Huang Qi, Huang Xu-Yun, Xue Yu-Ting, Wu Xiao-Hui, Wu Yu-Peng, Ke Zhi-Bin, Kang Zhen, Xu Yi-Cheng, Chen Dong-Ning, Wei Yong, Xue Xue-Yi, Huang Zhi-Yang, Xu Ning
Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People's Republic of China.
Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, 350212, People's Republic of China.
J Inflamm Res. 2024 Oct 3;17:7017-7036. doi: 10.2147/JIR.S461489. eCollection 2024.
This study aims to explore the role of cuproptosis-related genes in ACC, utilizing data from TCGA and GEO repositories, and to develop a predictive model for patient stratification.
A cohort of 123 ACC patients with survival data was analyzed. RNA-seq data of 17 CRGs were examined, and univariate Cox regression identified prognostic CRGs. A cuproptosis-related network was constructed to show interactions between CRGs. Consensus clustering classified ACC into three subtypes, with transcriptional and survival differences assessed by PCA and survival analysis. Gene set variation analysis (GSVA) and ssGSEA evaluated functional and immune infiltration characteristics across subtypes. Differentially expressed genes (DEGs) were identified, and gene clusters were established. A risk score (CRG_score) was generated using LASSO and multivariate Cox regression, validated across datasets. Tumor microenvironment, stem cell index, mutation status, drug sensitivity, and hormone synthesis were examined in relation to the CRG_score. Protein expression of key genes was validated, and functional studies on ASF1B and NDRG4 were performed.
Three ACC subtypes were identified with distinct survival outcomes. Subtype B showed the worst prognosis, while subtype C had the best. We identified 214 DEGs linked to cell proliferation and classified patients into three gene clusters, confirming their prognostic value. The CRG_score predicted patient outcomes, with high-risk patients demonstrating worse survival and possible resistance to immunotherapy. Drug sensitivity analysis suggested higher responsiveness to doxorubicin and etoposide in high-risk patients.
This study suggests the potential prognostic value of CRGs in ACC. The CRG_score model provides a robust tool for risk stratification, with implications for treatment strategies.
本研究旨在利用来自TCGA和GEO数据库的数据，探索铜死亡相关基因在肾上腺皮质癌（ACC）中的作用，并开发一种用于患者分层的预测模型。
分析了一组123例有生存数据的ACC患者。检测了17个铜死亡相关基因（CRGs）的RNA测序数据，单变量Cox回归确定了预后CRGs。构建了一个铜死亡相关网络以显示CRGs之间的相互作用。一致性聚类将ACC分为三个亚型，通过主成分分析（PCA）和生存分析评估转录和生存差异。基因集变异分析（GSVA）和单样本基因集富集分析（ssGSEA）评估了各亚型的功能和免疫浸润特征。鉴定了差异表达基因（DEGs），并建立了基因簇。使用套索回归和多变量Cox回归生成风险评分（CRG_score），并在多个数据集中进行验证。研究了肿瘤微环境、干细胞指数、突变状态、药物敏感性和激素合成与CRG_score的关系。验证了关键基因的蛋白表达，并对ASF1B和NDRG4进行了功能研究。
鉴定出三种ACC亚型，其生存结果不同。B亚型预后最差，而C亚型预后最好。我们鉴定了214个与细胞增殖相关的DEGs，并将患者分为三个基因簇，证实了它们的预后价值。CRG_score可预测患者预后，高危患者生存较差且可能对免疫治疗耐药。药物敏感性分析表明高危患者对阿霉素和依托泊苷的反应性更高。
本研究提示CRGs在ACC中具有潜在的预后价值。CRG_score模型为风险分层提供了一个强大的工具，对治疗策略具有重要意义。
