Identification of immune subtypes of melanoma based on single-cell and bulk RNA sequencing data

Linqian Guo; Qingrong Meng; Wenqi Lin; Kaiyuan Weng; Linqian Guo; Qingrong Meng; Wenqi Lin; Kaiyuan Weng

doi:10.3934/mbe.2023138

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 2: 2920-2936. doi: 10.3934/mbe.2023138

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Identification of immune subtypes of melanoma based on single-cell and bulk RNA sequencing data

Pharmaceutical Business School of Guangdong Pharmaceutical University, Guangdong Pharmaceutical Regulatory Research Base, Guangzhou, Guangdong 510006, China

Academic Editor: Shixiong Zhang

Received: 09 October 2022 Revised: 06 November 2022 Accepted: 13 November 2022 Published: 01 December 2022

The tumor microenvironment plays a crucial role in melanoma. In this study, the abundance of immune cells in melanoma samples was assessed and analyzed using single sample gene set enrichment analysis (ssGSEA), and the predictive value of immune cells was assessed using univariate COX regression analysis. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis was applied to construct an immune cell risk score (ICRS) model with a high predictive value for identifying the immune profile of melanoma patients. The pathway enrichment between the different ICRS groups was also elucidated. Next, five hub genes for diagnosing the prognosis of melanoma were screened by two machine learning algorithms, LASSO and random forest. The distribution of hub genes in immune cells was analyzed on account of Single-cell RNA sequencing (scRNA-seq), and the interaction between genes and immune cells was elucidated by cellular communication. Ultimately, the ICRS model on account of two types of immune cells (Activated CD8 T cell and Immature B cell) was constructed and validated, which can determine melanoma prognosis. In addition, five hub genes were identified as potential therapeutic targets affecting the prognosis of melanoma patients.
- immune subtype,
- single-cell,
- machine learning,
- cellular communication
Citation: Linqian Guo, Qingrong Meng, Wenqi Lin, Kaiyuan Weng. Identification of immune subtypes of melanoma based on single-cell and bulk RNA sequencing data[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 2920-2936. doi: 10.3934/mbe.2023138

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Abstract

The tumor microenvironment plays a crucial role in melanoma. In this study, the abundance of immune cells in melanoma samples was assessed and analyzed using single sample gene set enrichment analysis (ssGSEA), and the predictive value of immune cells was assessed using univariate COX regression analysis. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression analysis was applied to construct an immune cell risk score (ICRS) model with a high predictive value for identifying the immune profile of melanoma patients. The pathway enrichment between the different ICRS groups was also elucidated. Next, five hub genes for diagnosing the prognosis of melanoma were screened by two machine learning algorithms, LASSO and random forest. The distribution of hub genes in immune cells was analyzed on account of Single-cell RNA sequencing (scRNA-seq), and the interaction between genes and immune cells was elucidated by cellular communication. Ultimately, the ICRS model on account of two types of immune cells (Activated CD8 T cell and Immature B cell) was constructed and validated, which can determine melanoma prognosis. In addition, five hub genes were identified as potential therapeutic targets affecting the prognosis of melanoma patients.

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