Fu Jiangping, Chen Yaohua, Li Jie, Tan Ming, Lin Rui, Wang Jiang, Wu Guirong, Rao Yao, Wu Fudao, Gao Youshu, Bai Maoshu, Wang Pingfei, Wu Fang
Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, Guangxi, China.
Department of General Respiratory, Dazhou Central Hospital, Dazhou, Sichuan, China.
Discov Oncol. 2024 Aug 30;15(1):388. doi: 10.1007/s12672-024-01233-4.
Non-small cell lung cancer (NSCLC) is the most common type of tumor globally and the leading cause of cancer-related deaths. Although treatment strategies such as immune checkpoint inhibitors and chemotherapy have advanced, the heterogeneity among NSCLC patients results in significant variability in treatment outcomes. Studies have shown that certain patients respond poorly to immune checkpoint inhibitors, indicating that treatment response is closely related to multiple factors. Therefore, it is necessary to develop predictive models to stratify patients based on gene expression and clinical characteristics, aiming for precision therapy.
This study aims to construct a stratified prognostic model for NSCLC patients based on lysosome-dependent cell death (LDCD) scoring by integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data. By analyzing the immune-related characteristics of high-risk and low-risk groups, we further explored the impact of cell death patterns on lung cancer and identified potential therapeutic targets.
This study obtained single-cell RNA sequencing data and gene expression data of NSCLC patients and normal lung tissues from the GEO and TCGA databases. We used R packages such as Seurat and CellChat for data preprocessing and analysis, and performed dimensionality reduction and visualization through Principal Component Analysis (PCA) and UMAP algorithms. LASSO regression analysis was used to construct the predictive model, followed by cross-validation and ROC curve analysis. The model's effectiveness was validated through survival analysis and immune microenvironment analysis.
The study showed a significant increase in the proportion of monocytes in NSCLC tissues, suggesting their important role in cancer progression. Cell communication analysis indicated that macrophages, smooth muscle cells, and myeloid cells exhibit strong intercellular communication during cancer progression. Using the constructed prognostic model based on 12 LDCD-related genes, we found significant differences in overall survival and immune microenvironment between the high-risk and low-risk groups.
非小细胞肺癌（NSCLC）是全球最常见的肿瘤类型，也是癌症相关死亡的主要原因。尽管免疫检查点抑制剂和化疗等治疗策略取得了进展，但NSCLC患者之间的异质性导致治疗结果存在显著差异。研究表明，某些患者对免疫检查点抑制剂反应不佳，这表明治疗反应与多种因素密切相关。因此，有必要开发预测模型，根据基因表达和临床特征对患者进行分层，以实现精准治疗。
本研究旨在通过整合单细胞RNA测序（scRNA-seq）和批量RNA测序数据，构建基于溶酶体依赖性细胞死亡（LDCD）评分的NSCLC患者分层预后模型。通过分析高危和低危组的免疫相关特征，我们进一步探讨了细胞死亡模式对肺癌的影响，并确定了潜在的治疗靶点。
本研究从GEO和TCGA数据库中获取了NSCLC患者和正常肺组织的单细胞RNA测序数据和基因表达数据。我们使用Seurat和CellChat等R包进行数据预处理和分析，并通过主成分分析（PCA）和UMAP算法进行降维和可视化。使用LASSO回归分析构建预测模型，随后进行交叉验证和ROC曲线分析。通过生存分析和免疫微环境分析验证模型的有效性。
研究表明，NSCLC组织中单核细胞的比例显著增加，表明它们在癌症进展中发挥重要作用。细胞通讯分析表明，巨噬细胞、平滑肌细胞和髓样细胞在癌症进展过程中表现出强烈的细胞间通讯。使用基于12个LDCD相关基因构建的预后模型，我们发现高危和低危组之间的总生存期和免疫微环境存在显著差异。
