Objective To screen out the biomarkers linked to prognosis of breast invasive carcinoma based on the analysis of transcriptome data by random forest (RF),extreme gradient boosting (XGBoost),light gradient boosting machine (LightGBM),and categorical boosting (CatBoost). Methods We obtained the expression data of breast invasive carcinoma from The Cancer Genome Atlas and employed DESeq2,t-test,and Cox univariate analysis to identify the differentially expressed protein-coding genes associated with survival prognosis in human breast invasive carcinoma samples.Furthermore,RF,XGBoost,LightGBM,and CatBoost models were established to mine the protein-coding gene markers related to the prognosis of breast invasive cancer and the model performance was compared.The expression data of breast cancer from the Gene Expression Omnibus was used for validation. Results A total of 151 differentially expressed protein-coding genes related to survival prognosis were screened out.The machine learning model established with C3orf80,UGP2,and SPC25 demonstrated the best performance. Conclusions Three protein-coding genes (UGP2,C3orf80,and SPC25) were screened out to identify breast invasive carcinoma.This study provides a new direction for the treatment and diagnosis of breast invasive carcinoma.
目的 应用随机森林(RF)、极限梯度提升算法(XGBoost)、轻量的梯度提升机(LightGBM)、类别型特征提升(CatBoost)4种机器学习算法分析浸润性乳腺癌转录组表达数据,筛选与浸润性乳腺癌预后相关的生物标志物。方法 通过癌症基因组图谱公共数据库下载浸润性乳腺癌的表达数据,采用DESeq2程序包、t检验及Cox单因素分析,对人类浸润性乳腺癌样本中与生存预后相关的差异蛋白质编码基因进行筛选。基于RF、XGBoost、LightGBM、CatBoost等机器学习模型的构建与比较,挖掘浸润性乳腺癌预后相关的蛋白质编码基因标志物,并使用基因表达综合数据库的乳腺癌表达数据作为外部测试进行验证。结果 共获得151个与生存预后相关的差异蛋白质编码基因,其中由C3orf80、UGP2和SPC25 3个基因构建的机器学习模型效果较好。结论 筛选出3个(UGP2、C3orf80、SPC25)与浸润性乳腺癌预后相关的生物标志物,为诊断和治疗浸润性乳腺癌提供了新的方向。.
Keywords: C3orf80; SPC25; UGP2; biomarker; breast invasive carcinoma; protein-coding genes.