Table2_Imaging genetic association analysis of triple-negative breast cancer based on the integration of prior sample information.XLS

Triple-negative breast cancer (TNBC) is one of the more aggressive subtypes of breast cancer. The prognosis of TNBC patients remains low. Therefore, there is still a need to continue identifying novel biomarkers to improve the prognosis and treatment of TNBC patients. Research in recent years has shown that the effective use and integration of information in genomic data and image data will contribute to the prediction and prognosis of diseases. Considering that imaging genetics can deeply study the influence of microscopic genetic variation on disease phenotype, this paper proposes a sample prior information-induced multidimensional combined non-negative matrix factorization (SPID-MDJNMF) algorithm to integrate the Whole-slide image (WSI), mRNAs expression data, and miRNAs expression data. The algorithm effectively fuses high-dimensional data of three modalities through various constraints. In addition, this paper constructs an undirected graph between samples, uses an adjacency matrix to constrain the similarity, and embeds the clinical stage information of patients in the algorithm so that the algorithm can identify the co-expression patterns of samples with different labels. We performed univariate and multivariate Cox regression analysis on the mRNAs and miRNAs in the screened co-expression modules to construct a TNBC-related prognostic model. Finally, we constructed prognostic models for 2-mRNAs (IL12RB2 and CNIH2) and 2-miRNAs (miR-203a-3p and miR-148b-3p), respectively. The prognostic model can predict the survival time of TNBC patients with high accuracy. In conclusion, our proposed SPID-MDJNMF algorithm can efficiently integrate image and genomic data. Furthermore, we evaluated the prognostic value of mRNAs and miRNAs screened by the SPID-MDJNMF algorithm in TNBC, which may provide promising targets for the prognosis of TNBC patients.

三阴性乳腺癌（Triple-negative breast cancer, TNBC）是侵袭性较强的乳腺癌亚型之一，该类患者的预后仍较差，因此仍需持续挖掘新型生物标志物以改善三阴性乳腺癌患者的预后与治疗方案。近年来研究表明，有效整合基因组数据与图像数据中的信息，将有助于疾病的预测与预后分析。鉴于影像遗传学可深入探究微观遗传变异对疾病表型的影响，本文提出一种样本先验信息诱导的多维联合非负矩阵分解（sample prior information-induced multidimensional combined non-negative matrix factorization, SPID-MDJNMF）算法，用于整合全切片图像（Whole-slide image, WSI）、mRNA表达数据与miRNA表达数据。该算法通过多重约束有效融合三种模态的高维数据。此外，本文构建了样本间的无向图，利用邻接矩阵约束样本相似性，并将患者的临床分期信息嵌入算法中，使算法能够识别不同标签样本的共表达模式。针对筛选得到的共表达模块中的mRNA与miRNA，我们开展了单因素与多因素Cox回归分析，以构建三阴性乳腺癌相关预后模型。最终分别构建了包含2个mRNA（IL12RB2与CNIH2）与2个miRNA（miR-203a-3p、miR-148b-3p）的预后模型，该模型可高精度预测三阴性乳腺癌患者的生存时间。综上，本文提出的SPID-MDJNMF算法能够高效整合图像与基因组数据；此外，我们评估了SPID-MDJNMF算法筛选出的mRNA与miRNA在三阴性乳腺癌中的预后价值，该结果可为三阴性乳腺癌患者的预后评估提供潜在的靶向靶点。