Identification of a predictive gene signature related to pyroptosis for the prognosis of cutaneous melanoma

被引:2
|
作者
Shi, Zhaoyang [1 ]
Gu, Jiaying [2 ]
Yao, Yi [1 ]
Wu, Zhengyuan [1 ]
机构
[1] First Peoples Hosp Linping Dist, Dept Hand Plast Surg, Hangzhou, Peoples R China
[2] Integrated Tradit Chinese & Western Med Hosp Linp, Dept Lab, Hangzhou, Peoples R China
关键词
bioinformatic analysis; cutaneous melanoma; prognosis; pyroptosis; ENDOTHELIAL GROWTH-FACTOR; AIM2; INFLAMMASOME; CELL-DEATH; CANCER; EXPRESSION; INDUCTION; BINDING; TUMORIGENICITY; METASTASIS; ACTIVATION;
D O I
10.1097/MD.0000000000030564
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Pyroptosis is a form of inflammatory programmed cell death. However, because of no specific molecular biomarker, pyroptosis has not been considered as a novel therapeutic method to treat cutaneous melanoma (CM). Here, we identified pyroptosis genes that associate with the prognosis of CM patients and constructed an effective model for the prognostic prediction of CM patients. To identify genes related to pyroptosis that are differentially expressed in CM, we obtained gene expression data of CM patients and normal skin tissues from the Cancer Genome Atlas and the Genotype-Tissue Expression databases, and used another cohort obtained from Gene Expression Omnibus database for validation. Three genes (BST2, GBP5, and AIM2) that were associated with prognosis were found and incorporated into our prognostic model. Furthermore, we divided the patients into 2 groups: a high-risk group and a low-risk group. Functional analyses indicated that our model was correlated with patient survival and cancer growth. Multivariate and univariate Cox regressions revealed that the constructed model could serve as an independent prognostic factor for CM patients. Meanwhile, compared with other clinical characteristics, our model significantly improved the diagnostic accuracy. Gene function analysis revealed that pyroptosis genes BST2, GBP5, and AIM2 were differentially expressed in CM patients and positively associated with patient prognosis. Finally, a risk score was used to generate nomograms that displayed favorable discriminatory abilities for CM. In summary, our model could significantly predict the prognosis of CM patients and be used for the development of CM therapy.
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页数:11
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