Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (6): 196-205.doi: 10.16088/j.issn.1001-6600.2021030702

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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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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

CLC Number: 

  • R739.5
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