Int J Biol Sci 2021; 17(2):448-459. doi:10.7150/ijbs.51207

Research Paper

Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma

Jinhui Liu1*, Jie Mei2*, Yichun Wang3*, Xucheng Chen4, Jiadong Pan2, Laigen Tong5✉, Yan Zhang6✉

1. Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
2. Wuxi School of Clinical Medicine, Nanjing Medical University, Wuxi 214023, Jiangsu, China.
3. Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
4. College of Pharmacy, Nanjing Medical University, Nanjing 211166, Jiangsu, China.
5. Department of Hematology, Yixing People's Hospital, The Affiliated Hospital of Jiangsu University, Yixing 214200, Jiangsu, China.
6. Department of Gynecology and Obstetrics, Wuxi Maternal and Child Health Hospital, the Affiliated Hospital of Nanjing Medical University, Wuxi 214000, Jiangsu, China.
*These authors contributed equally to this work.

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Citation:
Liu J, Mei J, Wang Y, Chen X, Pan J, Tong L, Zhang Y. Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma. Int J Biol Sci 2021; 17(2):448-459. doi:10.7150/ijbs.51207. Available from https://www.ijbs.com/v17p0448.htm

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Abstract

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high‑ and low‑risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.

Keywords: immune-related lncRNA, endometrial carcinoma, signature, bioinformatics