A prognostic signature consisting of metabolism-related genes and SLC17A4 serves as a potential biomarker of immunotherapeutic prediction in prostate cancer.
A prognostic signature consisting of metabolism-related genes and SLC17A4 serves as a potential biomarker of immunotherapeutic prediction in prostate cancer.
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由代谢相关基因和 SLC17A4 组成的预后特征可作为前列腺癌免疫治疗预测的潜在生物标志物
DOI：
10.3389/fimmu.2022.982628
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发表时间：
2022
影响因子：
7.3
通讯作者：
中科院分区：
文献类型：
--
作者：
前列腺癌（PCa）是全球男性中的一种常见恶性肿瘤，由于PCa患者的异质性，对可能从免疫/化疗中获益更多的患者进行筛查仍然不足且具有挑战性。因此，本研究旨在探索代谢（Meta）特征，并开发基于代谢的特征来预测PCa患者的预后和免疫/化疗反应。从2577个代谢相关基因中筛选出差异表达基因。单变量考克斯分析和随机森林算法用于特征筛选。进行多变量考克斯回归分析以构建基于代谢相关特征的所有组合的预后Meta模型。然后从预后、基因组变异、功能和免疫学角度以及化疗/免疫治疗反应等方面深入探讨MetaScore与肿瘤的相关性。应用多种算法来估计两个MeteScore组的免疫应答。使用PCa细胞进行进一步的体外功能实验以验证作为模型组分基因之一的中枢基因SLC 17 A4的表达与肿瘤进展之间的关联。采用GDSC数据库进行化疗药物敏感性测定。两个代谢相关簇在总生存期（OS）中表现出不同的特征。通过多变量考克斯回归分析中估计的回归系数（0.5154* GAS 2 + 0.395* SLC 17 A4 - 0.1211*NTM + 0.2939*GC）加权开发代谢模型。该Meta评分系统突出了代谢谱与PCa中的基因组改变、基因通路、功能注释和肿瘤微环境（包括基质、免疫细胞和免疫检查点）之间的关系。低MetaScore与增加的突变负荷和微卫星不稳定性相关，表明对免疫疗法的上级应答。确定了几种可能改善MetaScore组患者预后的药物。此外，我们的细胞实验表明，SLC 17 A4的敲低有助于抑制体外PCa细胞的侵袭、集落形成和增殖。我们的研究支持基于代谢的四基因签名作为预测PCa患者预后和化疗/免疫治疗反应的新型和稳健模型。代谢相关基因在前列腺癌发生和发展中的潜在机制进一步确定。
Prostate cancer (PCa), a prevalent malignant cancer in males worldwide, screening for patients might benefit more from immuno-/chemo-therapy remained inadequate and challenging due to the heterogeneity of PCa patients. Thus, the study aimed to explore the metabolic (Meta) characteristics and develop a metabolism-based signature to predict the prognosis and immuno-/chemo-therapy response for PCa patients. Differentially expressed genes were screened among 2577 metabolism-associated genes. Univariate Cox analysis and random forest algorithms was used for features screening. Multivariate Cox regression analysis was conducted to construct a prognostic Meta-model based on all combinations of metabolism-related features. Then the correlation between MetaScore and tumor was deeply explored from prognostic, genomic variant, functional and immunological perspectives, and chemo-/immuno-therapy response. Multiple algorithms were applied to estimate the immunotherapeutic responses of two MeteScore groups. Further in vitro functional experiments were performed using PCa cells to validate the association between the expression of hub gene SLC17A4 which is one of the model component genes and tumor progression. GDSC database was employed to determine the sensitivity of chemotherapy drugs. Two metabolism-related clusters presented different features in overall survival (OS). A metabolic model was developed weighted by the estimated regression coefficients in the multivariate Cox regression analysis (0.5154*GAS2 + 0.395*SLC17A4 - 0.1211*NTM + 0.2939*GC). This Meta-scoring system highlights the relationship between the metabolic profiles and genomic alterations, gene pathways, functional annotation, and tumor microenvironment including stromal, immune cells, and immune checkpoint in PCa. Low MetaScore is correlated with increased mutation burden and microsatellite instability, indicating a superior response to immunotherapy. Several medications that might improve patients` prognosis in the MetaScore group were identified. Additionally, our cellular experiments suggested knock-down of SLC17A4 contributes to inhibiting invasion, colony formation, and proliferation in PCa cells in vitro. Our study supports the metabolism-based four-gene signature as a novel and robust model for predicting prognosis, and chemo-/immuno-therapy response in PCa patients. The potential mechanisms for metabolism-associated genes in PCa oncogenesis and progression were further determined.
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作者：
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通讯作者：
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影响因子：
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作者：
Zou Z;Liu R;Liang Y;Zhou R;Dai Q;Han Z;Jiang M;Zhuo Y;Zhang Y;Feng Y;Zhu X;Cai S;Lin J;Tang Z;Zhong W;Liang Y
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