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基于机器学习从STING通路挖掘前列腺癌骨转移相关关键基因。

Mining bone metastasis related key genes of prostate cancer from the STING pathway based on machine learning.

作者信息

Li Guiqiang, Zhao Runhan, Xie Zhou, Qu Xiao, Duan Yingtao, Zhu Yafei, Liang Hao, Tang Dagang, Li Zefang, He Weiyang

机构信息

Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Department of Urology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, China.

出版信息

Front Med (Lausanne). 2024 May 21;11:1372495. doi: 10.3389/fmed.2024.1372495. eCollection 2024.

Abstract

BACKGROUND

Prostate cancer (PCa) is the second most prevalent malignant tumor in male, and bone metastasis occurs in about 70% of patients with advanced disease. The STING pathway, an innate immune signaling mechanism, has been shown to play a key role in tumorigenesis, metastasis, and cancerous bone pain. Hence, exploring regulatory mechanism of STING in PCa bone metastasis will bring novel opportunities for treating PCa bone metastasis.

METHODS

First, key genes were screened from STING-related genes (SRGs) based on random forest algorithm and their predictive performance was evaluated. Subsequently, a comprehensive analysis of key genes was performed to explore their roles in prostate carcinogenesis, metastasis and tumor immunity. Next, cellular experiments were performed to verify the role of RELA in proliferation and migration in PCa cells, meanwhile, based on immunohistochemistry, we verified the difference of RELA expression between PCa primary foci and bone metastasis. Finally, based on the key genes to construct an accurate and reliable nomogram, and mined targeting drugs of key genes.

RESULTS

In this study, three key genes for bone metastasis were mined from SRGs based on the random forest algorithm. Evaluation analysis showed that the key genes had excellent prediction performance, and it also showed that the key genes played a key role in carcinogenesis, metastasis and tumor immunity in PCa by comprehensive analysis. In addition, cellular experiments and immunohistochemistry confirmed that overexpression of RELA significantly inhibited the proliferation and migration of PCa cells, and RELA was significantly low-expression in bone metastasis. Finally, the constructed nomogram showed excellent predictive performance in Receiver Operating Characteristic (ROC, AUC = 0.99) curve, calibration curve, and Decision Curve Analysis (DCA) curve; and the targeted drugs showed good molecular docking effects.

CONCLUSION

In sum, this study not only provides a new theoretical basis for the mechanism of PCa bone metastasis, but also provides novel therapeutic targets and novel diagnostic tools for advanced PCa treatment.

摘要

背景

前列腺癌(PCa)是男性中第二常见的恶性肿瘤,约70%的晚期患者会发生骨转移。STING通路作为一种天然免疫信号机制,已被证明在肿瘤发生、转移和癌性骨痛中起关键作用。因此,探索STING在PCa骨转移中的调控机制将为治疗PCa骨转移带来新的机遇。

方法

首先,基于随机森林算法从STING相关基因(SRGs)中筛选关键基因,并评估其预测性能。随后,对关键基因进行综合分析,以探索它们在前列腺癌发生、转移和肿瘤免疫中的作用。接下来,进行细胞实验以验证RELA在PCa细胞增殖和迁移中的作用,同时,基于免疫组织化学,我们验证了PCa原发灶和骨转移灶之间RELA表达的差异。最后,基于关键基因构建一个准确可靠的列线图,并挖掘关键基因的靶向药物。

结果

在本研究中,基于随机森林算法从SRGs中挖掘出三个骨转移关键基因。评估分析表明,关键基因具有优异的预测性能,综合分析还表明关键基因在PCa的发生、转移和肿瘤免疫中起关键作用。此外,细胞实验和免疫组织化学证实,RELA的过表达显著抑制了PCa细胞的增殖和迁移,并且RELA在骨转移中显著低表达。最后,构建的列线图在受试者工作特征(ROC,AUC = 0.99)曲线、校准曲线和决策曲线分析(DCA)曲线中显示出优异的预测性能;靶向药物显示出良好的分子对接效果。

结论

总之,本研究不仅为PCa骨转移机制提供了新的理论基础,也为晚期PCa治疗提供了新的治疗靶点和新的诊断工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/acb0/11148254/0ae6301e2419/fmed-11-1372495-g001.jpg

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