Zhou Weijun, Hu Zhuozheng, Wu Jiajun, Liu Qinghua, Jie Zhangning, Sun Hui, Zhang Wenxiong
Department of Thoracic Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
Department of Thoracic Surgery, Ganzhou People's Hospital, Ganzhou, Jiangxi 341099, P.R. China.
Oncol Lett. 2025 Apr 7;29(6):271. doi: 10.3892/ol.2025.15017. eCollection 2025 Jun.
Plasma cells serve a crucial role in the human immune system and are important in tumor progression. However, the specific role of plasma cell immune-related genes (PCIGs) in tumor progression remains unclear. Therefore, the present study aimed to establish a risk assessment model for patients with lung adenocarcinoma (LUAD) based on PCIGs. The data used in the present study were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. After identifying nine PCIGs, a risk assessment model was constructed and a nomogram was developed for predicting patient prognosis. To explore the molecular mechanism and clinical significance, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis and drug sensitivity prediction were performed. Furthermore, the accuracy of the model was validated using reverse transcription-quantitative PCR (RT-qPCR). The present study constructed a risk assessment model consisting of nine PCIGs. Kaplan-Meier survival curves indicated a worse prognosis in the high-risk subgroup (risk score ≥0.982) compared with that in the low-risk subgroup. The nomogram exhibited predictive value for survival prediction (area under the curve=0.727). GSEA enrichment analysis revealed enrichment of the focal adhesion and extracellular matrix-receptor interaction pathways in the high-risk group. Moreover, the high-risk group exhibited a higher TMB, as demonstrated by the TME analysis showing lower ESTIMATE scores. Drug sensitivity prediction facilitated potential drug selection. Subsequently, differential gene expression was validated in multiple LUAD cell lines using RT-qPCR. In conclusion, the risk assessment model based on nine PCIGs may be used to predict the prognosis and drug selection in patients with LUAD.
浆细胞在人体免疫系统中发挥着关键作用，并且在肿瘤进展过程中也很重要。然而，浆细胞免疫相关基因（PCIGs）在肿瘤进展中的具体作用仍不清楚。因此，本研究旨在基于PCIGs建立肺腺癌（LUAD）患者的风险评估模型。本研究中使用的数据来自癌症基因组图谱（The Cancer Genome Atlas）和基因表达综合数据库（Gene Expression Omnibus）。在鉴定出9个PCIGs后，构建了风险评估模型并开发了列线图以预测患者预后。为了探索分子机制和临床意义，进行了基因集富集分析（GSEA）、肿瘤突变负荷（TMB）分析、肿瘤微环境（TME）分析和药物敏感性预测。此外，使用逆转录定量PCR（RT-qPCR）验证了模型的准确性。本研究构建了一个由9个PCIGs组成的风险评估模型。Kaplan-Meier生存曲线表明，与低风险亚组相比，高风险亚组（风险评分≥0.982）的预后更差。列线图对生存预测具有预测价值（曲线下面积=0.727）。GSEA富集分析显示高风险组中粘着斑和细胞外基质-受体相互作用途径富集。此外，高风险组表现出更高的TMB，TME分析显示ESTIMATE评分较低证明了这一点。药物敏感性预测有助于潜在药物的选择。随后，使用RT-qPCR在多个LUAD细胞系中验证了差异基因表达。总之，基于9个PCIGs的风险评估模型可用于预测LUAD患者的预后和药物选择。
