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《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:

31卷

期数:

2025年06期

页码:

497-504

栏目:

肿瘤疾病专题

出版日期:

2025-11-20

文章信息/Info

Title:

Construction of ovarian cancer prognosis prediction model based on lipid metabolism genes and study on the mechanism of lipid metabolism affecting ovarian cancer progression

文章编号:

1006-8147(2025)06-0497-08

作者:

孙 笑; 李咏梅

（天津医科大学基础医学院病原生物学系，天津 300070）

Author(s):

SUN Xiao;  LI Yongmei

（Department of Pathogen Biology， School of Basic Medical Science， Tianjin Medical University， Tianjin 300070， China）

关键词:

卵巢癌; 脂代谢; 预测模型; 免疫浸润

Keywords:

ovarian cancer;  lipid metabolism;  prediction model;  immune infiltration

分类号:

R737.31

DOI:

10.20135/j.issn.1006-8147.2025.06.0497

文献标志码:

A

摘要:

目的：构建基于脂代谢基因的卵巢癌（OV）预后预测模型，并初步探究脂代谢对OV进展的影响机制。方法：利用Lasso回归和单因素Cox分析构建模型。利用独立预后分析评价、受试者工作特征（ROC）曲线、5折交叉验证、列线图（nomogram）分析评价模型的预后预测能力。利用免疫浸润和免疫功能分析、差异基因分析、蛋白质相互作用（PPI）分析、GO功能富集分析探究脂代谢对OV进展的影响机制。利用Kaplan-Meier生存曲线和免疫浸润分析探究关键基因对OV患者预后和免疫浸润的影响，RT-qPCR检测其在卵巢正常上皮细胞系和癌细胞系中的表达差异。结果：使用与OV预后相关的 2 个脂代谢差异基因ELOVL3和UROD构建预后预测模型，低风险组OV患者的总生存期更长（P=0.003）。独立预后分析结果表明，该模型的风险评分可独立于其他临床性状预测OV患者生存（P<0.001）。1 年的曲线下面积（AUC）大于 0.5，3 年和 5 年的AUC均大于 0.6，该模型具有良好的敏感性和特异性。5折交叉验证结果显示每1折AUC值均在 0.5 以上，预后预测模型整体性能良好。列线图模型进一步展现了该模型较好的预测性能。脂代谢高风险组中OV患者的主要组织相容性复合物（MHC）-Ⅰ类分子评分升高，γ干扰素反应评分降低（P<0.05）。PPI分析发现ZIC2为核心作用蛋白，ZIC5与ZIC2有较强的联系。GO分析表明骨形态发生蛋白（BMP）信号通路与脂代谢有关。ELOVL3、ZIC5和ZIC2可促进OV患者不良预后，并且与免疫浸润有关，在癌细胞系中呈高表达（F=87.77、440.3、14.01，均P<0.05）。结论：构建了良好的基于脂代谢基因的OV预后预测模型，脂代谢可能通过调节免疫浸润、BMP信号通路影响OV患者预后，脂代谢基因ELOVL3、ZIC5和ZIC2是OV潜在的促癌基因。

Abstract:

Objective： To construct a prognostic prediction model for ovarian cancer （OV） based on lipid metabolism genes and explore the mechanism of lipid metabolism affecting OV progression. Methods： A model was constructed using Lasso regression and univariate Cox analysis. The predictive ability of the model was evaluated using independent prognostic analysis， receiver operating characteristic （ROC） curve， 5-fold cross validation and nomogram analysis. Immune infiltration and immune function analysis， differential gene analysis， protein-protein interaction （PPI） analysis， and GO functional enrichment analysis were used to explore the mechanism of lipid metabolism′s impact on OV progression. Kaplan Meier survival curve analysis and immune infiltration analysis were used to explore the impact of key genes on the prognosis and immune infiltration of OV patients， and RT-qPCR was used to detect their differential expression in normal ovarian epithelial cell lines and cancer cell lines. Results： Using two lipid metabolism related differential expressed genes ELOVL3 and UROD associated with OV prognosis， a prognostic prediction model was constructed. The low-risk group of OV patients had a longer overall survival period （P=0.003）. Independent prognostic analysis showed that the risk score of this model could predict the survival of OV patients independently of other clinical traits （P<0.001）. The area under the curve （AUC） of 1 year was greater than 0.5， and the AUC of 3 and 5 years were both greater than 0.6， indicating that the model has good sensitivity and specificity. The 5-fold cross validation results showed that the AUC value for each 1-fold was above 0.5， indicating that the overall performance of the prognosis prediction model was good. The nomogram model further demonstrated the good predictive performance of the prediction model. In the high-risk group of lipid metabolism， the major histocompatibility complex （MHC） -Ⅰ molecular scores increased and interferon gamma （INF-γ） response scores decreased （P<0.05） in OV patients. PPI analysis revealed that ZIC2 was the core functional protein， and there was a strong correlation between ZIC5 and ZIC2. GO analysis indicated that the bone morphogenetic protein （BMP） signaling pathway was associated with lipid metabolism. ELOVL3， ZIC5， and ZIC2 promoted poor prognosis in OV patients and was associated with immune infiltration， showing high expression in cancer cell lines （F=87.77， 440.3， 14.01， all P<0.05）. Conclusion： A good prognostic prediction model for OV based on lipid metabolism genes has been constructed. Lipid metabolism may affect the prognosis of OV patients through regulating the immune infiltration and BMP signaling pathways. Lipid metabolism genes ELOVL3， ZIC5， and ZIC2 are potential oncogenes for OV.

参考文献/References:

[1] SPRIGGS D R， ZIVANOVIC O. Ovarian cancer treatment-are we getting warmer?[J]. N Engl J Med， 2018， 378（3）： 293-294.
[2] CORTEZ A J， TUDREJ P， KUJAWA K A， et al. Advances in ovarian cancer therapy[J]. Cancer Chemother Pharmacol， 2018， 81（1）： 17-38.
[3] SIDERIS M， MENON U， MANCHANDA R. Screening and prevention of ovarian cancer[J]. Med J Aust， 2024， 220（5）： 264-274.
[4] BIAN X， LIU R， MENG Y， et al. Lipid metabolism and cancer[J]. J Exp Med， 2021， 218（1）：e20201606.
[5] KOUNDOUROS N， POULOGIANNIS G. Reprogramming of fatty acid metabolism in cancer[J]. Br J Cancer， 2020， 122（1）： 4-22.
[6] NIEMAN K M， KENNY H A， PENICKA C V， et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth[J]. Nat Med， 2011， 17（11）： 1498-1503.
[7] YANG S， JI J， WANG M， et al. Construction of ovarian cancer prognostic model based on the investigation of ferroptosis-related lncrna[J]. Biomolecules， 2023， 13（2）：306.
[8] JIANG J， CHEN Z， WANG H， et al. Screening and identification of a prognostic model of ovarian cancer by combination of transcriptomic and proteomic data[J]. Biomolecules， 2023， 13（4）：685.
[9] ZHANG M， ZHUANG G， SUN X， et al. Risk prediction model for epithelial ovarian cancer using molecular markers and clinical characteristics[J]. J Ovarian Res， 2015， 8：67.
[10] JIANG C， LIU Y， WEN S， et al. In silico development and clinical validation of novel 8 gene signature based on lipid metabolism related genes in colon adenocarcinoma[J]. Pharmacol Res， 2021， 169：105644.
[11] GUILLOU H， ZADRAVEC D， MARTIN P G， et al. The key roles of elongases and desaturases in mammalian fatty acid metabolism： insights from transgenic mice[J]. Prog Lipid Res， 2010， 49（2）： 186-199.
[12] XU C， WANG G， HAO Y， et al. Correlation analysis between gene expression profile of rat liver tissues and high-fat emulsion-induced nonalcoholic fatty liver[J]. Dig Dis Sci， 2011， 56（8）： 2299-2308.
[13] KANG I， KIM Y， TOMáS-BARBER?魣N F A， et al. Urolithin a， c， and d， but not iso-urolithin a and urolithin b， attenuate triglyceride accumulation in human cultures of adipocytes and hepatocytes[J]. Mol Nutr Food Res， 2016， 60（5）： 1129-1138.
[14] TSOU Y A， CHEN K C， CHANG S S， et al. A possible strategy against head and neck cancer： in silico investigation of three-in-one inhibitors[J]. J Biomol Struct Dyn， 2013， 31（12）： 1358-1369.
[15] MERINO M， KASAMON Y， THEORET M， et al. Irreconcilable differences： the divorce between response rates， progression-free survival， and overall survival[J]. J Clin Oncol， 2023， 41（15）： 2706-2712.
[16] WU X， LI T， JIANG R， et al. Targeting mhc-i molecules for cancer： function， mechanism， and therapeutic prospects[J]. Mol Cancer， 2023， 22（1）： 194.
[17] ST PAUL M， OHASHI P S. The roles of CD8（+） T cell subsets in antitumor immunity[J]. Trends Cell Biol， 2020， 30（9）： 695-704.
[18] SHEU B C， LIN R H， LIEN H C， et al. Predominant Th2/Tc2 polarity of tumor-infiltrating lymphocytes in human cervical cancer[J]. J Immunol， 2001， 167（5）： 2972-2978.
[19] SHIN D S， ZARETSKY J M， ESCUIN-ORDINAS H， et al. Primary resistance to PD-1 blockade mediated by JAK1/2 mutations[J]. Cancer Discov， 2017， 7（2）： 188-201.
[20] XU Z， ZHENG J， CHEN Z， et al. Multilevel regulation of wnt signaling by zic2 in colon cancer due to mutation of β-catenin[J]. Cell Death Dis， 2021， 12（6）： 584.
[21] LIU A， XIE H， LI R， et al. Silencing zic2 abrogates tumorigenesis and anoikis resistance of non-small cell lung cancer cells by inhibiting Src/FAK signaling[J]. Mol Ther Oncolytics， 2021， 22：195-208.
[22] INAGUMA S， ITO H， RIKU M， et al. Addiction of pancreatic cancer cells to zinc-finger transcription factor zic2[J]. Oncotarget， 2015， 6（29）： 28257-28268.
[23] LIU Q， YANG T， ZHANG Y， et al. Zic2 induces pro-tumor macr-ophage polarization in nasopharyngeal carcinoma by activating the JUNB/MCSF axis[J]. Cell Death Dis， 2023， 14（7）： 455.
[24] ZHONG Y， YANG S， WANG X， et al. Research progress of zic5 for tumor metastasis[J]. Biochem Soc Trans， 2024， 52（3）： 1363-1372.
[25] CRISTANCHO A G， LAZAR M A. Forming functional fat： a growing understanding of adipocyte differentiation[J]. Nat Rev Mol Cell Biol， 2011， 12（11）： 722-734.
[26] TANG Q Q， LANE M D. Adipogenesis： from stem cell to adipocyte[J]. Annu Rev Biochem， 2012， 81：715-736.
[27] GUSTAFSON B， HAMMARSTEDT A， HEDJAZIFAR S， et al. BMP4 and BMP antagonists regulate human white and beige adipogenesis[J]. Diabetes， 2015， 64（5）： 1670-1681.
[28] HERDENBERG C， MUTIE P M， BILLING O， et al. Lrig proteins regulate lipid metabolism via bmp signaling and affect the risk of type 2 diabetes[J]. Commun Biol， 2021， 4（1）： 90.
[29] CLARK J F， CICCARELLI E J， KAYASTHA P， et al. BMP pathway regulation of insulin signaling components promotes lipid storage in caenorhabditis elegans[J]. PLoS Genet， 2021， 17（10）： e1009836.
[30] PENG J， YOSHIOKA Y， MANDAI M， et al. The BMP signaling pathway leads to enhanced proliferation in serous ovarian cancer-a potential therapeutic target[J]. Mol Carcinog， 2016， 55（4）： 335-345.
[31] FUKUDA T， FUKUDA R， TANABE R， et al. BMP signaling is a therapeutic target in ovarian cancer[J]. Cell Death Discov， 2020， 6（1）： 139.

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备注/Memo

备注/Memo:

基金项目 国家自然科学基金项目（82373076）
作者简介 孙笑（1999-），女，硕士在读，研究方向：病原生物学；通信作者：李咏梅，E-mail：liym@tmu.edu.cn。

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更新日期/Last Update: 2025-11-20