SUMOylation patterns and signature characterize the tumor microenvironment and predict prognosis in lung adenocarcinoma

Zhike Chen; Jian Yang; Lijuan Tang; Xue Sun; Yu Li; Ziqing Sheng; Hao Ding; Chun Xu; Xin Tong; Jun Zhao

doi:10.3389/fcell.2023.1094588

SUMOylation patterns and signature characterize the tumor microenvironment and predict prognosis in lung adenocarcinoma

Front Cell Dev Biol. 2023 Apr 13:11:1094588. doi: 10.3389/fcell.2023.1094588. eCollection 2023.

Authors

Zhike Chen^{1

2}, Jian Yang^{1

2}, Lijuan Tang^{3

4}, Xue Sun⁵, Yu Li^{1

2}, Ziqing Sheng^{1

2}, Hao Ding^{1

2}, Chun Xu^{1

2}, Xin Tong^{1

2}, Jun Zhao^{1

2}

Affiliations

¹ Institute of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
² Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China.
³ Dalian Medical University, Dalian, China.
⁴ Department of Pathology, Affiliated Hospital of Nantong University, Nantong, China.
⁵ Department of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.

Abstract

Background: Recent studies have revealed that SUMOylation modifications are involved in various biological processes, including cancer development and progression. However, the precise role of SUMOylation in lung adenocarcinoma (LUAD), especially in the tumor immune microenvironment, is not yet clear. Methods: We identified SUMOylation patterns by unsupervised consensus clustering based on the expression of SUMOylation regulatory genes. The tumor microenvironment in lung adenocarcinoma was analyzed using algorithms such as GSVA and ssGSEA. Key genes of SUMOylation patterns were screened for developing a SUMOylation scoring model to assess immunotherapy and chemotherapy responses in lung adenocarcinoma patients. Experiments were conducted to validate the differential expression of model genes in lung adenocarcinoma. Finally, we constructed a nomogram based on the SUMOylation score to assess the prognosis of individual lung adenocarcinoma patients. Results: Two patterns of SUMOylation were identified, namely, SUMO-C1, which showed anti-tumor immune phenotype, and SUMO-C2, which showed immunosuppressive phenotype. Different genomic subtypes were also identified; subtype gene-T1 exhibited a reciprocal restriction between the immune microenvironment and stromal microenvironment. High SUMOylation scores were indicative of poor lung adenocarcinoma prognosis. SUMOylation score was remarkably negatively correlated with the infiltration of anti-tumor immune cells, and significantly positively correlated with immune cells promoting immune escape and immune suppression. In addition, patients with low scores responded better to immunotherapy. Therefore, the developed nomogram has a high prognostic predictive value. Conclusion: The SUMOylation patterns can well discriminate the tumor microenvironment features of lung adenocarcinoma, especially the immune cell infiltration status. The SUMOylation score can further assess the relationship between SUMOylation and immune cell crosstalk and has significant prognostic value and can be used to predict immunotherapy and chemotherapy response in patients with lung adenocarcinoma.

Keywords: SUMOylation; immune therapy; lung adenocarcinoma; prognosis; tumor microenvironment.

Grants and funding

This work was supported by the grants from Natural Science Foundation of Jiangsu province: BK20220250; Suzhou science and Technology Bureau (LCZX2019002).