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.

Baihuasheshecao(Hedyotis diffusa) is a commonly used traditional Chinese medicine derived from the whole herb of H. diffusa and has been widely utilized in folk medicine. It possesses anti-tumor, antibacterial, and anti-inflammatory properties, making it one of the frequently used herbs in TCM clinical practice. However, Shuixiancao(H. corymbosa) and Xianhuaercao(H. tenelliflora), species of the same genus, are often used as substitutes for Baihuasheshecao. To substantiate the medicinal basis of Baihuasheshecao, this study systematically reviewed classical herbal texts and modern literature, examining its nomenclature, botanical origin, harvesting, processing, properties, meridian tropism, pharmacological effects, and clinical applications. The results indicate that Baihuasheshecao was initially recorded as "Shuixiancao" in Preface to the Indexes to the Great Chinese Botany(Zhi Wu Ming Shi Tu Kao). Based on its morphological characteristics and habitat description, it was identified as H. diffusa in the Rubiaceae family. Subsequent records predominantly refer to it as Baihuasheshecao as its official name. In most regions, Baihuasheshecao is recognized as the authentic medicinal material, distinct from Shuixiancao and Xianhuaercao. Baihuasheshecao is harvested in late summer and early autumn, and the dried whole plant, including its roots, is used medicinally. The standard processing method involves cutting. It is known for its effects in clearing heat, removing toxins, reducing swelling and pain, and promoting diuresis to resolve abscesses. Initially, it was mainly used for treating appendicitis, intestinal abscesses, and venomous snake bites, and later, it became a treatment for cancer. The excavation of its clinical value followed a process in which overseas Chinese introduced the herb from Chinese folk medicine to other countries. After its unique anti-cancer effects were recognized abroad, it was reintroduced to China and gradually became a crucial TCM for cancer treatment. The findings of this study help clarify the historical and contemporary uses of Baihuasheshecao, providing literature support and a scientific basis for its rational development and precise clinical application.
Humans ; China ; Drugs, Chinese Herbal/chemistry* ; Hedyotis/classification* ; Medicine, Chinese Traditional/history*

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: miR-34c-3p is down-regulated in nasopharyngeal carcinoma (NPC). The biological role of miR-34c-3p in NPC and its underlying mechanisms are unknown and were explored in this study. METHODS: Flow cytometry and immunohistochemical staining were employed to detect cluster of differentiation 86 (CD86) and cluster of differentiation 206 (CD206) expression; quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting were employed to examine mRNA expression and protein levels; cell counting kit-8 (CCK8) and transwell assays were employed to assess cell proliferation, migration, and invasion; and hematoxylin-eosin (HE) staining was employed to assess pathological changes in tumor tissues. RESULTS: Our results revealed that the miR-34c-3p mimic markedly inhibited M2 polarization of macrophages by targeting SLC7A11, and M2 macrophages transfected with the miR-34c-3p mimic inhibited the proliferation, migration, and invasion of NPC cells. The in vivo experiments further confirmed that miR-34c-3p mimics blocked tumor growth and reduced inflammatory infiltration in tumor tissues. CONCLUSION This study provides novel insights into the pathogenesis of NPC and a new treatment strategy.
MicroRNAs/metabolism* ; Nasopharyngeal Carcinoma/genetics* ; Humans ; Animals ; Nasopharyngeal Neoplasms/genetics* ; Macrophages/physiology* ; Cell Line, Tumor ; Mice ; Cell Proliferation ; Mice, Inbred BALB C ; Cell Movement ; Male ; Gene Expression Regulation, Neoplastic ; Mice, Nude ; Female