间充质干细胞(MSC),也称为多能基质细胞,是一种首次在骨髓中发现的非造血干细胞群,目前已从各种成体组织来源中分离出来,是能够分化为多种间充质组织(如脂肪和骨骼)的成熟细胞的多能细胞。MicroRNAs (miRNA)是一种高度保守的内源性非蛋...间充质干细胞(MSC),也称为多能基质细胞,是一种首次在骨髓中发现的非造血干细胞群,目前已从各种成体组织来源中分离出来,是能够分化为多种间充质组织(如脂肪和骨骼)的成熟细胞的多能细胞。MicroRNAs (miRNA)是一种高度保守的内源性非蛋白质编码RNA,通过翻译抑制或降解其靶标来调节基因表达,在调节BMSC分化中起主要作用。本文探讨了miRNAs骨髓间充质干细胞成骨分化中的作用。Mesenchymal stem cells (MSCs), also known as pluripotent stromal cells, are a population of non-hematopoietic stem cells first discovered in the bone marrow that have been isolated from various adult tissue sources and are pluripotent cells capable of differentiating into mature cells of a variety of mesenchymal tissues, such as fat and bone. MicroRNAs (miRNAs) are highly conserved endogenous non-protein-coding RNAs that play a major role in regulating BMSC differentiation by translating inhibition or degradation of their targets to regulate gene expression. This article explores the role of miRNAs in osteogenic differentiation of bone marrow mesenchymal stem cells.展开更多
Cells are exposed to various mechanical forces,including extracellular and intracellular forces such as stiffness,tension,compression,viscosity,and shear stress,which regulate cell biology.The process of transducing m...Cells are exposed to various mechanical forces,including extracellular and intracellular forces such as stiffness,tension,compression,viscosity,and shear stress,which regulate cell biology.The process of transducing mechanical stimuli into biochemical signals is termed mechanotransduction.These mechanical forces can regulate protein and gene expression,thereby impacting cell morphology,adhesion,proliferation,apoptosis,and migration.During cancer development,significant changes in extracellular and intracellular mechanical properties occur,resulting in altered mechanical inputs to which cells are exposed.MicroRNAs(miRNAs),key post-transcriptional regulators of gene and protein expression,are increasingly recognized as mechanosensitive molecules involved in cancer development.In this review,we summarize the primary cellular pathways involved in force sensing and mechanotransduction,emphasizing the role of forces in miRNA biogenesis and expression,as well as their influence on the regulation of key mechanotransducers.Furthermore,we focus on recent evidence regarding the induction or repression of miRNAs involved in cancer development by mechanical forces and their impact on the regulation of proteins that contribute to cancer progression.展开更多
BACKGROUND Early screening methods for gastric cancer(GC)are lacking;therefore,the disease often progresses to an advanced stage when patients first start to exhibit typical symptoms.Endoscopy and pathological biopsy ...BACKGROUND Early screening methods for gastric cancer(GC)are lacking;therefore,the disease often progresses to an advanced stage when patients first start to exhibit typical symptoms.Endoscopy and pathological biopsy remain the primary diagnostic approaches,but they are invasive and not yet widely applicable for early popu-lation screening.miRNA is a highly conserved type of RNA that exists stably in plasma.Dysfunction of miRNA is linked to tumorigenesis and progression,indicating that individual miRNAs or combinations of multiple miRNAs may serve as potential biomarkers.AIM To identify effective plasma miRNA biomarkers and investigate the clinical value of combining multiple miRNAs for early detection of GC.METHODS Plasma samples from multiple centres were collected.Differentially expressed genes among healthy controls,early-stage GC patients,and advanced-stage GC patients were identified through small RNA sequencing(sRNA-seq)and validated via real-time quantitative reverse transcription polymerase chain reaction(RT-qPCR).A Wilcoxon signed-rank test was used to investigate the differences in miRNAs.Sequencing datasets of GC serum samples were retrieved from the Gene Expression Omnibus(GEO),ArrayExpress,and The Cancer Genome Atlas databases,and a multilayer perceptron-artificial neural network(MLP-ANN)model was constructed for the key risk miRNAs.The pROC package was used to assess the discriminatory efficacy of the model.RESULTS Plasma samples of 107 normal,71 early GC and 97 advanced GC patients were obtained from three centres,and serum samples of 8443 normal and 1583 GC patients were obtained from the GEO database.The sRNA-seq and RT-qPCR experiments revealed that miR-452-5p,miR-5010-5p,miR-27b-5p,miR-5189-5p,miR-552-5p and miR-199b-5p were significantly increased in early GC patients compared with healthy controls and in advanced GC patients compared with early GC patients(P<0.05).An MLP-ANN model was constructed for the six key miRNAs.The area under the curve(AUC)within the training cohort was 0.983[95% confidence interval(CI):0.980–0.986].In the two validation cohorts,the AUCs were 0.995(95%CI:0.987 to nearly 1.000)and 0.979(95%CI:0.972–0.986),respectively.CONCLUSION Potential miRNA biomarkers,including miR-452-5p,miR-5010-5p,miR-27b-5p,miR-5189-5p,miR-552-5p and miR-199b-5p,were identified.A GC classifier based on these miRNAs was developed,benefiting early detection and population screening.展开更多
文摘背景与目的肺癌是全球癌症死亡的主要原因之一,约80%的肺癌属于非小细胞肺癌(non-small cell lung cancer,NSCLC),其中肺鳞癌(lung squamous cell carcinoma,LUSC)在NSCLC中占据重要比例。尽管肿瘤的综合治疗极大提升了患者的总生存期,但晚期LUSC患者的预后较差。急需一种生物标志物来预测晚期LUSC患者的预后,以便通过早期诊断,改善预后。研究发现miRNAs在肺癌组织中差异表达,并作为潜在的致癌或抑癌基因发挥作用,本研究旨在筛选出早期和晚期LUSC差异表达的miRNAs,构建用于预测晚期LUSC患者预后的一组miRNAs标志物。方法从癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库中下载LUSC患者临床信息及miRNAs的相关数据。应用生物信息学方法分析数据,绘制受试者工作特征(receiver operating characteristic,ROC)曲线,利用多种在线分析工具预测靶基因,分析靶基因的潜在生物学机制。结果两组间共鉴定出58个差异表达的miRNAs。根据LASSO回归筛选出7个miRNAs拟构建miRNAs标志物,又根据每个miRNAs的ROC曲线下面积(area under the curve,AUC)值,最终选取其中的4个mRNAs(miR-377-3p、miR-4779、miR-6803-5p、miR-3960)作为预测晚期LUSC患者的生物标志物。4个miRNAs联合的AUC值为0.865。富集分析显示这些靶基因富集在癌症通路、促分裂素原活化蛋白激酶(mitogen-activated protein kinase,MAPK)通路、丝氨酸/苏氨酸激酶(serine/threonine kinase,STK)及酪氨酸激酶信号通路等多种通路。结论miR-377-3p、miR-4779、miR-6803-5p、miR-3960联合预测晚期LUSC患者预后能力良好,AUC可达0.865。
文摘间充质干细胞(MSC),也称为多能基质细胞,是一种首次在骨髓中发现的非造血干细胞群,目前已从各种成体组织来源中分离出来,是能够分化为多种间充质组织(如脂肪和骨骼)的成熟细胞的多能细胞。MicroRNAs (miRNA)是一种高度保守的内源性非蛋白质编码RNA,通过翻译抑制或降解其靶标来调节基因表达,在调节BMSC分化中起主要作用。本文探讨了miRNAs骨髓间充质干细胞成骨分化中的作用。Mesenchymal stem cells (MSCs), also known as pluripotent stromal cells, are a population of non-hematopoietic stem cells first discovered in the bone marrow that have been isolated from various adult tissue sources and are pluripotent cells capable of differentiating into mature cells of a variety of mesenchymal tissues, such as fat and bone. MicroRNAs (miRNAs) are highly conserved endogenous non-protein-coding RNAs that play a major role in regulating BMSC differentiation by translating inhibition or degradation of their targets to regulate gene expression. This article explores the role of miRNAs in osteogenic differentiation of bone marrow mesenchymal stem cells.
文摘Cells are exposed to various mechanical forces,including extracellular and intracellular forces such as stiffness,tension,compression,viscosity,and shear stress,which regulate cell biology.The process of transducing mechanical stimuli into biochemical signals is termed mechanotransduction.These mechanical forces can regulate protein and gene expression,thereby impacting cell morphology,adhesion,proliferation,apoptosis,and migration.During cancer development,significant changes in extracellular and intracellular mechanical properties occur,resulting in altered mechanical inputs to which cells are exposed.MicroRNAs(miRNAs),key post-transcriptional regulators of gene and protein expression,are increasingly recognized as mechanosensitive molecules involved in cancer development.In this review,we summarize the primary cellular pathways involved in force sensing and mechanotransduction,emphasizing the role of forces in miRNA biogenesis and expression,as well as their influence on the regulation of key mechanotransducers.Furthermore,we focus on recent evidence regarding the induction or repression of miRNAs involved in cancer development by mechanical forces and their impact on the regulation of proteins that contribute to cancer progression.
基金Supported by the Guangxi Zhuang Autonomous Region Health Commission Scientific Research Project,No.Z-A20220465Guangxi Key R and D Plan,No.AB20297021+2 种基金Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project,No.S2022107China Undergraduate Innovation and Entrepreneurship Training Program,No.S202310598074Future Academic Star of Guangxi Medical University,No.WLXSZX23109.
文摘BACKGROUND Early screening methods for gastric cancer(GC)are lacking;therefore,the disease often progresses to an advanced stage when patients first start to exhibit typical symptoms.Endoscopy and pathological biopsy remain the primary diagnostic approaches,but they are invasive and not yet widely applicable for early popu-lation screening.miRNA is a highly conserved type of RNA that exists stably in plasma.Dysfunction of miRNA is linked to tumorigenesis and progression,indicating that individual miRNAs or combinations of multiple miRNAs may serve as potential biomarkers.AIM To identify effective plasma miRNA biomarkers and investigate the clinical value of combining multiple miRNAs for early detection of GC.METHODS Plasma samples from multiple centres were collected.Differentially expressed genes among healthy controls,early-stage GC patients,and advanced-stage GC patients were identified through small RNA sequencing(sRNA-seq)and validated via real-time quantitative reverse transcription polymerase chain reaction(RT-qPCR).A Wilcoxon signed-rank test was used to investigate the differences in miRNAs.Sequencing datasets of GC serum samples were retrieved from the Gene Expression Omnibus(GEO),ArrayExpress,and The Cancer Genome Atlas databases,and a multilayer perceptron-artificial neural network(MLP-ANN)model was constructed for the key risk miRNAs.The pROC package was used to assess the discriminatory efficacy of the model.RESULTS Plasma samples of 107 normal,71 early GC and 97 advanced GC patients were obtained from three centres,and serum samples of 8443 normal and 1583 GC patients were obtained from the GEO database.The sRNA-seq and RT-qPCR experiments revealed that miR-452-5p,miR-5010-5p,miR-27b-5p,miR-5189-5p,miR-552-5p and miR-199b-5p were significantly increased in early GC patients compared with healthy controls and in advanced GC patients compared with early GC patients(P<0.05).An MLP-ANN model was constructed for the six key miRNAs.The area under the curve(AUC)within the training cohort was 0.983[95% confidence interval(CI):0.980–0.986].In the two validation cohorts,the AUCs were 0.995(95%CI:0.987 to nearly 1.000)and 0.979(95%CI:0.972–0.986),respectively.CONCLUSION Potential miRNA biomarkers,including miR-452-5p,miR-5010-5p,miR-27b-5p,miR-5189-5p,miR-552-5p and miR-199b-5p,were identified.A GC classifier based on these miRNAs was developed,benefiting early detection and population screening.
