Introduction
Worldwide, breast cancer raises concerns to human health, women especially, with continuously increasing incidence and high mortality. 2.1 million new cases diagnosed and 626,679 deaths found in 2018 make breast cancer the most commonly diagnosed cancer and the leading cause of cancer death in women [
1]. Great efforts are put by clinicians and researchers and progressions are seen in early detection, diagnosis, and treatments of breast cancer over the years with a significant extension of breast cancer survival [
2]. Nevertheless, early recurrence, distant metastasis and drug resistance are still commonly seen, which hold threads to the prognosis of breast cancer patients and mount challenges for clinicians [
3‐
5]. Further researches were urgently needed to unravel the molecular mechanism underlying and discovering valuable prognostic biomarkers for breast cancer survival.
Kinesin superfamily (KIFs) were a group of proteins featured to be microtubule-based motors and functioned as intracellular transporters that directionally transport various cargos, including organelles, protein complexes and mRNAs, along microtubules in an adenosine triphosphate (ATP)-dependent way and played crucial roles in not only cellular morphogenesis and fundamental biology, like mitosis and meiosis, but also various mechanisms for higher life functions, including higher brain functions like memory and learning, left–right asymmetry formation, etc. [
6‐
8]. There are 45 KIFs discovered and identified in human, among which several family members were demonstrated varied functions in tumor pathobiology [
9]. KIF11 was identified as a molecular target that shuttles between the proliferation and invasion of glioblastoma. Administration of KIF11 inhibitors in glioblastoma-bearing mice had a significantly extended survival indicating a putative therapeutic target for glioblastoma [
10]. KIF20A peptide-based immunotherapy for cancer treatment was demonstrated availability and putative efficacy with promiscuous T
-H-cell epitopes derived from KIF20A identified in solid tumor tissue and distinguished KIF20A-specific T
H1-cell responses were found in patients with HNMT receiving immunotherapy [
11]. Microarray data analyses revealed the highly transactivated status of KIF4A in non-small cell lung cancer and targeting KIF4A might hold a promise for the development of anticancer drugs and cancer vaccines as well as a prognostic biomarker in the clinic [
12]. Numerous researches were done highlighting the importance of KIFs in various aspects of breast cancer [
13]. KIF2A, KIF14 and KIF26B were found overexpressed in lymph nodes-positive breast cancer patients indicating putative impacts on tumor metastasis [
14‐
16]. Knocking down of KIF2C, KIF3C, KIF22, KIF18A and KIF24 inhibited proliferation of breast cancer cells via different mechanisms including G2/M phase arrest, delayed exit from mitosis, deregulating cell division and restoring ciliation [
17‐
22]. Recent researches demonstrated implications of KIF1A, KIF5A, KIF12, KIF14, KIFC1 and KIFC3 in resistance to docetaxel by destabilizing microtubule [
23‐
26], while KIF5A, KIF5B, KIF12, KIF20A and KIFC3 were found to reduce the efficacy of paclitaxel by inducing abnormal breakdown of microtubules in breast cancer treatment [
24,
27‐
29].
Given the essential roles of KIFs reported in cancer, KIF-targeting cancer therapies were highly expected to be of great efficacy and several KIF-inhibitors were invented and tested in clinical trials. Ispinesib, a KIF11-targeted inhibitor, was the first KIF-inhibitor that was evaluated both safety and efficacy in breast cancer in phase I clinical study [
30]. Other KIF-targeted drugs further tested in various cancers by clinical trials including KIF11 inhibitors (litronesib [
31,
32], filanesib [
33‐
35], SB-743921 [
36], AZD4877 [
37]), KIF5C inhibitors (Lidocaine and Tetracaine [
38]) and KIFC1 inhibitors (AZ82 and SR31527 [
39,
40]). However, limited efficacy was seen in all inhibitors reported. Therefore, despite numerous researches done, the prognostic and therapeutic value of all KIFs remains uncorroborated. Considering the intricate functions of KIFs in mitosis, singling out any particular KIFs may not be an efficient way to fulfill the therapeutic capacity of KIFs, while common regulatory network of all KIFs are little known, which may give new insight into the limited therapeutic efficacy shown in clinical trials and provide putative drug target by mutually regulating KIFs in cancer.
By adopting comprehensive multi-dataset bioinformatics analyses, our study intends to demonstrate the value of kinesin superfamily members as prognostic biomarkers of breast cancer, explore the putative regulatory network of KIFs, discover common functions and pathways shared among members and provide promising insights into breast cancer treatment.
Discussion
Kinesin superfamily has a long-reported significant influence on the initiation, development and progress of breast cancer [
15,
16,
20,
21,
25,
26]. However, the prognostic value of whole family members was poorly done. Therefore, comprehensive bioinformatics analyses were done in our study using data from multi-dataset to explore the prognostic value, as well as regulatory mechanism, functions and putative pathways, of kinesin superfamily. A total of 20 differentially expressed KIFs were identified between breast cancer and normal tissue with 4 downregulated and 16 overexpressed. Survival analyses revealed 11 overexpressed KIFs (KIF10, KIF11, KIF14, KIF15, KIF18A, KIF18B, KIF20A, KIF23, KIF2C, KIF4A, KIFC1) significantly correlated with worse OS, RFS and DMFS of breast cancer, indicating efficient biomarkers for predicting the prognosis of breast cancer. Further analyses were done with a 6-KIFs-based risk score generated by LASSO regression, a nomogram was constructed with elements selected by multivariate survival analysis and an accurate predictive efficacy was validated. Expression profiles of the 6 KIFs selected (KIF10, KIF15, KIF18A, KIF18B, KIF20A, KIF4A) were testified by quantitative RT-PCR and immunohistochemistry. Overexpression was seen in all 6 KIFs in both mRNA and protein levels, which agrees with bioinformatics analyses, demonstrating stable and significant upregulation in breast cancer. Enrichments of regulatory mechanism revealed MSX1 a putative transcription factor that negatively regulates KIFs expression in breast cancer. GO and KEGG analyses were also done to explore mutual functions and pathways of KIFs in breast cancer.
Given the results done in our study, KIFs were demonstrated solid prognostic value with significantly differential expressions and strong correlations with the survival of breast cancer by bioinformatics analyses and further quantitative RT-PCR and immunohistochemistry of patient samples also demonstrated a significant difference between cancer and normal tissue, indicating putative efficacy as biomarkers for breast cancer. Previous work is done by Song et al. using only TCGA data found 21 significantly differential-expressed KIFs, among which just KIF4A was further identified as OS-related, while overexpression of KIF15, KIF20A, KIF23, KIF2C related to OS after adjusted for tumor stage and age [
57]. Comparing to the results given in our study, similar expression profiles were seen with an extension of normal samples from GTEx. However, by using combined data from multi-dataset, the significant prognostic value was seen in most KIFs regarding either OS, RFS or DMFS. Furthermore, survival analyses were done with only TCGA data also showed significant correlations between the expression of KIFs and survival outcomes of breast cancer, OS and RFS. Given the purpose of exploring the prognostic value of KIFs, best cutoffs were used for grouping instead of median expressions, meanwhile, larger samples ensured better accuracy and sensitivity. Additionally, validations made with operating samples from breast cancer patients in both mRNA and protein levels further supported KIFs working as executable clinical biomarkers. Therefore, our study demonstrated greater efficacy and availability of KIFs in predicting breast cancer prognosis.
The 6 KIFs selected by LASSO regression in our study were demonstrated significant prognostic value with overexpression strongly correlated with worse outcomes in breast cancer, not only overall survival but also relapse and distant metastasis, by bioinformatics analyses. Validations can be made from studies published, focusing on the biological and tumorigenic mechanism of KIFs. Previously reported biological functions of KIFs mainly involved in the regulation of mitosis [
58]. During prophase to prometaphase transition, KIF15 works as an interaction partner Ki67 and is required for spindle elongation and the maintenance of spindle bipolarity [
59,
60]. KIF10, KIF18A and KIF18B are reported to be essential to the progression from metaphase to anaphase with different functions [
61,
62]. KIF10 mainly participates in microtubule–kinetochore capture and mitotic checkpoint signaling, therefore plays an important role in chromosome congression and alignment [
61,
62], while KIF18A and KIF18B, two related members of kinesin-8 family, both regulate microtubule dynamics at the plus end, controlling correct chromosome positioning and the length of astral microtubules, respectively [
63‐
65]. KIF20A was reported to be functioning during cytokinesis by regulating furrow ingression and several other events that are essential for successful cytokinesis [
66,
67]. KIF4A, among all six KIFs selected in our study, is the only one that functionally involved in multi-stages of mitosis, participating in chromosome condensation, anaphase spindle mid-zone formation and cytokinesis [
50,
62,
68]. Given the hyperactive proliferation of tumor cells, overexpression of the six KIFs selected as expected, which in accordance with the results given in our study, and further demonstrations were found on both cellular and molecular levels reported in previous studies [
69‐
72]. Tumorigenic functions of KIFs affect various aspects of breast cancer, including metastasis, progression and chemotherapy resistance. Silencing of KIF10 and KIF18A were both reported inhibitions to the proliferation of breast cancer cells via deregulating cell division [
20,
69]. Lysosomal stability was demonstrated to enhance the survival of breast cancer cells while the knocking down of KIF20A conduced the permeabilization of the lysosomal membrane, which in turn, causing cellular death [
73]. KIF18A, KIF15 and KIF4A were demonstrated prognostic biomarkers for prediction of clinical outcomes [
20,
70]. Furthermore, expression of KIF18A was associated with cancer grade and metastasis status and may facilitate cancer cell migration by deregulating microtubule stability [
20]. Given both biological and tumorigenic functions of the six KIFs selected, which is highly consistent with our results from bioinformatics analyses, the prognostic value of six KIFs was seen in predicting clinical outcomes of breast cancer patients with high expression of KIFs highly correlated with worse survival endings, including overall survival, relapse-free survival and distant metastasis-free survival.
Numerous works have been done focusing on the exploration and validation of breast cancer biomarkers for better clinical stratification of patients and more efficacious treatment. Early predictive models generated from clinical data and SEER database with only clinical factors had been demonstrated a lack of efficacy in the surge of sequencing technology. Recent days have seen models using multi-omics data to pursue better accuracy but failed in clinical transformation owing to the limitation of detection technology and standardized criteria. Meanwhile, several multi-gene test panels had already validated good utility by randomized clinical trials and been recommended by NCCN guidelines like the 21-gene test [
74], MammaPrint [
75] or PAM50 [
76]. A within-patient comparison had been done by Ivana Sestak et al. between multiple molecular signatures that are available for managing ER-positive, ERBB2-negative breast cancer after 5-year endocrine therapy in the TransATAC cohort. The signatures providing the most prognostic information were the PAM50 (hazard ratio [HR], 2.56; 95% CI 1.96–3.35), followed by the Breast Cancer Index (HR, 2.46; 95% CI 1.88–3.23) and EndoPredict (HR, 2.14; 95% CI 1.71–2.68). Each provided significantly more information than the Clinical Treatment Score (HR, 1.99; 95% CI 1.58–2.50), the 21-gene score (HR, 1.69; 95% CI 1.40–2.03), and the 4-marker immunohistochemical score (HR, 1.95; 95% CI 1.55–2.45) [
77]. These results demonstrated better efficacy of predictive models combined with molecular and clinical information than each alone. Given the essential roles of KIFs in breast cancer, a comprehensive analysis combining molecular expression and clinical features has been done in our study tying to highlight the prognostic potency of a six-KIFs score based predictive model. Despise a lack of large cohort comparison with any other biomarkers, instead, we validated good efficacy as well as a clinical utility by qPCR and IHC which are easy-access and standardized methods.
Although the KIF family has been shown to play an essential role in various aspects of breast cancer, the development of drugs targeting KIFs has not been satisfactory. Previously reported KIFs-targeting drugs including GSK923295 (a KIF10 inhibitor) [
78], Quinazolinedione and phthalimide inhibitors (both KIF15 inhibitors) [
79], BTB1 (an inhibitor of KIF18A) [
80], Paprotrain (the first known inhibitor of MKLP2) [
81]. However, no clinical trials were done in breast cancer and a ‘double-edged sword’ effect was seen in the therapeutic efficacy of the KIF10 inhibitor [
82], indicating an unclear treatment window. The limitation shown in drug development raised controversy in the clinical significance of kinesin superfamily. Furthermore, analyses found regulatory correlations between members, with KIF10 regulated by KIF18A [
83], which indicates a putative deficiency in singling out any KIFs to analyze alone rather than balancing the interplay between tumor-related KIFs [
84]. Therefore, our analyses combined all KIFs to explore the prognostic value and putative regulatory mechanism of KIFs. Despise the correlations between KIFs and the prognosis of breast cancer, a putative transcription factor MSX1 was identified as a repressive upstream with a significant under-expression in breast cancer, which may lead to the overexpression of KIFs and further contribute to the initiation, progression and prognosis of breast cancer. This may give a new perspective into the therapeutic value of KIFs by revealing a putative mutual regulator which significantly affects the expression of tumor-related KIFs, therefore, may serve as a potential drug target by influencing kinesin superfamily.
MSX1, a member of the muscle segment homeobox gene family, was long identified as a transcriptional repressor during various biological processes [
85]. The essential roles of MSX1 were demonstrated in multiple malignancies. High-throughput global expression profiling of lung cancer cells revealed promoter methylation of MSX1 a novel biomarker for primary lung, breast, colon, and prostate cancers [
86]. Cellular experiments validated hypomethylation of CpG sites within the MSX1 gene highly associated with resistant high-grade serous ovarian cancer (HGSOC) disease at presentation and identified expression of MSX1 as conferring platinum drug sensitivity [
87]. By interacting with P53 tumor suppressor, MSX1 was demonstrated as an inhibitor to tumor growth as well as an inducer to cancer cell apoptosis [
88]. From our bioinformatics enrichments of KIFs, MSX1 showed potency in functioning as a therapeutic target for breast cancer treatment by generally repressing the expression of survival-related KIFs, which may need further tests in both pharmaceutical development and clinical trials.
KEGG analyses found various putative downstream pathways affected by the alteration of KIFs, among which human T-cell leukemia virus 1 infection pathway indicating potential correlations between KIFs and immunity. Researches published demonstrated the correlation predicted by our bioinformatics analyses. KIF7 was reported to be required for T-cell development with the deficiency of KIF7 leading to the increase of premature CD44+CD25+CD4−CD8− thymocyte progenitor population while a decrease of differentiated CD4+CD8+ double- positive (DP) cell [
89]. Furthermore, KIF20A-derived long peptides were identified bearing naturally processed epitopes recognized by CD4(+) T cells and CTLs, which induce tumor-specific T-helper type 1 (TH1) cells and CTLs in head-and-neck malignant tumor tissues [
11]. Other pathways enriched include Fanconi anemia pathway and p53 signaling pathway. Previously published studies validated repressed expression of KIF2C regulated by P53 via down-regulation of Sp1 level in human tumor cells [
90], however, no report was found focusing on Fanconi anemia and KIFs, which need further exploration.
In conclusion, our study demonstrated the significant overexpression of tumor-related KIFs by bioinformatics analyses, which correlate with worse outcomes of breast cancer patients, therefore may work as prognostic biomarkers. A nomogram containing LASSO-generated six-KIFs-index was generated and validated a good prediction efficacy. Further analyses revealed MSX1 a putative transcription factor that negatively regulates the expression of KIFs in breast cancer and may work as a putative drug target.
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