Development and validation of a tumor immune cell infiltration-related gene signature for recurrence prediction by weighted gene co-expression network analysis in prostate cancer

Frontiers in Genetics

Genetics

Enwei Wei¹  Lin-Ying Xie¹  Wenju Zhang¹  Lei Zeng¹  Chunfeng Gao¹  Miaomiao Yu¹  Chang Wang²  Yu-Lei Hao³  Han-Ying Huang⁴ 

[1] Bethune Institute of Epigenetic Medicine, The First Hospital of Jilin University, Changchun, Jilin, China;International Center of Future Science, Jillin University, Changchun, Jilin, China;Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, China;Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, Jilin, China;State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China;

关键词: prostate cancer;    tumor microenvironment;    tumor immune infiltrating cells;    prognosis prediction;    tumor mutation burden;    clinical therapy;    dendritic cells;   

DOI  :  10.3389/fgene.2023.1067172

 received in 2022-10-11, accepted in 2023-02-23,  发布年份 2023

来源: Frontiers

【 摘 要 】

Introduction: Prostate cancer (PCa) is the second most common malignancy in men. Despite multidisciplinary treatments, patients with PCa continue to experience poor prognoses and high rates of tumor recurrence. Recent studies have shown that tumor-infiltrating immune cells (TIICs) are associated with PCa tumorigenesis.Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to derive multi-omics data for prostate adenocarcinoma (PRAD) samples. The CIBERSORT algorithm was used to calculate the landscape of TIICs. Weighted gene co-expression network analysis (WGCNA) was performed to determine the candidate module most significantly associated with TIICs. LASSO Cox regression was applied to screen a minimal set of genes and construct a TIIC-related prognostic gene signature for PCa. Then, 78 PCa samples with CIBERSORT output p-values of less than 0.05 were selected for analysis. WGCNA identified 13 modules, and the MEblue module with the most significant enrichment result was selected. A total of 1143 candidate genes were cross-examined between the MEblue module and active dendritic cell-related genes.Results: According to LASSO Cox regression analysis, a risk model was constructed with six genes (STX4, UBE2S, EMC6, EMD, NUCB1 and GCAT), which exhibited strong correlations with clinicopathological variables, tumor microenvironment context, antitumor therapies, and tumor mutation burden (TMB) in TCGA-PRAD. Further validation showed that the UBE2S had the highest expression level among the six genes in five different PCa cell lines.Discussion: In conclusion, our risk-score model contributes to better predicting PCa patient prognosis and understanding the underlying mechanisms of immune responses and antitumor therapies in PCa.

【 授权许可】

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Copyright © 2023 Xie, Huang, Hao, Yu, Zhang, Wei, Gao, Wang and Zeng.