Immune Infiltration Analysis by TIMER, quanTIseq, and Cibersort

Authors

  • Chenling Zhou
  • Shiyu Fan

DOI:

https://doi.org/10.54097/ba7qmm88

Keywords:

Immune Infiltration Analysis, High-throughput Sequencing, TIMER, QuanTIseq, Cibersort.

Abstract

Immune Infiltration analysis is of great significance in cancer research and can provide insight into the activity of the immune system in different disease states. The expression profile analysis of high-throughput sequencing provides a powerful tool for immune cell research, but different methods may lead to different results. This study used a dataset published by Ma et al in August 2023, which included peripheral blood mononuclear cells (PBMCs) from 19 patients with non-small cell lung cancer (NSCLC) and four healthy human donors. This papper used three immune cell estimation methods (TIMER, quanTIseq, and Cibersort) for analysis, and found differential immune cell proportions from the three methods. To accurately determine the proportion of immune cells, this passage weighted the results of the three methods. The results show that the weighted average method can more comprehensively reveal the presence and distribution of different immune cell types. This provides important insights for immunological research and cancer treatment, underscoring the importance of integrating multiple analytical approaches.

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References

Zhang Z, Bao S, Yan C, Hou P, Zhou M, Sun J. Computational Principles and Practice for Decoding Immune Contexture in the Tumor Microenvironment. Brief Bioinform, 2020.

Zeng D, Ye Z, Shen R, et al. IOBR: multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures [J]. Frontiers in immunology, 2021.

Ma W, Wei S, Long S, Tian EC et al. Dynamic evaluation of blood immune cells predictive of response to immune checkpoint inhibitors in NSCLC by multicolor spectrum flow cytometry. Front Immunol 2023.

Li B, Severson E, Pignon J C, et al. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy[J]. Genome biology, 2016.

Finotello F, Mayer C, Plattner C, et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data[J]. Genome medicine, 2019.

Newman A M, Liu C L, Green M R, et al. Robust enumeration of cell subsets from tissue expression profiles [J]. Nature methods, 2015.

Lv H, Liu X, Zeng X, et al. Comprehensive analysis of cuproptosis-related genes in immune infiltration and prognosis in melanoma[J]. Frontiers in pharmacology, 2022.

Jiang Y, Chen Z, Han N, et al. sc-ImmuCC: hierarchical annotation for immune cell types in single-cell RNA-seq[J]. Frontiers in Immunology, 2023.

Sun J, Zhang Z, Bao S, et al. Identification of tumor immune infiltration-associated lncRNAs for improving prognosis and immunotherapy response of patients with non-small cell lung cancer [J]. Journal for immunotherapy of cancer, 2020.

Kim S I, Cassella C R, Byrne K T. Tumor burden and immunotherapy: impact on immune infiltration and therapeutic outcomes [J]. Frontiers in immunology, 2021.

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Published

29-12-2023

How to Cite

Zhou, C., & Fan, S. (2023). Immune Infiltration Analysis by TIMER, quanTIseq, and Cibersort. Highlights in Science, Engineering and Technology, 74, 1461-1468. https://doi.org/10.54097/ba7qmm88