Validation of endoplasmic reticulum stress-related gene signature to predict prognosis and immune landscape of patients with non-small cell lung cancer.
{"title":"Validation of endoplasmic reticulum stress-related gene signature to predict prognosis and immune landscape of patients with non-small cell lung cancer.","authors":"Yingying Cui, Xiaoli Zhou, Dan Zheng, Yumei Zhu","doi":"10.3233/THC-241059","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Lung cancer is one of the most common cancers worldwide, with the incidence increasing each year. It is crucial to improve the prognosis of patients who have lung cancer. Non-Small Cell Lung Cancer (NSCLC) accounts for the majority of lung cancer. Though its prognostic significance in NSCLC has not been often documented, Endoplasmic Reticulum (ER) stress has been identified to be implicated in tumour malignant behaviours and resistance to treatment.</p><p><strong>Objective: </strong>This work aimed to develop a gene profile linked to ER stress that could be applied to predictive and risk assessment for non-small cell lung cancer.</p><p><strong>Methods: </strong>Data from 1014 NSCLC patients were sourced from The Cancer Genome Atlas (TCGA) database, integrating clinical and Ribonucleic Acid (RNA) information. Diverse analytical techniques were utilized to identify ERS-associated genes associated with patients' prognoses. These techniques included Kaplan-Meier analysis, univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression analysis (LASSO) regression, and Pearson correlation analysis. Using a risk score model obtained from multivariate Cox analysis, a nomogram was created and validated to classify patients into high- and low-risk groups. The study employed the CIBERSORT algorithm and Single-Sample Gene Set Eenrichment Analysis (ssGSEA) to investigate the tumour immune microenvironment. We used the Genomics of Drug Sensitivity in Cancer (GDSC) database and R tools to identify medicines that could be responsive.</p><p><strong>Results: </strong>Four genes - FABP5, C5AR1, CTSL, and LTA4H - were chosen to create the risk model. Overall Survival (OS) was considerably lower (P< 0.05) in the high-risk group. When it came to predictive accuracy, the risk model outperformed clinical considerations. Several medication types that are sensitive to high-risk groups were chosen.</p><p><strong>Conclusion: </strong>Our study has produced a gene signature associated with ER stress that may be employed to forecast the prognosis and therapeutic response of non-small cell lung cancer patients.</p>","PeriodicalId":48978,"journal":{"name":"Technology and Health Care","volume":" ","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Technology and Health Care","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3233/THC-241059","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Lung cancer is one of the most common cancers worldwide, with the incidence increasing each year. It is crucial to improve the prognosis of patients who have lung cancer. Non-Small Cell Lung Cancer (NSCLC) accounts for the majority of lung cancer. Though its prognostic significance in NSCLC has not been often documented, Endoplasmic Reticulum (ER) stress has been identified to be implicated in tumour malignant behaviours and resistance to treatment.
Objective: This work aimed to develop a gene profile linked to ER stress that could be applied to predictive and risk assessment for non-small cell lung cancer.
Methods: Data from 1014 NSCLC patients were sourced from The Cancer Genome Atlas (TCGA) database, integrating clinical and Ribonucleic Acid (RNA) information. Diverse analytical techniques were utilized to identify ERS-associated genes associated with patients' prognoses. These techniques included Kaplan-Meier analysis, univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression analysis (LASSO) regression, and Pearson correlation analysis. Using a risk score model obtained from multivariate Cox analysis, a nomogram was created and validated to classify patients into high- and low-risk groups. The study employed the CIBERSORT algorithm and Single-Sample Gene Set Eenrichment Analysis (ssGSEA) to investigate the tumour immune microenvironment. We used the Genomics of Drug Sensitivity in Cancer (GDSC) database and R tools to identify medicines that could be responsive.
Results: Four genes - FABP5, C5AR1, CTSL, and LTA4H - were chosen to create the risk model. Overall Survival (OS) was considerably lower (P< 0.05) in the high-risk group. When it came to predictive accuracy, the risk model outperformed clinical considerations. Several medication types that are sensitive to high-risk groups were chosen.
Conclusion: Our study has produced a gene signature associated with ER stress that may be employed to forecast the prognosis and therapeutic response of non-small cell lung cancer patients.
期刊介绍:
Technology and Health Care is intended to serve as a forum for the presentation of original articles and technical notes, observing rigorous scientific standards. Furthermore, upon invitation, reviews, tutorials, discussion papers and minisymposia are featured. The main focus of THC is related to the overlapping areas of engineering and medicine. The following types of contributions are considered:
1.Original articles: New concepts, procedures and devices associated with the use of technology in medical research and clinical practice are presented to a readership with a widespread background in engineering and/or medicine. In particular, the clinical benefit deriving from the application of engineering methods and devices in clinical medicine should be demonstrated. Typically, full length original contributions have a length of 4000 words, thereby taking duly into account figures and tables.
2.Technical Notes and Short Communications: Technical Notes relate to novel technical developments with relevance for clinical medicine. In Short Communications, clinical applications are shortly described. 3.Both Technical Notes and Short Communications typically have a length of 1500 words.
Reviews and Tutorials (upon invitation only): Tutorial and educational articles for persons with a primarily medical background on principles of engineering with particular significance for biomedical applications and vice versa are presented. The Editorial Board is responsible for the selection of topics.
4.Minisymposia (upon invitation only): Under the leadership of a Special Editor, controversial or important issues relating to health care are highlighted and discussed by various authors.
5.Letters to the Editors: Discussions or short statements (not indexed).