4.6 Article

Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer

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FRONTIERS IN ONCOLOGY
卷 11, 期 -, 页码 -

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FRONTIERS MEDIA SA
DOI: 10.3389/fonc.2021.616133

关键词

ovarian cancer; prognostic risk signature; autophagy-related genes; tumor immune microenvironment; immunotherapy

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资金

  1. National Natural Science Foundation of China [81874137]
  2. Science and Technology Innovation Program of Hunan Province [2020RC4011]
  3. Outstanding Youth Foundation of Hunan Province [2018JJ1047]
  4. Hunan Province Science and Technology Talent Promotion Project [2019TJ-Q10]
  5. Young Scholars of Furong Scholar Program in Hunan Province
  6. Wisdom Accumulation and Talent Cultivation Project of the Third Xiangya Hospital of Central South University [BJ202001]
  7. Philosophy and Social Science Foundation Project of Hunan Province [19YBA349]
  8. Clinical Medical Technology Innovation Guidance Plan of Hunan Province [2020SK53607]

向作者/读者索取更多资源

This study investigated the relationship between autophagy and the tumor immune microenvironment in ovarian cancer. By clustering and analyzing levels of immune cell infiltration, two subclasses of OC were identified, with cluster A showing higher immune infiltration. The establishment of a risk model and evaluation of its efficiency revealed that patients with low-risk had better overall survival outcomes than high-risk patients. Additionally, potential targets ULK2 and GABARAPL1 were identified in OC for further validation in clinical samples.
Ovarian cancer (OC) is one of the most lethal gynecologic malignant tumors. The interaction between autophagy and the tumor immune microenvironment has clinical importance. Hence, it is necessary to explore reliable biomarkers associated with autophagy-related genes (ARGs) for risk stratification in OC. Here, we obtained ARGs from the MSigDB database and downloaded the expression profile of OC from TCGA database. The k-means unsupervised clustering method was used for clustering, and two subclasses of OC (cluster A and cluster B) were identified. SsGSEA method was used to quantify the levels of infiltration of 24 subtypes of immune cells. Metascape and GSEA were performed to reveal the differential gene enrichment in signaling pathways and cellular processes of the subtypes. We found that patients in cluster A were significantly associated with higher immune infiltration and immune-associated signaling pathways. Then, we established a risk model by LASSO Cox regression. ROC analysis and Kaplan-Meier analysis were applied for evaluating the efficiency of the risk signature, patients with low-risk got better outcomes than those with high-risk in overall survival. Finally, ULK2 and GABARAPL1 expression was further validated in clinical samples. In conclusion, Our study constructed an autophagy-related prognostic indicator, and identified two promising targets in OC.

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