Finding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of the tumor – the laterality – can be unilateral (the tumor is affecting one side of the prostate), or bilateral on both sides. Nevertheless, the tumor can be overestimated or underestimated by standard screening methods. In this work, a combination of efficient machine learning methods for feature selection and classification are proposed to analyze gene activity and select them as relevant biomarkers for different laterality samples. A data set that consists of 450 samples was used in this study. The samples were divided into three laterality classes (left, right, bilateral). The aim of this work is to understand the genomic activity in each class and find relevant genes as indicators for each class with nearly 99% accuracy. The system identified groups of differentially expressed genes (RTN1, HLA-DMB, MRI1) that are able to differentiate samples among the three classes. The proposed method was able to detect sets of genes that can identify different laterality classes. The resulting genes are found to be strongly correlated with disease progression. HLA-DMB and EIF4G2, which are detected in the set of genes can detect the left laterality, were reported earlier to be in the same pathway called Allograft rejection SuperPath.

中文翻译：

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使用基因表达数据的机器学习系统预测前列腺癌组织中的肿瘤位置。

寻找前列腺中的肿瘤位置是前列腺癌诊断和治疗的重要病理步骤。肿瘤的位置——偏侧性——可以是单侧的（肿瘤影响前列腺的一侧），也可以是双侧的。然而，标准筛查方法可能会高估或低估肿瘤。在这项工作中，提出了用于特征选择和分类的有效机器学习方法的结合来分析基因活性并选择它们作为不同侧向样本的相关生物标志物。本研究使用了包含 450 个样本的数据集。样本被分为三个偏侧性类别（左、右、双侧）。这项工作的目的是了解每个类别的基因组活性，并以近 99% 的准确度找到相关基因作为每个类别的指标。该系统识别了能够区分三类样本的差异表达基因组（RTN1、HLA-DMB、MRI1）。所提出的方法能够检测可以识别不同侧向类别的基因组。发现由此产生的基因与疾病进展密切相关。在一组可检测左侧性的基因中检测到的 HLA-DMB 和 EIF4G2 早先被报道位于称为同种异体移植排斥 SuperPath 的同一途径中。