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American Journal of Cancer Research logoLink to American Journal of Cancer Research
. 2022 Nov 15;12(11):4954–4976.

Multi-omics analysis of kinesin family member 2C in human tumors: novel prognostic biomarker and tumor microenvironment regulator

Bixi Zhang 1,*, Peng Liu 2,*, Yanchun Li 1, Qing Hu 1, Huan Li 2, Xiaoyang Pang 3, Hao Wu 2,4
PMCID: PMC9729912  PMID: 36504885

Abstract

Kinesin family member 2C (KIF2C) is the best-characterized member of the kinesin-13 family and is involved in accurately fine-tuned dynamics of mitotic spindles. As KIF2C is involved in both spindle formation and regulation of DNA double-strand breaks, precise regulation of KIF2C is essential to prevent malignant transformation associated with gains and losses of DNA content. In the present study, we initially reviewed The Cancer Genome Atlas database and observed that KIF2C is abundantly expressed in most tumor types. We then analyzed the gene alteration profile, protein expression, prognosis, and immune reactivities of KIF2C in more than 10,000 samples from several well-established databases. In addition, we conducted a gene enrichment set analysis to investigate the potential mechanisms underlying the role of KIF2C in tumorigenesis. Multi-omics analysis of KIF2C demonstrated significant statistical correlations between KIF2C expression and clinical prognosis, oncogenic signature gene sets, myeloid-derived suppressor cell infiltration, ImmunoScore, immune checkpoints, microsatellite instability, and tumor mutational burden across multiple tumors. Single-cell data showed that KIF2C is abundantly expressed in malignant cells. The experimental validation demonstrated that KIF2C is highly expressed in gastric cancer cell lines, gastric adenocarcinoma, and hepatocelluar carcinoma. The findings of this study provide important insight for understanding the role and mechanisms of KIF2C in tumorigenesis and immunotherapy in a variety of cancers.

Keywords: KIF2C, pan-cancer, MDSCs, immunotherapy, prognosis

Introduction

A preprint has previously been published [1]. Kinesin family member 2C (KIF2C), also known as mitotic centromere-associated kinesin (MCAK), is the best-characterized member of the kinesin-13 family and is involved in accurately fine-tuned dynamics of mitotic spindles [2]. KIF2C is predominantly found in centrosomes, centromeres, and microtubules [3,4]. Chromosomal instability, a hallmark of tumor cells, can be induced by defective DNA repair due to erroneous chromosome segregation during mitosis [5]. As KIF2C is involved in both spindle formation and regulation of DNA double-strand breaks, precise regulation of KIF2C is essential to prevent malignant transformation associated with gains and losses of DNA content [6]. Hyperactive kinases (e.g., aurora kinase A and polo-like kinase 1) or inactive p53 could deregulate KIF2C to the extent that chromosomal instability and aneuploidy are promoted [7].

Accumulating evidence has demonstrated that KIF2C is aberrantly regulated during malignancy. KIF2C is highly expressed in hepatocellular carcinoma and colorectal cancer and is associated with a poor prognosis [8,9]. However, the mechanisms underlying the role of KIF2C in tumorigenesis in various cancers have not been well defined. We reviewed The Cancer Genome Atlas (TCGA) database and found that KIF2C is abundantly expressed in most cancers of diverse origins. Thus, pan-cancer analysis of KIF2C may provide new insights into the molecular mechanisms underlying tumor occurrence, recurrence, and immunotherapy.

In the present study, we analyzed the profiles of gene alterations, protein expression, prognosis, and immune reactivities of KIF2C in more than 10,000 samples from several well-established datasets. We also conducted a KIF2C-related gene enrichment analysis to investigate the potential mechanisms underlying the role of KIF2C in tumorigenesis. In addition, the expression of KIF2C have been validated in human gastric cancer cell lines and patients tumor samples. This study aimed to examine the roles and potential mechanisms of KIF2C in the development and progression of human cancers.

Materials and methods

Genetic alteration analysis

The genetic alteration characteristics of KIF2C were queried from “TCGA PanCancer Atlas Studies” module of cBioPortal (https://www.cbioportal.org/, accessed on January 15, 2022) [10]. The details of the tumor entity summary, alteration frequency, and copy number alteration (CNA) are shown in the “Cancer Types Summary” module. The diagram of KIF2C alteration sites that included the alteration types, case number, and 3D molecular structure were obtained from the “mutations” module.

Gene expression analysis

Considering adjacent normal tissue as a control or even no control group in tumors from TCGA database, Genotype-Tissue Expression (GTEx) database was obtained for corresponding normal tissues as the control group. The difference of KIF2C expression between certain tumor tissues and normal tissues was carried out through the “Box Plot” module of Gene Expression Profiling Interactive Analysis, version 2 (GEPIA2) (http://gepia2.cancer-pku.cn/, accessed on January 15, 2022) [11]. Additionally, we obtained plots of the KIF2C expression in different pathological stages of TCGA tumors via the “Pathological Stage Plot” module of GEPIA2. We further obtained plots of the KIF2C expression in different cancer cell lines via Cancer Cell Line Encyclopedia (CCLE) (https://depmap.org/portal/gene/, accessed on January 16, 2022) [12]. UALCAN (http://ualcan.path.uab.edu/analysis-prot.html, accessed on January 17, 2022) [13] was used to conduct KIF2C protein expression analysis in the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery database. The Human Protein Atlas (HPA) database (https://www.proteinatlas.org/, accessed on January 17, 2022) was used to obtain the RNA expression level of KIF2C in normal tissues and immunohistochemical staining images of KIF2C expression in human tumors and normal tissues.

Survival analysis

The Kaplan-Meier survival map module of GEPIA2 was used to obtain the overall survival (OS) and disease-free survival (DFS) heatmap data of KIF2C across all cancers in TCGA cohort. The log-rank test was applied in the hypothesis test, and survival plots were obtained through the Kaplan-Meier “Survival Analysis” module of GEPIA2.

The protein-protein interaction of KIF2C and similar genes in pan-cancer

Using STRING (https://string-db.org/, accessed on January 18, 2022) tool, protein-protein interaction (PPI) analysis of KIF2C will present 50 available experimentally determined KIF2C-interacted proteins and visualize the PPI network. Moreover, based on cancer data from TCGA cohort, the top 100 KIF2C-correlated targeting genes were obtained from the “Similar Genes Detection” module of GEPIA2. Utilizing the “correlation analysis” module of GEPIA2, a pairwise gene Pearson’s correlation analysis of KIF2C and the top five selected genes was performed. Spearman’s correlation test was performed on the top five selected genes using the “Gene_Corr” module of TIMER2 to obtain a heatmap.

Using Venn Diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/, accessed on January 19, 2022), we conducted an intersection analysis to compare the KIF2C-interacted and KIF2C-correlated genes. In addition, combining the two sets of data, R package “clusterProfiler” in R software (Version 4.1.1, R Foundation for Statistical Computing, Vienna, Austria) was used to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analysis including biological process, molecular function, and cellular component.

Gene set enrichment analysis of KIF2C in pan-cancer

To explore the biological and oncogenic signaling pathways, Gene Set Enrichment Analysis (GSEA) was performed in high-and low-expression groups based on the mean expression value of KIF2C in 33 cancers of TCGA dataset. R package “clusterProfiler” in R software was used to perform MSigDB H (hallmark gene sets) and C6 (oncogenic signature gene sets) enrichment analysis. Gene sets with |NES|>1, p.adjust <0.05, and FDR<0.25 were considered significantly enriched.

Immune infiltration analysis

To explore the association between KIF2C expression and immune infiltration of all cancers in TCGA database, we input KIF2C in “gene expression” module, while myeloid derived suppressor cells (MDSCs) in “immune infiltrates” module of TIMER2 to obtain a heatmap and the scatter plots.

The Sangerbox platform (http://sangerbox.com/, accessed on January 20, 2022) was used to generate the Stromal, Immune, and ESTIMATE scores. In addition, the relationship between KIF2C expression and various immune checkpoints (inhibitory and stimulatory) was explored. The correlation between KIF2C expression and tumor mutational burden (TMB) and microsatellite instability (MSI) in different cancers in TCGA database was also analyzed using Sangerbox. Cellular heterogeneity of KIF2C expression in various cancers was conducted using Cancer Single-cell Expression Map (https://ngdc.cncb.ac.cn/cancerscem/index, last accessed on March 1, 2022) [14].

Cell culture

GES-1, SGC-7901, HGC-27, MKN-45, and AGS are purchased from ATCC. All the cells were cultured in Roswell Park Memorial Institute 1640 (RPMI 1640, Gibco) supplemented with 10% FBS and 1% Penicillin-Streptomycin in a 37°C humidified incubator with a 5% CO2 environment.

Quantitative RT-PCR

Total RNA was extracted with Trizol (15596026, Invitrogen) and cDNA was synthesized using the PrimeScript RT-PCR kit (RR055B, Takara) according to the manufacturer’s instructions. A SYBR Green PCR Kit (4344463, Applied Biosystems) was used to conduct qRT-PCR on the CFX96 real-time PCR detection system. Relative KIF2C mRNA expression in cancer cells were measured by the 2-ΔΔCt method. We normalized the expression levels to those of the internal control glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Primer sequences are as follows: KIF2C (F: 5’-GGAGGAGAAGGCTATGGAAGA-3’, R: 5’-TCGCAGGGCTGAGAAATG-3’); GAPDH (F: 5’-GGTCACCAGGGCTGCTTTA-3’, R: 5’-GGATCTCGCTCCTGGAAGATG-3’).

Western blot

Western blot the proteins through an SDS-PAGE gel and subsequently transferring them to 0.45 μm PVDF membrane (Merck). The membrane was blocked with 5% milk for 1 hour, and then incubated with the primary antibody (KIF2C: 28372-1-AP, Proteintech; GAPDH: 60004-1-lg, Proteintech) overnight at 4°C. After three washes with TBST (each for 6 min), the membrane was incubated with the appropriate secondary antibodies at room temperature for 1 hour, washed with TBST, and visualized using the ECL by ChemoStudio Imaging System (Analytik Jena).

Immunohistochemical staining

The tumor tissue samples used in this study were obtained from patients who underwent surgical treatment and diagnosed with gastric cancer, liver hepatocellular, and lung adenocarcinoma at Hunan Provincial People’s Hospital. All patients provided written informed consent. The present study was conducted in accordance with the Declaration of Helsinki, and all experiments were approved by the ethics committees of Hunan Provincial People’s Hospital. Tissues were subjected to standard tissue processing and paraffin embedding. The tissues were sliced serially into sections 3 μm thick for hematoxylin and eosin (H&E) staining. For immunohistochemical staining (IHC), the tissue sections were preheated in Tris-EDTA buffer (pH 9.0) and then maintain at a sub-boiling temperature for 20 minutes to retrieve the immunoreactivities of antigens. To block endogenous peroxidase activity, quench the tissue sections with 3.0% hydrogen peroxide at room temperature for 10 minutes. Antibody against KIF2C (1:200 dilution, 28372-1-AP; Proteintech, Wuhan, China) was used to incubate the tissue sections for 1 hour at room temperature. Sections were incubated with MaxVision HRP-Polymer anti-Rabbit IHC Kit (KIT-5030, MXB Biotechnologies, Foochow, China) for 30 minutes. The working solution of DAB (DAB-2032, MXB Biotechnologies, Foochow, China) was applied to the tissue sections for the chromogenic reaction. The tissue sections were examined using an upright microscope (BX53, Olympus, Japan).

Statistical analysis

Gene expression data from the TCGA and GTEx databases were analyzed using Wilcoxon test. Protein expression data from UALCAN dataset was analyzed using Student’s t-test. The survival data from GEPIA2 database was analyzed using log-rank test. R package “clusterProfiler” in R software (Version 4.1.1, R Foundation for Statistical Computing, Vienna, Austria) was used to perform KEGG pathway, GO, MSigDB H and C6 enrichment analysis. The correlation analysis was evaluated in the TIMER2 database using purity-adjusted Spearman’s rho. The correlation analysis of ImmunoScore, StromalScore, and ESTIMATEScore, immune checkpoints, MSI, and TMB using Pearson’s correlation coefficient. The P<0.05 was considered as statistically significant.

Results

Genetic alteration of KIF2C in cancers

The following tumor entities from TCGA cohort were included in this study: ACC, BLCA, BRCA, COAD, CHOL, CESC, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, MESO, OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, THYM, UCEC, UCS, and UVM (abbreviations and acronyms are listed in Table S1). The KIF2C genetic alteration profile of cancers in TCGA cohort showed that 1.8% of enrolled patients had genetic alterations (predominantly missense mutations and amplifications), and the patients with UCEC had the highest frequency (6.99%) of KIF2C genetic alterations (Figure 1A). As shown in Figure 1B, missense mutations were the most common, followed by splice site mutations. The 3D molecular structures of the KIF2C and A500T missense mutation are shown in Figure 1C. Notably, all these somatic mutations were classified as variants of uncertain significance.

Figure 1.

Figure 1

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Genetic alterations of KIF2C in different tumors in TCGA database. A. Alteration frequencies with mutation types were displayed. B. The mutation site and case number of KIF2C genetic alterations were displayed. C. 3D molecular structure of KIF2C. CNA, copy number alteration; SV, structural variation.

Gene expression analysis data

Based on the consensus databases of HPA and GTEx, the expression patterns of KIF2C in different normal tissues are shown in Figure S1. KIF2C is abundantly expressed in the testis, bone marrow, and lymphoid tissues with high RNA levels. Analysis of the expression profile of KIF2C in different tumor and normal tissues in the consensus datasets from TCGA and GTEx demonstrated that the expression level of KIF2C was significantly higher in tumor tissues than in normal tissues across different types of cancers, such as BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, LIHC, LUAD, LUSC, OV, PAAD, READ, SARC, SKCM, STAD, THYM, UCEC, and UCS (Figure 2A). In addition, KIF2C expression was significantly related to the pathological stages of ACC, BRCA, KIRC, KIRP, LIHC, and LUAD (Figure S2). KIF2C was abundantly expressed in different cancer cell lines in the CCLE dataset (Figure 2B; Table S2). In the CPTAC dataset, the total KIF2C protein expression was higher in primary cancers than in normal tissues for BRCA, LUAD, HNSC, COAD, LIHC, PAAD, OV, UCEC, and KIRC (Figure 3A). Immunohistochemical staining images from the HPA dataset showed that positive KIF2C staining was present in COAD, LUAD, and CESC tissues, but not in normal tissues (Figure 3B).

Figure 2.

Figure 2

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mRNA level expression of KIF2C in different tumors and cancer cell lines. A. The KIF2C expression level of different cancers in TCGA database compared with normal tissue in GTEx database. B. The KIF2C expression level of different cancer cell lines in the CCLE database. Log2(TPM+1) transformed expression data for plotting. *P<0.05, in Wilcoxon test. TPM, transcripts per million; N and T, normal and tumor tissue.

Figure 3.

Figure 3

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Protein expression of KIF2C in different tumors. A. The KIF2C total protein expression between normal tissue and primary tumor tissue according to the CPTAC database, Z-value represent standard deviations from the median across samples for the given cancer type. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, in Student’s t-test. TPM, transcripts per million. B. KIF2C immunohistochemical staining images in human cancers compared with normal tissue from HPA database.

Survival analysis data

Based on the levels of KIF2C expression in cancers, the correlation between KIF2C expression and cancer prognosis was explored in TCGA cohort. It was found that increased levels of KIF2C expression were significantly associated with poor OS in ACC [HR, 9.2; P<0.0001], KICH [HR, 8.2; P=0.018], KIRC [HR, 1.5; P=0.0057], KIRP [HR, 2.9; P=0.001], LGG [HR, 2.9; P<0.0001], LIHC [HR, 2.2; P<0.0001], LUAD [HR, 1.4; P=0.016], MESO [HR, 3.9; P<0.0001], PAAD [HR, 1.6; P=0.02], and SKCM [HR, 1.4; P=0.01] (Figure 4A). High expression of KIF2C was also significantly associated with poor DFS in ACC [HR, 5.2; P<0.0001], KIRC [HR, 1.8; P=0.0018], KIRP [HR, 3.8; P<0.0001], LGG [HR, 1.8; P=0.0001], LIHC [HR, 1.8; P<0.0001], MESO [HR, 2.1; P=0.016], PAAD [HR, 2.2; P=0.0005], PRAD [HR, 2.4; P=0.0001], and THCA [HR, 1.9; P=0.03] (Figure 4B).

Figure 4.

Figure 4

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Relations between KIF2C expression and survival prognosis of different tumors in TCGA database are shown in survival maps and Kaplan-Meier curves. GEPIA2 tool was used to obtain overall survival (A) and disease-free survival (B) analyses. Cutoff-high (50%) and cutoff-low (50%) values were used as the expression thresholds for separating the high-expression and low-expression cohorts. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, in log-rank test. HR, hazard ratio.

Protein-protein interactions of KIF2C and similar genes in pan-cancer

To further investigate the potential mechanism of KIF2C in tumorigenesis, we conducted a series of enrichment analyses for proteins interacting with KIF2C and genes correlated with KIF2C based on STRING and GEPIA2. A total of 50 proteins that experimentally interacted with KIF2C are shown in the PPI network (Figure 5A). In addition, the top 100 KIF2C-correlated genes (Table S3) were identified, among which the top five genes were kinesin family member C1 (KIFC1) (R=0.85), cell division cycle 20 (CDC20) (R=0.84), non-SMC condensin I complex subunit H (NCAPH) (R=0.84), kinesin family member 4A (KIF4A) (R=0.83), and DLG-associated protein 5 (DLGAP5) (R=0.83) (Figure 5B). The corresponding heatmap data showed a significant and positive correlation between KIF2C and the top five genes in all cancer types in TCGA cohort (Figure 5C). Intersection analysis of the genes directly interacting with or related to KIF2C identified six genes, namely, aurora kinase A (AURKA), kinesin family member 14 (KIF14), kinesin family member 18B (KIF18B), polo-like kinase 1 (PLK1), shugoshin 1 (SGO1), and shugoshin 1 (SGO2) (Figure 5D). Applying the combination of the two datasets, GO enrichment analysis indicated that the genes directly interacting with or related to KIF2C were mainly related to the biological processes of “mitotic nuclear division” and “chromosome segregation”, the cellular component of “spindle” and “chromosome”, and the molecular functions of “microtubule binding” and “tubulin binding”. KEGG pathway analysis further suggested that the “cell cycle” and “p53 signaling pathway” might be potential mechanisms underlying the effect of KIF2C on tumorigenesis (Figure 5E).

Figure 5.

Figure 5

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KIF2C related gene network, KEGG pathway analysis and GO enrichment analysis. A. 50 available experimentally determined KIF2C-interacted proteins using STRING tool. B. Top 100 KIF2C-correlated genes in TCGA database and selected targeting genes, including CDC20, DLGAP5, KIF4A, KIFC1, and NCAPH, in Pearson’s correlation coefficient. C. The corresponding heatmap in the detailed tumor types, in purity-adjusted partial Spearman’s rho. D. An intersection analysis of the KIF2C-interacted and KIF2C-correlated genes. E. The bar plot of GO enrichment analysis in biological process, molecular function, and cellular component. KEGG pathway analysis based on the KIF2C-interacted and KIF2C-correlated genes. Adjusted p-values were obtained from multiple hypotheses test using the Benjamini-Hochberg procedure, p.adjust <0.05 was considered statistically significant.

Gene set enrichment analysis data

MSigDB H (hallmark gene sets) and C6 (oncogenic gene sets) were analyzed in the present study. Enrichment of H analysis indicated that high expression of KIF2C was associated with genes involved in mitotic spindle assembly, cell cycle-related targets of E2F transcription factors, genes regulated by MYC, and genes regulated by KRAS (Figure 6). C6 enrichment analysis demonstrated that high expression of KIF2C was associated with various oncogenes such as E2F, EGFR, MYC, and KRAS (Figure 7).

Figure 6.

Figure 6

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Hallmark Gene set enrichment analysis of KIF2C. Hallmark gene sets enriched in high KIF2C expression group. ES, enrichment score; NES, normalized enrichment score; FDR, false discovery rate.

Figure 7.

Figure 7

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Oncogenic Gene set enrichment analysis of KIF2C. Oncogenic signature gene sets enriched in high KIF2C expression group. ES, enrichment score; NES, normalized enrichment score; FDR, false discovery rate.

Immune reactivities analysis data

Using the Sangerbox Estimate infiltration module, we determined the correlation of ImmunoScore, StromalScore, and ESTIMATEScore with KIF2C expression in 33 cancer types based on TCGA dataset (Table S4). KIF2C expression in GBM, UCEC, CESC, LUAD, SARC, STAD, LUSC, SKCM, and TGCT was significantly and negatively correlated with ImmunoScore, StromalScore, and ESTIMATEScore. In contrast, KIF2C expression in LGG, KIRC, and THCA was significantly and positively correlated with these three scores (Figure 8A). A negative correlation between KIF2C expression and ImmunoScore, StromalScore, and ESTIMATEScore was observed for GBM, STAD, and LUSC as shown in the representative scatter plots (Figure 8B). The correlation between the expression of KIF2C and infiltration level of MDSCs was estimated using TCGA dataset. Surprisingly, a significant and positive correlation between the expression of KIF2C and infiltration of MDSCs was observed in all cancer types, except HNSC-HPV+ and THCA (Figure 8C). A positive correlation between KIF2C expression and the infiltration estimation value was observed for GBM, STAD, and LUSC, as illustrated in the representative scatter plots (Figure 8D).

Figure 8.

Figure 8

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Immune reactivities analysis of KIF2C in different tumors of TCGA database. A, B. Correlation of ImmunoScore, StromalScore, and ESTIMATEScore with KIF2C expression in tumors, in Pearson’s correlation coefficient. C, D. Correlation analysis between KIF2C expression and immune infiltration of myeloid derived suppressor cells, in purity-adjusted Spearman’s rho, with TIDE algorithm. TIDE, tumor immune dysfunction and exclusion.

The immune checkpoint analysis demonstrated that the expression of KIF2C in most cancers was positively correlated with CD276 and High Mobility Group Box 1 (HMGB1) (Figure 9A). Analysis of the relationship between KIF2C expression and MSI/TMB of cancers in TCGA cohort indicated that KIF2C expression was significantly and positively correlated with MSI in LUSC, PRAD, SARC, BRCA, COAD, STAD, and KICH, whereas it was negatively correlated with MSI in DLBC as illustrated in the radar chart (Figure 9B). The analysis also showed that KIF2C expression significantly and positively correlated with TMB in LUAD, PRAD, UCEC, TGCT, BRCA, COAD, STAD, SKCM, KIRC, READ, KICH, ACC, and PCPG (Figure 9C). Single-cell data demonstrated that KIF2C mainly expressed in malignant cells (Figure 10).

Figure 9.

Figure 9

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Correlation of KIF2C expression with immune checkpoints, MSI, and TMB in TCGA database. A. Heatmap represents the color-coded correlations of immune checkpoints and KIF2C across different tumors. B. Radar chart displays the overlap between KIF2C and MSI. C. Radar chart displays the overlap between KIF2C and TMB. *P<0.05, **P<0.01, ***P<0.001, in Pearson’s correlation coefficient.

Figure 10.

Figure 10

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Single-cell data analysis of KIF2C expression in Cancer Single-cell Expression Map. KIF2C expression in malignant cells and various immune cells. CRC, colorectal cancer; MCC, Merkel cell carcinoma; PDAC, pancreatic ductal adenocarcinoma; TNBC, triple-negative breast cancer.

Experimental validation

To validate the KIF2C expression in human tumors, we verified bioinformatics data using qRT-PCR and western blot analysis to examine the RNA and protein levels of KIF2C in several gastric cancer cell lines. The results showed that KIF2C is highly expressed in SGC-7901, HGC-27, and MKN-45 when compared to GES-1 (Figure 11A, 11B). In addition, the immunohistochemistry stainning demonstrated that KIF2C is positively expressed in STAD and LIHC when compared to adjacent normal tissues (Figure 11C, 11D). However, there is no difference in KIF2C expression between the tumor tissues and adjacent normal lung tissues (Figure 11E).

Figure 11.

Figure 11

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KIF2C is highly expressed in gastric cancer cell lines, STAD, and LIHC. A. KIF2C expression in human gastric cancer cell lines analyzed by real-time PCR. B. KIF2C expression in human gastric cancer cell lines analyzed by western blot. C-E. KIF2C immunohistochemical staining images in human cancers compared with adjacent normal tissue. The red arrow showed positive expression of KIF2C in the nucleus of tumor cells. **P<0.01 and ****P<0.0001 in Student’s t-test.

Discussion

In the present study, we demonstrated that: 1) the expression level of KIF2C was significantly higher in tumor tissues than in normal tissues across most cancer types in TCGA cohort; 2) total KIF2C protein expression was higher in the primary cancers than in normal tissues for BRCA, COAD, LUAD, LIHC, HNSC, PAAD, KIRC, UCEC, and OV in the CPTAC dataset; 3) high expression of KIF2C was significantly associated with poor OS and DFS of various tumors in TCGA database; 4) KIF2C was significantly and positively correlated with KIFC1, CDC20, NCAPH, KIF4A, and DLGAP5 in all tumor types in TCGA cohort; 5) KEGG pathway analysis and GO enrichment analysis based on the KIF2C-interacted and -correlated genes showed that “cell cycle” and “p53 signaling pathway” might be the mechanisms for the effect of KIF2C on tumorigenesis; 6) high expression of KIF2C was significantly associated with E2F, EGFR, MYC, and KRAS signature oncogenes in BLCA, BRCA, HNSC, KIRP, LIHC, LUAD, PAAD, PCPG, and THCA; 7) KIF2C expression was significantly and negatively correlated with ImmunoScore, StromalScore, and ESTIMATEScore in most of the cancers in TCGA cohort; 8) a significant and positive correlation between the expression of KIF2C and infiltration of MDSCs was present in all tumor types except for HNSC-HPV+ and THCA; 9) KIF2C expression was significantly and positively correlated with MSI, TMB, CD276, and HMGB1 immune checkpoints in most of the cancers in TCGA cohort.

KIF2C is the best-characterized member of the kinesin-13 family, and is involved in the fine-tuned dynamics of mitotic spindles, and reductions in kinetochore-microtubule turnover induce severe chromosome segregation defects [2]. Previous studies have shown that KIF2C is highly expressed and associated with poor prognosis for LIHC, STAD, and COAD [8,9,15]. A recent study showed that KIF2C acts as a key factor in mediating the crosstalk between Wnt/β-catenin and mTORC1 signaling and promotes LIHC cell proliferation, migration, invasion, and metastasis both in vitro and in vivo [8]. In addition, KIF2C expression was significantly suppressed by ectopic introduction of p53 [16]. The present study also showed that KIF2C expression was significantly increased in most tumors in TCGA dataset and was related to poor prognosis. These data demonstrate that the expression of KIF2C could lead to tumorigenesis and cancer metastasis in a variety of tumors, warranting further investigation.

Among the top 100 genes with similar expression patterns as KIF2C in the tumors of TCGA cohort, KIF2C expression was significantly and positively correlated with KIFC1, CDC20, NCAPH, KIF4A, and DLGAP5 expression in all tumor types in TCGA database. Although there is no physical interaction between KIF2C and these five genes, these genes are all involved in “cell cycle” and “p53 signaling pathway”. KIFC1 drives chromosome segregation errors and aneuploidy, resulting in tumorigenesis initiation and/or acceleration [17]. The oncogenic role of CDC20 has been reported in various human cancers, including PAAD, BRCA, PRAD, COAD, LUAD, and LIHC [18]. Although NCAPH plays a central role in mitotic chromosome assembly and segregation in humans [19], there is little data on the relationship between NCAPH and cancer in the literature. A recent study showed that KIF4A drives aggressive PRAD phenotypes and is associated with poor outcome [20]. circKIF4A has been demonstrated to be a prognostic factor and mediator that regulates the progression of triple-negative breast cancer [21]. Pan-cancer analysis showed that DLGAP5 is highly expressed in most types of cancers and is associated with poor prognosis [22].

Using the data for both KIF2C-interacted proteins and KIF2C-correlated genes, KEGG pathway analysis showed that targeting the cell cycle and p53 signaling pathway might be important mechanisms for the effect of KIF2C on tumorigenesis. GO enrichment analysis further suggested that the genes directly interacting with or related to KIF2C were mainly related to mitotic nuclear division and chromosome segregation. GSEA for the hallmark gene sets demonstrated that high expression of KIF2C was associated with genes involved in mitotic spindle assembly in various tumors of TCGA cohort. As KIF2C is involved in both spindle formation and regulation of DNA double-strand breaks, precise regulation of KIF2C is essential to prevent malignant transformation associated with gains or losses of DNA content [6]. Thus, perturbations in KIF2C may lead to chromosomal instability and aneuploidy. In the present study, intersection analysis of KIF2C-interacted proteins and KIF2C-correlated genes identified six genes AURKA, KIF14, KIF18B, PLK1, SGO1, and SGO2, which are potentially important regulatory molecules associated with KIF2C for tumorigenesis. Indeed, the interactions between KIF2C and AURKA, and between KIF2C and PLK1 have been detected using enzymatic assays [23,24]. Kinases such as AURKA and PLK1 are often deregulated in cancer cells, leading to perturbations in the proper regulation of KIF2C, which in turn could enhance mitotic defects [25]. In addition, the interaction between KIF2C and KIF14, and between KIF2C and KIF18B has been detected using affinity chromatography technology assays [26,27]. AURKA regulates spindle microtubule dynamics through the KIF2C-KIF18B complex [28]. A study showed that the interaction between KIF2C and SGO1 was detected by an anti-tag co-immunoprecipitation assay [29], but no further study has investigated the effect of SGO1 on the regulation of KIF2C. Indeed, SGO1 downregulation leads to chromosomal instability in COAD [30], and it is reasonable to hypothesize that SGO1 downregulation through KIF2C leads to chromosomal instability. The interaction between KIF2C and SGO2 has been detected using anti-bait co-immunoprecipitation assay [31], and phosphorylation of SGO2 is essential for localizing KIF2C to centromeres [32]. However, it is unclear whether the interactions between KIF2C, KIF14, KIF18B, SGO1, and SGO2 have synergistic effects on cancer progression. In this study, GSEA showed that high expression of KIF2C was associated with the oncogenic signatures such as E2F, EGFR, MYC, and KRAS. Notably, the roles of E2F, EGFR, MYC and KRAS in cancer have been extensively studied [33-36].

In the present study, we observed a significant and positive correlation between the expression of KIF2C and infiltration of MDSCs in all tumor types except for HNSC-HPV+ and THCA, while expression of KIF2C negatively correlated with ImmunoScore, which has been used to quantify the in situ T cell infiltration in most of the tumors in the TCGA cohort. In addition, single-cell data shows that KIF2C is mainly expressed in malignant cells. MDSCs are immunomodulatory cells that suppress adaptive immune responses and promote tumor progression and metastasis and are involved in multidrug resistance [37,38]. A clinical study has shown that increased MDSC levels in patients with recurrent GBM are associated with poor prognosis [39]. It has been reported that MDSCs produce eotaxin-1 to promote LUSC metastasis via activation of ERK and AKT signaling [40]. A clinical study has suggested that patients with STAD have higher levels of circulating MDSCs than healthy individuals, and high levels of MDSCs are correlated with advanced cancer stage and reduced survival [41]. These data support the hypothesis that KIF2C overexpression in cancer cells may recruit more MDSCs, not T cells, to infiltrate the tumor microenvironment, thus leading to poor prognosis.

Immunotherapy is an evolving cancer treatment that helps the immune system fight cancer. Among the most promising approaches to activate therapeutic antitumor immunity is blocking immune checkpoints [42]. KIF2C is positively correlated with most of the immune checkpoint genes in tumors of TCGA cohort, especially CD276 and HMGB1. CD276 is highly expressed in a wide range of human cancers and plays an important role in the inhibition of T-cell functions [43]. HMGB1 plays both oncogenic and tumor-suppressive roles during tumor development and therapy [44]. Unfortunately, many patients with a positive initial response later develop resistance to immune checkpoint inhibitors [45]. Notably, MDSCs can blunt the anticancer activity of immune checkpoint inhibitors [46]. Indeed, HMGB1 inhibition drastically reduces MDSCs and improves the efficacy of anti-PD-1 cancer monoimmunotherapy [47]. Both MSI and TMB are promising predictive biomarkers for the efficacy of immune checkpoint inhibitors in cancer treatment [48]. The findings of the present study suggest that KIF2C expression is significantly and positively correlated with MSI and TMB in a portion of tumors in TCGA cohort. KIF2C depletion or inhibition of its microtubule depolymerase activity impairs the formation of DNA damage foci and reduces the mobility of DNA double-strand breaks [6], suggesting that KIF2C may be an attractive therapeutic target for human cancers. UMK57 is a novel chromosomal instability inhibitor that potentiates KIF2C in vivo and transiently suppresses chromosome mis-segregation in cancer cells with chromosomal instability, while cancer cells with chromosomal instability display adaptive resistance to UMK57 [49]. Rigosertib, a non-ATP-competitive inhibitor of PLK1, kills cancer cells via microtubule destabilization [50]. Indeed, KIF2C overexpression enhances microtubule destabilization by rigosertib [51], indicating that rigosertib could be an effective antitumor drug among patients with KIF2C highly expressed cancer.

In conclusion, the present pan-cancer analyses of KIF2C revealed that KIF2C expression was correlated with oncogenic signature gene sets, MDSC infiltration, ImmunoScore, immune checkpoints, MSI, TMB, and clinical prognosis across multiple tumors. These data may aid in the understanding of the role of KIF2C in tumorigenesis and immunotherapy.

Disclosure of conflict of interest

None.

Supporting Information

ajcr0012-4954-f12.pdf (1.1MB, pdf)

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