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Current Cancer Drug Targets

Editor-in-Chief

ISSN (Print): 1568-0096
ISSN (Online): 1873-5576

Research Article

A Comprehensive Exploration of Metabolism and Tumor Microenvironment and Immunotherapy Response: Evidence From Large Populations in Non-small Cell Lung Cancer

Author(s): Baorong Chen, Qinghua Hou, Linzhuang Liu, Liusheng Wu, Hanwen Wang, Xinyi Lai, Haozhen Liu, Xiaoqiang Li and Jixian Liu*

Volume 24, Issue 1, 2024

Published on: 18 May, 2023

Page: [46 - 58] Pages: 13

DOI: 10.2174/1568009623666230503094327

Price: $65

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Abstract

Aim: The study aimed to explore the effect of metabolism on lung cancer.

Background: The tumor microenvironment is largely influenced by metabolism, tightly involved in tumor progression.

Objective: We try to investigate the effect of tumor metabolism terms on non-small cell lung cancer (NSCLC) prognosis, drug and immunotherapy sensitivity, as well as its underlying mechanisms.

Methods: All the data was obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. R software was used to perform all statistical analyses and plots.

Results: This study conducted 21 metabolism statuses in NSCLC to identify their underlying roles. We found that alpha-linolenic acid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, fatty acid degradation, linoleic acid metabolism, primary bile acid biosynthesis, and fatty acid metabolism were protective factors for NSCLC. Next, we constructed a prognosis model based on primary bile acid biosynthesis, glycerophospholipid, and sphingolipid metabolism. Results in the present study showed that our model could effectively predict patients' prognosis in both training and validation cohorts. A clinical correlation revealed that patients at high-risk exhibited more progressive clinical characteristics. Biological enrichment indicated that MYC targets, E2F targets, mTORC1 signaling, G2/M checkpoint, and epithelial-mesenchymal transition were activated in the high-risk group. Immune relation analysis showed that risk score positively correlated with Th2 cells, yet a negative correlation with CD56 bright NK, Th17, mast and CD8+ T cells. Moreover, our model was related to NSCLC patients' sensitivity to immunotherapy and chemotherapy. Ultimately, eight characteristic genes were identified to distinguish the patients' risk group in the real application.

Conclusions: The model we developed is a useful tool to predict NSCLC patients' prognosis and is associated with the sensitivity of immunotherapy and chemotherapy. Meanwhile, our results can guide the following metabolism-related studies in NSCLC.

Keywords: Metabolism, immune, chemotherapy, prognosis, immunotherapy, non-small cell lung cancer.

Graphical Abstract
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