Using Immune-Related lncRNA Signature for Prognosis and Response to Immunotherapy in Cutaneous Melanoma

Ling Xue; Pingfan Wu; Xiaowen Zhao; Xiaojie Jin; Jingjing Wang; Yuxiang Shi; Xiaojing Yang; Yali She; Yaling Li; Changtian Li

doi:10.2147/IJGM.S335266

Using Immune-Related lncRNA Signature for Prognosis and Response to Immunotherapy in Cutaneous Melanoma

Int J Gen Med. 2021 Oct 8:14:6463-6475. doi: 10.2147/IJGM.S335266. eCollection 2021.

Authors

Ling Xue^{1

2}, Pingfan Wu^{1

2}, Xiaowen Zhao^{1

2}, Xiaojie Jin³, Jingjing Wang¹, Yuxiang Shi¹, Xiaojing Yang¹, Yali She³, Yaling Li^{1

3}, Changtian Li¹

Affiliations

¹ College of Basic Medicine, Gansu University of Chinese Medicine, Lanzhou, 730000, People's Republic of China.
² Department of Pathology, The 940th Hospital of the Joint Logistic Support of the People's Liberation Army, Lanzhou, 730050, People's Republic of China.
³ Provincial-Level Key Laboratory of Molecular Medicine of Major Diseases and Study on Prevention and Treatment of Traditional Chinese Medicine, Gansu University of Chinese Medicine, Lanzhou, Gansu, People's Republic of China.

Abstract

Background: Cutaneous melanoma is a highly malignant skin tumor, and most patients have a poor prognosis. In recent years, immunotherapy has assumed an important role in the treatment of advanced cutaneous melanoma, but only a small percentage of patients benefit from immunotherapy. A growing number of studies have demonstrated that the prognosis of patients with cutaneous melanoma is closely related to long non-coding RNA and the tumor immune microenvironment.

Methods: We downloaded RNA expression data and immune-related gene lists of cutaneous melanoma patients separately from The Cancer Genome Atlas database and ImmPort website and identified immune-related lncRNAs by co-expression analysis. The prognostic model was constructed by applying least absolute shrinkage and selection operator regression, and all patients were classified into high- and low-risk groups according to the risk score of the model. We evaluated the differences between the two groups in terms of survival outcomes, immune infiltration, pathway enrichment, chemotherapeutic drug sensitivity and immune checkpoint gene expression to verify the impact of lncRNA signature on clinical prognosis and immunotherapy efficacy.

Results: By correlation analysis and LASSO regression analysis, we constructed an immune-related lncRNA prognostic model based on five lncRNA: HLA-DQB1-AS1, MIR205HG, RP11-643G5.6, USP30-AS1 and RP11-415F23.4. Based on this model, we plotted Kaplan-Meier survival curves and time-dependent ROC curves and analyzed its ability as an independent prognostic factor for cutaneous melanoma in combination with clinicopathological features. The results showed that these lncRNA signature was an independent prognostic factor of cutaneous melanoma with favorable prognostic ability. Our results also show a higher degree of immune infiltration, higher expression of immune checkpoint-associated genes, and better outcome of immunotherapy in the low-risk group of the lncRNA signature.

Conclusion: The 5 immune-related lncRNA signatures constructed in our study can predict the prognosis of cutaneous melanoma and contribute to the selection of immunotherapy.

Keywords: GSEA; TCGA; immune checkpoint; long non-coding RNA; melanoma.

Grants and funding

Gansu Provincial Higher Education Innovation Fund (2021B-166). Longyuan Youth Innovation and Entrepreneurship Talent Project (2021-17-2). The Key Laboratory of Dunhuang Medicine and Transformation Constructed by Chinese Ministry of Education and Gansu Province (No. DHYX20-07). Natural Science Foundation of Gansu Province, China (20JR10RA312).