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开发与黑色素瘤临床特征和免疫状况相关的与谷氨酸代谢相关的基因预后模型

Hongyan Hu ¹, Jing Yang ², Jin Miao ¹

¹Department of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
²Department of Oncology, First People's Hospital of Kunming, Kunming, China.
³Scientific Research Department, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
⁴Department of Orthopedics, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
⁵Department of Gynecology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
⁶Department of Head and Neck Cancer, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
⁷Department of Gastrointestinal Oncology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
⁸Department of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.

⁰Department of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.

Frontiers in Oncology +

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2025年九月5日

在PubMed上查看摘要

概括

这项研究确定了关键的与谷氨酸代谢相关的基因 (GRGs),以构建皮肤黑色素瘤的预后模型,揭示了与免疫透和拉巴胺素敏感性的联系.

科学领域

癌症学
遗传学
生物信息学

背景情况

皮肤黑色素瘤由于其侵袭性和异质性,对谷氨酸代谢在其进展中的作用的理解有限,因此具有重大挑战.
研究与谷氨酸代谢相关的基因 (GRGs) 对于发现黑色素瘤的新预后标志物和治疗点至关重要.

研究的目的

使用生物信息学识别GRG并构建皮肤黑色素瘤的可靠预后模型.
在黑色素瘤患者中探索GRG,临床特征,免疫透和药物敏感性之间的关系.

主要方法

利用基因表达总汇 (GEO) 和癌症基因组图谱-皮肤黑色素瘤 (TCGA-SKCM) 的患者数据进行生物信息学分析.
从MSigDB提取GRG并使用scRNA-seq数据处理R包"Seurat".
基于使用RT-PCR识别的关键基因和验证的结果构建了一个预后风险模型.

主要成果

为风险模型确定了八个关键GRG (CHMP4A,IFFO1,ANKRD10,ZDHHC11,CLPB,ANKMY1,TCAP,POLG2);克拉克期和状况是独立的预后因素.
高风险组的总生存率显著降低,免疫细胞透率明显降低,免疫检查点表达模式明显降低.
高风险组对拉巴胺素的敏感性增加,其中特定的基因 (ANKRD10,ANKMY1,TCAP) 在糖解/葡萄糖生成途径中富含.

结论

基于GRG开发的预后模型为黑色素瘤预后和免疫状态提供了宝贵的见解.
这些发现突显了针对特定GRG的潜力,以改善黑色素瘤治疗策略和基于拉巴的疗法.

关键词:

生物信息学胺的代谢免疫微环境黑色素瘤预后情况