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Background: Ovarian cancer (OC) is the most lethal gynecologic malignancy, yet the clinical results for OC patients are still variable. Therefore, we examined how elafin expression affects the patients' prognoses and immunotherapy responses in OC, which may facilitate treatment selection and improve prognosis. Methods: The elafin mRNA expression profile was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus. Elafin's prognostic potential and its relationship with clinical variables were investigated using Kaplan-Meier survival curves, time-dependent receiver operating characteristic curves as well as univariate and multivariate Cox regression models. As validation, protein expression in the tumor and adjacent tissues of OC patients was investigated by using immunohistochemistry (IHC). Comprehensive analyses were then conducted to explore the correlation between immune infiltration and elafin expression. Results: A higher mRNA expression of elafin was associated with an unfavorable prognosis in TCGA cohort and was validated in GSE31245 and IHC. Moreover, elafin was indicated as an independent risk factor for OC. A significantly higher protein expression of elafin was detected in the adjacent tissues of OC patients with shorter overall survival (OS). The immune-related pathways were mainly enriched in the high-elafin-mRNA-expression group. However, the mRNA expression of elafin was favorably correlated with indicators of the immune filtration and immunotherapy response, which also proved better immunotherapy outcomes. Conclusion: The high elafin expression was associated with an unfavorable OS, while it also indicated better immunotherapy responses. Thus, the detection of elafin is beneficial to diagnosis and treatment selection.

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BACKGROUND: Breast cancer (BRCA) is one of the most common cancers in women worldwide and a leading cause of death from malignancy. This study was designed to identify a novel biomarker for prognosticating the survival of BRCA patients. METHODS: The prognostic potential of eukaryotic translation initiation factor 4 gamma 1 (EIF4G1) was assessed using RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) as training cohort and validation set, respectively. The functional enrichment analysis of differentially expressed genes (DEGs) was performed. The relationship between EIF4G1 and tumor microenvironment (TME) was analyzed. Immunotherapy responses were explored by the immunophenoscores (IPS) and tumor immune dysfunction and exclusion (TIDE) score. The Connectivity Map (CMap) was used to discover potentially effective therapeutic molecules against BRCA. Immunohistochemistry (IHC) was applied to compare the protein levels of EIF4G1 in normal and cancer tissues and to verify the prognostic value of EIF4G1. RESULTS: BRCA patients with increased expression of EIF4G1 had a shorter overall survival (OS) in all cohorts and results from IHC. EIF4G1-related genes were mainly involved in DNA replication, BRCA metastasis, and the MAPK signaling pathway. Infiltration levels of CD4+-activated memory T cells, macrophages M0, macrophages M1, and neutrophils were higher in the EIF4G1 high-expression group than those in the EIF4G1 low-expression group. EIF4G1 was positively correlated with T cell exhaustion. Lower IPS was revealed in high EIF4G1 expression patients. Five potential groups of drugs against BRCA were identified. CONCLUSION: EIF4G1 might regulate the TME and affect BRCA metastasis, and it is a potential prognostic biomarker and therapeutic target for BRCA.

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Polycystic ovary syndrome (PCOS) exerts negative effects on females of childbearing age. It is important to identify more suitable models for fundamental research on PCOS. We evaluated animal models from a novel perspective with the aim of helping researchers select the best model for PCOS. RNA sequencing was performed to investigate the mRNA expression profiles in the ovarian tissues of mice with dehydroepiandrosterone (DHEA) plus high-fat diet (HFD)-induced PCOS. Meanwhile, 14 datasets were obtained from the Gene Expression Omnibus (GEO), including eight studies on humans, three on rats and three on mice, and genes associated with PCOS were obtained from the PCOSKB website. We compared the consistency of each animal model and human PCOS in terms of DEGs and pathway enrichment analysis results. There were 239 DEGs shared between prenatally androgenized (PNA) mice and PCOS patients. Moreover, 1113 genes associated with PCOS from the PCOSKB website were identified among the DEGs of PNA mice. A total of 134 GO and KEGG pathways were shared between PNA mice and PCOS patients. These findings suggest that the PNA mouse model is the best animal model to simulate PCOS.

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Background: Ovarian cancer (OC) is one of the most lethal gynecologic malignancies and a leading cause of death in the world. Thus, this necessitates identification of prognostic biomarkers which will be helpful in its treatment. Methods: The gene expression profiles from The Cancer Genome Atlas (TCGA) and GSE31245 were selected as the training cohort and validation cohort, respectively. The Kaplan-Meier (KM) survival analysis was used to analyze the difference in overall survival (OS) between high and low RB transcriptional corepressor 1 (RB1) expression groups. To confirm whether RB1 was an independent risk factor for OC, we constructed a multivariate Cox regression model. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses were conducted to identify the functions of differentially expressed genes (DEGs). The associations of RB1 with immune infiltration and immune checkpoints were studied by the Tumor Immune Estimation Resource (TIMER 2.0) and the Gene Expression Profiling Interactive Analysis (GEPIA). The immunohistochemistry (IHC) was performed to compare the expression level of RB1 in normal tissues and tumor samples, and to predict the prognosis of OC. Results: The KM survival curve of the TCGA indicated that the OS in the high-risk group was lower than that in the low-risk group (HR = 1.61, 95% CI: 1.28-2.02, P = 3×10-5), which was validated in GSE31245 (HR = 4.08, 95% CI: 1.21-13.74, P = 0.01) and IHC. Multivariate Cox regression analysis revealed that RB1 was an independent prognostic biomarker (HR = 1.66, 95% CI: 1.31-2.10, P = 2.02×10-5). Enrichment analysis suggested that the DEGs were mainly involved in cell cycle, DNA replication, and mitochondrial transition. The infiltration levels of fibroblast, neutrophil, monocyte and macrophage were positively correlated with RB1. Furthermore, RB1 was associated with immune checkpoint molecules (CTLA4, LAG3, and CD274). The IHC staining revealed higher expression of RB1 in tumor tissues as compared to that in normal tissues (P = 0.019). Overexpression of RB1 was associated with poor prognosis of OC (P = 0.01). Conclusion: These findings suggest that RB1 was a novel and immune-related prognostic biomarker for OC, which may be a promising target for OC treatment.