广西师范大学学报（自然科学版） ›› 2022, Vol. 40 ›› Issue (6): 196-205.doi: 10.16088/j.issn.1001-6600.2021030702

• 研究论文 • 上一篇    下一篇

一种新型黑色素瘤的蛋白质预后模型

陈雅静1,2,3, 张桐玮1,4, 周俊1,2,3, 陈舒曼1,2,3, 蒲仕明1,2,3*   

  1. 1.广西师范大学生命科学学院, 广西桂林541006;
    2.广西高校干细胞与医药生物技术重点实验室(广西师范大学),广西桂林541004;
    3.广西师范大学生物医学研究中心,广西桂林541004;
    4.广西珍稀濒危动物生态学重点实验室(广西师范大学), 广西桂林541006
  • 收稿日期:2021-03-07 修回日期:2021-06-02 出版日期:2022-11-25 发布日期:2023-01-17
  • 通讯作者: 蒲仕明(1987—),男,四川南充人,广西师范大学助理研究员。E-mail:pushiming77@163.com
  • 基金资助:
    国家自然科学基金(81972700,61827819);广西自然科学基金(2018GXNSFBA281115);广西大学生创新创业训练计划(202010602052)

A Novel Protein Prognostic Model for Melanoma

CHEN Yajing1,2,3, ZHANG Tongwei1,4, ZHOU Jun1,2,3, CHEN Shuman1,2,3, PU Shiming1,2,3*   

  1. 1. College of Life Sciences, Guangxi Normal University, Guilin Guangxi 541006, China;
    2. Guangxi Universities Key Laboratory of Stem Cell and Biopharmaceutical Technology (Guangxi Normal University), Guilin Guangxi 541004, China;
    3. Research Center for Biomedical Science, Guangxi Normal University, Guilin Guangxi 541004, China;
    4. Guangxi Key Laboratory of Rare and Endangered Animal Ecology (Guangxi Normal University), Guilin Guangxi 541006, China
  • Received:2021-03-07 Revised:2021-06-02 Online:2022-11-25 Published:2023-01-17

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41

摘要： 黑色素瘤是一种最具侵袭性和最难以治疗的癌症之一,目前黑色素瘤的治疗主要是使用靶向药物和免疫治疗药物。然而,受限于耐药性,这些疗法也无法有效改善黑色素瘤患者的预后。因此,需要新的预后方法来指导个体化治疗和改善预后。本文利用TCPA和TCGA数据集的多因素COX分析,建立基于P21、YAP、X1433ZETA、CKIT、S6、CD20、LCK、P27、CD49B、GATA6和SRC_pY416蛋白质的预后模型。风险评分分析显示,风险评分高的患者预后较差。单因素和多因素COX模型分析显示预后与风险评分相关。共表达分析确定了与预后模型中蛋白质共表达的各种蛋白质。将低风险蛋白质GATA6和高风险蛋白质X1433ZETA在黑色素瘤细胞中过表达,GATA6抑制肿瘤生长,X1433ZETA促进肿瘤生长。本文所建立的黑色素瘤预后模型可潜在应用于个体化治疗指导。

关键词: 预后模型, 黑色素瘤, 蛋白质组学, 生物标志物

Abstract: Melanoma is one of the most aggressive and difficult to treat cancers. Currently, Melanoma is mainly treated by using targeted and immunotherapeutic agents. However, their success is limited by the development of resistance, which curtails long-term response rates. Thus, novel prognostic approaches are needed to guide individualized treatment and improve outcomes. In this paper, a multicox analysis of TCPA and TCGA datasets is used to develop a prognostic model based on P21, YAP, X1433ZETA, CKIT, S6, CD20, LCK, P27, CD49B, GATA6 and SRC_pY416 proteins. Risk score analysis for each patient indicated that those with high risk scores had worse prognosis relative to those with low risk scores. Cox and multi-Cox model analyses revealed that prognosis correlated with risk scores. Co-expression analysis identified various proteins that were co-expressed with the prognostic model’s proteins. Overexpression of the low risk protein, GATA6, and the high risk protein X1433ZETA, in melanoma cells revealed tumor growth suppression by GATA6, and tumor growth promotion by X1433ZETA, in vitro and in vivo. In conclusion, this novel melanoma prognostic model can guide individualized treatment.

Key words: prognostic model, melanoma, proteomics, biological marker

中图分类号: 

  • R739.5
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