ceRNA network development and tumour-infiltrating immune cell analysis of metastatic breast cancer to bone

Shuzhong Liu; An Song; Xi Zhou; Zhen Huo; Siyuan Yao; Bo Yang; Yong Liu; Yipeng Wang

doi:10.1016/j.jbo.2020.100304

ceRNA network development and tumour-infiltrating immune cell analysis of metastatic breast cancer to bone

J Bone Oncol. 2020 Jul 20:24:100304. doi: 10.1016/j.jbo.2020.100304. eCollection 2020 Oct.

Authors

Shuzhong Liu¹, An Song², Xi Zhou¹, Zhen Huo³, Siyuan Yao¹, Bo Yang¹, Yong Liu¹, Yipeng Wang¹

Affiliations

¹ Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
² Department of Endocrinology, Key Laboratory of Endocrinology, National Health and Family Planning Commission, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.
³ Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, China.

Abstract

Purpose: Advanced breast cancer commonly metastasises to bone; however, the molecular mechanisms underlying the affinity for breast cancer cells to bone remains unclear. Thus, we developed nomograms based on a competing endogenous RNA (ceRNA) network and analysed tumour-infiltrating immune cells to elucidate the molecular pathways that may predict prognosis in patients with breast cancer.

Methods: We obtained the RNA expression profile of 1091 primary breast cancer samples included in The Cancer Genome Atlas database, 58 of which were from patients with bone metastasis. We analysed the differential RNA expression patterns between breast cancer with and without bone metastasis and developed a ceRNA network. Cibersort was employed to differentiate between immune cell types based on tumour transcripts. Nomograms were then established based on the ceRNA network and immune cell analysis. The value of prognostic factors was evaluated by Kaplan-Meier survival analysis and a Cox proportional risk model.

Results: We found significant differences in long non-coding RNAs (lncRNAs), 18 microRNAs (miRNAs), and 20 messenger RNAs (mRNAs) between breast cancer with and without bone metastasis, which were used to construct a ceRNA network. We found that the protein-coding genes GJB3, CAMMV, PTPRZ1, and FBN3 were significantly differentially expressed by Kaplan-Meier analysis. We also observed significant differences in the abundance of plasma cell and follicular helper T cell populations between the two groups. In addition, the proportion of mast cells, gamma delta T cells, and plasma cells differed depending on disease location and stage. Our analysis showed that a high proportion of follicular helper T cells and a low proportion of eosinophils promoted survival and that DLX6-AS1, Wnt6, and GABBR2 expression may be associated with bone metastasis in breast cancer.

Conclusions: We developed a bioinformatic tool for exploring the molecular mechanisms of bone metastasis in patients with breast cancer and identified factors that may predict the occurrence of bone metastasis.

Keywords: AIC, Akaike information criterion; AUC, Area under curve; Bone metastasis; Breast cancer; DE, Differentially expressed; DEmRNA, differentially expressed messenger RNA; EMT, epithelial-mesenchymal transition; ER, estrogen receptor; FPKM, fragments per kilobase per million mapped reads; GO, Gene ontology; HER2, human epidermal growth factor receptor 2; Immune infiltration; KEGG, Kyoto Encyclopedia of Genes and Genomes; Nomogram; PCC, Pearson correlation coefficient; Prognosis; ROC curve, receiver operating characteristic curve; Runx2, runt related transcription factor 2; TCGA, The Cancer Genome Atlas; TNM, Tumor, Node, Metastases; ceRNA network; ceRNA, competing endogenous RNA; lncRNA, long non-coding RNA; mRNA, messenger RNA; miRNA, microRNA.