Objective: Cancer is one of the most serious diseases affecting human health. Among all current cancer treatments, early diagnosis and control significantly help increase the chances of cure. Detecting cancer biomarkers in body fluids now is attracting more attention within oncologists. In-silico predictions of body fluid-related proteins, which can be served as cancer biomarkers, open a door for labor-intensive and time-consuming biochemical experiments.

Methods: In this work, we propose a novel method for high-throughput identification of cancer biomarkers in human body fluids. We incorporate physicochemical properties into the weighted observed percentages (WOP) and position-specific scoring matrices (PSSM) profiles to enhance their attributes that reflect the evolutionary conservation of the body fluid-related proteins. The least absolute selection and shrinkage operator (LASSO) feature selection strategy is introduced to generate the optimal feature subset.

Results: The ten-fold cross-validation results on training datasets demonstrate the accuracy of the proposed model. We also test our proposed method on independent testing datasets and apply it to the identification of potential cancer biomarkers in human body fluids.

Conclusion: The testing results promise a good generalization capability of our approach.

目的：癌症是影响人类健康的最严重疾病之一。在目前所有的癌症治疗方法中，早期诊断和控制显着有助于增加治愈的机会。现在，检测体液中的癌症生物标志物已引起肿瘤学家的更多关注。可作为癌症生物标记物的体液相关蛋白的计算机模拟预测为劳动密集型和费时的生化实验打开了大门。

方法：在这项工作中，我们提出了一种用于高通量鉴定人体液体中癌症生物标记物的新方法。我们将理化性质纳入加权观察百分率（WOP）和位置特异性评分矩阵（PSSM）谱中，以增强其属性，以反映体液相关蛋白的进化保守性。引入最小绝对选择和收缩算子（LASSO）特征选择策略以生成最佳特征子集。

结果：训练数据集上的十倍交叉验证结果证明了所提出模型的准确性。我们还将在独立的测试数据集上测试我们提出的方法，并将其应用于识别人体液中潜在的癌症生物标志物。

结论：测试结果表明我们的方法具有良好的泛化能力。