Pancreatic ductal adenocarcinoma (PDAC) is an invasive and aggressive cancer that remains a major threat to human health across the globe. Despite advances in cancer treatments and diagnosis, the prognosis of PDAC patients remains poor. New and more effective PDAC therapies are therefore urgently required. In this study, we identified a novel host factor, namely the LncRNA TP73-AS1, as overexpressed in PDAC tissues compared to adjacent healthy tissue samples. The overexpression of TP-73-AS1 was found to correlate with both PDAC stage and lymph node metastasis. To reveal its role in PDCA, we targeted TP73-AS1 using LnRNA inhibitors in a range of pancreatic cancer (PC) cell lines. We found that the inhibition of TP73-AS1 led to a loss of MMP14 expression in PC cells and significantly inhibited their migratory and invasive capacity. No effects of TP73-AS1 on cell survival or proliferation were observed. Mechanistically, we found that TP73-AS1 suppressed the expression of the known oncogenic miR-200a. Taken together, these data highlight the prognostic potential of TP73-AS1 for PC patients and highlight it as a potential anti-PDAC therapeutic target. 相似文献
动物群落是构成城市绿地生态系统的关键要素,声景作为野生动物重要的生态信息,掌握其时空变化及其影响因素,对于指导城市绿地景观设计与生物多样性保护具有重要意义。本文以Web of Science数据库的核心合集2005–2022年收录的67篇研究文献为对象,综合梳理与分析了城市绿地动物声景的时空模式及其驱动因素。城市绿地动物声景在空间上表现出环境空间梯度和植被空间结构的差异,动物声音多样性随海拔、纬度、城市化程度的降低以及植被类型和高度的增加呈现升高趋势。时间尺度呈现出昼夜、季节和年度变化差异,表现为鸟类在黎明和黄昏合唱、昆虫和两栖动物在夜间鸣叫以及季节性和年度性发声规律等。影响城市动物声景模式的因素主要包括植被、环境、人为干扰和动物自身驱动等。动物声景作为当前声景生态学研究的热点之一,面临大时空尺度演变规律研究不足、动物声景分析有限等挑战,建议未来着重开展多时空尺度变化规律研究、创新动物声景分析方法、定量解析影响因素及其响应机制、建立全球动物声景数据库等。 相似文献
Background: Esophageal cancer (ESCA) is one of the most commonly diagnosed cancers in the world. Tumor immune microenvironment is closely related to tumor prognosis. The present study aimed at analyzing the competing endogenous RNA (ceRNA) network and tumor-infiltrating immune cells in ESCA.Methods: The expression profiles of mRNAs, lncRNAs, and miRNAs were downloaded from the Cancer Genome Atlas database. A ceRNA network was established based on the differentially expressed RNAs by Cytoscape. CIBERSORT was applied to estimate the proportion of immune cells in ESCA. Prognosis-associated genes and immune cells were applied to establish prognostic models basing on Lasso and multivariate Cox analyses. The survival curves were constructed with Kaplan–Meier method. The predictive efficacy of the prognostic models was evaluated by the receiver operating characteristic (ROC) curves.Results: The differentially expressed mRNAs, lncRNAs, and miRNAs were identified. We constructed the ceRNA network including 23 lncRNAs, 19 miRNAs, and 147 mRNAs. Five key molecules (HMGB3, HOXC8, HSPA1B, KLHL15, and RUNX3) were identified from the ceRNA network and five significant immune cells (plasma cells, T cells follicular helper, monocytes, dendritic cells activated, and neutrophils) were selected via CIBERSORT. The ROC curves based on key genes and significant immune cells all showed good sensitivity (AUC of 3-year survival: 0.739, AUC of 5-year survival: 0.899, AUC of 3-year survival: 0.824, AUC of 5-year survival: 0.876). There was certain correlation between five immune cells and five key molecules.Conclusion: The present study provides an effective bioinformatics basis for exploring the potential biomarkers of ESCA and predicting its prognosis. 相似文献
The skin secretions of amphibians are a rich source of bioactive peptides. We isolated chensirin-1 and chensirin-2 from the skin secretion of the Chinese frog Rana chensinensis. Sephadex-G-50 and RP-HPLC were employed to purify these peptides. The amino acid sequences of these peptides were VLPLVGNLLNDLLGE and IIPLPLGYFAKKT, respectively, as determined by Edman degradation. The molecular weights were 1578.7 and 1460.8 Da, respectively, as analyzed by HPLC-ESI-MS. The chensirin cDNA was cloned by 5′ and 3′ amplification of cDNA ends, synthesized and purified. The antibacterial activities of the chensirins were tested using minimum inhibitory concentration, the results indicated that chensirins inhibit the growth of gram-negative and gram-positive bacteria. Among them, chensirin-1 is a novel peptide with a higher antibacterial activity compared to other similar antimicrobial peptides. These low molecular weight peptides with good antimicrobial efficacy are considered potential sources for developing new antimicrobial agents to improve traditional drug resistance.