Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis
Skin cutaneous melanoma (SKCM) is a chronically malignant tumor with a high mortality rate. Pyroptosis, a kind of pro-inflammatory programmed cell death, has been linked to cancer in recent studies. However, the value of pyroptosis in the diagnosis and prognosis of SKCM is not clear. In this study,...
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doaj-9515f14d26f847af96e3bba51270da332021-07-13T06:03:34ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-07-011110.3389/fonc.2021.709077709077Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the PrognosisAnji Ju0Anji Ju1Anji Ju2Jiaze Tang3Jiaze Tang4Jiaze Tang5Shuohua Chen6Shuohua Chen7Shuohua Chen8Yan Fu9Yan Fu10Yan Fu11Yongzhang Luo12Yongzhang Luo13Yongzhang Luo14The National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, ChinaBeijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, ChinaCancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, ChinaThe National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, ChinaBeijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, ChinaCancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, ChinaThe National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, ChinaBeijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, ChinaCancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, ChinaThe National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, ChinaBeijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, ChinaCancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, ChinaThe National Engineering Laboratory for Anti-Tumor Protein Therapeutics, Tsinghua University, Beijing, ChinaBeijing Key Laboratory for Protein Therapeutics, Tsinghua University, Beijing, ChinaCancer Biology Laboratory, School of Life Sciences, Tsinghua University, Beijing, ChinaSkin cutaneous melanoma (SKCM) is a chronically malignant tumor with a high mortality rate. Pyroptosis, a kind of pro-inflammatory programmed cell death, has been linked to cancer in recent studies. However, the value of pyroptosis in the diagnosis and prognosis of SKCM is not clear. In this study, it was discovered that 20 pyroptosis-related genes (PRGs) differed in expression between SKCM and normal tissues, which were related to diagnosis and prognosis. Firstly, based on these genes, nine machine-learning algorithms were shown to perform well in constructing diagnostic classifiers, including K-Nearest Neighbor (KNN), logistic regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), decision tree, random forest, XGBoost, LightGBM, and CatBoost. Secondly, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied and the prognostic model was constructed based on 9 PRGs. Subgroups in low and high risks determined by the prognostic model were shown to have different survival. Thirdly, functional enrichment analyses were performed by applying the gene set enrichment analysis (GSEA), and results suggested that the risk was related to immune response. In conclusion, the expression signatures of pyroptosis-related genes are effective and robust in the diagnosis and prognosis of SKCM, which is related to immunity.https://www.frontiersin.org/articles/10.3389/fonc.2021.709077/fullpyroptosis-related genesdiagnosisprognosisclassifierprognostic modelimmunity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Anji Ju Anji Ju Anji Ju Jiaze Tang Jiaze Tang Jiaze Tang Shuohua Chen Shuohua Chen Shuohua Chen Yan Fu Yan Fu Yan Fu Yongzhang Luo Yongzhang Luo Yongzhang Luo |
spellingShingle |
Anji Ju Anji Ju Anji Ju Jiaze Tang Jiaze Tang Jiaze Tang Shuohua Chen Shuohua Chen Shuohua Chen Yan Fu Yan Fu Yan Fu Yongzhang Luo Yongzhang Luo Yongzhang Luo Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis Frontiers in Oncology pyroptosis-related genes diagnosis prognosis classifier prognostic model immunity |
author_facet |
Anji Ju Anji Ju Anji Ju Jiaze Tang Jiaze Tang Jiaze Tang Shuohua Chen Shuohua Chen Shuohua Chen Yan Fu Yan Fu Yan Fu Yongzhang Luo Yongzhang Luo Yongzhang Luo |
author_sort |
Anji Ju |
title |
Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis |
title_short |
Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis |
title_full |
Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis |
title_fullStr |
Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis |
title_full_unstemmed |
Pyroptosis-Related Gene Signatures Can Robustly Diagnose Skin Cutaneous Melanoma and Predict the Prognosis |
title_sort |
pyroptosis-related gene signatures can robustly diagnose skin cutaneous melanoma and predict the prognosis |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Oncology |
issn |
2234-943X |
publishDate |
2021-07-01 |
description |
Skin cutaneous melanoma (SKCM) is a chronically malignant tumor with a high mortality rate. Pyroptosis, a kind of pro-inflammatory programmed cell death, has been linked to cancer in recent studies. However, the value of pyroptosis in the diagnosis and prognosis of SKCM is not clear. In this study, it was discovered that 20 pyroptosis-related genes (PRGs) differed in expression between SKCM and normal tissues, which were related to diagnosis and prognosis. Firstly, based on these genes, nine machine-learning algorithms were shown to perform well in constructing diagnostic classifiers, including K-Nearest Neighbor (KNN), logistic regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), decision tree, random forest, XGBoost, LightGBM, and CatBoost. Secondly, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied and the prognostic model was constructed based on 9 PRGs. Subgroups in low and high risks determined by the prognostic model were shown to have different survival. Thirdly, functional enrichment analyses were performed by applying the gene set enrichment analysis (GSEA), and results suggested that the risk was related to immune response. In conclusion, the expression signatures of pyroptosis-related genes are effective and robust in the diagnosis and prognosis of SKCM, which is related to immunity. |
topic |
pyroptosis-related genes diagnosis prognosis classifier prognostic model immunity |
url |
https://www.frontiersin.org/articles/10.3389/fonc.2021.709077/full |
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