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Recent studies using several teleost models have revealed that androgens increase the size of previtellogenic (primary and/or early secondary) ovarian follicles. To explore our hypothesis that androgens drive the development of primary follicles into early secondary follicles, and to determine the mechanisms underlying these androgenic effects, we exposed juvenile coho salmon to nearphysiological and relatively sustained levels of the nonaromatizable androgen 11-ketotestosterone (11-KT). This resulted in significant growth of primary ovarian follicles after 10 and 20 days, with follicles after 20 days displaying a morphological phenotype characteristic of early secondary follicles (presence of cortical alveoli). Utilizing the same experimental approach, we then analyzed how 11-KT rapidly altered the ovarian transcriptome after 1 and 3 days of treatment. RNA-Seq analysis revealed that 69 (day 1) and 1,022 (day 3) contiguous sequences (contigs) were differentially expressed relative to controls. The differentially expressed contigs mapped to genes including those encoding proteins involved in gonadotropin, steroid hormone, and growth factor signaling, and in cell and ovarian development, including genes with putative androgen-response elements. Biological functions and canonical pathways identified as potentially altered by 11-KT include those involved in ovarian development, tissue differentiation and remodeling, and lipid metabolism. We conclude that androgens play amajor role in stimulating primary ovarian follicle development and the transition into secondary growth. Summary Sentence In vivo treatment of coho salmon with the androgen, 11-ketotestosterone, dramatically alters the ovarian transcriptome during primary oocyte growth, and promotes the completion of primary ovarian follicle growth.

While transposable elements (TE) are critical drivers of genomic diversity, their influence on phenotypic traits in geese remain largely unexplored, primarily because most research has focused on single nucleotide polymorphisms (SNP). In this study, we identified 157,044 TE absence polymorphisms (TAP) in the genome of 566 Sichuan White geese through whole-genome resequencing (with an average coverage depth of 12.44 ×) to evaluate their influence across different populations, and we extended our investigation to include a TE genome-wide association study (TE-GWAS) encompassing 48 traits, with a particular focus on abdominal fat weight. Notably, a TE within an intron of the CDCC171 gene was identified and significantly affects abdominal fat deposition, exhibiting minimal Linkage with SNPs within a 100 kb region. Additionally, co-expression analysis of ovarian transcriptome data from four geese populations revealed significant correlations between long terminal repeat (LTR) and terminal inverted repeat (TIR) elements and genes related to egg production. Collectively, these findings highlight the potential of TE as key drivers of phenotypic variation in geese, presenting new opportunities for targeted breeding strategies.

Nuclear receptor HR97 is considered as a non-insect arthropod–specific receptor, but its roles in decapod reproduction remain poorly understood. Here, we identified and characterized an HR97 ortholog from the swimming crab Portunus trituberculatus (PtHR97) and verified its placement within the NR1L nuclear receptor family by phylogenetic analysis. PtHR97 encodes a canonical nuclear receptor with a conserved DNA-binding domain (DBD) and ligand-binding domain (LBD). Quantitative PCR revealed predominant PtHR97 expression in the ovary and stage-dependent changes during ovarian development. Using an ovarian explant culture system, we found that arachidonic acid (AA) consistently suppressed PtHR97 transcript levels, while methyl farnesoate (MF) and pyriproxyfen (P) had no significant effect, indicating a potential inhibitory role for AA in PtHR97 expression. RNA interference of HR97 caused significant changes in ovarian development, including reduced GSI, smaller oocytes, and uneven eosinophilic granule distribution. Transcriptomic profiling of HR97-silenced ovaries indicated that the major responses involved genes associated with substrate transport/exchange, cell boundary–related signaling and transduction, and disturbed nuclear transcriptional regulation. Short-term in vivo perturbations (HR97 RNAi and AA treatment) further supported these expression changes and revealed that AA- and HR97 RNAi–elicited transcriptional responses only partially overlapped. Taken together, these results suggest that HR97 contributes to ovarian development, potentially through broad transcriptional responses related to transport, signaling, and gene regulation. Although AA may suppress HR97 expression, HR97 does not fully explain AA-mediated regulation of ovarian development.

The porcine estrous cycle is influenced by reproductive hormones, which affect porcine reproduction and result in physiological changes in the reproductive organs. The ovary is involved in ovulation, luteinization, corpus luteum development, and luteolysis. Here, we aimed to provide a comprehensive understanding of the gene expression patterns in porcine ovarian transcriptomes during the estrous cycle through differentially expressed genes profiling and description of molecular mechanisms. The transcriptomes of porcine ovary were obtained during the estrous cycle at three-day intervals from day 0 to day 18 using RNA-seq. At seven time points of the estrous cycle, 4414 DEG were identified; these were classified into three clusters according to their expression patterns. During the late metestrus and diestrus periods, the expression in cluster 1 increased rapidly, and steroid biosynthesis was significant in the pathway. Cluster 2 gene expression patterns represented the cytokine–cytokine receptor interaction in significant pathways. In cluster 3, the hedgehog signaling pathway was selected as the significant pathway. Our study exhibited dynamic gene expression changes with these three different patterns of cluster 1, 2, and 3. The results helped identify the functions and related significant genes especially during the late metestrus and diestrus periods in the estrous cycle.

Mucinous ovarian cancer accounts for 3% of epithelial ovarian cancers and must be distinguished from mucinous carcinomas that have metastasized to the ovary. Most cases manifest as large, localized masses; resection is associated with a good prognosis.

Epithelial‐mesenchymal transition (EMT) is a reversible and dynamic process hypothesized to be co‐opted by carcinoma during invasion and metastasis. Yet, there is still no quantitative measure to assess the interplay between EMT and cancer progression. Here, we derived a method for universal EMT scoring from cancer‐specific transcriptomic EMT signatures of ovarian, breast, bladder, lung, colorectal and gastric cancers. We show that EMT scoring exhibits good correlation with previously published, cancer‐specific EMT signatures. This universal and quantitative EMT scoring was used to establish an EMT spectrum across various cancers, with good correlation noted between cell lines and tumours. We show correlations between EMT and poorer disease‐free survival in ovarian and colorectal, but not breast, carcinomas, despite previous notions. Importantly, we found distinct responses between epithelial‐ and mesenchymal‐like ovarian cancers to therapeutic regimes administered with or without paclitaxel in vivo and demonstrated that mesenchymal‐like tumours do not always show resistance to chemotherapy. EMT scoring is thus a promising, versatile tool for the objective and systematic investigation of EMT roles and dynamics in cancer progression, treatment response and survival. Synopsis A novel EMT scoring method reveals that EMT status does not unanimously correlate with poorer overall and disease‐free survival. Different EMTed tumours show distinct responses to certain chemotherapeutics, with the potential to stratify patients by EMT status. A novel scoring method was developed based on transcriptomics to universally estimate and compare the Epithelial‐Mesenchymal Transition (EMT) phenotype across cancer types. A spectrum of EMT was established across more than 15 cancers using this EMT scoring method. Correlations of EMT status with poorer overall‐ and disease‐free survival were not unanimously observed in all cancers. Differential and preferential responses of EMTed tumours to certain chemotherapeutics were observed, suggesting the potential to stratify patients by EMT status. Graphical Abstract A novel EMT scoring method reveals that EMT status does not unanimously correlate with poorer overall and disease‐free survival. Different EMTed tumours show distinct responses to certain chemotherapeutics, with the potential to stratify patients by EMT status.

Intratumoral cellular heterogeneity necessitates multi-targeting therapies for improved clinical benefits in advanced malignancies. However, systematic identification of patient-specific treatments that selectively co-inhibit cancerous cell populations poses a combinatorial challenge, since the number of possible drug-dose combinations vastly exceeds what could be tested in patient cells. Here, we describe a machine learning approach, scTherapy, which leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors. Patient-specific treatments reveal a wide spectrum of co-inhibitors of multiple biological pathways predicted for primary cells from heterogenous cohorts of patients with acute myeloid leukemia and high-grade serous ovarian carcinoma, each with unique resistance patterns and synergy mechanisms. Experimental validations confirm that 96% of the multi-targeting treatments exhibit selective efficacy or synergy, and 83% demonstrate low toxicity to normal cells, highlighting their potential for therapeutic efficacy and safety. In a pan-cancer analysis across five cancer types, 25% of the predicted treatments are shared among the patients of the same tumor type, while 19% of the treatments are patient-specific. Our approach provides a widely-applicable strategy to identify personalized treatment regimens that selectively co-inhibit malignant cells and avoid inhibition of non-cancerous cells, thereby increasing their likelihood for clinical success. The identification of treatments that selectively co-inhibit cancerous cell populations remains a challenge. Here, a machine learning approach, scTherapy, leverages single-cell transcriptomic profiles to prioritize multi-targeting treatment options for individual patients with hematological cancers or solid tumors.

Despite advances in cancer treatments, epithelial ovarian cancer (EOC) remains the leading cause of death among gynecologic cancers. EOC is stratified into five main histopathological subtypes: high-grade serous carcinoma (HGSC), low-grade serous carcinoma (LGSC), endometrioid carcinoma (EC), clear cell carcinoma (CCC), and mucinous carcinoma (MC). However, personalized treatment strategies and reliable biomarkers for all histotypes remain elusive. Building on our previous work with early-stage EOC, we aim to explore diagnostic and prognostic biomarkers in advanced-stage EOC, updated to the latest World Health Organization classification guidelines from 2020, using comprehensive transcriptomic profiling from total RNA sequencing of 146 EOCs. Differential expression analysis identified top 9 histotype-specific gene panels for HGSC, CCC, MC, and EC, including S100A1 (HGSC), ARID3A (CCC), LGALS4 (MC), and PAX9 (EC). We also identified gene candidates associated with overall survival and disease-specific survival, reflecting both favorable (e.g., OTOF , EEF1E1-BLOC1S5 , and STAC3 ) and unfavorable (e.g., SMOC1 , GDPGP1 , EPRS1 ) clinical outcome. Additionally, enrichment analysis revealed tumor progression-related pathways unique to each histotype, offering insights into the molecular mechanisms underlying disease progression and potential therapeutic targets. These findings provide valuable insights into the molecular landscape of advanced-stage EOC, paving the way for more effective diagnostic and prognostic tools across diverse histotypes.

Identifying tumor antigen-specific T cells from cancer patients has important implications for immunotherapy diagnostics and therapeutics. Here, we show that CD103 + CD39 + tumor-infiltrating CD8 T cells (CD8 TIL) are enriched for tumor-reactive cells both in primary and metastatic tumors. This CD8 TIL subset is found across six different malignancies and displays an exhausted tissue-resident memory phenotype. CD103 + CD39 + CD8 TILs have a distinct T-cell receptor (TCR) repertoire, with T-cell clones expanded in the tumor but present at low frequencies in the periphery. CD103 + CD39 + CD8 TILs also efficiently kill autologous tumor cells in a MHC-class I-dependent manner. Finally, higher frequencies of CD103 + CD39 + CD8 TILs in patients with head and neck cancer are associated with better overall survival. Our data thus describe an approach for detecting tumor-reactive CD8 TILs that will help define mechanisms of existing immunotherapy treatments, and may lead to future adoptive T-cell cancer therapies. Identifying and enumerating tumor-specific CD8 T cells are important for assessing cancer prognosis and therapy efficacy. Here the authors show that CD39 and CD103 mark a subset of tumor-infiltrating CD8 T cells that are tumor-reactive and exhibit characteristics of exhausted or tissue-resident memory T cells.

In this study, spatial and single-cell transcriptome techniques were used to investigate the role of beta-galactoside alpha-2,6-sialyltransferase 1 (ST6GAL1) in promoting peritoneal metastasis in ovarian cancer epithelial cells. We collected single-cell transcriptomic (GSE130000) and spatial transcriptomic datasets (GSE211956) from the Gene Expression Omnibus and RNA-sequencing data from The Cancer Genome Atlas. The Robust Cell Type Decomposition (RCTD) approach was implemented to integrate spatial and single-cell transcriptomic data. In addition, pseudo-time trajectory analysis, cell-cell communication networks, transcription factor activity profiling, spatial interaction mapping, and prognostic significance of gene expression were assessed. A significant enrichment of ST6GAL1 was observed in the epithelial cells of ovarian cancer, particularly in peritoneal metastases, which exhibited elevated metabolic activity compared to primary tumors. The levels of ST6GAL1 were significantly high in peritumoral and adjacent non-tumorous tissues, with increased metabolic activity, while the tumor core demonstrated ST6GAL1-negative epithelial cells. Extensive cell-cell communication and transcription factor networks were unraveled, potentially influencing vascular permeability and intracellular signaling. Clinically, high expression of ST6GAL1 in epithelial cells is associated with diminished progression-free survival, indicating its prognostic potential. In conclusion, ST6GAL1 is likely to significantly impact the progression and metastasis of ovarian cancer.