Construction and Verification of the Molecular Subtype and a Novel Prognostic Signature Based on Inflammatory Response-Related Genes in Uveal Melanoma

Author:

Zhang Feng1,Deng Yan2,Wang Dong3,Wang Shuai2

Affiliation:

1. Department of Ophthalmology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou 434020, China

2. Department of Hepatobiliary Surgery, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou 434020, China

3. Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, China

Abstract

The significance of inflammation in tumorigenesis and progression has become prominent. This study aimed to construct and validate the molecular subtype and a novel prognostic signature based on inflammatory response-related genes in uveal melanoma (UM). Patients from the TCGA, GSE84976, and GSE22138 UM cohorts were enrolled. According to the consensus cluster analysis, patients were divided into two molecular subtypes, namely IC1 and IC2. Survival curves showed that patients in IC1 had a better prognosis. The IC2 subgroup had higher levels of immune cell infiltration and more enriched immunological pathways. There were statistical differences in the immune-inflammation microenvironment, immune checkpoint genes expression, and drug sensitivity. The prognostic signature constructed based on inflammatory response-related genes exhibited a stable predictive power. Multivariate analysis confirmed that the signature was a prognostic factor independent of clinical characteristics. Functional analyses showed that the high-risk group was associated with immunological response, inflammatory cell activation, and tumor-related signal pathways. The riskscore had a negative relationship with tumor purity and was positively correlated with immune and stromal scores. Furthermore, the prognostic signature could sensitively predict the response to drug treatments. In conclusion, the prognostic signature might aid in stratifying patients at risk premised on the prognosis and immunotherapy sensitivity.

Funder

National Science Foundation of Shandong Province of China

Publisher

MDPI AG

Subject

General Medicine

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning Methods for Gene Selection in Uveal Melanoma;International Journal of Molecular Sciences;2024-02-01

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