Kinesin family member 11 is a potential therapeutic target and is suppressed by microRNA‐30a in breast cancer

Abstract Kinesin family member 11 (KIF11) is a plus end‐directed kinesin indispensable for the formation of the bipolar spindle in metaphase, where it objects to the action of minus end‐directed molecular motors. Here, we hypothesize that KIF11 might be a therapeutic target of breast cancer and regulated by miR‐30a. Cell Counting Kit 8 assays were used to investigate cell proliferation. Invasion assays were used to survey the motility of cells. Kaplan‐Meier and Cox proportional analyses were employed for this outcome study. The prognostic significance and performance of KIF11 were validated on 17 worldwide independent microarray datasets and two The Cancer Genome Atlas‐Breast Invasive Carcinoma sets. microRNA was predicted targeting KIF11 through sequence alignment in microRNA.org and confirmed by coexpression analysis in human breast cancer samples. Dual‐luciferase reporter assays were employed to validate the interaction between miR‐30a and KIF11 further. Higher KIF11 mRNA levels and lower miR‐30a were significantly associated with poor survival of breast cancer patients. Inhibition of KIF11 by small‐hairpin RNA significantly reduced the proliferation and invasion capabilities of the breast cancer cells. Meanwhile, downregulation of KIF11 could enhance the cytotoxicity of adriamycin in breast cancer cell lines MCF‐7 and MDA‐MB‐231. A population study also validated that chemotherapy and radiotherapy significantly improved survival in early‐stage breast cancer patients with low KIF11 expression levels. Further bioinformatics analysis demonstrated that miR‐30a could interact with KIF11 and validated by dual‐luciferase reporter assays. Therefore, KIF11 is a potential therapeutic target of breast cancer. miR‐30a could specifically interact with KIF11 and suppress its expression in breast cancer.


| INTRODUCTION
Breast cancer is one kind of the most common malignant cancers among females in the world, with approximately 1 000 000 new cases each year. 1,2 It is also the second-leading cause of death among women, accounts for 15% of all cancer deaths. 3  type. [4][5][6][7][8] This classification of breast cancers has been used for selecting the appropriate therapeutic method. Currently, personalized precision medicine is an emerging field, however, underdeveloped in breast cancer. More targets and corresponding inhibitors need to be explored to improve treatment efficacy and to reduce adverse side effects.
The protein of the kinesin family could function as molecular nanomotors. It converts the free energy of nucleotide hydrolysis in coordinating the mechanical movement of microtubules. 9,10 As a member of the kinesin family, KIF11 is a microtubule-dependent motor protein encoded by the KIF11 gene located at 10q24.1, with a primary function in mitotic spindle formation. 11 KIF11 is still an essential element for maintaining proper spindle dynamics and preserving spindle bipolarity in cell division. It has a catalytic motor/ATPase domain that mediates its interaction with ATP and microtubules. KIF11 utilizes the energy released by ATP hydrolysis to move forward along microtubules. It facilitates spindle assembly by forming a homotetramer. The homotetramer can cross-link and push apart antiparallel microtubules. 12,13 In the previous study, the KIF11 has been implicated in tumourigenesis. It overexpresses in blast crisis chronic myeloid leukemia, activation in mouse B-cell leukemia, and triggering of genomic instability in transgenic mice. [14][15][16] KIF11 has also been identified as a molecule involved in pancreatic cancer, non-muscle invasive bladder urothelial carcinoma, non-small cell lung cancer, and glioblastoma. [17][18][19][20] These studies suggest that KIF11 may be involved in the pathogenesis of multiple kinds of cancer. Because of its participating in dividing cells, KIF11 is an essential anticancer target with the trait to avoid the deficiencies of traditional anti-mitosis drugs. 21,22 Drug candidates like ispinesib inhibit KIF11 and cause mitotic arrest, then apoptosis.
The research and development of ligand are continually driven partly by the observation of deactivating mutations in the drug binding region, and lack of successful monotherapies based on KIF11 inhibition. Although in the course of our research, one study has discussed the function of KIF11 in breast cancer, 23 whether KIF11 is a potential therapeutic target for breast cancer remains unclarified currently, and the transcriptional regulation on KIF11 also needs to be elucidated.
As an essential transcriptional regulator, the differential expression pattern of microRNAs (miRNAs) in health and disease, therapeutic response, and resistance has resulted in its application as robust biomarkers. 24 Gene regulation by miRNAs and reciprocal regulation of miRNAs have now been studied for over 15 years and extensively reviewed. 25 In general, one miRNA could target multiple genes.
Meanwhile, one messenger RNA (mRNA) can be targeted by multiple miRNAs, which highlighted the complexity of miRNA biology. 26 Previous studies showed that the outcome of cancer is closely related to the variable expression and the specific expression signatures of miR-NA in cancer tissues. 27 In particular, there is some existing evidence that miRNAs are tightly linked to the development of human breast cancer. [28][29][30][31] miRNAs are attractive candidates as upstream regulators of breast tumor progression and metastasis by regulating entire sets of genes. miRNA signature can subclassify breast cancer 32 and can even determine new subtypes, as recently reported. 33 miR-30a has been validated as a tumor suppressor via targeting multiple genes in diverse cancer. [34][35][36][37] Here, we predicted target miRNAs of KIF11 using both predicting KIF11-related miRNAs in microRNA.org and correlation analysis for KIF11 and miRNAs in the GSE22220 dataset, screening out target-KIF11 miRNAs. Favorably, miR-30a was one of five miRNAs that could bind to the 3'-untranslated region (3'-UTR) of KIF11 mRNA. Thus, miR-30a may be involved in the regulation of KIF11 in cancer progression.
In the present study, we investigated the role of KIF11 in the tumorigenesis and treatment of breast cancer and the possible role of miR-30a in the regulation of KIF11 in this process.

| The quantitative reverse transcriptionpolymerase chain reaction analysis
A detailed protocol of quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis could be found in our previous publication. 38 Quantitative RT-PCR for mRNA was detected using an ABI 7500 real-time PCR system and Absolute qPCR SYBR Green Mix (Applied Biosystems, Foster City, CA). The primer sequences used for KIF11 mRNA detection were 5′-GATGGACGTAAGGCAGCTCA-3′ (forward) and 5′-TGTGGTGTCGTACCTGTTGG-3′ (reverse). C t values for KIF11 mRNA were normalized to β-actin mRNA, which was used as internal controls. The −ΔΔ 2 Ct method was applied to calculate the relative expression of mRNA.

| Invasion assays
Details of the invasion assay were described in a previous publication. 39 About Table S1.
The disease-free survival (DFS) period was defined as the time from initial surgery until tumor recurrence, including local relapse and distant metastasis. The overall survival (OS) period was calculated as the time from initial surgery to the date when the patient was last seen. To normalize the mRNA expression levels among the above datasets, we restratified the scores of KIF11 and other related genes into four grades (Q 1 , Q 2 , Q 3 , and Q 4 ) based on the percentile for each independently downloaded dataset. For Cox analysis, less than the median was regarded as KIF11-low (Q 1 + Q 2 ), while greater than or equal to the median was regarded as KIF11-high (Q 3 + Q 4 ).
The demographic distribution of KIF11 is presented in Table S2.

| Gene set enrichment analysis
To evaluate the correlations between KIF11 expression and cancerrelated pathways, we conducted gene set enrichment analysis (GSEA) using the above microarray dataset GSE1456. The detailed protocol of GSEA was available on the Broad Institute GSEA website (www.broad. mit.edu/gsea) or from related references. 57 Briefly, datasets and phenotype label files were created and loaded into GSEA software (v2.0.13). The gene sets were downloaded from the Gene Expression Omnibus (http://www.cancergenome.nih.gov/geo/). The phenotype label was KIF11 expression. We set the number of permutations to 1000.

| Target prediction and functional analysis of miRNA
The presumed target of KIF11-related miRNA, especially the most significant hsa-miR-30a, we searched in microRNA.org (http://www. microrna.org/microrna/home.do). The above breast cancer microarray dataset GSE22220 was used to assess the role of miR-30a and KIF11 in breast cancer progression and prognosis.

| Data management and statistical analysis
All data were analyzed using the SAS statistical software, version 9.2 (SAS Institute, Cary, NC), unless otherwise noted. The Student t test and one-way analysis of variance were used for continuous data analyses, and the Pearson χ 2 test was used for categorical data analyses. We used Kaplan-Meier survival analysis to draw the proportion of the population that was OS or DFS by follow-up time in months. We calculated hazard ratios (HR) with 95% confidence intervals (CI) using Cox proportional hazards regression analysis to survey the association of KIF11 expression levels with patient survival. Two-sided P values less than .05 were considered statistically significant. Missing data were coded and excluded from the analysis.  Figure 2E). The correlation between KIF11 and poor prognosis was further verified. Besides, more NES related to poor differentiation, metastasis, and so forth were indicated in breast cancer ( Figure 2F). All of the above findings validated that mRNA levels of KIF11 were significantly associated with aggressive phenotypes in breast cancer.

| Prognostic significance of KIF11 for breast cancers
Since NES of KIF11 was associated with poor prognosis, poor differentiation, and metastasis of breast cancer, the expression of KIF11   58 Our results in Figure 2A,B, showed that KIF11 had higher expression levels in ER-negative breast cancers. Further stratified Kaplan-Meier analysis with the pooled data explored that KIF11 mRNA levels were significantly associated with poor OS ( Figure 3C) and poor DFS ( Figure 3D) in not only ER-negative but also in ER-positive breast cancers. It was confirmed that KIF11 expression significantly impacted the poor survival of breast cancer.
Further survival analysis was conducted on every independent dataset by employing unique and multiple Cox proportional hazard analysis. The results are listed in Table 1. Q 1 was the lowest expression subgroup, which was set as the relative point of reference.

| Screening of KIF11-targeting miR-30a
miRNA is an essential transcriptional regulator involved in the various cancerous process. Here, we screened out eligible target miRNAs using both predicting KIF11-related miRNAs in microRNA.org and correlation analysis for KIF11 and miRNAs in the GSE22220 dataset ( Figure 5A), to validate in cell culture study and to conduct clinical relevance analysis.
Hsa-miR-30a expression was negatively correlated with KIF11 mRNA expression ( Figure 5B). The sequence of hsa-miR-30a target KIF11 was shown in Figure 5C. miR-30a transfection inhibited KIF11 mRNA and protein expression ( Figure 5D, left), luciferase assay further verified inhibited effects of miR-30a to KIF11 ( Figure 5D, right). Analysis results based on the GSE22220 set showed that expression levels of hsa-miR-30a were significantly positively correlated with diseaserelapse-free survival of breast cancer ( Figure 5E). The prognostic performance of KIF11 and miR-30a could be compared with age, tumor size, and grade. KIF11 and hsa-miR-30a had better prognostic capabilities than lymph node involvement ( Figure 5F). The above findings suggest that KIF11 and miR-30a could serve as a prognostic biomarker to predict poor outcome in breast cancers, and miR-30a in breast cancer could suppress the expression of KIF11. ATPase activity-dependent manner, leading to the accumulation of polyploid cells. 17 Another research showed that overexpression of KIF11 was related to poor differentiation of bladder cancer. 18 Inhibition of KIF11 with a highly specific small-molecule inhibitor has been proven to delay the growth of commonly treatment-resistant Expression of KIF11 and miR-30a is associated with the development and outcome of breast cancer. First, in vitro assays with KIF11 knockdown significantly inhibited cell proliferation and invasion. Second, the OS and DFS in breast cancer databases were significantly lower in high-KIF11 breast cancer than in low-KIF11 breast cancer.
Third, the expression between KIF11 and miR-30a shared a negative correlation ( Figure 5B). Given these findings, we propose that KIF11 contributes to the development of breast cancer, and miR-30a suppresses the KIF11 expression. Undoubtedly, extensive investigations are required to illuminate the elaborate mechanism of KIF11 in the development and regulation of breast cancer, and in-depth studies are also needed to uncover the interactions between KIF11 and miR-30a.
Taken together, we demonstrate a critical role of KIF11 in promoting invasion and predicting poor prognosis in breast cancer patients.
The levels between KIF11 and miR-30a present a significantly negative correlation in breast cancer databases. Our findings highlight that miR-30a and KIF11 could be employed as promising prognostic biomarkers and therapeutic targets for breast cancers.

ACKNOWLEDGMENTS
This study was supported by an unrestricted starting package from The First Affiliated Hospital of Soochow University to Yiqiang Wang and a grant from the National Natural Science Foundation of China (81600076) to Dandan Lin.