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Research Paper|Volume 15, Issue 9|pp 3442—3464

Identification and validation of immunogenic cell death-related score in uveal melanoma to improve prediction of prognosis and response to immunotherapy

Xiaoyan Li1,2, Jing Kang3, Jing Yue3, Dawei Xu2, Chunhua Liao4, Huina Zhang5, Jin Zhao5, Qiongwen Liu5, Jinke Jiao5, Lin Wang6, Guoyin Li5,7,8
  • 1Department of Central Laboratory, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi, China
  • 2Department of Blood Transfusion, Shanxi Provincial People’s Hospital, Taiyuan, Shanxi, China
  • 3Department of Clinical Laboratory, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
  • 4Department of Physiotherapy and Rehabilitation, The Second Affiliated Hospital of Air Force Military Medical University, Xi'an, Shaanxi, China
  • 5College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, Henan, China
  • 6Department of Geriatrics, Xijing Hospital, The Air Force Military Medical University, Xi'an, Shaanxi, China
  • 7Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, Xi’an, Shaanxi, China
  • 8Academy of Medical Science, Zhengzhou University, Zhengzhou, Henan, China
* Equal contribution
Received: February 13, 2023Accepted: April 17, 2023Published: May 3, 2023
https://doi.org/10.18632/aging.204680

Copyright: © 2023 Li 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

Background: Immunogenic cell death (ICD) could activate innate and adaptive immune response. In this work, we aimed to develop an ICD-related signature in uveal melanoma (UVM) patients and facilitate assessment of their prognosis and immunotherapy.

Methods: A set of machine learning methods, including non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithms were used to evaluate the infiltration of immune cells. The Genomics of Drug Sensitivity in Cancer (GDSC), cellMiner and tumor immune dysfunction and exclusion (TIDE) databases were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures was also compared.

Results: The ICDscore could predict the prognosis of UVM patients in both the training and four validating cohorts. The ICDscore outperformed 19 previously published signatures. Patients with high ICDscore exhibited a substantial increase in immune cell infiltration and expression of immune checkpoint inhibitor-related genes, leading to a higher response rate to immunotherapy. Furthermore, the downregulation of poly (ADP-ribose) polymerase family member 8 (PARP8), a critical gene involved in the development of the ICDscore, resulted in decreased cell proliferation and slower migration of UVM cells.

Conclusion: In conclusion, we developed a robust and powerful ICD-related signature for evaluating the prognosis and benefits of immunotherapy that could serve as a promising tool to guide decision-making and surveillance for UVM patients.