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Background: Despite evidence indicating the dominance of cell-of-origin signatures in molecular tumor patterns, translating these genome-wide patterns into actionable insights has been challenging. This study introduces breast cancer cell-of-origin signatures that offer significant prognostic value across all breast cancer subtypes and various clinical cohorts, compared to previously developed genomic signatures. Methods: We previously reported that triple hormone receptor (THR) co-expression patterns of androgen (AR), estrogen (ER), and vitamin D (VDR) receptors are maintained at the protein level in human breast cancers. Here, we developed corresponding mRNA signatures (THR-50 and THR-70) based on these patterns to categorize breast tumors by their THR expression levels. The THR mRNA signatures were evaluated across 56 breast cancer datasets (5040 patients) using Kaplan–Meier survival analysis, Cox proportional hazard regression, and unsupervised clustering. Results: The THR signatures effectively predict both overall and progression-free survival across all evaluated datasets, independent of subtype, grade, or treatment status, suggesting improvement over existing prognostic signatures. Furthermore, they delineate three distinct ER-positive breast cancer subtypes with significant survival in differences—expanding on the conventional two subtypes. Additionally, coupling THR-70 with an immune signature identifies a predominantly ER-negative breast cancer subgroup with a highly favorable prognosis, comparable to ER-positive cases, as well as an ER-negative subgroup with notably poor outcome, characterized by a 15-fold shorter survival. Conclusions: The THR cell-of-origin signature introduces a novel dimension to breast cancer biology, potentially serving as a robust foundation for integrating additional prognostic biomarkers. These signatures offer utility as a prognostic index for stratifying existing breast cancer subtypes and for de novo classification of breast cancer cases. Moreover, THR signatures may also hold promise in predicting hormone treatment responses targeting AR and/or VDR. [ABSTRACT FROM AUTHOR]

Breast cancer (BC) is the most common cancer among women with high morbidity and mortality. Therefore, new research is still needed for biomarker detection. GSE101124 and GSE182471 datasets were obtained from the Gene Expression Omnibus (GEO) database to evaluate differentially expressed circular RNAs (circRNAs). The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used to identify the significantly dysregulated microRNAs (miRNAs) and genes considering the Prediction Analysis of Microarray classification (PAM50). The circRNA-miRNA-mRNA relationship was investigated using the Cancer-Specific CircRNA, miRDB, miRTarBase, and miRWalk databases. The circRNA–miRNA–mRNA regulatory network was annotated using Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database. The protein-protein interaction network was constructed by the STRING database and visualized by the Cytoscape tool. Then, raw miRNA data and genes were filtered using some selection criteria according to a specific expression level in PAM50 subgroups. A bottleneck method was utilized to obtain highly interacted hub genes using cytoHubba Cytoscape plugin. The Disease-Free Survival and Overall Survival analysis were performed for these hub genes, which are detected within the miRNA and circRNA axis in our study. We identified three circRNAs, three miRNAs, and eighteen candidate target genes that may play an important role in BC. In addition, it has been determined that these molecules can be useful in the classification of BC, especially in determining the basal-like breast cancer (BLBC) subtype. We conclude that hsa_circ_0000515/miR-486-5p/SDC1 axis may be an important biomarker candidate in distinguishing patients in the BLBC subgroup of BC. [ABSTRACT FROM AUTHOR]

Objective: Acute myeloid leukemia (AML) is a lethal type of cancer associated with dysregulation of progenitor hematopoietic stem cell behavior and its incidence is, unfortunately, increasing. Although there are various applications in treatment, since most of them are insufficient in early diagnosis, treatment and new prognostic biomarkers should be investigated. Materials and Methods: In this study, three Gene Expression Omnibus (GEO) datasets Genomic Spatial Event (GSE); GSE94591, GSE116617, and GSE163386) were used to investigate dysregulated expressions of circular RNAs (circRNAs), and the GSE142699 and GSE142698 datasets were analyzed to detect dysregulated expressions of microRNAs (miRNAs) and mRNAs, respectively. Filtering was applied with p value <0.05, log2FC=0.5 (circRNA), and log2FC=1 (miRNA and mRNA) from the raw data analyzed using the limma R package (v.3.46.0). We investigated circRNA-miRNA-mRNA interactions using special tools including CSCDv2.0, circBank, miRTarBase, miRDB, multiMiR, miRWalk, DIANA-microT, TarBase, miRanda, and TargetScan. The pathway analysis was performed using KEGG and GO programs. The STRING database and Cytoscape tool were used to construct and view protein interaction. Hub gene analysis was constructed using the MCODE tool. We have utilized the GEPIA tool to determine the Overall Survival of the hub genes. Results: In our study, 4 circRNAs, 3 miRNAs, and 6 genes that may be closely related to AML were detected. Conclusion: According to our bioinformatics analysis results, hsa_circ_0012152/miR-199a-5p/HOXA9 axis could be more important in AML. Therefore, in vitro and in vivo investigations are recommended. [ABSTRACT FROM AUTHOR]