Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs
BackgroundClear cell renal cell carcinoma (ccRCC) is the most frequently occurring malignant tumor within the kidney cancer subtype. It has low sensitivity to traditional radiotherapy and chemotherapy, the optimal treatment for localized ccRCC has been surgical resection, but even with complete rese...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-06-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1145450/full |
| _version_ | 1797808333371998208 |
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| author | Chao Hao Rumeng Li Zeguang Lu Kuang He Jiayun Shen Tengfei Wang Tingting Qiu |
| author_facet | Chao Hao Rumeng Li Zeguang Lu Kuang He Jiayun Shen Tengfei Wang Tingting Qiu |
| author_sort | Chao Hao |
| collection | DOAJ |
| description | BackgroundClear cell renal cell carcinoma (ccRCC) is the most frequently occurring malignant tumor within the kidney cancer subtype. It has low sensitivity to traditional radiotherapy and chemotherapy, the optimal treatment for localized ccRCC has been surgical resection, but even with complete resection the tumor will be eventually developed into metastatic disease in up to 40% of localized ccRCC. For this reason, it is crucial to find early diagnostic and treatment markers for ccRCC.MethodsWe obtained anoikis-related genes (ANRGs) integrated from Genecards and Harmonizome dataset. The anoikis-related risk model was constructed based on 12 anoikis-related lncRNAs (ARlncRNAs) and verified by principal component analysis (PCA), Receiver operating characteristic (ROC) curves, and T-distributed stochastic neighbor embedding (t-SNE), and the role of the risk score in ccRCC immune cell infiltration, immune checkpoint expression levels, and drug sensitivity was evaluated by various algorithms. Additionally, we divided patients based on ARlncRNAs into cold and hot tumor clusters using the ConsensusClusterPlus (CC) package.ResultsThe AUC of risk score was the highest among various factors, including age, gender, and stage, indicating that the model we built to predict survival was more accurate than the other clinical features. There was greater sensitivity to targeted drugs like Axitinib, Pazopanib, and Sunitinib in the high-risk group, as well as immunotherapy drugs. This shows that the risk-scoring model can accurately identify candidates for ccRCC immunotherapy and targeted therapy. Furthermore, our results suggest that cluster 1 is equivalent to hot tumors with enhanced sensitivity to immunotherapy drugs.ConclusionCollectively, we developed a risk score model based on 12 prognostic lncRNAs, expected to become a new tool for evaluating the prognosis of patients with ccRCC, providing different immunotherapy strategies by screening for hot and cold tumors. |
| first_indexed | 2024-03-13T06:36:00Z |
| format | Article |
| id | doaj.art-68059ba6c3f649e7bfb7ae0397bbbee9 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-13T06:36:00Z |
| publishDate | 2023-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-68059ba6c3f649e7bfb7ae0397bbbee92023-06-09T04:48:50ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-06-011410.3389/fimmu.2023.11454501145450Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAsChao Hao0Rumeng Li1Zeguang Lu2Kuang He3Jiayun Shen4Tengfei Wang5Tingting Qiu6Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Nanchang, ChinaDepartment of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaDepartment of Anesthesiology, Sun Yat-sen University Cancer Center/State Key Laboratory of Oncology in South China, Guangzhou, ChinaDepartment of Pathology, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, ChinaAfliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, ChinaDepartment of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, ChinaJiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Nanchang, ChinaBackgroundClear cell renal cell carcinoma (ccRCC) is the most frequently occurring malignant tumor within the kidney cancer subtype. It has low sensitivity to traditional radiotherapy and chemotherapy, the optimal treatment for localized ccRCC has been surgical resection, but even with complete resection the tumor will be eventually developed into metastatic disease in up to 40% of localized ccRCC. For this reason, it is crucial to find early diagnostic and treatment markers for ccRCC.MethodsWe obtained anoikis-related genes (ANRGs) integrated from Genecards and Harmonizome dataset. The anoikis-related risk model was constructed based on 12 anoikis-related lncRNAs (ARlncRNAs) and verified by principal component analysis (PCA), Receiver operating characteristic (ROC) curves, and T-distributed stochastic neighbor embedding (t-SNE), and the role of the risk score in ccRCC immune cell infiltration, immune checkpoint expression levels, and drug sensitivity was evaluated by various algorithms. Additionally, we divided patients based on ARlncRNAs into cold and hot tumor clusters using the ConsensusClusterPlus (CC) package.ResultsThe AUC of risk score was the highest among various factors, including age, gender, and stage, indicating that the model we built to predict survival was more accurate than the other clinical features. There was greater sensitivity to targeted drugs like Axitinib, Pazopanib, and Sunitinib in the high-risk group, as well as immunotherapy drugs. This shows that the risk-scoring model can accurately identify candidates for ccRCC immunotherapy and targeted therapy. Furthermore, our results suggest that cluster 1 is equivalent to hot tumors with enhanced sensitivity to immunotherapy drugs.ConclusionCollectively, we developed a risk score model based on 12 prognostic lncRNAs, expected to become a new tool for evaluating the prognosis of patients with ccRCC, providing different immunotherapy strategies by screening for hot and cold tumors.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1145450/fullclear cell renal cell carcinomaanoikisimmunotherapylncRNAhot and cold tumorsprognostic biomarkers |
| spellingShingle | Chao Hao Rumeng Li Zeguang Lu Kuang He Jiayun Shen Tengfei Wang Tingting Qiu Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs Frontiers in Immunology clear cell renal cell carcinoma anoikis immunotherapy lncRNA hot and cold tumors prognostic biomarkers |
| title | Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs |
| title_full | Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs |
| title_fullStr | Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs |
| title_full_unstemmed | Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs |
| title_short | Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs |
| title_sort | predicting prognosis immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis related lncrnas |
| topic | clear cell renal cell carcinoma anoikis immunotherapy lncRNA hot and cold tumors prognostic biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1145450/full |
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