Validation of endoplasmic reticulum stress-related gene signature to predict prognosis and immune landscape of patients with non-small cell lung cancer
Affiliations: [a] College of Basic Medicine, Zhengzhou University, Henan, China | [b] Charité-Universitäts Medizin Berlin, Berlin, Germany
Correspondence:
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Corresponding author: Yingying Cui, College of Basic Medicine, Zhengzhou University, Henan, China. E-mail: cui_yy2017@126.com.
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.
