [16], GSE118370 [17], GSE19188 [18], GSE27262 [19], and GSE33532 [20]). Then, we used R package from the Bioconductor project [21] and Venn’s “LIMMA” graphic software to acquire sets of DEGs between tumor and normal samples in the above five datasets. Third, the Database for Annotation, visualization and comprehensive Discovery (DAVID) was used. Enrichment analysis of the DEGs revealed their related molecular functions (MFs), cell components (CCs), and biological processes (BPs) as well as the Kyoto Protocol Encyclopedia of Genes and Genomes (KEGG) pathways. The protein-protein interaction (PPI) network was built, after which cellular molecular complexity detection (MCODE) was performed to determine various important modules. MCODE was also used for screening 12 hub genes. To obtain vital prognostic data, the dominant genes were imported into the online Kaplan-Meier plotter database (). The levels of DEGs and hub genes in NSCLC as well as normal lung tissues were verified by Gene Expression Profiling Interactive Analysis (GEPIA; ). Overall, the goal of this research was to improve the understanding of the carcinogenic effects of NSCLC through the analysis of data about the processes of genetic variations that occur during disease development and reveal central genes that can be used as biomarkers for diagnosis, therapeutic outcomes, and disease progression.

2. Results

2.1. NSCLC-Associated DEGs

There were 562 DEGs in the 5 datasets, which consisted of 98 downregulated and 464 upregulated genes (Figure 1(a)).

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Figure 1

Venn diagram, PPI network, and the most significant module of DEGs. (a) DEGs were selected with a fold and value < 0.01 among the mRNA expression profiling set GSE19804, GSE118370, GSE19188, GSE27262, and GSE33532. The 5 datasets showed an overlap of 562 genes. (b) The most significant module was obtained from PPI network with 12 nodes and 66 edges. (c) The PPI network of DEGs was constructed using Cytoscape. Upregulated genes are marked in light red; downregulated genes are marked in light blue.

2.2. KEGG and Gene Ontology (GO) Enrichment Analyses

GO analysis revealed that the DEGs were markedly enriched in BPs, such as extracellular matrix organization, extracellular structure organization, nuclear division, mitotic nuclear division, organelle fission, cell-substrate adhesion, assembly of cell junctions, organization of cell junctions, vascular process in circulatory system, and ameboidal-type cell migration (Table 1). The enriched MFs included actin binding, extracellular constituents of matrix structures, amyloid-beta binding, peptide binding, amide binding, histone kinase activity, metalloendopeptidase activity, extracellular constituents of matrix structures conferring tensile strengths, and metallopeptidase activities (Table 1). The enriched CC terms were cell-cell junction, actin filament bundle, stress fiber, contractile actin filament bundle, contractile fiber part, midbody, condensed chromosome, centromeric region, chromosomal region, and condensed nuclear chromosome (Table 1). KEGG pathway analyses showed that downregulated DEGs were highly enriched in p53 signaling pathway, while upregulated DEGs were highly enriched in pathways related to cell adhesion molecules, transendothelial leukocyte migration, contractions of vascular smooth muscles, coagulation and complement cascades, and axon guidance.

2.3. The PPI Network and Module Analysis

The established DEG-associated PPI network is shown in Figure 1(c), with the most important module shown in Figure 1(b), as identified by Cytoscape. Functional assessments of genes in this module revealed high enrichment in nuclear division, organelle fission, mitotic nuclear division, histone phosphorylation, cell cycle checkpoint, condensed chromosome, centromeric region, chromosomal region, midbody, chromosomes, protein serine/threonine kinase activities, histone kinase activities, protein C-terminus binding, the cell cycle, progesterone-mediated oocyte maturation, ferric iron binding, oxidoreductase activities, acting on CH or CH2 groups, the p53 signaling pathway, cellular senescence, and oocyte meiosis (Table 2).

2.4. Hub Gene Identification and Analysis

Twelve genes were established to be hub genes with degree (Table 3). The 12 hub genes were used to draw the difference in the distribution of LUAD and LUSC tissues and adjacent tissues in TCGA database (https://portal.gdc.cancer.gov/) using ggplot2 in R language. Figure 2(a) shows the expression levels of hub genes in unpaired LUAD as well as adjacent tissues. The levels of hub genes in unpaired LUAD and adjacent tissues are shown in Figure 2(b). Figure 2(c) shows the levels of hub genes in paired LUAD tumor and adjacent tissues. Figure 2(d) shows the levels of hub genes in adjacent and paired LUSC tissues. The value was used to indicate significance as follows: ns, ; , ; , ; and , . Hub genes were downloaded from the DAVID website (https://david.ncifcrf.gov/), and then, the “ggplot2” and “clusterProfiler” in R language were used for visualization the GO and KEGG results. Enrichments of upregulated and downregulated genes are shown in Figures 3(a) and 3(b), respectively. Figure 3(c) shows a visualization of the enrichment analysis results for the hub genes. After the analysis of the 12 hub genes with the pROC package in R language and visualized with the ggplot2 package, receiver operating characteristic (ROC) curves were generated for LUAD (Figures 4(a) and 4(b)) and LUSC (Figures 4(c) and 4(d)). NUF2 assessment showed higher accuracy than other variables in predicting the tumor status (normal versus tumor). Subsequently, overall survival analysis according to hub gene expression was conducted via the Kaplan-Meier curves. Relative to low expression patients, NSCLC patients with elevated CDK1, CCNB1, TOP2A, PRM2, CHEK1, AURKA, ZWINT, NUF2, MKI67, BIRC5, CEP55, and ANLN levels showed worse overall survival (Figures 5(a)–5(h)). We noticed that NSCLC patients with changes of NUF2-related genes showed decreased overall survival, while NSCLC patients with NUF2 genome changes showed the highest hazard ratio. These observations were statistically significant (, CI 1.7-2.39, ) (Figure 5(h)). Assessment of NUF2 mRNA levels in a variety of tumor types revealed that they were elevated in tumor tissues, relative to adjacent tissues (Figure 6(a)). Comparison analysis showed that NUF2 mRNA is high (left column, red) and suppressed (right column, blue) in tumor and normal tissues, respectively. The diagram comes from the Oncomine database (available from https://www.oncomine.org/resource/login.html) with thresholds as: value, 1E-4; fold change, 2; and gene rank, 10%. Figure 6(b) shows the expression of NUF2 in 33 human cancer datasets (GEPIA2) (URL https://gepia2.cancer-pku.cn) obtained from the TCGA via GEPIA2; dot plots were generated to show all the gene expression profiles. The cancer and their matching normal tissues were collected. Each point represents the expression of the sample. Figure 6(c) shows NUF2 mRNA levels in tumors and tissues. Data for adjacent normal tissues were acquired from the Gene Expression in Normal and Tumor Tissues (GENT) database (http://medicalgenomics.kribb.re.kr/GENT/). The boxes denote the median as well as the 25^th and 75^th percentiles. Dots denote outliers. The red box represents cancer tissue, and the blue box represents normal tissue.

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Figure 2

The 12 hub genes were used to draw the difference in the distribution of lung adenocarcinoma and lung squamous cell carcinoma tissues and adjacent tissues in the TCGA (https://portal.gdc.cancer.gov/) database using ggplot2 in R language. (a) is the expression of hub gene in cancer tissues and adjacent tissues of unpaired samples in LUAD, and (b) is the expression of hub gene in cancer tissues and adjacent tissues of unpaired samples in LUSD. (c) represents the expression of hub gene in LUAD paired sample cancer tissues and adjacent tissues, and (d) represents the expression of hub gene in LUSD paired sample cancer tissues and adjacent tissues. The value adopts scientific notation and all have statistical significance. ; ; and .

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Figure 6

NUF2 mRNA expression in a variety of cancer types: (a) Comparison shows that the datasets of NUF2 mRNA overexpression (left column, red) and underexpression (right column, blue) are in cancer and normal tissues. The graphic representation is derived from the Oncomine database (available from https://www.oncomine.org/resource/login.html), and the threshold is to use the following parameters: value is 1E-4, fold change is 2, and gene ranking is 10%. (b) Expression of NUF2 expression in 33 human cancers (Gene Expression Profiling Interactive Analysis 2) (URL https://gepia2.cancer-pku.cn) obtained from the Cancer Genome Atlas via GEPIA2: dot map. The gene expression profiles in all tumor samples and paired normal tissues are shown. Each point represents the expression of the sample. (c) The expression pattern of NUF2 mRNA in tumors and tissues. The corresponding normal tissues: the data on the expression of NUF2 mRNA in various types of cancer is retrieved from the GENT (gene expression in normal and tumor tissue) database (available at http://medicalgenomics.kribb.re.kr/GENT/). The boxes represent the median and the 25th and 75th percentiles. Dots represent outliers. The red box represents tumor tissue, and the blue box represents normal tissue.

The Protein Atlas network database (https://www.proteinatlas.org/) was utilized for analyses. Figure 7(a) shows the hematoxylin and eosin (HE) staining result for NUF2 in LUAD, and Figure 7(b) shows the HE staining result for NUF2 in LUSC. Figure 7(c) shows the HE staining result for NUF2 in normal bronchial tissues. Figure 7(d) shows the Serial Analysis of Gene Expression (SAGE) findings for the analysis of NUF2 in human cancers. The Tumor IMmune Estimation Resource (TIMER) website (https://cistrome.shinyapps.io/timer/) was used to show the associations between infiltrations of immune cells in tumors and NUF2 somatic copy numbers in LUSC and LUAD. According to the copy number of NUF2, the samples were divided into five categories (arm-level deletion, deep deletion, diploid/normal, high amplification, and arm-level gain), and distributions of infiltrated immune cells in the five samples were compared. In LUAD, except for the marked variations in the number of arm-level deletions in B cells, the other five types of cells (neutrophils, CD8+ T cells, macrophages, CD4+ T cells, and dendritic cells) were diploid/normal or showed significant differences in arm-level gain. However, the results were not exactly the same for LUSC. The copy numbers in neutrophils, B cells, and dendritic cells showed obvious differences. The rates of arm-level deletions as well as gains and increased amplification in CD4+ T cells showed obvious differences. The arm-level gain in macrophages was obviously different, and all CD8+ T cell copy number observations were significantly different between normal and tumor tissues. values are as follows: ns, ; , ; , ; and , (Figure 8(a)). The TIMER website (https://cistrome.shinyapps.io/timer/) was used to view the correlations of immune cells and tumor purity and NUF2 levels in LUSC and LUAD in the TCGA database (Figure 8(b)). Table 4 shows the correlation analysis of NUF2 and immune cell-associated genes as well as biomarkers in TIMER (). The UALCAN website (http://ualcan.path.uab.edu/cgi-bin/ualcan-res.pl) was used to show differences in the levels of NUF2 in LUAD cancers of different grades and in the normal population versus LUAD patients. The expression of NUF2 in patients with different smoking statuses and the difference in the expression of NUF2 in TP53 mutant and nonmutated LUAD were assessed (Figures 9(a)–9(c)). The differences in the expression of NUF2 in LUSC tumors of different grades, the expression of NUF2 in normal people and LUSC patients with different smoking statuses, and the difference in the expression of NUF2 in TP53-mutated and nonmutated LUSC samples were also assessed. indicates that the difference is statistically significant (Figures 9(d)–9(f)).

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Figure 8

(a) Use the TIMER website (https://cistrome.shinyapps.io/timer/) to explore the correlation between tumor immune cell infiltration and somatic cell copy number of NUF2 in LUAD and LUSD, respectively. According to the copy number of NUF2, samples are divided into five categories (deep deletion, arm-level deletion, diploid/normal, arm-level gain, and high amplication); compare the distribution of immune cells infiltrated among the five types of samples. value significant codes: , , and . (b) Use the TIMER website (https://cistrome.shinyapps.io/timer/) to view the correlation between immune cell and tumor purity and NUF2 expression in LUAD and LUSD in the TCGA database.

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Figure 9

(a–c) Use the UALCAN website (http://ualcan.path.uab.edu/cgi-bin/ualcan-res.pl), respectively, to show the expression differences of NUF2 lung adenocarcinoma tumors in different grades, normal population, and lung adenocarcinoma. The expression of NUF2 in different smokers and the difference in the expression of NUF2 in TP53 mutant and nonmutated lung adenocarcinoma. (d–f) The differences in the expression of NUF2 lung squamous cell carcinoma tumors of different grades, the expression of NUF2 in normal people and different smokers of lung squamous cell carcinoma, and the difference in expression of NUF2 in TP53 mutated and nonmutated lung squamous cell carcinoma. indicates that the difference is statistically significant.

2.5. NUF2 Expression in NSCLC Tissues

To evaluate the significance of NUF2 in NSCLC, we investigated NUF2 protein levels in four randomly selected paired NSCLC specimens. Western blot showed elevated NUF2 levels in NSCLC tissues, relative to adjacent nontumor samples (Figures 10(a) and 10(b), ).

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3. Discussion

Globally, lung carcinoma is the most prevalent cause of tumor-associated death. About 1.6 million people die from lung carcinoma each [22]; approximately 85% of lung cancer patients have NSCLC, among which LUAD and LUSC are the most prevalent subtypes [23]. With continuous advances in molecular biology and information technology, in the past two decades, significant progress in NSCLC treatment has been reported [24]. Smoking is highly correlated with the development of lung carcinoma, and it is also related to environmental exposures, such as secondhand smoke, occupational carcinogens and pollution, and genetic susceptibility [25, 26]. However, the pathomechanisms for NSCLC occurrence as well as development of NSCLC are not extremely clear. Regulators of the cell cycle play major roles in NSCLC [27–29]. Most NSCLC cases are not detected early, making patients ineligible for treatment, which could explain for poor prognostic outcomes. Thus, the need for development of potential markers for efficient diagnosis and treatment is urgent. Microarray technologies allow the exploration of genetic changes in NSCLC and have been proven to be important methods for identifying new disease markers [30].

In this study, the pathways in which the DEGs were found to be enriched are closely related to immune infiltration. Mami-Chouaib et al. studied resident memory T cells and found that they are critical components in tumor immunology [31]. The tumor microenvironment (TME) affects the progression of many malignant tumors in humans. The infiltration of immune-related cells into tumors increases the recruitment of immune activation signals and antidisease immune effector cells and activates related pathways [32]. KEGG pathway analyses showed that downregulated DEGs were highly enriched in p53 signaling pathway. The p53 protein can mediate nucleolar stress responses, leading to cell cycle arrest, apoptosis, senescence, or differentiation, thereby affecting the occurrence as well as development of tumors [33]. Mutations of the p53 tumor suppressor gene often occur in lung carcinoma. Mutant p53 (mtp53) suppresses wild-type p53 protein activities and destroy its tumor suppressor function. Moreover, mtp53 usually functions as an oncogene. The posttranslational modification of p53 protein is vital for its transcription as well as tumor suppressor function [34]. These conclusions are in tandem with ours.

Twelve DEGs with were obtained as hub genes. We noticed that NSCLC patients with NUF2-related genomic changes showed a decrease in overall survival, while NSCLC patients with NUF2 genome changes showed the highest hazard ratio. NUF2 is a component of the essential kinetochore-associated NDC80 complex, which is important in chromosomal segregation as well as spindle checkpoint activities. It is also vital for the maintenance of the integrity of the kinetochore and organization of stable microtubule binding sites in the outer plate of the kinetochore. The complex promotes the affinity of the SKA1 complex for microtubules, which allows the NDC80 complex to track depolymerizing microtubules [35]. NUF2 is reported as one of tumor testis antigens that is secreted ectopically by cancers, and NUF2 levels are increased in prostate tumor tissues [36]. Xie et al. found that NUF2 is involved in cell apoptosis and proliferation regulation by controlling the binding of spindle microtubules and the centromere to attain the correct chromosome separation. NUF2, a prognostic-associated marker, is correlated with infiltrations of immune cells in hepatocellular carcinoma [37]. In addition, NUF2 is elevated in breast cancer, human osteosarcoma, pancreatic tumor, and colorectal cancer and is an important diagnostic, treatment, and prognostic marker of tumors [38–41]. In conclusion, NUF2 is a potential predictor of NSCLC prognosis.

The TME is a key regulator of tumorigenesis, tumor progression, and drug resistance [42]. In the TME, tumors and cells continue to evolve to reduce the production of new antigens and the burden of mutations to facilitate the evasion of antitumor responses. This reduces the tumor’s responsiveness to adaptive immune responses and facilitates cancer-supportive changes inside the tumor, such as changes in the expression of immunomodulatory molecules on cancer cells. External tumor factors, including soluble inhibitory molecules, immunosuppressive cells, or inhibitory receptors expressed by immune cells, can change the compositions and activities of tumor-infiltrating lymphocytes (TILs) (by enhancing the T regulatory cell, effector T cell ratio, and suppressing the roles of effector T cells and enhancing tumor proliferation as well as metastasis) [43]. We found that NUF2 is associated with immune infiltration of several cell types, such as CD8+ T cells, B cells, T cells (general), tumor-associated macrophages (TAMs), monocytes, M1 and M2 macrophages, natural killer cells, neutrophils, dendritic cells, and Th1, Th2, Tfh, Th17, Treg, and exhausted T cells. The association between NUF2 and immunosuppressive gene levels implies that NUF2 has a major function in regulation of cancer immunology.

In summary, this study was aimed at identifying DEGs that play key roles in NSCLC occurrence or progression. There were 562 DEGs and 12 hub genes, and these genes can be used as diagnostic markers for NSCLC. These results also prove that NUF2 can be used as an effective immunotherapy target. In the next step, our research group will use molecular biology experiments to further verify the biological functions of NUF2 in NSCLC in vivo and in vitro. Finally, we will use western blotting to evaluate the levels of NUF2 in NSCLC and adjacent tissues to verify that NUF2 can indeed be used as a target for in-depth research on NSCLC treatment.

4. Methods

4.1. Microarray Data

The Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo) [44] is a public functional genomics data repository of high-throughput gene expression and chip and microarray data. Five gene expression datasets (GSE19804 [16], GSE118370 [17], GSE19188 [18], GSE27262 [19], and GSE33532 [20]) were retrieved from the GEO (GPL570 Platform Affymetrix Human Genome U133 Plus 2.0 Array). The conversion of probes into their corresponding gene symbols was based on annotation information for the platform. The GSE19804 dataset had 60 NSCLC tissue and 60 noncancerous samples. The GSE118370 dataset had 6 NSCLC tissue and 6 noncancerous samples. The GSE19188 dataset had 91 NSCLC tissue and 65 noncancerous samples. The GSE27262 dataset had 25 NSCLC tissue and 25 noncancerous samples. The GSE33532 dataset had 80 NSCLC tissue and 20 noncancerous samples.

4.2. Identification DEGS

GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r) was used for screening DEGs between NSCLC samples and noncancer samples. GEO2R, an interactive web tool, enables the comparisons of two or more GEO datasets. To identify DEGs, we applied thresholds for the adjusted (adj.) value and Benjamini and Hochberg false discovery rate to establish a balance between the limitations of finding significant (statistical) genes as well as false positives. Probe sets lacking the corresponding gene symbols or genes exhibiting multiple probe sets were, respectively, eliminated or averaged. Log fold change and adj. denoted statistical significance [45].

4.3. KEGG and GO Analyses of the DEGs

DAVID (http://david.ncifcrf.gov) (version6.7) [46] is an online biological information database integrated with a comprehensive set of analysis tools. Functional annotation of genes and proteins can be used to extract biological information. KEGG is a database resource for understanding advanced and biological functions. Systems generated from large-scale molecular datasets are considered high-throughput experimental techniques [47] GO is an established gene analysis method.

4.4. PPI Network Construction and Module Analysis

The Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org) (version 10.0) [48] online database was used for PPI network prediction. Analysis of functional interactions between and among proteins may elucidate on the pathomechanisms of various diseases. We used the STRING database to build a PPI network of DEGs, and interactions with a combined were considered statistically significant. Cytoscape (version 3.4.0) is an open source bioinformatics software platform for visualizing molecular interaction networks [49]. The MCODE (version 1.4.2) plug-in of Cytoscape is an app for clustering a given network based on topology to find densely connected regions [50]. The PPI networks were drawn using Cytoscape, with the most significant module in the networks identified using MCODE. The selection criteria were MCODE , degree , node score , max , and . Then, KEGG and GO analyses of the genes in this module were conducted using DAVID.

4.5. Hub Gene Selection and Analysis

Hub genes with were selected for analysis. A network of the genes and their coexpressed genes was analyzed using cBioPortal (http://www.cbioportal.org) [51, 52] online platform. The biological process analysis of hub genes was performed and visualized using the Biological Networks Gene Oncology (BiNGO) (version 3.0.3) plugin of Cytoscape [53]. Hierarchical clustering of hub genes was performed using the UCSC Cancer Genomics Browser (http://genome-cancer.ucsc.edu) [54]. Overall survival and disease-free survival analyses of hub genes were performed using the Kaplan-Meier curves in cBioPortal. The expression profiles of NUF2 were analyzed and displayed using the online database SAGE (http://www. http://ncbi.nlm.nih.gov/SAGE). The relationships between expression patterns and tumor grades, infection status, metastasis, and vascular invasion were analyzed using the online database Oncomine (http://www.oncomine.com) [55–57].

4.6. NSCLC Patient Specimens

To investigate NUF2 levels in human NSCLC, we obtained tumor tissues and paired adjacent nontumorous tissues during radical resection of patients without prior chemotherapy or radiotherapy at the Department of Thoracic Surgery, Sixth Affiliated Hospital of Nantong University. Resected NSCLC-adjacent nontumor samples and matched tumor tissues were obtained and instantly stored in liquid nitrogen (Table 5). From May 2021 to July 2021, 2 pairs of lung squamous cell carcinoma tissues and adjacent nontumor tissues and 2 pairs of lung adenocarcinoma tissues and adjacent nontumor tissues were randomly selected from patients in The Sixth Affiliated Hospital of Nantong University (Yancheng Third People’s Hospital) (T1 and T2 in Figure 10 are LUSCs, and T3 and T4 are LUADs). All patients or their guardians provided informed consents, and this study was approved by the ethical committee of The Sixth Affiliated Hospital of Nantong University (Yancheng, China). The grade and histological type of all tissue samples were independently verified by two professional pathologists.

4.7. Western Blot Analysis

A lysis buffer (Beyotime Institute of Biotechnology, Nantong, China) was used to prepare total protein extracts from cell lines as well as tumor tissues. Then, protein concentrations were evaluated by a BCA kit (Beyotime Institute of Biotechnology, Nantong, China). An equal protein amount (40 μg per lane) was separated by SDS-polyacrylamide gel electrophoresis (PAGE) in 12% acrylamide gels and transferred onto polyvinylidine difluoride (PVDF) membranes (Millipore Corporation, Billerica, USA) which were blocked in 5% fat-free milk followed by overnight incubation at 4°C with the rabbit anti-NUF2 primary antibody (1 : 5000 dilution; Sangon Biotech, Shanghai, China). The secondary antibody was horseradish peroxidase- (HRP-) conjugated goat anti-rabbit antibody (1 : 2000; Beyotime Institute of Biotechnology). After stripping, the membrane was reprobed overnight at 4°C with antibody against GAPDH (1 : 2000; Beyotime Institute of Biotechnology), followed by incubation with secondary antibodies as above at room temperature (RT) for 2 h. An enhanced chemiluminescence system (ECL; Beyotime Institute of Biotechnology) was used for band visualization. The band intensities were quantified by densitometry.

4.8. Real-Time Quantitative PCR

Quantitative real-time PCR analysis was measured as previously described. Total RNA was isolated from cultured cells or muscle tissues using an RNeasy plus mini kit. cDNA was obtained using a GoScript Reverse Transcription System and analyzed by quantitative real-time PCR using SYBR Green kit. The data were normalized to expression of ribosomal gene NUF2 or GAPDH. The primer sequences are NUF2-forward (ATGGAAGGCTTCTTACCATTCA) and NUF2-reverse (CTTAAAAACCGACTTGTCCGTT).

4.9. Statistical Analysis

GraphPad Prism 7.0 software was used for statistical analyses. Between-group differences were evaluated by the two-tailed Student’s -test. A value < 0.05 indicated significance. All assays were repeated thrice.

Data Availability

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

Disclosure

We assure that the material is original and a preprint has previously been published [58].

Conflicts of Interest

The authors declared that no competing interests exist in this study.

Authors’ Contributions

Xia Li, Zhongquan Yi, and Lianlian zhang researched and analyzed data. Wenchun Song, Panwen Zhao, and Jixiang Wu contributed to the discussion. Jianxiang Song and Qinggan Ni designed the study, and Xia Li wrote the manuscript. All authors read and approved the final manuscript. Xia Li, Lianlian Zhang, and Zhongquan Yi equally contributed to this work and co-first authors. .

Acknowledgments

This study was supported in part by grant from the 2020 Clinical College (Yancheng Third People’s Hospital) Special Scientific Research and Development Fund Project (20209113); Medical Scientific Research Guiding Project of Jiangsu Provincial Health Commission in 2021 (Z2021087); and the 2021 Clinical College (Yancheng Third People’s Hospital) Special Scientific Research and Development Fund Project (20219105).

References

L. Osmani, F. Askin, E. Gabrielson, and Q. K. Li, “Current WHO guidelines and the critical role of immunohistochemical markers in the subclassification of non-small cell lung carcinoma (NSCLC): Moving from targeted therapy to immunotherapy,” Seminars in Cancer Biology, vol. 52, Part 1, pp. 103–109, 2018.
View at: Publisher Site | Google Scholar
E. Khaksar, M. Askarishahi, S. Hekmatimoghaddam, H. Vahedian-Ardakani, C. Regression, and P. Models, “Cox regression and parametric models: comparison of how they determine factors influencing survival of patients with non-small cell lung carcinoma,” Asian Pacific Journal of Cancer Prevention, vol. 18, no. 12, pp. 3389–3393, 2017.
View at: Google Scholar
G. Boulle, Y. Velut, A. Mansuet-Lupo et al., “Chemoradiotherapy efficacy is predicted by intra-tumour CD8+/FoxP3+ double positive T cell density in locally advanced N2 non-small-cell lung carcinoma,” European Journal of Cancer, vol. 135, pp. 221–229, 2020.
View at: Publisher Site | Google Scholar
T. Kishikawa, T. Kasai, M. Okada et al., “Osimertinib, a third-generation EGFR tyrosine kinase inhibitor: a retrospective multicenter study of its real-world efficacy and safety in advanced/recurrent non-small cell lung carcinoma,” Thoracic Cancer, vol. 11, no. 4, pp. 935–942, 2020.
View at: Publisher Site | Google Scholar
L. Li, Q. Lei, S. Zhang, L. Kong, and B. Qin, “Screening and identification of key biomarkers in hepatocellular carcinoma: evidence from bioinformatic analysis,” Oncology Reports, vol. 38, no. 5, pp. 2607–2618, 2017.
View at: Publisher Site | Google Scholar
K. Gennen, L. Käsmann, J. Taugner et al., “Prognostic value of PD-L1 expression on tumor cells combined with CD8+ TIL density in patients with locally advanced non-small cell lung cancer treated with concurrent chemoradiotherapy,” Radiation Oncology, vol. 15, no. 1, p. 5, 2020.
View at: Google Scholar
Y. Bossé and C. I. Amos, “A decade of GWAS results in lung cancer,” Cancer Epidemiology, Biomarkers & Prevention, vol. 27, no. 4, pp. 363–379, 2018.
View at: Publisher Site | Google Scholar
B. Matthew, “Cancer progress and priorities: lung cancer,” Cancer Epidemiology, Biomarkers & Prevention, vol. 28, no. 10, pp. 1563–1579, 2019.
View at: Publisher Site | Google Scholar
R. Edgar, M. Domrachev, and A. E. Lash, “Gene expression omnibus: NCBI gene expression and hybridization array data repository,” Nucleic Acids Research, vol. 30, no. 1, pp. 207–210, 2002.
View at: Publisher Site | Google Scholar
S. Shukla, J. R. Evans, R. Malik et al., “Development of a RNA-Seq based prognostic signature in lung adenocarcinoma,” JNCI: Journal of the National Cancer Institute, vol. 109, no. 1, 2016.
View at: Google Scholar
N. Duma, R. Santana-Davila, and J. R. Molina, “Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment,” Mayo Clinic Proceedings, vol. 94, no. 8, pp. 1623–1640, 2019.
View at: Publisher Site | Google Scholar
S. Jonna and D. S. Subramaniam, “Molecular diagnostics and targeted therapies in non-small cell lung cancer (NSCLC): an update,” Discovery Medicine, vol. 27, no. 148, pp. 167–170, 2019.
View at: Google Scholar
K. Takada, G. Toyokawa, F. Shoji, T. Okamoto, and Y. Maehara, “The significance of the pd-l1 expression in non�small-cell lung cancer: trenchant double swords as predictive and prognostic markers,” Clinical Lung Cancer, vol. 19, no. 2, pp. 120–129, 2018.
View at: Google Scholar
C. Evan, “Targeted therapy and immunotherapy for lung cancer,” Surgical Oncology Clinics of North America, vol. 25, no. 3, pp. 601–609, 2016.
View at: Publisher Site | Google Scholar
B. Sandfeld-Paulsen, K. R. Jakobsen, R. Bæk et al., “Exosomal proteins as diagnostic biomarkers in lung cancer,” Journal of Thoracic Oncology, vol. 11, no. 10, pp. 1701–1710, 2016.
View at: Publisher Site | Google Scholar
T. P. Lu, M. H. Tsai, J. M. Lee et al., “Identification of a novel biomarker, SEMA5A, for non-small cell lung carcinoma in nonsmoking women,” Cancer Epidemiology, Biomarkers & Prevention, vol. 19, no. 10, pp. 2590–2597, 2010.
View at: Publisher Site | Google Scholar
L. Xu, C. Lu, Y. Huang et al., “SPINK1 promotes cell growth and metastasis of lung adenocarcinoma and acts as a novel prognostic biomarker,” BMB Reports, vol. 51, no. 12, pp. 648–653, 2018.
View at: Publisher Site | Google Scholar
J. Hou, J. Aerts, B. den Hamer et al., “Gene expression-based classification of non-small cell lung carcinomas and survival prediction,” PLoS One, vol. 5, no. 4, article e10312, 2010.
View at: Publisher Site | Google Scholar
T. Y. Wei, C. C. Juan, J. Y. Hisa et al., “Protein arginine methyltransferase 5 is a potential oncoprotein that upregulates G1 cyclins/cyclin-dependent kinases and the phosphoinositide 3-kinase/AKT signaling cascade,” Cancer Science, vol. 103, no. 9, pp. 1640–1650, 2012.
View at: Publisher Site | Google Scholar
M. Meister, X. E. C. Anton Belousov, P. Schnabel et al., “Intra-tumor heterogeneity of gene expression profiles in early stage non-small cell lung cancer,” Journal of Bioinformatics Research Studies, vol. 1, 2014.
View at: Google Scholar
S. C. Mok, T. Bonome, V. Vathipadiekal et al., “A gene signature predictive for outcome in advanced ovarian cancer identifies a survival factor: microfibril-associated glycoprotein 2,” Cancer Cell, vol. 16, no. 6, pp. 521–532, 2009.
View at: Publisher Site | Google Scholar
L. A. Torre, F. Bray, R. L. Siegel, J. Ferlay, J. Lortet-Tieulent, and A. Jemal, “Global cancer statistics, 2012,” CA: A Cancer Journal for Clinicians, vol. 65, no. 2, pp. 87–108, 2015.
View at: Publisher Site | Google Scholar
J. R. Molina, P. Yang, S. D. Cassivi, S. E. Schild, and A. A. Adjei, “Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship,” Mayo Clinic Proceedings, vol. 83, no. 5, pp. 584–594, 2008.
View at: Publisher Site | Google Scholar
S. Roy, “The biology and management of non-small cell lung cancer,” Nature, vol. 553, no. 7689, pp. 446–454, 2018.
View at: Publisher Site | Google Scholar
A. J. Alberg, M. V. Brock, J. G. Ford, J. M. Samet, and S. D. Spivack, “Epidemiology of lung cancer: diagnosis and management of lung cancer American College of Chest Physicians evidence-based clinical practice guidelines,” Chest, vol. 143, Supplement_5, pp. e1S–e29S, 2013.
View at: Google Scholar
P. Vineis, L. Airoldi, F. Veglia et al., “Environmental tobacco smoke and risk of respiratory cancer and chronic obstructive pulmonary disease in former smokers and never smokers in the EPIC prospective study,” British Medical Journal, vol. 330, no. 7486, p. 277, 2005.
View at: Publisher Site | Google Scholar
Y. Qiang, F. Ma, Z. Wang et al., “LukS-PV induces cell cycle arrest and apoptosis through p38/ERK MAPK signaling pathway in NSCLC cells,” Biochemical and Biophysical Research Communications, vol. 521, no. 4, pp. 846–852, 2020.
View at: Publisher Site | Google Scholar
H. Feng, F. Ge, L. du, Z. Zhang, and D. Liu, “MiR-34b-3p represses cell proliferation, cell cycle progression and cell apoptosis in non-small-cell lung cancer (NSCLC) by targeting CDK4,” Journal of Cellular and Molecular Medicine, vol. 23, no. 8, pp. 5282–5291, 2019.
View at: Publisher Site | Google Scholar
Y. F. Wu, C. C. Ou, P. J. Chien, H. Y. Chang, J. L. Ko, and B. Y. Wang, “Chidamide-induced ROS accumulation and mi R-129-3p-dependent cell cycle arrest in non-small lung cancer cells,” Phytomedicine, vol. 56, pp. 94–102, 2019.
View at: Google Scholar
Q. Xu, Y. Wang, and W. Huang, “Identification of immune-related lncRNA signature for predicting immune checkpoint blockade and prognosis in hepatocellular carcinoma,” International Immunopharmacology, vol. 92, article 107333, 2021.
View at: Google Scholar
F. Mami-Chouaib, C. Blanc, S. Corgnac et al., “Resident memory T cells, critical components in tumor immunology,” Journal for Immunotherapy of Cancer, vol. 6, no. 1, p. 87, 2018.
View at: Publisher Site | Google Scholar
J. M. T. Janco, P. Lamichhane, L. Karyampudi, and K. L. Knutson, “Tumor-infiltrating dendritic cells in cancer pathogenesis,” The Journal of Immunology, vol. 194, no. 7, pp. 2985–2991, 2015.
View at: Google Scholar
E. Matos-Perdomo and F. Machín, “Nucleolar and ribosomal DNA structure under stress: yeast lessons for aging and cancer,” Cell, vol. 8, no. 8, p. 779, 2019.
View at: Publisher Site | Google Scholar
X. Tang, Y. Li, L. Liu et al., “Sirtuin 3 induces apoptosis and necroptosis by regulating mutant p53 expression in small-cell lung cancer,” Oncology Reports, vol. 43, no. 2, pp. 591–600, 2020.
View at: Publisher Site | Google Scholar
D. Subramonian, T.-A. Chen, N. Paolini, and X.-D. “. D.”. Zhang, “Poly-SUMO-2/3 chain modification of Nuf2 facilitates CENP-E kinetochore localization and chromosome congression during mitosis,” Cell Cycle, vol. 20, no. 9, pp. 855–873, 2021.
View at: Publisher Site | Google Scholar
S. Yamamoto, K.-i. Takayama, D. Obinata et al., “Identification of new octamer transcription factor 1-target genes upregulated in castration-resistant prostate cancer,” Cancer Science, vol. 110, no. 11, pp. 3476–3485, 2019.
View at: Publisher Site | Google Scholar
X. Xie, S. Jiang, and X. Li, “Nuf 2 is a prognostic-related biomarker and correlated with immune infiltrates in hepatocellular carcinoma,” Frontiers in Oncology, vol. 11, no. 11, article 621373, 2021.
View at: Publisher Site | Google Scholar
X. Wenjie, Y. Wang, Y. Wang, S. Lv, X. Xu, and X. Dong, “Screening of differentially expressed genes and identification of NUF2 as a prognostic marker in breast cancer,” International Journal of Molecular Medicine, vol. 44, no. 2, pp. 390–404, 2019.
View at: Google Scholar
H.-L. Fu and L. Shao, “Silencing of NUF2 inhibits proliferation of human osteosarcoma Saos-2 cells,” European Review for Medical and Pharmacological Sciences, vol. 20, no. 6, pp. 1071–1079, 2016.
View at: Google Scholar
H. Peng, X. Chen, J. Sun, P. Bie, and L.-D. Zhang, “SiRNA-mediated knockdown against NUF2 suppresses pancreatic cancer proliferation in vitro and in vivo,” Bioscience Reports, vol. 35, no. 1, article e00170, 2015.
View at: Publisher Site | Google Scholar
H. Sugimasa, K. Taniue, A. Kurimoto, Y. Takeda, Y. Kawasaki, and T. Akiyama, “Heterogeneous nuclear ribonucleoprotein K upregulates the kinetochore complex component NUF2 and promotes the tumorigenicity of colon cancer cells,” Biochemical and Biophysical Research Communications, vol. 459, no. 1, pp. 29–35, 2015.
View at: Publisher Site | Google Scholar
W. Peijie, W. Gao, S. Miao et al., “Adaptive mechanisms of tumor therapy resistance driven by tumor microenvironment,” Frontiers in Cell and Development Biology, vol. 9, no. 9, article 641469, 2021.
View at: Publisher Site | Google Scholar
R. Saleh and E. Elkord, “Acquired resistance to cancer immunotherapy: role of tumor-mediated immunosuppression,” Seminars in Cancer Biology, vol. 65, pp. 13–27, 2020.
View at: Publisher Site | Google Scholar
T. Barrett, T. O. Suzek, D. B. Troup et al., “NCBI GEO: mining millions of expression profiles—database and tools,” Nucleic Acids Research, vol. 33, pp. D562–D566, 2005.
View at: Publisher Site | Google Scholar
X. Wen, M. Fu, W. Wu, Z. Zeng, and K. Zheng, “Identification of key genes and their microRNAs in glioma: evidence from bioinformatic analysis,” Tech. Rep., Research Square, 2020.
View at: Publisher Site | Google Scholar
D. W. Huang, B. T. Sherman, Q. Tan et al., “The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists,” Genome Biology, vol. 8, no. 9, p. R183, 2007.
View at: Publisher Site | Google Scholar
M. Kanehisa, “The KEGG database,” Novartis Foundation Symposium, vol. 247, pp. 91–252, 2002.
View at: Publisher Site | Google Scholar
A. Franceschini, D. Szklarczyk, S. Frankild et al., “STRING v9.1: protein-protein interaction networks, with increased coverage and integration,” Nucleic Acids Research, vol. 41, pp. D808–D815, 2013.
View at: Publisher Site | Google Scholar
M. E. Smoot, K. Ono, J. Ruscheinski, P. L. Wang, and T. Ideker, “Cytoscape 2.8: new features for data integration and network visualization,” Bioinformatics, vol. 27, no. 3, pp. 431-432, 2011.
View at: Publisher Site | Google Scholar
W. P. Bandettini, P. Kellman, C. Mancini et al., “Multi contrast delayed enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study,” Journal of Cardiovascular Magnetic Resonance, vol. 14, no. 1, p. 83, 2012.
View at: Publisher Site | Google Scholar
J. Gao, B. A. Aksoy, U. Dogrusoz et al., “Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal,” Science Signaling, vol. 6, 2013.
View at: Google Scholar
E. Cerami, J. Gao, U. Dogrusoz et al., “The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data,” Cancer Discovery, vol. 2, no. 5, pp. 401–404, 2012.
View at: Publisher Site | Google Scholar
S. Maere, K. Heymans, and M. Kuiper, “BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks,” Bioinformatics, vol. 21, no. 16, pp. 3448-3449, 2005.
View at: Publisher Site | Google Scholar
W. J. Kent, C. W. Sugnet, T. S. Furey et al., “The human genome browser at UCSC,” Genome Research, vol. 12, no. 6, pp. 996–1006, 2002.
View at: Publisher Site | Google Scholar
X. Chen, S. T. Cheung, S. So et al., “Gene expression patterns in human liver cancers,” Molecular Biology of the Cell, vol. 13, no. 6, pp. 1929–1939, 2002.
View at: Publisher Site | Google Scholar
S. Roessler, H. L. Jia, A. Budhu et al., “A unique metastasis gene signature enables prediction of tumor relapse in early-stage hepatocellular carcinoma patients,” Cancer Research, vol. 70, no. 24, pp. 10202–10212, 2010.
View at: Publisher Site | Google Scholar
E. Wurmbach, Y. B. Chen, G. Khitrov et al., “Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma,” Hepatology, vol. 45, no. 4, pp. 938–947, 2007.
View at: Publisher Site | Google Scholar
X. Li, Z. Yi, L. Zhang et al., Systematic Analysis Identies NUF2 as an Immunological and Prognostic Biomarker for Nonsmall cell lung cancer, Research Square, 2021.
View at: Publisher Site

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