Abstract 2004: Integrative bioinformatics approach reveals potential liquid biopsy-based biomarkers for breast cancer metastasis monitoring

Abstract Background: Despite recent advances in screening and therapeutic strategies, breast cancer remains the first leading cause of cancer death among females. The 5-year survival rate is high as 95% at localized stages but drops significantly to 31% once the cancer metastasizes. Therefore, developing predictive tools of metastatic recurrence is an unmet clinical need to design preventive clinical trials. Methods: This study used a public dataset (GEO ID: GSE102484), which is a microarray dataset that contains primary tumor samples (n = 683) sourced from subjects with (n = 95) and without (n = 582) distant recurrence (metastasis) of breast cancer (met and non-met, respectively). Our approach began with two sets of differentially expressed genes (DEGs). The first set of DEGs are from all the samples in the dataset regardless of breast cancer subtypes, while the second set of DEGs are from triple negative breast cancer (TNBC) samples only. To identify subjects with TNBC, basal-like breast cancer samples were selected using PAM50 algorithm with genefu R package. To select potential blood biomarker candidates, we developed an integrative bioinformatics pipeline. Briefly, 1) Differential expression analysis between met and non-met were performed using limma R package. We then selected DEGs with log2 fold change ≥ 0.58 and false discovery rate (FDR) < 0.1, 2) Genes with housekeeping/hematopoietic markers were removed, and 3) The genes that can be detectable in human bloods were selected using the human secretome database from the Human Protein Atlas (HPA). Results: The differential expression analysis of all breast cancer samples identified 134 significantly upregulated genes in met and the selection process with the pipeline resulted in 13 marker candidates including COL10A1, CD93, HPRT1, MMP1, EPHB4, HSPD1, MAP4, CCDC80, MMP11, SAFB2, CALD1, CRISP3, and GAPDH. Subsequent GO enrichment analysis identified functions associated with these markers including angiogenesis, calcium ion binding, and extracellular matrix and structure organizations. Nucleotide metabolism was observed significance in pathway. We then focused on the TNBC samples from the dataset and identified 162 significantly upregulated genes in met from TNBC. Out of the 13 marker candidates from the 134 upregulated genes, 8 marker candidates including CALD1, CD93, EPHB4, SAFB2, HSPD1, MAP4, CCDC80, and HPRT1 were also found in TNBC. Conclusion: Through an integrative computational analysis, we identified 13 markers associated with breast cancer metastasis, of which 8 were specifically linked to metastasis in TNBC. Future studies will focus on validating these biomarkers using liquid biopsy samples from breast cancer patients, potentially offering a less invasive diagnostic approach. Citation Format: Jina Kim, Cynthia Jinno, Hyoyoung Kim, Sunao Tanaka, Takeo Fujii, Sungyong You, Hideki Furuya. Integrative bioinformatics approach reveals potential liquid biopsy-based biomarkers for breast cancer metastasis monitoring [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2004.