A novel identified pyroptosis-related prognostic signature of colorectal cancer

Colorectal cancer (CRC), one of the most common malignancies worldwide, leads to abundant cancer-related mortalities annually. Pyroptosis, a new kind of programmed cell death, plays a critical role in immune response and tumor progression. Our study aimed to identify a prognostic signature for CRC b...

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Autores principales:	Chen Zheng, Zhaobang Tan
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	colorectal cancer pyroptosis prognosis tcga immune infiltration Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/75368c0f40d447efb1b1e0785d500245
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spelling	oai:doaj.org-article:75368c0f40d447efb1b1e0785d5002452021-11-29T02:35:56ZA novel identified pyroptosis-related prognostic signature of colorectal cancer10.3934/mbe.20214331551-0018https://doaj.org/article/75368c0f40d447efb1b1e0785d5002452021-10-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021433?viewType=HTMLhttps://doaj.org/toc/1551-0018Colorectal cancer (CRC), one of the most common malignancies worldwide, leads to abundant cancer-related mortalities annually. Pyroptosis, a new kind of programmed cell death, plays a critical role in immune response and tumor progression. Our study aimed to identify a prognostic signature for CRC based on pyroptosis-related genes (PRGs). The difference in PRGs between CRC tissues and normal tissues deposited in the TCGA database was calculated by "limma" R package. The tumor microenvironment (TME) of CRC cases was accessed by the ESTIMATE algorithm. The prognostic PRGs were identified using Cox regression analysis. A least absolute shrinkage and selector operation (LASSO) algorithm was used to calculate the risk scores and construct a clinical predictive model of CRC. Gene Set Enrichment Analysis (GSEA) was performed for understanding the function annotation of the signature in the tumor microenvironment. We found that most PRGs were significantly dysregulated in CRC. Through the LASSO method, three key PRGs were selected to calculate the risk scores and construct the prognostic model for CRC. The risk score was an independent indicator of patient's prognosis. In addition, we classified the CRC patients into two clusters based on risk scores and discovered that CRC patients in cluster 2 underwent worse overall survival and owned higher expression levels of immune checkpoint genes in tumor tissues. In conclusion, our study identified a PRG-related prognostic signature for CRC, according to which we classified the CRC patients into two clusters with distinct prognosis and immunotherapy potential.Chen ZhengZhaobang TanAIMS Pressarticlecolorectal cancerpyroptosisprognosistcgaimmune infiltrationBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 6, Pp 8783-8796 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	colorectal cancer pyroptosis prognosis tcga immune infiltration Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	colorectal cancer pyroptosis prognosis tcga immune infiltration Biotechnology TP248.13-248.65 Mathematics QA1-939 Chen Zheng Zhaobang Tan A novel identified pyroptosis-related prognostic signature of colorectal cancer
description	Colorectal cancer (CRC), one of the most common malignancies worldwide, leads to abundant cancer-related mortalities annually. Pyroptosis, a new kind of programmed cell death, plays a critical role in immune response and tumor progression. Our study aimed to identify a prognostic signature for CRC based on pyroptosis-related genes (PRGs). The difference in PRGs between CRC tissues and normal tissues deposited in the TCGA database was calculated by "limma" R package. The tumor microenvironment (TME) of CRC cases was accessed by the ESTIMATE algorithm. The prognostic PRGs were identified using Cox regression analysis. A least absolute shrinkage and selector operation (LASSO) algorithm was used to calculate the risk scores and construct a clinical predictive model of CRC. Gene Set Enrichment Analysis (GSEA) was performed for understanding the function annotation of the signature in the tumor microenvironment. We found that most PRGs were significantly dysregulated in CRC. Through the LASSO method, three key PRGs were selected to calculate the risk scores and construct the prognostic model for CRC. The risk score was an independent indicator of patient's prognosis. In addition, we classified the CRC patients into two clusters based on risk scores and discovered that CRC patients in cluster 2 underwent worse overall survival and owned higher expression levels of immune checkpoint genes in tumor tissues. In conclusion, our study identified a PRG-related prognostic signature for CRC, according to which we classified the CRC patients into two clusters with distinct prognosis and immunotherapy potential.
format	article
author	Chen Zheng Zhaobang Tan
author_facet	Chen Zheng Zhaobang Tan
author_sort	Chen Zheng
title	A novel identified pyroptosis-related prognostic signature of colorectal cancer
title_short	A novel identified pyroptosis-related prognostic signature of colorectal cancer
title_full	A novel identified pyroptosis-related prognostic signature of colorectal cancer
title_fullStr	A novel identified pyroptosis-related prognostic signature of colorectal cancer
title_full_unstemmed	A novel identified pyroptosis-related prognostic signature of colorectal cancer
title_sort	novel identified pyroptosis-related prognostic signature of colorectal cancer
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/75368c0f40d447efb1b1e0785d500245
work_keys_str_mv	AT chenzheng anovelidentifiedpyroptosisrelatedprognosticsignatureofcolorectalcancer AT zhaobangtan anovelidentifiedpyroptosisrelatedprognosticsignatureofcolorectalcancer AT chenzheng novelidentifiedpyroptosisrelatedprognosticsignatureofcolorectalcancer AT zhaobangtan novelidentifiedpyroptosisrelatedprognosticsignatureofcolorectalcancer
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A novel identified pyroptosis-related prognostic signature of colorectal cancer

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