鉴定和验证与顺铂耐药性卵巢癌预后相关的基因。

Identification and validation of genes associated with prognosis of cisplatin-resistant ovarian cancer.

机构信息

Department of Obstetrics and Gynecology, The First Hospital of Lanzhou University, Lanzhou, China.

The First Clinical Medical College, Lanzhou University, Lanzhou, China.

出版信息

BMC Cancer. 2024 Aug 5;24(1):508. doi: 10.1186/s12885-024-12264-z.

DOI:10.1186/s12885-024-12264-z

PMID:39103807

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11302001/

Abstract

PURPOSE

To investigate the role of prognostic genes related to cisplatin resistance in ovarian cancer during disease progression.

METHOD

The gene expression profile of the NCI-60 cell line was acquired through comprehensive analysis of the GEO database accession GSE116439. We performed a thorough analysis of gene expression differences in samples from seven individuals exposed to cisplatin concentrations of 0 nM compared to seven samples exposed to 15000 nM over a 24-h period. Key genes were initially identified through LASSO regression, followed by their enrichment through differential gene function analysis (GO) and pathway enrichment analysis (KEGG). Subsequently, a prognostic risk model was established for these key genes. The prognostic model's performance was assessed through K-M survival curves and ROC curves. To examine the variance in immune cell infiltration between the high and low-risk groups, CIBERSORTx analysis was employed. Finally, validation of prognostic gene expression in cisplatin-resistant ovarian cancer was carried out using clinical samples, employing RT-qPCR and Western Blot techniques.

RESULTS

A total of 132 differential genes were found between cisplatin resistance and control group, and 8 key prognostic genes were selected by analysis, namely VPS13B, PLGRKT, CDKAL1, TBC1D22A, TAP1, PPP3CA, CUX1 and PPP1R15A. The efficacy of the risk assessment model derived from prognostic biomarkers, as indicated by favorable performance on both Kaplan-Meier survival curves and ROC curves. Significant variations in the abundance of Macrophages M1, T cells CD4 memory resting, T cells follicular helper, and T cells gamma delta were observed between the high and low-risk groups. To further validate our findings, RT-qPCR and Western Blot analyses were employed, confirming differential expression of the identified eight key genes between the two groups.

CONCLUSION

VPS13B, TBC1D22A, PPP3CA, CUX1 and PPP1R15A were identified as poor prognostic genes of cisplatin resistance in ovarian cancer, while PLGRKT, CDKAL1 and TAP1 were identified as good prognostic genes. This offers a novel perspective for future advancements in ovarian cancer treatment, suggesting potential avenues for the development of new therapeutic targets.

摘要

目的

探讨顺铂耐药相关预后基因在卵巢癌疾病进展过程中的作用。

方法

通过综合分析 GEO 数据库中的 GSE116439 号数据集，获得 NCI-60 细胞系的基因表达谱。我们对 7 名个体在暴露于 0 nM 顺铂浓度和 7 名个体在暴露于 15000 nM 顺铂浓度的情况下 24 小时内的样本进行了基因表达差异的深入分析。通过 LASSO 回归初步确定关键基因，然后通过差异基因功能分析（GO）和途径富集分析（KEGG）进行富集。随后，为这些关键基因建立了预后风险模型。通过 K-M 生存曲线和 ROC 曲线评估了预后模型的性能。为了检查高风险组和低风险组之间免疫细胞浸润的差异，我们使用 CIBERSORTx 分析。最后，使用 RT-qPCR 和 Western Blot 技术在临床样本中验证顺铂耐药卵巢癌中预后基因的表达。

结果

在顺铂耐药组和对照组之间共发现 132 个差异基因，通过分析选择了 8 个关键预后基因，分别是 VPS13B、PLGRKT、CDKAL1、TBC1D22A、TAP1、PPP3CA、CUX1 和 PPP1R15A。基于预后生物标志物的风险评估模型的疗效通过 Kaplan-Meier 生存曲线和 ROC 曲线均得到了很好的体现。在高风险组和低风险组之间，巨噬细胞 M1、T 细胞 CD4 记忆静息、滤泡辅助 T 细胞和 T 细胞 γδ的丰度存在显著差异。为了进一步验证我们的发现，我们使用 RT-qPCR 和 Western Blot 分析，证实了两组之间这 8 个关键基因的差异表达。