三阴性乳腺癌m6A相关lncRNA的免疫预后模型构建

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三阴性乳腺癌m6A相关lncRNA的免疫预后模型构建

更新时间：2026-01-21

- 三阴性乳腺癌m6A相关lncRNA的免疫预后模型构建
- Journal of Xinjiang University (Natural Science Edition in Chinese and English) Vol. 41, Issue 1, Pages: 78-86(2024)
- 作者机构：
  
  1. 新疆大学软件学院
  2. 新疆大学信息科学与工程学院
- 作者简介：
- 基金信息：
- DOI：10.13568/j.cnki.651094.651316.2023.03.17.0001
  CLC： R737.9
- Published：2024
- 稿件说明：
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[1]鲍淑梅,管浩钦,苏莹,等.三阴性乳腺癌m6A相关lncRNA的免疫预后模型构建[J].新疆大学学报(自然科学版)(中英文),2024,41(01):78-86.
[1]鲍淑梅,管浩钦,苏莹,等.三阴性乳腺癌m6A相关lncRNA的免疫预后模型构建[J].新疆大学学报(自然科学版)(中英文),2024,41(01):78-86. DOI： 10.13568/j.cnki.651094.651316.2023.03.17.0001.

DOI：10.13568/j.cnki.651094.651316.2023.03.17.0001.

摘要

三阴性乳腺癌(TNBC)是一种特殊的乳腺癌亚型．因其异质性以及缺乏可靠的分子靶点使其无法获得有效靶向治疗，导致TNBC患者的存活率仍然很低．N6-甲基腺苷(m6A)和长链非编码核糖核酸(lncRNA)在TNBC的预后价值和免疫治疗反应中起着至关重要的作用．因此，辨别TNBC患者中与m6A相关的lncRNA至关重要．通过共表达的方式分析并获得m6A相关的lncRNA，之后进行单变量比例风险(Cox)回归分析、随机生存森林(RSF)、最小绝对收缩和选择算子(LASSO)和多变量Cox回归分析以构建m6A相关lncRNA模型．随后使用卡普兰-梅尔(KM)生存分析、主成分分析(PCA)、功能富集分析和列线图分析风险模型．最后，讨论了针对该模型的潜在免疫治疗特征和药物敏感性预测．包含3个m6A相关lncRNA的风险模型被确定为预后的独立预测因子．通过使用该模型对患者进行重新分组，可以对患者在免疫治疗反应方面进行更有效的区分．使用pRRhetic算法根据每个样本的癌症药物敏感性基因组学(GDSC)数据库中可用的半数最大抑制浓度(IC50)估计治疗反应，确定了针对TNBC亚型分化的候选药物．结果表明，这种基于m6A的lncRNA风险模型有望用于临床预测TNBC患者的预后和免疫治疗反应．

Abstract

Triple-negative breast cancer(TNBC) is a specific subtype of breast cancer. Because of its heterogeneity and the lack of reliable molecular targets for effective targeted therapy

the survival rate of TNBC patients remains low. N6-methyladenosine(m6A) and long-stranded non-coding ribonucleic acid(lncRNA) play a critical role in the prognostic value and immunotherapeutic response of TNBC. Therefore

it is important to identify m6A-associated lncRNAs in TNBC patients. In this study

m6A-associated lncRNAs were analyzed and obtained by co-expression. Univariate Cox

random survival forest(RSF)

least absolute shrinkage and selection operator(LASSO) and multivariate Cox regression analyses were then performed to construct m6A-associated lncRNA models. Kaplan-Meier(KM) survival analysis

principal component analysis(PCA)

functional enrichment analysis

and column line plots were then used to analyze the risk models. Finally

potential immunotherapy profiles and drug sensitivity predictions for the model were also discussed. A risk model containing 3 m6A-associated lncRNAs was identified as an independent predictor of prognosis. By regrouping patients using this model

we can differentiate patients more effectively in terms of their response to immunotherapy. Drug candidates targeting TNBC subtype differentiation were identified using the pRRhetic algorithm to estimate treatment response based on the half-maximal inhibitory concentration(IC50) available in the Genomics of Drug Sensitivity in Cancer(GDSC)database for each sample. The findings suggest that this m6A-based lncRNA risk model may be promising for clinical prediction of prognosis and immunotherapy response in TNBC patients.

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references

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