Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Purpose: The genomic characteristics of uterine sarcomas have not been fully elucidated. This study aimed to explore the genomic landscape of the uterine sarcomas (USs). Materials and Methods: Comprehensive genomic analysis through RNA-sequencing was conducted. Gene fusion, differentially expressed genes (DEGs), signaling pathway enrichment, immune cell infiltration, and prognosis were analyzed. A deep learning model was constructed to predict the survival of US patients. Results: A total of 71 US samples were examined, including 47 endometrial stromal sarcomas (ESS), 18 uterine leiomyosarcomas (uLMS), three adenosarcomas, two carcinosarcomas, and one uterine tumor resembling an ovarian sex-cord tumor. ESS (including high-grade ESS [HGESS] and low-grade ESS [LGESS]) and uLMS showed distinct gene fusion signatures; a novel gene fusion site, MRPS18A–PDC-AS1 could be a potential diagnostic marker for the pathology differential diagnosis of uLMS and ESS; 797 and 477 uterine sarcoma DEGs (uDEGs) were identified in the ESS vs. uLMS and HGESS vs. LGESS groups, respectively. The uDEGs were enriched in multiple pathways. Fifteen genes including LAMB4 were confirmed with prognostic value in USs; immune infiltration analysis revealed the prognositic value of myeloid dendritic cells, plasmacytoid dendritic cells, natural killer cells, macrophage M1, monocytes and hematopoietic stem cells in USs; the deep learning model named Max-Mean Non-Local multi-instance learning (MMN-MIL) showed satisfactory performance in predicting the survival of US patients, with the area under the receiver operating curve curve reached 0.909 and accuracy achieved 0.804. Conclusion USs harbored distinct gene fusion characteristics and gene expression features between HGESS, LGESS, and uLMS. The MMN-MIL model could effectively predict the survival of US patients.

Objective To investigate the role and mechanism of the zinc finger protein Kruppel-like transcription factor 9 (KLF9) in the stimulation of type I interferon expression induced by herpes simplex virus type 1 (HSV-1) in macrophages. Methods Agarose Gel electrophoresis, quantitative real-time PCR (qRT-PCR) and western blot analyses were employed to detect the KLF9 relative expression in bone marrow-derived macrophages (BMDMs) from Klf9^-/- (gKO) mice and wild-type (WT) mice. RNA-seq analysis was utilized to identify the potential targeted genes upon HSV-1 stimulation in BMDMs. ELISA was used to measure the potent of IFN-β in the supernatant of BMDMs derived from gKO and WT mice after HSV-1 stimulation. qRT-PCR analysis was employed to further confirm the changes of Ifnb1 and interferon-stimulated gene (ISG) such as interferon-induced protein with tetratricopeptide repeats 1 (Ifit1), interferon-stimulated exonuclease gene 20 (Isg20), cholesterol 25-hydroxylase (Ch25h) and 2'-5' oligoadenylate synthetase-like 1 (Oasl1). Western blot was used to detect the expression of phosphorylated interferon regulatory factor-3 (p-IRF3), IRF3, phosphorylated interferon regulatory factor-7 (p-IRF7), IRF7, phosphorylated nuclear factor-kappa B p65 (p-NF-κB p65) and NF-κB p65. CUT-Tag and ChIP-qPCR assay were utilized to confirm the binding region of KLF9 in Ifnb1. Results The KLF9 expression was significantly decreased in BMDMs from gKO mice compared with that from WT mice. The RNA-seq analysis showed that Klf9 deletion in BMDMs resulted in an impaired type I interferon signaling pathway. The qRT-PCR analysis revealed that Klf9 deletion in BMDMs led to a significant decrease of Ifnb1 and ISG such as Ifit1, Ch25h and Oasl1 except Isg20. Moreover, ELISA revealed that Klf9 knockout in BMDMs resulted in a significant decrease of IFN-β secreted from BMDMs. Mechanistically, KLF9 directly binds to the promoter of Ifnb1. Conclusion KLF9 is essential for macrophages to resist HSV-1 infection.
Animals ; Kruppel-Like Transcription Factors/physiology* ; Interferon-beta/metabolism* ; Macrophages/virology* ; Mice ; Herpesvirus 1, Human/physiology* ; Mice, Knockout ; Signal Transduction ; Mice, Inbred C57BL ; Interferon Regulatory Factor-3/genetics* ; Interferon Regulatory Factor-7/genetics* ; Gene Expression Regulation