NEK2 is a potential pan-cancer biomarker and immunotherapy target

doi:10.21203/rs.3.rs-4998438/v1

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Research Article

NEK2 is a potential pan-cancer biomarker and immunotherapy target

https://doi.org/10.21203/rs.3.rs-4998438/v1

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Background.

NEK2 is a member of the NEKs family and plays an important role in cell mitosis. Increasing evidence suggests that NEK2 is associated with the development of multiple tumors, but systematic studies of NEK2 in cancer are still lacking. Therefore, we evaluated the prognostic value of NEK2 in 33 cancers to elucidate the potential function of NEK2 in pan-cancers.

Methods.

We explored the role of NEK2 in pan-cancers using The Cancer Genome Atlas(TCGA)and Genotype-tissue expression༈GTEx༉database, and we also analyzed the association between NEK2 pan-cancers gene expression, protein expression, tumor microenvironment༈TME), and drug sensitivity through various software and web platforms such as R, CCLE, the Human protein atlas༈HPA༉, cBioPortal, CancerSEA and GEPIA 2. 0.We also conducted in vitro experiments to preliminarily verify the function of NEK2 in cervical cancer.

Results.

NEK2 is overexpressed in almost all tumors, and mutation of NEK2 are associated with a poorer tumor prognosis. In addition, the correlation between NEK2 and immune features such as immune cell infiltration, immune checkpoint genes, tumor mutational burden(TMB), Microsatellite instability(MSI) etc. suggest that NEK2 could potentially be applied in the immunotherapy of tumors.

Conclusion.

NEK2 may be a potential pan-cancer biomarker and immunotherapeutic target for improving the efficacy of tumor therapy.

NEK2

Pan-cancers

Diagnosis

Prognosis

Tumor immunity

Cancer is a prominent global public health problem and has long been recognized as the second leading cause of death worldwide after cardiovascular disease[1].In 2022, there will be an estimated 20 million new cancer cases and nearly 9. 7 million cancer deaths globally, with lung, breast and colorectal cancers ranking among the top three cancers [2]. Cancer imposes an enormous social burden and suffering on the global community. Cancer has been somewhat controlled in the last decade due to advances in early detection, surgical techniques and targeted therapies[1], however, the presence of tumour biology such as genomic mutations and reprogramming of epigenetic variants has led to features such as low response rates and immune escape in oncology treatments[3]. Cancer immunotherapy is a promising cancer treatment strategy that has changed the landscape of malignancy treatment[4].Cancer immunotherapy has revolutionized cancer treatment. Compared with radiotherapy, cancer immunotherapy can stimulate or mobilize the body's immune function to enhance various anti-tumor capabilities in the tumor microenvironment, indirectly attacking or directly killing tumor cells and reducing off-target effects [5]. However, tumor immunotherapy also has certain drawbacks, such as nanoparticles and scaffolds. With the rapid development of public databases such as the TCGA database, it is easier to further analyze the relevance and impact of individual genes on cancer prognosis and immune infiltration. In addition, the great success of immunotherapy has made immune-related biomarkers even more important.Therefore, exploring new targets and new biomarkers for tumor immunotherapy is becoming increasingly important in the diagnosis and treatment of tumors.

Cancer is characterized by uncontrolled cell proliferation due to abnormal activity of various proteins. Cell cycle-related proteins are thought to be important in a variety of functions[6].Never In Mitosis Gene A (NIMA)-Related Kinase 2 (NEK2) is a member of the NEKs. NEKs are members of the Ser/Thr kinase family, with a total of 11 (NEK1–11) members. NEKs are key proteins involved in cellular differentiation and the maintenance of cellular homeostasis, such as the cell cycle, mitosis, cilia formation, and the DNA damage response [7]. Of these, NEK2 is the member with the highest homology to NIMA [8]. NEK2 plays an important role in cellular mitosis. NEK2 expression is low in the G1 phase of the cell cycle, elevated in the S and G2 phases, peaks in late G2/M, and decreases as the cell enters mitosis. NEK2 is involved in the control of centrosome segregation and bipolar spindle formation in mitotic cells by phosphorylating specific substrates during the G2/M phase of the cell cycle and chromatin condensation in meiotic cells [9]. Abnormal expression and high activity of NEK2 in cancer cells lead to mitotic disorders such as centrosome over-replication, abnormal spindle formation, and chromosome segregation errors, resulting in aneuploidy or chromosomally unstable cells [10], which is an important cause of tumor initiation. Initially, the pro-tumorigenic effects of NEK2 were mainly attributed to its key role in promoting the cell cycle, and early studies focused on the cancer cell cycle [11]. Studies have shown that NEK2 over expression occurs in many human solid tumors and its expression is associated with the onset, progression, metastasis and poor prognosis of a variety of solid tumors[12], such as hepatocellular carcinoma[13], pancreatic cancer [14], glioblastoma[15], esophageal squamous cell carcinoma[16] and non-small-cell lung carcinoma[17], among other tumors. Newly discovered findings reveal an immunomodulatory role for NEK2, and NEK2 inhibition triggers anti-pancreatic cancer immunity by targeting PD-L1[14].Therefore, targeting NEK2 may be a promising cancer therapy, especially for solid tumors.However, the exact mechanism of NEK2 in tumorigenesis and development is still unclear, and there are no systematic studies on NEK2 in pan-cancer, so it is essential to explore the role of NEK2 in pan-cancer.

In this study, we utilized databases such as TCGA and GTEx for pan-cancer analysis of NEK2 and investigated its impact on cancer prognosis. In addition, to deeply analyze the association between NEK2 and tumor immunity, this study evaluated the correlation between NEK2 expression and TMB, MSI status, immune cell infiltration, immune stroma score, immune checkpoint gene expression, somatic mutation, and drug correlation with drug sensitivity. The NEK2 Protein-protein interaction (PPI) network was generated, and Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis were performed. The aim of this study was to investigate the role of NEK2 in pan-cancer and its correlation with the immune microenvironment, and to preliminary explore the mechanism of NEK2 in tumors.

2. 1 NEK2 mRNA and protein expression

We downloaded mRNA expression profiles and related clinical data from TCGA ( https://portal.gdc.cancer.gov/ ) for 33 cancer samples and corresponding normal samples. Gene expression data from 31 different tissues were downloaded from GTEx ( https://commonfund.nih.gov/GTEx ). Cancer cell line data from 37 human tissues was downloaded from the CCLE database ( https://sites.broadinstitute.org/ccle ) and analyzed for their NEK2 expression. The downloaded data allowed us to assess NEK2 expression levels in 31 normal and 33 tumor tissues and to compare cancer samples with paired standard samples from the 33 cancers.Log2 transformation and t tests were performed on expression data and these tumor types. Differences in expression between tumor and normal tissue samples were identified by a P-value < 0.05 criterion. The R software (version 4.3.2, https://www.r-project.org/) was used for data analysis, and the "ggplot2" R package[18] was applied to draw the box graph. HPA ( https://www.proteinatlas.org/ ) is a human proteome mapping database that contains information about the distribution of proteins in human tissues and cells. To analyse the differential expression of NEK2 at the protein level, we downloaded immunohistochemical images of four tumor tissues and their corresponding normal tissues from HPA.

2. 2 Prognostic analysis

Pan-cancer survival information, including overall survival(OS),disease specific survival༈DSS༉,disease free interval༈DFI༉and progression free interval༈PFI༉,was downloaded from the TCGA database to assess the prognostic significance of NEK2. The high and low expression clusters of NEK2 were obtained by expression thresholds of critical high (50%) and critical low (50%) values[19]. The cox proportional-hazards model and Kaplan-Meier༈KM༉survival analysis were used in order to analyse the correlation between NEK2 expression and patient prognosis. The forest plots plotted the hazard ratio (HR), 95% confidence interval and p-value of survival curves were calculated.

2. 3 Mutations in the NEK2 gene

cBioPortal[20]( http://cbioportal. org ) was used to collect the mutation types and mutation sites of relevant proteins in all TCGA tumors. Mutation sites were obtained from the "Mutations" module. The somatic mutation frequency and genomic information of NEK2 mutations in cancer were explored in the "Cancer Types Summary" and "mRNA vs study" modules.

2. 4 Immune infiltration analysis

ESTIMATE is an algorithm that uses expression data to assess stromal and immune cells in 33 tumors, and estimates tumor purity based on immune and stromal scores. The association between NEK2 expression and stromal and immune cells in 33 cancer types was investigated using the "ESTIMATE" package[21] in R. We used the "ggplot2" package in R to evaluate the association between stromal and immune cells in 33 cancer types. We used the R-packages "ggplot2" and "ggpubr"[22] to explore the correlation between NEK2 expression and different immune cell infiltrations in pan-cancer. In addition, we analysed the co-expression of NEK2 with immune-related genes. Based on the somatic mutation data downloaded from TCGA, the Spearman's rank correlation test was used to generate the partial correlation(cor) and P-value between NEK2 expression and TMB and MSI[23]. The results are presented as radar plots.In addition, a total of 11 immune checkpoint genes (including PDCD1, CTLA4, C10orf54, HAVCR2, LAG3, TIGIT, SIRPA, BTLA, SIGLEC7, LILRB2, and LILRB4)[24] were extracted from the TCGA dataset for immune checkpoint gene correlation analysis.

2.5 Single-Cell Functional Analysis

CancerSEA[25] ( http://biocc.hrbmu.edu.cn/CancerSEA/ ), the first dedicated database designed to comprehensively explore the different functional states of cancer cells at the single-cell level. Correlations between NEK2 expression and 16 different cancer functional states were analyzed based on single-cell sequencing data, including angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, epithelial–mesenchymal transition (EMT), hypoxia, inflammation, invasion, metastasis, proliferation, quiescence and stemness. The correlation threshold between NEK2 and cancer functional status was set at a correlation strength of 0.3 with a p-value of less than 0.05.

2. 6 Functional analysis and protein-protein interaction network

GEPIA2 is a website developed in the laboratory of Mr Zhang Zemin at Peking University, capable of analysing RNA-seq expression data from a total of 9,736 tumour samples and 8,587 normal samples from the TCGA and GTEx projects[26]. The top 100 NEK2-related targeted Genes in TCGA tumors were generated by GEPIA2's "Most Similar Genes" module.Then, STRING database ( https://cn. string-db. org/ ) was used to construct the PPI network for these 100 genes[27]. GO analysis and KEGG analysis of these 100 NEK2-related genes were performed by "clusterProfiler"[28] and "org.Hs.eg.db" R packages. The results were presented as bubble plots by "ggplot2". Differential expression analysis of NEK2 was performed using the "DESeq" R-package[29] in cancers in which NEK2 may affect prognosis. Subsequently, Gene set enrichment analysis(GSEA) was performed using the “clusterProfiler” R-package based on the results obtained from NEK2 differential expression analysis in different tumors.

2. 7 Analysis of drug sensitivity

NCI-60 compound activity data and RNA-seq expression profiles from the CallMiner database were downloaded to analyse drug sensitivity of NEK2 in pan-cancer ( https://discover.nci.nih.gov/cellminer/loadDownload. do ) [30]. Drugs that were FDA approved or Clinical trial were selected for analysis. The "readxl", "Hmisc"[31], "ggplot2" and "ggpubr" R-packages were used.

2. 8 Patients and clinical specimens

Cervical cancer tissues were obtained from patients undergoing surgery in the Obstetrics and Gynecology Department of Wuhan University People's Hospital, with the patients' written informed consent and authorized by the Ethics Committee of Wuhan University People's Hospital (Ethics number: WDRY2024-K178). Cervical cancer and normal specimens were routinely fixed, embedded, dewaxed, hydrated and dehydrated after 5µm continuous sections. Rabbit anti-human NEK2 antibody (Proteintech Company, article No: 24171-1-AP, diluted at 1:200) was incubated overnight at 4℃. The brown particles were identified as positive staining cells under microscope (×400).

2.9 Cell culture and treatment

Normal cervical epithelial cells Hacat, cervical cancer cell lines Hela and Siha were purchased from the American Type Culture Collection. Human cervical cancer cell lines and normal cervical epithelial cells were cultured in DMEM containing 10% fetal bovine serum at 37℃ and 5% CO2. When the cell density was 80%, the cells were passed, cultured for 2–3 generations, and collected for follow-up experiments. The NEK2 inhibitor JH295 was dissolved in DMSO, then Hela and Siha cells were treated with JH295 at a concentration of 1 uM, and cells stimulated by DMSO at the same concentration served as a control group. JH295 (Cat No. 1311143-71-1) was obtained from MedChemExpres.

2.10 Western blot

RIPA cell lysate and PMSF were prepared into lysate at a rate of 100:1, and the cells were placed in lysate for cracking for 30min. Supernatant was obtained by centrifugation for 12000r. The protein concentration was determined with BCA assay kit (Wuhan Sevier Biotechnology Co., Ltd., Article No: G2026-200 T). 5x protein loading buffer was added to the lysate, boiled in a water bath at 100℃ for 10min, electrophoretic with SDS-PAGE, and transferred to PVDF membrane. 5% skim milk was closed at room temperature for 2 hours. The closed film was treated with NEK2 monoclonal antibody (Proteintech Company, article No: 24171-1-AP, diluted at 1:500) and GAPDH primary antibody (Proteintech Company, article No: 10494-1-AP, diluted at 1:10,000) were incubated overnight at 4°C. HRP labeled goat anti-rabbit IgG antibody (Wuhan Sevier Biotechnology Co., Ltd., article No: GB23303, diluted at 1:10,000) was incubated for 1 hour and developed with ECL luminescent kit. The analysis was performed using the ChemiDocTM imaging system (Bio-Rad Laboratories, Inc., USA).

2.11 RNA extraction and qRT-PCR

Total RNA was isolated from cultured cells using Trizol reagent (Vazyme Biotech Co, Ltd, Nanjing, China). DNA is reversely transcribed by Yeasen Biotechnology (article No:11141ES60). PCR Master Mix Kit (Yeasen Biotechnology, article No:11198ES08) was used for PCR amplification. The relative expression of NEK2 in cervical cancer cells was determined by RT-PCR, and GAPDH mRNA was used as the internal reference. The following primers are used:

NEK2 forward primer:5'-TGCTTCGTGAACTGAAACATCC-3';

NEK2 reverse primer:5'-CCAGAGTCAACTGAGTCATCACT-3';

GAPDH forward primer:5 ' -GGAGTCCACTGGCGTCTTCA-3 ';

GAPDH reverse primer:5 ' -GTCATGAGTCCTTCCACGATACC-3 '.

2.12 Cell proliferation

For cell proliferation assays, cervical cancer cells are inoculated into a 96-well plate and live cells are measured daily using the CCK8 reagent for up to 4 days. Cell Counting Kit-8 (Cat No. C0037) was obtained from beyotime.

3. 1 Pan-cancer analysis of NEK2 expression

We extracted mRNA levels of NEK2 from 33 cancer types in the TCGA database and plotted box plots of NEK2 expression in cancerous tissues and normal tissues adjacent to the cancer. NEK2 mRNA expression was significantly upregulated in most cancers, including BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PCPG, PRAD, READ, SARC, STAD, THCA, and UCEC, with no significant difference in SKCM and THYM (Fig. 1A). Since the transcript levels of the corresponding paracancerous normal tissues in ACC, DLBC, LAML, LGG, MESO, OV, TGCT, UCS, and UVM were not available, we integrated the normal tissue data into the GTEx database data. As shown in Fig. 1B, NEK2 mRNA levels were also higher in ACC, DLBC, LGG, OV, SKCM, THYM and UCS, and NEK2 mRNA expression levels were lower in TGCT. In addition, Fig. 1C shows the relative expression levels of NEK2 in various cancer cell lines in the CCLE database. As can be seen from the results, NEK2 is usually expressed at higher levels in tumor cell lines from 33 tissues. These results indicate that NEK2 expression is up-regulated in many types of cancers, suggesting that NEK2 may play a key role in cancer diagnosis and treatment.

In addition, we investigated the expression level of NEK2 protein in pan-cancer. We investigated the expression of NEK2 protein in normal and tumor tissues of different organs of the human body by HPA database and showed IHC images of normal and tumor tissues of lymph nodes, cervix, stomach, and uroepithelium (Supplementary Figure S1).

3. 2 The correlation between NEK2 expression and adverse consequences of cancer

To further determine the prognostic value of NEK2, we performed survival analysis on data retrieved from the TCGA database to observe the correlation of NEK2 with OS, DSS, DFI and PFI in different cancers. The Cox proportional-hazards model demonstrated that high expression of NEK2 mRNA was correlated with OS in ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PCPG and UVM, and was negatively correlated with the OS of THYM (Fig. 2A). KM survival analysis was used to evaluate the relationship between NEK2 expression and clinical outcomes. In patients with ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PCPG and UVM, high NEK2 expression was associated with poorer OS, whereas in patients with THYM, high NEK2 expression was associated with better OS (Fig. 2B-L).

In addition, high NEK2 expression correlated with poorer DSS in patients with ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PCPG, PRAD and UVM (Supplementary Figure S2); KIRP, LIHC, LUAD, PAAD, PRAD, SARC, THCA patients correlated with poor DFI (Supplementary Figure S3); high NEK2 expression in ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PCPG, PRAD, SARC, THCA, and UVM patients correlated with poor PFI (Supplementary Figure S3); high NEK2 expression in ACC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PCPG, PRAD, SARC, THCA, and UVM patients correlated with poorer PFI (Supplementary Figure S4).

3. 3 Mutations in NEK2

Mutations in NEK2 expression in cancer were analyzed by the cBioPortal online tool. Including all TCGA pan-cancer studies, a total of 32 studies and 10,967 samples, and we identified 88 mutation sites between amino acids 0 and 445, including 72 Missense, 9 Truncating, 6 Splice, and 1 Fusion, with R337CH as the most common mutation site (Fig. 3A). NEK2 mutations were most common in BRCA, UCEC, LIHC, CHOL and SKCM, and the predominant mutation types were Missense mutation, Amplification and Deep Deletion (Fig. 3B). Among the 32 cancers, there was a SHALLOW deletion in NEK2 mRNA expression in all cancers except LAML, THYM and UVM (Fig. 3C).

3. 4 Correlations between NEK2 expression and immune checkpoints

Immune checkpoint inhibitors (ICI) therapy works by blocking immunosuppressive checkpoints such as PD-1, CTLA-4, and TIGIT in order to activate immunostimulatory checkpoints such as CD226 and CD28 in effector T cells and myeloid cells[32], and this treatment has revolutionized the landscape of malignant tumors. Tumor mutational burden (TMB) is defined as the total number of base mutations per million cells in a tumor. TMB reflects the number of cancer mutations, which can stimulate the production of tumor-specific and highly immunogenic antibodies, and is a new target for predicting the efficacy of tumor immunotherapy[33]. Microsatellite instability (MSI) refers to any change in microsatellite length of a microsatellite due to insertion or deletion of repetitive units in a microsatellite in a tumor compared to normal tissues, and the phenomenon of new microsatellite alleles, which leads to impaired gene replication and tumor progression, and affects the prognosis of the tumor[34]. TMB and MSI are relevant biomarkers for ICI[35]. We investigated the correlation between NEK2 expression and TMB and MSI in all TCGA cancers. NEK2 was found in ACC, BLCA, BRCA, COAD, HNSC, KICH, KIRC, LAML, LGG, LUAD, LUSC, MESO, PAAD, PRAD, READ, SARC, SKCM, STAD, Expression in TGCT, THCA, and UCEC was positively correlated with TMB and negatively correlated with TMB in THYM (Fig. 4A). A significant positive correlation of NEK2 with MSI was observed in BLCA, COAD, ESCA, LIHC, MESO, READ, SARC, STAD, and UCEC (Fig. 4B).

Next, we extracted a total of 11 immune checkpoint genes (including PDCD1, CTLA4, C10orf54, HAVCR2, LAG3, TIGIT, SIRPA, BTLA, SIGLEC7, LILRB2, and LILRB4 from the TCGA dataset for the immune checkpoint gene correlation analysis. The results showed that most immune checkpoint genes were positively correlated with NEK2 in all tumor types (Fig. 4C).

3. 5 Correlations between NEK2 expression and immune infiltration

An increasing number of reports suggest that the tumor immune microenvironment plays a crucial role in tumorigenesis and progression[36]. Therefore, we further explored the pan-cancer relationship between the tumor microenvironment and NEK2 expression. The ESTIMATE algorithm was used to calculate stroma and immune cell scores for 33 cancers and analyse the relationship between NEK2 expression levels and the two scores. The stromal score reflected the proportion of stromal cells in tumor tissues; the immune score reflected the proportion of infiltrating immune cells in tumor tissues. The results showed a significant negative correlation between NEK2 expression and stromal score in BRCA, CESC, COAD, GBM, HNSC, LIHC, LUAD, LUSC, OV, PAAD, PRAD, SKCM, STAD, TGCT, THYM and UCEC. This suggests that in these types of tumours, high expression of NEK2 is associated with infiltration of stromal cells, leading to high tumour purity, whereas it is positively correlated in KIRC and THCA (Supplementary Fig. S5).In CESC, COAD, ESCA, GBM, LUAD, LUSC, OV, PAAD, SARC, SKCM, STAD, TGCT, and UCEC, NEK2 expression was significantly negatively correlated with immunological scores. This suggests that high NEK2 expression is associated with reduced immune cell infiltration in these tumours, resulting in high tumour purity, whereas a positive correlation was observed in KIRC and THCA (Supplementary Fig. S6).

Next, we investigated the relationship between NEK2 expression levels and the level of infiltration of 39 immune-related cells. The results showed that for most cancers, the immune cell infiltration level was significantly negatively correlated with NEK2 expression (Fig. 5). Among them, NEK2 expression was significantly positively correlated with the immune-related cell infiltration level of Common lymphoid progenitor in 29 cancer types, and T cell CD4 + Th2 in 31 cancer types. The expression level of NEK2 was positively correlated with THYM B cell, CD4 + T cell, and CD8 + T cell.

In addition, co-expression analysis was performed in 33 tumors to detect the relationship between NEK2 expression and immune-related genes. As visualized in the heatmap (Supplementary Figure S7), almost all immune-related genes were co-expressed with NEK2, and most immune-related genes were positively correlated with NEK2 in all types of tumors.

3. 6 The expression pattern of NEK2 at single-cell levels

Single-cell transcriptome sequencing is an important method for studying different types of cancer, immune cells, endothelial cells and stromal cells[37].Next, we explored the relationship between NEK2 expression and different functional states of tumors using the CancerSEA tool, which allowed us to analyse the correlation between NEK2 and multiple functional states of 16 cancers at the single-cell level. NEK2 was positively correlated with cellcycle, invasion, and proliferation in most cancers and negatively correlated with angiogenesis, apoptosis, inflammation, and quiescence (Supplementary Figure S8), which is consistent with previous studies linking NEK2 expression with tumor functional status[12].The results showed a positive correlation between NEK2 expression and cellcycle, invasion and proliferation, and a negative correlation between NEK2 expression and angiogenesis, apoptosis, inflammation and quiescence (Supplementary Figure S8 ). We then explored the correlation between NEK2 and specific tumor functional states. The results showed that NEK2 was positively correlated with proliferation, cellcycle, invasion and EMT and negatively correlated with inflammation and hypoxia in Acute myeloid leukemia (AML). NEK2 was positively associated with cell cycle, proliferation and DNA damage and DNA repair in colorectal cancer (CRC) and LUAD. NEK2 was negatively correlated with DNA repair, DNA damage, apoptosis, EMT and metastasis in uveal melanoma (UM) (Supplementary Figure S9). All of the above data suggest that NEK2 plays an important role in the biological processes of tumorigenesis and progression.

3. 7 PPI network and GO and KEGG enrichment analysis of NEK2 and related genes

To further elucidate the biological function of NEK2 in tumors, the 100 most relevant genes for NEK2 were obtained from the GEPIA2 database (Supplementary Table S1). These 100 NEK2-associated genes were generated as a PPI network on the STRING website (Fig. 6A). Figure 6 shows the results of GO analysis and KEGG pathway analysis, which includes three categories: biological pathways (BP), cellular components (CC) and molecular functions (MF). GO analysis (Fig. 6B) indicated that NEK2-related genes may be involved in biological pathways such as“organelle fission”, “nuclear division”, “mitotic nuclear division”and“chromosome segregation".NEK2-related genes may be involved in cellular components such as “spindle”,“chromosomal region ”,“chromosome, centromeric region”and“condensed chromosome, centromeric region”. NEK2-related genes may be involved in molecular functions such as “tubulin binding”,“microtubule binding”,“cytoskeletal motor activity”and“microtubule motor activity”. KEGG pathway analysis (Fig. 6C) indicated that NEK2-associated genes may be associated with "Cell cycle", "Oocyte meiosis", " Progesterone-mediated oocyte maturation", "Cellular senescence" and "p53 signalling pathway".

Subsequently, we applied GSEA to determine the biological function of NEK2 in tumors. NEK2 is significantly associated with cell cycle signalling pathways,such as Cell Cycle Checkpoints and Mitotic Spindle Checkpoints. These results suggest a molecular mechanism of NEK2 in tumorigenesis(Supplementary Figure S10).

3. 8 Analysis of drug sensitivity to the NEK2 gene

Finally, we used the CellMiner database to elucidate the potential correlation between NEK2 expression and pan-cancer drug sensitivity. NEK2 expression was positively correlated with the drug sensitivity of B-7100, BMS-754807, CCT-128930, Dexrazoxane, ENMD-2076, Epothilone B, Imiquimod, LEE- 011, LY-2835219, maritoclax, Nitrogen mustard and VT-464 were positively correlated with drug sensitivity(Fig. 7) .

3.9 NEK2 regulates the proliferation of cervical cancer cells

To determine the expression levels of NEK2 in tumor and normal cell lines, immunohistochemical, Western blot and RT-qPCR analyses were performed. The results showed that the expression level of NEK2 in cervical cancer tissues and cell lines was higher than that in normal cells (Figs. 8A, 8B, 8C). Based on these results, the function of NEK2 in Hela and Siha cell lines was further investigated. CCK-8 assay showed that cell proliferation was inhibited in the JH295 group compared to the control group (Fig. 8D). Therefore, these results suggest that NEK2 is highly expressed in cervical cancer and promotes the proliferation of cervical cancer cells.

NEK2 is a core protein of centrosomes and is required for centrosome segregation at the onset of mitosis [38]. A growing number of studies have shown that NEK2 plays a crucial role in the development of a variety of solid tumors[12]. However, the role of NEK2 in pan-cancer has not been fully characterized. In this study, we used multiple bioinformatics methods to firstly reveal the aberrant expression of NEK2 in human cancers, and then explored the diagnostic and prognostic value of NEK2 in various cancers. Secondly, we also analysed the gene mutation level of NEK2 in pan-cancer. In addition, the correlation between NEK2 expression and the level of immune cell and stromal cell infiltration was investigated, and the potential function of NEK2 at the single-cell level was determined. Finally, we implemented functional enrichment analysis to identify potential mechanisms by which NEK2 affects cancer pathogenesis.

Our analysis combining TCGA and GTEx database data showed that NEK2 was overexpressed in most cancers compared to neighbouring normal tissues. We used KM survival analysis to assess the prognostic value of NEK2 in 33 cancers. For OS, NEK2 exhibited high HR in multiple cancer types, including ACC (HR = 3.256, 95% CI 2.182–4.859, P < 0.001), KICH(HR = 3.068, 95% CI 1.818–5.177,P < 0.001),KIRC(HR = 2.314, 95% CI 1.843–2.905, P < 0.001), KIRP(HR = 3.114, 95% CI 2.321–4.178, P < 0.001), LGG (HR = 1.848, 95% CI 1.556–2.195, P < 0.001), LIHC(HR = 1.415, 95% CI 1.193–1.678, P < 0.001), LUAD(HR = 1.276, 95% CI 1.117–1.457, P < 0.001), MESO(HR = 2.625, 95% CI 1.894–3.640, P < 0.001), PAAD(HR = 1.703, 95% CI 1.285–2.257, P < 0.001), PCPG(HR = 4.529, 95% CI 1.591–12.896, P = 0.005) and UVM (HR = 3.233, 95% CI 1.239–8.440, P = 0.017).Therefore, we deduced that NEK2 might be a risk factor for the above cancers but not for the above cancers in THYM(HR = 0.531, 95% CI 0.305–0.927, P = 026) was a protective factor.We also analysed NEK2 in 33 cancers by DSS, DFI and PFI. We further found that high expression of NEK2 was associated with poorer DSS, PFI and DFI in multiple tumors. This suggests that NEK2 has a better prognostic predictive value in a variety of cancers. Previous studies have linked NEK2 to poor prognosis in various cancers, e. g., NEK2 promotes gastric cancer progression by activating the AKT-mediated signalling pathway [39]; NEK2 inactivates the Hippo pathway to advance the proliferation of cervical cancer cells by cooperating with STRIPAK complexes [40]; Nek2 expression is up-regulated in many breast cancer cells, and silencing of Nek2 can inhibit cell proliferation, invasion and metastasis by regulating ERK/MAPK signalling [41]. Based on these results, NEK2 may be a meaningful prognostic biomarker for tumor patients and provide new targeted therapeutic strategies for various tumor treatments.

Cancer cells develop from genetic mutations in individual somatic cells. The accumulation of somatic mutations intensifies with age and the body's risk of developing cancer increases [42]. Tumor-specific gene mutations can be therapeutic targets for immunotherapy and can also predict the ability to respond to immune checkpoint inhibitors. Relevant studies have shown that the mutation rate of NEK family genes is relatively high in non-small-cell lung carcinoma, in which NEK2 is over expressed in LUSC patients with a mutation rate as high as 53 percent[43].However, there are no systematic studies on NEK2 mutations in human pan-cancer.The human NEK2 gene is located on chromosome 1q32. 2-1q41. From the results interpreted in the cBioPortal platform, we know that NEK2 is mutated in most forms of tumors. NEK2 has a high frequency of mutations in BRCA, such as Missense mutation and Amplification, and the major mutation site in NEK2 is R337CH. With Immune checkpoint inhibitors (ICI) therapies have significantly improved ORR and OS in patients with advanced malignancies; however, despite this, tumor resistance to ICIs and ICI-mediated toxicity have hampered their clinical application [44]. Therefore, there is an urgent need to find accurate and reliable biomarkers to screen patients for potential benefits of immunotherapy. In recent years, TMB has received significant attention in ICI-related biomarker studies, and a significant association between high TMB and ICI response has been demonstrated in a variety of cancer types, such as breast [45], colon [46], and prostate [47]: on the other hand, high MSI is associated with an increased density of mutations in tumor DNA, which is associated with a higher incidence of TMB and neoantigen generation. Tumors with high MSI (MSI-H), TMB, or neoantigens are more likely to be infiltrated by immune cells, which promotes a stronger anti-tumor immune response. In the present study, we found that high expression of NEK2 was positively correlated with TMB and MSI in a variety of tumors. Related studies have shown that NEK2 phosphorylates PD-L1 to maintain its stability, leading to poor efficacy of immunotherapy for PD-L1-targeted tumors [14], which reveals that NEK2 expression may have immune checkpoint genes associated with it. In our current study, we found that NEK2 was positively associated with PDCD1, CTLA4, C10orf54, HAVCR2, LAG3, TIGIT, SIRPA, BTLA, SIGLEC7, LILRB2, and LILRB4 checkpoint markers. These results suggest that NEK2 may be involved in immune escape in human cancer immunotherapy. NEK2 may be a biomarker for predicting the efficacy of ICI therapy in cancer patients.

Constant interactions between tumor cells and the tumor microenvironment play a decisive role in tumorigenesis, development, metastasis, and therapeutic response. The tumor microenvironment has great clinical research value as a therapeutic target for cancer[48]. To further investigate the potential value of NEK2 in pan-cancer, we explored the correlation between NEK2 expression and tumor microenvironment. We could find that NEK2 expression was mostly negatively correlated with the level of immune cell infiltration. In addition, there was a significant positive correlation between NEK2 and immune-related genes. It has been shown that NEK2 is a novel regulator of B cell development and immunological response [49]; NEK2 also induces M2-like polarization of macrophages [50]; and high expression of NEK2 inhibits T cell immunity in multiple myeloma [51]. Therefore, it is reasonable to speculate that NEK2 has potential value as an effective target for immunotherapy, and its expression may regulate the level of tumor immune cell infiltration and ultimately affect the prognosis of tumor patients. However, more preclinical and clinical trials are needed to explore the relationship between NEK2 expression and the tumor immune microenvironment.

Undoubtedly, NEK2 plays a crucial role in tumorigenesis and is an effective target for cancer therapy, but most of the current studies on NEK2 have focused on individual cancers[52], and there has been no systematic report on its mechanism of action in pan-cancer. Single cell RNA sequencing (scRNA-seq) has emerged as a state-of-the-art method to reveal the heterogeneity and complexity of RNA transcripts within a single cell, as well as to unravel the different cell types and functional compositions [53]. Using CancerSEA, we found that NEK2 is significantly associated with many biological behaviours of cancer, such as cellcycle, invasion, proliferation, angiogenesis, apoptosis, inflammation and quiescence. This is consistent with previous findings: increased NEK2 promotes cell growth and siRNA targeting NEK2 inhibits proliferation in cholangiocarcinoma[54]; NEK2 expression is closely associated with the proliferation marker Ki-67 in a variety of malignant tumours[55, 56]; Xia et al. reported that NEK2 plays an important role in myeloma and lung cancer metastasis by inducing nuclear accumulation of β-catenin[57];NEK2 overexpression induces triple-negative breast cancer cells epithelial-to-mesenchymal transition (EMT) [58]. To explore the biological functions of NEK2, we performed GO and KEGG analysis on 100 NEK2-associated genes. We found that "Cell cycle" and "p53 signaling pathway" may be the key mechanisms of NEK2 involved in pan-cancer. However, further studies are needed to determine the mechanism of NEK2 and these signaling pathways, and to explore whether it can be used as an indicator for tumor diagnosis and prognosis.

Finally, NEK2 is highly expressed in human tissues and cells of cervical cancer through in vitro experiments, and NEK2 inhibitor JH295 can inhibit the proliferation of cervical cancer cells. However, the results of this study have not been further validated in vivo. In addition, epidemiological studies on proteomics and biomarker databases have been increasing in recent years, and their ability to extensively quantify the large amount of traditional proteomics data provides a platform for efficient processing of cancer-related protein data[59, 60]. Therefore, we will focus on further validating the function of NEK2 in pan-cancer by cell and animal experiments and combining with the latest protein databases in our future studies.

This study explored NEK2 expression, prognostic significance, gene mutations, MSI, TMB, immune checkpoints, and associated signaling pathways in pan-cancer through comprehensive bioinformatics analysis. The findings revealed that NEK2 overexpression holds diagnostic value across various cancer types and may serve as a prognostic and immune-related biomarker. By examining NEK2's role in tumorigenesis from different angles, this research lays the groundwork for future studies on its specific mechanisms in cancer progression and therapy.

TCGA The Cancer Genome Atlas

GTEx Genotype-tissue expression

HPA the Human protein atlasv

TMB Tumor mutational burden

TME Tumor microenvironment

MSI Microsatellite instability

OS Overall survival

DSS Disease specific survival

PFI Progression-free interval

DFI Disease-free interval

KM Kaplan-Meier

GO Gene Ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

GSEA Gene set enrichment analysis

PPI Protein-protein interaction

ACC Adrenocortical carcinoma

BLCA Bladder Urothelial Carcinoma

BRCA Breast invasive carcinoma

CESC Cervical squamous cell carcinoma and endocervical adenocarcinoma

CHOL Cholangiocarcinoma

COAD Colon adenocarcinoma

DLBC Lymphoid Neoplasm Diffuse Large B-cell Lymphoma

ESCA Esophageal carcinoma

GBM Glioblastoma multiforme

HNSC Head and Neck squamous cell carcinoma

KICH Kidney Chromophobe

KIRC Kidney renal clear cell carcinoma

KIRP Kidney renal papillary cell carcinoma

LAML Acute Myeloid Leukemia

LGG Brain Lower Grade Glioma

LIHC Liver hepatocellular carcinoma

LUAD Lung adenocarcinoma

LUSC Lung squamous cell carcinoma

MESO Mesothelioma

OV Ovarian serous cystadenocarcinoma

PAAD Pancreatic adenocarcinoma

PCPG Pheochromocytoma and Paraganglioma

PRAD Prostate adenocarcinoma

READ Rectum adenocarcinoma

SARC Sarcoma

SKCM Skin Cutaneous Melanoma

STAD Stomach adenocarcinoma

TGCT Testicular Germ Cell Tumors

THCA Thyroid carcinoma

THYM Thymoma

UCEC Uterine Corpus Endometrial Carcinoma

UCS Uterine Carcinosarcoma

UVM Uveal Melanoma

Funding Statement

This research was funded by the National Natural Science Foundation of China（grant number 82301828)、Natural Science Foundation of Hubei Province of China (grant number 2021CFB430) and Special Fund for Basic Scientific Research Expenses of Central Universities（grant number 2042022kf1110）.

Author Contributions

L.Z. and Y.L. wrote the article; W.L., T.L. and J.D. processed the data analysis; F.S. revised the final manuscript. All authors contributed to the study conception and design.All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors certify that all original data in this study are available from the article, supplementary materials, and public databases. The raw data and R package for this paper have been deposited in the Mendeley Data public database at the link https://data.mendeley.com/datasets/bn23yc7cwv/1.

Acknowledgments

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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You are reading this latest preprint version

NEK2 is a potential pan-cancer biomarker and immunotherapy target

Status:

Version 1

Abstract

Background.

Methods.

Results.

Conclusion.

Figures

1. Introduction

2. Materials & Methods

2. 1 NEK2 mRNA and protein expression

2. 2 Prognostic analysis

2. 3 Mutations in the NEK2 gene

2. 4 Immune infiltration analysis

2.5 Single-Cell Functional Analysis

2. 6 Functional analysis and protein-protein interaction network

2. 7 Analysis of drug sensitivity

2. 8 Patients and clinical specimens

2.9 Cell culture and treatment

2.10 Western blot

2.11 RNA extraction and qRT-PCR

2.12 Cell proliferation

3. Results

3. 1 Pan-cancer analysis of NEK2 expression

3. 2 The correlation between NEK2 expression and adverse consequences of cancer

3. 3 Mutations in NEK2

3. 4 Correlations between NEK2 expression and immune checkpoints

3. 5 Correlations between NEK2 expression and immune infiltration

3. 6 The expression pattern of NEK2 at single-cell levels

3. 7 PPI network and GO and KEGG enrichment analysis of NEK2 and related genes

3. 8 Analysis of drug sensitivity to the NEK2 gene

3.9 NEK2 regulates the proliferation of cervical cancer cells

4. Discussion

5. Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1