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

Shipeng Ning; Juan Xie; Jianlan Mo; You Pan; Rong Huang; Qinghua Huang; Jifeng Feng

doi:10.3389/fgene.2023.1090847

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

Front Genet. 2023 Feb 22:14:1090847. doi: 10.3389/fgene.2023.1090847. eCollection 2023.

Authors

Shipeng Ning¹, Juan Xie², Jianlan Mo³, You Pan¹, Rong Huang¹, Qinghua Huang¹, Jifeng Feng³

Affiliations

¹ Department of Breast Surgery, Guangxi Medical University Cancer Hospital, Nanning, China.
² Department of Clinical Laboratory, Guangxi Medical University Cancer Hospital, Nanning, China.
³ Department of Anesthesiology, Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.

Abstract

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.

Keywords: NMF; biomarkers; imaging genetics; immunity; prior information; prognosis; triple-negative breast cancer.

Grants and funding

The work was supported by National Natural Science Foundation of China grants (82002779, 81760478, 82060024); The Seedling Plan of Maternal and Child Health Hospital of Guangxi Zhuang Autonomous Region (GXWCH-YMJH-2018005); And Guangxi Provincial Natural Science Foundation of China grants (2019GXNSFAA245083).