KIF2C/MCAK a prognostic biomarker and its oncogenic potential in malignant progression, and prognosis of cancer patients: a systematic review and meta-analysis as biomarker

Figures & data

Figure 1. Illustration of KIF2C’s phosphorylation sites, domains, interaction partners and molecular functions. (A): KIF2C’s domains, functions and phosphorylation sites are depicted. The phosphorylation sites are marked as green for Aurora A, blue for Aurora B, light blue for Cdk1, red for Plk1, purple for PAK1 and black for a phosphorylation by an unknown kinase. (B): schematic illustration of KIF2C/MCAK’s molecular involvement in regulating chromosome congression/segregation, FA turnover, ciliogenesis and DSB repair. The figure was modified and updated from [17]. Abbreviations: Cdk1: Cyclin-dependent kinase 1; Plk1: Polo-like kinase 1; PAK1: p21-activated kinase; EB1: end-binding protein 1; FA: focal adhesion; DSB: double-strand break; NT: NH2-terminus; CT: carboxy-terminus; NHEJ: non-homologous end joining; HR: homologous recombination.

Table 1. Correlation between KIF2C/MCAK expression and the clinicopathologic features of various cancer entities.

Tumor entities	Clinicopathologic features associated with KIF2C/MCAK overexpression	Reference
Gastric cancer	OS: OR 1.95 CI (1.21–3.36) p = 0.008) MLN: OR 4.18, 95% CI (1.87–17.86) p = 0.001 SI: OR 1.54, 95% CI (1.02–2.33) p = 0.039	[113]
	OS: HR = 0.59 [0.41–0.83] p = 0.003 DSS: HR = 0.58 [0.37–0.90] p = 0.015 PFI: HR = 0.52 [0.36–0.76] p = 0.001	[177]
Colorectal cancer	KI-67 expression: r = 0.69; p = 0.0003	[115]
	MLN: p = 0.0023 VI: p = 0.019 PI: p = 0.021 Duke’ classification: p = 0.0023	[114]
	OS as gene signature with PARPBP, KNSTRN, and KIF2C: p = 0.0038	[187]
Breast Cancer	OS: HR = 3.77 [2.61–5.59] p = 0.0001 (GEO dataset) OS: HR = 2.55 [2.03–3.22] p = 0.0001 (TCGA dataset) DFS: HR = 2.49 [1.58–4.18] p = 0.0001 (GEO dataset) DFS: HR = 2.66 [1.93–3.66] p = 0.0001 (TCGA dataset) Basal-like BC: OS: HR = 1.61 [1.01–2.69] p = 0.0508 (GEO dataset) DFS: HR = 1.62 [1.08–2.53] p = 0.0248 (TCGA dataset)	[117]
	OS: HR = 2.65 [1.28–5.49] p = 2e−^03	[123]
	NKI295 dataset: OS: p = 0.0001, DMSF: p = 0.0004 GIS251 dataset: OS: p = 0.0224, RFS: p = 0.0042	[122]
	OS: p = 0.0088 Stage: p = 0.001 Tumor size: p = 0.001	[127]
	OS: HR = 1.65 [1.07–2.53] p = 0.022 (http://kmplot.com/)	[132]
	DMFS: HR = 1.87 [1.53–2.27] p = 0.001 (http://kmplot.com/) stage: F-value = 5.20 (Pr(>F) = 0.0229	[131]
	OS: HR = 1.63 [1.46–1.82] p = 1e−^16 (http://kmplot.com/)	[134]
	OS: HR = 1.69 [1.39–2.04] p = 5.8e−^8 (http://kmplot.com/) CD4^+ T-cell: rho = 0.599, p = 2.2e^−16	[136]
	OS: HR = 1.81 [1.28–2.55] adjusted p = 0.001	[129]
	OS: HR = 1.69 [1.51–1.89] p = 1e−^16 (http://kmplot.com/)	[126]
	OS: HR = 1.74 [1.40–2.16] p = 4.3e−^7 (http://kmplot.com/)	[128]
	OS: p = 0.0001, grade: p = 0.05 (grade 2 vs grade 3)	[209]
	OS: p > 0.0001, RFS: p > 0.0001, DMFS: p > 0.0001	[135]
	OS: p = 2.72e−^2 (TCGA dataset) OS: p = 9.02e−^3 (GSE42568 dataset) OS: p = 3.83e−^2 (GSE20685 dataset) OS: HR = 1.57 [1.08–2.27] p = 0.018 (univariate cox reg.) OS: HR = 1.60 [1.05–2.43] p = 0.027 (multivariate cox reg.)	[130]
	OS: HR = 1.69 [1.39–2.04] p = 5.8e−^08 (http://kmplot.com/) RFS: HR = 1.66 [1.50–1.84] p = 1.0e−^16 (http://kmplot.com/) TNM staging: p ≤ 0.05 CD4+ T cell infiltration: p ≤ 0.05 CD8+ T cell infiltration: p ≤ 0.05	[138]
Non-small cell lung cancer (NSCLC)	OS: HR =1.78 [1.57–2.03] TNM staging: F-value = 5.38 (Pr(>F) = 0.00119 (GEPIA)	[161]
	OS: HR =1.98 [1.48–2.65] p = 2.5e−^06 (GSE30219) OS: HR =3.88 [1.76–8.55] p = 3e−^04 (GSE31210) OS: HR = 1.82 [1.14–2.9] p = 0.011 (GSE50081) OS: HR = 1.57 [1.22–2.02] p = 0.00037 (CaArray) TNM staging: p = 0.0049 Differentiation: p = 0.0078 MLN: p = 0.0001	[163]
	OS: HR = 1.4 [n.s.] p = 0.017 (GEPIA) TNM staging: F-value = 5.38 (Pr(>F) = 0.00119 (GEPIA)	[162]
	OS: HR = 1.6 [n.s.] p = 0.0044 (GEPIA)	[168]
	OS: HR =2.58 [2.16–3.09] p = 1e−^16 (http://kmplot.com/)	[169]
	OS: p = 0.015 (lung adenocarcinoma) OS: p = 0.020 (lung squamous cell carcinoma)	[165]
	OS: HR = 2.18 [1.48–3.22] p = 5.2e−^05 (http://kmplot.com/) OS: HR = 1.4 p = 0.017 (GEPIA dataset) CD4+ T cell infiltration: p = 2.46e−^2 (TIME dataset) CD8+ T cell infiltration: p = 8.01e−^9 (EPIC dataset)	[164]
Hepatocellular carcinoma (HCC)	OS: p = 0.005, RFS: p = 0.041, Grade: p = 0.002, TNM: p = 0.013 survival outcomes: HR = 1.832 [1.06–2.51] p = 0.027	[142]
	OS: univariate analysis HR = 1.053 [1.01–1.03] p = 0.0001 OS: multivariate analysis HR = 1.043 [1.01–1.08] p = 0.0171	[149]
	OS: HR = 2.96 [1.31–6.71] p = 0.0063 (http://kmplot.com/) RFS: HR = 2.13 [1.15–3.94] p = 0.014 (http://kmplot.com/)	[150]
	OS: HR = 2.16 [1.51–3.08] p < 0.001 PFI: HR = 2.01 [1.50–2.70] p < 0.001	[151]
	patient collective: OS: HR = 4.00 [1.65–9.74] p = 0.002 DFS: HR = 1.76 [1.09–2.85] p = 0.021 Differentiation: p = 0.016 Relapse: p = 0.025 TCGA collective: OS: HR = 2.13 [1.49–3.04] p = 0.003 DFS: HR = 1.77 [1.31–2.39] p = 0.021	[144]
	OS: HR = 2.2 [n.s.] p = 1.1e−^05 DFS: HR = 1.8 [n.s.] p = 6.2e−^05 NTN: p = 0.015 Tumor size: p = 0.009	[141]
	OS: p = 0.012	[152]
	OS: HR = 1.31, z = 4.71, p = 2.49e−^06 (GEPIA) HR = 2.2, p = 0.0012 (http://kmplot.com/) DFS: HR = 1.8 p = 7.5e−^05 Grading: p > 0.0001 AFPE: p = 0.001	[153]
	OS: HR = 2.2, p = 1.8e−^05 (GEPIA) OS: HR = 2.13 [1.49–3.04] p = 2.3e−^05 (http://kmplot.com/) DFS: HR = 1.9, p = 5.9e−^05 (GEPIA) RFS: HR = 1.77 [1.27–2.47] p = 0.0006 (http://kmplot.com/) Staging: F-value = 11.1 (Pr(>F) = 5.59e−^07	[98]
	OS: HR = 1.2 [0.99–1.50] p = 0.05	[145]
	OS: HR = 2.29 [1.69–3.10] p < 0.001 (ICGC) OS: p = 1.1e−^05 (GEPIA) OS: HR = 1.47 [1.22–1.77] p < 0.001 (TCGA)	[154]
	OS: HR = 2.2, p = 1.7e−^05 Immune cell infiltration: p < 0.001	[102]
Glioma	OS: p < 0.0001	[174]
	OS: HR = 4.73 [1.79–12.45] p < 0.001	[173]
	OS: HR = 2.15 [1.26–3.65] p = 0.005 Grading: HR = 3.40 [2.30–5.02] p < 0.0001 Radio therapy: HR = 2.05 [1.19–3.54] p = 0.01	[103]
	Grading: p < 0.005 OS: WHO Grade I–II; p = 0.026 OS: WHO Grade III; p = 0.043 OS: WHO Grade IV; p = 0.059	[172]
Endometrial cancer (EC)	OS: HR = 1.4 [0.97–1.9] p = 0.019	[189]
	OS: p = 0.0097 Differentiation: HR = 1.23 [1.03–1.42] p = 5.45e−^35	[191]
	OS: p = 0.00537	[190]
	OS: p = 0.012 Staging: p < 0.01 Grading: p < 0.01 Histological type: p < 0.01	[192]
Ovarian cancer (OC)	OS: KIF2C low expression – HR = 1.15 [1.01–1.32] p = 0.038 (http://kmplot.com/)	[193]
	OS: HR = 0.94 [n.s.] p = 0.59 Staging: F-value = 2.92 (Pr(>F) = 0.0548	[197]
	OS: KIF2C low expression – HR = 1.15 [1.01–1.32] p = 0.038 (http://kmplot.com/)	[196]
Esophageal squamous cell carcinoma (ESCC)	OS: HR = 1.48 [1.085–2.020] p = 0.013 (male) DFS: HR = 1.42 [1.048–1.918] p = 0.024 (male) OS: p = 0.880 (female) DFS: p = 0.864 (female) Differentiation: p = 0.022 pT status: p = 0.038	[176]
	OS: HR = 0.37 [0.13–1.04] p = 0.05	[210]
Kidney renal clear cell carcinoma (KIRC)	OS: HR = 1.5 [n.s.] p = 0.0057 DFS: HR = 1.8 [n.s.] p = 0.0018	[95]
	OS: HR = 2.14 [1.756–2613] p < 0.001	[105]
	Staging: p < 0.001 OS: HR = 1.5, pHR = 0.006, p = 0.0057 DFS: p = 0.0018	[184]
	OS: p < 0.001	[118]
Pancreatic Ductal Adenocarcinoma (PDAC) & Pancreatic Adenocarcinoma (PAAD)	OS: HR = 1.6, pHR = 0.021, p = 0.02 (GEPIA dataset) DFS: HR = 2.2, pHR = 0.00072, p = 5e−^4 PFS: p = 0.007 PST: p = 0.0033	[99]
	OS: HR = 1.6, pHR = 0.021, p = 0.02 (GEPIA dataset) OS: HR = 1.4 [1.04–1.9] p = 0.026 (TCGA-PAAD) Grading: p = 0.001 Tumor growth: R = 0.73, p < 2.2e−^16	[179]
	OS: HR = 1.6, pHR = 0.021, p = 0.02 (GEPIA dataset) DFS: HR = 2.2, pHR = 0.00072, p = 5e−^4 OS: p = 0.057 (GSE 62452 dataset)	[180]
	OS: HR = 1.42 [1.177–1.723] p < 0.001 DFI: HR = 1.54 [1.087–2.183] p = 0.015 DSS: HR = 1.48 [1.193–1.844] p < 0.001 PFI: HR = 1.41 [1.179–1.684] p < 0.001	[181]

Abbreviations: MLN: metastatic lymph node; SI: serosal invasion; OR: odds ratio; VI: venous invasion; PFI: progression free interval; PI: peritoneal dissemination; PST: postoperative survival time; PAAD: pancreatic adenocarcinoma; DFS: disease-free survival; DMSF: distant metastasis free survival; DSS: disease specific survival; RFS: relapse free survival; TCGA: the cancer genome atlas; NTN: number of tumor nodes; AFPE: α-fetoprotein expression; pT: pathologic tumor.

* All values show the significance of high KIF2C expression on the malignant features of the cancer entities.

Table 2. Molecular mechanisms involved in KIF2Cs oncogenic potential in multiple cancer entities/cell lines.

Tumor Entity	Experimental Setup	Molecular mechanism and main results	Reference
Breast cancer (BC)	• Breast cancer tissue, KIF2C RT-PCR, n = 14 • In vitro: cell lines T47D and HBC5, knockdown by siRNA	KIF2C is overexpressed in 12 of 14 clinical breast cancer cases. Knockdown of MCAK inhibited cell growth.	[121]
	• In vitro: breast cancer cell line MDA-MB-231, siRNA	In absence of KIF2C: lower migration and invasion capacity with reduced paxillin, p-paxillin, FAK, and p-FAK intensity in focal adhesions (FAs), and a decreased FA size	[36]
	• In silico: public databases concerning expression profile of miR-10b-5p in breast cancer (METABRIC, TCGA, GSE40267 and GSE19783) • In vitro: breast cancer cell line MDA-MB-231, miR-10b-inhibitor, RT-PCR	KIF2C is a key target of miR-10b-5p. KIF2C was mainly involved in sustaining proliferative signaling in cancer development. Inhibitor treatment enhanced KIF2C gene expression (n.s.).	[116]
	• In silico: large-scale gene expression datasets of breast cancer (total n = 4,677) and microRNAs targeting MCAK predicted by bioinformatic analysis (TCGA, GSE7390, GSE2034, GSE1456, GSE4922, GSE22226, GSE24450, GSE53031, GSE25066, GSE10885, GSE58812, and NKI) • In vitro: validated by a dual-luciferase reporter assay in breast cancer cell lines MCF-7 and MDA-MB-231	KIF2C expression was significantly associated with aggressive features of breast cancer and associated with poor outcome in a dose-dependent manner for either ER-positive or ER-negative breast cancer. miR-485-5p and miR-181c might target KIF2C and suppress its expression, associated with better outcome.	[117]
	• In vitro: 39 FFPE and fine needle aspiration biopsies (FNAB) of 25 breast cancers of different clinical subtypes (based on ER and Her2/neu status); RNA-seq. and RT-PCR	Overexpression of ANLN and KIF2C, and lower expression of MAPT strongly correlated with poor outcomes in breast cancer patients.	[122]
	• In silico: public database BioGRID and six GEO datasets (GSE70947, GSE15852, GSE20711, GSE65212, GSE18229-GPL887, GSE65194)	Four genes, FAM134B, KIF2C, ALCAM, KIF1A, were identified having comparable subtyping efficiency with the initial 1015 breast cancer subtyping genes. KIF2C and KIFC1 had a negative effect on patient clinical outcome.	[126]
	• In silico: GEO database: GSE10780 with 42 normal breast tissues and 143 IDC (invasive ductal carcinoma) tissues	Top 10 significantly up-regulated hub genes, CDK1, CCNB1, CENPE, CENPA, PLK1, CDC20, MAD2L1, HIST1H2BK, KIF2C and CCNA2 KIF2C, MAD2L1 and PLK1 were associated with decreased overall survival (OS).	[127]
	• In silico: GEO database: GSE25066 and GSE21422; basal-like breast cancer (BLBC)	Top 10 hub genes: CCNB2, BUB1, NDC80, CENPE, KIF2C, TOP2A, MELK, TPX2, CKS2 and KIF20A Four hub genes, BUB1, CCNB2, KIF2C and CDCA8, were confirmed to be enhanced in basal subtype by RT-PCR.	[128]
	• In silico: TCGA with 1055 BC patients	High levels of KIF15, KIF20A, KIF23, KIF2C and KIF4A genes were significantly correlated with poor OS. BC patients with high expression of KIF15, KIF20A, KIF23, KIF2C and KIF4A were enriched in the cell cycle process, p53 regulation pathway and mismatch repair.	[129]
	• In silico: TIMER, TCGA, KM-plotter, GSE36295, GSE42568, GSE20685	KIF2C is upregulated in 18 different types of cancer, including breast cancer. KIF2C^high expression was correlated with poor OS, TMN staging, T status and molecular subtypes. KIF2C was associated with immune cell infiltration, tumor mutation burden and response to immunotherapy.	[130]
	• In silico: GSE27447, GSE43358, GSE36295, GSE61724, GSE75678, TCGA	Top 5 hub genes are associated with triple negative breast cancer: TPX2, CTPS1, KIF2C, MELK and CDCA8. Highest deregulated pathways: cell cycle, oocyte meiosis and spliceosome.	[131]
	• In silico: KM-plotter, TCGA, Oncomine, GEPIA, GSE81540	The following genes were found to be important for stemness maintenance of TNBC: BIRC5, CDC25A, KIF18B, KIF2C, ORC1, RAD54L and TPX2. A high expression of BIRC5, KIF2C, KIF18B, and TPX2 reduced RFS and free survival.	[132]
	• In silico: KM-plotter, GSE102484, GSE102484, TCGA	Five hub genes for breast cancer were identified: TPX2, KIF2C, CDCA8, BUB1B, and CCNA2. The expression of KIF2C correlated with higher breast cancer staging, had a diagnostic value in identifying normal and breast cancer tissue and had prognostic value for DMFS.	[133]
	• In silico: GSE109169, GSE115144, TCGA, GEPIA, cBioPortal, TISIDB, E-MTAB-365, GSE11121, GSE12093, GSE12276, GSE1456, GSE16391, GSE16446, GSE16716, GSE17705, GSE17907, GSE19615, GSE20271, GSE2034, GSE20685, GSE20711, GSE21653, GSE2603, GSE26971, GSE2990, GSE31519, GSE3494, GSE37946, GSE42568, GSE45255, GSE4611, GSE4922, GSE5327, GSE6532, GSE7390, GSE9195	KIF2C was found to be a hub gene among 9 other genes. The overexpression of these genes was verified in breast cancer tissue and all of these hub genes were associated with a negative overall survival in breast cancer patients. The expression levels of CDK1, CENPF, KIF2C, KIF4A, MELK, PBK, PRC1, and TPX2 were associated with increased CD4^+ T cell infiltration.	[136]
	• In silico: GSE29044, GSE42568, GSE50428, TCGA, KM-plotter	Twelve hub genes were found, including KIF2C, and the expression of KIF2C was associated with reduced OS.	[134]
	• In silico: TCGA, GTEx project, GEPIA 2, KM-plotter, METABRIC	This study found 20 different expressed genes between normal tissue and breast cancer tissue. Among these 20 genes, KIF2C was highly overexpressed and correlated with poor OS, RFS and DMFS.	[135]
	• In silico: TCGA, GEPIA2, HPA, ROC, KM-plotter	Thirteen hub genes were found: CDK1, BUB1, BUB1B, CDC20, CCNB2, CCNB1, KIF2C, NDC80, CDCA8, CENPF, BIRC5, AURKB, PLK1, MAD2L1, and CENPE. The performance as prediction marker was corroborated by a receiver operating characteristic analysis and found that the area under the curve value for KIF2C was 97.6%, highlighting its prognostic potential.	[140]
	• In silico: TCGA, KM-plotter, GSE47561, GSE25066 • In vitro (cell lines): MDA-MB-231, PTXR231, MCF10A, and E8.1 HeLa • In vitro: MT depolymerization assay was used to verify potential KIF2C inhibitors	KIF2C was significantly overexpressed in breast cancer tissue. This expression was the highest in triple negative breast cancers. KIF2C^high expression correlated with reduced OS and DMSF. Knockdown of KIF2C significantly enhances the paclitaxel sensitivity of breast cancer cells, without affecting normal cells. In a drug-discovery approach they found a potent KIF2C inhibitor, which induced paclitaxel sensitization and reduced the clonogenic survival of the triple negative breast cancer cell line MDA-MB-231.	[137]
	• In silico: GSE10797, GSE15852, GSE92697, GSE102484, GSE65212, GSE43837, GSE23988, GSE20194, GSE42568, GSE75333, GSE5847, GSE22035, GSE3744, GSE5764, GSE21422, GSE26910, GSE41970, GSE8977, GSE45827, GSE71142, GSE86945, GSE86946, GSE29431, GSE65194, GSE22093, GSE31192, GSE9014, GSE10780, GSE29431, GSE61304, GSE10810 and KM-plotter	Six hub genes were found in the comprehensive breast invasive carcinoma samples including Centrosomal protein of 55 kDa (CEP55), KIF2C, KIF20A, Ribonucleotide Reductase Regulatory Subunit M2 (RRM2), AURKA, and Protein Regulator of cytokinesis 1 (PRC1). These were verified by expression profiling and validation analyses, Further, these genes were associated with altered promoter methylation status, genetic alteration, reduced overall survival (OS), relapse-free survival (RFS), tumor purity, CD8+ T, CD4+ T immune cell infiltration, and different mutant genes across BRIC samples.	[138]
Hepatocellular carcinoma (HCC)	• In silico: TCGA database and tissue: 66 HCC surgical specimens, IHC • In vitro: HCC cell lines Hep3B and SNU475 treated with shRNA against KIF2C • In vivo: xenograft mouse model with control or KIF2C stably depleted Hep3B cells	High KIF2C expression in HCC. KIF2C expression was associated with HCC prognosis, including OS and DFS. KIF2C expression was also associated with clinical pathological characteristics including the number of tumor nodes and tumor size. KIF2C knockdown inhibited proliferation of HCC cells in vitro and suppressed tumor growth in mice.	[141]
	• In silico: TCGA with 295 HCC patients	Expression of 32 KIF genes was studied. KIF2C, KIF4A, and KIF11 overexpression were significantly associated with shorter relapse-free survival times. KIF2A, KIF2C, KIF3A, KIF4B, KIF11, KIF15, KIFC1, and KIFC3 overexpression was associated with shorter OS times. KIF2A, KIF2C, KIF4A, KIF4B, KIF5B, KIF7, KIF11, KIF15, KIF24, KIF27, and KIFC1 were biomarkers for HCC progression	[142]
	• In silico: TCGA database with 424 tissue samples including 374 HCC and 50 normal samples, merged with the corresponding mRNA expression-based stemness index (mRNAsi)	44 highly-correlated significant elevated genes were screened and functionally related to cell cycle, cellular senescence, p53 signaling pathway, DNA replication, and mismatch repair. 15 key biomarkers, i.a. KIF4A and KIF2C.	[143]
	• In silico: TCGA, Oncomine data, a transcriptome study of 225 primary HCC and 220 normal liver tissues, verified with 39 and 12 paired HCC and adjacent tissues (RT-PCR and WB) and IHC from 149 patients with HCC • In vitro: gain and loss of function assays: HCC cell lines SK-Hep1 and SNU449 with shRNA, and HepG2 and SNU387 cells with overexpressed KIF2C • In vivo: xenograft nude mice experiments with HepG2-KIF2C cells and KIF2C-depleted SK-Hep1 cells	KIF2C expression was increased in almost all cancer types, including HCC. KIF2C was significantly overexpressed in HCC compared to normal liver tissues. KIF2C upregulation is frequently observed in HCC and correlates with a poor prognosis. KIF2C promoted HCC cell proliferation, migration, invasion, and metastasis both in vitro and in vivo. TBC1D7 was identified as a binding partner of KIF2C, this interaction disrupts the formation of the TSC complex, causing enhancement of mammalian target of rapamycin complex1 (mTORC1) signal transduction. KIF2C is a direct target of the Wnt/β-catenin pathway, establishing a link between Wnt/β-catenin and mTORC1 signaling, highlighting KIF2C as a potential target for the treatment of HCC.	[144]
	• KIF2C IHC (20 HCC and adjacent non-tumor liver tissues) and RT-PCR (76 patients, including cancerous and matched non-cancerous tissues) from tissue samples, verified by GEO (GSE14520 dataset), TCGA and Oncomine database included 225 HCC and 220 normal cases • In vitro: HCC cell lines Hep3B and Huh7, overexpression with lentiviral vectors and downregulation with shRNA; RNA-seq.	KIF2C is overexpressed in HCC, related to neoplasm histologic grade, pathology stage, and a dismal prognosis (overall, recurrence-free, and disease-free survival). The diagnostic efficacy of KIF2C was >90% in HCC. Cell culture: KIF2C promoted cell proliferation, migration and invasion through EMT, and an accelerated cell cycle, and inhibited apoptosis. KIF2C promoted HCC through the Ras/MAPK and PI3K/Akt signaling pathway.	[98]
	• TCGA database • In vitro: Hep-3B, MHCC97H, HCCLM3, Huh7, and an immortalized normal liver epithelial cell line (THLE-3), KIF2C siRNA	Increased KIF2C expression in HCC associated with poor prognosis. Silencing KIF2C significantly suppressed proliferation, migration, and invasion in HCC cells with decreased expression of Snail, Vimentin, p-MEK, and p-ERK, but increased E-cadherin. The MEK/ERK inhibitor U0126 enhanced the impact on cell proliferation, migration, and invasion induced by silencing, the MEK/ERK activator PAF.	[145]
	• In vitro: RNA-seq. of Huh-7-control and ANLN-KD cells, RT-PCR confirmation; ectopic KIF2C expression	HCC BM was associated with enhanced nuclear ANLN, which formed a transcriptional complex with SP1, which enhanced KIF2C transcriptional activity to activate the mTORC1 pathway. KIF2C significantly reduced osteoclast differentiation activity promoted by ectopic ANLN expression. Decreased phosphorylation of AKT/increased β-catenin observed in ANLN-KD HCC cells could be partially recovered by ectopic KIF2C expression.	[147]
	• In silico: various databases, i.a. ONCOMINE database, TCGA, human protein atlas; verification: cancer tissues and normal liver tissues of 15 patients with liver cancer for IHC • In vitro: HCC cell lines SK-Hep-1, Huh7, and HepG2. The HCC cell lines BEL-7404 and HCC-LM3 and the normal human liver epithelial cell line HL-7702; siRNA	Kinesin family members KIF2C/4A/10/11/14/18B/20A/23 predict poor prognosis (significantly associated with the tumor stage and pathological tumor grade; shorter OS and DFS). Cox regression analysis showed the mRNA expression levels of these KIFs is an independent prognostic factor for worse outcomes in HCC. KEGG: the cell cycle, p53 signaling pathway, PPAR signaling pathway and DNA replication In vitro: downregulation of KIF proteins effectively decreased the proliferation and colony formation, increased G1 arrest.	[153]
	• In silico: TCGA, Human-GEM, BRENDA, GSE14520	The study found two genes with independent prognostic value: KIF2C and ATIC. The overexpression of both genes was associated with poor OS.	[149]
	• In silico: GSE47197, GSE55092, GSE121248	KIF2C was found within the highest deregulated genes in the HCC cohort. Its expression was verified in HBV-related HCC and KIF2C^high expression was correlated with poor OS and decreased RFS.	[150]
	• In silico: GSE17548, TCGA GSE25097, KM-plotter	Six hub genes were identified: BUB1B, NUSAP1, TTK, HMMR, CCNA2, and KIF2C. All of these genes had a high diagnostic value for HCC with reduced OS, PFI and an altered immune cell infiltration.	[151]
	• In silico: GSE62322, GSE112790, GSE102079, GSE14323, GSE14520, GSE89377, GSE64041, TCGA	Fourteen hub genes were identified including KIF2C. These genes were significantly upregulated in HCC and associated with a decreased OS. KIF2C was also correlated with an increased infiltration of regulatory T cells (Treg), T follicular helper (TFH) cells, macrophages M0, but negatively correlated with the infiltration of monocytes.	[152]
	• In silico: GSE14520, GSE22058, ICGC, TCGA	A network between 10 genes including KIF2C was found associated with clinicopathological features (reduced OS) of HCC. All of these genes are independent predictors of HCC and were connected to immune cell infiltration.	[154]
	• In silico: TCGA, ICGC, TCIA	This study generated a hallmark-guided subtypes-based immunologic signature (HGSIS) including eight genes. KIF2C was calculated as a key target concerning this HGSIS and ten small molecules were predicted to bind to the active site of KIF2C via molecular docking analysis, which might be useful for future therapies.	[155]
	• In silico: TCGA-LIHC, GSE14520	This study generated cuproptosis-linked subtypes and found a prognosis risk model related to cuproptosis with seven key genes: KIF2C, PTTG1, CENPM, CDC20, CYP2C9, SFN, and CFHR3. This HCC subtype had a reduced survival prediction.	[157]
	• In silico: TCGA-LIHC, GSE14520, HPA	An endoplasmic reticulum-related prognostic signature for HCC was generated included KIF2C. The generated signature was associated with reduced OS and RFS. Additionally, it was connected to increased macrophages M2/M0 and regulatory T cells infiltration.	[159]
	• In silico: GEO (normal samples-PRJNA494560, tumor samples-PRJNA414787), KM-plotter	Identification of thirteen hub genes for HCC and glioblastoma multiforme including KIF2C. KIF2C was overexpressed in both cancer entities, hypomethylated and KIF2C^high expression was correlated with poor OS.	[160]
	• In silico: TCGA (included 374 HCC tissue samples and 50 adjacent non-tumor samples), ICGC, WGCNA, IMvigor210	A novel prognostic signature for HCC was established using the protein and gene expression profiles of KIF2C and RAC1. Patients with high expression of both genes were assigned to a “high-risk group” with significant worse prognosis, higher clinicopathological grade, increased levels of tumor mutation burden, enhanced CD8+ T cell infiltration, increased expression of immune checkpoint genes, enhanced immunotherapy efficacy and an altered sensitivity toward chemotherapy drugs.	[156]
Non-small cell lung cancer (NSCLC)	• In silico: GSE18842 (included 46 NSCLC tissue samples and 45 paired non-tumor samples), GSE21933 (21 NSCLC tissue samples and 21 corresponding normal samples), and GSE89039 (eight LAC tissues samples and eight controls); TCGA	The hub genes, including AURKB, BUB1B, KIF2C, HMMR, CENPF, and CENPU, were overexpressed compared with the normal group and associated with worse OS and TNM stage. Genomic alterations of KIF2C: 2.1%	[161]
	• In silico: GSE31210 containing LUAD (n = 226) and normal lung tissue (n = 20)	High expression of KIF2C is related to the relapse and tumor stage of LUAD and reduced OS.	[162]
	• NSCLC tissues and normal lung tissues; 100 patients • In vitro: NSCLC cell lines A549, H1299, H226, and H520. A549 and H226 were transfected with shRNA	KIF2C is up-regulated in NSCLC tissues and cell lines. High expression level of KIF2C predicted worse survival. KIF2C knockdown inhibited proliferation, colony formation, migration and invasion of NSCLC cell lines. KIF2C was a target gene of miR-325-3p, which was reported to be a tumor suppressor in NSCLC.	[163]
	• In silico: GEO dataset (GSE18842, GSE32863, GSE75037, GSE81089)	key genes involved in the process of lung cancer: AURKA, CCNB1, KIF11, CCNA2, TOP2A, CENPF, KIF2C, TPX2, HMMR, and MAD2L1	[167]
	• In silico: microarray datasets GSE18842 and GSE118370 GSE18842: 91 samples (45 control samples and 46 NSCLC samples) and GSE118370: 12 samples (6 controls and 6 NSCLC samples)	Protein protein interaction network analysis revealed several hub genes: CDK1, CDC20, BUB1, BUB1B, TOP2A, CCNA2, KIF20A, CCNB1, KIF2C, and NUSAP1. High KIF2C expression associated with reduced OS.	[168]
	• In silico: GSE37745 including a total of 196 NSCLC samples; CIBERSORT package	High expression of AURKB, CDC20, TPX2, and KIF2C correlated with poor prognosis. AURKB, CDC20, TPX2, and KIF2C are potential CD8+ T cell infiltration-related biomarkers and therapeutic targets.	[169]
	• In silico: GSE74706 and GSE149507 The NSCLC and SCLC datasets comprised 36 samples each with 18 normal and 18 tumor paired samples	9 genes (AURKA, AURKB, KIF23, RACGAP1, KIF2C, KIF20A, CENPE, TPX2 and PRC1) were overexpressed in LC patients, potential prognostic biomarkers. Overexpression leads to increased cell number and proliferation, potentially leading to cancer formation. Frequency of mutations: 1.5%.	[170]
	• In silico: collectively analyzing 31 GEO expression datasets of lung adenocarcinoma (LUAD), KIF2C included in GSE68465; UALCAN/TCGA (consisting of 515 cancerous and 59 normal samples); GENT2, GEPIA, DriverDBV2, ENCORI	Upregulated hub genes: CDC6, PBK, AURKA, KIF2C, OIP5, and PRC1, associated with poor OS KIF2C is significantly hypomethylated relative to controls in LUAD patients. KIF2C had a genomic alteration rate of 3%. CD8+ T, and CD4+ T immune cells infiltration were negatively correlated with KIF2C. E2F1 and hsa-mir-34a-5p targeted all hub genes and were significantly upregulated in LUAD.	[164]
	• In silico: TCGA • In vitro: normal lung epithelial cell line BEAS-2B and lung cancer cell lines, including A549, A549-DDP, LLC, PC-9, SHP-77 and NCI-H1703; vector expression or siRNA	High KIF2C expression in LUAD and LUSC tissues were associated with worse prognosis. The results showed that KIF2C was upregulated in NSCLC cell lines. KIF2C overexpression promoted proliferation, migration, and invasion, and inhibited apoptosis. KIF2C was as a key target of miR-186-3p. High expression of KIF2C increased the levels of β-catenin, p-GSK-3β and p-AKT. KIF2C downregulation and miR-186-3p upregulation reversed these results.	[165]
Gastric cancer (GC)	• tissue: 65 cases of gastric cancer; RT-PCR and IHC • In vitro: cell line AZ521 KIF2C vector-overexpression	Elevated KIF2C expression was associated with lymphatic invasion and lymph node metastasis. Patients with high KIF2C expression had a significantly poorer survival rate. Overexpression of KIF2C increased cell proliferation, migration and resistance against anoikis.	[113]
	• In silico: GEPIA, TIMER • tissue samples: 35 GC samples and their corresponding normal samples • In vitro: cell lines SNU-1 knockdown by siRNA and AGS KIF2C-overexpression vector	KIF2C was overexpressed and might be used as a diagnostic and prognostic biomarker. KIF2C expression was correlated with immune infiltration and cell cycle-related genes. Knockdown of KIF2C caused cell cycle arrest and decreased the proliferation of GC cell lines. Overexpression of KIF2C had the opposite effect.	[177]
	• In vitro: cytotoxic T lymphocytes (CTLs) were primed with KIF2C peptides	KIF2C peptide primed CTLs were able to lyse various colon and NUGC3 gastric cancer cell lines in a peptide-based immunotherapeutic assay.	[178]
	• In silico: TCGA, GTEx database, GEPIA2, CCLE, UALCAN, CPTAC, HPA, TIMER2; verified with IHC of gastric cancer samples • In vitro: GES-1, SGC-7901, HGC-27, MKN-45, and AGS	KIF2C is significantly higher in tissue of gastric tumor patients. KIF2C is highly expressed in gastric cancer cell lines, gastric adenocarcinoma, and hepatocellular carcinoma. KIF2C gene is highly expressed in SGC-7901, HGC-27, and MKN-45 when compared to GES-1 and AGS.	[95]
Colorectal cancer (CC)	• cell lines: colorectal cancer cell lines HT29, COLO205, LIM1215, SKCO1, DLD1 • tissue: tumor samples and adjacent healthy tissues from 75 colorectal patients, RT-PCR + WB	KIF2C is significantly overexpressed and correlates with Ki-67 and the proliferative activity of CC. KIF2C was able to induce spontaneous CD4+ T cell responses of the Th1-type.	[115]
	• cell lines: DLD1, COLO201, COLO205, COLO320DM, HT29, HCT15, WiDr, SW480, LS174T, CCK81, RCM1 and CaR1, RT-PCR • tissue: paired colorectal tissue samples from tumors and the corresponding normal tissues were obtained from 120 patients, RT-PCR + IHC	KIF2C is expressed in various colorectal cancer cell lines, exception COLO201. KIF2C expression was higher in colorectal cancer tissue, and markedly associated with lymph node metastasis, venous invasion, peritoneal dissemination and Dukes’ classification. Patients with high KIF2C mRNA expression showed a poorer survival rate.	[114]
	• In silico: gene expression profiles of 17 paired CRC and adjacent normal tissues (GSE110224) from the GEO database	Higher expression of the key genes (KIF2C, CDC45, CEP55 and DTL) in CRC stages I–IV, adenocarcinoma and mucinous adenocarcinoma were identified as a stemness-related gene signature.	[188]
	• In silico: RNA-seq. data from 616 CRC patients from TCGA: 433 tissues and 408 colon adenocarcinoma (COAD), and 221 tissues and 208 rectum adenocarcinoma (READ)	The study identified 34 key genes as candidate prognosis biomarkers. A three-gene prognostic signature (PARPBP, KNSTRN, and KIF2C) was correlated with poor prognosis.	[187]
	• In silico: computational prediction of disease-associated non-synonymous polymorphism (nsSNP)	Mutation of E403K in KIF2C is associated with colorectal cancer interfering with protein conformation and stability.	[96]
Glioma	• tissue: 40 tumor samples and 6 normal brain tissues • In vitro: biomarker KIF2C, AURKB and Ki-67 were tested by RT-PCR and KIF2C by WB in patient tumor samples	KIF2C, AURKB and Ki-67 genes were higher expressed in glioma samples and associated with histopathological grades. Multivariate survival analysis identified high expression of KIF2C and Ki-67 as independent prognostic factors for survival time. KIF2C overexpression correlates with poor outcome. The expression level of KIF2C strongly correlated with proliferation marker Ki-67.	[172]
	• In silico: TCGA; including 142 glioblastoma multiforme (GBM) patients	Top 10 hub genes (CDC20, NCAPH, CDCA5, BUB1, CDCA8, PBK, KIF2C, TPX2, TTK and TOP2A)	[173]
	• In silico: TCGA, 530 cases of low-grade glioma (LGG) and 173 cases of (GBM); GSE16011, 284 gliomas	10 hub genes (CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE). CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE may be potential biomarkers and therapeutic targets for LGG.	[174]
	• In silico: CGGA database, 142 LGG and 34 sGBM samples • tissue: LGG, n = 8; sGBM, n = 4; IHC	Five hub genes: CCNB2, KIF2C, CDC20, TPX2, and PLK1 play an important role in promoting the transition of LGG to sGBM. High KIF2C expression is associated with poor OS.	[175]
	• In silico: CGGA, TCGA; GSE63678 and GSE17025	KIF2C was identified as a hub gene in GBM with positive association between KIF2C expression and the advanced stages of gliomas. Higher expression of KIF2C in WHO grade IV samples relative to that in grade III and grade II samples. Higher KIF2C expression correlated with shorter survival time in both primary and recurrent gliomas. Higher KIF2C expression was related to higher levels of endothelial cell, T cell CD8+ naïve, common lymphoid progenitor, T cell CD4+ Th2, T cell CD4+ Th2, macrophage, macrophage M1, T cell CD4+ memory, and T cell CD4+ effector memory, but was related to lower levels of NK cell, B cell plasma, T cell CD4+ Th1, T cell regulatory (Tregs), neutrophil, and T cell NK.	[103]
	• In silico: GSEA for immune activity of glioma, TCGA database (from 508 glioma patients) and GEO (GSE4412, 85 glioma patients)	14 core genes were identified, namely: TOP2A, TPX2, BUB1, AURKB, AURKA, CDK1, BUB1B, CCNA2, CCNB2, CDCA8, CDC20, KIF11, KIF20A, and KIF2C.	[104]
Endometrial cancer (EC)	• In silico: GEO datasets (i.e. GSE63678, GSE17025) and TCGA	15 genes were associated with the prognosis of EC (CCNB2, CDC20, BUB1B, UBE2C, AURKB, FOXM1, NCAPG, RRM2, TPX2, DLGAP5, CDCA8, CDC45, MKI67, BUB1, KIF2C). The high expression was associated with poor prognosis.	[189]
	• In silico: TCGA of 552 primary tumor samples and 35 solid normal tissue samples	AURKA, CENPA, and KIF2C, were found to be potential biomarkers for EC with a significant prognostic effect.	[190]
	• In silico: TCGA including 548 patients with uterine corpus endometrial carcinoma (UCEC)	AURKA, BUB1, CDCA8, DLGAP5, KIF2C, and TPX2 genes were significantly negatively related to the 5-year OS in patients.	[191]
	• In silico: TCGA (575 cases, including 23 normal cases) • tissue: twelve EC tissues and twelve adjacent tissues; RT-PCR, WB + IHC • In vitro: human EC cell lines Ishikawa and RL95-2; shRNA knockdown • mouse model with KIF2C-silenced Ishikawa cells	KIF2C expression was higher in EC. Bioinformatics indicated that KIF2C is negatively correlated with the infiltration level of CD8+ T cells but positively with poor prognosis. KIF2C knockdown prolonged G1 and inhibited EC cell proliferation, migration, and invasion in vitro. Apoptosis of CD8+ T cell was inhibited after KIF2C knockdown. KIF2C knockdown inhibited Ki-67 expression and tumor growth in a xenograft mouse model.	[192]
Ovarian cancer (OC)	• GEO datasets (GSE6008, GSE18520, GSE26712, GSE27651, GSE29450, GSE36668, GSE52037) containing 396 ovarian cancer samples and 54 healthy control; differentially expressed genes (DEGs)	12 hub genes: CDK1, TOP2A, CDC20, CCNB2, BIRC5, UBE2C, BUB1, NCAPG, RRM2, KIF2C, CENPA, and MELK were analyzed. KIF2C gene expression is upregulated in OC and negatively associated with OS.	[193]
	• In silico: GEO mRNA dataset (GSE28739, 30 chemo-resistant serous EOC samples and 20 chemo-sensitive serous EOC samples) and one miRNA dataset (GSE25202, n = 55 including 30 early relapsing patients and 25 late relapsing patients) • In vitro methods: validated using A2780 ovarian cancer cell line, RT-PCR	KIF2C was downregulated in OC. KIF2C, STAT3 and BUB1 were regulated by hsa-miR-494. Seven hub genes (BUB1, KIF2C, NUP43, NDC80, NUF2, CCNB2, and CENPN) were differentially expressed in platinum-resistant ovarian cancer cells; KIF2C is downregulated.	[194]
	• In silico: GEO dataset GSE14407 including 12 healthy controls and 12 OC patients	Expression of CCNB1, CCNB2, BUB1B, CCNA2, KIF2C, and TOP2A are associated with decreased survival and disease-free survival rates of OC.	[196]
	• In silico: GEO datasets GSE14407 (12 controls, 12 serous ovarian cancer epithelium samples), GSE29450 (10 controls, 10 ovarian cancer cell specimens), and GSE54388 (6 controls, 16 tumor epithelial component samples)	Expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in OC.	[197]
Esophageal squamous cell carcinoma (ESCC)	• tissue: 415 surgically resected primary tumor tissues and 40 adjacent non-cancerous tissues, IHC of KIF2C	Higher KIF2C expression was associated with significantly increased risks of higher pathologic tumor status and poorer tumor differentiation. KIF2C expression in tumor tissues seems to serve as an independent prognostic marker for male (OS and DFS), but not female.	[176]
Renal cell cancer (RCC)	• In silico: public RNA-seq. data from TCGA; n = 533 KIRC	Ten genes associated with the cell cycle and p53 signaling pathway were validated as prognostic markers: CCNA2, CDC20, CDCA8, GTSE1, KIF23, KIF2C, KIF4A, MELK, TOP2A, and TPX2.	[182]
	• In silico: GEO dataset (GSE168845); clear cell renal cell carcinoma (ccRCC); n = 8, 4 ccRCC and 4 controls; UALCAN database for methylation analysis	The most enriched pathways were T cell activation and cytokine-cytokine receptor interaction. 22 hub genes related to ccRCC. KIF2C had lower methylation levels in ccRCC tissues compared with paired tumor-free kidney tissues.	[185]
	• In silico: TCGA, UALCAN, TIMER, and CellMiner GSCA database for methylation analysis	KIF2C was highly expressed in several tumors, including KIRC and KIRP, correlated with poor prognosis. KIF2C expression was significantly associated with TMB, MSI, MMRs, and immune checkpoint genes, and with the level of immune cell infiltration such as tumor-associated macrophage (TAM), cancer-associated fibroblasts (CAFs), myeloid-derived suppressor cells (MDSCs) and Tregs. KIF2C is also associated with DNA methylation, m6A and m7G modifications.	[105]
	• In silico: TCGA, GTEx database, GEPIA2, CCLE, UALCAN, CPTAC, HPA, TIMER2; analyses of various tumor entities including KIRC and KIRP	High KIF2C expression was significantly related to the pathological stages of KIRC and KIRP. Increased KIF2C is associated with poor OS and DFS.	[95]
	• In silico: TCGA database	Nine highly expressed KIF genes (KIF2C, KIF4A, KIF7, KIF11, KIF14, KIF18A, KIF18B, KIF20A, KIF20B) KIF2C gene expression was significantly related to reduced OS.	[118]
	• In silico: TCGA, chromophobe renal cell carcinoma	Five hub lncRNAs (TTK, CENPE, KIF2C, BUB1, and RAD51AP1) may act as potential biomarkers for chromophobe renal cell carcinoma progression and prognosis.	[183]
Pancreatic carcinoma (PCa)	• TCGA/GEPIA analysis and PDAC tissue: 14 pairs of cancer and paracancerous tissues; RT-PCR, IHC • cell lines: ASPC-1 and MIA-PaCa2 (siRNA/shRNA and overexpression) and animal model with MIA-PaCa2	KIF2C expression is significantly upregulated in human PDAC tissues associated with a poor prognosis. KIF2C promotes PDAC cell proliferation, migration, invasion, and metastasis, in vitro and in vivo. KIF2C overexpression causes a decrease in some proinflammatory factors and chemokines. In animal models, downregulation of KIF2C greatly inhibits the formation of subcutaneous tumors and lung metastasis.	[99]
	• In silico: TCGA, GTEx database, GEPIA2, CCLE, UALCAN, CPTAC, HPA, TIMER2; analyses of various tumor entities including PAAD • scRNA-seq. of 24 primary PDAC tumors and 11 control pancreases (database: CancerSCEM)	The gene and protein expression level of KIF2C was higher in tumor tissue of PAAD. Increased levels of KIF2C expression were significantly associated with poor OS and DSF. High expression of KIF2C was significantly associated with E2F, EGFR, MYC and KRAS. KIF2C expression is highly enriched in CD4+ T cells, fibroblasts, NK cells, and most significant in malignant cells of PDAC.	[95]
	• In silico: PDAC TCGA/ GEPIA, Oncomine datasets, cBioPortal, LOGpc, TIMER, and STRING bioinformatics tools	KIFC1/2C/4A/11/14/15/18A/18B/20B/23 were upregulated and associated with unfavorable clinical outcome. Poorer patient survival is associated with high KIF2C expression. KIFC1/2C/4A/11/14/15/18A/18B/20B/23 were significantly associated with the mutation status of KRAS and TP53. KIF2C/4A/11/14/15/16B/20A/22/23/25 were core enriched in immunologic signature. KIF genes were correlated with tumor stage and myeloid-derived suppressor cells infiltration (MDSCs).	[179]
	• localization: HPA and IF in U2OS, SHIA, RH-3, ASPC1 and BXPC3 • In silico databases: GEPIA2, TISIDB, Linkdeomics, miRDB, miRanda, miRTairBase, Targetscan and miRmap, TISIDB, GSE62452 and PACA-UA	nuclear localization of KIF2C KIF2C mRNA expression levels were higher in pancreatic cancer tissues and significantly associated with decreased OS and DFS. KIF2C expression was closely related to CD4 + T cells.	[180]
	• In silico: TCGA, GTEx, CCLE; 31 normal tissues, 30 tumor cell lines, and 33 tumor tissues and compared KIF2C expression levels in 33 cancer samples and corresponding paracancer samples and TCGA prostate adenocarcinoma (PRAD) dataset with 249 patients in the KIF2C high expression group and 250 patients in the KIF2C low expression group • tissue: tumor samples and related paracrine tissues from 14 pairs of PCa patients	KIF2C was significantly upregulated, associated with age, pathological stage, lymph node metastases, prostate-specific antigen, and Gleason score and significantly predicted an unfavorable prognosis. Increased KIF2C was significantly correlated with poor OS, DSS, DFI, and PFI. KIF2C was involved in the cell cycle and immune response. KIF2C DNA methylation was reduced and inversely linked with KIF2C expression. KIF2C has a strong relationship with the TME, infiltrating cells, and immune checkpoint genes. High KIF2C expression was significantly resistant to a variety of MAPK signaling pathway-related inhibitors.	[181]
Acute lymphoblastic leukemia (ALL)	• In silico: databases including TARGET, GSE60926 and GSE28460; all patients n = 134, relapse n = 116 • In vivo: zebrafish embryos (50% sequence homology)	KIF2C and KIF18B are overexpressed in patients with relapsed ALL compared to patients diagnosed with ALL for the first time. KIF2C is necessary for lymphopoiesis in zebrafish embryos. KIF2C is important for the maintenance and survival of hematopoietic stem cells.	[198]
Cervical cancer	• In silico: 33 TCGA cancer types, TIMER 2.0, GETx, GEPIA2, UALCAN • tissue: 35 cervical squamous tissue samples, 25 cervical adenocarcinoma tissue samples, and 40 normal cervical tissues samples • cell lines: cervical cancer cell lines SiHa, HeLa, C33a, and Caski and normal cervical cell line HCerEpic; shRNA used for C33a and SiHa cells, also for subcutaneous xenograft model.; vector DNA for Caski cells	KIF2C expression was significantly upregulated in cervical cancer tissues and cervical cancer cells. KIF2C is associated with enhanced proliferation, invasion, and migration in vitro and increased tumor growth in vivo. KIF2C knockdown promotes the p53 signaling pathway A KIF2C and p53 double knockdown partially reversed the inhibitory influence of KIF2C silencing on cervical cancer processes.	[184]

Figure 2. The model depicts how deregulated KIF2C/MCAK is associated with some of the fourteen known and discussed hallmarks of cancer including acquired capabilities and enabling characteristics [84]. These hallmarks encompass the following terms: deregulated cellular metabolism, sustaining proliferative signaling, resisting cell death, evading growth suppressors, polymorphic microbiomes, enabling replicative immortality, avoiding immune destruction, nonmutational epigenetic reprogramming, unlocking phenotypic plasticity, activating invasion and metastasis, tumor-promoting inflammation, cellular senescence, inducing or accessing vasculature and genome instability and mutation. Deregulated KIF2C may be associated with at least five of these hallmarks by promoting migration and invasion, disrupting MT dynamics, impairing the immune response, inducing senescence and increasing therapy resistance to MT interfering agents.

Figure 3. High expression of KIF2C is associated with reduced OS and DSF in BC, HCC and NSCLC. (A–C) The meta-analyses concerning KIF2C/MCAK’s importance as a prognostic marker is depicted as forest plots for OS of BC, NSCLC and HCC patients (A–C). (D) Meta-quantification of KIF2C/MCAK involvement in DSF in HCC patients. All individual studies were scored based on their patient cohort. The weighted score (ws) is visualized in a heatmap, dark blue (high ws), yellow (intermediate ws) and red (low ws). The overall mean value (MV) extracted from all studies was calculated by integrating the weighted score. Abbreviations: OS: overall survival; DFS: disease-free survival.

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Data availability statement

All data generated or analyzed during this study are included in this article and its supplementary information.