The use of fentanyl and other opioids during pregnancy is a pressing public health issue due to its association with Neonatal Opioid Withdrawal Syndrome (NOWS) and long-term neurobehavioral deficits. Human epidemiologic studies are confounded by both genetic and environmental factors that differ between exposed and unexposed children. We developed a novel rat model of perinatal fentanyl exposure in heterogeneous stock (HS) rats characterized by high genetic diversity, to investigate NOWS symptoms and its long-term effects on adult fentanyl self-administration, drug-seeking behavior, and central amygdala (CeA) transcriptomic changes, addressing a critical gap in understanding synthetic opioid impacts. Offspring born to fentanyl-exposed dams exhibited reduced survival, lower body weight, spontaneous withdrawal symptoms, and mechanical hypersensitivity during adolescence. These rats displayed negative affect in adolescence, while they showed increased fentanyl self-administration, heightened drug-seeking during reinstatement, and elevated corticosterone levels during withdrawal in adulthood. To explore the molecular underpinnings of these physiological and behavioral outcomes, we conducted RNA-seq in the CeA of adult rats, revealing dysregulated pathways related to GPCR signaling, adaptive immune response and neurodevelopmental processes. These transcriptional changes provide insights into the mechanisms driving addiction vulnerability and stress-related behaviors following early fentanyl exposure. Our findings highlight the lasting impact of perinatal opioid exposure in an experimental system that avoids many of the confounds that plague studies in humans, underscoring the need for preclinical models to study NOWS and its long-term consequences. This model offers translational relevance for developing therapeutic strategies to mitigate NOWS and reduce neuropsychiatric risks associated with perinatal opioid exposure.

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.

Gene fusion between androgen receptor (AR) response genes and E26 transformation-specific (ETS) family members increases the gene expression of ETS family members, and promotes tumorigenesis in prostate cancer. However, the molecular features of ETV4 fusion in prostate cancer are not fully understood, and drugs targeting ETV4 fusion have not been developed. To examine key cellular signaling pathways and explore therapeutic targets and drugs for ETV4-fusion-positive prostate cancer, we analyzed RNA sequencing data and clinical information for prostate cancer. The ETV4-fusion-positive group was selected through prior study and analysis comparing ETV4-fusion-positive and -negative groups was conducted using a Pearson correlation test. We obtained 393 genes correlated with ETV4 expression. Pathway analysis was performed using over-representation analysis (ORA), and six cancer-specific molecular signaling pathways (the irinotecan pathway, metabolism, androgen receptor signaling, interferon signaling, MAPK/NF-kB signaling, and the tamoxifen pathway) were altered in the ETV4-fusion-positive group. Furthermore, a gene–drug database was used to find an actionable drug and therapeutic target for the ETV4-fusion-positive group. Here, we have identified significantly altered genes and oncogenic signaling pathways in ETV4-fusion-positive prostate cancer, and we suggest therapeutic targets and potential drugs for ETV4-fusion-positive prostate patients.