Joe Soobok, Nam Hojung
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea.
BMC Med Inform Decis Mak. 2016 Jul 18;16 Suppl 1(Suppl 1):56. doi: 10.1186/s12911-016-0292-5.
The survival of patients with breast cancer is highly sporadic, from a few months to more than 15 years. In recent studies, the gene expression profiling of tumors has been used as a promising means of predicting prognosis factors.
In this study, we used gene expression datasets of tumors to identify prognostic factors in breast cancer. We conducted log-rank tests and used unsupervised clustering methods to find reciprocally expressed gene sets associated with worse survival rates. Prognosis prediction scores were determined as the ratio of gene expressions.
As a result, four prognosis prediction gene set modules were constructed. The four prognostic gene sets predicted worse survival rates in three independent gene expression data sets. In addition, we found that cancer patient with poor prognosis, i.e., triple-negative cancer, HER2-enriched, TP53 mutated and high-graded patients had higher prognosis prediction scores than those with other types of breast cancer.
In conclusion, based on a gene expression analysis, we suggest that our well-defined scoring method of the prediction of survival outcome may be useful for developing prognostic factors in breast cancer.
乳腺癌患者的生存期差异很大，从几个月到超过15年不等。在最近的研究中，肿瘤的基因表达谱已被用作预测预后因素的一种有前景的方法。
在本研究中，我们使用肿瘤基因表达数据集来识别乳腺癌的预后因素。我们进行了对数秩检验，并使用无监督聚类方法来寻找与较差生存率相关的相互表达的基因集。预后预测分数被确定为基因表达的比率。
结果构建了四个预后预测基因集模块。这四个预后基因集在三个独立的基因表达数据集中预测了较差的生存率。此外，我们发现预后不良的癌症患者，即三阴性癌症、HER2富集型、TP53突变型和高级别患者的预后预测分数高于其他类型乳腺癌患者。
总之，基于基因表达分析，我们认为我们定义明确的生存结果预测评分方法可能有助于开发乳腺癌的预后因素。
