Background

Dysregulation of iron metabolism has been shown to have significant implications for cancer development. We aimed to investigate the prognostic and immunological significance of iron metabolism-related genes (IMRGs) in nasopharyngeal carcinoma (NPC).

Methods

Multiple Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets were analyzed to identify key IMRGs associated with prognosis. Additionally, the immunological significance of IMRGs was explored.

Results

A novel risk model was established using the LASSO regression algorithm, incorporating three genes (TFRC, SLC39A14, and ATP6V0D1).This model categorized patients into low and high-risk groups, and Kaplan–Meier analysis revealed significantly shorter progression-free survival for the high-risk group (P < 0.0001). The prognostic model’s accuracy was additionally confirmed by employing time-dependent Receiver Operating Characteristic (ROC) curves and conducting Decision Curve Analysis (DCA). High-risk patients were found to correlate with advanced clinical stages, specific tumor microenvironment subtypes, and distinct morphologies. ESTIMATE analysis demonstrated a significant inverse relationship between increased immune, stromal, and ESTIMATE scores and lowered risk score. Immune analysis indicated a negative correlation between high-risk score and the abundance of most tumor-infiltrating immune cells, including dendritic cells, CD8⁺ T cells, CD4⁺ T cells, and B cells. This correlation extended to immune checkpoint genes such as PDCD1, CTLA4, TIGIT, LAG3, and BTLA. The protein expression patterns of selected genes in clinical NPC samples were validated through immunohistochemistry.

Conclusion

This study presents a prognostic model utilizing IMRGs in NPC, which could assist in assessing patient prognosis and provide insights into new therapeutic targets for NPC.

中文翻译：

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整合铁代谢相关基因特征来评估鼻咽癌的预后和免疫浸润

背景

铁代谢失调已被证明对癌症的发展具有重大影响。我们的目的是研究铁代谢相关基因（IMRG）在鼻咽癌（NPC）中的预后和免疫学意义。

方法

对多基因表达综合 (GEO) 和癌症基因组图谱 (TCGA) 数据集进行分析，以确定与预后相关的关键 IMRG。此外，还探讨了 IMRG 的免疫学意义。

结果

使用 LASSO 回归算法建立了一个新的风险模型，纳入了三个基因（TFRC、SLC39A14 和 ATP6V0D1）。该模型将患者分为低风险组和高风险组，Kaplan-Meier 分析显示，患者的无进展生存期显着缩短。高危组（P  <0.0001）。通过采用时间相关的受试者工作特征 (ROC) 曲线并进行决策曲线分析 (DCA)，还进一步确认了预后模型的准确性。研究发现高风险患者与晚期临床分期、特定的肿瘤微环境亚型和不同的形态相关。估计分析表明，免疫、基质和估计评分的增加与风险评分的降低之间存在显着的负相关关系。免疫分析表明，高风险评分与大多数肿瘤浸润免疫细胞（包括树突状细胞、CD8 ⁺ T细胞、CD4 ⁺ T细胞和B细胞）的丰度呈负相关。这种相关性扩展到免疫检查点基因，例如 PDCD1、CTLA4、TIGIT、LAG3 和 BTLA。通过免疫组织化学验证临床鼻咽癌样本中选定基因的蛋白质表达模式。

结论

这项研究提出了一种在鼻咽癌中利用 IMRG 的预后模型，可以帮助评估患者预后并为鼻咽癌的新治疗靶点提供见解。