Multi-omics analysis reveals an anoikis-related signature for non-small cell lung cancer

Author:

Ma Yuqi1,Li Jia1,Shen Tao1

Affiliation:

1. Chengdu University of Traditional Chinese Medicine

Abstract

Abstract

Background Non-small cell lung cancer (NSCLC) is a prevalent form of lung cancer characterized by a significant death rate. Anoikis (ANO), refers to a distinct kind of programmed cell death that is strongly linked to the body's immune response to cancer. Nevertheless, the precise function of ANO in NSCLC is still not well understood. Methods ANO-related genes were analysed using multiple methods, including AUCell, UCell, single-sample gene set enrichment analysis (ssGSEA), Singscore, AddModuleScore, GSVA and weighted gene co-expression network analysis (WGCNA). We have developed an innovative machine learning framework that combines 10 different machine learning algorithms and 101 possible combinations of these algorithms. The goal of this framework is to build a reliable signature, known as the Anoikis-related signature (ARS), which is related to the phenomenon of anoikis. The performance of ARS was evaluated in both the training and validation sets. Column line graphs using ARS were developed as a quantitative technique to predict prognosis in clinical settings. Multi-omics studies, including genomic and bulk transcriptomic, were performed to gain more in-depth knowledge of prognostic features. We analysed the responsiveness of risk groups to immunotherapy and searched for tailored drugs to target specific risk categories. Results We discovered 103 genes associated with ANO at both single cell and bulk transcriptome levels. A computational framework using machine learning and 101 combinations was used to generate the consensus ARS. This framework showed exceptional performance in accurately predicting prognosis and clinical change, and the ARS can also be used to predict the initiation, progression and spread of NSCLC. Statistical studies have shown that it is an independent prognostic determinant of (OS) and disease-specific survival (DSS) in NSCLC. The integrated column line graphs of the ARS provide an accurate and quantitative tool for clinical practice. We also identified distinct metabolic processes, patterns of genetic mutations and the presence of immune cells in the tumour microenvironment that differed between the high-risk and low-risk groups. Significantly, there were significant changes in the immunophenotype score (IPS) between the risk groups, suggesting that the high-risk group is likely to have a more favourable response to immunotherapy. In addition, potential drugs targeting specific at-risk populations were identified. Conclusion The purpose of our work is to create a signature associated with immunogenic cell death. This signature has the potential to be a useful tool for predicting the prognosis of NSCLC, as well as for targeted prevention and personalised therapy. We are also providing new insights into the molecular pathways involved in the growth and progression of NSCLC through the use of mass transcriptomics and genomics research.

Publisher

Springer Science and Business Media LLC

Reference44 articles.

1. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries;Sung H;CA: a cancer journal for clinicians,2021

2. Cancer Progress and Priorities: Lung Cancer. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology;Schabath MB,2019

3. Five-Year Overall Survival for Patients With Advanced Non–Small-Cell Lung Cancer Treated With Pembrolizumab: Results From the Phase I KEYNOTE-001 Study;Garon EB;Journal of clinical oncology: official journal of the American Society of Clinical Oncology,2019

4. The present and future of immunocytokines for cancer treatment;Gout DY;Cellular and molecular life sciences: CMLS,2022

5. Cancer immunotherapy using checkpoint blockade;Ribas A;Science (New York, NY),2018

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3