Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs

Chao Hao; Rumeng Li; Zeguang Lu; Kuang He; Jiayun Shen; Tengfei Wang; Tingting Qiu

doi:10.3389/fimmu.2023.1145450

Predicting prognosis, immunotherapy and distinguishing cold and hot tumors in clear cell renal cell carcinoma based on anoikis-related lncRNAs

Front Immunol. 2023 Jun 9:14:1145450. doi: 10.3389/fimmu.2023.1145450. eCollection 2023.

Authors

Chao Hao¹, Rumeng Li², Zeguang Lu³, Kuang He⁴, Jiayun Shen⁵, Tengfei Wang², Tingting Qiu¹

Affiliations

¹ Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Jiangxi Clinical Research Center for Cancer, Nanchang, China.
² Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China.
³ Department of Anesthesiology, Sun Yat-sen University Cancer Center/State Key Laboratory of Oncology in South China, Guangzhou, China.
⁴ Department of Pathology, Dushu Lake Hospital Affiliated of Soochow University, Suzhou, China.
⁵ Afliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China.

Abstract

Background: Clear 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.

Methods: We 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.

Results: The 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.

Conclusion: Collectively, 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.

Keywords: anoikis; clear cell renal cell carcinoma; hot and cold tumors; immunotherapy; lncRNA; prognostic biomarkers.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Anoikis / genetics
Carcinoma*
Carcinoma, Renal Cell* / diagnosis
Carcinoma, Renal Cell* / genetics
Carcinoma, Renal Cell* / therapy
Humans
Immunotherapy
Kidney Neoplasms* / diagnosis
Kidney Neoplasms* / genetics
Kidney Neoplasms* / therapy
Prognosis
RNA, Long Noncoding* / genetics

Substances

RNA, Long Noncoding

Grants and funding

This work was supported by the Jiangxi Provincial Health Commission Science and Technology Plan (grant number 202211019).