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Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis
Cell death-related genes indicate prognosis in cancer patients. PANoptosis is a newly observed form of cell death that researchers have linked to cancer cell death and antitumor immunity. Even so, its significance in lung adenocarcinomas (LUADs) has yet to be elucidated. We extracted and analyzed da...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589340/ https://www.ncbi.nlm.nih.gov/pubmed/37864090 http://dx.doi.org/10.1038/s41598-023-45005-6 |
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author | Zhang, Chi Xia, Jiangnan Liu, Xiujuan Li, Zexing Gao, Tangke Zhou, Tian Hu, Kaiwen |
author_facet | Zhang, Chi Xia, Jiangnan Liu, Xiujuan Li, Zexing Gao, Tangke Zhou, Tian Hu, Kaiwen |
author_sort | Zhang, Chi |
collection | PubMed |
description | Cell death-related genes indicate prognosis in cancer patients. PANoptosis is a newly observed form of cell death that researchers have linked to cancer cell death and antitumor immunity. Even so, its significance in lung adenocarcinomas (LUADs) has yet to be elucidated. We extracted and analyzed data on mRNA gene expression and clinical information from public databases in a systematic manner. These data were utilized to construct a reliable risk prediction model for six regulators of PANoptosis. The Gene Expression Omnibus (GEO) database validated six genes with risk characteristics. The prognosis of LUAD patients could be accurately estimated by the six-gene-based model: NLR family CARD domain-containing protein 4 (NLRC4), FAS-associated death domain protein (FADD), Tumor necrosis factor receptor type 1-associated DEATH domain protein (TRADD), Receptor-interacting serine/threonine-protein kinase 1 (RIPK1), Proline-serine-threonine phosphatase-interacting protein 2 (PSTPIP2), and Mixed lineage kinase domain-like protein (MLKL). Group of higher risk and Cluster 2 indicated a poor prognosis as well as the reduced expression of immune infiltrate molecules and human leukocyte antigen. Distinct expression of PANoptosis-related genes (PRGs) in lung cancer cells was verified using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, we evaluated the relationship between PRGs and somatic mutations, tumor immune dysfunction exclusion, tumor stemness indices, and immune infiltration. Using the risk signature, we conducted analyses including nomogram construction, stratification, prediction of small-molecule drug response, somatic mutations, and chemotherapeutic response. |
format | Online Article Text |
id | pubmed-10589340 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105893402023-10-22 Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis Zhang, Chi Xia, Jiangnan Liu, Xiujuan Li, Zexing Gao, Tangke Zhou, Tian Hu, Kaiwen Sci Rep Article Cell death-related genes indicate prognosis in cancer patients. PANoptosis is a newly observed form of cell death that researchers have linked to cancer cell death and antitumor immunity. Even so, its significance in lung adenocarcinomas (LUADs) has yet to be elucidated. We extracted and analyzed data on mRNA gene expression and clinical information from public databases in a systematic manner. These data were utilized to construct a reliable risk prediction model for six regulators of PANoptosis. The Gene Expression Omnibus (GEO) database validated six genes with risk characteristics. The prognosis of LUAD patients could be accurately estimated by the six-gene-based model: NLR family CARD domain-containing protein 4 (NLRC4), FAS-associated death domain protein (FADD), Tumor necrosis factor receptor type 1-associated DEATH domain protein (TRADD), Receptor-interacting serine/threonine-protein kinase 1 (RIPK1), Proline-serine-threonine phosphatase-interacting protein 2 (PSTPIP2), and Mixed lineage kinase domain-like protein (MLKL). Group of higher risk and Cluster 2 indicated a poor prognosis as well as the reduced expression of immune infiltrate molecules and human leukocyte antigen. Distinct expression of PANoptosis-related genes (PRGs) in lung cancer cells was verified using quantitative reverse transcription polymerase chain reaction (qRT-PCR). Furthermore, we evaluated the relationship between PRGs and somatic mutations, tumor immune dysfunction exclusion, tumor stemness indices, and immune infiltration. Using the risk signature, we conducted analyses including nomogram construction, stratification, prediction of small-molecule drug response, somatic mutations, and chemotherapeutic response. Nature Publishing Group UK 2023-10-20 /pmc/articles/PMC10589340/ /pubmed/37864090 http://dx.doi.org/10.1038/s41598-023-45005-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Zhang, Chi Xia, Jiangnan Liu, Xiujuan Li, Zexing Gao, Tangke Zhou, Tian Hu, Kaiwen Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis |
title | Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis |
title_full | Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis |
title_fullStr | Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis |
title_full_unstemmed | Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis |
title_short | Identifying prognostic genes related PANoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis |
title_sort | identifying prognostic genes related panoptosis in lung adenocarcinoma and developing prediction model based on bioinformatics analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10589340/ https://www.ncbi.nlm.nih.gov/pubmed/37864090 http://dx.doi.org/10.1038/s41598-023-45005-6 |
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