Cargando…

Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma

BACKGROUND: Tumours with no evidence of neuroendocrine transformation histologically but harbouring neuroendocrine features are collectively referred to as non-small cell lung cancer (NSCLC) with neuroendocrine differentiation (NED). Investigating the mechanisms underlying NED is conducive to design...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Hao, Han, Yan, Liu, Zhantao, Gao, Liping, Yi, Tienan, Yu, Yuandong, Wang, Yu, Qu, Ping, Xiang, Longchao, Li, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10195954/
https://www.ncbi.nlm.nih.gov/pubmed/37199872
http://dx.doi.org/10.1007/s12672-023-00693-4
_version_ 1785044239995895808
author Liu, Hao
Han, Yan
Liu, Zhantao
Gao, Liping
Yi, Tienan
Yu, Yuandong
Wang, Yu
Qu, Ping
Xiang, Longchao
Li, Yong
author_facet Liu, Hao
Han, Yan
Liu, Zhantao
Gao, Liping
Yi, Tienan
Yu, Yuandong
Wang, Yu
Qu, Ping
Xiang, Longchao
Li, Yong
author_sort Liu, Hao
collection PubMed
description BACKGROUND: Tumours with no evidence of neuroendocrine transformation histologically but harbouring neuroendocrine features are collectively referred to as non-small cell lung cancer (NSCLC) with neuroendocrine differentiation (NED). Investigating the mechanisms underlying NED is conducive to designing appropriate treatment options for NSCLC patients. METHODS: In the present study, we integrated multiple lung cancer datasets to identify neuroendocrine features using a one-class logistic regression (OCLR) machine learning algorithm trained on small cell lung cancer (SCLC) cells, a pulmonary neuroendocrine cell type, based on the transcriptome of NSCLC and named the NED index (NEDI). Single-sample gene set enrichment analysis, pathway enrichment analysis, ESTIMATE algorithm analysis, and unsupervised subclass mapping (SubMap) were performed to assess the altered pathways and immune characteristics of lung cancer samples with different NEDI values. RESULTS: We developed and validated a novel one-class predictor based on the expression values of 13,279 mRNAs to quantitatively evaluate neuroendocrine features in NSCLC. We observed that a higher NEDI correlated with better prognosis in patients with LUAD. In addition, we observed that a higher NEDI was significantly associated with reduced immune cell infiltration and immune effector molecule expression. Furthermore, we found that etoposide-based chemotherapy might be more effective in the treatment of LUAD with high NEDI values. Moreover, we noted that tumours with low NEDI values had better responses to immunotherapy than those with high NEDI values. CONCLUSIONS: Our findings improve the understanding of NED and provide a useful strategy for applying NEDI-based risk stratification to guide decision-making in the treatment of LUAD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00693-4.
format Online
Article
Text
id pubmed-10195954
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer US
record_format MEDLINE/PubMed
spelling pubmed-101959542023-05-20 Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma Liu, Hao Han, Yan Liu, Zhantao Gao, Liping Yi, Tienan Yu, Yuandong Wang, Yu Qu, Ping Xiang, Longchao Li, Yong Discov Oncol Research BACKGROUND: Tumours with no evidence of neuroendocrine transformation histologically but harbouring neuroendocrine features are collectively referred to as non-small cell lung cancer (NSCLC) with neuroendocrine differentiation (NED). Investigating the mechanisms underlying NED is conducive to designing appropriate treatment options for NSCLC patients. METHODS: In the present study, we integrated multiple lung cancer datasets to identify neuroendocrine features using a one-class logistic regression (OCLR) machine learning algorithm trained on small cell lung cancer (SCLC) cells, a pulmonary neuroendocrine cell type, based on the transcriptome of NSCLC and named the NED index (NEDI). Single-sample gene set enrichment analysis, pathway enrichment analysis, ESTIMATE algorithm analysis, and unsupervised subclass mapping (SubMap) were performed to assess the altered pathways and immune characteristics of lung cancer samples with different NEDI values. RESULTS: We developed and validated a novel one-class predictor based on the expression values of 13,279 mRNAs to quantitatively evaluate neuroendocrine features in NSCLC. We observed that a higher NEDI correlated with better prognosis in patients with LUAD. In addition, we observed that a higher NEDI was significantly associated with reduced immune cell infiltration and immune effector molecule expression. Furthermore, we found that etoposide-based chemotherapy might be more effective in the treatment of LUAD with high NEDI values. Moreover, we noted that tumours with low NEDI values had better responses to immunotherapy than those with high NEDI values. CONCLUSIONS: Our findings improve the understanding of NED and provide a useful strategy for applying NEDI-based risk stratification to guide decision-making in the treatment of LUAD. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00693-4. Springer US 2023-05-18 /pmc/articles/PMC10195954/ /pubmed/37199872 http://dx.doi.org/10.1007/s12672-023-00693-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Liu, Hao
Han, Yan
Liu, Zhantao
Gao, Liping
Yi, Tienan
Yu, Yuandong
Wang, Yu
Qu, Ping
Xiang, Longchao
Li, Yong
Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma
title Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma
title_full Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma
title_fullStr Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma
title_full_unstemmed Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma
title_short Depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma
title_sort depiction of neuroendocrine features associated with immunotherapy response using a novel one-class predictor in lung adenocarcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10195954/
https://www.ncbi.nlm.nih.gov/pubmed/37199872
http://dx.doi.org/10.1007/s12672-023-00693-4
work_keys_str_mv AT liuhao depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma
AT hanyan depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma
AT liuzhantao depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma
AT gaoliping depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma
AT yitienan depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma
AT yuyuandong depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma
AT wangyu depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma
AT quping depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma
AT xianglongchao depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma
AT liyong depictionofneuroendocrinefeaturesassociatedwithimmunotherapyresponseusinganoveloneclasspredictorinlungadenocarcinoma