Identification and Analysis of Novel Biomarkers Involved in Chromophobe Renal Cell Carcinoma by Integrated Bioinformatics Analyses

CENPA knockdown restrains cell progression and tumor growth in breast cancer by reducing PLA2R1 promoter methylation and modulating PLA2R1/HHEX axis

BACKGROUND

Breast cancer is a lethal malignancy affecting females worldwide. It has been reported that upregulated centromere protein A (CENPA) expression might indicate unfortunate prognosis and can function as a prognostic biomarker in breast cancer. This study aimed to investigate the accurate roles and downstream mechanisms of CENPA in breast cancer progression.

METHODS

CENPA protein levels in breast cancer tissues and cell lines were analyzed by Western blot and immunohistochemistry assays. We used gain/loss-of-function experiments to determine the potential effects of CENPA and phospholipase A2 receptor (PLA2R1) on breast cancer cell proliferation, migration, and apoptosis. Co-IP assay was employed to validate the possible interaction between CENPA and DNA methyltransferase 1 (DNMT1), as well as PLA2R1 and hematopoietically expressed homeobox (HHEX). PLA2R1 promoter methylation was determined using methylation-specific PCR assay. The biological capabilities of CENPA/PLA2R1/HHEX axis in breast cancer cells was determined by rescue experiments. In addition, CENPA-silenced MCF-7 cells were injected into mice, followed by measurement of tumor growth.

RESULTS

CENPA level was prominently elevated in breast cancer tissues and cell lines. Interestingly, CENPA knockdown and PLA2R1 overexpression both restrained breast cancer cell proliferation and migration, and enhanced apoptosis. On the contrary, CENPA overexpression displayed the opposite results. Moreover, CENPA reduced PLA2R1 expression through promoting DNMT1-mediated PLA2R1 promoter methylation. PLA2R1 overexpression could effectively abrogate CENPA overexpression-mediated augment of breast cancer cell progression. Furthermore, PLA2R1 interacted with HHEX and promoted HHEX expression. PLA2R1 knockdown increased the rate of breast cancer cell proliferation and migration but restrained apoptosis, which was abrogated by HHEX overexpression. In addition, CENPA silencing suppressed tumor growth in vivo.

CONCLUSION

CENPA knockdown restrained breast cancer cell proliferation and migration and attenuated tumor growth in vivo through reducing PLA2R1 promoter methylation and increasing PLA2R1 and HHEX expression. We may provide a promising prognostic biomarker and novel therapeutic target for breast cancer.

GeneSelectML: a comprehensive way of gene selection for RNA-Seq data via machine learning algorithms

Selection of differentially expressed genes (DEGs) is a vital process to discover the causes of diseases. It has been shown that modelling of genomics data by considering relation among genes increases the predictive performance of methods compared to univariate analysis. However, there exist serious differences among most studies analyzing the same dataset for the reasons arising from the methods. Therefore, there is a strong need for easily accessible, user-friendly, and interactive tool to perform gene selection for RNA-seq data via machine learning algorithms simultaneously not to miss DEGs. We develop an open-source and freely available web-based tool for gene selection via machine learning algorithms that can deal with high performance computation. This tool includes six machine learning algorithms having different aspects. Moreover, the tool involves classical pre-processing steps; filtering, normalization, transformation, and univariate analysis. It also offers well-arranged graphical approaches; network plot, heatmap, venn diagram, and box-and-whisker plot. Gene ontology analysis is provided for both mRNA and miRNA DEGs. The implementation is carried out on Alzheimer RNA-seq data to demonstrate the use of this web-based tool. Eleven genes are suggested by at least two out of six methods. One of these genes, hsa-miR-148a-3p, might be considered as a new biomarker for Alzheimer's disease diagnosis. Kidney Chromophobe dataset is also analyzed to demonstrate the validity of GeneSelectML web tool on a different dataset. GeneSelectML is distinguished in that it simultaneously uses different machine learning algorithms for gene selection and can perform pre-processing, graphical representation, and gene ontology analyses on the same tool. This tool is freely available at www.softmed.hacettepe.edu.tr/GeneSelectML .

CENP-A is a potential prognostic biomarker and correlated with immune infiltration levels in glioma patients

Background: Centromeric protein A (CENP-A), an essential protein involved in chromosomal segregation during cell division, is associated with several cancer types. However, its role in gliomas remains unclear. This study examined the clinical and prognostic significance of CENP-A in gliomas.

Methods: Data of patients with glioma were collected from the Cancer Genome Atlas. Logistic regression, the Kruskal–Wallis test, and the Wilcoxon signed-rank test were performed to assess the relationship between CENP-A expression and clinicopathological parameters. The Cox regression model and Kaplan–Meier curve were used to analyze the association between CENP-A and survival outcomes. A prognostic nomogram was constructed based on Cox multivariate analysis. Gene set enrichment analysis (GSEA) was conducted to identify key CENP-A-related pathways and biological processes.

Results:CENP-A was upregulated in glioma samples. Increased CENP-A levels were significantly associated with the world health organization (WHO) grade [Odds ratio (OR) = 49.88 (23.52–129.06) for grade 4 vs. grades 2 and 3], primary therapy outcome [OR = 2.44 (1.64–3.68) for progressive disease (PD) and stable disease (SD) vs. partial response (PR) and complete response (CR)], isocitrate dehydrogenase (IDH) status [OR = 13.76 (9.25–20.96) for wild-type vs. mutant], 1p/19q co-deletion [OR = 5.91 (3.95–9.06) for no codeletion vs. co-deletion], and age [OR = 4.02 (2.68–6.18) for > 60 vs. ≤ 60]. Elevated CENP-A expression was correlated with shorter overall survival in both univariate [hazard ratio (HR): 5.422; 95% confidence interval (CI): 4.044–7.271; p < 0.001] and multivariate analyses (HR: 1.967; 95% CI: 1.280–3.025; p < 0.002). GSEA showed enrichment of numerous cell cycle-and tumor-related pathways in the CENP-A high expression phenotype. The calibration plot and C-index indicated the favorable performance of our nomogram for prognostic prediction in patients with glioma.

Conclusion: We propose a role for CENP-A in glioma progression and its potential as a biomarker for glioma diagnosis and prognosis.

CENPA promotes clear cell renal cell carcinoma progression and metastasis via Wnt/β-catenin signaling pathway

Clear cell renal cell carcinoma (ccRCC) is the most common malignant tumor of the kidney. New and reliable biomarkers are in urgent need for ccRCC diagnosis and prognosis. The CENP family is overexpressed in many types of cancers, but its functions in ccRCC have not been fully clarified. In this paper, we found that several CENP family members were highly expressed in ccRCC tissues. Also, CENPA expression level was related to clinicopathological grade and prognosis by weighted gene co-expression network analysis (WGCNA). CENPA served as a representative CENP family member as a ccRCC biomarker. Further in vitro experiments verified that overexpression of CENPA promoted ccRCC proliferation and metastasis by accelerating the cell cycle and activating the Wnt/β-catenin signaling pathway. The elevated β-catenin led by CENPA overexpression translocated to nucleus for downstream effect. Functional recovery experiment confirmed that Wnt/β-catenin pathway was essential for ccRCC progression and metastasis. Developing selective drugs targeting CENPA may be a promising direction for cancer treatment.