Determining the tumor origin in humans is vital in clinical applications of molecular diagnostics. Metastatic cancer is usually a very aggressive disease with limited diagnostic procedures, despite the fact that many protocols have been evaluated for their effectiveness in prognostication. Research has shown that dysregulation in miRNAs (a class of non-coding, regulatory RNAs) is remarkably involved in oncogenic conditions. This research paper aims to develop a machine learning model that processes an array of miRNAs in 1097 metastatic tissue samples from patients who suffered from various stages of breast cancer. The suggested machine learning model is fed with miRNA quantitative read count data taken from The Cancer Genome Atlas Data Repository. Two main feature-selection techniques have been used, mainly Neighborhood Component Analysis and Minimum Redundancy Maximum Relevance, to identify the most discriminant and relevant miRNAs for their up-regulated and down-regulated states. These miRNAs are then validated as biological identifiers for each of the four cancer stages in breast tumors. Both machine learning algorithms yield performance scores that are significantly higher than the traditional fold-change approach, particularly in earlier stages of cancer, with Neighborhood Component Analysis and Minimum Redundancy Maximum Relevance achieving accuracy scores of up to 0.983 and 0.931, respectively, compared to 0.920 for the FC method. This study underscores the potential of advanced feature-selection methods in enhancing the accuracy of cancer stage identification, paving the way for improved diagnostic and therapeutic strategies in oncology.
在分子诊断的临床应用中，确定人类肿瘤起源至关重要。尽管已有多种方案被评估其在预后判断中的有效性，但转移性癌症通常是一种极具侵袭性的疾病，且诊断手段有限。研究表明，miRNA（一类非编码调控RNA）的失调与致癌状态密切相关。本研究旨在开发一种机器学习模型，用于分析来自不同分期乳腺癌患者的1097份转移组织样本中的miRNA表达谱。该模型采用源自癌症基因组图谱数据仓库的miRNA定量读数数据作为输入。研究主要运用邻域成分分析和最小冗余最大相关性两种特征选择技术，以识别在表达上调和下调状态下最具判别力且相关性最强的miRNA。这些miRNA随后被验证可作为乳腺癌四个不同分期的生物学标识物。两种机器学习算法的性能评分均显著高于传统的倍数变化法，尤其在癌症早期阶段表现突出：邻域成分分析和最小冗余最大相关性分别达到0.983和0.931的准确率，而倍数变化法仅为0.920。本研究强调了先进特征选择方法在提升癌症分期识别准确性方面的潜力，为改善肿瘤学诊断与治疗策略开辟了新途径。
Identification of Gene Expression in Different Stages of Breast Cancer with Machine Learning
肿瘤(癌症)患者之家