A novel identified pyroptosis-related prognostic signature of colorectal cancer

Chen Zheng; Zhaobang Tan; Chen Zheng; Zhaobang Tan

doi:10.3934/mbe.2021433

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 6: 8783-8796. doi: 10.3934/mbe.2021433

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A novel identified pyroptosis-related prognostic signature of colorectal cancer

Chen Zheng ¹,
Zhaobang Tan ^{2
,
,}

1.
First Department of General Surgery, Xi'an Central Hospital, Xi'an, Shaanxi Province, China
2.
Department of digestive surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, Shaanxi Province, China

Received: 13 August 2021 Accepted: 30 September 2021 Published: 14 October 2021

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.
- colorectal cancer,
- pyroptosis,
- prognosis,
- TCGA,
- immune infiltration
Citation: Chen Zheng, Zhaobang Tan. A novel identified pyroptosis-related prognostic signature of colorectal cancer[J]. Mathematical Biosciences and Engineering, 2021, 18(6): 8783-8796. doi: 10.3934/mbe.2021433

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Abstract

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.

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