# Integration of Transcriptome Profiling and Single‐Cell Sequencing Analysis to Establish a CD8+ T Cell–Related Prognostic Model for Patients With NSCLC: From Assessment to Therapy

## TL;DR

This study creates a CD8+ T cell-related model to predict outcomes and treatment responses in non-small cell lung cancer patients.

## Contribution

An innovative CD8+ T cell-related risk model was developed using transcriptome and single-cell data for NSCLC prognosis and therapy guidance.

## Key findings

- CD52, CD69, and PLIN2 were identified as key biomarkers for the risk model.
- The model accurately classified patients into high- and low-risk groups with strong predictive performance.
- Paclitaxel showed greater effectiveness in the high-risk group, suggesting treatment strategy implications.

## Abstract

Non‐small cell lung cancer (NSCLC) is the leading cause of cancer‐related mortality, characterized by a poor prognosis. The advent of immunotherapy has significantly altered the treatment landscape for NSCLC. CD8+ T cells, key mediators of immune responses, play a pivotal role in the prognosis and progression of the disease. This study aims to develop a CD8+ T‐cell‐related prognostic model to enable more precise prognostic evaluations and enhance clinical decision‐making in immunotherapy for patients with NSCLC.

Three datasets (TCGA‐NSCLC, GSE183219, and GSE42127) were analyzed using Weighted Gene Co‐expression Network Analysis and single‐cell analysis. A risk model was constructed through least absolute shrinkage and selection operator, univariate Cox regression, and multivariate Cox regression analyses. A prognostic nomogram was subsequently developed, integrating the risk model and clinical characteristics of patients with NSCLC, and validated using multiple methods. Additionally, Gene Set Enrichment Analysis, immune‐related analyses, and drug susceptibility assays were performed to assess responses to immunotherapy and chemotherapy.

CD52, CD69, and PLIN2 were identified as biomarkers and used to construct a risk model with high accuracy. Based on the risk model, patients were classified into high‐ and low‐risk subgroups. The model demonstrated strong predictive performance in both the training and validation cohorts. When combined with pathologic N and T stages, a clinical prognostic nomogram was developed, outperforming individual indicators in prognostic prediction. Immune landscape analyses revealed a robust immune system in the low‐risk group, whereas immune dysfunction was observed in the high‐risk group, suggesting differential immunotherapy efficacy between the cohorts. Additionally, paclitaxel showed significantly greater effectiveness in the high‐risk group.

This study constructed an innovative CD8+ T cell‐related risk model, advancing clinical diagnosis and offering valuable therapeutic strategies for patients with NSCLC.

CD52, CD69, and PLIN2 were identified as potential biomarkers in NSCLC. Immune‐related pathways were enriched and immune system dysfunction occurred. Paclitaxel was significantly more effective in the high‐risk group.