通过整合单细胞RNA测序(sing-cell RNA sequencing, scRNA-seq)和癌症基因组图谱(The Cancer Genome Atlas, TCGA)数据，探索肺鳞状细胞癌(lung squamous cell carcinoma, LUSC)中差异表达基因(differentially expressed genes, DEGs)和预后相关基因，并构建基于这些基因的预后模型。为了更好地了解LUSC中的免疫微环境(tumor microenvironment, TME)，进一步分析了免疫细胞的浸润特征，揭示其与LUSC患者预后的潜在关联。

从基因表达综合数据库GEO(GSE118245)中获取LUSC单细胞RNA测序数据，并通过质量控制和数据标准化，鉴定出不同的细胞群体。使用Seurat软件包进行主成分分析(principal component analysis, PCA)和统一流形近似投影(uniform manifold approximation and projection, UMAP)进行降维聚类。基于TCGA数据库中的LUSC患者样本，使用TCGAbiolinks包获取批量RNA测序数据，并对肿瘤样本和正常样本之间的DEGs进行筛选，采用加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA)构建基因共表达网络。使用Cox回归和最小绝对收缩和选择算子(least absolute shrinkage and selection operator, LASSO)回归构建基于DEGs的预后模型，Kaplan-Meier生存曲线评估患者总生存期(overall survival, OS)。使用CIBERSORT算法评估不同风险组中的免疫细胞浸润比例，比较22种免疫细胞在高风险和低风险组中的差异。

从LUSC单细胞数据中质控得到出5 360个细胞，注释为13个不同的细胞群，并通过SingleR注释为8种细胞类型。与对照组相比，LUSC组中的中性粒细胞、CD4^＋ T细胞和骨骼肌细胞比例显著上升。差异表达基因分析共筛选出3 396个DEGs，其中1 851个基因上调，1 545个基因下调。GO和KEGG富集分析表明，DEGs主要参与细胞周期、感染和补体级联反应等重要生物过程。在TCGA-LUSC数据中，使用WGCNA识别出与LUSC发展显著相关的蓝色模块，在此基础上筛选出61个交集基因进行进一步分析。单因素Cox回归和LASSO回归分析共识别出4个独立的预后相关基因(ITIH3、MME、PLAAT1和ATP13A5)，构建了风险评分模型。Kaplan-Meier生存曲线显示高风险组患者的总生存期显著低于低风险组。免疫浸润分析结果表明，高风险组患者肿瘤中的CD8^＋ T细胞、活化的记忆CD4^＋ T细胞和滤泡辅助性T细胞比例显著高于低风险组，进一步支持了免疫微环境对LUSC进展的关键作用。

通过整合单细胞和TCGA数据，成功鉴定了LUSC中的关键差异表达基因，构建了有效的预后模型。模型在多个验证队列中表现出预后预测，揭示的免疫细胞浸润特征为LUSC的免疫微环境提供了新的见解。免疫细胞在LUSC的发生和进展中发挥重要作用。

This study aims to explore differentially expressed genes (DEGs) and prognostic genes in lung squamous cell carcinoma (LUSC) by integrating single-cell RNA sequencing (scRNA-seq) and data from The Cancer Genome Atlas (TCGA), and to construct a prognostic model based on these genes. To further understand the immune microenvironment (TME) in LUSC, we analyzed immune cell infiltration characteristics and revealed their potential association with patient prognosis.

Single-cell RNA-seq data for LUSC were obtained from the Gene Expression Omnibus (GEO) database (GSE118245). After quality control and data normalization, distinct cell populations were identified. Principal component analysis (PCA) and uniform manifold approximation and projection (UMAP) were performed using the Seurat package to cluster the data. Bulk RNA sequencing data from LUSC patient samples in the TCGA database were obtained using the TCGAbiolinks package, and DEGs between tumor and normal samples were identified. Weighted gene co-expression network analysis (WGCNA) was employed to construct a gene co-expression network. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were applied to develop a prognostic model based on DEGs, and Kaplan-Meier survival curves were used to evaluate overall survival (OS). Additionally, the CIBERSORT algorithm was used to assess immune cell infiltration proportions in different risk groups, and the infiltration levels of 22 immune cell types were compared between the high-risk and low-risk groups.

After quality control, 5, 360 cells were identified from the LUSC single-cell data and annotated into 13 distinct cell clusters, which were further categorized into 8 cell types using SingleR. Compared with the control group, the proportions of neutrophils, CD4⁺ T cells, and skeletal muscle cells were significantly elevated in the LUSC group. DEG analysis identified 3, 396 DEGs, of which 1, 851 were upregulated and 1, 545 were downregulated. Gene Ontology (GO) and KEGG enrichment analyses revealed that these DEGs were mainly involved in biological processes such as the cell cycle, infection, and complement cascade reactions. In the TCGA-LUSC data, WGCNA identified the blue module as significantly associated with LUSC progression, from which 61 intersecting genes were selected for further analysis. Univariate Cox regression and LASSO regression identified four independent prognostic genes (ITIH3, MME, PLAAT1, and ATP13A5), and a risk score model was constructed based on these genes. Kaplan-Meier survival curves showed that patients in the high-risk group had significantly shorter OS than those in the low-risk group. The prognostic model performed well in both GEO validation cohorts (GSE192870 and GSE180712). Immune infiltration analysis indicated significant differences in the proportions of CD8⁺ T cells, activated memory CD4⁺ T cells, and follicular helper T cells between the high-risk and low-risk groups, supporting the crucial role of TME in LUSC progression.

This study successfully identified key DEGs in LUSC through the integration of single-cell RNA-seq and TCGA data and developed an effective prognostic model. The model demonstrated robust prognostic prediction in multiple validation cohorts, and the immune infiltration characteristics uncovered by this study provide new insights into the TME in LUSC. The findings suggest that immune cells play a critical role in LUSC development and progression, potentially offering new therapeutic targets for immunotherapy in LUSC.