TP53及其调控基因与皮肤黑色素瘤预后的关系。

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Cancer Informatics Pub Date : 2023-01-01 DOI:10.1177/11769351231177267
Safir Ullah Khan, Zahid Ullah, Hadia Shaukat, Sheeza Unab, Saba Jannat, Waqar Ali, Amir Ali, Muhammad Irfan, Muhammad Fiaz Khan, Rodolfo Daniel Cervantes-Villagrana
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引用次数: 0

摘要

本研究首次通过提供大型数据集的各种遗传数据库对皮肤黑色素瘤中p53信号基因的基因突变和表达水平进行了全面研究。p53及其信号基因的突变情况比预期的要高,TP53其次是CDKN2A是皮肤黑色素瘤中突变最多的基因。此外,表达分析显示TP53、MDM2、CDKN2A和TP53BP1在皮肤黑色素瘤中过表达,而MDM4和CDKN2B在皮肤黑色素瘤中过表达。总体而言,TCGA数据显示,在所有其他p53信号蛋白中,CDKN2A在阳光照射和非阳光照射的健康组织中均显著高于黑色素瘤。同样,与其他组相比,MDM4和TP53BP1在非阳光照射的健康组织中的表达明显更高。然而,CDKN2B在阳光照射的健康组织中的表达高于其他组织。此外,各基因在雄性和雌性之间的表达也存在显著差异。此外,CDKN2A在SK-MEL-30皮肤癌细胞系中高表达,而免疫细胞类型表达分析显示MDM4在naïve b细胞中高表达。此外,与健康组织相比,所有六个基因在异常超重或肥胖的肿瘤组织中都显着过表达。MDM2的表达与肿瘤分期密切相关。不同患者年龄组和阳性淋巴结状态的基因表达存在差异。TP53与B细胞呈正相关,MDM2与CD8+T细胞、巨噬细胞、中性粒细胞呈正相关,MDM4与中性粒细胞呈正相关。CDKN2A/B与所有六种免疫细胞均无显著相关性。而TP53BP1与除B细胞外的5种免疫细胞均呈正相关。只有TP53、MDM2和CDKN2A在皮肤黑色素瘤特异性肿瘤免疫中起作用。所有TP53及其调控基因均可预测预后。目前研究的结果需要通过未来的筛选、体内和体外研究来验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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TP53 and its Regulatory Genes as Prognosis of Cutaneous Melanoma.

The present study was the first comprehensive investigation of genetic mutation and expression levels of the p53 signaling genes in cutaneous melanoma through various genetic databases providing large datasets. The mutational landscape of p53 and its signaling genes was higher than expected, with TP53 followed by CDKN2A being the most mutated gene in cutaneous melanoma. Furthermore, the expression analysis showed that TP53, MDM2, CDKN2A, and TP53BP1 were overexpressed, while MDM4 and CDKN2B were under-expressed in cutaneous melanoma. Overall, TCGA data revealed that among all the other p53 signaling proteins, CDKN2A was significantly higher in both sun and non-sun-exposed healthy tissues than in melanoma. Likewise, MDM4 and TP53BP1 expressions were markedly greater in non-sun-exposed healthy tissues compared to other groups. However, CDKN2B expression was higher in the sun-exposed healthy tissues than in other tissues. In addition, various genes were expressed significantly differently among males and females. In addition, CDKN2A was highly expressed in the SK-MEL-30 skin cancer cell line, whereas, Immune cell type expression analysis revealed that the MDM4 was highly expressed in naïve B-cells. Furthermore, all six genes were significantly overexpressed in extraordinarily overweight or obese tumor tissues compared to healthy tissues. MDM2 expression and tumor stage were closely related. There were differences in gene expression across patient age groups and positive nodal status. TP53 showed a positive correlation with B cells, MDM2 with CD8+T cells, macrophages and neutrophils, and MDM4 with neutrophils. CDKN2A/B had a non-significant correlation with all six types of immune cells. However, TP53BP1 was positively correlated with all five types of immune cells except B cells. Only TP53, MDM2, and CDKN2A had a role in cutaneous melanoma-specific tumor immunity. All TP53 and its regulating genes may be predictive for prognosis. The results of the present study need to be validated through future screening, in vivo, and in vitro studies.

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来源期刊
Cancer Informatics
Cancer Informatics Medicine-Oncology
CiteScore
3.00
自引率
5.00%
发文量
30
审稿时长
8 weeks
期刊介绍: The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.
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