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Identification of prognostic molecular subtypes and model based on CD8+ T cells for lung adenocarcinoma

by HONGMIN CAO1,#,*, YING XUE2,#, FEI WANG1, GUANGYAO LI1, YULAN ZHEN1, JINGWEN GUO1

1 Internal Medicine-Oncology, Dongguan Songshan Lake Central Hospital, Dongguan, 523326, China
2 Tumor Center, First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523888, China

* Corresponding Author: HONGMIN CAO. Email: email
# Equal Contribution

(This article belongs to the Special Issue: Bioinformatics Study of Diseases)

BIOCELL 2024, 48(3), 473-490. https://doi.org/10.32604/biocell.2024.048946

Abstract

Background: Cytotoxic T lymphocytes (CD8+ T) cells function critically in mediating anti-tumor immune response in cancer patients. Characterizing the specific functions of CD8+ T cells in lung adenocarcinoma (LUAD) could help better understand local anti-tumor immune responses and estimate the effect of immunotherapy. Methods: Gens related to CD8+ T cells were identified by cluster analysis based on the single-cell sequencing data of three LUAD tissues and their paired normal tissues. Weighted gene co-expression network analysis (WGCNA), consensus clustering, differential expression analysis, least absolute shrinkage and selection operator (LASSO) and Cox regression analysis were conducted to classify molecular subtypes for LUAD and to develop a risk model using prognostic genes related to CD8+ T cells. Expression of the genes in the prognostic model, their effects on tumor cell invasion, and interactions with CD8+ T cells were verified by cell experiments. Results: This study defined two LUAD clusters (CD8+ 0 and CD8+ 1) based on CD8+ T cells, with cluster CD8+ 0 being significantly associated with the prognosis of LUAD. Three heterogeneous subtypes (clusters 1, 2, and 3) differing in prognosis, genome mutation events, and immune status were categorized using 42 prognostic genes. A prognostic model created based on 11 significant genes (including CD200R1, CLEC17A, ZC3H12D, GNG7, SNX30, CDCP1, NEIL3, IGF2BP1, RHOV, ABCC2, and KRT81) was able to independently estimate the death risk for patients in different LUAD cohorts. Moreover, the model also showed general applicability in external validation cohorts. Low-risk patients could benefit more from taking immunotherapy and were significantly related to the resistance to anticancer drugs. The results from cell experiments demonstrated that the expression of CD200R1, CLEC17A, ZC3H12D, GNG7, and SNX30 was significantly downregulated, while that of CDCP1, NEIL3, IGF2BP1, RHOV, ABCC2 and KRT81 was upregulated in LUAD cells. Inhibition of CD200R1 greatly increased the invasiveness of the LUAD cells, but inhibiting CDCP1 expression weakened the invasion ability of LUAD cells. Conclusion: This study defined two prognostic CD8+ T cell clusters and classified three heterogeneous molecular subtypes for LUAD. A prognostic model predictive of the potential effects of immunotherapy on LUAD patients was developed.

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Identification of prognostic molecular subtypes and model based on CD8+ T cells for lung adenocarcinoma

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APA Style
CAO, H., XUE, Y., WANG, F., LI, G., ZHEN, Y. et al. (2024). Identification of prognostic molecular subtypes and model based on CD8+ T cells for lung adenocarcinoma. BIOCELL, 48(3), 473-490. https://doi.org/10.32604/biocell.2024.048946
Vancouver Style
CAO H, XUE Y, WANG F, LI G, ZHEN Y, GUO J. Identification of prognostic molecular subtypes and model based on CD8+ T cells for lung adenocarcinoma. BIOCELL . 2024;48(3):473-490 https://doi.org/10.32604/biocell.2024.048946
IEEE Style
H. CAO, Y. XUE, F. WANG, G. LI, Y. ZHEN, and J. GUO, “Identification of prognostic molecular subtypes and model based on CD8+ T cells for lung adenocarcinoma,” BIOCELL , vol. 48, no. 3, pp. 473-490, 2024. https://doi.org/10.32604/biocell.2024.048946



cc Copyright © 2024 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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