Cargando…

Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis

Melanoma is one of the deadliest skin cancers. Recently, developed single-cell sequencing has revealed fresh insights into melanoma. Cytokine signaling in the immune system is crucial for tumor development in melanoma. To evaluate melanoma patient diagnosis and treatment, the prediction value of cyt...

Descripción completa

Detalles Bibliográficos
Autores principales: Pu, Zuhui, Zhao, Qing, Chen, Jiaqun, Xie, Yubin, Mou, Lisha, Zha, Xushan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10070796/
https://www.ncbi.nlm.nih.gov/pubmed/37026000
http://dx.doi.org/10.3389/fimmu.2023.1148130
_version_ 1785019070686429184
author Pu, Zuhui
Zhao, Qing
Chen, Jiaqun
Xie, Yubin
Mou, Lisha
Zha, Xushan
author_facet Pu, Zuhui
Zhao, Qing
Chen, Jiaqun
Xie, Yubin
Mou, Lisha
Zha, Xushan
author_sort Pu, Zuhui
collection PubMed
description Melanoma is one of the deadliest skin cancers. Recently, developed single-cell sequencing has revealed fresh insights into melanoma. Cytokine signaling in the immune system is crucial for tumor development in melanoma. To evaluate melanoma patient diagnosis and treatment, the prediction value of cytokine signaling in immune-related genes (CSIRGs) is needed. In this study, the machine learning method of least absolute selection and shrinkage operator (LASSO) regression was used to establish a CSIRG prognostic signature of melanoma at the single-cell level. We discovered a 5-CSIRG signature that was substantially related to the overall survival of melanoma patients. We also constructed a nomogram that combined CSIRGs and clinical features. Overall survival of melanoma patients can be consistently predicted with good performance as well as accuracy by both the 5-CSIRG signature and nomograms. We compared the melanoma patients in the CSIRG high- and low-risk groups in terms of tumor mutation burden, infiltration of the immune system, and gene enrichment. High CSIRG-risk patients had a lower tumor mutational burden than low CSIRG-risk patients. The CSIRG high-risk patients had a higher infiltration of monocytes. Signaling pathways including oxidative phosphorylation, DNA replication, and aminoacyl tRNA biosynthesis were enriched in the high-risk group. For the first time, we constructed and validated a machine-learning model by single-cell RNA-sequencing datasets that have the potential to be a novel treatment target and might serve as a prognostic biomarker panel for melanoma. The 5-CSIRG signature may assist in predicting melanoma patient prognosis, biological characteristics, and appropriate therapy.
format Online
Article
Text
id pubmed-10070796
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-100707962023-04-05 Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis Pu, Zuhui Zhao, Qing Chen, Jiaqun Xie, Yubin Mou, Lisha Zha, Xushan Front Immunol Immunology Melanoma is one of the deadliest skin cancers. Recently, developed single-cell sequencing has revealed fresh insights into melanoma. Cytokine signaling in the immune system is crucial for tumor development in melanoma. To evaluate melanoma patient diagnosis and treatment, the prediction value of cytokine signaling in immune-related genes (CSIRGs) is needed. In this study, the machine learning method of least absolute selection and shrinkage operator (LASSO) regression was used to establish a CSIRG prognostic signature of melanoma at the single-cell level. We discovered a 5-CSIRG signature that was substantially related to the overall survival of melanoma patients. We also constructed a nomogram that combined CSIRGs and clinical features. Overall survival of melanoma patients can be consistently predicted with good performance as well as accuracy by both the 5-CSIRG signature and nomograms. We compared the melanoma patients in the CSIRG high- and low-risk groups in terms of tumor mutation burden, infiltration of the immune system, and gene enrichment. High CSIRG-risk patients had a lower tumor mutational burden than low CSIRG-risk patients. The CSIRG high-risk patients had a higher infiltration of monocytes. Signaling pathways including oxidative phosphorylation, DNA replication, and aminoacyl tRNA biosynthesis were enriched in the high-risk group. For the first time, we constructed and validated a machine-learning model by single-cell RNA-sequencing datasets that have the potential to be a novel treatment target and might serve as a prognostic biomarker panel for melanoma. The 5-CSIRG signature may assist in predicting melanoma patient prognosis, biological characteristics, and appropriate therapy. Frontiers Media S.A. 2023-03-21 /pmc/articles/PMC10070796/ /pubmed/37026000 http://dx.doi.org/10.3389/fimmu.2023.1148130 Text en Copyright © 2023 Pu, Zhao, Chen, Xie, Mou and Zha https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Pu, Zuhui
Zhao, Qing
Chen, Jiaqun
Xie, Yubin
Mou, Lisha
Zha, Xushan
Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis
title Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis
title_full Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis
title_fullStr Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis
title_full_unstemmed Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis
title_short Single-cell RNA analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis
title_sort single-cell rna analysis to identify five cytokines signaling in immune-related genes for melanoma survival prognosis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10070796/
https://www.ncbi.nlm.nih.gov/pubmed/37026000
http://dx.doi.org/10.3389/fimmu.2023.1148130
work_keys_str_mv AT puzuhui singlecellrnaanalysistoidentifyfivecytokinessignalinginimmunerelatedgenesformelanomasurvivalprognosis
AT zhaoqing singlecellrnaanalysistoidentifyfivecytokinessignalinginimmunerelatedgenesformelanomasurvivalprognosis
AT chenjiaqun singlecellrnaanalysistoidentifyfivecytokinessignalinginimmunerelatedgenesformelanomasurvivalprognosis
AT xieyubin singlecellrnaanalysistoidentifyfivecytokinessignalinginimmunerelatedgenesformelanomasurvivalprognosis
AT moulisha singlecellrnaanalysistoidentifyfivecytokinessignalinginimmunerelatedgenesformelanomasurvivalprognosis
AT zhaxushan singlecellrnaanalysistoidentifyfivecytokinessignalinginimmunerelatedgenesformelanomasurvivalprognosis