Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2021.616133/full |
| _version_ | 1818734411548459008 |
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| author | Xingyu Chen Hua Lan Dong He Zhanwang Wang Runshi Xu Jing Yuan Mengqing Xiao Yao Zhang Lian Gong Songshu Xiao Ke Cao |
| author_facet | Xingyu Chen Hua Lan Dong He Zhanwang Wang Runshi Xu Jing Yuan Mengqing Xiao Yao Zhang Lian Gong Songshu Xiao Ke Cao |
| author_sort | Xingyu Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-18T00:04:56Z |
| format | Article |
| id | doaj.art-fabbdbfd2b7a442fa67e2a491710f6b9 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-12-18T00:04:56Z |
| publishDate | 2021-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-fabbdbfd2b7a442fa67e2a491710f6b92022-12-21T21:27:50ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2021-05-011110.3389/fonc.2021.616133616133Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian CancerXingyu Chen0Hua Lan1Dong He2Zhanwang Wang3Runshi Xu4Jing Yuan5Mengqing Xiao6Yao Zhang7Lian Gong8Songshu Xiao9Ke Cao10Department of Oncology, Third Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Obstetrics and Gynecology, Third Xiangya Hospital of Central South University, Changsha, ChinaThe Second People’s Hospital of Hunan Province, Hunan University of Chinese Medicine, Changsha, ChinaDepartment of Oncology, Third Xiangya Hospital of Central South University, Changsha, ChinaMedical School, Hunan University of Chinese Medicine, Changsha, ChinaDepartment of Obstetrics and Gynecology, Third Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Oncology, Third Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Oncology, Third Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Oncology, Third Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Obstetrics and Gynecology, Third Xiangya Hospital of Central South University, Changsha, ChinaDepartment of Oncology, Third Xiangya Hospital of Central South University, Changsha, ChinaOvarian 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.https://www.frontiersin.org/articles/10.3389/fonc.2021.616133/fullovarian cancerprognostic risk signatureautophagy-related genestumor immune microenvironmentimmunotherapy |
| spellingShingle | Xingyu Chen Hua Lan Dong He Zhanwang Wang Runshi Xu Jing Yuan Mengqing Xiao Yao Zhang Lian Gong Songshu Xiao Ke Cao Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer Frontiers in Oncology ovarian cancer prognostic risk signature autophagy-related genes tumor immune microenvironment immunotherapy |
| title | Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer |
| title_full | Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer |
| title_fullStr | Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer |
| title_full_unstemmed | Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer |
| title_short | Analysis of Autophagy-Related Signatures Identified Two Distinct Subtypes for Evaluating the Tumor Immune Microenvironment and Predicting Prognosis in Ovarian Cancer |
| title_sort | analysis of autophagy related signatures identified two distinct subtypes for evaluating the tumor immune microenvironment and predicting prognosis in ovarian cancer |
| topic | ovarian cancer prognostic risk signature autophagy-related genes tumor immune microenvironment immunotherapy |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2021.616133/full |
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