Department of Obstetrics and Gynecology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai 200072, China.
Institute of Gynecological Minimally Invasive Medicine, Tongji University School of Medicine, Shanghai 200072, China.
Biomolecules. 2023 Feb 9;13(2):339. doi: 10.3390/biom13020339.
Ovarian cancer (OC) is one of the most malignant tumors in the female reproductive system, with a poor prognosis. Various responses to treatments including chemotherapy and immunotherapy are observed among patients due to their individual characteristics. Applicable prognostic markers could make it easier to refine risk stratification for OC patients. Autophagy is closely implicated in the occurrence and development of tumors, including OC. Whether autophagy -related genes can be used as prognostic markers for OC patients remains unclear.
The gene transcriptome data of 374 OC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The correlation between the autophagy levels and outcomes of OC patients was identified through the single sample gene set enrichment analysis (ssGSEA). Recognized molecular markers of autophagy in different clinical specimens were detected by immunohistochemistry (IHC) assay. The gene set enrichment analysis (GSEA), ESTIMATE, and CIBERSORT analysis were applied to explore the correlation of autophagy with the tumor immune microenvironment (TIME). Single-cell RNA-sequencing (scRNA-seq) data from seven OC patients were included for characterizing cell-cell interaction patterns of autophagy-high or low tumor cells. Machine learning, Stepwise Cox regression and LASSO-Cox analysis were used to screen autophagy hub genes, which were used to establish an autophagy-related signature for prognosis evaluation. Four tumor immunotherapy cohorts were obtained from the GEO (Gene Expression Omnibus) database and the literature for autophagy risk score validation.
The autophagy levels were closely related to the prognosis of the OC patients. Additionally, the autophagy levels were correlated with TIME status including immune score, and immune-cell infiltration. The scRNA-seq analysis found that tumor cells with high or low autophagy levels had different interactions with immune cells, especially macrophages. Eight autophagy-hub genes (ZFYVE1, AMBRA1, LAMP2, TRAF6, PDPK1, ATG2B, DAPK1 and TP53INP2) were screened for an autophagy-related signature. According to this signature, higher risk score was correlated with poor prognosis and better immunotherapy response in the OC patients.
The autophagy-related signature is applicable to predict the prognosis and immune checkpoint inhibitors (ICIs) therapy efficiency in OC patients. It is possible to identify OC patients who will respond to ICIs therapy and have a favorable prognosis, although more verification is needed.
卵巢癌（OC）是女性生殖系统中最恶性的肿瘤之一，预后较差。由于患者个体特征不同，对化疗和免疫治疗等各种治疗的反应也不同。适用的预后标志物可以更容易地对 OC 患者进行风险分层。自噬与肿瘤的发生发展密切相关，包括 OC。自噬相关基因是否可以作为 OC 患者的预后标志物尚不清楚。
从癌症基因组图谱（TCGA）数据库下载 374 名 OC 患者的基因转录组数据。通过单样本基因集富集分析（ssGSEA）确定自噬水平与 OC 患者结局的相关性。通过免疫组织化学（IHC）检测不同临床标本中自噬的公认分子标志物。应用基因集富集分析（GSEA）、ESTIMATE 和 CIBERSORT 分析探讨自噬与肿瘤免疫微环境（TIME）的相关性。纳入 7 名 OC 患者的单细胞 RNA 测序（scRNA-seq）数据，用于描绘自噬高或低肿瘤细胞的细胞间相互作用模式。应用机器学习、逐步 Cox 回归和 LASSO-Cox 分析筛选自噬枢纽基因，建立自噬相关的预后评估签名。从 GEO（基因表达综合）数据库和文献中获取四个肿瘤免疫治疗队列进行自噬风险评分验证。
自噬水平与 OC 患者的预后密切相关。此外，自噬水平与 TIME 状态相关，包括免疫评分和免疫细胞浸润。scRNA-seq 分析发现，自噬水平高或低的肿瘤细胞与免疫细胞，尤其是巨噬细胞的相互作用不同。筛选出 8 个自噬枢纽基因（ZFYVE1、AMBRA1、LAMP2、TRAF6、PDPK1、ATG2B、DAPK1 和 TP53INP2）用于建立自噬相关的签名。根据该签名，较高的风险评分与 OC 患者的预后不良和免疫检查点抑制剂（ICIs）治疗反应较好相关。
自噬相关签名可用于预测 OC 患者的预后和免疫检查点抑制剂（ICIs）治疗效率。虽然需要更多的验证，但有可能识别出对 ICIs 治疗有反应且预后良好的 OC 患者。
