
==== Front
Medicine (Baltimore)
Medicine (Baltimore)
MD
Medicine
0025-7974
1536-5964
Lippincott Williams & Wilkins Hagerstown, MD

39029088
MD-D-23-08194
00090
10.1097/MD.0000000000038961
3
5200
Research Article
Observational Study
Prognostic value and immune infiltration of the NEK family in clear cell renal cell carcinoma
Zhu Yingli BM 1486981123@qq.com
a
Lin Jianfan BM 1107019587@qq.com
a
Li Yufei BM 1770335341@qq.com
a
https://orcid.org/0000-0003-2969-8329
Luo Zuojie PhD a*
a Department of Endocrinology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
* Correspondence: Zuojie Luo. Department of Endocrinology, the First Affiliated Hospital of Guangxi Medical University, No. 6 ShuangYong Road, Nanning 530021, China (e-mail: zluo888@163.com).
19 7 2024
19 7 2024
103 29 e3896120 9 2023
22 4 2024
23 5 2024
Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.
2024
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.

Clear cell renal cell carcinoma (ccRCC) is a fatal urological malignancy. Members of the never-in mitosis gene A (NIMA)-related kinase (NEK) family have been found to participate in the progression of several cancers and could be used as target genes to treat corresponding diseases. Nonetheless, the prognostic value and immune infiltration levels of NEK family genes in ccRCC remain unknown. The GSCA, TIMER, and GEPIA databases were utilized to examine the differential expression of NEK family members in ccRCC, and the Kaplan–Meier plotter was utilized to analyze the prognosis. The STRING database was used to construct a protein-protein interaction network. Analysis of function was performed by the Sangerbox tool. In addition, the relationship between NEK family genes and immune cells was explored using the TIMER and TISIDB databases. Finally, we used quantitative real-time PCR (qPCR) and immunohistochemistry (IHC) for experimental verification. Transcriptional levels of NEK2, NEK3, NEK5, NEK6, and NEK11 significantly differed between ccRCC and normal tissues. Moreover, there was a significant correlation between NEK1, NEK2, NEK4, NEK8, NEK9, and NEK10 and their clinicopathological stages in patients with ccRCC. Based on survival analysis, ccRCC patients with high transcriptional levels of NEK2, NEK3, NEK8, and NEK10 and low transcriptional levels of NEK1, NEK4, NEK5, NEK6, NEK7, NEK9, NEK11 had shorter survival times. Additionally, a significant relationship was observed between NEK family members and immune cell infiltration, immune cell markers, and immune subtypes. These results indicate that NEK family members are significantly differentially expressed in ccRCC, and a significant correlation exists between the NEK family and prognosis and immune infiltration. NEK family members may act as therapeutic targets and prognostic indicators in ccRCC.

clear cell renal cell carcinoma
immune infiltration
NEK family
prognosis
OPEN-ACCESSTRUE
==== Body
pmc1. Introduction

Renal cell carcinoma (RCC) is a common lethal urinary tract tumor associated with morbidity, which is increasing annually. Approximately 430,000 cases of RCC and over 179,000 RCC-related deaths are reported yearly.[1] Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of RCC with the highest severity, accounting for 70% of all RCC cases.[2,3] At present, patients diagnosed with early ccRCC are mainly treated with surgical resection; however, 30% to 35% of patients still suffer from distant metastases. Immunotherapy and targeted drug therapy have emerged as important treatment options for advanced and metastatic ccRCC.[4,5] However, the 5-year survival rate for patients with ccRCC is only 10%; ccRCC treatment still faces a myriad of challenges,[6] and only a few prognostic biomarkers are used in clinics. Hence, identifying more immunotherapeutic targets and prognostic markers is urgent to better understand the pathogenesis of ccRCC.

The never-in mitosis A-related kinase (NEK) family also known as the NIMA kinase family contains 11 members (NEK1-NEK11), which are highly homologous in structure.[7] Previous studies have demonstrated that NEK family members are implicated in several biological processes, including the cell cycle, cell damage repair, cell division, apoptosis, and cilia formation.[8] NEK1 acts as a central signal to mediate mitochondrial function and DNA repair pathways.[9] In RCC, NEK1 may influence the sensitivity of DNA damage therapy.[10] NEK2 was reported to regulate B-cell and T-cell immune responses.[11] NEK6 participates in the castration resistance of prostate cancer (PC) by stimulating cytoskeleton, differentiation, and immune signaling pathways.[12] There is evidence showing that NEK family members can be used as cancer biomarkers.[13] Nevertheless, the pathogenesis, development, and prognostic value of the NEK family in ccRCC remain elusive.

Herein, we performed a comprehensive analysis of transcriptional and protein levels of NEKs in ccRCC patients using bioinformatics and explored their prognostic value. Furthermore, constructing a protein-protein interaction (PPI) network, enrichment analysis, and immune infiltration analysis of NEKs were conducted based on multiple public databases. This study may provide insight into the clinical diagnosis and precise treatment of ccRCC.

2. Methods

2.1. Gene Set Cancer Analysis (GSCA) database

GSCA[14] is an online database for multiple genetic analyses. A total of 11160 samples were included in this dataset from The Cancer Genome Atlas (TCGA) and Genomics of Drug Sensitivity in Cancer databases. The GSCA is divided into 4 modules, including “expression,” “immune,” “mutation,” and “drug.” Here, we explored the differential expression of NEK family members between normal and cancerous samples using the “expression module.”

2.2. Gene Expression Profiling Interactive Analysis (GEPIA) database

GEPIA[15] is a large gene expression profiling database that can match TCGA and Genotype-Tissue Expression datasets. GEPIA was utilized to analyze messenger RNA (mRNA) levels and pathological stages of NEK family members between ccRCC and normal tissues.

2.3. Kaplan–Meier (KM) plotter database

The KM plotter database is used for survival analysis. It integrates multiple datasets, including Gene Expression Omnibus and TCGA. The KM plotter database was utilized to explore the correlation between the gene expression of NEK family members and overall survival (OS) and disease-free survival (DFS) in ccRCC.

2.4. cBioportal database

As a visual online tool, the cBioPortal database[16] contains nearly 200 cancer genome research data. The cBioPortal database was used to investigate the genetic variation of NEK family members in ccRCC. Meanwhile, genes that were co-expressed with NEK family genes in ccRCC were mined.

2.5. Search Tool for Retrieval of Interacting Genes database

STRING[17] is widely studied as a PPI database covering a large number of species and information. A visual association network diagram of NEK family members and their co-expressed genes was constructed based on the STRING database.

2.6. Functional enrichment analysis

Sangerbox, as an integrated visual analysis tool, has Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment. Here, the Sangerbox tool was used to draw a functional enrichment bubble map for the NEK members and co-expressed genes.

2.7. Tumor Immune Estimation Resource (TIMER) database

Based on the TCGA dataset, the TIMER database[18] provides a detailed analysis of tumor immune infiltration. The “Gene Module” was used to evaluate the association between NEK family genes and the abundance of immune infiltration in ccRCC, including B cells, CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells. The “Gene Comparison Module” was employed to further examine the differential expression of NEK family members in tumors and adjacent normal tissues. The “survival module” was used to explore clinical correlations in the immune subpopulations of ccRCC. Furthermore, the “Correlation Module” was utilized to validate the correlation between NEK family expression and immune cell markers in ccRCC.

2.8. TISIDB database

TISIDB[19] is an integrated repository portal for tumor-immune system interactions, containing 43 cancer types with over 39,706 samples and 546 datasets. It is divided into several modules, the most widely used of which are “Immunotherapy,” “Lymphocyte,” “Immunomodulator,” “Chemokine,” and “Subtype.” Here, we used the “Subtype module” to explore the distribution of NEK family members among immune subtypes. We also applied the “Immunomodulator module” to explore the correlation between the NEK family and immune checkpoints.

2.9. Patients and tissue specimens

All clinical samples were collected from the First Affiliated Hospital of Guangxi Medical University. From January 2024 to April 2024, tissue specimens from 16 patients were collected. All samples were collected with the consent of the patients. This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (2024-E014-01).

2.10. Quantitative real-time PCR

Total RNA was extracted using Trizol (TaKaRa, Chicago) and RNA concentration and purity were determined by NanoDrop spectrophotometer. Then, Reversed into cDNA by PrimeScript RT Master Mix kit (Takara). All mRNA levels were assessed using the SYBR Green PCR Mix (SEVEN, Beijing, China). All experiments were analyzed with the 2−ΔΔCT method and β-actin primers were used as control. Table 1 lists the primer sequences.

Table 1 Primer sequence.

Gene name	Forward primer	Reverse primer	
NEK2	GAGAAGAAAGATTGGAGCAGAAAG	TCATTTGTAGCACCAGCTTCTGT	
NEK3	TGTGATGGAGGGGATCTAATGC	CTCCAAGGCACATTTGGGTA	
NEK5	AAACTTCACTGCCCAGAAGCAG	CTTGGTCTTCCCTTCCTGACGAC	
NEK6	GTCTGCTGTACGAGATGGCA	TCCGATGTCAGGTCTCTGGT	
NEK11	GAAAGCCAAACGAGGAGAGGA	CTCGGCCCTCACAGTACTCC	
β-actin	CCAACCGCGAGAAGATGACC	GAGTCCATCACGATGCCAGT	

2.11. Immunohistochemistry analysis (IHC)

We measured NEK2, and NEK6 protein expression by IHC in ccRCC tissue and adjacent normal kidney tissue. Immunohistochemical procedures were performed in strict accordance with the kit instructions. Paraffin-embedded tissue sections were degreased by soaking xylene for 10 minutes, then hydrated with alcohol of different concentrations, repaired by antigen in EDTA solution, boiled in a pressure cooker, and cooled to room temperature naturally. Then add 3% H2O2, wash with phosphate-buffered saline, and press NEK2 primary antibody [1:200, Rabbit, Cat No.BS7552; Biogot Company, Nanjing, China], NEK6 primary antibody [1:100, Mouse, Cat No.MA03740; Boster Company, California], then placed in the refrigerator at 4°C overnight incubation, placed in the incubator at 37°C for 1 hour, dropped the reaction enhancement solution, incubated for 20 minutes, and then dropped the goat anti-rabbit IgG polymer with enhanced enzyme target. Incubate for 20 minutes. DAB color-developing liquid drops were added to the sample, and then hematoxylin was re-dyed and blue was reversed. Finally, it is sealed with a neutral resin. We calculated the positive staining signal in each field of view by using ImageJ software.

2.12. Statistical analysis

Use GraphPad Prism 8.0 (La Jolla) for data statistics and plot the results. Using the log-rank test, survival analysis was performed. Spearman or Pearson correlations were used to analyze the correlation between gene expression and immune checkpoints, and immune infiltration. Cox proportional risk models were used to the association between gene expression and immune cells. The difference is statistically significant when a P value is less than .05.

3. Results

3.1. The mRNA and protein expression of NEKs in ccRCC

The expression level of NEK mRNA in different tumor types was conducted using the GSCA database. The results showed that NEKs were highly expressed in 5 types of cancer, including colon adenocarcinoma, ccRCC/kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma, and stomach adenocarcinomas. However, NEKs were lowly expressed in kidney chromophobe, and thyroid carcinoma (Fig. 1A). Next, the TIMER database was employed to assess differential expression among 11 members of NEKs (NEK1-NEK11) in different tumors and normal tissues. It was found that the transcriptional levels of NEK2 (P < .001), NEK3 (P < .001), NEK5, NEK6 (P < .001), and NEK11 (P < .01) were significantly increased in ccRCC, while those of NEK1 (P < .001), NEK4 (P < .001), NEK7 (P < .001), NEK8 (P < .001), NEK9 (P < .001), and NEK10 (P < .001) were significantly decreased (Fig. 1B).

Figure 1. The expression level of NEK mRNA in different tumor types. (A) The mRNA expression levels of NEK genes in different tumors were explored through the GSCA database. (B) The transcription levels of NEKs in tumor and normal tissues were determined using the TIMER database. ***P < .001; **P < .01; *P < .05. GSCA = Gene Set Cancer Analysis, mRNA = messenger RNA, NEK = never in mitosis A-related kinase, TIMER = Tumor Immune Estimation Resource.

Subsequently, the levels of NEKs in ccRCC and non-tumor were further validated using the GEPIA database. It was found that GEPIA and TIMER databases had similar results (Fig. 2).

Figure 2. The expression levels of NEKs in ccRCC tissues (n = 523) and normal kidney tissues (n = 100) from the GEPIA database. *P < .05. ccRCC = clear cell renal cell carcinoma, GEPIA = Gene Expression Profiling Interactive Analysis, NEK = never in mitosis A-related kinase.

3.2. Correlation between NEKs and clinicopathological characteristics in ccRCC patients

The association between NEK family genes and pathological staging in ccRCC patients was assessed using the GEPIA dataset. Pathological staging was significantly associated with expression of NEK1 (P = .000655), NEK2 (P = 4.12E-6), NEK4 (P = .00037), NEK8 (P = .0256), NEK9 (P = .00416), and NEK10 (P = .0486). Our data also revealed that NEK1, NEK4, and NEK9 gradually decreased while NEK2 gradually increased with the progression of tumor stages I, II, III, and IV while NEK2 gradually increased (Fig. 3).

Figure 3. The pathological stage of NEKs in ccRCC patients was displayed from the GEPIA database. ccRCC = clear cell renal cell carcinoma, GEPIA = Gene Expression Profiling Interactive Analysis, NEK = never in mitosis A-related kinase.

3.3. Prognostic values of NEKs in ccRCC

Differential expression of NEKs in ccRCC was used as a prognostic factor to evaluate their prognostic value. The KM plotter was applied to analyze the relationship between NEK family members and survival time in ccRCC patients. We found that the mRNA levels of NEK family members were significantly associated with the prognosis of ccRCC patients. The high expression of NEK1 (P = 3.5e-05), NEK4 (P = .00073), NEK5 (P = .048), NEK6 (P = 7.2e-06), NEK7 (P = .00051), NEK9 (P = .0092), and NEK11 (P = .00017) were associated with longer OS in ccRCC patients. In contrast, the high expression of NEK2 (P = 2.4e-09), NEK3 (P = .00053), NEK8 (P = .0014), and NEK10 (P = .00051) were associated with shorter OS. Additionally, we also found that the expression of NEK3 (P = .02), NEK4 (P = .034), NEK5 (P = .01), NEK6 (P = .041), and NEK9 (P = .028) were associated with DFS in ccRCC patients. The high levels of NEK3, NEK4, and NEK9 were correlated with better DFS, while patients with the high levels of NEK5 and NEK6 had worse DFS. All the results were statistically significant (P < .05) (Fig. 4).

Figure 4. The survival curves of NEK family genes in ccRCC were analyzed by the Kaplan–Meier Plotter database. Including the association between NEK family genes and OS in ccRCC patients and the association between NEK family genes and DFS in ccRCC patients. ccRCC = clear cell renal cell carcinoma, DFS = disease-free survival, NEK = never in mitosis A-related kinase, OS = overall survival.

3.4. Genetic variations of NEKs in ccRCC patients

The cBioPortal database was employed to explore the frequencies and variety of mutations in NEK family members. NEK1-NEK11 were altered by 0.9%, 0.2%, 0.9%, 11%, 0.7%, 0%, 0.2%, 0.7%, 1.1%, 13%, and 1.1%, respectively. NEK4 and NEK10 had the highest mutation frequency, while no mutation was found in NEK6. The main mutation type of NEK4, NEK9, and NEK10 was “deep deletion,” and NEK11 was dominated by amplification (Fig. 5A).

Figure 5. Genetic alteration and the relative expression levels of NEK family members in ccRCC patients. (A) Genetic alterations of NEKs in ccRCC patients. (B) The relative expression levels of NEK family in ccRCC tissues. ccRCC = clear cell renal cell carcinoma, NEK = never in mitosis A-related kinase.

Furthermore, a relative-level analysis of NEK family members was conducted in ccRCC tissues. The results showed that the levels of NEK6, NEK7, and NEK9 were the highest among all NEK family members (Fig. 5B).

3.5. Functional enrichment analysis of NEKs in ccRCC

First, the cBioPortal database was used to identify a series of genes that were co-expressed with NEK genes in ccRCC. NEK1 was co-expressed with TRAPPC11, CLOCK, CCDC186, DNAJB14, ZFP91, ASXL2, STRN, ANKRD17, LMBRD2, and TRIM44. NEK2 was co-expressed with TOP2A, BUB1B, TPX2, DEPDC1, CCNB2, HMMR, KIF14, CDK1, MELK, and NCAPG. NEK3 was co-expressed with THSD1P1, ARGLU1, CEP95, PRPF3, MTRF1, N4BP2L2, TRMT10B, CLK1, RRN3P1, and PNISR. NEK4 was co-expressed with CAPN7, RALGAPB, PDCD6IP, ARFGEF2, STT3B, METTL26, SUZ12, RFXANK, MED13, and ITCH. NEK5 was co-expressed with ATP7B, DYNC2H1, SPAG9, HEATR5B, ATP11A, MYO9A, WDR35, SEC13, AFF3, and ARHGAP32. NEK6 was co-expressed with ACLY, ABCA1, TLR3, MFSD9, GPR107, CCDC12, TRIM14, RASEF, TGOLN2, and KIAA1958. NEK7 was co-expressed with CDC73, OSBPL8, ZBTB41, POLR2M, SDE2, ATF1, CTDSPL2, TSNAX, TOR1AIP1, and MFSD14A. NEK8 was co-expressed with RECQL5, NEURL4, MED24, CDK5RAP3, TUBG2, SLC26A11, COG1, ASB16-AS1, TRPV1, and SAFB2. NEK9 was co-expressed with ATG2B, DCAF5, ZFYVE26, NEMF, RCOR1, RALGAPA1, DICER1, BCL2L2, CDC42BPB, and KIAA0586. NEK10 was co-expressed with SLC4A7, ODF2L, SPAG17, ZNF621, FAM227A, EFHC1, PCNX2, KATNIP, ZFC3H1, and ILVBL. NEK11 was co-expressed with SNX4, IFT122, EFCAB12, IFT57, ASTE1, ZNF396, PLS1, CFAP91, DNAI4, and ACAD9.

Next, we imported NEK family genes and their co-expressed genes into the STRING online tool to map the PPI network and explored the interactions between NEK family genes and co-expressed genes. Image visualization was performed using Cytoscape software (Fig. 6A). Meanwhile, the cytoHubba toolkit in Cytoscape software was employed to screen the top 10 hub genes for PPI, including BUB1B, TPX2, NEK2, TOP2A, KIF14, NCAPG, HMMR, MELK, CDK1, and CCNB2. The results are shown in Figure 6B.

Figure 6. PPI network map and functional enrichment diagram of NEKs in ccRCC. (A) A PPI network between NEK family genes and 110 co-expressed genes was constructed by Cytoscape. (B) The 10 hub genes were identified through the cytoHubba toolkit in Cytoscape. (C–E) GO enrichment dot bubble in biological process terms, cellular component terms, and molecular function terms. (F) KEGG enrichment dot bubble. ccRCC = clear cell renal cell carcinoma, NEK = never in mitosis A-related kinase, PPI = protein-protein interaction.

Finally, Gene Ontology and KEGG pathway enrichment analyses were performed for NEK family genes and their co-expressed genes using the Sangerbox online tool. It was found that biological processes (BP) were mainly enriched in the cell cycle process, cell division, mitotic cell cycle process, and endomembrane system organization (Fig. 6C). Cellular components (CC) were mainly associated with the intracellular nonmembrane-bounded organelle, nonmembrane-bounded organelle, cytoskeletal part, cytoskeleton, and microtubule cytoskeleton (Fig. 6D). Molecular functions were mainly related to anion binding, purine ribonucleoside triphosphate binding, purine ribonucleotide binding, ATP binding, and adenyl ribonucleotide binding (Fig. 6E). The KEGG results showed that these genes were involved in various pathways, including endocytosis, cell cycle, p53 signaling pathway, platinum drug resistance, and progesterone-mediated oocyte maturation (Fig. 6F).

3.6. Relationship between NEKs and immune cell infiltration in ccRCC patients

A correlation between the expression of NEKs and the infiltration of immune cells was examined using the TIMER database. Results revealed a significant correlation between B cells, CD8+, CD4 + T cells, macrophages, neutrophils, and dendritic cell infiltration and all NEK family genes except for NEK8. NEK1, NEK2, NEK4, NEK6, and NEK7 were significantly positively correlated with the 6 immune cells (all P < .001). In addition, NEK3 showed a significant positive correlation with CD8 + T cells (P = 1.10e-02), CD4 + T cells (P = 2.33e-13), Macrophage (P = 4.68e-03), and Neutrophil (P = 5.66e-10) infiltration. NEK10 also showed a significant positive correlation with B cell (P = 9.92e-04), CD4 + T cells (P = 2.17e-09), Macrophage (P = 1.36e-07), and Neutrophil (P = 2.12e-11) infiltration, and NEK8 was only positively correlated with CD4 + T cells (P = 1.65e-06) (Fig. 7). Next, the “survival module” was used to explore clinical correlations in the immune subpopulations of ccRCC. The clinical outcomes of patients with ccRCC were significantly associated with CD8 + T cells (P < .001), CD4 + T cells (P = .036), Dendritic (P = .029), NEK2 (P < .001), NEK3 (P = .029), NEK5 (P = .022), NEK6 (P = .016), and NEK9 (P = .019) after adjusting for confounding factors (Table 2).

Table 2 Cox proportional risk models of NEK family members and 6 immune cells in 415 ccRCC patients using GEPIA database.

Description	Coef	HR	95% CI l	95% CI u	P	
B cell	−1.099	0.333	0.006	18.565	.59	
CD8 + Tcell	−4.074	0.017	0.002	0.140	***	
CD4 + Tcell	−4.418	0.012	0.000	0.751	*	
Macrophage	−0.917	0.400	0.025	6.401	.52	
Neutrophil	3.702	40.541	0.148	11095.803	.20	
Dendritic	2.771	15.970	1.322	192.934	*	
NEK1	−0.385	0.680	0.356	1.302	.25	
NEK2	0.484	1.623	1.237	2.128	***	
NEK3	0.494	1.639	1.053	2.551	*	
NEK4	−0.566	0.568	0.270	1.196	.14	
NEK5	−0.673	0.510	0.287	0.906	*	
NEK6	−0.313	0.731	0.567	0.944	*	
NEK7	0.067	1.070	0.629	1.819	.80	
NEK8	0.131	1.140	0.689	1.887	.61	
NEK9	0.716	2.045	1.122	3.727	*	
NEK10	0.258	1.294	0.661	2.532	.45	
NEK11	−0.054	0.948	0.650	1.381	.78	
ccRCC = clear cell renal cell carcinoma, GEPIA = Gene Expression Profiling Interactive Analysis, NEK = never in mitosis A-related kinase.

* P < .05,

*** P < .001.

Figure 7. The correlation between NEK members and immune cell infiltration in ccRCC was examined using the TIMER database. ccRCC = clear cell renal cell carcinoma, NEK = never in mitosis A-related kinase, TIMER = Tumor Immune Estimation Resource.

3.7. Association between the NEK family and immune subtypes, and immune checkpoints

We explored the association between NEKs and immune subtypes (C1, C2, C3, C4, C5, C6), and immune checkpoints in ccRCC using the TISIDB database. We found that the expression of NEK genes was significantly correlated with different immune subtypes, including NEK1 (P = 3.17e-05), NEK2 (P = 2.89e-10), NEK3 (P = 6.21e-02), NEK4 (P = 6.59e-04), NEK5 (P = 8.54e-07), NEK6 (P = 7.55e-03), NEK7 (P = 2e-01), NEK8 (P = 1.34e-02), NEK9 (P = 4.66e-05), NEK10 (P = 6.56e-01), and NEK11 (P = 2.21e-02). We found that C5 subtype was at high expression in NEK1, NEK4, NEK8, NEK9, NEK10 and low expression in NEK2, NEK3, NEK5, NEK6, NEK11. In addition, the C1 subtype was the least expressed in NEK1 (Fig. 8). Next, we also examined the correlation between the NEK family and immune checkpoints, and we saw that NEK2, NEK6, NEK7, and NEK10 showed a significant positive correlation with immune checkpoints (Fig. 9).

Figure 8. The associations between the NEK family and 6 immune subtypes (C1-C6) were explored by the TISIDB database. NEK = never in mitosis A-related kinase.

Figure 9. The correlation between the NEK family and immune checkpoints was examined using the TISIDB database. NEK = never in mitosis A-related kinase.

3.8. Correlation between NEK family members and immune cell markers

We used the TIMER database to validate the correlation between the NEK family and immune cell markers in ccRCC. The immune cells markers included B cells (CD19, CD79A, MS4A1), CD4 + T cells (CD2, CD3D, CD3E), CD8 + T cells (CD8A, CD8B, GZMA), Monocyte (C3AR1, CD86, CSF1R), Neutrophils (CCR7, ITGAM, SIGLEC5), Dendritic (CD1C, HLA-DPA1, HLA-DPB1, HLA-DQB1, HLA-DRA, ITGAX, NRP1), M1 macrophage (IRF5, PTGS2), M2 macrophage (CD163, VSIG4), tumor-associated macrophages (CCL2, CD68, IL-10). We found that NEK1, NEK2, NEK6, NEK7, and NEK10 were significantly positively correlated with multiple immune cell marker genes. The results are shown in Table 3.

Table 3 Correlation analysis between NEK family members and gene markers of immune cells in 533 ccRCC patients using TIMER database.

Description	Gene markers	NEK1	NEK2	NEK3	NEK4	NEK5	NEK6	NEK7	NEK8	NEK9	NEK10	NEK11	
Cor	P	Cor	P	Cor	P	Cor	P	Cor	P	Cor	P	Cor	P	Cor	P	Cor	P	Cor	P	Cor	P	
B cell	CD19	−0.070	.11	0.232	***	0.021	.63	−0.053	.22	−0.171	***	−0.024	.58	−0.010	.82	−0.059	.18	−0.072	.10	0.190	***	−0.042	.33	
CD79A	−0.101	*	0.159	***	−0.052	.24	−0.078	.07	−0.146	***	−0.010	.81	0.005	.92	−0.130	**	−0.146	***	0.192	***	−0.073	.09	
MS4A1	0.104	*	0.169	***	0.209	***	0.073	.09	0.022	.62	0.073	.09	0.179	***	−0.062	.15	0.078	.07	0.249	***	0.058	.18	
CD4 + T cell	CD2	0.029	.50	0.327	***	0.078	.07	−0.044	*	0.022	.62	0.107	*	0.105	*	−0.127	**	−0.066	.13	0.126	**	0.032	.46	
CD3D	−0.072	.10	0.285	***	0.046	.29	−0.140	**	−0.051	.24	0.041	.34	0.027	.53	−0.132	**	−0.141	**	0.083	.06	−0.019	.66	
CD3E	−0.026	.55	0.269	***	0.072	.10	−0.079	.07	−0.025	.57	0.069	.11	0.055	.21	−0.089	*	−0.073	.09	0.109	*	0.008	.85	
CD8 + T cell	CD8A	0.036	.41	0.304	***	0.054	.21	−0.026	.55	0.007	.87	0.128	**	0.112	**	−0.095	*	−0.058	.18	0.104	*	0.043	.32	
CD8B	−0.029	.50	0.255	***	0.037	.39	−0.096	*	−0.004	.93	0.096	*	0.053	.22	−0.070	.11	−0.104	*	0.090	*	0.023	.60	
GZMA	−0.058	.18	0.234	***	0.058	.18	−0.136	**	−0.047	.28	0.018	.68	0.053	.22	−0.150	***	−0.105	*	0.055	.20	−0.013	.77	
Monocyte	C3AR1	0.419	***	0.360	***	0.074	.09	0.351	***	0.270	***	0.456	***	0.409	***	−0.147	***	0.181	***	0.158	***	0.186	***	
CD86	0.265	***	0.410	***	0.028	.51	0.196	***	0.151	***	0.322	***	0.287	***	−0.196	***	0.040	.35	0.169	***	0.122	**	
CSF1R	0.332	***	0.328	***	0.075	.08	0.265	***	0.195	***	0.406	***	0.330	***	−0.056	.20	0.186	***	0.221	***	0.175	***	
Neutrophils	CCR7	0.079	.07	0.223	***	0.067	.12	0.047	***	0.043	.32	0.133	**	0.123	**	−0.033	.45	0.048	.27	0.121	**	0.007	.87	
ITGAM	0.342	***	0.343	***	0.086	*	0.281	***	0.226	***	0.422	***	0.328	***	−0.026	.55	0.165	***	0.209	***	0.181	***	
SIGLEC5	0.453	***	0.346	***	0.113	**	0.389	***	0.290	***	0.438	***	0.437	***	−0.020	.64	0.251	***	0.219	***	0.237	***	
Dendritic	CD1C	0.216	***	0.144	***	0.204	***	0.213	***	0.196	***	0.195	***	0.297	***	0.087	*	0.227	***	0.101	*	0.186	***	
HLA-DPA1	0.270	***	0.328	***	0.095	*	0.152	***	0.253	***	0.337	***	0.290	***	−0.117	**	0.055	.20	0.100	*	0.176	***	
HLA-DPB1	0.169	***	0.276	***	0.078	.07	0.059	.17	0.171	***	0.294	***	0.202	***	−0.101	*	0.005	.91	0.093	*	0.133	**	
HLA-DQB1	0.127	**	0.113	**	0.152	***	0.003	.95	0.117	**	0.198	***	0.112	**	0.007	.87	0.038	.38	0.012	.78	0.091	*	
HLA-DRA	0.263	***	0.345	***	0.070	*	0.164	***	0.235	***	0.366	***	0.310	***	−0.153	***	0.041	.34	0.091	*	0.174	***	
ITGAX	0.095	**	0.275	***	0.260	***	0.092	*	0.052	.23	0.156	***	0.032	.46	0.115	**	0.086	*	0.248	***	−0.007	.88	
NRP1	0.570	***	0.115	**	0.238	***	0.492	***	0.467	***	0.435	***	0.611	***	0.075	.08	0.587	***	0.090	*	0.353	***	
M1	IRF5	0.1	*	0.277	***	0.291	***	0.11	*	0.17	***	0.187	***	0.004	.93	0.234	***	0.014	.74	0.157	***	0.169	***	
PTGS2	0.221	***	0.12	**	0.078	.07	0.282	***	0.078	.07	0.057	.19	0.294	***	−0.143	**	0.208	***	0.08	.07	−0.036	.404	
M2	CD163	0.457	***	0.372	***	0.058	.18	0.407	***	0.254	***	0.486	***	0.439	***	−0.118	**	0.275	***	0.185	***	0.140	**	
VSIG4	0.269	***	0.369	***	−0.016	.71	0.236	***	0.114	**	0.358	***	0.293	***	−0.173	***	0.099	*	0.196	***	0.108	*	
TAM	CCL2	0.036	.40	−0.020	.64	0.211	***	0.012	.79	0.162	***	−0.021	.63	0.142	**	0.012	.78	0.076	.08	−0.093	*	0.173	***	
CD68	0.187	***	0.408	***	−0.128	**	0.130	**	0.152	***	0.405	***	0.157	***	−0.116	**	−0.058	.18	0.052	.23	0.137	**	
IL-10	0.255	***	0.347	***	0.070	.11	0.239	***	0.110	*	0.264	***	0.316	***	−0.149	**	0.146	**	0.127	**	0.051	.24	
ccRCC = clear cell renal cell carcinoma, NEK = never in mitosis A-related kinase, TAM, tumor-associated macrophage, TIMER = Tumor Immune Estimation Resource.

* P < .05,

** P < .01,

*** P < .001.

3.9. Validation of NEK family members’ expression in ccRCC

Given our finding that NEK2, NEK3, NEK5, NEK6, and NEK11 were overexpressed in ccRCC, we hypothesized that they may play an important role in the progression of ccRCC. We further detected the expression levels of the 5 members of the NEKs family in ccRCC and adjacent normal kidney tissues by quantitative real-time PCR. The results showed that the mRNA expression level of the NEK family was significantly higher than that of adjacent normal kidney tissues, and the differences were statistically significant in NEK2 (P < .001), and NEK6(P < .001). (Fig. 10A). In addition, we performed IHC to verify the protein expression of NEK2, and NEK6 in ccRCC and normal kidney tissues. We found that NEK2(P = .011), and NEK6(P < .001) were highly expressed in ccRCC compared to normal renal tissues, and the differences were all statistically significant. Among them, NEK2 was mainly expressed in the cytoplasm in normal renal tissues, while in ccRCC, both cytoplasm and nucleus were expressed (Fig. 10B).

Figure 10. The NEK family expression in ccRCC. (A) qPCR detected mRNA expression of NEK2, NEK3, NEK5, NEK6, and NEK11 in adjacent normal kidney tissues and ccRCC tissues, and β-actin was used as control. (B) IHC detected NEK2, and NEK6 protein expression in adjacent normal kidney tissues and ccRCC tissues (200×). ns P > .05, *P < .05, ***P < .001. ccRCC = clear cell renal cell carcinoma, IHC = immunohistochemistry, mRNA = messenger RNA, NEK = never in mitosis A-related kinase.

4. Discussion

It has been reported that the number of patients with ccRCC has increased over the last decade, posing a great threat to human health. Although the diagnosis and treatment of ccRCC have improved, specific and effective treatments are still lacking. As a family of cyclin kinases, NEKs are essential proteins in cell processes such as cell cycle, mitosis, and DNA damage response. NEK family members are extensively involved in the DNA damage response (DDR) pathway, and they can regulate the process of cell mitosis by transporting cell division cycle kinase 2 to the nucleus.[20,21] It is well-established that tumorigenesis is the result of multifactorial, multigene, and multistage interactions. An abnormal cell cycle is a distinctive sign of tumors. Chromosome instability (CIN) during mitosis can result when chromosome segregation errors or DDR occur, which has become a driving factor for tumor evolution.[22] Some scholars have also proposed that a treatment strategy based on DNA damage repair may be a new immunotherapy approach for cancer, which is conducive to the development of novel predictive biomarkers.[23]

A series of evidence indicates that NEKs are critical in tumor development, and some members have been confirmed to be related to ccRCC.[13,24] NEK1, the first NEK to be identified, mediates the DDR pathway.[25] As aforementioned, alterations in the DDR pathway can lead to CIN and further promote cancer progression, suggesting that NEK1 may be a new therapeutic target for cancer.[26,27] Over-expression of NEK2 is correlated with CIN, tumor progression, and poor prognosis in various forms of tumors.[28–30] NEK2 deficiency can lead to the inhibition of lymphocyte infiltration.[31] It is also defined as a novel molecular target in the field of breast cancer (BC) therapy.[32] The potential efficacy of NEK3 as a biomarker has been extensively studied and is associated with a poor prognosis in gastric cancer (GC).[33] Similarly, BC cells can migrate and invade more quickly when NEK3 is present.[34] NEK4 promotes malignant progression by regulating transformation in lung cancer,[35,36] and it may predict the stages of cancer.[37] NEK5 can facilitate the proliferation of BC cells[38,39] and the invasion of nasopharyngeal carcinoma (NPC).[40] NEK6 is overexpressed in several cancers, including THCA,[41] HCC,[42] BC,[43] PC,[12] colon adenocarcinoma,[44] and RCC.[45,46] In RCC, miRNA can promote the progression of RCC by targeting NEK6.

NEK7 is upregulated in GC,[47] pancreatic cancer,[48] and HCC,[49] promoting cell proliferation to influence cancer progression. A recent study confirmed that NEK7 can affect the progression of tumors by activating markers of pyroptosis.[50] It is a promising potential prognostic marker. There is a significant correlation between the infiltration of immune cells and NEK8 in gliomas, which affects the tumor microenvironment through the DDR pathway.[51] NEK8 and NEK9 act as an effector for inflammation-related signal transduction in GC, affecting metastasis and cytoskeletal reorganization of GC cells.[52,53] NEK10 regulates tumor protein p53 through tyrosine phosphorylation and affects the DDR pathway, which is related to the occurrence and development of tumors.[54] Liu et al reported for the first time the link between NEK11 and cancer resistance. They found that NEK11 participated in resistance by regulating the cell cycle.[55]

Although the NEK family has been reported to be involved in the regulation of various tumors, a comprehensive analysis of NEK family members in ccRCC has not been reported, so we detailed explored the expression level, prognostic value, and biological function of NEKs in ccRCC. First, we compared the differential expression of NEKs in ccRCC and normal samples using several well-known databases. The results showed that 5 genes (NEK2, NEK3, NEK5, NEK6, and NEK11) were upregulated in ccRCC compared with normal samples, which is consistent with previously reported results.[45,46,56,57] We speculate that aberrantly expressed NEK genes might promote the development of ccRCC. We then examined the correlation between NEK family members and pathological staging in ccRCC. Our results displayed that the expression levels of NEK1, NEK4, and NEK9 gradually decreased while the expression levels of NEK2 gradually increased with an increase in pathological stages, suggesting that NEK genes may contribute to pathological progression in ccRCC. Subsequently, the prognosis of patients with ccRCC based on NEK family members was investigated. The survival curve demonstrated that all NEK family members were associated with survival time, implying that NEK family members may serve as potential prognostic indicators for ccRCC.

The occurrence of tumors inevitably involves genetic alterations. Therefore, we also evaluated the genetic alterations of NEK family genes in ccRCC, and NEK family genes had a high proportion of deep deletions and amplifications. Next, we built a PPI network and screened the top 10 core genes, including BUB1B, TPX2, NEK2, TOP2A, KIF14, NCAPG, HMMR, MELK, CDK1, and CCNB2, which may play important regulatory roles in ccRCC and NEKs. Immune cells act as defenders of homeostasis in the internal environment and eliminate diseased cells. A study by Vuong and colleagues reported a high degree of immune infiltration in RCC.[58] Similarly, our study also shows that NEK family genes were significantly associated with immune cells in ccRCC, indicating that the NEK family may regulate the function of immune cells in ccRCC patients as an indicator of the immune infiltration status. Immune subtypes were used to predict the probability of patients being in 6 immune states including wound healing, IFN-γ dominant, inflammatory, lymphocyte-critical, immune-silent, and TGF-β dominant, named C1- C6, respectively, and we found that members of the NEK family were highly correlated with the immune subtypes. It has been reported that there is a close relationship between tumor immune cell infiltration and immune checkpoints.[24] Next, we further explored the association between the NEK family and immune checkpoints. We found that NEK2, NEK6, NEK7, and NEK10 showed a significant positive correlation with immune checkpoints. We also used the TIMER database to validate the correlation between the NEK family and immune cell markers in ccRCC. We found that NEK1, NEK2, NEK6, NEK7, and NEK10 showed significantly positively correlated with various immune cell marker genes. Therefore, these results indicate that the NEK family members can regulate interactions in the tumor microenvironment. Finally, we further validated our results by qRCR and IHC experiments. Our findings further offer the theoretical foundation for the role of NEK family members in the pathogenesis of ccRCC. It also provides resources to support future research on immune-targeted therapies.

Nonetheless, there are some limitations to this study. Firstly, we analyzed the expression, prognosis, and immune infiltration of the NEK family in ccRCC using a variety of databases, which may have yielded biased results due to the different sources of the datasets. Secondly, we selected some human tissues for our experiments, and the detailed mechanism of the NEK family in ccRCC still needs to be further verified by animal and cell experiments.

5. Conclusions

In conclusion, the current study explored the expression level, prognostic value, and immune infiltration of NEK family members in ccRCC in a systematic manner. Findings from this study provide new insight into the role of NEK family members in ccRCC, which may be used as potential prognostic markers for ccRCC.

Acknowledgments

We appreciate a lot for the public databases. We thank the Home for Researchers editorial team (www.home-for-researchers.com) for the language editing service.

Author contributions

Conceptualization: Yingli Zhu.

Data curation: Jianfan Lin, Yufei Li.

Formal analysis: Jianfan Lin.

Funding acquisition: Zuojie Luo.

Writing – original draft: Yingli Zhu.

Writing – review & editing: Yingli Zhu, Zuojie Luo.

Abbreviations:

BC breast cancer

BP biological processes

CC cellular components

ccRCC clear cell renal cell carcinoma

CIN chromosomal instability

DDR DNA damage response

DFS disease-free survival

GC gastric cancer

GEPIA Gene Expression Profiling Interactive Analysis

GSCA Gene Set Cancer Analysis

HCC hepatocellular carcinoma

IHC immunohistochemistry

KM Kaplan-Meier

mRNA messenger RNA

NEK never in mitosis A-related kinase

OS overall survival

PC prostate cancer

PPI protein-protein interaction

RCC renal cell carcinoma

TCGA The Cancer Genome Atlas

TIMER Tumor Immune Estimation Resource

This work was supported by The National Natural Science Foundation of China (grant no. 82260159).

This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University, Project number 2024-E014-01, and informed consent was obtained from all participants. The experiments were carried out in accordance with the Declaration of Helsinki.

The authors have no conflicts of interest to disclose.

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

How to cite this article: Zhu Y, Lin J, Li Y, Luo Z. Prognostic value and immune infiltration of the NEK family in clear cell renal cell carcinoma. Medicine 2024;103:29(e38961).
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