Oxidative Stress Associated LncRNAs as Potential Biomarkers for Prognosis and Immune Responses in Lung Squamous Cell Carcinoma Patients

Summary

This study identifies a 10-lncRNA oxidative stress–related signature in lung squamous cell carcinoma (LUSC) using data from The Cancer Genome Atlas. Although validation showed limited clinical predictive performance, the signature differentiated immune microenvironment patterns, and in vitro assays supported LINC01615 as a migration-invasion-related candidate for further mechanistic investigation.

Abstract

Long-chain non-coding RNA (lncRNA) significantly influences lung squamous cell carcinoma's (LUSC) prognostic value and immune infiltration. This study aimed to demonstrate how oxidative stress-related lncRNAs impact lung squamous cell carcinoma (SCC). The Cancer Genome Atlas (TCGA) dataset gathered transcriptome information and related clinical data for LUSC. To build a prognostic model, 10 prognostic-related genes were identified using a series of bioinformatics analyses that compared the OS gene's aberrant expression in tumor and healthy tissues, as well as its association with malignancy. Subjects were stratified into high- and low-risk groups based on the median risk score derived from the 10-gene signature. While the mathematical risk model demonstrated limited independent predictive performance in the validation cohort (AUC ~ 0.5), functional and immunological evaluations revealed significant differences in the tumor microenvironment (TME) across risk strata. Specifically, high-risk patients exhibited distinct immune infiltration profiles and altered immunological scores relative to their low-risk counterparts. Therefore, rather than serving as a direct clinical prediction tool, this oxidative stress-related lncRNA signature provides valuable biological insights into the immune landscape of LUSC and highlights potential therapeutic targets for further mechanistic investigation.

Introduction

Lung cancer demonstrates the highest global incidence and mortality among malignancies¹. Within the non-small cell lung cancer (NSCLC) histological variants, lung squamous cell carcinoma (LUSC) is a predominant subtype, often correlating with unfavorable clinical outcomes and a lack of highly effective targeted treatments compared to lung adenocarcinoma². While primary therapeutic approaches encompass surgical intervention, radiotherapy, and chemotherapy, patients with advanced LUSC frequently exhibit therapeutic resistance³. Consequently, there is an urgent need to identify robust molecular bioma....

Protocol

Data acquisition and preprocessing

All data analyzed in this study were obtained from the publicly accessible TCGA database listed in the Table of Materials. This study strictly adhered to the National Institutes of Health (NIH) Genomic Data Sharing Policy and the publication guidelines provided by TCGA. Because the datasets consist of de-identified, publicly available clinical and multi-omics data, this study was exempt from further Institutional Review Board (IRB) approval. Transcriptome data and corresponding clinical information for LUSC were downloaded from the Cancer Genome Atlas (TCGA) database. Initia....

Results

Identification of differentially expressed genes for oxidative stress

The workflow is shown in Figure 1. The clinical characteristics of the final analytic cohort comprising 470 LUSC patients are detailed in Table 1. Based on the defined threshold of a relevance score ≥ 7, the authors retrieved a total of 792 oxidative stress-related genes from the GeneCards database (Supplementary File 1). Between normal and LUSC.......

Discussion

Lung squamous cell carcinoma (LUSC) is a predominant and highly aggressive histological subtype of non-small cell lung cancer, often characterized by grim survival rates and resistance to conventional therapeutic modalities^8,9. Emerging evidence underscores oxidative stress as a pivotal driver in the pathogenesis and progression of various malignancies. The pathological accumulation of reactive oxygen species (ROS) and the dysregulation of cellular antioxidant de.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
4% Paraformaldehyde	Beyotime	P0099	Used for fixing cells
Cytoscape	Cytoscape Consortium	https://cytoscape.org/	Used for co-expression network creation
Fetal Bovine Serum (FBS)	Gibco	10099-141	Final concentration 10% (v/v)
GeneCards database	GeneCards Human Gene Database	https://www.genecards.org/	Oxidative stress-related genes screening
Giemsa Stain	Sigma-Aldrich	GS500	Used for staining migrated/invaded cells
ImageJ	NIH	https://imagej.nih.gov/ij/	Used for wound-gap quantification
Lipo3000 Transfection Reagent	GLPBIO	GK20006	Used for siRNA transfection
Matrigel	Corning	356234	Diluted 1:8 in serum-free medium
NCI-H520 human lung squamous cell carcinoma	ATCC	HTB-182	Used for in vitro validation assays
PBS (Phosphate-Buffered Saline)	Gibco	10010-023	Used for cell washing
Penicillin-Streptomycin (1%)	Gibco	15140-122	Cell culture antibiotic supplement
R software (v4.1.2)	R Foundation	https://www.r-project.org/	Statistical analysis and visualization
RPMI-1640 Medium	Gibco	11875-093	Cell culture medium
The Cancer Genome Atlas (TCGA)	Genomic Data Commons	https://portal.gdc.cancer.gov/	Transcriptome & clinical data source
Transwell Chambers (8.0 μm)	Corning	3422	Used for migration and invasion assays