Ning Shipeng, Xie Juan, Mo Jianlan, Pan You, Huang Rong, Huang Qinghua, Feng Jifeng
Department of Breast Surgery, Guangxi Medical University Cancer Hospital, Nanning, China.
Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China.
Front Genet. 2023 Feb 22;14:1090847. doi: 10.3389/fgene.2023.1090847. eCollection 2023.
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（TNBC）是乳腺癌中侵袭性较强的亚型之一。TNBC患者的预后仍然较差。因此，仍需要继续寻找新的生物标志物来改善TNBC患者的预后和治疗。近年来的研究表明，有效利用和整合基因组数据和图像数据中的信息将有助于疾病的预测和预后评估。鉴于影像遗传学能够深入研究微观基因变异对疾病表型的影响，本文提出了一种样本先验信息诱导的多维联合非负矩阵分解（SPID-MDJNMF）算法，以整合全切片图像（WSI）、mRNA表达数据和miRNA表达数据。该算法通过各种约束有效地融合了三种模态的高维数据。此外，本文在样本之间构建了一个无向图，使用邻接矩阵来约束相似性，并将患者的临床分期信息嵌入到算法中，以便算法能够识别不同标签样本的共表达模式。我们对筛选出的共表达模块中的mRNA和miRNA进行单变量和多变量Cox回归分析，以构建一个与TNBC相关的预后模型。最后，我们分别构建了由2个mRNA（IL12RB2和CNIH2）和2个miRNA（miR-203a-3p和miR-148b-3p）组成的预后模型。该预后模型能够高精度地预测TNBC患者的生存时间。总之，我们提出的SPID-MDJNMF算法能够有效地整合图像和基因组数据。此外，我们评估了通过SPID-MDJNMF算法筛选出的mRNA和miRNA在TNBC中的预后价值，这可能为TNBC患者的预后提供有前景的靶点。
