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Abstract：
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a type of interstitial lung disease characterized by unknown causes and a poor prognosis. Recent research indicates that age-related mechanisms, such as cellular senescence, may play a role in the development of this condition. However, the relationship between cellular senescence and clinical outcomes in IPF remains uncertain.
METHODS: Data from the GSE70867 database were meticulously analyzed in this study. The research employed differential expression analysis, as well as univariate and multivariate Cox regression analysis, to pinpoint senescence-related genes (SRGs) linked to prognosis and construct a prognostic risk model. The model\'s clinical relevance and its connection to potential biological processes were systematically assessed in training and testing datasets. Additionally, the expression location of prognosis-related SRGs was identified through immunohistochemical staining, and the correlation between SRGs and immune cell infiltration was deduced using the GSE28221 dataset.
RESULTS: The prognostic risk model was constructed based on five SRGs (cellular communication network factor 1, CYR61, stratifin, SFN, megakaryocyte-associated tyrosine kinase, MATK, C-X-C motif chemokine ligand 1, CXCL1, LIM domain, and actin binding 1, LIMA1). Both Kaplan-Meier (KM) curves (p = 0.005) and time-dependent receiver operating characteristic (ROC) analysis affirmed the predictive accuracy of this model in testing datasets, with respective areas under the ROC curve at 1-, 2-, and 3-years being 0.721, 0.802, and 0.739. Furthermore, qRT-RCR analysis and immunohistochemical staining verify the differential expression of SRGs in IPF samples and controls. Moreover, patients in the high-risk group contained higher infiltration levels of neutrophils, eosinophils, and M1 macrophages in BALF, which appeared to be independent indicators of poor prognosis in IPF patients.
CONCLUSIONS: Our research reveals the effectiveness of the 5 SRGs model in BALF for risk stratification and prognosis prediction in IPF patients, providing new insights into the immune infiltration of IPF progression.
METHODS: Data from the GSE70867 database were meticulously analyzed in this study. The research employed differential expression analysis, as well as univariate and multivariate Cox regression analysis, to pinpoint senescence-related genes (SRGs) linked to prognosis and construct a prognostic risk model. The model\'s clinical relevance and its connection to potential biological processes were systematically assessed in training and testing datasets. Additionally, the expression location of prognosis-related SRGs was identified through immunohistochemical staining, and the correlation between SRGs and immune cell infiltration was deduced using the GSE28221 dataset.
RESULTS: The prognostic risk model was constructed based on five SRGs (cellular communication network factor 1, CYR61, stratifin, SFN, megakaryocyte-associated tyrosine kinase, MATK, C-X-C motif chemokine ligand 1, CXCL1, LIM domain, and actin binding 1, LIMA1). Both Kaplan-Meier (KM) curves (p = 0.005) and time-dependent receiver operating characteristic (ROC) analysis affirmed the predictive accuracy of this model in testing datasets, with respective areas under the ROC curve at 1-, 2-, and 3-years being 0.721, 0.802, and 0.739. Furthermore, qRT-RCR analysis and immunohistochemical staining verify the differential expression of SRGs in IPF samples and controls. Moreover, patients in the high-risk group contained higher infiltration levels of neutrophils, eosinophils, and M1 macrophages in BALF, which appeared to be independent indicators of poor prognosis in IPF patients.
CONCLUSIONS: Our research reveals the effectiveness of the 5 SRGs model in BALF for risk stratification and prognosis prediction in IPF patients, providing new insights into the immune infiltration of IPF progression.
摘要：
背景：特发性肺纤维化（IPF）是一种间质性肺病，其特征是原因不明，预后差。最近的研究表明，与年龄相关的机制，如细胞衰老，可能在这种情况的发展中发挥作用。然而,IPF患者细胞衰老与临床结局之间的关系尚不确定.
方法：本研究对来自GSE70867数据库的数据进行了细致分析。研究采用差异表达分析，以及单变量和多变量Cox回归分析，明确与预后相关的衰老相关基因（SRGs），并构建预后风险模型。在训练和测试数据集中系统地评估了该模型的临床相关性及其与潜在生物过程的联系。此外,通过免疫组织化学染色确定预后相关SRGs的表达位置，并使用GSE28221数据集推断SRGs与免疫细胞浸润之间的相关性。
结果：基于五个SRG（蜂窝通信网络因子1，CYR61，分层，SFN,巨核细胞相关酪氨酸激酶,MATK,C-X-C基序趋化因子配体1,CXCL1,LIM结构域,和肌动蛋白结合1，LIMA1）。Kaplan-Meier(KM)曲线(p=0.005)和时间相关的受试者工作特性(ROC)分析证实了该模型在测试数据集中的预测准确性。ROC曲线下各自的面积在1-，2-,3年分别为0.721、0.802和0.739。此外，qRT-RCR分析和免疫组织化学染色验证了SRGs在IPF样品和对照中的差异表达。此外，高危人群的中性粒细胞浸润水平较高，嗜酸性粒细胞,和BALF中的M1巨噬细胞，这似乎是IPF患者预后不良的独立指标。
结论：我们的研究揭示了5个SRGs模型在BALF中用于IPF患者的危险分层和预后预测的有效性，为IPF进展的免疫浸润提供新的见解。
方法：本研究对来自GSE70867数据库的数据进行了细致分析。研究采用差异表达分析，以及单变量和多变量Cox回归分析，明确与预后相关的衰老相关基因（SRGs），并构建预后风险模型。在训练和测试数据集中系统地评估了该模型的临床相关性及其与潜在生物过程的联系。此外,通过免疫组织化学染色确定预后相关SRGs的表达位置，并使用GSE28221数据集推断SRGs与免疫细胞浸润之间的相关性。
结果：基于五个SRG（蜂窝通信网络因子1，CYR61，分层，SFN,巨核细胞相关酪氨酸激酶,MATK,C-X-C基序趋化因子配体1,CXCL1,LIM结构域,和肌动蛋白结合1，LIMA1）。Kaplan-Meier(KM)曲线(p=0.005)和时间相关的受试者工作特性(ROC)分析证实了该模型在测试数据集中的预测准确性。ROC曲线下各自的面积在1-，2-,3年分别为0.721、0.802和0.739。此外，qRT-RCR分析和免疫组织化学染色验证了SRGs在IPF样品和对照中的差异表达。此外，高危人群的中性粒细胞浸润水平较高，嗜酸性粒细胞,和BALF中的M1巨噬细胞，这似乎是IPF患者预后不良的独立指标。
结论：我们的研究揭示了5个SRGs模型在BALF中用于IPF患者的危险分层和预后预测的有效性，为IPF进展的免疫浸润提供新的见解。
