Shen Dongsheng, Wu Chenghao, Chen Meiyi, Zhou Zixuan, Li Huaifang, Tong Xiaowen, Chen Zhenghu, Guo Yi
Department of Obstetrics and Gynecology, Shanghai Tongji Hospital, School of Medicine, Tongji University, 200120, PR China; Department of Obstetrics and Gynecology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200065, PR China.
Department of Obstetrics and Gynecology, International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200030, PR China.
Cancer Genet. 2025 Apr;292-293:1-13. doi: 10.1016/j.cancergen.2024.12.005. Epub 2024 Dec 29.
Mitochondrial dysregulation contributes to the chemoresistance of multiple cancer types. Yet, the functions of mitochondrial dysregulation in Ovarian serous cystadenocarcinoma (OSC) remain largely unknown.
We sought to investigate the function of mitochondrial dysregulation in OSC from the bioinformatics perspective. We aimed to establish a model for prognosis prediction and chemosensitivity evaluation of the OSC patients by targeting mitochondrial dysregulation.
Differentially expressed genes (DEGs) were screened from the Cancer Genome Atlas (TCGA)-OV dataset and the mitochondrial-related DEGs were identified from the Human MitoCarta 3.0 database. Prognosis-related mitochondria-related genes (MRGs) were screened to establish the MRGs-based risk score model for prognosis prediction. To validate the risk score model, the risk score model was then evaluated by IHC staining intensity and survival curves from clinical specimens of OSC patients. Migration and proliferation assays were performed to elucidate the role of carcinogenic gene ACSS3 in serous ovarian cancer cell lines.
Using consensus clustering algorithm, we identified 341 MRGs and two subtypes of OSC patients. Moreover, we established a novel prognostic risk score model by combining the transcription level, intensity and extent scores of MRGs for prognosis prediction purpose. The model was established using 7 MRGs (ACOT13, ACSS3, COA6, HINT2, MRPL14, NDUFC2, and NDUFV2) significantly correlated to the prognosis of OSC. Importantly, by performing the drug sensitivity analysis, we found that the OSC patients in the low-risk group were more sensitive to cisplatin, paclitaxel and docetaxel than those in the high-risk group, while the latter ones were more sensitive to VEGFR inhibitor Axitinib and BRAF inhibitors Vemurafenib and SB590885. In addition, patients in the low-risk group were predicted to have better response in anti-PD-1 immunotherapy than those in the high-risk group. The risk score model was then validated by survival curves of high-risk and low-risk groups determined by IHC staining scores of OSC clinical samples. The carcinogenic effect of ACSS3 in OSC was confirmed through the knockdown of ACSS3 in SKOV3 and HO-8910 cells.
To summarize, we established a novel 7 MRGs - based risk score model that could be utilized for prognosis prediction and chemosensitivity assessment in OSC patients.
线粒体功能失调与多种癌症类型的化疗耐药性有关。然而，线粒体功能失调在卵巢浆液性囊腺癌（OSC）中的作用仍 largely 未知。
我们试图从生物信息学角度研究线粒体功能失调在 OSC 中的作用。我们旨在通过针对线粒体功能失调建立一个用于 OSC 患者预后预测和化疗敏感性评估的模型。
从癌症基因组图谱（TCGA）-OV 数据集中筛选差异表达基因（DEGs），并从人类线粒体基因图谱 3.0 数据库中鉴定线粒体相关的 DEGs。筛选与预后相关的线粒体相关基因（MRGs）以建立基于 MRGs 的风险评分模型用于预后预测。为验证风险评分模型，随后通过 OSC 患者临床标本的免疫组化染色强度和生存曲线对风险评分模型进行评估。进行迁移和增殖实验以阐明致癌基因 ACSS3 在浆液性卵巢癌细胞系中的作用。
使用一致性聚类算法，我们鉴定出 341 个 MRGs 和 OSC 患者的两种亚型。此外，我们通过结合 MRGs 的转录水平、强度和范围评分建立了一种用于预后预测的新型预后风险评分模型。该模型使用与 OSC 预后显著相关的 7 个 MRGs（ACOT13、ACSS3、COA6、HINT2、MRPL14、NDUFC2 和 NDUFV2）建立。重要的是，通过进行药物敏感性分析，我们发现低风险组的 OSC 患者比高风险组的患者对顺铂、紫杉醇和多西他赛更敏感，而后者对 VEGFR 抑制剂阿西替尼以及 BRAF 抑制剂维莫非尼和 SB590885 更敏感。此外，预测低风险组患者在抗 PD-1 免疫治疗中的反应比高风险组患者更好。然后通过 OSC 临床样本的免疫组化染色评分确定的高风险和低风险组的生存曲线对风险评分模型进行验证。通过在 SKOV3 和 HO-8910 细胞中敲低 ACSS3 证实了 ACSS3 在 OSC 中的致癌作用。
总之，我们建立了一种新型的基于 7 个 MRGs 的风险评分模型，可用于 OSC 患者的预后预测和化疗敏感性评估。
