An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer

Exploring Prognostic Gene Factors in Breast Cancer via Machine Learning

Breast cancer remains the most prevalent cancer in women. To date, its underlying molecular mechanisms have not been fully uncovered. The determination of gene factors is important to improve our understanding on breast cancer, which can correlate the specific gene expression and tumor staging. However, the knowledge in this regard is still far from complete. Thus, this study aimed to explore these knowledge gaps by analyzing existing gene expression profile data from 3149 breast cancer samples, where each sample was represented by the expression of 19,644 genes and classified into Nottingham histological grade (NHG) classes (Grade 1, 2, and 3). To this end, a machine learning-based framework was designed. First, the profile data were analyzed by using seven feature ranking algorithms to evaluate the importance of features (genes). Seven feature lists were generated, each of which sorted features in accordance with feature importance evaluated from a special aspect. Then, the incremental feature selection method was applied to each list to determine essential features for classification and building efficient classifiers. Consequently, overlapping genes, such as AURKA, CBX2, and MYBL2, were deemed as potentially related to breast cancer malignancy and prognosis, indicating that such genes were identified to be important by multiple feature ranking algorithms. In addition, the study formulated classification rules to reflect special gene expression patterns for three NHG classes. Some genes and rules were analyzed and supported by recent literature, providing new references for studying breast cancer.

ANO6 is a reliable prognostic biomarker and correlates to macrophage polarization in breast cancer

To investigate the value of Anoctamin 6 (ANO6) in breast cancer (BC) by analyzing its expression, prognostic impact, biological function, and its association with immune characteristics. We initially performed the expression and survival analyses, followed by adopting restricted cubic spline to analyze the nonlinear relationship between ANO6 and overall survival (OS). Stratified and interaction analyses were conducted to further evaluate its prognostic value in BC. Next, we performed enrichment analyses to explore the possible pathways regulated by ANO6. Finally, the correlations between ANO6 and immune characteristics were analyzed to reveal its role in immunotherapy. Lower ANO6 expression was observed in BC than that in the normal breast group, but its overexpression independently predicted poor OS among BC patients (P < .05). Restricted cubic spline analysis revealed a linear relationship between ANO6 and OS (P-Nonlinear > 0.05). Interestingly, menopause status was an interactive factor in the correlation between ANO6 and OS (P for interaction = 0.016). Additionally, ANO6 was involved in stroma-associated pathways, and its elevation was significantly linked to high stroma scores and macrophage polarization (P < .05). Moreover, ANO6 was notably correlated with immune checkpoint expression levels, and scores of tumor mutation burden and microsatellite instability (all P < .05). ANO6 was an independent prognostic factor for BC, and might be a potential target for the BC treatment. Besides, ANO6 might affect BC progression via the regulation of stroma-related pathways and macrophage polarization.

Demystifying the Role of Prognostic Biomarkers in Breast Cancer through Integrated Transcriptome and Pathway Enrichment Analyses

Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of death in women. Researchers have discovered an increasing number of molecular targets for BC prognosis and therapy. However, it is still urgent to identify new biomarkers. Therefore, we evaluated biomarkers that may contribute to the diagnosis and treatment of BC. We searched TCGA datasets and identified differentially expressed genes (DEGs) by comparing tumor (100 samples) and non-tumor (100 samples) tissues using the Deseq2 package. Pathway and functional enrichment analysis of the DEGs was performed using the DAVID (Database for Annotation, Visualization, and Integrated Discovery) database. The protein–protein interaction (PPI) network was identified using the STRING database and visualized through Cytoscape software. Hub gene analysis of the PPI network was completed using cytohubba plugins. The associations between the identified genes and overall survival (OS) were analyzed using a Kaplan–Meier plot. Finally, we have identified hub genes at the transcriptome level. A total of 824 DEGs were identified, which were mostly enriched in cell proliferation, signal transduction, and cell division. The PPI network comprised 822 nodes and 12,145 edges. Elevated expression of the five hub genes AURKA, BUB1B, CCNA2, CCNB2, and PBK are related to poor OS in breast cancer patients. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation causing aneuploidy. The enriched functions and pathways included the cell cycle, oocyte meiosis, and the p53 signaling pathway. The identified five hub genes in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

MiR-379-5p inhibits the proliferation, migration, and invasion of breast cancer by targeting KIF4A

BACKGROUND

Many studies have shown that microRNAs (miRNAs) play an essential role in gene regulation and tumor development. This study aimed to explore the expression of miR-379-5p and its mechanisms of affecting proliferation, migration, and invasion in breast cancer (BC).

METHODS

MiRNAs and mRNAs expression data of BC and normal breast tissue samples were downloaded from the TCGA and GEO databases. qRT-PCR was used to detect the expression of miR-379-5p in human normal breast epithelial cell lines and human BC cell lines. The proliferation ability of transfected cells was detected by colony formation and EdU assays. The mobility and invasion ability of transfected cells was measured by wound healing and transwell assays. The relative protein expression of transfected cells was detected by western blot. Dual luciferase reporter assay was performed to identify the targeted binding of miR-379-5p and KIF4A.

RESULTS

MiR-379-5p was lowly expressed in BC tissue samples and BC cell lines. The target genes of miR-379-5p were involved in many cancer-related signaling pathways. PPI analysis and the cytoHubba algorithm of Cytoscape identified 10 genes as the hub genes. Survival analysis showed that only KIF4A expression in 10 hub genes was significantly associated with the prognosis of BC patients and was significantly upregulated in BC. Overexpression of miR-379-5p inhibited proliferation, migration, and invasion in the BC cell line MDA-MB-231, which could be reversed by KIF4A.

CONCLUSIONS

MiR-379-5p inhibits proliferation, migration, and invasion of BC by targeting KIF4A.

A Novel Five-Gene Signature Related to Clinical Outcome and Immune Microenvironment in Breast Cancer

Breast cancer (BC) is the most frequent cancer in women and the main cause of cancer-related deaths in the globe, according to the World Health Organization. The need for biomarkers that can help predict survival or guide treatment decisions in BC patients is critical in order to provide each patient with an individualized treatment plan due to the wide range of prognoses and therapeutic responses. A reliable prognostic model is essential for determining the best course of treatment for patients. Patients’ clinical and pathological data, as well as their mRNA expression levels at level 3, were gleaned from the TCGA databases. Differentially expressed genes (DEGs) between BC and non-tumor specimens were identified. Tumor immunity analyses have been utilized in order to decipher molecular pathways and their relationship to the immune system. The expressions of KIF4A in BC cells were determined by RT-PCR. To evaluate the involvement of KIF4A in BC cell proliferation, CCK-8 tests were used. In this study, utilizing FC > 4 and p < 0.05, we identified 140 upregulated genes and 513 down-regulated genes. A five-gene signature comprising SFRP1, SAA1, RBP4, KIF4A and COL11A1 was developed for the prediction of overall survivals of BC. Overall survival was distinctly worse for patients in the high-risk group than those in the low-risk group. Cancerous and aggressiveness-related pathways and decreased B cell, T cell CD4⁺, T cell CD8⁺, Neutrophil and Myeloid dendritic cells levels were seen in the high-risk group. In addition, we found that KIF4A was highly expressed in BC and its silence resulted in the suppression of the proliferation of BC cells. Taken together, as a possible prognostic factor for BC, the five-gene profile created and verified in this investigation could guide the immunotherapy selection.

Chondrocyte-derived Exosomal miR-195 Inhibits Osteosarcoma Cell Proliferation and Anti-Apoptotic by Targeting KIF4A in vitro and in vivo

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Osteosarcoma (OS) chemoresistance and distant metastasis are directly associated with OS recurrence and dismal patient prognosis, which are serious concerns for the medical community.

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However, current knowledge on OS pathogenesis and treatment remains limited. We found that kinesin superfamily protein 4A (KIF4A) acts as a potential OS biomarker.

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KIF4A promoted OS cell proliferation and anti-apoptotic in vitro and enhanced tumor growth in vivo. Our results indicate that miR-195 inhibits the expression of KIF4A by directly targeting its 3’-untranslated region

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Hence, targeting KIF4A could be a novel therapeutic strategy for OS and miR-195 may be a potential KIF4A-targeting drug. Furthermore, this study demonstrates that normal human chondrocytes can be used to produce miR-195-carrying exosomes to successfully deliver miR-195 into OS cells.

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Thus, our results suggest that chondrocyte-derived exosomal miR-195 may be developed into a potential adjuvant chemotherapeutic drug.

Background

Osteosarcoma (OS) is a primary malignant tumor of the bone that occurs in adolescents and is characterized by a young age at onset, high malignancy, high rate of metastasis, and poor prognosis. However, the factors influencing disease progression and prognosis remain unclear.

Methods

In this study, we aimed to investigate the role of chondrocyte-derived exosomal miR-195 in OS. We used normal human chondrocytes to form miR-195-carrying exosomes to deliver miR-195 into OS cells. Xenograft tumor experiments were performed in mice intratumorally injected with exosomal miR-195. We found that kinesin superfamily protein 4A (KIF4A) promoted OS tumor progression and anti-apoptotic.

Resules

We demonstrated that miR-195 inhibited the expression of KIF4A by directly targeting its 3’-untranslated region. Moreover, we observed that exosomal miR-195 successfully inhibited OS cell tumor growth and antiapoptotic in vitro and suppressed tumor growth in vivo.

Conclusion

Collectively, these results demonstrate that normal human chondrocyte-derived exosomal miR-195 can be internalized by OS cells and inhibit tumor growth and antiapoptotic by targeting KIF4A, providing a new direction for clarifying the molecular mechanism underlying OS development.

Potential Prognostic Biomarkers of NIMA (Never in Mitosis, Gene A)-Related Kinase (NEK) Family Members in Breast Cancer

Breast cancer remains the most common malignant cancer in women, with a staggering incidence of two million cases annually worldwide; therefore, it is crucial to explore novel biomarkers to assess the diagnosis and prognosis of breast cancer patients. NIMA-related kinase (NEK) protein kinase contains 11 family members named NEK1-NEK11, which were discovered from Aspergillus Nidulans; however, the role of NEK family genes for tumor development remains unclear and requires additional study. In the present study, we investigate the prognosis relationships of NEK family genes for breast cancer development, as well as the gene expression signature via the bioinformatics approach. The results of several integrative analyses revealed that most of the NEK family genes are overexpressed in breast cancer. Among these family genes, NEK2/6/8 overexpression had poor prognostic significance in distant metastasis-free survival (DMFS) in breast cancer patients. Meanwhile, NEK2/6 had the highest level of DNA methylation, and the functional enrichment analysis from MetaCore and Gene Set Enrichment Analysis (GSEA) suggested that NEK2 was associated with the cell cycle, G2M checkpoint, DNA repair, E2F, MYC, MTORC1, and interferon-related signaling. Moreover, Tumor Immune Estimation Resource (TIMER) results showed that the transcriptional levels of NEK2 were positively correlated with immune infiltration of B cells and CD4+ T Cell. Collectively, the current study indicated that NEK family genes, especially NEK2 which is involved in immune infiltration, and may serve as prognosis biomarkers for breast cancer progression.

Enhanced expression of KIF4A in osteosarcoma predicts a poor prognosis and facilitates tumor growth by activation of the MAPK pathway

The present study aimed to explore the prognostic value and role of kinesin family member 4A (KIF4A) expression in human osteosarcoma. KIF4A expression was evaluated in human osteosarcoma tissues from The Cancer Genome Atlas and Gene Expression Omnibus datasets. Reverse transcription-quantitative PCR was then applied to assess KIF4A level in both osteosarcoma cell lines and tissues. The association between KIF4A expression and clinical results in patients with osteosarcoma was detected by survival analysis. MTT assays and colony formation assays were used to evaluate the effects of KIF4A on osteosarcoma cell proliferation. The results indicated that the level of KIF4A was increased and associated with a poor prognosis in osteosarcoma tissues. Knockdown of KIF4A was shown to inhibit osteosarcoma cellular proliferation by affecting the MAPK pathway. The level of KIF4A was high in the human osteosarcoma tissues and this could be considered as a tumor induction gene, which may be used as an indicator of prognosis.

DSN1 is a prognostic biomarker and correlated with clinical characterize in breast cancer

DSN1 affects cell cycle progression and is associated with clinical-pathological features in colorectal and hepatocellular carcinomas. However, the biological function of DSN1 in breast cancer is still indistinct. In this study, we comprehensively analyzed the correlation between DSN1 expression in different molecular subtypes or stages of breast cancer and investigated the prognostic value of DSN1 in databases such as Oncomine, Cancer Cell Line Encyclopedia, UALCAN, Human Protein Atlas, Kaplan-Meier Plotter, OncoLnc, GEPIA. Moreover, we investigated the correlation of DSN1 with tumor-infiltrating immune cells in the different tumor microenvironments via Tumor Immune Estimation Resource database and explore DSN1 co-expression networks in breast cancer via LinkedOmics analysis, NetworkAnalyst database analysis. Finally, we also performed our immunohistochemical experiments to explore the expression of DSN1 in different stages or subtypes of breast cancer. The findings in this article shed light on the essential role of DSN1 in breast cancers as well as suggested that the upregulation of DSN1 expression was strongly associated with poor prognosis and decreased survival in breast cancer, and there were significant differences in its expression in different pathological subtypes and stages of breast cancer.

YAP/TEAD4-induced KIF4A contributes to the progression and worse prognosis of esophageal squamous cell carcinoma

Aberrant expression of kinesin family member 4A (KIF4A), which is associated with tumor progression, has been reported in several types of cancer. However, its expression and the underlying molecular mechanisms regulating the transcription of KIF4A in esophageal squamous cell carcinoma (ESCC) remain largely unclear. Here, we found that high KIF4A expression was positively correlated with tumor stage and poor prognosis in ESCC patients. KIF4A silencing significantly inhibited the growth and migration of ESCC cells, arrested cell cycle, and induced apoptosis. Interestingly, KIF4A expression was positively related to the expression of YAP in human ESCC tissues. YAP knockdown or disrupting YAP/TEAD4 interaction by verteporfin repressed KIF4A expression. Also, KIF4A knockdown significantly inhibited the cell growth induced by YAP overexpression. Mechanistically, YAP activated KIF4A transcriptional expression by TEAD4-mediated direct binding to KIF4A promoter. Finally, KIF4A knockdown and verteporfin treatment synergistically inhibited tumor growth in xenograft models. Together, these results indicated that KIF4A, a novel target gene of YAP/TEAD4, may be a progression and prognostic biomarker of ESCC. Targeting drugs for KIF4A combined with YAP inhibitor may be a novel therapeutic strategy for ESCC.

KIF4A knockdown suppresses ovarian cancer cell proliferation and induces apoptosis by downregulating BUB1 expression

Ovarian cancer is one of the most common lethal gynecological malignancies worldwide. Abnormal kinesin family member 4A (KIF4A) expression has been implicated in ovarian cancer progression; however, the potential mechanism underlying KIF4A in ovarian cancer is not completely understood. The present study aimed to clarify the molecular basis of KIF4A in ovarian cancer. KIF4A and budding uninhibited by benzimidazoles 1 (BUB1) expression levels were detected via reverse transcription-quantitative PCR and western blotting. Cell Counting Kit-8, colony formation, wound healing, TUNEL and flow cytometry assays were performed to assess cell proliferation, migration, apoptosis and cell cycle distribution, respectively. Ki67 expression levels were detected by conducting immunofluorescence assays. The expression levels of migration- and apoptosis-related proteins were measured via western blotting. A co-immunoprecipitation assay was conducted to determine the association between KIF4A and BUB1. The results demonstrated that KIF4A was expressed at significantly higher levels in ovarian cancer cell lines compared with IOSE-80 cells. Compared with the short hairpin RNA-negative control group, KIF4A knockdown significantly inhibited cell viability, colony formation and migration, and markedly induced cell apoptosis. The results indicated that KIF4A could bind to BUB1 and regulate BUB1 expression. BUB1 overexpression weakened KIF4A knockdown-mediated effects on cell viability, colony formation, migration and apoptosis. Overall, the present study demonstrated that KIF4A knockdown suppressed ovarian cancer progression by regulating BUB1, and suggested the potential value of KIF4A and BUB1 as therapeutic targets for ovarian cancer.

Feature Selection for Breast Cancer Classification by Integrating Somatic Mutation and Gene Expression

Exploring the molecular mechanisms of breast cancer is essential for the early prediction, diagnosis, and treatment of cancer patients. The large scale of data obtained from the high-throughput sequencing technology makes it difficult to identify the driver mutations and a minimal optimal set of genes that are critical to the classification of cancer. In this study, we propose a novel method without any prior information to identify mutated genes associated with breast cancer. For the somatic mutation data, it is processed to a mutated matrix, from which the mutation frequency of each gene can be obtained. By setting a reasonable threshold for the mutation frequency, a mutated gene set is filtered from the mutated matrix. For the gene expression data, it is used to generate the gene expression matrix, while the mutated gene set is mapped onto the matrix to construct a co-expression profile. In the stage of feature selection, we propose a staged feature selection algorithm, using fold change, false discovery rate to select differentially expressed genes, mutual information to remove the irrelevant and redundant features, and the embedded method based on gradient boosting decision tree with Bayesian optimization to obtain an optimal model. In the stage of evaluation, we propose a weighted metric to modify the traditional accuracy to solve the sample imbalance problem. We apply the proposed method to The Cancer Genome Atlas breast cancer data and identify a mutated gene set, among which the implicated genes are oncogenes or tumor suppressors previously reported to be associated with carcinogenesis. As a comparison with the integrative network, we also perform the optimal model on the individual gene expression and the gold standard PMA50. The results show that the integrative network outperforms the gene expression and PMA50 in the average of most metrics, which indicate the effectiveness of our proposed method by integrating multiple data sources, and can discover the associated mutated genes in breast cancer.

Kinesin family members KIF2C/4A/10/11/14/18B/20A/23 predict poor prognosis and promote cell proliferation in hepatocellular carcinoma

Kinesin superfamily proteins (KIFs) comprise a family of molecular motors that transport membranous organelles and protein complexes in a microtubule- and ATP-dependent manner, with multiple roles in cancers. Little is known about the function of KIFs in hepatocellular carcinoma (HCC). Here, we investigate the roles of KIFs in the prognosis and progression of HCC. Upregulation of eight KIFs (KIF2C, KIF4A, KIF10, KIF11, KIF14, KIF18B, KIF20A, and KIF23) was found to be significantly associated with the tumor stage and pathological tumor grade of HCC patients. Additionally, a high expression of these eight KIFs was significantly associated with shorter overall survival (OS) and disease-free survival (DFS) in patients with HCC. Cox regression analysis showed the mRNA expression levels of these eight KIF members to be independent prognostic factors for worse outcomes in HCC. Moreover, a risk score model based on the mRNA levels of the eight KIF members effectively predicted the OS rate of patients with HCC. Additional experiments revealed that downregulation of each of the eight KIFs effectively decreased the proliferation and increased the G1 arrest of liver cancer cells in vitro. Taken together, these results indicate that KIF2C/4A/10/11/14/18B/20A/23 may serve as prognostic biomarkers for survival and potential therapeutic targets in HCC patients.

Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

Prognostic value of Kinesin-4 family genes mRNA expression in early-stage pancreatic ductal adenocarcinoma patients after pancreaticoduodenectomy

BACKGROUND

The aim of this study was to investigate the potential prognostic value of Kinesin-4 family genes mRNA expression in early-stage pancreatic ductal adenocarcinoma (PDAC) patients after pancreaticoduodenectomy.

METHODS

Kaplan-Meier survival analysis method with log-rank test and Cox proportional hazards regression analysis were performed to figure out the association between Kinesin-4 family genes expression and PDAC patients overall survival time. Joint-effect survival analysis and stratified survival analysis were carried out to assess the prognosis prediction value of prognosis-related gene. Nomogram was constructed for the individualized prognosis prediction. In addition, we had used the gene set enrichment analysis and genome-wide co-expression analysis to further explore the potential mechanism.

RESULTS

KIF21A expression level was significantly associated with PDAC patient clinical prognosis outcome and patient with a high expression of KIF21A would have a shorter overall survival time. The prognosis prediction significance of KIF21A was well validated by the joint-effect survival analysis, stratified survival analysis, and nomogram. Meanwhile, the gene set enrichment analysis and genome-wide co-expression analysis revealed that KIF21A might involve in DNA damage and repair, transcription and translation process, post-translation protein modification, cell cycle, carcinogensis genes and pathways.

CONCLUSIONS

Our current research demonstrated that KIF21A could serve as a potential prognostic biomarker for patient with early-stage PDAC after pancreaticoduodenectomy.

Genome‑wide investigation of the clinical significance and prospective molecular mechanisms of kinesin family member genes in patients with lung adenocarcinoma

The current study aimed to identify the potential clinical significance and molecular mechanisms of kinesin (KIF) family member genes in lung adenocarcinoma (LUAD) using genome-wide RNA sequencing (RNA-seq) datasets derived from The Cancer Genome Atlas (TCGA) database. Clinical parameters and RNA-seq data of patients with LUAD from the TCGA database enabled the assessment of the clinical significance of KIF genes, while the potential mechanisms of their interactions in LUAD were investigated by gene set enrichment analysis (GSEA). A gene signature with potential prognostic value was constructed via a stepwise multivariable Cox analysis. In total, 23 KIF genes were identified to be differentially expressed genes (DEGs) between the LUAD tumor and adjacent non-cancerous tissues. Of these, 8 differentially expressed KIF genes were strongly found to be strongly associated with the overall survival of patients with LUAD. Three of these genes were found to be able to be grouped as a potential prognostic gene signature. Patients with higher risk scores calculated using this gene signature were found to have a markedly higher risk of mortality (adjusted P=0.003; adjusted HR, 1.576; 95% CI, 1.166–2.129). Time-dependent receiver operating characteristic analysis indicated that this prognostic signature was able to accurately predict patient prognosis with an area under curve of 0.636, 0.643,0.665, 0.670 and 0.593 for the 1-, 2-, 3-, 4- and 5-year survival, respectively. This prognostic gene signature was identified as an independent risk factor for LUAD and was able to more accurately predict prognosis in comparison to other known clinical parameters, as shown via comprehensive survival analysis. GSEA enrichment revealed that that KIF14, KIF18B and KIF20A mediated basic cell physiology through the regulation of the cell cycle, DNA replication, and DNA repair biological processes and pathways. On the whole, the findings of this study identified 23 KIF genes that were DEGs between LUAD tumor and adjacent non-cancerous tissues. In total, 8 of these genes had the potential to function as prognostic and diagnostic biomarkers in patients with LUAD.

Screening of differentially expressed genes and identification of NUF2 as a prognostic marker in breast cancer

The aims of the present study were to screen differentially expressed genes (DEGs) in breast cancer (BC) and investigate NDC80 kinetochore complex component (NUF2) as a prognostic marker of BC in detail. A total of four BC microarray datasets, downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, were used to screen DEGs. A total of 190 DEGs with the same expression trends were identified in the 4 datasets, including 65 upregulated and 125 downregulated DEGs. Functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery. The upregulated DEGs were enriched for 10 Gene Ontology (GO) terms and 7 pathways, and the downregulated DEGs were enriched for 10 GO terms and 10 pathways. A protein‑protein interaction network containing 149 nodes and 930 edges was constructed using the Search Tool for the Retrieval of Interacting Genes, and 2 functional modules were identified using the MCODE plugin of Cytoscape. Based on an in‑depth analysis of module 1 and literature mining, NUF2 was selected for further research. Oncomine database analysis and reverse transcription‑quantitative PCR showed that NUF2 is significantly upregulated in BC tissues. In analyses of correlations between NUF2 and clinical pathological characteristics, NUF2 was significantly associated with the malignant features of BC. Using 5 additional datasets from GEO, it was demonstrated that NUF2 has a significant prognostic role in both ER‑positive and ER‑negative BC. A Gene Set Enrichment Analysis indicated that NUF2 may regulate breast carcinogenesis and progression via cell cycle‑related pathways. The results of the present study demonstrated that NUF2 is overexpressed in BC and is significantly associated with its multiple pathological features and prognosis.

Identification of KIF11 As a Novel Target in Meningioma

Kinesins play an important role in many physiological functions including intracellular vesicle transport and mitosis. The emerging role of kinesins in different cancers led us to investigate the expression and functional role of kinesins in meningioma. Therefore, we re-analyzed our previous microarray dataset of benign, atypical, and anaplastic meningiomas (n = 62) and got evidence for differential expression of five kinesins (KIFC1, KIF4A, KIF11, KIF14 and KIF20A). Further validation in an extended study sample (n = 208) revealed a significant upregulation of these genes in WHO°I to °III meningiomas (WHO°I n = 61, WHO°II n = 88, and WHO°III n = 59), which was most pronounced in clinically more aggressive tumors of the same WHO grade. Immunohistochemical staining confirmed a WHO grade-associated upregulated protein expression in meningioma tissues. Furthermore, high mRNA expression levels of KIFC1, KIF11, KIF14 and KIF20A were associated with shorter progression-free survival. On a functional level, knockdown of kinesins in Ben-Men-1 cells and in the newly established anaplastic meningioma cell line NCH93 resulted in a significantly inhibited tumor cell proliferation upon siRNA-mediated downregulation of KIF11 in both cell lines by up to 95% and 71%, respectively. Taken together, in this study we were able to identify the prognostic and functional role of several kinesin family members of which KIF11 exhibits the most promising properties as a novel prognostic marker and therapeutic target, which may offer new treatment options for aggressive meningiomas.