Research Paper Volume 12, Issue 2 pp 1446—1464

Immunological analyses reveal an immune subtype of uveal melanoma with a poor prognosis

Hui Pan1,2, *, , Linna Lu1,2, *, , Junqi Cui3, *, , Yuan Yang1,2, , Zhaoyang Wang1,2, , Xianqun Fan1,2, ,

  • 1 Department of Ophthalmology, Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
  • 2 Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology, Shanghai, China
  • 3 Department of Pathology, Ninth People’s Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China
* Co-first authors

Received: October 20, 2019       Accepted: December 25, 2019       Published: January 18, 2020      

https://doi.org/10.18632/aging.102693
How to Cite

Copyright © 2020 Pan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Uveal melanoma is an aggressive intraocular malignancy that often exhibits low immunogenicity. Metastatic uveal melanoma samples frequently exhibit monosomy 3 or BAP1 deficiency. In this study, we used bioinformatic methods to investigate the immune infiltration of uveal melanoma samples in public datasets. We first performed Gene Set Enrichment/Variation Analyses to detect immunological pathways that are altered in tumors with monosomy 3 or BAP1 deficiency. We then conducted an unsupervised clustering analysis to identify distinct immunologic molecular subtypes of uveal melanoma. We used CIBERSORT and ESTIMATE with RNA-seq data from The Cancer Genome Atlas and the GSE22138 microarray dataset to determine the sample-level immune subpopulations and immune scores of uveal melanoma samples. The Kaplan-Meier method and log-rank test were used to assess the prognostic value of particular immune cells and genes in uveal melanoma samples. Through these approaches, we discovered uveal melanoma-specific immunologic features, which may provide new insights into the tumor microenvironment and enhance the development of immunotherapies in the future.

Abbreviations

AJCC: American Joint Committee on Cancer stage; ANOVA: analysis of variance; CIBERSORT: Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts; CM: cutaneous melanoma; D3: Disomy3; ESTIMATE: Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data; FPKM: Fragments Per Kilobase of transcript per Million fragments mapped; GSEA/GSVA: Gene Set Enrichment/Variation Analysis; HR: hazard ratio; IR: Ionizing radiation; M3: Monosomy3; PFI: Progression-free interval; SCNA: somatic copy number alteration; TCGA: The Cancer Genome Atlas; UM: Uveal melanoma.