Abstract
BACKGROUND: Globally, non-small-cell lung cancer (NSCLC) has a high incidence, and NSCLC patients have poor prognoses. Lung squamous carcinoma (LUSC) is a major pathological type of NSCLC. LncRNAs play important roles in tumor progression and immune system functions. The aim of this study was to construct a predictive model with immune-related lncRNAs and to assess the immune microenvironment in middle- or advanced-stage LUSC patients. METHODS: RNA sequencing data and corresponding clinical LUSC data were downloaded from The Cancer Genome Atlas. Immune genes were obtained from the Molecular Signatures Database. Immune-related lncRNAs were identified by Pearson correlation analysis in R. The model was constructed using univariate and multivariate Cox regression analyses. Finally, we validated the prognostic immune-related lncRNA model in a cohort from the Fudan University Shanghai Cancer Center. RESULTS: Our risk model included four immune-related lncRNAs (LINC00944, AL034550.2, AC020907.1 and AC027682.6). Survival analysis revealed that overall and disease-free survival were shorter in the high-risk group than in the low-risk group. Independent prognostic analysis showed that our model could be used as an independent prognostic predictor. The high-risk group was positively associated with CD8+ T cells, B cells, myeloid dendritic cells, macrophages, regulatory T cells (Tregs) and cancer-associated fibroblasts and high expression of PD1 and CTLA4. Additionally, a low-risk score was correlated with lower half maximal inhibitory concentrations (IC(50)s) of cisplatin, docetaxel, vinorelbine and paclitaxel and a higher IC(50) of gemcitabine. Gene set enrichment analysis suggested that these lncRNAs may participate in tumor progression and immune processes. Validation with the clinical cancer cohort demonstrated that higher risk scores were associated with a higher, but not statistically significant, likelihood of recurrence. CONCLUSION: We established a risk score model including four immune-related lncRNAs. The model accurately predicts the prognosis of middle- or advanced-stage LUSC patients and provides an important reference for individualized treatment.