Role and Validation of Lactylation-Related Gene Markers in Postmenopausal Osteoporosis.

IF 3.1 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Fuzhu Tan, Xinmei Cui, Shujun Ren, Yu Zhang
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引用次数: 0

Abstract

Background: Osteoporosis (OP) is a systemic bone disease characterized by bone loss, disrupted bone structure, and increased susceptibility to fractures. Postmenopausal osteoporosis (PMOP) refers to OP that occurs in women during the late postmenopausal period, with the main cause being a decrease in estrogen levels. Lactylation, as a glycation modification, may affect bone cell function in PMOP. However, its specific role in the development of PMOP remains unclear.

Methods: In this study, we collected single-cell RNA sequencing (scRNA-seq) data and transcriptome data of PMOP. The scRNA-seq data were processed using the "Seurat" package, including cell filtering, normalization, dimensionality reduction, and clustering. The cell types were annotated using the "singleR" package. Based on lactylation-related genes (LRGs), all cells were divided into high- and low-expression cell groups. Differences in signaling pathways, developmental trajectories, and transcription factor activity between the two cell groups were explored using the "fgsea," "monocle," and "DoRothEA" packages, respectively. Mendelian randomization (MR) analysis was performed to identify genes with significant differential expression between the two cell groups that are causally related to PMOP. The differentially expressed genes (DEGs) between the high- and low-expression cell groups were selected using the "limma" package, and the intersection with DEGs was taken. A diagnostic model for PMOP was constructed using multiple machine learning algorithms and their combinations based on the intersection genes. Immune infiltration analysis was performed on the transcriptome data using the ssGSEA algorithm. Finally, a column line plot model of PMOP was constructed based on diagnostic genes.

Results: After annotating the cell types in the scRNA-seq data, a total of 11 cell types were obtained, including neutrophils, tissue stem cells, monocyte, macrophage, erythroblast, myelocyte, Pre-B cell CD34-, BM, T cells, B cell, and Pro-B cell CD34 + . The high- and low-expression cell groups divided based on the expression levels of LRGs showed significant differences in signaling pathways, developmental trajectories, and transcription factor activity. The MR analysis identified RPS10 and RPL12 as risk factors causally related to PMOP. A diagnostic model for PMOP was constructed based on the transcriptome data and the intersection DEGs between the high- and low-expression cell groups. The model achieved AUCs of 0.961, 0.730, and 0.9 in the training set and two testing sets, respectively, indicating high predictive accuracy. Additionally, eight diagnostic genes, including S100A9, ARHGEF10, RPL30, ANGPT1, RPL18, LAMB1, RBMS3, and RPL27A, were identified. The column line plot model constructed based on these diagnostic genes also showed high AUCs and clinical utility.

Conclusion: This study revealed the important role of LRGs in the development of PMOP. Genes causally related to PMOP and genes related to the diagnosis of PMOP were identified using multi-omics data, MR analysis, and machine learning algorithms. The findings of this study provide potential diagnostic biomarkers for PMOP.

乳酸酰化相关基因标记在绝经后骨质疏松症中的作用和验证。
背景:骨质疏松症(Osteoporosis, OP)是一种以骨质流失、骨结构破坏和骨折易感性增加为特征的全身性骨病。绝经后骨质疏松症(postmenopause osteoporosis, PMOP)是指发生在绝经后期的女性骨质疏松症,其主要原因是雌激素水平下降。乳酸化作为一种糖基化修饰,可能影响PMOP的骨细胞功能。然而,其在PMOP发展中的具体作用尚不清楚。方法:本研究收集了PMOP的单细胞RNA测序(scRNA-seq)数据和转录组数据。使用“Seurat”软件包对scRNA-seq数据进行处理,包括细胞过滤、归一化、降维和聚类。使用“singleR”包对细胞类型进行注释。基于乳酸相关基因(LRGs),将所有细胞分为高表达和低表达细胞组。分别使用“fgsea”、“monocle”和“DoRothEA”包研究了两组细胞之间信号通路、发育轨迹和转录因子活性的差异。采用孟德尔随机化(MR)分析来鉴定与ppu有因果关系的两个细胞组之间表达差异显著的基因。采用“limma”包筛选高、低表达细胞组间的差异表达基因(differential expression genes, deg),并与deg相交。基于交叉基因,利用多种机器学习算法及其组合构建了PMOP诊断模型。使用ssGSEA算法对转录组数据进行免疫浸润分析。最后,基于诊断基因构建了PMOP的柱线图模型。结果:对scRNA-seq数据中的细胞类型进行注释后,共获得了11种细胞类型,包括中性粒细胞、组织干细胞、单核细胞、巨噬细胞、红母细胞、髓母细胞、Pre-B细胞CD34-、BM、T细胞、B细胞和Pro-B细胞CD34 +。根据LRGs表达水平划分的高表达和低表达细胞组在信号通路、发育轨迹和转录因子活性方面存在显著差异。MR分析确定RPS10和RPL12是与ppu有因果关系的危险因素。基于转录组数据和高、低表达细胞组间的交叉deg,构建了PMOP诊断模型。该模型在训练集和两个测试集上的auc分别为0.961、0.730和0.9,具有较高的预测精度。此外,还鉴定出8个诊断基因,包括S100A9、ARHGEF10、RPL30、ANGPT1、RPL18、LAMB1、RBMS3和RPL27A。基于这些诊断基因构建的柱线图模型也显示出较高的auc和临床应用价值。结论:本研究揭示了LRGs在ppu发生发展中的重要作用。使用多组学数据、MR分析和机器学习算法鉴定与PMOP因果相关的基因和与PMOP诊断相关的基因。本研究结果为ppu的诊断提供了潜在的生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Applied Biochemistry and Biotechnology
Applied Biochemistry and Biotechnology 工程技术-生化与分子生物学
CiteScore
5.70
自引率
6.70%
发文量
460
审稿时长
5.3 months
期刊介绍: This journal is devoted to publishing the highest quality innovative papers in the fields of biochemistry and biotechnology. The typical focus of the journal is to report applications of novel scientific and technological breakthroughs, as well as technological subjects that are still in the proof-of-concept stage. Applied Biochemistry and Biotechnology provides a forum for case studies and practical concepts of biotechnology, utilization, including controls, statistical data analysis, problem descriptions unique to a particular application, and bioprocess economic analyses. The journal publishes reviews deemed of interest to readers, as well as book reviews, meeting and symposia notices, and news items relating to biotechnology in both the industrial and academic communities. In addition, Applied Biochemistry and Biotechnology often publishes lists of patents and publications of special interest to readers.
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