基于尼古丁代谢基因特征的乳腺癌预后模型

doi:10.19586/j.2095-2341.2025.0013

生物技术进展 ›› 2025, Vol. 15 ›› Issue (4): 735-742.DOI: 10.19586/j.2095-2341.2025.0013

• 研究论文 • 上一篇

基于尼古丁代谢基因特征的乳腺癌预后模型

张黄子怡(), 黄莉莎, 李燕奇, 熊晨露, 于瀛, 谢飞()

北京工业大学化学与生命科学学院，北京 100124

收稿日期:2025-02-11 接受日期:2025-06-04 出版日期:2025-07-25 发布日期:2025-09-08
通讯作者: 谢飞
作者简介:张黄子怡 E-mail: zhzy1129@email.bjut.edu.cn；
基金资助:
军委后勤保障部开放研究重点项目(BHJ17L08)

Construction of a Breast Cancer Prognostic Model Based on Nicotine Metabolism Gene Signatures

Ziyi ZHANG-HUANG(), Lisha HUANG, Yanqi LI, Chenlu XIONG, Ying YU, Fei XIE()

College of Chemistry and Life Sciences，Beijing University of Technology，Beijing 100124，China

Received:2025-02-11 Accepted:2025-06-04 Online:2025-07-25 Published:2025-09-08
Contact: Fei XIE

摘要/Abstract

摘要：

乳腺癌作为全球女性发病率最高的恶性肿瘤，其预后存在显著异质性。基于癌症基因组图谱(the cancer genome atlas，TCGA)数据库，对乳腺癌患者的转录组数据进行差异分析和相关性分析，筛选出尼古丁代谢相关的差异表达基因。采用单因素Cox回归分析和Lasso-Cox回归分析方法构建预后模型，并通过Kaplan-Meier生存曲线、ROC曲线、校准曲线及包含临床因素的列线图进行评估。使用基因表达总库(gene expressi on omnibus，GEO)中的GSE42568数据集作为外部验证集进行模型验证，并进一步开展免疫浸润分析和药物敏感性分析。结果发现，模型由10个尼古丁代谢相关基因组成，可作为乳腺癌的独立预后因素，并且高低风险组在免疫细胞浸润及药物敏感性上具有显著差异。构建的风险预后模型能够独立预测乳腺癌的预后情况，可为乳腺癌预后评估和个体化治疗提供新的分子标志物和潜在治疗靶点。

关键词: 乳腺癌, 尼古丁代谢, 预后模型

Abstract:

Breast cancer， as the most prevalent malignancy among women globally， exhibits significant prognostic heterogeneity. Transcriptomic data from breast cancer patients in the cancer genome atlas （TCGA） database were analyzed through differential expression and correlation analyses to identify nicotine metabolism-related differentially expressed genes. A prognostic model was constructed using univariate Cox regression and Lasso-Cox regression analyses， and its performance was evaluated using Kaplan-Meier survival curves， ROC curves， calibration curves， and clinical factor-integrated nomograms. The GSE42568 dataset from the gene expression omnibus （GEO） was employed as an external validation cohort for model verification， and further condueted immune infiltration and drug sensitivity assessments. The results showed that the model was composed of 10 nicotine metabolism-related genes， which could be validated as an independent prognostic factor for breast cancer. Immune infiltration and drug sensitivity analyses revealed significant differences in immune cell infiltration and drug responsiveness between high- and low-risk groups. This risk prognostic model can independently predict breast cancer outcomes and provide novel molecular markers and potential therapeutic targets for prognostic evaluation and personalized treatment of breast cancer.

Key words: breast cancer, nicotine metabolism, prognostic model

中图分类号:

Q987

张黄子怡, 黄莉莎, 李燕奇, 熊晨露, 于瀛, 谢飞. 基于尼古丁代谢基因特征的乳腺癌预后模型[J]. 生物技术进展, 2025, 15(4): 735-742.

Ziyi ZHANG-HUANG, Lisha HUANG, Yanqi LI, Chenlu XIONG, Ying YU, Fei XIE. Construction of a Breast Cancer Prognostic Model Based on Nicotine Metabolism Gene Signatures[J]. Current Biotechnology, 2025, 15(4): 735-742.

图/表 4

图 1 NR-DEGs的鉴定A：差异基因火山图；B：肿瘤样本和正常样本之间NMGs的差异分析箱线图；C：DEGs中前10个显著富集的GO功能注释条目；D：NRGs与DEGs取交集的Venn图

Fig. 1 Identification of NR-DEGs

图 2 NR-DEGs相关预后模型的构建A：Lasso回归分析的系数曲线； B：用于确定最优λ值的10折交叉验证结果； C：TCGA-BRCA队列的K-M生存曲线； D：GSE42568队列的K-M生存曲线； E：TCGA-BRCA队列的ROC曲线； F：GSE42568队列的ROC曲线

Fig. 2 Construction of the NR-DEGs-related prognostic model

图 3 列线图的构建和验证A：多因素Cox回归分析森林图；B：预测乳腺癌患者1、3、5年生存率的列线图；C：列线图校准曲线；D：列线图的ROC曲线

Fig. 3 Construction and validation of the nomogram

图 4 预后模型的免疫浸润特征及药物敏感性评估A：高低风险组之间的ESTIMATE评分比较；B：高低风险组之间的肿瘤纯度比较；C：22种免疫细胞在高低风险组中的相对比例；D：高低风险组的化疗反应箱线图

Fig. 4 Immune infiltration characteristics and drug sensitivity evaluation of the prognostic model

参考文献 28

[1]	GIAQUINTO A N, SUNG H, NEWMAN L A, et al.. Breast cancer statistics 2024[J]. CA Cancer J. Clin., 2024, 74(6): 477-495.
[2]	JIANG Z, JU Y, ALI A, et al.. Distinct shared and compartment-enriched oncogenic networks drive primary versus metastatic breast cancer[J/OL]. Nat. Commun., 2023, 14(1): 4313[2025-06-04]. .
[3]	NATHANSON S D, DETMAR M, PADERA T P, et al.. Mechanisms of breast cancer metastasis[J]. Clin. Exp. Metastasis, 2022, 39(1): 117-137.
[4]	BROWN R B, BIGELOW P, DUBIN J A, et al.. High dietary phosphorus is associated with increased breast cancer risk in a U.S. cohort of middle-aged women[J/OL]. Nutrients, 2023, 15(17): 3735[2025-06-04]. .
[5]	PASSARELLI M N, NEWCOMB P A, HAMPTON J M, et al.. Cigarette smoking before and after breast cancer diagnosis: mortality from breast cancer and smoking-related diseases[J]. J. Clin. Oncol., 2016, 34(12): 1315-1322.
[6]	BENOWITZ N L. Nicotine and smokeless tobacco[J]. CA Cancer J. Clin., 1988, 38(4): 244-247.
[7]	HECHT S S, HOFFMANN D. Tobacco-specific nitrosamines, an important group of carcinogens in tobacco and tobacco smoke[J]. Carcinogenesis, 1988, 9(6): 875-884.
[8]	CHEN P C, LEE W Y, LING H H, et al.. Activation of fibroblasts by nicotine promotes the epithelial-mesenchymal transition and motility of breast cancer cells[J]. J. Cell. Physiol., 2018, 233(6): 4972-4980.
[9]	COOKE J P, BITTERMAN H. Nicotine and angiogenesis: a new paradigm for tobacco-related diseases[J]. Ann. Med., 2004, 36(1): 33-40.
[10]	ZHAN Y, WENG M, GUO Y, et al.. Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning[J/OL]. Front. Immunol., 2024, 15: 1465638[2025-06-04]. .
[11]	LOVE M I, HUBER W, ANDERS S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[J/OL]. Genome Biol., 2014, 15(12): 550[2025-06-04]. .
[12]	WU T, HU E, XU S, et al.. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data[J/OL]. Innovation, 2021, 2(3): 100141[2025-06-04]. .
[13]	YOSHIHARA K, SHAHMORADGOLI M, MARTÍNEZ E, et al.. Inferring tumour purity and stromal and immune cell admixture from expression data[J/OL]. Nat. Commun., 2013, 4: 2612[2025-06-04]. .
[14]	NEWMAN A M, STEEN C B, LIU C L, et al.. Determining cell type abundance and expression from bulk tissues with digital cytometry[J]. Nat. Biotechnol., 2019, 37(7): 773-782.
[15]	MAESER D, GRUENER R F, HUANG R S. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data[J/OL]. Brief. Bioinform., 2021, 22(6): bbab260[2025-06-04]. .
[16]	COLEMAN M P, QUARESMA M, BERRINO F, et al.. Cancer survival in five continents: a worldwide population-based study (CONCORD)[J]. Lancet Oncol., 2008, 9(8): 730-756.
[17]	KHODABANDEH Z, VALILO M, VELAEI K, et al.. The potential role of nicotine in breast cancer initiation, development, angiogenesis, invasion, metastasis, and resistance to therapy[J]. Breast Cancer, 2022, 29(5): 778-789.
[18]	GRANDO S A. Connections of nicotine to cancer[J]. Nat. Rev. Cancer, 2014, 14(6): 419-429.
[19]	TYAGI A, SHARMA S, WU K, et al.. Nicotine promotes breast cancer metastasis by stimulating N₂ neutrophils and generating pre-metastatic niche in lung[J/OL]. Nat. Commun., 2021, 12(1): 474[2025-06-04]. .
[20]	ZHANG N, ZHU T, YU K, et al.. Elevation of O-GlcNAc and GFAT expression by nicotine exposure promotes epithelial-mesenchymal transition and invasion in breast cancer cells[J/OL]. Cell Death Dis., 2019, 10(5): 343[2025-06-04]. .
[21]	OSANAI M, LEE G H. Nicotine-mediated suppression of the retinoic acid metabolizing enzyme CYP26A1 limits the oncogenic potential of breast cancer[J]. Cancer Sci., 2011, 102(6): 1158-1163.
[22]	MURAYAMA M A, TAKADA E, TAKAI K, et al.. Nicotine treatment regulates PD-L1 and PD-L2 expression via inhibition of Akt pathway in HER2-type breast cancer cells[J/OL]. PLoS ONE, 2022, 17(1): e0260838[2025-06-04]. .
[23]	LI X, WANG J, WANG L, et al.. Lipid metabolism dysfunction induced by age-dependent DNA methylation accelerates aging[J/OL]. Signal Transduct. Target. Ther., 2022, 7(1): 162[2025-06-04]. .
[24]	WU K J, WANG W, WANG H D, et al.. Interfering with S100B-effector protein interactions for cancer therapy[J]. Drug Discov. Today, 2020, 25(9): 1754-1761.
[25]	BAXTER R C. Endocrine and cellular physiology and pathology of the insulin-like growth factor acid-labile subunit[J]. Nat. Rev. Endocrinol., 2024, 20(7): 414-425.
[26]	WANG Y W, CHENG H L, DING Y R, et al.. EMP1, EMP 2, and EMP3 as novel therapeutic targets in human cancer[J]. Biochim. Biophys. Acta Rev. Cancer, 2017, 1868(1): 199-211.
[27]	王言,周娟.PLK1通过自噬调节肿瘤的作用及研究进展[J].赣南医学院学报,2023,43(2):109-112.
	WANG Y, ZHOU J. The role of PLK1 in regulating tumors through autophagy and the research progress[J]. J. Gannan Med. Univ., 2023, 43(2): 109-112.
[28]	KUO Y C, CHEN C L, LEE K L, et al.. Nicotine-driven enhancement of tumor malignancy in triple-negative breast cancer via additive regulation of CHRNA9 and IGF1R[J]. J. Pathol., 2025, 266(2): 230-245.

基于尼古丁代谢基因特征的乳腺癌预后模型

Construction of a Breast Cancer Prognostic Model Based on Nicotine Metabolism Gene Signatures

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