The effect of centromere protein U silencing by lentiviral mediated RNA interference on the proliferation and apoptosis of breast cancer

Promising novel biomarkers and candidate small-molecule drugs for lung adenocarcinoma: Evidence from bioinformatics analysis of high-throughput data

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer associated with an unstable prognosis. Thus, there is an urgent demand for the identification of novel diagnostic and prognostic biomarkers as well as targeted drugs for LUAD. The present study aimed to identify potential new biomarkers associated with the pathogenesis and prognosis of LUAD. Three microarray datasets (GSE10072, GSE31210, and GSE40791) from the Gene Expression Omnibus database were integrated to identify the differentially expressed genes (DEGs) in normal and LUAD samples using the limma package. Bioinformatics tools were used to perform functional and signaling pathway enrichment analyses for the DEGs. The expression and prognostic values of the hub genes were further evaluated by Gene Expression Profiling Interactive Analysis and real-time quantitative polymerase chain reaction. Furthermore, we mined the “Connectivity Map” (CMap) to explore candidate small molecules that can reverse the tumoral of LUAD based on the DEGs. A total of 505 DEGs were identified, which included 337 downregulated and 168 upregulated genes. The PPI network was established with 1,860 interactions and 373 nodes. The most significant pathway and functional enrichment associated with the genes were cell adhesion and extracellular matrix-receptor interaction, respectively. Seven DEGs with high connectivity degrees (ZWINT, RRM2, NDC80, KIF4A, CEP55, CENPU, and CENPF) that were significantly associated with worse survival were chosen as hub genes. Lastly, top 20 most important small molecules which reverses the LUAD gene expressions were identified. The findings contribute to revealing the molecular mechanisms of the initiation and progression of LUAD and provide new insights for integrating multiple biomarkers in clinical practice.

High mRNA Expression of CENPL and Its Significance in Prognosis of Hepatocellular Carcinoma Patients

Centromere proteins (CENPs) are the main constituent proteins of kinetochore, which are essential for cell division. In recent years, several studies have revealed that several CENPs were aberrantly expressed in hepatocellular carcinoma (HCC). However, numerous centromere proteins have not been studied in HCC. In this study, we used databases of Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), the Kaplan-Meier Plotter, cBioPortal, the Human Protein Atlas (HPA), and TIMER (Tumor Immune Estimation Resource) and immunohistochemical staining of clinical specimens to investigate the expression of 15 major centromere proteins in HCC to evaluate their potential prognostic value. We found that the mRNA levels of 4 out of 15 centromere proteins (CENPL, CENPQ, CENPR, and CENPU) were significantly higher in HCC than in normal tissues, and their mRNA levels were associated with the tumor stages (p values < 0.01). Patients with higher mRNA levels of CENPL had poorer overall survival, progression-free survival, relapse-free survival, and disease-specific survival (p values < 0.05). Furthermore, the higher levels of CENPL mRNA were associated with worse overall survival in males without hepatitis virus infection (p values < 0.05). The protein expression level of CENPL in human HCC tissue was higher than that in normal liver tissue. In addition, the expression of CENPL was positively correlated with the levels of the tumor-infiltrating lymphocytes. The results suggest that the high mRNA expression of CENPL may be a potential predictor of prognosis in HCC patients.

A nomogram based on CENPP expression for survival prediction in breast cancer

Background

In recent years, it has been found that the expression of 17 centromere proteins (CENPs) was closely related to malignant tumors, however, the role of CENPs in breast cancer (BC) has not been fully investigated. This study intends to investigate the prognostic value of CENPs in BC and establish nomogram based on expression of CENPs to predict BC patients' prognosis.

Methods

A total of 800 BC patients with complete relevant data were included from the TCGA database and were further randomly divided into training set (N=480) and validation set (N=320). Univariate and multivariate Cox regression analysis were used to screen independent factors for overall survival (OS) prediction of BC patients in the training set. Then, the nomogram was established based on these independent predictors and further validated by receiver-operating characteristic (ROC) curves and calibration plots. The GEPIA and bcGenExMiner v4.4 databases were utilized to analyze mRNA expression of candidate gene in BC patients with different clinicopathological features, respectively.

Results

Multivariate Cox regression analysis showed that age, Her2 status, pathologic_T stage, pathologic_M stage and CENPP expression were of independent prognostic value for BC. CENPP was overexpressed in BC tissues (P<0.01) and lower expression of CENPP was associated with worse OS (P=0.005, HR =2.35; 95% CI: 1.30-4.23). We then established a nomogram based on those independent predictors, and the calibration curve demonstrated good fitness of the nomogram for OS prediction. In the training set, the AUCs of 3- and 5-year survival were 0.757 and 0.797, respectively. In the validation set, the AUCs of 3- and 5-year survival were 0.727 and 0.71, respectively.

Conclusions

Our study showed that CENPP was a novel prognostic factor for patients with BC, and the established nomogram could provide valuable information on prognostic prediction for patients with BC.

The lncRNA GATA3-AS1/miR-495-3p/CENPU axis predicts poor prognosis of breast cancer via the PLK1 signaling pathway

The function of centromere protein U (CENPU) gene in breast cancer has not been well understood. Therefore, we explored the expression profiles of CENPU gene in breast carcinoma to better understand the functions of this gene, as well as the relationship between CENPU expression and the prognosis of breast carcinoma patients. Our results indicate that CENPU was expressed at significantly higher levels in cancerous tissues than in normal tissues. Furthermore, CENPU expression correlated significantly with many clinicopathological characteristics of breast cancer. In addition, we discovered that high levels of CENPU expression predicted poor prognosis in patients with breast cancer. Functional investigation revealed that 180 genes exhibited co-expression with CENPU. Functional annotation indicated that 17 of these genes were involved in the PLK1 signaling pathway, with most of them (16/17) being expressed at significantly higher levels in malignant tissues compared with normal controls and correlating with a poor prognosis. Subsequently, we found that four miRNAs, namely hsa-miR-543, hsa-miR-495-3p, hsa-miR-485-3p, and hsa-miR-337-3p, could be regarded as potential CENPU expression regulators. Then, five lncRNAs were predicted to potentially bind to the four miRNAs. Combination of the results from expression, survival, correlation analysis and functional experiments analysis demonstrated the link between lncRNA GATA3-AS1/miR-495-3p/CENPU axis and prognosis of breast cancer. In conclusion, CENPU could be involved in cell cycle progression through PLK1 signaling pathway.

Over-Expression of Centromere Protein U Participates in the Malignant Neoplastic Progression of Breast Cancer

The expression of Centromere Protein U (CENP-U) is closely related to tumor malignancy. Till now, the role of CENP-U in the malignant progression of breast cancer remains unclear. In this study, we found that CENP-U protein was highly expressed in the primary invasive breast cancer tissues compared to the paired adjacent histologically normal tissues and ductal carcinoma in situ (DCIS) tissues. After CENP-U was knocked down, the proliferation and colony-forming abilities of breast cancer cells were significantly suppressed, whereas the portion of apoptotic cells was increased. Meanwhile, the PI3K/AKT/NF-κB pathway was significantly inhibited. In vivo studies showed that, the inhibition of CENP-U repressed the tumor growth in orthotopic breast cancer models. Therefore, our study demonstrated that the CENP-U might act as an oncogene and promote breast cancer progression via activation of the PI3K/AKT/NF-κB pathway, which suggests a promising direction for targeting therapy in breast cancer.

Centromere protein U enhances the progression of bladder cancer by promoting mitochondrial ribosomal protein s28 expression

Bladder cancer is one of the most common types of cancer. Most gene mutations related to bladder cancer are dominantly acquired gene mutations and are not inherited. Previous comparative transcriptome analysis of urinary bladder cancer and control samples has revealed a set of genes that may play a role in tumor progression. Here we set out to investigate further the expression of two candidate genes, centromere protein U (CENPU) and mitochondrial ribosomal protein s28 (MRPS28) to better understand their role in bladder cancer pathogenesis. Our results confirmed that CENPU is up-regulated in human bladder cancer tissues at mRNA and protein levels. Gain-of-function and loss-of-function studies in T24 human urinary bladder cancer cell line revealed a hierarchical relationship between CENPU and MRPS28 in the regulation of cell viability, migration and invasion activity. CENPU expression was also up-regulated in in vivo nude mice xenograft model of bladder cancer and mice overexpressing CENPU had significantly higher tumor volume. In summary, our findings identify CENPU and MRPS28 in the molecular pathogenesis of bladder cancer and suggest that CENPU enhances the progression of bladder cancer by promoting MRPS28 expression.

Clinical Implications of Chromosomal Instability (CIN) and Kinetochore Abnormalities in Breast Cancers

Genetic instability is a defining property of cancer cells and is the basis of various lesions including point mutations, copy number alterations and translocations. Chromosomal instability (CIN) is part of the genetic instability of cancer and consists of copy number alterations in whole or parts of cancer cell chromosomes. CIN is observed in differing degrees in most cancers. In breast cancer, CIN is commonly part of the genomic landscape of the disease and has a higher incidence in aggressive sub-types. Tumor suppressors that are commonly mutated or disabled in cancer, such as p53 and pRB, play roles in protection against CIN, and as a result, their dysfunction contributes to the establishment or tolerance of CIN. Several structural and regulatory proteins of the centromeres and kinetochore, the complex structure that is responsible for the correct distribution of genetic material in the daughter cells during mitosis, are direct or, mostly, indirect transcription targets of p53 and pRB. Thus, despite the absence of structural defects in genes encoding for centromere and kinetochore components, dysfunction of these tumor suppressors may have profound implications for the correct function of the mitotic apparatus contributing to CIN. CIN and its prognostic and therapeutic implications in breast cancer are discussed in this article.

LOTUS: A single- and multitask machine learning algorithm for the prediction of cancer driver genes

Cancer driver genes, i.e., oncogenes and tumor suppressor genes, are involved in the acquisition of important functions in tumors, providing a selective growth advantage, allowing uncontrolled proliferation and avoiding apoptosis. It is therefore important to identify these driver genes, both for the fundamental understanding of cancer and to help finding new therapeutic targets or biomarkers. Although the most frequently mutated driver genes have been identified, it is believed that many more remain to be discovered, particularly for driver genes specific to some cancer types. In this paper, we propose a new computational method called LOTUS to predict new driver genes. LOTUS is a machine-learning based approach which allows to integrate various types of data in a versatile manner, including information about gene mutations and protein-protein interactions. In addition, LOTUS can predict cancer driver genes in a pan-cancer setting as well as for specific cancer types, using a multitask learning strategy to share information across cancer types. We empirically show that LOTUS outperforms five other state-of-the-art driver gene prediction methods, both in terms of intrinsic consistency and prediction accuracy, and provide predictions of new cancer genes across many cancer types.

Cancer development is driven by mutations and dysfunction of important, so-called cancer driver genes, that could be targeted by specific therapies. While a number of such cancer genes have already been identified, it is believed that many more remain to be discovered. To help prioritize experimental investigations of candidate genes, several computational methods have been proposed to rank promising candidates based on their mutations in large cohorts of cancer cases, or on their interactions with known driver genes in biological networks. We propose LOTUS, a new computational approach to identify genes with high oncogenic potential. LOTUS implements a machine learning approach to learn an oncogenic potential score from known driver genes, and brings two novelties compared to existing methods. First, it allows to easily combine heterogeneous sources of information into the scoring function, which we illustrate by learning a scoring function from both known mutations in large cancer cohorts and interactions in biological networks. Second, using a multitask learning strategy, it can predict different driver genes for different cancer types, while sharing information between them to improve the prediction for every type. We provide experimental results showing that LOTUS significantly outperforms several state-of-the-art cancer gene prediction software.