ceRNA network development and tumour-infiltrating immune cell analysis of metastatic breast cancer to bone
Shuzhong Liu1, An Song2, Xi Zhou1
1Department of Orthopaedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
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.
Insights
This study identifies key molecular pathways and immune cells involved in breast cancer bone metastasis. Findings may help predict prognosis and develop targeted therapies for advanced breast cancer patients.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Advanced breast cancer frequently metastasizes to bone, but the underlying molecular mechanisms are not fully understood.
- Identifying these mechanisms is crucial for predicting prognosis and developing effective treatments.
Purpose of the Study:
- To elucidate molecular pathways predicting prognosis in breast cancer bone metastasis.
- To develop nomograms based on competing endogenous RNA (ceRNA) networks and tumor-infiltrating immune cells.
Main Methods:
- Analyzed RNA expression profiles from The Cancer Genome Atlas (TCGA) database (1091 primary breast cancer samples).
- Constructed a ceRNA network using differential expression analysis of long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs).
- Utilized Cibersort for immune cell profiling and established nomograms, validated by Kaplan-Meier and Cox regression analyses.
Main Results:
- Identified significant differences in lncRNAs, miRNAs, and mRNAs between breast cancer with and without bone metastasis.
- Found differential expression of GJB3, CAMMV, PTPRZ1, and FBN3. Observed distinct plasma cell and follicular helper T cell populations.
- High follicular helper T cell and low eosinophil proportions correlated with improved survival. DLX6-AS1, Wnt6, and GABBR2 expression linked to bone metastasis.
Conclusions:
- Developed a bioinformatics tool to explore molecular mechanisms of breast cancer bone metastasis.
- Identified potential predictive factors for bone metastasis occurrence and prognosis.
