摘要
出身背景
胃癌症是一种重要的疾病，是世界上第五常见的恶性肿瘤。自噬是细胞内物质周转的一个重要过程。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）在预测预后和治疗效果方面具有临床病理意义。
方法
GC的临床和基因表达数据来自癌症基因组图谱和基因表达综合数据库。从232个ARGs中筛选出22个在表达和预后方面存在差异的基因。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，在癌症细胞中验证了自噬与铁下垂之间的关系。
后果
胃癌患者ARGs的表达表现出明显的异质性。确定了三种自噬模式，并用于预测GC患者的总生存率。这三种模式与免疫表型非常匹配。《京都基因与基因组百科全书》和《基因本体论》的富集分析表明，三种自噬模式的生物学功能不同。然后建立评分系统来量化自噬模型，并进一步评估患者对免疫疗法的反应。自噬评分高的患者有更严重的肿瘤突变负担和更好的预后。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表达显著升高，生存率增加。实验结果证实，在不同的自噬簇中，脱铁基因的表达与自噬基因的表达呈正相关，抑制自噬显著逆转了脱铁细胞死亡和脂质积聚的减少。
结论
自噬模式与TME的多样性和复杂性有关。自噬评分可作为GC患者的独立预后生物标志物，并可预测免疫疗法和脱铁性贫血治疗的效果。这可能有利于GC的个体化治疗。
Abstract
Background
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.

Methods
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.

Results
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.

Conclusions
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.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9424910/