Exploring the Prognostic Significance and Immunotherapeutic Potential of Single-Cell Sequencing-Identified Long Noncoding RNA (LncRNA) in Patients With Non-small Cell Lung Cancer.

BACKGROUND

Single-cell RNA sequencing technology can provide insight into lung cancer. The purpose of this study was to analyze the relationship between long noncoding RNA (lncRNA) discovered by RNA sequencing and immunotherapy in patients with non-small cell lung cancer (NSCLC).

METHODS

In this study, we utilized data from The Cancer Genome Atlas (TCGA) to extract gene expression data and prognostic information from patients with NSCLC. We employed univariate, least absolute shrinkage and selection operator (LASSO), multivariate Cox regression analyses to construct risk models, and Kaplan-Meier (KM) analysis to compare survival differences between high- and low-risk groups. To evaluate the accuracy of our risk model predictions, we utilized a nomogram, calibration curve, correlation index curve (C-index), and receiver operating characteristic (ROC). Additionally, we conducted Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to investigate the differential expression of lncRNA genes. We also used the tumor immune dysfunction and exclusivity (TIDE) algorithm and the R package "pRRophetic" to analyze the tumor microenvironment. Finally, we utilized stem cell indices based on mRNA expression-based stemness index (mRNAsi) expression to better assess patient prognosis.

RESULTS

Our analysis identified a set of 28 lncRNAs with prognostic risk profiles in patients with lung adenocarcinoma. Notably, patients in the low-risk group exhibited significantly better overall survival (OS) compared to those in the high-risk group. Kaplan-Meier (KM) survival curves revealed that these prognostic risk markers accurately predicted survival outcomes in non-small cell lung cancer (NSCLC) patients. MerCK18 and myeloid-derived suppressor cells (MDSC) were strongly associated with immune escape and immunotherapy in high- and low-risk subgroups. In our investigation of potential chemotherapeutic agents for the treatment of NSCLC, we screened a total of 60 agents and found that PPM1D was more effective in the low-risk group. However, we did not observe a strong correlation between the stem cell index mRNAsi and OS.

CONCLUSION

Our study highlights the close association between lncRNAs and prognostic risk profiles and the prognosis of patients with non-small cell lung cancer, offering a promising avenue for the clinical implementation of immunotherapy.