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A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma

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Abstract

Chemoresistance is a key obstacle in the long-term survival of patients with locally and advanced lung adenocarcinoma (LUAD). This study used bioinformatic analysis to reveal the chemoresistance of gene-neutrophil extracellular traps (NETs) associated with LUAD. RNA sequencing data and LUAD expression patterns were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The GeneCards database was used to identify NETosis-related genes (NRGs). To identify hub genes with significant and consistent expression, differential analysis was performed using the TCGA-LUAD and GEO datasets. LUAD subtypes were determined based on these hub genes, followed by prognostic analysis. Immunological scoring and infiltration analysis were conducted using NETosis scores (N-scores) derived from the TCGA-LUAD dataset. A clinical prognostic model was established and analyzed, and its clinical applications explored. Twenty-two hub genes were identified, and consensus clustering was used to identify two subgroups based on their expression levels. The Kaplan–Meier (KM) curves demonstrated statistically significant differences in prognosis between the two LUAD subtypes. Based on the median score, patients were further divided into high and low N-score groups, and KM curves showed that the N-scores were more precise at predicting the prognosis of patients with LUAD for overall survival (OS). Immunological infiltration analysis revealed significant differences in the abundances of 10 immune cell infiltrates between the high and low N-score groups. Risk scores indicated significant differences in prognosis between the two extreme score groups. The risk scores for the prognostic model also indicated significant differences between the two groups. The results provide new insights into NETosis-related differentially expressed genes (NRDEGs) associated with chemotherapy resistance in patients with LUAD. The established prognostic model is promising and could help with clinical applications to evaluate patient survival and therapeutic efficiency.

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Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article.

Abbreviations

LUAD:

Lung adenocarcinoma

NETs:

Gene-neutrophil extracellular traps

TCGA:

The Cancer Genome Atlas

GEO:

Gene expression omnibus

NRGs:

NETosis-related genes

Nscores:

NETosis scores

KM:

Kaplan–Meier

OS:

Overall survival

NRDEGs:

NETosis-related differentially expressed genes

FPKM:

Fragments per kilobase per million

TMB:

Tumor mutation burden

MSI:

Microsatellite instability

SNPs:

Single nucleotide polymorphisms

CNV:

Copy number variation

PPI:

Protein–protein interaction

GO:

Gene Ontology

BP:

Biological processes

MF:

Molecular functions

CC:

Cellular components

KEGG:

Kyoto encyclopedia of genes and genomes

GSVA:

Gene set variation analysis

ssGSEA:

Single-sample gene-set enrichment analysis

TME:

Tumor immunological microenvironment

INS:

Number of insertions

CDF:

Cumulative distribution function

PCA:

Principal component analysis

ROC:

Receiver operating characteristic curve

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Acknowledgements

We express our gratitude to all the databases mentioned in the article for granting us permission to utilize their data.

Funding

This study was funded by the Shandong Provincial Medical and Health Science and Technology Development Plan Project (No.202203100054).

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Author notes

  1. Long Xing, Shuangli Wu and Shiyue Xue have contributed equally to this work and share the first authorship.

Authors and Affiliations

  1. Department of Oncology, The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe East Road, Zhengzhou, 450000, Henan, China

    Long Xing & Xingya Li

  2. Department of Oncology, Affiliated Hospital of Qingdao Binhai University, Qingdao, Shandong, China

    Long Xing

  3. Department of Special Examination, Affiliated Hospital of Qingdao Binhai University, Qingdao, Shandong, China

    Shuangli Wu

  4. Department of Cardiothoracic Surgery, The First Affiliated Hospital of Henan University of Traditional Chinese Medicine, Zhengzhou, Henan, China

    Shiyue Xue

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  1. Long Xing
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Correspondence to Xingya Li.

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Xing, L., Wu, S., Xue, S. et al. A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma. Mol Biotechnol (2024). https://doi.org/10.1007/s12033-024-01170-1

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