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通过加权基因网络和通路追踪方法对子宫平滑肌瘤和平滑肌肉瘤进行分子差异分析。

Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches.

作者信息

Sahly Nora Naif, Banaganapalli Babajan, Sahly Ahmed N, Aligiraigri Ali H, Nasser Khalidah K, Shinawi Thoraia, Mohammed Arif, Alamri Abdulhakeem S, Bondagji Nabeel, Elango Ramu, Shaik Noor Ahmad

机构信息

Department of Obstetrics and Gynecology, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.

Department of Genetic Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.

出版信息

Syst Biol Reprod Med. 2021 Jun;67(3):209-220. doi: 10.1080/19396368.2021.1876179. Epub 2021 Mar 8.

Abstract

Uterine smooth muscular neoplastic growths like benign leiomyomas (UL) and metastatic leiomyosarcomas (ULMS) share similar clinical symptoms, radiological and histological appearances making their clinical distinction a difficult task. Therefore, the objective of this study is to identify key genes and pathways involved in transformation of UL to ULMS through molecular differential analysis. Global gene expression profiles of 25 ULMS, 25 UL, and 29 myometrium (Myo) tissues generated on Affymetrix U133A 2.0 human genome microarrays were analyzed by deploying robust statistical, molecular interaction network, and pathway enrichment methods. The comparison of expression signals across Myo vs UL, Myo vs ULMS, and UL vs ULMS groups identified 249, 1037, and 716 significantly expressed genes, respectively (p ≤ 0.05). The analysis of 249 DEGs from Myo vs UL confirms multistage dysregulation of various key pathways in extracellular matrix, collagen, cell contact inhibition, and cytokine receptors transform normal myometrial cells to benign leiomyomas (p value ≤ 0.01). The 716 DEGs between UL vs ULMS were found to affect cell cycle, cell division related Rho GTPases and PI3K signaling pathways triggering uncontrolled growth and metastasis of tumor cells (p value ≤ 0.01). Integration of gene networking data, with additional parameters like estimation of mutation burden of tumors and cancer driver gene identification, has led to the finding of 4 hubs (JUN, VCAN, TOP2A, and COL1A1) and 8 bottleneck genes (PIK3R1, MYH11, KDR, ESR1, WT1, CCND1, EZH2, and CDKN2A), which showed a clear distinction in their distribution pattern among leiomyomas and leiomyosarcomas. This study provides vital clues for molecular distinction of UL and ULMS which could further assist in identification of specific diagnostic markers and therapeutic targets. UL: Uterine Leiomyomas; ULMS: Uterine Leiomyosarcoma; Myo: Myometrium; DEGs: Differential Expressed Genes; RMA: Robust Multiarray Average; DC: Degree of Centrality; BC: Betweenness of Centrality; CGC: Cancer Gene Census; FDR: False Discovery Rate; TCGA: Cancer Genome Atlas; BP: Biological Process; CC: Cellular Components; MF: Molecular Function; PPI: Protein-Protein Interaction.

摘要

子宫平滑肌瘤(UL)和转移性平滑肌肉瘤(ULMS)等子宫平滑肌肿瘤性生长具有相似的临床症状、影像学和组织学表现,这使得它们的临床鉴别成为一项艰巨的任务。因此,本研究的目的是通过分子差异分析来确定参与UL向ULMS转化的关键基因和通路。通过运用稳健的统计、分子相互作用网络和通路富集方法,对在Affymetrix U133A 2.0人类基因组微阵列上生成的25个ULMS、25个UL和29个子宫肌层(Myo)组织的全基因组表达谱进行了分析。Myo与UL、Myo与ULMS以及UL与ULMS组之间表达信号的比较分别确定了249个、1037个和716个显著表达的基因(p≤0.05)。对Myo与UL之间249个差异表达基因(DEGs)的分析证实,细胞外基质、胶原蛋白、细胞接触抑制和细胞因子受体等各种关键通路的多阶段失调将正常子宫肌层细胞转化为良性平滑肌瘤(p值≤0.01)。发现UL与ULMS之间的716个DEGs影响细胞周期、与细胞分裂相关的Rho GTP酶和PI3K信号通路,从而引发肿瘤细胞的失控生长和转移(p值≤0.01)。基因网络数据与肿瘤突变负担估计和癌症驱动基因鉴定等其他参数的整合,导致发现了4个枢纽基因(JUN、VCAN、TOP2A和COL1A1)和8个瓶颈基因(PIK3R1、MYH11、KDR、ESR1、WT1、CCND1、EZH2和CDKN2A),它们在平滑肌瘤和平滑肌肉瘤中的分布模式有明显差异。本研究为UL和ULMS的分子鉴别提供了重要线索,这可能进一步有助于识别特定的诊断标志物和治疗靶点。UL:子宫平滑肌瘤;ULMS:子宫平滑肌肉瘤;Myo:子宫肌层;DEGs:差异表达基因;RMA:稳健多阵列平均法;DC:中心度;BC:中介中心度;CGC:癌症基因普查;FDR:错误发现率;TCGA:癌症基因组图谱;BP:生物学过程;CC:细胞成分;MF:分子功能;PPI:蛋白质-蛋白质相互作用

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