ETS-related gene (ERG) fusion affects prostate cancer depending on the degree of expression of ERG. Solute Carrier Family 45 Member 3 (SLC45A3) is the second-most common 5′ partner gene of ERG rearrangement. However, the molecular pathological features of SLC45A3:ERG (S:E) fusion and therapeutic methods have not been studied at all. S:E fusion-positive cancers (n = 10) were selected from the Tumor Fusion Gene Data Portal website. Fusion-negative cancers (n = 50) were selected by sorting ERG expression level in descending order and selecting the bottom to 50th sample. Totally, 1325 ERG correlated genes were identified by a Pearson correlation test using over 0.3 of absolute correlation coefficiency (|R| > 0.3). Pathway analysis was performed using over-representation analysis of correlated genes, and seven cancer-related pathways (focal adhesion kinase (FAK)/PI3K-Akt, JAK-STAT, Notch, receptor tyrosine kinase/PDGF, TGF-β, VEGFA, and Wnt signaling) were identified. In particular, focal adhesion kinase (FAK)/PI3K-Akt signaling and JAK-STAT signaling were significantly enriched in S:E fusion-positive prostate cancer. We further identified therapeutic targets and candidate drugs for S:E fusion-positive prostate cancer using gene–drug network analysis. Interestingly, PDGFRA and PDGFRB were the most frequently predicted therapeutic targets, and imatinib targeted both genes. In this study, we provide extensive information on cellular signaling pathways involved in S:E fusion-positive prostate cancer and also suggest therapeutic methods.

Housekeeping genes (HKGs) are crucial for maintaining basic cellular functions and are consistently expressed across various tissues and cell types, making them essential for normalizing gene expression. Their application is crucial in both basic research and clinical settings, such as breast cancer, where they help in accurate gene expression measurement and tumor subtype classification such as the PAM50 system. However, HKGs are often used without thorough assessment of their variability across different conditions, which may affect the reliability of normalization. We identified 16 candidate HKGs in breast tissue using TCGA RNA-seq data. These genes, along with previously known HKGs such as GAPDH, were evaluated across several breast cancer cell lines and experimental conditions that mimic clinical cancer treatment using quantitative real-time PCR (qPCR). The candidate HKGs were further validated using additional bulk and single cell RNA-seq datasets from the Gene Expression Omnibus (GEO) and by performing droplet digital PCR (ddPCR). We finally concluded that our candidate HKGs, especially EIF4H, GHITM, ATP5F1B, BRK1, and OS9, demonstrated greater stability than GAPDH and RPLP0. These genes were subsequently tested within the PAM50 breast cancer subtyping system, where they improved normalization performance over GAPDH. We have identified novel HKGs useful in breast cancer research and proved that they exhibit more stable expression compared to previously known HKGs. These findings may offer researchers and clinicians a more reliable normalization standard for gene expression analysis, potentially enhancing the accuracy of breast cancer diagnosis and the selection of personalized treatments.