Institute for Systems Analysis and Computer Science "Antonio Ruberti", National Research Council, Rome, Italy.
Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
PLoS One. 2022 Feb 15;17(2):e0264024. doi: 10.1371/journal.pone.0264024. eCollection 2022.
Triple-negative breast cancers (TNBCs) display poor prognosis, have a high risk of tumour recurrence, and exhibit high resistance to drug treatments. Based on their gene expression profiles, the majority of TNBCs are classified as basal-like breast cancers. Currently, there are not available widely-accepted prognostic markers to predict outcomes in basal-like subtype, so the selection of new prognostic indicators for this BC phenotype represents an unmet clinical challenge.
Here, we attempted to address this challenging issue by exploiting a bioinformatics pipeline able to integrate transcriptomic, genomic, epigenomic, and clinical data freely accessible from public repositories. This pipeline starts from the application of the well-established network-based SWIM methodology on the transcriptomic data to unveil important (switch) genes in relation with a complex disease of interest. Then, survival and linear regression analyses are performed to associate the gene expression profiles of the switch genes with both the patients' clinical outcome and the disease aggressiveness. This allows us to identify a prognostic gene signature that in turn is fed to the last step of the pipeline consisting of an analysis at DNA level, to investigate whether variations in the expression of identified prognostic switch genes could be related to genetic (copy number variations) or epigenetic (DNA methylation differences) alterations in their gene loci, or to the activities of transcription factors binding to their promoter regions. Finally, changes in the protein expression levels corresponding to the so far identified prognostic switch genes are evaluated by immunohistochemical staining results taking advantage of the Human Protein Atlas.
The application of the proposed pipeline on the dataset of The Cancer Genome Atlas (TCGA)-Breast Invasive Carcinoma (BRCA) patients affected by basal-like subtype led to an in silico recognition of a basal-like specific gene signature composed of 11 potential prognostic biomarkers to be further investigated.
三阴性乳腺癌（TNBC）预后不良，肿瘤复发风险高，对药物治疗高度耐药。根据其基因表达谱，大多数 TNBC 被归类为基底样乳腺癌。目前，尚无广泛接受的预后标志物可预测基底样亚型的结局，因此，为这种 BC 表型选择新的预后指标代表着未满足的临床挑战。
在这里，我们试图通过利用一个生物信息学管道来解决这个具有挑战性的问题，该管道能够整合来自公共存储库的转录组学、基因组学、表观基因组学和临床数据。该管道从在转录组数据上应用成熟的基于网络的 SWIM 方法开始，以揭示与复杂疾病相关的重要（开关）基因。然后，进行生存和线性回归分析，将开关基因的基因表达谱与患者的临床结局和疾病侵袭性相关联。这使我们能够确定一个预后基因特征，该特征反过来又被输入到管道的最后一步，该步骤由在 DNA 水平上进行的分析组成，以研究鉴定的预后开关基因的表达变化是否与它们基因座的遗传（拷贝数变异）或表观遗传（DNA 甲基化差异）改变相关，或与转录因子结合到其启动子区域的活性相关。最后，通过利用人类蛋白质图谱，评估对应于迄今为止鉴定的预后开关基因的蛋白质表达水平的免疫组织化学染色结果的变化。
将所提出的管道应用于癌症基因组图谱（TCGA）-乳腺浸润性癌（BRCA）患者数据集，这些患者受基底样亚型影响，导致在计算机上识别出由 11 个潜在预后生物标志物组成的基底样特异性基因特征，需要进一步研究。
