Background: 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. Methods: 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. Results: 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. Conclusions: 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.

Autophagy is a crucial cellular process responsible for sustaining homeostasis through the degradation and recycling of proteins and organelles, providing energy during amino acid starvation and hypoxia. In cancer, autophagy can either inhibit tumor growth or support cancer cell survival. Our previous studies have shown that re-expression of the tumor suppressor gene DIRAS3 inhibits growth of ovarian cancer cells, promotes autophagic cell death in vitro, and induces tumor dormancy in vivo. Growth factors and extracellular matrix (ECM) components can, however, inhibit DIRAS3-induced autophagic cell death. This study explores whether fibronectin (FN) can counteract the growth inhibition induced by DIRAS3 in ovarian cancer cells. FN was found to inhibit DIRAS3-induced autophagy and to partially rescue ovarian cancer cells from DIRAS3-induced cell death while reducing DIRAS3-induced inhibition of p-FAK and p-AKT. Inhibiting FAK with defactinib in ovarian cancer cells enhanced DIRAS3-induced autophagy and cell death. Re-expression of DIRAS3 and treatment with defactinib produced tumor regression in xenograft models. Our findings suggest that ECM components in the tumor microenvironment like FN enhance the activities of β1 integrin, FAK, and AKT to inhibit DIRAS3-induced autophagic cell death, thereby promoting ovarian cancer cell survival.