An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer
IntroductionBreast cancer (BRCA) is the most common malignancy among women worldwide. It was widely accepted that autophagy and the tumor immune microenvironment play an important role in the biological process of BRCA. Long non-coding RNAs (lncRNAs), as vital regulatory molecules, are involved in t...
| Main Authors: | , , , , , , , , , , |
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Frontiers Media S.A.
2022-12-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.929240/full |
| _version_ | 1811177731007709184 |
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| author | Jiafeng Chen Jiafeng Chen Xinrong Li Xinrong Li Shuixin Yan Shuixin Yan Jiadi Li Jiadi Li Yuxin Zhou Yuxin Zhou Minhua Wu Jinhua Ding Jiahui Yang Yijie Yuan Ye Zhu Weizhu Wu |
| author_facet | Jiafeng Chen Jiafeng Chen Xinrong Li Xinrong Li Shuixin Yan Shuixin Yan Jiadi Li Jiadi Li Yuxin Zhou Yuxin Zhou Minhua Wu Jinhua Ding Jiahui Yang Yijie Yuan Ye Zhu Weizhu Wu |
| author_sort | Jiafeng Chen |
| collection | DOAJ |
| description | IntroductionBreast cancer (BRCA) is the most common malignancy among women worldwide. It was widely accepted that autophagy and the tumor immune microenvironment play an important role in the biological process of BRCA. Long non-coding RNAs (lncRNAs), as vital regulatory molecules, are involved in the occurrence and development of BRCA. The aim of this study was to assess the prognosis of BRCA by constructing an autophagy-related lncRNA (ARlncRNA) prognostic model and to provide individualized guidance for the treatment of BRCA.MethodsThe clinical data and transcriptome data of patients with BRCA were acquired from the Cancer Genome Atlas database (TCGA), and autophagy-related genes were obtained from the human autophagy database (HADb). ARlncRNAs were identified by conducting co‑expression analysis. Univariate and multivariate Cox regression analysis were performed to construct an ARlncRNA prognostic model. The prognostic model was evaluated by Kaplan–Meier survival analysis, plotting risk curve, Independent prognostic analysis, clinical correlation analysis and plotting ROC curves. Finally, the tumor immune microenvironment of the prognostic model was studied.Results10 ARlncRNAs(AC090912.1, LINC01871, AL358472.3, AL122010.1, SEMA3B-AS1, BAIAP2-DT, MAPT-AS1, DNAH10OS, AC015819.1, AC090198.1) were included in the model. Kaplan–Meier survival analysis of the prognostic model showed that the overall survival(OS) of the low-risk group was significantly better than that of the high-risk group (p< 0.001). Multivariate Cox regression analyses suggested that the prognostic model was an independent prognostic factor for BRCA (HR = 1.788, CI = 1.534–2.084, p < 0.001). ROCs of 1-, 3- and 5-year survival revealed that the AUC values of the prognostic model were all > 0.7, with values of 0.779, 0.746, and 0.731, respectively. In addition, Gene Set Enrichment Analysis (GSEA) suggested that several tumor-related pathways were enriched in the high-risk group, while several immune‑related pathways were enriched in the low-risk group. Patients in the low-risk group had higher immune scores and their immune cells and immune pathways were more active. Patients in the low-risk group had higher PD-1 and CTLA-4 levels and received more benefits from immune checkpoint inhibitors (ICIs) therapy.DiscussionThe ARlncRNA prognostic model showed good performance in predicting the prognosis of patients with BRCA and is of great significance to guide the individualized treatment of these patients. |
| first_indexed | 2024-04-11T06:07:22Z |
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| language | English |
| last_indexed | 2024-04-11T06:07:22Z |
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| series | Frontiers in Oncology |
| spelling | doaj.art-3c134895f8f244c1b6f2c5b8163e2a822022-12-22T04:41:28ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-12-011210.3389/fonc.2022.929240929240An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancerJiafeng Chen0Jiafeng Chen1Xinrong Li2Xinrong Li3Shuixin Yan4Shuixin Yan5Jiadi Li6Jiadi Li7Yuxin Zhou8Yuxin Zhou9Minhua Wu10Jinhua Ding11Jiahui Yang12Yijie Yuan13Ye Zhu14Weizhu Wu15Department of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaSchool of Medicine, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaSchool of Medicine, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaSchool of Medicine, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaSchool of Medicine, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaSchool of Medicine, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaDepartment of Thyroid and Breast surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, ChinaIntroductionBreast cancer (BRCA) is the most common malignancy among women worldwide. It was widely accepted that autophagy and the tumor immune microenvironment play an important role in the biological process of BRCA. Long non-coding RNAs (lncRNAs), as vital regulatory molecules, are involved in the occurrence and development of BRCA. The aim of this study was to assess the prognosis of BRCA by constructing an autophagy-related lncRNA (ARlncRNA) prognostic model and to provide individualized guidance for the treatment of BRCA.MethodsThe clinical data and transcriptome data of patients with BRCA were acquired from the Cancer Genome Atlas database (TCGA), and autophagy-related genes were obtained from the human autophagy database (HADb). ARlncRNAs were identified by conducting co‑expression analysis. Univariate and multivariate Cox regression analysis were performed to construct an ARlncRNA prognostic model. The prognostic model was evaluated by Kaplan–Meier survival analysis, plotting risk curve, Independent prognostic analysis, clinical correlation analysis and plotting ROC curves. Finally, the tumor immune microenvironment of the prognostic model was studied.Results10 ARlncRNAs(AC090912.1, LINC01871, AL358472.3, AL122010.1, SEMA3B-AS1, BAIAP2-DT, MAPT-AS1, DNAH10OS, AC015819.1, AC090198.1) were included in the model. Kaplan–Meier survival analysis of the prognostic model showed that the overall survival(OS) of the low-risk group was significantly better than that of the high-risk group (p< 0.001). Multivariate Cox regression analyses suggested that the prognostic model was an independent prognostic factor for BRCA (HR = 1.788, CI = 1.534–2.084, p < 0.001). ROCs of 1-, 3- and 5-year survival revealed that the AUC values of the prognostic model were all > 0.7, with values of 0.779, 0.746, and 0.731, respectively. In addition, Gene Set Enrichment Analysis (GSEA) suggested that several tumor-related pathways were enriched in the high-risk group, while several immune‑related pathways were enriched in the low-risk group. Patients in the low-risk group had higher immune scores and their immune cells and immune pathways were more active. Patients in the low-risk group had higher PD-1 and CTLA-4 levels and received more benefits from immune checkpoint inhibitors (ICIs) therapy.DiscussionThe ARlncRNA prognostic model showed good performance in predicting the prognosis of patients with BRCA and is of great significance to guide the individualized treatment of these patients.https://www.frontiersin.org/articles/10.3389/fonc.2022.929240/fullbreast cancerlong non-coding RNAstumor immune microenvironmentprognostic modelautophagysurvival |
| spellingShingle | Jiafeng Chen Jiafeng Chen Xinrong Li Xinrong Li Shuixin Yan Shuixin Yan Jiadi Li Jiadi Li Yuxin Zhou Yuxin Zhou Minhua Wu Jinhua Ding Jiahui Yang Yijie Yuan Ye Zhu Weizhu Wu An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer Frontiers in Oncology breast cancer long non-coding RNAs tumor immune microenvironment prognostic model autophagy survival |
| title | An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer |
| title_full | An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer |
| title_fullStr | An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer |
| title_full_unstemmed | An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer |
| title_short | An autophagy-related long non-coding RNA prognostic model and related immune research for female breast cancer |
| title_sort | autophagy related long non coding rna prognostic model and related immune research for female breast cancer |
| topic | breast cancer long non-coding RNAs tumor immune microenvironment prognostic model autophagy survival |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.929240/full |
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