Zhang Zhengjun
Department of Statistics, University of Wisconsin, Madison, WI, USA.
Cancer Inform. 2022 Feb 14;21:11769351221076360. doi: 10.1177/11769351221076360. eCollection 2022.
Known genes in the breast cancer study literature could not be confirmed whether they are vital to breast cancer formations due to lack of convincing accuracy, although they may be biologically directly related to breast cancer based on present biological knowledge. It is hoped vital genes can be identified with the highest possible accuracy, for example, 100% accuracy and convincing causal patterns beyond what has been known in breast cancer. One hope is that finding gene-gene interaction signatures and functional effects may solve the puzzle. This research uses a recently developed competing linear factor analysis method in differentially expressed gene detection to advance the study of breast cancer formation. Surprisingly, 3 genes are detected to be differentially expressed in TNBC and non-TNBC (Her2, Luminal A, Luminal B) samples with 100% sensitivity and 100% specificity in 1 study of triple-negative breast cancers (TNBC, with 54 675 genes and 265 samples). These 3 genes show a clear signature pattern of how TNBC patients can be grouped. For another TNBC study (with 54 673 genes and 66 samples), 4 genes bring the same accuracy of 100% sensitivity and 100% specificity. Four genes are found to have the same accuracy of 100% sensitivity and 100% specificity in 1 breast cancer study (with 54 675 genes and 121 samples), and the same 4 genes bring an accuracy of 100% sensitivity and 96.5% specificity in the fourth breast cancer study (with 60 483 genes and 1217 samples). These results show the 4-gene-based classifiers are robust and accurate. The detected genes naturally classify patients into subtypes, for example, 7 subtypes. These findings demonstrate the clearest gene-gene interaction patterns and functional effects with the smallest numbers of genes and the highest accuracy compared with findings reported in the literature. The 4 genes are considered to be essential for breast cancer studies and practice. They can provide focused, targeted researches and precision medicine for each subtype of breast cancer. New breast cancer disease types may be detected using the classified subtypes, and hence new effective therapies can be developed.
乳腺癌研究文献中的已知基因，尽管基于目前的生物学知识它们可能与乳腺癌有生物学上的直接关联，但由于缺乏令人信服的准确性，无法确定它们对乳腺癌形成是否至关重要。人们希望能够以尽可能高的准确性识别出关键基因，例如，达到100%的准确性以及具有超出乳腺癌已知范畴的令人信服的因果模式。一种期望是，找到基因-基因相互作用特征和功能效应或许能解决这一难题。本研究采用一种最近开发的竞争线性因子分析方法进行差异表达基因检测，以推进对乳腺癌形成的研究。令人惊讶的是，在一项三阴性乳腺癌（TNBC，有54675个基因和265个样本）研究中，检测到3个基因在TNBC和非TNBC（Her2、Luminal A、Luminal B）样本中差异表达，灵敏度和特异性均为100%。这3个基因呈现出一种清晰的特征模式，可用于对TNBC患者进行分组。在另一项TNBC研究（有54673个基因和66个样本）中，4个基因的灵敏度和特异性同样达到100%的准确性。在一项乳腺癌研究（有54675个基因和121个样本）中，发现4个基因的灵敏度和特异性达到100%的准确性，并且在第四项乳腺癌研究（有60483个基因和1217个样本）中，同样的4个基因的灵敏度为100%，特异性为96.5%。这些结果表明基于4个基因的分类器具有稳健性和准确性。检测到的基因能自然地将患者分为不同亚型，例如7种亚型。与文献报道的结果相比，这些发现展示了最清晰的基因-基因相互作用模式和功能效应，涉及的基因数量最少且准确性最高。这4个基因被认为对乳腺癌研究和实践至关重要。它们可为乳腺癌的每种亚型提供有针对性的研究和精准医疗。利用分类后的亚型可能检测出新的乳腺癌疾病类型，从而开发出新的有效治疗方法。
