KIF20A- A New Discovery in Tumor Therapy

KIF20A- A New Discovery in Tumor Therapy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article KIF20A- A New Discovery in Tumor Therapy Zhenzhen Feng, Minmin Li, Songling Lu, Jian Sun, Naifei Xing, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7044011/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background KIF20A is a cell cycle-related protein playing a crucial role in cell mitosis and participating in important steps such as chromosome segregation, spindle assembly, and cytoplasmic division. Methods Based on bioinformatics databases, KIF20A expression levels and their relationship with immune cell infiltration (ICI) were examined by means of TIMER and TCGA, tumor prognosis was assessed through one-way COX regression and Kaplan-Meier (KM) analyses, while genetic mutations were analyzed using cBioPortal. The R programming language was adopted for immune checkpoint (ICP) and pathway enrichment analyses. Results KIF20A exhibits significant expression elevation in the majority of cancers, and KIF20A protein is also highly expressed. Tumors demonstrating high KIF20A expression are associated with worse prognostic outcomes. Elevated KIF20A expression is positively linked to ICI, tumor mutational burden (TMB), and ICPs across most tumors. Amplification is the most frequent genetic alteration that the KIF20A gene undergoes. This extensive pan-cancer analysis provides enhanced understanding of KIF20A's role in tumorigenesis and metastasis. Conclusions KIF20A as a new target gene opens a new path for tumor treatment and diagnosis, and inhibiting this gene has the potential to improve tumor prognosis and prevent tumorigenesis. KIF20A pan-cancer bioinformatics immune correlation prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction With environmental changes and an aging population, cancer has become the leading death cause, challenging global human health [ 1 – 3 ] . A study shows that by 2070, the global population size of cancer patients is projected to rise to 34 million, about twice the current number [ 4 ] . The high medical expenses and the surge in cancer cases are bound to seriously affect the development of society and bring about a huge economic burden [ 5 , 6 ] . In recent years, advancements in medical technology have led to improvements in treatments such as radiotherapy, surgery, molecular targeting, and hormone therapy. While these approaches have achieved some success in cancer management, the overall five-year survival rates remain relatively low [ 7 – 9 ] . Therefore, the battle against cancer is far from over, necessitating the urgent identification of new biomarkers for early cancer detection and treatment. KIF20A, a kinesin with ATP activity, is also referred to as mitotic kinesin-like protein 2 because of its ability to encode a mitotic kinesin-like molecule, whose chromosome is located at 5q31 and consists of 19 exons [ 10 ] . KIF20A is mainly involved in mitosis, including spindle formation, cytokinesis, and microtubule movement [ 11 ] . In addition, it is involved in the maintenance of Golgi integrity and the Golgi-to-endoplasmic reticulum vesicle transport [ 12 , 13 ] . It has recently been shown that KIF20A is overexpressed in various cancers (e.g., renal, lung, colorectal, and ovarian cancers), and that by participating in phosphorylation and cell cycle regulation, it is strongly linked to cancer prognosis; in most cases, higher KIF20A expression correlates with poorer outcomes [ 14 – 19 ] . However, research on the specific and comprehensive function of KIF20A in various cancers remains insufficient. During the last few years, early cancer diagnosis and treatment has increasingly adopted pan-cancer analysis. However, pan-cancer studies on KIF20A are currently limited to specific cancers, and research on its application potential in multiple cancers is lacking. We herein examined the differential KIF20A expression in diverse cancerous cells relative to normal tissues by the means of bioinformatics databases Cancer Cell Line Encyclopedia (CCLE), the Gene Expression Omnibus (GEO), the Genotype-Tissue Expression (GTEx) Project, the Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA). We also examined KIF20A expression association with cancer immunology and genetics, laying the foundation for its prognostic analysis. Finally, KIF20A-associated cellular pathways in the pathogenesis of a variety of cancers were investigated at the cellular molecular level. These investigations provided new information about the possible use of KIF20A in early cancer diagnosis, treatment, and prognosis. The KIF20A-related findings may introduce innovative treatment strategies and offer renewed hope for cancer patients' recovery, which could ultimately enhance their chances of rehabilitation. Materials and methods Common Methods Unless otherwise noted, R 4.0.3 was used for all statistical analyses. Unless otherwise stated, the Wilcox test was adopted for assessing differences between two groups, which were considered as having statistical significance when the P-value was below 0.05. Genetic and proteomic profiling TIMER2.0, a bioinformatics platform developed using TCGA and GTEx, was adopted to assess the differential KIF20A expression in cancerous versus normal tissues. Log2 TPM values were used to measure gene expression levels. Additionally, RNA-seq expression profiles (level 3) with KIF20A-related clinical data were retrieved from TCGA, and between-group differences were assessed. Thirty-three cancers in total were included in this study, comprising the following categories: cancers of the digestive system (colon adenocarcinoma or COAD, esophageal carcinoma or ESCA, liver hepatocellular carcinoma or LIHC, pancreatic adenocarcinoma or PAAD, rectal adenocarcinoma or READ, stomach adenocarcinoma or STAD, and cholangiocarcinoma or CHOL); cancers of the urinary and reproductive systems (bladder urothelial carcinoma or BLCA, cervical squamous cell carcinoma and endocervical adenocarcinoma or CESC, ovarian serous cystadenocarcinoma or OV, prostate adenocarcinoma or PRAD, testicular germ cell tumors or TGCT, uterine corpus endometrial carcinoma or UCEC, and uterine carcinosarcoma or UCS); cancers of the respiratory system (lung adenocarcinoma or LUAD and lung squamous cell carcinoma or LUSC); cancers of the nervous system (glioblastoma multiforme or GBM and brain lower grade glioma or LGG); cancers of the endocrine system (adrenocortical carcinoma or ACC, pheochromocytoma and paraganglioma or PCPG, and thyroid carcinoma or THCA); cancers of the skin and soft tissues (skin cutaneous melanoma or SKCM, sarcoma or SARC, mesothelioma or MESO, and uveal melanoma or UVM); hematologic and lymphatic cancers (acute myeloid leukemia or AML, lymphoid neoplasm diffuse large B-cell lymphoma or DLBC, and thymoma or THYM); cancers of the head and neck (head and neck squamous cell carcinoma or HNSC); cancers of the breast (breast invasive carcinoma or BRCA); and cancers of the kidney (kidney chromophobe or KICH, kidney renal clear cell carcinoma or KIRC, and kidney renal papillary cell carcinoma or KIRP). Furthermore, we used the UALCAN tool to not only extract the differential expression of total KIF20A protein in malignant tissues relative to normal tissues from CPTAC, but also evaluate the association between tumor stages and KIF20A expression. Survival prognosis analyses From the TCGA dataset, we extracted level-3 RNA-seq expression profiles with clinical data related to 33 distinct cancers. The online tool KM plotter was utilized for the assessment of KIF20A expression association with cancer prognosis. This tool allows for the analysis of survival data and the examination of the influence of KIF20A expression on disease outcomes. Using the R package "forestplot", we generated a forest plot to illustrate univariate Cox regression analysis results, displaying each variable's hazard ratio (HR) along with its 95% confidence interval (CI) and P-value. Between-group differences were assessed. Immune cell infiltration (ICI) and immune checkpoint (ICP) analyses Level-3 RNA-seq expression profiles with clinical information related to 33 cancers were extracted from TCGA, followed by using the immunedeconv software to ensure accurate immune scoring. To evaluate immunity scores, the R package incorporated the most recent methods, such as CIBERSORT, EPIC, quantization, MCP-counter, TIMER, and xCell. This study also examined IC-associated transcripts, specifically SIGLEC15, PDCD1LG2, PDCD1, LAG3, IDO1, HAVCR2, CTLA4, and CD274. These eight genes' expression levels were retrieved, and their relationship to IC activation was examined. Data analyses were performed in R 4.0.3. Between-group differences were assessed. TMB and microsatellite instability (MSI)analyses Clinical data and level-3 RNA-seq expression profiles for 33 cancers were acquired from TCGA. TMB calculation was performed according to a 2018 publication [10]. MSI data were obtained from a 2017 publication [11]. Gene enrichment analyses We retrieved 50 proteins that have the highest association with KIF20A using the tool STRING, and then constructed a protein-protein interaction (PPI) network by means of Cytoscape. Among the KIF20A-associated genes, the top 100 ones exhibiting the highest association were identified, followed by Pearson's correlation test on the top 5 ones using the GEPIA2 platform. To better understand the relationships between the five genes, TIMER2.0 was used to create a heatmap of them. Furthermore, eight genes that were common to both databases were identified, and then a Venn diagram was generated by means of the Draw Venn Diagram tool. Moreover, using the R 4.0.3 package 'clusterProfiler', we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on the combined targets from both databases. Enrichment was deemed statistically significant when showing a P-value below 0.05 and a Q-value below 1. Genetic alteration analyses Using UALCAN, we assessed the differential levels of KIF20A DNA methylation in various cancerous tissues relative to normal tissues. The genetic variants of KIF20A were examined using cBioPortal. Data on alteration frequency, mutation location, and survival outcome were acquired by querying the database using its search function with terms such as "cancer types summary," "mutations," and "comparison/survival". Results KIF20A expression in multiple cancers The differential KIF20A expression in cancerous tissues relative to normal tissues was analyzed using different databases—first the TIMER database and second the UCEC database. TIMER analysis (Fig. 1 A) revealed positive differential KIF20A expression for BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PCPG, PRAD, READ, STAD, THCA, and UCEC. TCGA analysis revealed positive differential KIF20A expression in BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PCPG, PRAD, READ, SARC, STAD, THCA, and UCEC, which generally agree with the TIMER database findings. In cases where cancers lacked normal tissue comparisons, the TCGA dataset was merged with the GTEx dataset for data analysis, which revealed that the majority of the 33 cancers (except for AML, MESO, THYM, and UVM) had high expression of FANCI mRNA (Fig. S1 ). Next, the differential protein expression of KIF20A in several primary cancers relative to normal tissues was assessed using CPTAC. It was observed that compared to normal tissues, clear cell RCC (ccRCC), GBM, HNSC, LIHC, LUAD, LUSC, and UCEC exhibited positive differential KIF20A protein expression, but negative differential expression in breast cancer (Fig. 1 B). KIF20A expression association with cancer stage KIF20A expression in different cancer stages was assessed using GEPIA. As shown in Fig. 2 , the expression was associated with cancer stage in ACC, BRCA, CESC, ESCA, KICH, KIRC, KIRP, LIHC, LUAD, and SKCM. KIF20A expression association with cancer prognosis The link of KIF20A expression to cancer prognosis was analyzed using the TCGA database. Cancer cases were categorized into a high-KIF20A-expression (HKE) group and a low-KIF20A-expression (LKE) group. KM analysis revealed KIF20A expression association with disease-free survival (DFS) and overall survival (OS). Specifically, the LKE group in ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, SARC, and UVM had longer OS, in contrast to AML and THYM where the HKE group had longer OS (Fig. 3 A). Additionally, the HKE group in ACC, BLCA, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PRAD, SARC, and UVM exhibited shorter DFS and were more prone to relapse (Fig. 3 B). In ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, THYM, UCEC, and UVM, KIF20A expression was associated with OS, as confirmed by Cox regression analysis (Fig. 3 C). Furthermore, KIF20A expression association with DFS was observed in GBM, AML, SKCM, THYM, UVM, UCEC, LIHC, KIRP, ACC, and PAAD (Fig. 3 D). Mutations of KIF20A Genetic mutations can disrupt normal cellular regulatory mechanisms, which in turn promotes the development of cancer [ 20 ] . Using cBioPortal, we analyzed the mutation characteristics of KIF20A. KIRC, UCEC, and UCS were the three cancers that exhibited the most frequent KIF20A mutations, among which KIRC showed the highest mutation frequency at about 6.25%, and the most predominant mutation type was amplification (Fig. 4 A). Furthermore, the 106 mutation sites identified across amino acids 0 to 890 consisted of 89 missense mutations, 12 truncating mutations, and 5 splicing mutations, with no inframe mutations or fusion alterations. The most common mutation site for KIF20A is located at R445H/C (Fig. 4 B). KIF20A gene mutation association with OS, DFS, disease-specific survival (DSS), and progression-free survival (PFS) was examined for various cancers. In LIHC patients, those with KIF20A gene mutations had a worse prognosis for DSS than those without, but the prognosis for OS, DFS, or PFS did not worsen (Fig. 4 C). KIF20A DNA methylation Using the UALCAN database, the differential methylation of KIF20A DNA in several primary cancers relative to normal tissues was assessed. Negative differential methylation was observed in BLCA, BRCA, HNSC, LIHC, LUAD, PRAD, READ, and UCEC, but positive differential methylation was observed in COAD, ESCA, KIRC, and KIRP (Fig. 5 ). KIF20A expression association with ICI KIF20A expression association with ICI was explored for cancers. First, TIMER analysis identified six immune cell types associated with KIF20A expression in various cancers. Specifically, KIF20A expression in KIRC, LGG, and LIHC showed positive association with all six immune cell types studied. KIF20A expression showed significant positive association with CD8 + T cells, myeloid dendritic cells, and B cells in THYM, but negative association with all six immune cell types in LUSC. Meanwhile, TGCT exhibited significant negative association of KIF20A expression with myeloid dendritic cells and CD4 + T cells (Fig. 6 A). Next, 38 immune cells associated with KIF20A expression in cancers we identified through XCELL analysis. Among them, positive association with KIF20A expression was observed for Th2 CD4 + T cells in 32 cancers and for common lymphoid progenitor in 21 cancers, but negative association was observed for central memory CD4 + T cells in 29 cancers. Moreover, Macrophage M2 and eosinophils were negatively associated with KIF20A expression in 20 and 16 cancers, respectively. Remaining immune cells, as shown in Fig. 6 B, exhibited negative or positive KIF20A expression association in 33 cancers. KIF20A expression association with immunotherapy ICPs are molecular mechanisms that affect immune cell function during immune responses, including immunosuppression and immune stimulation [ 21 ] . We found that KIF20A expression in pan-cellular carcinomas was associated with the following ICPs: CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, SIGLEC15, and TIGIT (Fig. 7 A). TMB is a sequencing-based biomarker used to assess the frequency of gene mutations and predict the effectiveness of ICP inhibitor (ICPI) therapy [ 22 ] . Additionally, KIF20A expression association with TMB was observed in various cancers (Fig. 7 B), namely negative association in ESCA and THYM, and positive association in ACC, BLCA, BRCA, CHOL, COAD, KICH, KIRC, LGG, LUAD, LUSC, PAAD, PRAD, SARC, SKCM, STAD, UCEC, and UCS (Fig. 7 B). Finally, KIF20A expression was observed to be associated with MSI as well. MSI, referring to an alteration in microsatellite length caused by genetic changes, can predict the effect of immunotherapy [ 23 ] . Our results showed positive KIF20A-MSI association in ACC, CESC, COAD, GBM, LIHC, LUSC, MESO, STAD, UCEC, and UVM, in contrast to negative association in DLBC (Fig. 7 C). KIF20A-associated gene enrichment analyses Enrichment analyses of KIF20A-associated genes and KIF20A-binding proteins were performed to reveal the molecular mechanisms by which KIF20A contributes to cancer development. First, a PPI network comprising 50 KIF20A-binding proteins was discovered using the STRING database (Fig. 8 A). Second, the top 100 KIF20A-associated genes were analyzed using GEPIA2, and the findings indicated that KIF20A expression exhibited positive association with CCNB1,CDC25C, DLGAP5, KIF2C, and KIF23 (R = 0.8, 0.81, 0.79, 0.81, and 0.8 respectively) (Fig. 8 B). The pan-cancer heatmap analysis revealed that KIF20A expression exhibited positive association with all five of the aforementioned genes (Fig. 8 C). Furthermore, three common genes, RACGAP1, AURKB and CDCA8 (Fig. 8 D), were identified by cross-tabulation analysis of KIF20A-related genes and binding genes. Finally, the two datasets were pooled for KEGG and GO enrichment analyses. As revealed by GO enrichment analysis, KIF20A-related genes were involved in cancer development by regulating cell cycle and DNA-related pathways (Fig. 8 E). As revealed by KEGG enrichment analysis, KIF20A-associated genes contributed to cancer development through DNA replication, the p53 signaling pathway, Human T − cell leukemia virus 1 infection, cellular senescence, oocyte meiosis, progesterone-mediated oocyte maturation, and other cell cycle-associated pathways (Fig. 8 F). Discussion Cancer is subtly affecting our lives, and the search for therapeutic targets for cancer from the molecular level has now become a hot topic [ 24 ] . KIF20A, as a novel target gene, plays a role in an increasing number of cancers [ 25 – 27 ] , and researchers predict that blocking this gene target may lead to new breakthroughs in cancer therapy. However, KIF20A research is currently limited to certain cancers, with few research on pan-cancer analysis. We herein analyzed KIF20A expression in various cancers and explored its association with genetics, immunology, and cancer prognosis by using various bioinformatics databases, revealing new ideas for its use in cancer therapy. TIMER analysis revealed that compared to normal tissues, BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PCPG, PRAD, READ, STAD, THCA, and UCEC tissues exhibited highly elevated KIF20A expression. Additionally, ccRCC, GBM, HNSC, LIHC, LUAD, LUSC, and UCEC exhibited highly elevated KIF20A protein expression. The findings of the TCGA and HPA databases are broadly consistent with the above databases. KIF20A was observed to be highly expressed in ovarian cancer, with higher expression levels leading to shorter survival times [ 28 ] . Low KIF20A expression has been known to inhibit cell proliferation, enhance chemotherapy sensitivity, and improve prognosis for hepatocellular carcinoma patients [ 29 ] , consistent with our findings. Moreover, KIF20A expression association with pathological cancer stage was observed in ACC, BRCA, CESC, ESCA, KICH, KIRC, KIRP, LIHC, LUAD, and SKCM, as revealed by GEPIA-based analysis. Thus, we hypothesized that KIF20A could act as a new biological target in the prognosis and therapy of cancers. Moreover, KIF20A expression association with cancer prognosis were assessed using COX regression and KM analyses through the GEPIA2 platform. In ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, SARC, and UVM, the LKE group had longer OS. This suggests that KIF20A may be a carcinogenic factor for these malignancies, which aligns with previous studies [ 30 , 31 ] . However, the HKE group in AML and THYM had longer OS, indicating that KIF20A may be a protective factor for these cancers. This finding contradicts previous research, necessitating further validation. Moreover, it was observed in this study that HKE was linked to shorter DFS in ACC, BLCA, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PRAD, SARC, and UVM. This finding reinforces the notion that KIF20A might contribute to cancer progression and serve as a risk factor for these malignancies, consistent with earlier studies [ 32 , 33 ] . In light of these results, KIF20A could serve as a valuable prognostic cancer biomarker. Genetic mutations are key drivers of cancer development, and abnormal mutations during cell proliferation and differentiation can lead to tumor formation [ 34 ] . Previous research has shown that inhibiting KIF20A mutation reduces abnormal proliferation of renal cells, thus reducing the incidence of renal cancer [ 35 ] . Our analysis using cBioPortal revealed that the KIF20A gene has a high mutation rate in the majority of the cancers studied, with KIRC displaying the highest mutation rate, approximately 6.25%. This is consistent with previous research. We further assessed the association of KIF20A gene mutation with OS, DSS, DFS, and PFS across various cancers. KIF20A gene mutations in LIGC resulted in worse DSS, but did not significantly impact OS, DFS, or PFS. However, the clinical implications of these mutations to other cancer types remain uncertain and require further investigation. Finally, the association of KIF20A methylation with the development and progression of various cancers was explored. Methylation, a type of structural alteration of RNA, DNA, and proteins, can change genetic expression without changing the sequence, with its dysregulation contributing to oncogenesis and advancement [ 36 ] . Our findings indicate that levels of KIF20A DNA methylation vary across cancers, with some cancers exhibiting increased methylation while others display reduced levels. However, details on the mechanisms underlying these changes remain poorly understood and need further research. In conclusion, targeting the mutation sites of KIF20A to suppress its mutations could be a beneficial method for inhibiting cancers, and KIF20A could represent a novel target in oncological treatment. However, the precise mechanism requires further investigation. ICI, an essential part of the tumor microenvironment, is present throughout tumorigenesis and cancer progression, closely linked to tumor immunotherapy [ 37 ] . Previous studies have shown association of KIF20A expression with T-cell immunity in ovarian cancer [ 38 ] . KIF20A expression in breast cancer is associated with immune cells [ 39 ] . We herein observed that multiple cancers exhibited KIF20A expression association with various immune cells, including positive association with Th2 CD4 + T cells in 32 cancers. This is consistent with previous research. This finding provides a new idea for cancer immunotherapy. Specifically, inhibition of KIF20A could potentially inhibit Th2 CD4 + T cells and thus hinder tumorigenesis. Th2 CD4 + T cells, an essential part of the body's immune cells, are involved in the inflammatory response mainly through humoral immunity, but in some cases, they can promote tumor progression. In tumor progression, the balance between Th1 and Th2 is disrupted, with Th2 over-suppressing Th1 cells and generating immune evasion, which subsequently promotes tumor growth [ 40 ] . Despite these findings, the exact mechanism by which Th2 cells influence tumor immunity needs to be thoroughly investigated. IC proteins are small-molecule proteins produced by immune cells for self-regulation and are an important cause of immune tolerance in the development of tumors; therefore, ICPI therapy is becoming a promising approach in cancer treatment [ 41 ] . By improving the tumor immune microenvironment and blocking aberrant immunosuppression, ICPI therapy activates the normal immune response and thus suppresses tumor progression [ 42 ] . Our study also observed KIF20A expression association with LAG3 in 18 cancers, CD274 in 17 cancers, and HAVCR2 in 16 cancers. This provides clues in the search for new biological targets in tumor immunotherapy. Currently, more and more studies have shown that MSI and TMB are gradually becoming key metrics for assessing the effectiveness of immunotherapy across various cancers and serving as a marker of cancer prognosis [ 43 , 44 ] . KIF20A expression association with MSI in renal clear cell carcinoma has been confirmed [ 45 ] . In colorectal cancer, KIF20A expression is associated with TMB [ 46 ] . We herein observed positive KIF20A expression association with TMB and MSI in 17 and 10 cancers, respectively. This is also consistent with previous studies. Therefore, we hypothesized that elevated KIF20A expression is linked to increased MSI and TMB, which in turn influence the response to ICIs. This relationship may enhance the effectiveness of immunotherapy, leading to better patient prognosis and identifying KIF20A as a promising biological target for immunotherapy of tumors. Enrichment analyses are widely applied in biological research to assess the expression characteristics of disease-associated genes and understand the function of drug-targeted genes [ 47 ] . Previous research has observed that KIF20A-associated gene expression is up-regulated in hepatocellular carcinoma, and the upregulation contributes to cancer development by impairing mitosis, which in turn affects genetic stability [ 48 ] . In ovarian cancer, the expression of KIF20A-related genes is up-regulated, which promotes cancer development by increasing cell proliferation, invasion, and so on [ 49 ] . Consistent with some previous studies, our findings confirmed that KIF20A-associated genes are involved in cancer development through regulating the cell cycle and DNA-related pathways, especially through the P53 signaling pathway. KIF20A protein functions as a key driver of the cell cycle, playing a role in spindle formation during early mitosis. It is involved in centromeres and cytoplasmic divisions during mid and late mitosis. In tumors, overexpression of KIF20A may lead to spindle abnormalities, chromosome hyper condensation, abnormal cell divisions, and other consequent genetic alterations, resulting in uncontrolled cell cycle progression [ 50 , 51 ] . The P53 signaling pathway modulates cell cycle progression by regulating the G1 and G2/M phases, and is also involved in DNA repair. If the pathway is abnormal, it will interfere with normal physiological processes and promote tumorigenesis [ 52 ] . In conclusion, research focusing on gene-level mechanisms and signaling pathways provides novel insights and potential therapeutic targets for tumor treatment, yet the exact role of KIF20A in DNA-related pathways needs further investigation. In conclusion, KIF20A is markedly upregulated in various cellular carcinomas, with its high expression adversely affecting OS and DFS in most cancers. KIF20A expression is association with the tumor microenvironment and immune infiltration. Enrichment analyses have confirmed that KIF20A acts mainly through regulating cell cycle and DNA-related pathways. Therefore, it is speculated in the present study that KIF20A, as a novel biomarker, holds significant potential in tumor development and diagnosis, and opens up new avenues for tumor treatment. However, our study still needs to be further validated by corresponding trials. Ultimately, understanding the role of KIF20A may provide insights that contribute not only to effective treatment strategies but also to improved recovery outcomes for cancer patients. Declarations Authors’ contributions Zhenzhen Feng, Minmin Li, and Songling Lu conceptualized the study. Zhenzhen Feng, Minmin Li, Songling Lu, Jian Sun, Naifei Xing, Meng Guo, Zedan Zhao, and Jiyao Zhang drafted the manuscript. Jiyao Zhang, Zedan Zhao, Meng Guo, Zhenzhen Feng, Minmin Li, Songling Lu, Jian Sun, and Naifei Xing were responsible for data collection and analysis. Jiyao Zhang, Zedan Zhao, and Meng Guo revised the manuscript. Financial Support The Youth Fund Project of National Natural Science Foundation of China (No. 82205241), the Science and Technology Project of Binzhou Medical University (grant no. BY2022KYQD33), the Yantai Science and Technology Program (No. 2022YD072), and the Yantai Chronic Senile Disease Rehabilitation Clinical Medical Center provided financial support for the study's design, data collection, analysis, and interpretation, as well as manuscript drafting and revision. Availability of data and materials The data and materials supporting this study's findings can be obtained from the corresponding authors upon request. Ethics approval and participant consent Not applicable. Clinical trial number Not applicable. Consent for publication Not applicable. Conflicting interests The authors assert the absence of conflicting interests. Related Resources TIMER2.0 (http://timer.cistrome.org/), TCGA database (https://portal.gdc.com), UALCAN tool (http://ualcan.path.uab.edu/analysis-prot.html), Kaplan-Meier plotter (http://www.kmplot.com), STRING program (https://cn.string-db.org/), cBioPortal tool (http://www.cbiop ortal.org/). References SUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209-49. MILLER K D, NOGUEIRA L, DEVASIA T, et al. Cancer treatment and survivorship statistics, 2022 [J]. CA Cancer J Clin, 2022, 72(5): 409-36. CHEN S, CAO Z, PRETTNER K, et al. Estimates and Projections of the Global Economic Cost of 29 Cancers in 204 Countries and Territories From 2020 to 2050 [J]. JAMA Oncol, 2023, 9(4): 465-72. SOERJOMATARAM I, BRAY F. Planning for tomorrow: global cancer incidence and the role of prevention 2020-2070 [J]. Nat Rev Clin Oncol, 2021, 18(10): 663-72. QI J, LI M, WANG L, et al. National and subnational trends in cancer burden in China, 2005-20: an analysis of national mortality surveillance data [J]. Lancet Public Health, 2023, 8(12): e943-e55. MAO J J, PILLAI G G, ANDRADE C J, et al. Integrative oncology: Addressing the global challenges of cancer prevention and treatment [J]. CA Cancer J Clin, 2022, 72(2): 144-64. SIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023 [J]. CA Cancer J Clin, 2023, 73(1): 17-48. BAGCHI S, YUAN R, ENGLEMAN E G. Immune Checkpoint Inhibitors for the Treatment of Cancer: Clinical Impact and Mechanisms of Response and Resistance [J]. Annu Rev Pathol, 2021, 16(223-49. ROCK C L, THOMSON C A, SULLIVAN K R, et al. American Cancer Society nutrition and physical activity guideline for cancer survivors [J]. CA Cancer J Clin, 2022, 72(3): 230-62. WU W D, YU K W, ZHONG N, et al. Roles and mechanisms of Kinesin-6 KIF20A in spindle organization during cell division [J]. Eur J Cell Biol, 2019, 98(2-4): 74-80. YANG C, ZHANG Y, LIN S, et al. Suppressing the KIF20A/NUAK1/Nrf2/GPX4 signaling pathway induces ferroptosis and enhances the sensitivity of colorectal cancer to oxaliplatin [J]. Aging (Albany NY), 2021, 13(10): 13515-34. HIEDA M, MATSUMOTO T, ISOBE M, et al. The SUN2-nesprin-2 LINC complex and KIF20A function in the Golgi dispersal [J]. Sci Rep, 2021, 11(1): 5358. RANAIVOSON F M, CROZET V, BENOIT M, et al. Nucleotide-free structures of KIF20A illuminate atypical mechanochemistry in this kinesin-6 [J]. Open Biol, 2023, 13(9): 230122. REN X, CHEN X, JI Y, et al. Upregulation of KIF20A promotes tumor proliferation and invasion in renal clear cell carcinoma and is associated with adverse clinical outcome [J]. Aging (Albany NY), 2020, 12(24): 25878-94. WANG Q, WU H, WU Q, et al. Berberine targets KIF20A and CCNE2 to inhibit the progression of nonsmall cell lung cancer via the PI3K/AKT pathway [J]. Drug Dev Res, 2023, 84(5): 907-21. ZHANG L, SONG W, SHI J, et al. Circ_0084188 Regulates the progression of colorectal cancer through the miR-769-5p/KIF20A axis [J]. Biochem Genet, 2023, 61(5): 1727-44. LI Y, GUO H, WANG Z, et al. Cyclin F and KIF20A, FOXM1 target genes, increase proliferation and invasion of ovarian cancer cells [J]. Exp Cell Res, 2020, 395(2): 112212. QIU R, WU J, GUDENAS B, et al. Depletion of kinesin motor KIF20A to target cell fate control suppresses medulloblastoma tumour growth [J]. Commun Biol, 2021, 4(1): 552. SHE Z Y, LI Y L, LIN Y, et al. Kinesin-6 family motor KIF20A regulates central spindle assembly and acrosome biogenesis in mouse spermatogenesis [J]. Biochim Biophys Acta Mol Cell Res, 2020, 1867(4): 118636. JASSIM A, RAHRMANN E P, SIMONS B D, et al. Cancers make their own luck: theories of cancer origins [J]. Nat Rev Cancer, 2023, 23(10): 710-24. MORAD G, HELMINK B A, SHARMA P, et al. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade [J]. Cell, 2022, 185(3): 576. JARDIM D L, GOODMAN A, DE MELO GAGLIATO D, et al. The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker [J]. Cancer Cell, 2021, 39(2): 154-73. VAN WIETMARSCHEN N, SRIDHARAN S, NATHAN W J, et al. Repeat expansions confer WRN dependence in microsatellite-unstable cancers [J]. Nature, 2020, 586(7828): 292-8. BARBIERI I, KOUZARIDES T. Role of RNA modifications in cancer [J]. Nat Rev Cancer, 2020, 20(6): 303-22. ZHANG W, ZHANG J, HU Z, et al. LncRNA ARAP1-AS1 Promotes Bladder Cancer Development by Regulating the miR-3918/KIF20A Axis [J]. Mol Biotechnol, 2022, 64(11): 1259-69. COPELLO V A, BURNSTEIN K L. The kinesin KIF20A promotes progression to castration-resistant prostate cancer through autocrine activation of the androgen receptor [J]. Oncogene, 2022, 41(20): 2824-32. NAKAMURA M, TAKANO A, THANG P M, et al. Characterization of KIF20A as a prognostic biomarker and therapeutic target for different subtypes of breast cancer [J]. Int J Oncol, 2020, 57(1): 277-88. WEI M, BAI X, DONG Q. Identification of novel candidate genes and small molecule drugs in ovarian cancer by bioinformatics strategy [J]. Transl Cancer Res, 2022, 11(6): 1630-43. WU C, QI X, QIU Z, et al. Low expression of KIF20A suppresses cell proliferation, promotes chemosensitivity and is associated with better prognosis in HCC [J]. Aging (Albany NY), 2021, 13(18): 22148-63. XIAO W, CHEN K, LIANG H G, et al. Identification of KIF20A as a tumor biomarker and forwarder of clear cell renal cell carcinoma [J]. Chin Med J (Engl), 2021, 134(17): 2137-9. HE H, LIANG L, HUANG J, et al. KIF20A is associated with clinical prognosis and synergistic effect of gemcitabine combined with ferroptosis inducer in lung adenocarcinoma [J]. Front Pharmacol, 2022, 13(1007429. SUN D, ZHANG H, ZHANG C, et al. An evaluation of KIF20A as a prognostic factor and therapeutic target for lung adenocarcinoma using integrated bioinformatics analysis [J]. Front Bioeng Biotechnol, 2022, 10(993820. ZHANG Z, CHAI C, SHEN T, et al. Aberrant KIF20A Expression Is Associated with Adverse Clinical Outcome and Promotes Tumor Progression in Prostate Cancer [J]. Dis Markers, 2019, 2019(4782730. DIETLEIN F, WANG A B, FAGRE C, et al. Genome-wide analysis of somatic noncoding mutation patterns in cancer [J]. Science, 2022, 376(6589): eabg5601. WANG H, MA X, LI S, et al. KIF20A as a potential biomarker of renal and bladder cancers based on bioinformatics and experimental verification [J]. Aging (Albany NY), 2023, 15(11): 4714-33. DAI X, REN T, ZHANG Y, et al. Methylation multiplicity and its clinical values in cancer [J]. Expert Rev Mol Med, 2021, 23(e2. DE VISSER K E, JOYCE J A. The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth [J]. Cancer Cell, 2023, 41(3): 374-403. SCHOSSIG P, COSKUN E, ARSENIC R, et al. Target Selection for T-Cell Therapy in Epithelial Ovarian Cancer: Systematic Prioritization of Self-Antigens [J]. Int J Mol Sci, 2023, 24(3): LI T F, ZENG H J, SHAN Z, et al. Overexpression of kinesin superfamily members as prognostic biomarkers of breast cancer [J]. Cancer Cell Int, 2020, 20(123. TAY C, TANAKA A, SAKAGUCHI S. Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy [J]. Cancer Cell, 2023, 41(3): 450-65. LI Y, WANG Y, LI N, et al. Immune checkpoint inhibitors-associated cardiotoxicity in immunotherapy trials on gastrointestinal cancer patients [J]. Chin Med J (Engl), 2022, 135(8): 988-90. DAL COLLO G, TAKAM KAMGA P. Unlocking the Potential of Biomarkers for Immune Checkpoint Inhibitors in Cancer Therapy [J]. Cancers (Basel), 2023, 15(18): NIKNAFS N, BALAN A, CHERRY C, et al. Persistent mutation burden drives sustained anti-tumor immune responses [J]. Nat Med, 2023, 29(2): 440-9. HAN S, CHOK A Y, PEH D Y Y, et al. The distinct clinical trajectory, metastatic sites, and immunobiology of microsatellite-instability-high cancers [J]. Front Genet, 2022, 13(933475. FANG K, GONG M, LIU D, et al. FOXM1/KIF20A axis promotes clear cell renal cell carcinoma progression via regulating EMT signaling and affects immunotherapy response [J]. Heliyon, 2023, 9(12): e22734. GUO M, LI X, LI J, et al. Identification of the prognostic biomarkers and their correlations with immune infiltration in colorectal cancer through bioinformatics analysis and in vitro experiments [J]. Heliyon, 2023, 9(6): e17101. MECHTERIDIS K, LAUBER M, BAUMBACH J, et al. KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem [J]. Front Genet, 2021, 12(812853. GASNEREAU I, BOISSAN M, MARGALL-DUCOS G, et al. KIF20A mRNA and its product MKlp2 are increased during hepatocyte proliferation and hepatocarcinogenesis [J]. Am J Pathol, 2012, 180(1): 131-40. KAWAI Y, SHIBATA K, SAKATA J, et al. KIF20A expression as a prognostic indicator and its possible involvement in the proliferation of ovarian clear‑cell carcinoma cells [J]. Oncol Rep, 2018, 40(1): 195-205. ZHAO X, ZHOU L L, LI X, et al. Overexpression of KIF20A confers malignant phenotype of lung adenocarcinoma by promoting cell proliferation and inhibiting apoptosis [J]. Cancer Med, 2018, 7(9): 4678-89. LIU C, JI J, LI C. Cucurbitacin B Inhibits the Malignancy of Esophageal Carcinoma through the KIF20A/JAK/STAT3 Signaling Pathway [J]. Am J Chin Med, 2024, 52(1): 275-89. HUANG J. Current developments of targeting the p53 signaling pathway for cancer treatment [J]. Pharmacol Ther, 2021, 220(107720. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7044011","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486458712,"identity":"e19a9f59-c5cb-4900-bfc2-53062a973c7c","order_by":0,"name":"Zhenzhen Feng","email":"","orcid":"","institution":"Binzhou Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"Feng","suffix":""},{"id":486458713,"identity":"a6b4214d-59b7-4269-abca-fbe86bd7c029","order_by":1,"name":"Minmin Li","email":"","orcid":"","institution":"Binzhou Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Minmin","middleName":"","lastName":"Li","suffix":""},{"id":486458715,"identity":"7b50c91f-542e-4a6f-af82-090e5ef9f4e7","order_by":2,"name":"Songling Lu","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Songling","middleName":"","lastName":"Lu","suffix":""},{"id":486458717,"identity":"57df8851-48ea-422b-a38e-2af7474a2af2","order_by":3,"name":"Jian Sun","email":"","orcid":"","institution":"Binzhou Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Sun","suffix":""},{"id":486458719,"identity":"f601f10f-034d-449c-bea2-b0448fd4d1c2","order_by":4,"name":"Naifei Xing","email":"","orcid":"","institution":"Yantai Affiliated Hospital of Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Naifei","middleName":"","lastName":"Xing","suffix":""},{"id":486458720,"identity":"fb55221c-a205-459e-95c4-cd0659560de6","order_by":5,"name":"Meng Guo","email":"","orcid":"","institution":"Binzhou Medical University Hospital","correspondingAuthor":false,"prefix":"","firstName":"Meng","middleName":"","lastName":"Guo","suffix":""},{"id":486458721,"identity":"15b73f67-8500-45dc-bec4-daceb1d479e8","order_by":6,"name":"Zedan Zhao","email":"","orcid":"","institution":"Central Hospital Affiliated to Shandong First Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zedan","middleName":"","lastName":"Zhao","suffix":""},{"id":486458722,"identity":"838977f1-2dbf-4199-91e2-d5dfb52a7b33","order_by":7,"name":"Jiyao Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACCRDBAyIZGx98+GHDw8/eQLQW5mbDmT1pMpI9B4jRAgbsbdI8bIdtDG444NchP7v52cMvMhaJ/e2NQC0853kYbjAwfviYg1sL45xj5sYyPBKJM84cbLacY3Gbh3F2A7PkzG24tTBLJJhJSwC1NNxIbLzxhuc2D7PMATZmXjxa2CTSv4G1zL+R2CDBw3aOh00iAb8WHokcM8kPQC0bbiQ2SfKwHeDhIaRFQiKnTBqo0Xgj0C/AQE7mkeA52IzXL/Iz0rdJ/uypk513vP0hMCrt7O2PNx/88BGPFnAQ8PYwODYg+IwNuFQilPz4wWBPUNUoGAWjYBSMXAAABkpR2EBTJlMAAAAASUVORK5CYII=","orcid":"","institution":"Shandong University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Jiyao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-07-04 07:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7044011/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7044011/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86930179,"identity":"1acd2aca-b085-4c96-929c-0af3fe4d15bf","added_by":"auto","created_at":"2025-07-17 09:30:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":456636,"visible":true,"origin":"","legend":"\u003cp\u003eKIF20A expression (*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, and ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). (A) KIF20A gene expression determined using TIMER. (B) KIF20A protein expression in normal tissues and primary tumors (breast cancer, RCC, GBM, HNSC, LIHC, LUAD, LUSC, and UCEC) determined using CPTAC. (C) FANCI expression in normal and tumor tissues determined using HPA IHC.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/5dcedda2e92b4b08907eba92.png"},{"id":86931452,"identity":"1fa38363-0029-4edb-98f7-2d47a6c95b88","added_by":"auto","created_at":"2025-07-17 09:46:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":349180,"visible":true,"origin":"","legend":"\u003cp\u003eKIF20A expression in various cancer stages (I-IV) analyzed using GEPIA.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/07955374394cdc4b0ba3a324.png"},{"id":86930453,"identity":"9ebc4064-f0c6-4196-a606-e70be6ad4553","added_by":"auto","created_at":"2025-07-17 09:38:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":877212,"visible":true,"origin":"","legend":"\u003cp\u003eKIF20A expression association with survival prognosis in cancers. (A) Association with OS revealed by KM analysis. (B) Association with DFS revealed by KM analysis. (C) Forest plots illustrating the association with OS. (D) Forest plots illustrating the association with DFS.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/b91e6683088d051ac988176e.png"},{"id":86930182,"identity":"18995b80-8798-4afb-b3e3-a37e3a370c3d","added_by":"auto","created_at":"2025-07-17 09:30:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":372972,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of KIF20A mutations using cBioPortal. (A) Frequency and type of KIF20A mutations in cancer. (B) Mutational sites of KIF20A in cancer. (C) Association of KIF20A gene mutations with OS, DSS, DFS, and PFS in LIHC.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/04eb1be10eb7fabcf4beb227.png"},{"id":86930190,"identity":"108e8f75-12a5-4956-b415-80568c74d2fe","added_by":"auto","created_at":"2025-07-17 09:30:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":216208,"visible":true,"origin":"","legend":"\u003cp\u003eKIF20A DNA methylation in primary tumors (BLCA, BRCA, COAD, ESCA, HNSC, KIRC, KIRP, LIHC, LUAD, PRAD, READ, and UCEC) versus normal tissues analyzed using the UALCAN database.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/3132ee0215914eb599645edd.png"},{"id":86930457,"identity":"9c126cae-ea35-4a3b-9af6-fc06fcce4fb9","added_by":"auto","created_at":"2025-07-17 09:38:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":493445,"visible":true,"origin":"","legend":"\u003cp\u003eKIF20A expression association with ICI in various cancers (*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, and ***\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001). (A) TIMER analysis results. (B) xCell analysis results.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/db75d573b601dc3d37f14316.png"},{"id":86930459,"identity":"0d0463e6-eb9d-4891-9ccf-cfa0a2c0a522","added_by":"auto","created_at":"2025-07-17 09:38:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":327246,"visible":true,"origin":"","legend":"\u003cp\u003eKIF20A expression association with immunotherapy in various cancers (*\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05 and **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). (A) Association with ICP genes. (B) Association with TMB. (C) Association with MSI.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/3745a1765e2346a939ac13c6.png"},{"id":86930188,"identity":"8767d5f0-24e2-4bdb-8adf-6765529aac6f","added_by":"auto","created_at":"2025-07-17 09:30:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":589706,"visible":true,"origin":"","legend":"\u003cp\u003eKIF20A-associated gene enrichment analyses. (A) STRING-based PPI analysis of KIF20A-binding proteins. (B) Identification of KIF20A-associated genes using GEPIA2 and association of five specific genes with KIF20A expression. (C) Heatmap illustrating the KIF20A expression association with five specific genes in various cancers . (D) Analysis of KIF20A-binding proteins and associated genes using Venn diagrams. (E) GO enrichment analysis of KIF20A-associated genes. (F) KEGG enrichment analysis of KIF20A-associated genes.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/189fd1c4f39e5bcdd8f60c4d.png"},{"id":87186689,"identity":"62a1666c-c695-4d0e-82d0-1cc040d93475","added_by":"auto","created_at":"2025-07-21 10:32:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4209289,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/0f384619-4542-4493-88a3-61353ada1820.pdf"},{"id":86930454,"identity":"f534d3ef-9d9c-414f-96eb-ef110907f12a","added_by":"auto","created_at":"2025-07-17 09:38:10","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":474369,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7044011/v1/5d77eaab81584ab9e9bf4016.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"KIF20A- A New Discovery in Tumor Therapy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith environmental changes and an aging population, cancer has become the leading death cause, challenging global human health \u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. A study shows that by 2070, the global population size of cancer patients is projected to rise to 34\u0026nbsp;million, about twice the current number \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. The high medical expenses and the surge in cancer cases are bound to seriously affect the development of society and bring about a huge economic burden \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In recent years, advancements in medical technology have led to improvements in treatments such as radiotherapy, surgery, molecular targeting, and hormone therapy. While these approaches have achieved some success in cancer management, the overall five-year survival rates remain relatively low \u003csup\u003e[\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Therefore, the battle against cancer is far from over, necessitating the urgent identification of new biomarkers for early cancer detection and treatment.\u003c/p\u003e\u003cp\u003eKIF20A, a kinesin with ATP activity, is also referred to as mitotic kinesin-like protein 2 because of its ability to encode a mitotic kinesin-like molecule, whose chromosome is located at 5q31 and consists of 19 exons \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. KIF20A is mainly involved in mitosis, including spindle formation, cytokinesis, and microtubule movement \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. In addition, it is involved in the maintenance of Golgi integrity and the Golgi-to-endoplasmic reticulum vesicle transport \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. It has recently been shown that KIF20A is overexpressed in various cancers (e.g., renal, lung, colorectal, and ovarian cancers), and that by participating in phosphorylation and cell cycle regulation, it is strongly linked to cancer prognosis; in most cases, higher KIF20A expression correlates with poorer outcomes \u003csup\u003e[\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. However, research on the specific and comprehensive function of KIF20A in various cancers remains insufficient.\u003c/p\u003e\u003cp\u003eDuring the last few years, early cancer diagnosis and treatment has increasingly adopted pan-cancer analysis. However, pan-cancer studies on KIF20A are currently limited to specific cancers, and research on its application potential in multiple cancers is lacking. We herein examined the differential KIF20A expression in diverse cancerous cells relative to normal tissues by the means of bioinformatics databases Cancer Cell Line Encyclopedia (CCLE), the Gene Expression Omnibus (GEO), the Genotype-Tissue Expression (GTEx) Project, the Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA). We also examined KIF20A expression association with cancer immunology and genetics, laying the foundation for its prognostic analysis. Finally, KIF20A-associated cellular pathways in the pathogenesis of a variety of cancers were investigated at the cellular molecular level. These investigations provided new information about the possible use of KIF20A in early cancer diagnosis, treatment, and prognosis. The KIF20A-related findings may introduce innovative treatment strategies and offer renewed hope for cancer patients' recovery, which could ultimately enhance their chances of rehabilitation.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eCommon Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUnless otherwise noted, R 4.0.3 was used for all statistical analyses. Unless otherwise stated, the Wilcox test was adopted for assessing differences between two groups, which were considered as having statistical significance when the P-value was below 0.05.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic and proteomic profiling\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTIMER2.0, a bioinformatics platform developed using TCGA and GTEx, was adopted to assess the differential KIF20A expression in cancerous versus normal tissues. Log2 TPM values were used to measure gene expression levels. Additionally, RNA-seq expression profiles (level 3) with KIF20A-related clinical data were retrieved from TCGA, and between-group differences were assessed. Thirty-three cancers in total were included in this study, comprising the following categories: cancers of the digestive system (colon adenocarcinoma or COAD, esophageal carcinoma or ESCA, liver hepatocellular carcinoma or LIHC, pancreatic adenocarcinoma or PAAD, rectal adenocarcinoma or READ, stomach adenocarcinoma or STAD, and cholangiocarcinoma or CHOL); cancers of the urinary and reproductive systems (bladder urothelial carcinoma or BLCA, cervical squamous cell carcinoma and endocervical adenocarcinoma or CESC, ovarian serous cystadenocarcinoma or OV, prostate adenocarcinoma or PRAD, testicular germ cell tumors or TGCT, uterine corpus endometrial carcinoma or UCEC, and uterine carcinosarcoma or UCS); cancers of the respiratory system (lung adenocarcinoma or LUAD and lung squamous cell carcinoma or LUSC); cancers of the nervous system (glioblastoma multiforme or GBM and brain lower grade glioma or LGG); cancers of the endocrine system (adrenocortical carcinoma or ACC, pheochromocytoma and paraganglioma or PCPG, and thyroid carcinoma or THCA); cancers of the skin and soft tissues (skin cutaneous melanoma or SKCM, sarcoma or SARC, mesothelioma or MESO, and uveal melanoma or UVM); hematologic and lymphatic cancers (acute myeloid leukemia or AML, lymphoid neoplasm diffuse large B-cell lymphoma or DLBC, and thymoma or THYM); cancers of the head and neck (head and neck squamous cell carcinoma or HNSC); cancers of the breast (breast invasive carcinoma or BRCA); and cancers of the kidney (kidney chromophobe or KICH, kidney renal clear cell carcinoma or KIRC, and kidney renal papillary cell carcinoma or KIRP). Furthermore, we used the UALCAN tool to not only extract the differential expression of total KIF20A protein in malignant tissues relative to normal tissues from CPTAC, but also evaluate the association between tumor stages and KIF20A expression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurvival prognosis analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom the TCGA dataset, we extracted level-3 RNA-seq expression profiles with clinical data related to 33 distinct cancers. The online tool KM plotter was utilized for the assessment of KIF20A expression association with cancer prognosis. This tool allows for the analysis of survival data and the examination of the influence of KIF20A expression on disease outcomes. Using the R package \"forestplot\", we generated a forest plot to illustrate univariate Cox regression analysis results, displaying each variable's hazard ratio (HR) along with its 95% confidence interval (CI) and P-value. Between-group differences were assessed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImmune cell infiltration (ICI) and immune checkpoint (ICP) analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLevel-3 RNA-seq expression profiles with clinical information related to 33 cancers were extracted from TCGA, followed by using the immunedeconv software to ensure accurate immune scoring. To evaluate immunity scores, the R package incorporated the most recent methods, such as CIBERSORT, EPIC, quantization, MCP-counter, TIMER, and xCell. This study also examined IC-associated transcripts, specifically SIGLEC15, PDCD1LG2, PDCD1, LAG3, IDO1, HAVCR2, CTLA4, and CD274. These eight genes' expression levels were retrieved, and their relationship to IC activation was examined. Data analyses were performed in R 4.0.3. Between-group differences were assessed.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTMB and microsatellite instability (MSI)analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eClinical data and level-3 RNA-seq expression profiles for 33 cancers were acquired from TCGA. TMB calculation was performed according to a 2018 publication [10]. MSI data were obtained from a 2017 publication [11].\u003c/p\u003e\u003cp\u003e\u003cb\u003eGene enrichment analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe retrieved 50 proteins that have the highest association with KIF20A using the tool STRING, and then constructed a protein-protein interaction (PPI) network by means of Cytoscape. Among the KIF20A-associated genes, the top 100 ones exhibiting the highest association were identified, followed by Pearson's correlation test on the top 5 ones using the GEPIA2 platform. To better understand the relationships between the five genes, TIMER2.0 was used to create a heatmap of them. Furthermore, eight genes that were common to both databases were identified, and then a Venn diagram was generated by means of the Draw Venn Diagram tool. Moreover, using the R 4.0.3 package 'clusterProfiler', we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on the combined targets from both databases. Enrichment was deemed statistically significant when showing a P-value below 0.05 and a Q-value below 1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGenetic alteration analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing UALCAN, we assessed the differential levels of KIF20A DNA methylation in various cancerous tissues relative to normal tissues. The genetic variants of KIF20A were examined using cBioPortal. Data on alteration frequency, mutation location, and survival outcome were acquired by querying the database using its search function with terms such as \"cancer types summary,\" \"mutations,\" and \"comparison/survival\".\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eKIF20A expression in multiple cancers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe differential KIF20A expression in cancerous tissues relative to normal tissues was analyzed using different databases\u0026mdash;first the TIMER database and second the UCEC database. TIMER analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) revealed positive differential KIF20A expression for BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PCPG, PRAD, READ, STAD, THCA, and UCEC. TCGA analysis revealed positive differential KIF20A expression in BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PCPG, PRAD, READ, SARC, STAD, THCA, and UCEC, which generally agree with the TIMER database findings. In cases where cancers lacked normal tissue comparisons, the TCGA dataset was merged with the GTEx dataset for data analysis, which revealed that the majority of the 33 cancers (except for AML, MESO, THYM, and UVM) had high expression of FANCI mRNA (Fig.\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Next, the differential protein expression of KIF20A in several primary cancers relative to normal tissues was assessed using CPTAC. It was observed that compared to normal tissues, clear cell RCC (ccRCC), GBM, HNSC, LIHC, LUAD, LUSC, and UCEC exhibited positive differential KIF20A protein expression, but negative differential expression in breast cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eKIF20A expression association with cancer stage\u003c/b\u003e\u003c/p\u003e\u003cp\u003eKIF20A expression in different cancer stages was assessed using GEPIA. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the expression was associated with cancer stage in ACC, BRCA, CESC, ESCA, KICH, KIRC, KIRP, LIHC, LUAD, and SKCM.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eKIF20A expression association with cancer prognosis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe link of KIF20A expression to cancer prognosis was analyzed using the TCGA database. Cancer cases were categorized into a high-KIF20A-expression (HKE) group and a low-KIF20A-expression (LKE) group. KM analysis revealed KIF20A expression association with disease-free survival (DFS) and overall survival (OS). Specifically, the LKE group in ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, SARC, and UVM had longer OS, in contrast to AML and THYM where the HKE group had longer OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, the HKE group in ACC, BLCA, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PRAD, SARC, and UVM exhibited shorter DFS and were more prone to relapse (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, THYM, UCEC, and UVM, KIF20A expression was associated with OS, as confirmed by Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Furthermore, KIF20A expression association with DFS was observed in GBM, AML, SKCM, THYM, UVM, UCEC, LIHC, KIRP, ACC, and PAAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMutations of KIF20A\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGenetic mutations can disrupt normal cellular regulatory mechanisms, which in turn promotes the development of cancer \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Using cBioPortal, we analyzed the mutation characteristics of KIF20A. KIRC, UCEC, and UCS were the three cancers that exhibited the most frequent KIF20A mutations, among which KIRC showed the highest mutation frequency at about 6.25%, and the most predominant mutation type was amplification (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Furthermore, the 106 mutation sites identified across amino acids 0 to 890 consisted of 89 missense mutations, 12 truncating mutations, and 5 splicing mutations, with no inframe mutations or fusion alterations. The most common mutation site for KIF20A is located at R445H/C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). KIF20A gene mutation association with OS, DFS, disease-specific survival (DSS), and progression-free survival (PFS) was examined for various cancers. In LIHC patients, those with KIF20A gene mutations had a worse prognosis for DSS than those without, but the prognosis for OS, DFS, or PFS did not worsen (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eKIF20A DNA methylation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eUsing the UALCAN database, the differential methylation of KIF20A DNA in several primary cancers relative to normal tissues was assessed. Negative differential methylation was observed in BLCA, BRCA, HNSC, LIHC, LUAD, PRAD, READ, and UCEC, but positive differential methylation was observed in COAD, ESCA, KIRC, and KIRP (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eKIF20A expression association with ICI\u003c/b\u003e\u003c/p\u003e\u003cp\u003eKIF20A expression association with ICI was explored for cancers. First, TIMER analysis identified six immune cell types associated with KIF20A expression in various cancers. Specifically, KIF20A expression in KIRC, LGG, and LIHC showed positive association with all six immune cell types studied. KIF20A expression showed significant positive association with CD8\u003csup\u003e+\u003c/sup\u003e T cells, myeloid dendritic cells, and B cells in THYM, but negative association with all six immune cell types in LUSC. Meanwhile, TGCT exhibited significant negative association of KIF20A expression with myeloid dendritic cells and CD4\u003csup\u003e+\u003c/sup\u003e T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Next, 38 immune cells associated with KIF20A expression in cancers we identified through XCELL analysis. Among them, positive association with KIF20A expression was observed for Th2 CD4\u003csup\u003e+\u003c/sup\u003e T cells in 32 cancers and for common lymphoid progenitor in 21 cancers, but negative association was observed for central memory CD4\u003csup\u003e+\u003c/sup\u003e T cells in 29 cancers. Moreover, Macrophage M2 and eosinophils were negatively associated with KIF20A expression in 20 and 16 cancers, respectively. Remaining immune cells, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, exhibited negative or positive KIF20A expression association in 33 cancers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eKIF20A expression association with immunotherapy\u003c/b\u003e\u003c/p\u003e\u003cp\u003eICPs are molecular mechanisms that affect immune cell function during immune responses, including immunosuppression and immune stimulation \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. We found that KIF20A expression in pan-cellular carcinomas was associated with the following ICPs: CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, SIGLEC15, and TIGIT (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). TMB is a sequencing-based biomarker used to assess the frequency of gene mutations and predict the effectiveness of ICP inhibitor (ICPI) therapy \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Additionally, KIF20A expression association with TMB was observed in various cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), namely negative association in ESCA and THYM, and positive association in ACC, BLCA, BRCA, CHOL, COAD, KICH, KIRC, LGG, LUAD, LUSC, PAAD, PRAD, SARC, SKCM, STAD, UCEC, and UCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Finally, KIF20A expression was observed to be associated with MSI as well. MSI, referring to an alteration in microsatellite length caused by genetic changes, can predict the effect of immunotherapy \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. Our results showed positive KIF20A-MSI association in ACC, CESC, COAD, GBM, LIHC, LUSC, MESO, STAD, UCEC, and UVM, in contrast to negative association in DLBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eKIF20A-associated gene enrichment analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEnrichment analyses of KIF20A-associated genes and KIF20A-binding proteins were performed to reveal the molecular mechanisms by which KIF20A contributes to cancer development. First, a PPI network comprising 50 KIF20A-binding proteins was discovered using the STRING database (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Second, the top 100 KIF20A-associated genes were analyzed using GEPIA2, and the findings indicated that KIF20A expression exhibited positive association with CCNB1,CDC25C, DLGAP5, KIF2C, and KIF23 (R\u0026thinsp;=\u0026thinsp;0.8, 0.81, 0.79, 0.81, and 0.8 respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The pan-cancer heatmap analysis revealed that KIF20A expression exhibited positive association with all five of the aforementioned genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Furthermore, three common genes, RACGAP1, AURKB and CDCA8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD), were identified by cross-tabulation analysis of KIF20A-related genes and binding genes. Finally, the two datasets were pooled for KEGG and GO enrichment analyses. As revealed by GO enrichment analysis, KIF20A-related genes were involved in cancer development by regulating cell cycle and DNA-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). As revealed by KEGG enrichment analysis, KIF20A-associated genes contributed to cancer development through DNA replication, the p53 signaling pathway, Human T\u0026thinsp;\u0026minus;\u0026thinsp;cell leukemia virus 1 infection, cellular senescence, oocyte meiosis, progesterone-mediated oocyte maturation, and other cell cycle-associated pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCancer is subtly affecting our lives, and the search for therapeutic targets for cancer from the molecular level has now become a hot topic \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. KIF20A, as a novel target gene, plays a role in an increasing number of cancers \u003csup\u003e[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, and researchers predict that blocking this gene target may lead to new breakthroughs in cancer therapy. However, KIF20A research is currently limited to certain cancers, with few research on pan-cancer analysis. We herein analyzed KIF20A expression in various cancers and explored its association with genetics, immunology, and cancer prognosis by using various bioinformatics databases, revealing new ideas for its use in cancer therapy.\u003c/p\u003e\u003cp\u003eTIMER analysis revealed that compared to normal tissues, BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PCPG, PRAD, READ, STAD, THCA, and UCEC tissues exhibited highly elevated KIF20A expression. Additionally, ccRCC, GBM, HNSC, LIHC, LUAD, LUSC, and UCEC exhibited highly elevated KIF20A protein expression. The findings of the TCGA and HPA databases are broadly consistent with the above databases. KIF20A was observed to be highly expressed in ovarian cancer, with higher expression levels leading to shorter survival times \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. Low KIF20A expression has been known to inhibit cell proliferation, enhance chemotherapy sensitivity, and improve prognosis for hepatocellular carcinoma patients \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e, consistent with our findings. Moreover, KIF20A expression association with pathological cancer stage was observed in ACC, BRCA, CESC, ESCA, KICH, KIRC, KIRP, LIHC, LUAD, and SKCM, as revealed by GEPIA-based analysis. Thus, we hypothesized that KIF20A could act as a new biological target in the prognosis and therapy of cancers.\u003c/p\u003e\u003cp\u003eMoreover, KIF20A expression association with cancer prognosis were assessed using COX regression and KM analyses through the GEPIA2 platform. In ACC, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, SARC, and UVM, the LKE group had longer OS. This suggests that KIF20A may be a carcinogenic factor for these malignancies, which aligns with previous studies \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. However, the HKE group in AML and THYM had longer OS, indicating that KIF20A may be a protective factor for these cancers. This finding contradicts previous research, necessitating further validation. Moreover, it was observed in this study that HKE was linked to shorter DFS in ACC, BLCA, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD, PRAD, SARC, and UVM. This finding reinforces the notion that KIF20A might contribute to cancer progression and serve as a risk factor for these malignancies, consistent with earlier studies \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In light of these results, KIF20A could serve as a valuable prognostic cancer biomarker.\u003c/p\u003e\u003cp\u003eGenetic mutations are key drivers of cancer development, and abnormal mutations during cell proliferation and differentiation can lead to tumor formation \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Previous research has shown that inhibiting KIF20A mutation reduces abnormal proliferation of renal cells, thus reducing the incidence of renal cancer \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Our analysis using cBioPortal revealed that the KIF20A gene has a high mutation rate in the majority of the cancers studied, with KIRC displaying the highest mutation rate, approximately 6.25%. This is consistent with previous research. We further assessed the association of KIF20A gene mutation with OS, DSS, DFS, and PFS across various cancers. KIF20A gene mutations in LIGC resulted in worse DSS, but did not significantly impact OS, DFS, or PFS. However, the clinical implications of these mutations to other cancer types remain uncertain and require further investigation. Finally, the association of KIF20A methylation with the development and progression of various cancers was explored. Methylation, a type of structural alteration of RNA, DNA, and proteins, can change genetic expression without changing the sequence, with its dysregulation contributing to oncogenesis and advancement \u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Our findings indicate that levels of KIF20A DNA methylation vary across cancers, with some cancers exhibiting increased methylation while others display reduced levels. However, details on the mechanisms underlying these changes remain poorly understood and need further research. In conclusion, targeting the mutation sites of KIF20A to suppress its mutations could be a beneficial method for inhibiting cancers, and KIF20A could represent a novel target in oncological treatment. However, the precise mechanism requires further investigation.\u003c/p\u003e\u003cp\u003eICI, an essential part of the tumor microenvironment, is present throughout tumorigenesis and cancer progression, closely linked to tumor immunotherapy \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown association of KIF20A expression with T-cell immunity in ovarian cancer \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. KIF20A expression in breast cancer is associated with immune cells \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. We herein observed that multiple cancers exhibited KIF20A expression association with various immune cells, including positive association with Th2 CD4\u003csup\u003e+\u003c/sup\u003e T cells in 32 cancers. This is consistent with previous research. This finding provides a new idea for cancer immunotherapy. Specifically, inhibition of KIF20A could potentially inhibit Th2 CD4\u003csup\u003e+\u003c/sup\u003e T cells and thus hinder tumorigenesis. Th2 CD4\u003csup\u003e+\u003c/sup\u003e T cells, an essential part of the body's immune cells, are involved in the inflammatory response mainly through humoral immunity, but in some cases, they can promote tumor progression. In tumor progression, the balance between Th1 and Th2 is disrupted, with Th2 over-suppressing Th1 cells and generating immune evasion, which subsequently promotes tumor growth \u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Despite these findings, the exact mechanism by which Th2 cells influence tumor immunity needs to be thoroughly investigated. IC proteins are small-molecule proteins produced by immune cells for self-regulation and are an important cause of immune tolerance in the development of tumors; therefore, ICPI therapy is becoming a promising approach in cancer treatment \u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. By improving the tumor immune microenvironment and blocking aberrant immunosuppression, ICPI therapy activates the normal immune response and thus suppresses tumor progression \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Our study also observed KIF20A expression association with LAG3 in 18 cancers, CD274 in 17 cancers, and HAVCR2 in 16 cancers. This provides clues in the search for new biological targets in tumor immunotherapy. Currently, more and more studies have shown that MSI and TMB are gradually becoming key metrics for assessing the effectiveness of immunotherapy across various cancers and serving as a marker of cancer prognosis \u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. KIF20A expression association with MSI in renal clear cell carcinoma has been confirmed \u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. In colorectal cancer, KIF20A expression is associated with TMB \u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. We herein observed positive KIF20A expression association with TMB and MSI in 17 and 10 cancers, respectively. This is also consistent with previous studies. Therefore, we hypothesized that elevated KIF20A expression is linked to increased MSI and TMB, which in turn influence the response to ICIs. This relationship may enhance the effectiveness of immunotherapy, leading to better patient prognosis and identifying KIF20A as a promising biological target for immunotherapy of tumors.\u003c/p\u003e\u003cp\u003eEnrichment analyses are widely applied in biological research to assess the expression characteristics of disease-associated genes and understand the function of drug-targeted genes \u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. Previous research has observed that KIF20A-associated gene expression is up-regulated in hepatocellular carcinoma, and the upregulation contributes to cancer development by impairing mitosis, which in turn affects genetic stability \u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. In ovarian cancer, the expression of KIF20A-related genes is up-regulated, which promotes cancer development by increasing cell proliferation, invasion, and so on \u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. Consistent with some previous studies, our findings confirmed that KIF20A-associated genes are involved in cancer development through regulating the cell cycle and DNA-related pathways, especially through the P53 signaling pathway. KIF20A protein functions as a key driver of the cell cycle, playing a role in spindle formation during early mitosis. It is involved in centromeres and cytoplasmic divisions during mid and late mitosis. In tumors, overexpression of KIF20A may lead to spindle abnormalities, chromosome hyper condensation, abnormal cell divisions, and other consequent genetic alterations, resulting in uncontrolled cell cycle progression \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. The P53 signaling pathway modulates cell cycle progression by regulating the G1 and G2/M phases, and is also involved in DNA repair. If the pathway is abnormal, it will interfere with normal physiological processes and promote tumorigenesis \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. In conclusion, research focusing on gene-level mechanisms and signaling pathways provides novel insights and potential therapeutic targets for tumor treatment, yet the exact role of KIF20A in DNA-related pathways needs further investigation.\u003c/p\u003e\u003cp\u003eIn conclusion, KIF20A is markedly upregulated in various cellular carcinomas, with its high expression adversely affecting OS and DFS in most cancers. KIF20A expression is association with the tumor microenvironment and immune infiltration. Enrichment analyses have confirmed that KIF20A acts mainly through regulating cell cycle and DNA-related pathways. Therefore, it is speculated in the present study that KIF20A, as a novel biomarker, holds significant potential in tumor development and diagnosis, and opens up new avenues for tumor treatment. However, our study still needs to be further validated by corresponding trials. Ultimately, understanding the role of KIF20A may provide insights that contribute not only to effective treatment strategies but also to improved recovery outcomes for cancer patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZhenzhen Feng, Minmin Li, and Songling Lu conceptualized the study. Zhenzhen Feng, Minmin Li, Songling Lu, Jian Sun, Naifei Xing, Meng Guo, Zedan Zhao, and Jiyao Zhang drafted the manuscript. Jiyao Zhang, Zedan Zhao, Meng Guo, Zhenzhen Feng, Minmin Li, Songling Lu, Jian Sun, and Naifei Xing were responsible for data collection and analysis. Jiyao Zhang, Zedan Zhao, and Meng Guo revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Youth Fund Project of National Natural Science Foundation of China (No. 82205241), the Science and Technology Project of Binzhou Medical University (grant no. BY2022KYQD33), the Yantai Science and Technology Program (No. 2022YD072), and the Yantai Chronic Senile Disease Rehabilitation Clinical Medical Center provided financial support for the study\u0026apos;s design, data collection, analysis, and interpretation, as well as manuscript drafting and revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials supporting this study\u0026apos;s findings can be obtained from the corresponding authors upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and participant consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors assert the absence of conflicting interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelated Resources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTIMER2.0 (http://timer.cistrome.org/), TCGA database (https://portal.gdc.com), UALCAN tool (http://ualcan.path.uab.edu/analysis-prot.html), Kaplan-Meier plotter (http://www.kmplot.com), STRING program (https://cn.string-db.org/), cBioPortal tool (http://www.cbiop ortal.org/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSUNG H, FERLAY J, SIEGEL R L, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. CA Cancer J Clin, 2021, 71(3): 209-49.\u003c/li\u003e\n\u003cli\u003eMILLER K D, NOGUEIRA L, DEVASIA T, et al. Cancer treatment and survivorship statistics, 2022 [J]. CA Cancer J Clin, 2022, 72(5): 409-36.\u003c/li\u003e\n\u003cli\u003eCHEN S, CAO Z, PRETTNER K, et al. Estimates and Projections of the Global Economic Cost of 29 Cancers in 204 Countries and Territories From 2020 to 2050 [J]. JAMA Oncol, 2023, 9(4): 465-72.\u003c/li\u003e\n\u003cli\u003eSOERJOMATARAM I, BRAY F. Planning for tomorrow: global cancer incidence and the role of prevention 2020-2070 [J]. Nat Rev Clin Oncol, 2021, 18(10): 663-72.\u003c/li\u003e\n\u003cli\u003eQI J, LI M, WANG L, et al. National and subnational trends in cancer burden in China, 2005-20: an analysis of national mortality surveillance data [J]. Lancet Public Health, 2023, 8(12): e943-e55.\u003c/li\u003e\n\u003cli\u003eMAO J J, PILLAI G G, ANDRADE C J, et al. Integrative oncology: Addressing the global challenges of cancer prevention and treatment [J]. CA Cancer J Clin, 2022, 72(2): 144-64.\u003c/li\u003e\n\u003cli\u003eSIEGEL R L, MILLER K D, WAGLE N S, et al. Cancer statistics, 2023 [J]. CA Cancer J Clin, 2023, 73(1): 17-48.\u003c/li\u003e\n\u003cli\u003eBAGCHI S, YUAN R, ENGLEMAN E G. Immune Checkpoint Inhibitors for the Treatment of Cancer: Clinical Impact and Mechanisms of Response and Resistance [J]. Annu Rev Pathol, 2021, 16(223-49.\u003c/li\u003e\n\u003cli\u003eROCK C L, THOMSON C A, SULLIVAN K R, et al. American Cancer Society nutrition and physical activity guideline for cancer survivors [J]. CA Cancer J Clin, 2022, 72(3): 230-62.\u003c/li\u003e\n\u003cli\u003eWU W D, YU K W, ZHONG N, et al. Roles and mechanisms of Kinesin-6 KIF20A in spindle organization during cell division [J]. Eur J Cell Biol, 2019, 98(2-4): 74-80.\u003c/li\u003e\n\u003cli\u003eYANG C, ZHANG Y, LIN S, et al. Suppressing the KIF20A/NUAK1/Nrf2/GPX4 signaling pathway induces ferroptosis and enhances the sensitivity of colorectal cancer to oxaliplatin [J]. Aging (Albany NY), 2021, 13(10): 13515-34.\u003c/li\u003e\n\u003cli\u003eHIEDA M, MATSUMOTO T, ISOBE M, et al. The SUN2-nesprin-2 LINC complex and KIF20A function in the Golgi dispersal [J]. Sci Rep, 2021, 11(1): 5358.\u003c/li\u003e\n\u003cli\u003eRANAIVOSON F M, CROZET V, BENOIT M, et al. Nucleotide-free structures of KIF20A illuminate atypical mechanochemistry in this kinesin-6 [J]. Open Biol, 2023, 13(9): 230122.\u003c/li\u003e\n\u003cli\u003eREN X, CHEN X, JI Y, et al. Upregulation of KIF20A promotes tumor proliferation and invasion in renal clear cell carcinoma and is associated with adverse clinical outcome [J]. Aging (Albany NY), 2020, 12(24): 25878-94.\u003c/li\u003e\n\u003cli\u003eWANG Q, WU H, WU Q, et al. Berberine targets KIF20A and CCNE2 to inhibit the progression of nonsmall cell lung cancer via the PI3K/AKT pathway [J]. Drug Dev Res, 2023, 84(5): 907-21.\u003c/li\u003e\n\u003cli\u003eZHANG L, SONG W, SHI J, et al. Circ_0084188 Regulates the progression of colorectal cancer through the miR-769-5p/KIF20A axis [J]. Biochem Genet, 2023, 61(5): 1727-44.\u003c/li\u003e\n\u003cli\u003eLI Y, GUO H, WANG Z, et al. Cyclin F and KIF20A, FOXM1 target genes, increase proliferation and invasion of ovarian cancer cells [J]. Exp Cell Res, 2020, 395(2): 112212.\u003c/li\u003e\n\u003cli\u003eQIU R, WU J, GUDENAS B, et al. Depletion of kinesin motor KIF20A to target cell fate control suppresses medulloblastoma tumour growth [J]. Commun Biol, 2021, 4(1): 552.\u003c/li\u003e\n\u003cli\u003eSHE Z Y, LI Y L, LIN Y, et al. Kinesin-6 family motor KIF20A regulates central spindle assembly and acrosome biogenesis in mouse spermatogenesis [J]. Biochim Biophys Acta Mol Cell Res, 2020, 1867(4): 118636.\u003c/li\u003e\n\u003cli\u003eJASSIM A, RAHRMANN E P, SIMONS B D, et al. Cancers make their own luck: theories of cancer origins [J]. Nat Rev Cancer, 2023, 23(10): 710-24.\u003c/li\u003e\n\u003cli\u003eMORAD G, HELMINK B A, SHARMA P, et al. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade [J]. Cell, 2022, 185(3): 576.\u003c/li\u003e\n\u003cli\u003eJARDIM D L, GOODMAN A, DE MELO GAGLIATO D, et al. The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker [J]. Cancer Cell, 2021, 39(2): 154-73.\u003c/li\u003e\n\u003cli\u003eVAN WIETMARSCHEN N, SRIDHARAN S, NATHAN W J, et al. Repeat expansions confer WRN dependence in microsatellite-unstable cancers [J]. Nature, 2020, 586(7828): 292-8.\u003c/li\u003e\n\u003cli\u003eBARBIERI I, KOUZARIDES T. Role of RNA modifications in cancer [J]. Nat Rev Cancer, 2020, 20(6): 303-22.\u003c/li\u003e\n\u003cli\u003eZHANG W, ZHANG J, HU Z, et al. LncRNA ARAP1-AS1 Promotes Bladder Cancer Development by Regulating the miR-3918/KIF20A Axis [J]. Mol Biotechnol, 2022, 64(11): 1259-69.\u003c/li\u003e\n\u003cli\u003eCOPELLO V A, BURNSTEIN K L. The kinesin KIF20A promotes progression to castration-resistant prostate cancer through autocrine activation of the androgen receptor [J]. Oncogene, 2022, 41(20): 2824-32.\u003c/li\u003e\n\u003cli\u003eNAKAMURA M, TAKANO A, THANG P M, et al. Characterization of KIF20A as a prognostic biomarker and therapeutic target for different subtypes of breast cancer [J]. Int J Oncol, 2020, 57(1): 277-88.\u003c/li\u003e\n\u003cli\u003eWEI M, BAI X, DONG Q. Identification of novel candidate genes and small molecule drugs in ovarian cancer by bioinformatics strategy [J]. Transl Cancer Res, 2022, 11(6): 1630-43.\u003c/li\u003e\n\u003cli\u003eWU C, QI X, QIU Z, et al. Low expression of KIF20A suppresses cell proliferation, promotes chemosensitivity and is associated with better prognosis in HCC [J]. Aging (Albany NY), 2021, 13(18): 22148-63.\u003c/li\u003e\n\u003cli\u003eXIAO W, CHEN K, LIANG H G, et al. Identification of KIF20A as a tumor biomarker and forwarder of clear cell renal cell carcinoma [J]. Chin Med J (Engl), 2021, 134(17): 2137-9.\u003c/li\u003e\n\u003cli\u003eHE H, LIANG L, HUANG J, et al. KIF20A is associated with clinical prognosis and synergistic effect of gemcitabine combined with ferroptosis inducer in lung adenocarcinoma [J]. Front Pharmacol, 2022, 13(1007429.\u003c/li\u003e\n\u003cli\u003eSUN D, ZHANG H, ZHANG C, et al. An evaluation of KIF20A as a prognostic factor and therapeutic target for lung adenocarcinoma using integrated bioinformatics analysis [J]. Front Bioeng Biotechnol, 2022, 10(993820.\u003c/li\u003e\n\u003cli\u003eZHANG Z, CHAI C, SHEN T, et al. Aberrant KIF20A Expression Is Associated with Adverse Clinical Outcome and Promotes Tumor Progression in Prostate Cancer [J]. Dis Markers, 2019, 2019(4782730.\u003c/li\u003e\n\u003cli\u003eDIETLEIN F, WANG A B, FAGRE C, et al. Genome-wide analysis of somatic noncoding mutation patterns in cancer [J]. Science, 2022, 376(6589): eabg5601.\u003c/li\u003e\n\u003cli\u003eWANG H, MA X, LI S, et al. KIF20A as a potential biomarker of renal and bladder cancers based on bioinformatics and experimental verification [J]. Aging (Albany NY), 2023, 15(11): 4714-33.\u003c/li\u003e\n\u003cli\u003eDAI X, REN T, ZHANG Y, et al. Methylation multiplicity and its clinical values in cancer [J]. Expert Rev Mol Med, 2021, 23(e2.\u003c/li\u003e\n\u003cli\u003eDE VISSER K E, JOYCE J A. The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth [J]. Cancer Cell, 2023, 41(3): 374-403.\u003c/li\u003e\n\u003cli\u003eSCHOSSIG P, COSKUN E, ARSENIC R, et al. Target Selection for T-Cell Therapy in Epithelial Ovarian Cancer: Systematic Prioritization of Self-Antigens [J]. Int J Mol Sci, 2023, 24(3): \u003c/li\u003e\n\u003cli\u003eLI T F, ZENG H J, SHAN Z, et al. Overexpression of kinesin superfamily members as prognostic biomarkers of breast cancer [J]. Cancer Cell Int, 2020, 20(123.\u003c/li\u003e\n\u003cli\u003eTAY C, TANAKA A, SAKAGUCHI S. Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy [J]. Cancer Cell, 2023, 41(3): 450-65.\u003c/li\u003e\n\u003cli\u003eLI Y, WANG Y, LI N, et al. Immune checkpoint inhibitors-associated cardiotoxicity in immunotherapy trials on gastrointestinal cancer patients [J]. Chin Med J (Engl), 2022, 135(8): 988-90.\u003c/li\u003e\n\u003cli\u003eDAL COLLO G, TAKAM KAMGA P. Unlocking the Potential of Biomarkers for Immune Checkpoint Inhibitors in Cancer Therapy [J]. Cancers (Basel), 2023, 15(18): \u003c/li\u003e\n\u003cli\u003eNIKNAFS N, BALAN A, CHERRY C, et al. Persistent mutation burden drives sustained anti-tumor immune responses [J]. Nat Med, 2023, 29(2): 440-9.\u003c/li\u003e\n\u003cli\u003eHAN S, CHOK A Y, PEH D Y Y, et al. The distinct clinical trajectory, metastatic sites, and immunobiology of microsatellite-instability-high cancers [J]. Front Genet, 2022, 13(933475.\u003c/li\u003e\n\u003cli\u003eFANG K, GONG M, LIU D, et al. FOXM1/KIF20A axis promotes clear cell renal cell carcinoma progression via regulating EMT signaling and affects immunotherapy response [J]. Heliyon, 2023, 9(12): e22734.\u003c/li\u003e\n\u003cli\u003eGUO M, LI X, LI J, et al. Identification of the prognostic biomarkers and their correlations with immune infiltration in colorectal cancer through bioinformatics analysis and in vitro experiments [J]. Heliyon, 2023, 9(6): e17101.\u003c/li\u003e\n\u003cli\u003eMECHTERIDIS K, LAUBER M, BAUMBACH J, et al. KeyPathwayMineR: De Novo Pathway Enrichment in the R Ecosystem [J]. Front Genet, 2021, 12(812853.\u003c/li\u003e\n\u003cli\u003eGASNEREAU I, BOISSAN M, MARGALL-DUCOS G, et al. KIF20A mRNA and its product MKlp2 are increased during hepatocyte proliferation and hepatocarcinogenesis [J]. Am J Pathol, 2012, 180(1): 131-40.\u003c/li\u003e\n\u003cli\u003eKAWAI Y, SHIBATA K, SAKATA J, et al. KIF20A expression as a prognostic indicator and its possible involvement in the proliferation of ovarian clear‑cell carcinoma cells [J]. Oncol Rep, 2018, 40(1): 195-205.\u003c/li\u003e\n\u003cli\u003eZHAO X, ZHOU L L, LI X, et al. Overexpression of KIF20A confers malignant phenotype of lung adenocarcinoma by promoting cell proliferation and inhibiting apoptosis [J]. Cancer Med, 2018, 7(9): 4678-89.\u003c/li\u003e\n\u003cli\u003eLIU C, JI J, LI C. Cucurbitacin B Inhibits the Malignancy of Esophageal Carcinoma through the KIF20A/JAK/STAT3 Signaling Pathway [J]. Am J Chin Med, 2024, 52(1): 275-89.\u003c/li\u003e\n\u003cli\u003eHUANG J. Current developments of targeting the p53 signaling pathway for cancer treatment [J]. Pharmacol Ther, 2021, 220(107720.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"KIF20A, pan-cancer, bioinformatics, immune correlation, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7044011/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7044011/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eKIF20A is a cell cycle-related protein playing a crucial role in cell mitosis and participating in important steps such as chromosome segregation, spindle assembly, and cytoplasmic division.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eBased on bioinformatics databases, KIF20A expression levels and their relationship with immune cell infiltration (ICI) were examined by means of TIMER and TCGA, tumor prognosis was assessed through one-way COX regression and Kaplan-Meier (KM) analyses, while genetic mutations were analyzed using cBioPortal. The R programming language was adopted for immune checkpoint (ICP) and pathway enrichment analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eKIF20A exhibits significant expression elevation in the majority of cancers, and KIF20A protein is also highly expressed. Tumors demonstrating high KIF20A expression are associated with worse prognostic outcomes. Elevated KIF20A expression is positively linked to ICI, tumor mutational burden (TMB), and ICPs across most tumors. Amplification is the most frequent genetic alteration that the KIF20A gene undergoes. This extensive pan-cancer analysis provides enhanced understanding of KIF20A's role in tumorigenesis and metastasis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eKIF20A as a new target gene opens a new path for tumor treatment and diagnosis, and inhibiting this gene has the potential to improve tumor prognosis and prevent tumorigenesis.\u003c/p\u003e","manuscriptTitle":"KIF20A- A New Discovery in Tumor Therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-17 09:30:05","doi":"10.21203/rs.3.rs-7044011/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5d98e3cc-d8d9-4abc-8f3e-4ee2300b6b47","owner":[],"postedDate":"July 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-21T10:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-17 09:30:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7044011","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7044011","identity":"rs-7044011","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}