Zhang Jian, Li Huiying, Zhang Xi, Yang Yue, Sun Yue
Department of Thoracic Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China.
Department of Pathology, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, 150040, Heilongjiang, China.
Heliyon. 2024 Dec 11;11(1):e40869. doi: 10.1016/j.heliyon.2024.e40869. eCollection 2025 Jan 15.
Non-small cell lung cancer (NSCLC), which accounts for about 85 % of all lung cancers, currently exhibits insensitivity to most treatment regimens. Therefore, the identification of new and effective biomarkers for NSCLC is crucial for the development of treatment strategies. Immunogenic cell death (ICD), a form of regulated cell death capable of activating adaptive immune responses and generating long-term immune memory, holds promise for enhancing anti-tumor immunity and offering promising prospects for immunotherapy strategies in NSCLC.
Clinical information and expressive profiles of NSCLC genes were retrieved from the GEO and TCGA databases. By combining these databases, the researchers were able to identify the appropriate genes for use in forecasting outcomes of patients with this type of cancer. We further performed functional enrichment, gene variants and immune privilege correlation analysis to determine the underlying mechanisms. This was followed by univariate and multivariate Cox regression and LASSO regression analyses, we developed a prognostic risk model based on the TCGA cohort, which included 17 gene labels. The results of the external validation were then used to identify the appropriate genes for use in predicting the survival outcome of patients with this type of cancer. In addition, a nomogram was created to help visualise the clinical presentation of the patients. For the analyses, we performed 50 functional and immunoinfiltration assessments for two risk groups.
Using 17 genes (AIRE, APOH, CDKN2A, CEACAM4, COL4A3, CPA, DBH, F10, FCGRB, FGFR4, MMP1, PGLYRP1, SCGB2A2, SLC9A3, UGT2B17 and VIP), The researchers then created a gene signature that could be used to identify patients with an increased risk of contracting cancer. They divided the patients into two groups based on their risk score. The low-risk group exhibited a better prognosis (P < 0.01). The survival curve demonstrated that ICD-related models could accurately predict patient prognosis. Conversely, high-risk subgroups were closely associated with immune-related signaling pathways. The analysis of immune infiltration also showed that the infiltration levels of most immune cells were higher in the high risk sub-group than in the low risk sub-group. In comparison to the low-risk group, the high-risk group was more susceptible to the immune-checkpoint blockade (ICB) treatment.
Our researchers utilized a gene model to analyze the immune inflammation and prognosis of patients with non-small-cell lung cancer (NSCLC). The discovery of new ICD-related genes could lead to the development of new targeted treatments for this condition.
非小细胞肺癌（NSCLC）约占所有肺癌的85%，目前对大多数治疗方案表现出不敏感性。因此，鉴定NSCLC新的有效生物标志物对于制定治疗策略至关重要。免疫原性细胞死亡（ICD）是一种可调节的细胞死亡形式，能够激活适应性免疫反应并产生长期免疫记忆，有望增强抗肿瘤免疫力，并为NSCLC的免疫治疗策略提供广阔前景。
从GEO和TCGA数据库中检索NSCLC基因的临床信息和表达谱。通过整合这些数据库，研究人员能够确定用于预测此类癌症患者预后的合适基因。我们进一步进行了功能富集、基因变异和免疫豁免相关性分析，以确定潜在机制。随后进行单变量和多变量Cox回归以及LASSO回归分析，我们基于包含17个基因标签的TCGA队列开发了一个预后风险模型。然后使用外部验证结果来确定用于预测此类癌症患者生存结果的合适基因。此外，还创建了一个列线图以帮助直观呈现患者的临床表现。为进行分析，我们对两个风险组进行了50次功能和免疫浸润评估。
利用17个基因（AIRE、APOH、CDKN2A、CEACAM4、COL4A3、CPA、DBH、F10、FCGRB、FGFR4、MMP1、PGLYRP1、SCGB2A2、SLC9A3、UGT2B17和VIP），研究人员随后创建了一个基因特征，可用于识别患癌风险增加的患者。他们根据风险评分将患者分为两组。低风险组预后较好（P < 0.01）。生存曲线表明，与ICD相关的模型能够准确预测患者预后。相反，高风险亚组与免疫相关信号通路密切相关。免疫浸润分析还表明，大多数免疫细胞在高风险亚组中的浸润水平高于低风险亚组。与低风险组相比，高风险组对免疫检查点阻断（ICB）治疗更敏感。
我们的研究人员利用基因模型分析了非小细胞肺癌（NSCLC）患者的免疫炎症和预后。新的ICD相关基因的发现可能会为此病开发出新的靶向治疗方法。
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