DataSheet2_Clinical Roles of Risk Model Based on Differentially Expressed Genes in Mesenchymal Stem Cells in Prognosis and Immunity of Non-small Cell Lung Cancer.docx

The tumor microenvironment (TME) plays an important regulatory role in the progression of non-small cell lung cancer (NSCLC). Mesenchymal stem cells (MSCs) in the TME might contribute to the occurrence and development of cancer. This study evaluates the role of differentially expressed genes (DEGs) of MSCs and the development of NSCLC and develops a prognostic risk model to assess the therapeutic responses. The DEGs in MSCs from lung tissues and from normal tissues were analyzed using GEO2R. The functions and mechanisms of the DEGs were analyzed using the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Additionally, the Cancer Genome Atlas (TCGA) database was used to determine the expression levels of the DEGs of MSCs in the NSCLC tissues. The prognostic factors of NSCLC related to MSCs were screened by survival analysis, meta-analysis, Cox regression analysis, and a prognostic risk model and nomogram was developed. The signaling mechanisms and immune roles that risk model participate in NSCLC development were determined via Gene Set Enrichment Analysis and CIBERSORT analysis. Compared to the normal tissues, 161 DEGs were identified in the MSCs of the lung tissues. These DEGs were associated with mechanisms, such as DNA replication, nuclear division, and homologous recombination. The overexpression of DDIT4, IL6, ITGA11, MME, MSX2, POSTN, and TRPA1 were associated with dismal prognosis of NSCLC patients. A high-risk score based on the prognostic risk model indicated the dismal prognosis of NSCLC patients. The nomogram showed that the age, clinical stage, and risk score affected the prognosis of NSCLC patients. Further, the high-risk model was associated with signaling mechanisms, such as the ECM-receptor interaction pathways, cytokine-cytokine receptor interaction, and MAPK pathways, involved in the progression of NSCLC and was also related to the components of the immune system, such as macrophages M0, T follicular helper cells, regulatory T cells. Therefore, the risk model and nomogram that was constructed on the basis of MSC-related factors such as POSTN, TRPA1, and DDIT4 could facilitate the discovery of target molecules that participate in the progression of NSCLC, which might also serve as new candidate markers for evaluating the prognosis of NSCLC patients.

肿瘤微环境（tumor microenvironment, TME）在非小细胞肺癌（non-small cell lung cancer, NSCLC）的发生发展中发挥重要调控作用。肿瘤微环境中的间充质干细胞（mesenchymal stem cells, MSCs）或可参与癌症的发生与发展进程。本研究旨在解析间充质干细胞的差异表达基因（differentially expressed genes, DEGs）在非小细胞肺癌发生发展中的作用，并构建预后风险模型以评估治疗响应情况。本研究利用GEO2R工具对肺组织与正常组织来源的间充质干细胞中的差异表达基因进行分析。通过基因本体论（Gene Ontology, GO）与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）数据库，对差异表达基因的功能与作用机制进行富集分析。此外，本研究借助癌症基因组图谱（Cancer Genome Atlas, TCGA）数据库，检测非小细胞肺癌组织中间充质干细胞差异表达基因的表达水平。通过生存分析、荟萃分析（meta-analysis）、Cox回归分析筛选与间充质干细胞相关的非小细胞肺癌预后因子，并构建预后风险模型与列线图（nomogram）。通过基因集富集分析（Gene Set Enrichment Analysis, GSEA）与CIBERSORT分析，解析该风险模型参与非小细胞肺癌发生发展的信号通路机制与免疫调控作用。与正常组织相比，肺组织来源的间充质干细胞中共筛选得到161个差异表达基因。这些差异表达基因主要富集于DNA复制、核分裂、同源重组等生物学过程与信号通路。DDIT4、IL6、ITGA11、MME、MSX2、POSTN及TRPA1的高表达与非小细胞肺癌患者的不良预后显著相关。基于该预后风险模型计算得到的高风险评分，提示非小细胞肺癌患者预后较差。列线图分析结果显示，患者年龄、临床分期与风险评分均对非小细胞肺癌患者的预后产生影响。进一步分析发现，高风险模型与参与非小细胞肺癌进展的多条信号通路密切相关，包括细胞外基质-受体相互作用通路、细胞因子-细胞因子受体相互作用通路及丝裂原活化蛋白激酶（mitogen-activated protein kinase, MAPK）通路；同时该模型还与肿瘤免疫微环境中的多种免疫细胞组分相关，如M0型巨噬细胞、滤泡辅助性T细胞及调节性T细胞。综上，本研究基于POSTN、TRPA1及DDIT4等间充质干细胞相关因子构建的预后风险模型与列线图，可为解析非小细胞肺癌进展的关键靶分子提供助力，同时也可作为评估非小细胞肺癌患者预后的新型候选标志物。