GAIKER Technology Centre, Parque Tecnológico, Edificio 202, 48170 Zamudio, (Bizkaia), Spain.
BMC Cancer. 2012 Jan 26;12:43. doi: 10.1186/1471-2407-12-43.
Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries.
A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables.
After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples.
We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).
在大肠中发生的恶性肿瘤导致西方世界癌症死亡的第二大原因。尽管在过去几十年中取得了进展，但结直肠癌仍然是西方国家最常见和最致命的肿瘤之一。
对总共 31 个肿瘤样本和 33 个非肿瘤样本进行了人类结直肠癌的基因组研究。该研究通过将肿瘤样本与非肿瘤样本的参考池杂交，使用 Agilent Human 1A 60-mer oligo 微阵列进行。通过 qRT-PCR 验证了所得结果。在随后的生物信息学分析中，通过贝叶斯分类器、变量选择和自举重采样构建了基因网络。所有诱导模型之间的共识产生了依赖关系和变量的层次结构。
在进行了详尽的预处理过程以确保数据质量（缺失值插补、探针质量、数据平滑和类内变异性过滤）后，最终数据集共包含 8104 个探针。接下来，进行了监督分类方法和数据分析，以获得最相关的基因。其中有两个基因直接参与癌症的进展，特别是结直肠癌。最后，诱导了一个监督分类器来对新的未见过的样本进行分类。
我们基于生物标志物面板开发了一种用于结直肠癌诊断的试探性模型。我们的结果表明，使用不同的监督分类器，本文所述的基因谱可以以 94.45%的准确率区分非癌和癌样本（AUC 值在 0.997 和 0.955 之间）。
