Mutation analysis and copy number alterations of KIF23 in non-small-cell lung cancer exhibiting KIF23 over-expression

Identification and validation of a novel phagocytosis regulators-related signature with potential prognostic and immunotherapeutic value in patients with lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a malignant tumor that seriously affects the prognosis of patients. Tumor-associated macrophages (TAMs) play a vital role in the tumor microenvironment and can be used as a potential target for tumor therapy, and phagocytosis regulators (PRs) are particularly important in this process. However, the PRs-related signature that can predict the potential prognostic and immunotherapeutic value in patients with LUAD has not been discovered.

METHODS

In this study, we mainly analyzed the effect of phagocytosis regulators on the prognosis of LUAD, and based on multiple screening analyses including differential analysis, univariate Cox analysis, and Lasso analysis, we constructed a prognostic risk model consisting of five genes. To verify the stability of the model, survival analysis and ROC curve verification were carried out through multiple data sets. In addition, we also combined many factors, such as immune infiltrating cells, clinical grouping characteristics, immune examination sites, pro-inflammatory factors, and other factors as well as in vitro cell experiments and clinical tissue samples for further validation analysis.

RESULTS

After identifying 29 differentially expressed PRs in LUAD samples, we further constructed a prognostic model consisting of five prognostic signatures (FURIN, KIF23, SASH3, GNPNAT1, and ITGAL). Further survival analysis tests, ROC verification, as well as univariate and multivariate Cox regression analysis showed that the risk score of the model could well predict the prognosis of LUAD patients and could be used as an independent prognostic factor. In addition, we further found that these phagocytic regulators-related signatures were closely related to the immune microenvironment and immunotherapy in LUAD patients, and could well predict the efficacy of immunotherapy in patients. In vitro cell experiments and Immunohistochemistry of clinical tissues showed that the expressions of FURIN, KIF23, SASH3, GNPNAT1 and ITGAL in normal lung cells/tissues and LUAD cells/tissues were consistent with bioinformatics results, and 3 of them had significant differences.

CONCLUSION

Our study identified a novel PRs-related signature that has potential application value in predicting the prognosis of LUAD patients and predicting the efficacy of immunotherapy. This provides a new basis for the prognosis assessment of LUAD patients and provides a novel target for immunotherapy of LUAD patients.

Evaluating the Prognostic and Therapeutic Potentials of the Proteasome 26S Subunit, ATPase (PSMC) Family of Genes in Lung Adenocarcinoma: A Database Mining Approach

This study explored the prognostic and therapeutic potentials of multiple Proteasome 26S Subunit, ATPase (PSMC) family of genes (PSMC1-5) in lung adenocarcinoma (LUAD) diagnosis and treatment. All the PSMCs were found to be differentially expressed (upregulated) at the mRNA and protein levels in LUAD tissues. The promoter and multiple coding regions of PSMCs were reported to be differentially and distinctly methylated, which may serve in the methylation-sensitive diagnosis of LUAD patients. Multiple somatic mutations (alteration frequency: 0.6–2%) were observed along the PSMC coding regions in LUAD tissues that could assist in the high-throughput screening of LUAD patients. A significant association between the PSMC overexpression and LUAD patients’ poor overall and relapse-free survival (p < 0.05; HR: >1.3) and individual cancer stages (p < 0.001) was discovered, which justifies PSMCs as the ideal targets for LUAD diagnosis. Multiple immune cells and modulators (i.e., CD274 and IDO1) were found to be associated with the expression levels of PSMCs in LUAD tissues that could aid in formulating PSMC-based diagnostic measures and therapeutic interventions for LUAD. Functional enrichment analysis of neighbor genes of PSMCs in LUAD tissues revealed different genes (i.e., SLIRP, PSMA2, and NUDSF3) previously known to be involved in oncogenic processes and metastasis are co-expressed with PSMCs, which could also be investigated further. Overall, this study recommends that PSMCs and their transcriptional and translational products are potential candidates for LUAD diagnostic and therapeutic measure discovery.

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID’s tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan–Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Systematic Pan-Cancer Analysis of KIF23 and a Prediction Model Based on KIF23 in Clear Cell Renal Cell Carcinoma (ccRCC)

Purpose

This study aims to carry out a pan-cancer analysis of kinesin family member 23 (KIF23) and construct a predictive model for the prognosis of clear cell renal cell carcinoma (ccRCC) patients.

Methods

We evaluated the differential expression of KIF23 in pan-cancer by The Cancer Genome Atlas (TCGA) and Oncomine database. Then, the correlation between KIF23 with prognosis, clinical grade, stage, immune subtype, tumor mutation burden (TMB), microsatellite instability (MSI) and immune microenvironment was explored by TCGA, an integrated repository portal for tumor-immune system interactions (TISIDB) and cBioPortal. Subsequently, we screened out ferroptosis-related genes (FRGs) related to KIF23 and constructed a risk score model. Univariate Cox analysis was used to determine independent prognostic factors for ccRCC overall survival (OS), and a nomogram was established. Furthermore, gene set enrichment analysis (GSEA) was applied to study the biological functions and pathways of KIF23. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was carried out to evaluate the expression of KIF23.

Results

KIF23 was highly expressed in most tumors. Further, KIF23 was strongly correlated with prognosis, clinical grade, stage, immune subtype, TMB, MSI and immune microenvironment in different tumors. We found that KIF23 was significantly associated with all aspects of ccRCC. Then, 8 FRGs were identified to construct a risk score model together with KIF23. And a prognostic nomogram prediction model of OS was established. After GSEA analysis, cell cycle, condensed chromosome and other physiological processes were screened out. Finally, qRT-PCR verified the high expression of KIF23 in ccRCC cell lines than normal kidney cell line.

Conclusion

KIF23 may act as a pivotal part in occurrence and progression of different tumors. In ccRCC, KIF23 can be a great prognostic biomarker, and the nomogram based on KIF23 may contribute to better treatment plans for ccRCC patients.

Kinesin family member 23 exerts a protumor function in breast cancer via stimulation of the Wnt/β-catenin pathway

Kinesin family member 23 (KIF23) has been described as one of the main genes that are associated with malignant transformation in numerous cancers. However, the exact significance of KIF23 in breast cancer has not been well-addressed. The present study was dedicated to the comprehensive investigation of KIF23 in breast cancer. Initial expression analysis through The Cancer Genome Atlas (TCGA) demonstrated high KIF23 levels in breast cancer compared with normal controls. These in silico data showing high levels of KIF23 in breast cancer were verified by assessing clinical specimens using real-time quantitative PCR and immunoblot assays. Moreover, a high KIF23 level was correlated with adverse clinical outcomes in breast cancer patients. Cellular functional experiments showed that the down-regulation of KIF23 affected the malignant behaviors of breast cancer cells in vitro, whereas the forced expression of KIF23 stimulated them. Mechanistic studies revealed that KIF23 restraint down-regulated the levels of phosphorylated glycogen synthetase kinase-3β (GSK-3β), β-catenin, cyclin D1 and c-myc in breast cancer cells, showing an inhibitory effect on the Wnt/β-catenin pathway. The suppression of GSK-3β was able to reverse KIF23-silencing-induced inactivation of the Wnt/β-catenin pathway. Inhibition of the Wnt/β-catenin pathway abolished KIF23 overexpression-mediated protumor effects in breast cancer. A xenograft assay confirmed the in vivo antitumor function of KIF23 inhibition. In conclusion, these findings suggest that KIF23 may exert a protumor function in breast cancer by stimulating the Wnt/β-catenin pathway. This work suggests that KIF23 has potential values for targeted therapy and prognosis in breast cancer.

KIF23 promotes triple negative breast cancer through activating epithelial-mesenchymal transition

Background

KIF23 is a member of kinesin family, recent researches indicate KIF23 plays an important role in the proliferation and migration of malignant cancer cells. While the function and specific molecule mechanism of KIF23 in triple negative breast cancer remains unclear.

Methods

QRT-PCR and immunohistochemistry were conducted to analyze expression of KIF23 in triple negative breast cancer tissues and paired paracancer tissues. CCK-8 assay, colony formation assay, wound healing assay and transwell assay were applied for exploring phenotype changing of triple negative breast cancer cell lines MDA-MB-231 and BT549 after siRNA-induced knockdown of KIF23. Several bioinformatic databases were used for predicting miRNAs that combing with KIF23 mRNA and verified by dual luciferase reporter assay. Western blot assay was performed to explore downstream signaling pathway of KIF23.

Results

KIF23 was overexpressed in triple negative breast cancer, knockdown of KIF23 by siRNA inhibited proliferation and migration of TNBC cell lines MDA-MB-231 and BT549. Mechanistically, knockdown of KIF23 resulted in the suppression of Epithelial-Mesenchymal Transition. Meanwhile, miR-195-5p was downregulated in TNBC, and dual luciferase reporter assay indicated miR-195-5p could combine with 3'UTR of KIF23 thus promoting degradation of KIF23.

Conclusions

KIF23 is a potential oncogene in triple negative breast cancer, miR-195-5p could combine with 3'UTR of KIF23. Our study reveals a new sight into triple negative breast cancer.

Mutation and Copy Number Alterations Analysis of KIF23 in Glioma

In glioma, kinesin family member 23 (KIF23) is up-regulated and plays a vital role in oncogenesis. However, the mechanism underlying KIF23 overexpression in malignant glioma remains to be elucidated. This study aims to find potential causes of KIF23 high expression at genome level. To clarify this issue, we obtained point mutation and copy number alterations (CNAs) of KIF23 in 319 gliomas using whole-exome sequencing. Only two glioma samples with missense mutations in KIF23 coding region were identified, while 7 patients were detected with amplification of KIF23. Additional analysis showed that KIF23 amplification was significantly associated with higher expression of KIF23. Gene ontology analysis indicated that higher copy number of KIF23 was associated TNF-α signaling pathway and mitotic cell circle checkpoint, which probably caused by subsequent upregulated expression of KIF23. Moreover, pan-cancer analysis showed that gaining of copy number was significantly associated with higher expression of KIF23, consolidating our findings in glioma. Thus, it was deduced that elevated KIF23 expression in glioma tended to be caused by DNA copy number amplification, instead of mutation.

KIF23 enhances cell proliferation in pancreatic ductal adenocarcinoma and is a potent therapeutic target

Background

In recent research, high expression of kinesin family member 23 (KIF23), one of the kinesin motor proteins involved in the regulation of cytokinesis, has been shown to be related to poor prognosis in glioma and paclitaxel-resistant gastric cancer, as a results of the enhancement of proliferation, migration, and invasion. In this study, we analyzed the role of KIF23 in the progression of pancreatic ductal adenocarcinoma.

Methods

A bioinformatic method was used to analyze the KIF23 mRNA level in pancreatic tumor tissues compared with normal pancreatic tissues and to analyze the connection between high KIF23 expression and prognosis. We examined the expression of KIF23 using immunohistochemistry and analyzed the connection between the expression of KIF23 and clinicopathological features in pancreatic ductal adenocarcinoma patients. In addition, a colony formation assay, MTT assay, and western blot assay were performed in vitro, along with a mouse xenograft model in vivo, to analyze the effect of KIF23 on proliferation. Further, the correlation between KIF23 and CDCA8 was analyzed by TCGA and immunohistochemical data.

Results

Bioinformatic results showed that KIF23 mRNA expression was higher in pancreatic tumor tissues than in normal pancreatic tissues and a poor prognosis has been linked to the high expression of KIF23. Immunohistochemistry revealed that KIF23 was highly expressed at the protein level and high expression of KIF23 correlated with adverse clinicopathological features. Our experimental results demonstrated that knockdown of KIF23 could inhibit the proliferation of pancreatic cells. Further, a positive correlation between KIF23 and CDCA8 expression existed, and KIF23 might promote pancreatic cancer proliferation by affecting CDCA8 expression.

Conclusions

Our data showed that high expression of KIF23 is associated with a poor prognosis, and KIF23 might be a potential therapeutic target for pancreatic ductal adenocarcinoma.

Identification of KIF23 as a prognostic signature for ovarian cancer based on large-scale sampling and clinical validation

The purpose of this study is to explore the expression and clinical significance of KIF23 in ovarian cancer (OV) and identify potential targets for clinical treatment. Oncomine, GEO, and TCGA databases were used to analysis the expression of KIF23 in OV. The prognostic value of KIF23 gene was analyzed by the Kaplan-Meier plotter database. The molecular mechanism of KIF23 activity was analyzed from the perspective of immunology, gene mutation, copy number variation (CNV). Finally, immunohistochemistry was conducted to validate the expression of KIF23, univariable and multivariate cox analysis were used to determine its relationship with clinical characteristics and OV prognosis. It showed that highly expressed KIF23 is an adverse independent prognostic biomarker for OV patients. Genomics analysis showed that KIF23 expression was associated with mutations such as FLG2 and TTN, and was significantly enriched in DNA replication and the cell cycle tumor-related signaling pathways. Immunology analysis showed that KIF23 is closely related to the immune infiltration. KIF23 can not only performed as a prognosis signature in OV but also as a target of immune molecular therapeutics.

Identification of Metastasis-Associated Biomarkers in Synovial Sarcoma Using Bioinformatics Analysis

Synovial sarcoma (SS) is a highly aggressive soft tissue tumor with high risk of local recurrence and metastasis. However, the mechanisms underlying SS metastasis are still largely unclear. The purpose of this study is to screen metastasis-associated biomarkers in SS by integrated bioinformatics analysis. Two mRNA datasets (GSE40018 and GSE40021) were selected to analyze the differentially expressed genes (DEGs). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), functional and pathway enrichment analyses were performed for DEGs. Then, the protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. The module analysis of the PPI network and hub genes validation were performed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the hub genes were performed using WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). The expression levels and survival analysis of hub genes were further assessed through Gene Expression Profiling Interactive Analysis (GEPIA) and the Kaplan-Meier plotter database. In total, 213 overlapping DEGs were identified, of which 109 were upregulated and 104 were downregulated. GO analysis revealed that the DEGs were predominantly involved in mitosis and cell division. KEGG pathways analysis demonstrated that most DEGs were significantly enriched in cell cycle pathway. GSEA revealed that the DEGs were mainly enriched in oocyte meiosis, cell cycle and DNA replication pathways. A key module was identified and 10 hub genes (CENPF, KIF11, KIF23, TTK, MKI67, TOP2A, CDC45, MELK, AURKB, and BUB1) were screened out. The expression and survival analysis disclosed that the 10 hub genes were upregulated in SS patients and could result in significantly reduced survival. Our study identified a series of metastasis-associated biomarkers involved in the progression of SS, and may provide novel therapeutic targets for SS metastasis.

Transcriptome analyses identify hub genes and potential mechanisms in adenoid cystic carcinoma

Adenoid cystic carcinoma (ACC) is one of the most frequent malignancies of salivary glands. The objective of this study was to identify key genes and potential mechanisms during ACC samples.The gene expression profiles of GSE88804 data set were downloaded from Gene Expression Omnibus. The GSE88804 data set contained 22 samples, including 15 ACC samples and 7 normal salivary gland tissues. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, and protein-protein interaction network of differentially expressed genes (DEGs) was performed by Cytoscape. The top 10 hub genes were analyzed based on Gene Expression Profiling Interactive Analysis. Then, DEGs between ACC samples and normal salivary gland samples were analyzed by gene set enrichment analysis. Furthermore, miRTarBase and Cytoscape were used for visualization of miRNA-mRNA regulatory network. KEGG pathway analysis was undertaken using DIANA-miRPath v3.0.In total, 382 DEGs were identified, including 119 upregulated genes and 263 downregulated genes. GO analysis showed that DEGs were mainly enriched in extracellular matrix organization, extracellular matrix, and calcium ion binding. KEGG pathway analysis showed that DEGs were mainly enriched in p53 signaling pathway and salivary secretion. Expression analysis and survival analysis showed that ANLN, CCNB2, CDK1, CENPF, DTL, KIF11, and TOP2A are all highly expressed, which all may be related to poor overall survival. Predicted miRNAs of 7 hub DEGs mainly enriched in proteoglycans in cancer and pathways in cancer.This study indicated that identified DEGs and hub genes might promote our understanding of molecular mechanisms, which might be used as molecular targets or diagnostic biomarkers for ACC.

Gene signatures and prognostic analyses of the Tob/BTG pituitary tumor-transforming gene (PTTG) family in clinical breast cancer patients

Breast cancer is the most common cancer type in females, and exploring the mechanisms of disease progression is playing a crucial role in the development of potential therapeutics. Pituitary tumor-transforming gene (PTTG) family members are well documented to be involved in cell-cycle regulation and mitosis, and contribute to cancer development by their involvement in cellular transformation in several tumor types. The critical roles of PTTG family members as crucial transcription factors in diverse types of cancers are recognized, but how they regulate breast cancer development still remains mostly unknown. Meanwhile, a holistic genetic analysis exploring whether PTTG family members regulate breast cancer progression via the cell cycle as well as the energy metabolism-related network is lacking. To comprehensively understand the messenger RNA expression profiles of PTTG proteins in breast cancer, we herein conducted a high-throughput screening approach by integrating information from various databases such as Oncomine, Kaplan-Meier Plotter, Metacore, ClueGo, and CluePedia. These useful databases and tools provide expression profiles and functional analyses. The present findings revealed that PTTG1 and PTTG3 are two important genes with high expressions in breast cancer relative to normal breast cells, implying their unique roles in breast cancer progression. Results of our coexpression analysis demonstrated that PTTG family genes were positively correlated with thiamine triphosphate (TTP), deoxycytidine triphosphate (dCTP) metabolic, glycolysis, gluconeogenesis, and cell-cycle related pathways. Meanwhile, through Cytoscape analyzed indicated that in addition to the metastasis markers AURKA, AURKB, and NDC80, many of the kinesin superfamily (KIF) members including KIFC1, KIF2C, KIF4A, KIF14, KIF20A, KIF23, were also correlated with PTTG family transcript expression. Finally, we revealed that high levels of PTTG1 and PTTG3 transcription predicted poor survival, which provided useful insights into prospective research of cancer associated with the PTTG family. Therefore, these members of the PTTG family would serve as distinct and essential prognostic biomarkers in breast cancer.

Construction of a Competitive Endogenous RNA Network in Uterine Corpus Endometrial Carcinoma

Background

Long non-coding RNAs (lncRNAs) affect post-transcriptional regulation by interfering with microRNAs (miRNAs), and by acting as competitive endogenous RNAs (ceRNAs). The roles and mechanisms of lncRNAs as ceRNAs in the progression and prognosis of uterine corpus endometrial carcinoma are not well understood.

Material/Methods

We analyzed high-throughput transcriptome data downloaded from The Cancer Genome Atlas database for 548 patients with uterine corpus endometrial carcinoma, and the we constructed a ceRNA network. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses of differentially expressed messenger RNAs (DE-mRNAs) were performed using R software. Kaplan-Meier survival curves were generated for all RNAs in the ceRNA network.

Results

We identified 2612 messenger RNAs (mRNAs), 1111 lncRNAs, and 187 miRNAs that were differentially expressed in uterine corpus endometrial carcinoma. We then identified mutual regulatory relationships between lncRNA-miRNA pairs and miRNA-mRNA pairs. A ceRNA regulatory network for uterine corpus endometrial carcinoma was successfully constructed, and consisted of 87 lncRNAs, 74 mRNAs, and 20 miRNAs. Nine lncRNAs, 3 miRNAs, and 22 mRNAs were associated with prognosis of uterine corpus endometrial carcinoma. We also analyzed the linear relationships between the expression of the 9 DE-lncRNAs and 22 DE-mRNAs with prognostic value.

Conclusions

Our study showed that the lncRNAs C2orf48 and LINC00261 might be key regulators of uterine corpus endometrial carcinoma and might serve as prognostic indicators. Our study contributes to the understanding of the molecular mechanisms of uterine corpus endometrial carcinoma, and it identifies lncRNAs that might serve as prognostic markers and therapeutic targets.

Transcriptome sequencing profiles of cervical cancer tissues and SiHa cells

High-risk human papillomavirus (HPV) is a causal factor for cervical cancer, of which HPV16 is the predominant genotype, but the detailed mechanism remains to be elucidated. In this study, we performed transcriptome sequencing in cervical cancer tissues with HPV16-positive and normal tissues with HPV16-negative, and SiHa cells with or without HPV16 E6/E7 knockdown, and identified 140 differential expressed genes (DEGs) in two data sets. We carried out a series of bioinformatic analyses to learn more about the 140 DEGs, and found that 140 DEGs were mostly enriched in cell cycle and DNA repair through Kyoto Encyclopedia of Genes and Genomes pathway enrichment, Gene Ontology annotation, and gene set enrichment analysis. A total of 20 genes including RMI1, MKI67, FANCB, KIF14, CENPI, RACGAP1, EXO1, KIF4A, FOXM1, C19orf57, PSRC1, NUSAP1, CIT, NDC80, MCM7, GINS2, MCM6, ORC1, TLX2, and UHRF1 were screened by co-expression analysis; of those, the expressions of 6 (CENPI, FANCB, KIF14, ORC1, RACGAP1, and RMI1) were verified by qRT-PCR. Further, we found that E2F family, NF-Y, AhR:Arnt, and KROX family may be involved in modulating DEGs by TransFind prediction. TF2DNA database and co-expression analysis suggested that 12 TFs (ZNF367, TLX2, DEPDC1B, E2F8, ZNF541, EGR2, ZMAT3, HES6, CEBPA, MYBL2, FOXM1, and RAD51) were upstream modulators of DEGs. Our findings may provide a new understanding for effects of HPV oncogenes in the maintenance of cancerous state at the transcriptional level.

KIF23 Promotes Gastric Cancer by Stimulating Cell Proliferation

Gastric cancer (GC) is one of the most aggressive malignant tumors with low early diagnosis and high metastasis. Despite progress in treatment, to combat this disease, a better understanding of the underlying mechanisms and novel therapeutic targets is needed. KIF23, which belongs to the KIF family, plays a vital role in various cell processes, such as cytoplasm separation and axon elongation. Nowadays, KIF23 has been found to be highly expressed in multiple tumor tissues and cells, suggesting a potential link between KIF23 and tumorigenesis. Herein, we reported that KIF23 expression was correlated with poor prognosis of gastric cancer and found an association between KIF23 and pTNM stage. An in vitro assay proved that the proliferation of gastric cancer cells was significantly inhibited, which is caused by KIF23 depletion. Additionally, knockdown of KIF23 resulted in a marked inhibition of cell proliferation of gastric cancer in mice, with significant downregulation of Ki67 and PCNA expression. In conclusion, these data indicate that KIF23 is a potential therapeutic target for gastric cancer treatment.