Li Haoran, Xu Bing, Du Jing, Wu Yunyi, Shao Fangchun, Gao Yan, Zhang Ping, Zhou Junyu, Tong Xiangmin, Wang Ying, Li Yanchun
Laboratory Medicine Center, Department of Clinical Laboratory, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, China.
Department of Clinical Laboratory, Hangzhou Women's Hospital, Hangzhou, China.
Front Oncol. 2022 Aug 16;12:959337. doi: 10.3389/fonc.2022.959337. eCollection 2022.
Gastric cancer (GC) is an important disease and the fifth most common malignancy worldwide. Autophagy is an important process for the turnover of intracellular substances. Autophagy-related genes (ARGs) are crucial in cancer. Accumulating evidence indicates the clinicopathological significance of the tumor microenvironment (TME) in predicting prognosis and treatment efficacy.
Clinical and gene expression data of GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. A total of 22 genes with differences in expression and prognosis were screened from 232 ARGs. Three autophagy patterns were identified using an unsupervised clustering algorithm and scored using principal component analysis to predict the value of autophagy in the prognosis of GC patients. Finally, the relationship between autophagy and ferroptosis was validated in gastric cancer cells.
The expression of ARGs showed obvious heterogeneity in GC patients. Three autophagy patterns were identified and used to predict the overall survival of GC patients. These three patterns were well-matched with the immunophenotype. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses showed that the biological functions of the three autophagy patterns were different. A scoring system was then set up to quantify the autophagy model and further evaluate the response of the patients to the immunotherapy. Patients with high autophagy scores had a more severe tumor mutation burden and better prognosis. High autophagy scores were accompanied by high microsatellite instability. Patients with high autophagy scores had significantly higher PD-L1 expression and increased survival. The experimental results confirmed that the expression of ferroptosis genes was positively correlated with the expression of autophagy genes in different autophagy clusters, and inhibition of autophagy dramatically reversed the decrease in ferroptotic cell death and lipid accumulation.
Autophagy patterns are involved in TME diversity and complexity. Autophagy score can be used as an independent prognostic biomarker in GC patients and to predict the effect of immunotherapy and ferroptosis-based therapy. This might benefit individualized treatment for GC.
胃癌（GC）是一种重要疾病，是全球第五大常见恶性肿瘤。自噬是细胞内物质周转的重要过程。自噬相关基因（ARG）在癌症中至关重要。越来越多的证据表明肿瘤微环境（TME）在预测预后和治疗疗效方面的临床病理意义。
从癌症基因组图谱和基因表达综合数据库中获取GC的临床和基因表达数据。从232个ARG中筛选出22个表达和预后存在差异的基因。使用无监督聚类算法识别三种自噬模式，并使用主成分分析进行评分，以预测自噬在GC患者预后中的价值。最后，在胃癌细胞中验证自噬与铁死亡之间的关系。
ARG的表达在GC患者中表现出明显的异质性。识别出三种自噬模式并用于预测GC患者的总生存期。这三种模式与免疫表型匹配良好。京都基因与基因组百科全书和基因本体富集分析表明，三种自噬模式的生物学功能不同。然后建立一个评分系统来量化自噬模型，并进一步评估患者对免疫治疗的反应。自噬评分高的患者肿瘤突变负担更重，预后更好。高自噬评分伴随着高微卫星不稳定性。自噬评分高的患者PD-L1表达显著更高，生存期延长。实验结果证实，在不同的自噬簇中，铁死亡基因的表达与自噬基因的表达呈正相关，抑制自噬可显著逆转铁死亡细胞死亡和脂质积累的减少。
自噬模式参与TME的多样性和复杂性。自噬评分可作为GC患者独立的预后生物标志物，并用于预测免疫治疗和基于铁死亡治疗的效果。这可能有利于GC的个体化治疗。
