BIO22-031: Potential Implications in Immunotherapy and Prognostic Outcomes of Non-Small Cell Lung Cancer for Computational Recognition of lncRNA Signature of Tumor-Associated Neutrophils

Authors:
Zhuoran Tang Tongji University Medical School Cancer Institute, Shanghai, 200433, P.R. China

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 PhD
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Chunyu Li International Medical School, Tianjin Medical University, Tianjin 300070, P.R. China

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Peixin Chen Tongji University Medical School Cancer Institute, Shanghai, 200433, P.R. China

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Haoyue Guo Tongji University Medical School Cancer Institute, Shanghai, 200433, P.R. China

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Jinpeng Shi Tongji University Medical School Cancer Institute, Shanghai, 200433, P.R. China

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Yingying Pan Tongji University Medical School Cancer Institute, Shanghai, 200433, P.R. China

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Qi Wang Tongji University Medical School Cancer Institute, Shanghai, 200433, P.R. China

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 PhD
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Caicun Zhou Tongji University Medical School Cancer Institute, Shanghai, 200433, P.R. China

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Volume/Issue:
Volume 20: Issue 3.5
Online Publication Date:
31 Mar 2022
DOI:
https://doi.org/10.6004/jnccn.2021.7149
Full access

Long noncoding RNAs (lncRNAs) are correlated with the regulation of tumor microenvironment (TME) and cancer immunity. Nevertheless, the role of lncRNAs in tumor-associated neutrophils (TANs) has yet to be established. Here, a computing framework based on machine learning was used to identify neutrophil-specific lncRNA with prognostic significance in squamous cell carcinoma and lung adenocarcinoma using univariate Cox regression to comprehensively analyze immune, lncRNA, and clinical characteristics. The risk score was determined using LASSO Cox regression analysis. Meanwhile, we named this risk score as “TANlncSig”. TANlncSig was able to distinguish between better and worse survival outcomes in various patient datasets independently of other clinical variables. Functional assessment of TANlncSig showed it is a marker of myeloid cell infiltration into tumor infiltration and myeloid cells directly or indirectly inhibit the anti-tumor immune response by secreting cytokines, expressing immunosuppressive receptors, and altering metabolic processes. Our findings highlighted the value of TANlncSig in TME as a marker of immune cell infiltration and showed the values of lncRNAs as indicators of immunotherapy. In conclusion, we used a machine learning-based computational framework to identify lncRNA features of TANs (TANlncSig) via comprehensive analyses of lncRNA, immune, as well as clinical features. TANlncSig revealed a substantial and repeatable correlation with outcomes, even after adjustments of clinical covariates. Analysis of correlation between prognostic lncRNAs and risk score with the expression of immune checkpoint molecules demonstrated that TANlncSig can predict immunotherapy. This is the first study to define lncRNA characteristics of tumor-associated neutrophils, highlighting the significance of lncRNAs in immune responses and the potential for more precise and personalized cancer immunotherapy. Keywords: tumor-associated neutrophils; long noncoding RNA; immunotherapy; non-small cell lung cancer

Corresponding Author: Zhuoran Tang, PhD
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Copyright:
Copyright © 2022 by the National Comprehensive Cancer Network 2022
Article Category:
Abstract
Print Publication Date:
31 Mar 2022
Online Publication Date:
31 Mar 2022
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