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

Content Provider	Europe PMC
Author	Liu, Hao Han, Yan Liu, Zhantao Gao, Liping Yi, Tienan Yu, Yuandong Wang, Yu Qu, Ping Xiang, Longchao Li, Yong
Abstract	BackgroundTumours 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.MethodsIn 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.ResultsWe 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.ConclusionsOur 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 InformationThe online version contains supplementary material available at 10.1007/s12672-023-00693-4.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC10195954&blobtype=pdf
Journal	Discover. Oncology [Discov Oncol]
Volume Number	14
DOI	10.1007/s12672-023-00693-4
PubMed Central reference number	PMC10195954
Issue Number	1
PubMed reference number	37199872
e-ISSN	27306011
Language	English
Publisher	Springer US
Publisher Date	2023-05-18
Publisher Place	New York
Access Restriction	Open
Rights License	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/. © The Author(s) 2023
Subject Keyword	OCLR Machine learning Lung adenocarcinoma Neuroendocrine differentiation Immunotherapy
Content Type	Text
Resource Type	Article
Subject	Endocrinology Endocrinology, Diabetes and Metabolism Oncology Cancer Research Endocrine and Autonomic Systems