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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Pan-Cancer Analysis of CENPA as a Potential Prognostic and Immunological Biomarker in Human Cancer

Author(s): Zhongjiao Hu, Shutao Zhang, Xueling Yan, Lulu Zheng, Ke Ding, Shanshan Liu and Zheng Shi*

Volume 17, Issue 7, 2022

Published on: 23 August, 2022

Page: [599 - 614] Pages: 16

DOI: 10.2174/1574893617666220509205749

Price: $65

Abstract

Background: CENPA is a rare histone variant that regulates various active centromeres and neocentromeres via diverse signal pathways. However, the expression of CENPA correlated with the prognosis of patients in human pan-cancer is still largely underexplored.

Objective: To find the role of CENPA in the prognosis and immunotherapy of cancer patients.

Methods: In this study, multiple bioinformatic methods, including the ONCOMINE database, TCGA database, GEPIA database, DAVID database, and TIMER database were integrated to comprehensively investigate the prognosis and immunity of CENPA in pan-cancer.

Results: The results showed that CENPA was widely expressed in numerous cancer types, including liver cancer, lung cancer, bladder cancer, and gastric cancer. Meanwhile, the increased CENPA expression was significantly related with poor prognosis in breast cancer, lung cancer, and sarcoma. Additionally, CENPA expression had a positive coefficient for immune cell infiltration, including B cells, CD4+T cells, CD8+T cells, neutrophils, dendritic cells, and macrophages. Furthermore, we screened out TGCT, THCA, and LUSC as the most vital cancers correlated with CENPA expression in the immune microenvironment, according to immune score and stromal score. Notably, 47 common immune checkpoint genes were explored in 33 cancer types based on the coefficients of CENPA expression. In addition, CENPA expression was strongly associated with TMB and MSI in various cancers, like BLCA, BRCA, CESC, and CHOL. Moreover, there was a high correlation between CENPA expression and DNA methylation obtained by calculating relatedness coefficients. Enrichment analysis showed that CENPA might be involved in the progression of cancer through cell cycle-related pathways, p53 signaling pathways, and mismatch repair enrichment pathways.

Conclusion: Taken together, our results suggested that CEPNA could be considered a promising predictive biomarker affecting prognosis and immune infiltration in human pan-cancer.

Keywords: Bioinformatic analysis, CENPA, pan-cancer, cancer prognosis, immune infiltration, and systems biology

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