Objective To investigate the clinical value of transthyretin (TTR) from patients with early rheumatoid arthritis (ERA). Methods 58 patients with ERA , 34 patients with later RA (LRA) and 34 healthy control (HC) were included in the research. TTR was analyzed by ELISA, whose variance was analyzed. TTR density, disease activity score28 (DAS28) score and rheumatoid factor (RF) were tested, and their correlation with TTR was analyzed. Results Serum level of TTR with ERA significantly increased compared with that with LRA and HC (P < 0.05), no statistical significance with LRA group and HC. TTR level was no correlation with the number of swelling and tender joints, disease activity score 28, RF, ESR, CRP, anti-cyclic citrylinated peptide antibody and anti-keratin antibodies, hemoglobin, thrombocyte and albumin. Conclusion Serum level of TTR significantly increased with ERA patients, contributing to early diagnosis for RA.

Cardiac Catheterization ; Ductus Arteriosus, Patent ; therapy ; Heart Septal Defects, Atrial ; therapy ; Humans ; Printing, Three-Dimensional ; Vena Cava, Inferior

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.

Purpose: NUF2 has been implicated in multiple cancers recently, suggesting NUF2 may play a role in the common tumorigenesis process. In this study, we aim to perform comprehensive meta-analysis of NUF2 expression in the cancer types included in the Cancer Genome Atlas (TCGA). Materials and Methods: RNA-sequencing data in 31 cancer types in the TCGA data and 11 independent datasets were used to examine NUF2 expression. Silencing NUF2 using targeting shRNAs in hepatocellular carcinoma (HCC) cell lines was used to evaluate NUF2’s role in HCC in vitro and in vivo. Results: NUF2 up-regulation is significantly observed in 23 out of the 31 cancer types in the TCGA datasets and validated in 13 major cancer types using 11 independent datasets. NUF2 overexpression was clinically important as high NUF2 was significantly associated with tumor stages in eight different cancers. High NUF2 was also associated with significantly poorer patient overall survival and disease-free survival in eight and six cancers, respectively. We proceeded to validate NUF2 overexpression and its negative association with overall survival at the protein level in an independent cohort of 40 HCC patients. Compared to the non-targeting controls, NUF2 knockdown cells showed significantly reduced ability to grow, migrate into a scratch wound and invade the 8 μm porous membrane in vitro. Moreover, NUF2 knockdown cells also formed significantly smaller tumors than control cells in mouse xenograft assays in vivo. Conclusion NUF2 up-regulation is a common feature of many cancers. The prognostic potential and functional impact of NUF2 up-regulation warrant further studies.