PO.TB03.04 · 肿瘤生物学
整合转录组分析鉴定卵巢癌转移中疟疾相关信号通路和可重定位的候选药物
Integrative transcriptomic analysis identifies malaria-associated signaling pathways and repurposable drug candidates for ovarian cancer metastasis
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摘要 Abstract
中文摘要
卵巢癌转移仍是癌症相关死亡的主要原因，并对有效治疗提出重大挑战。本研究旨在鉴定参与卵巢癌转移的关键基因、蛋白和通路，并通过药物重定位框架探索有可能抑制这些转移机制的已获批药物。使用来自配对的原发和转移性卵巢肿瘤的转录组数据进行了比较性的计算机模拟（in silico）分析。在第1阶段，使用R（v4.3.1）分析了来自三个微阵列和四个RNA测序数据集的基因表达谱，以鉴定差异表达基因（DEG）。在第2阶段，进行了通路富集和蛋白-蛋白相互作用分析，以锁定关键信号网络，随后使用dgiDB和PanDrug数据库进行系统性药物重定位。在微阵列数据集的27,985个转录本中鉴定出前100个显著DEG，而RNA测序分析从25,551个转录本中揭示了DEG。KEGG通路分析突出了十条主要通路，其中三条——疟疾、TGF-beta和PPAR信号通路——为两个数据集所共有，并与转移性转化显著相关。基因文献挖掘鉴定出八个（THBS1、VCAM1、ADIPOQ、LPL、ACKR1、CD36、FABP4、HBA1）与疟疾相关和转移基因集重叠的基因。使用STRING数据库的网络分析显示，17个关键基因中的14个之间存在强相互作用，其中VCAM1成为与黏附和免疫相关通路关联的中心枢纽蛋白。随后的药物-基因相互作用筛选鉴定出37个潜在的重定位候选药物，最高优先级的药物包括氯喹（chloroquine，ACKR1，相互作用评分0.711）、非诺贝特（fenofibrate）和吡格列酮（pioglitazone，ADIPOQ/LPL，0.622）、吡非尼酮（pirfenidone，THBS1，0.702）以及贝伐珠单抗（bevacizumab，THBS1/VCAM1，0.908），均为已获批且具有确立的临床安全性特征的药物。这些发现强调了TGF-beta、PPAR和疟疾相关信号通路在卵巢癌转移中的汇聚，提示靶向这些通路的重定位药物，尤其是氯喹和PPAR激动剂，在此背景下可能具有治疗潜力。对贝伐珠单抗（一种已获批用于卵巢癌的药物）的计算机鉴定佐证了该方法。未来工作将聚焦于对优先候选药物（包括氯喹、PPAR激动剂和吡非尼酮）针对卵巢癌细胞系进行体外验证，以证实计算机预测，并支持开发源自现有药物、研发周期更短的新治疗策略。
查看英文原文 English abstract
Ovarian cancer metastasis remains a leading cause of cancer-related mortality and presents major challenges for effective therapy. This study aimed to identify pivotal genes, proteins, and pathways involved in ovarian cancer metastasis and to explore already approved drugs with potential to inhibit these metastatic mechanisms through a drug repurposing framework. A comparative in silico analysis was conducted using transcriptomic data from matched primary and metastatic ovarian tumors. In Phase 1, gene expression profiles from three microarray and four RNA-sequencing datasets were analyzed using R (v4.3.1) to identify differentially expressed genes (DEGs). In Phase 2, pathway enrichment and protein-protein interaction analyses were performed to pinpoint key signaling networks, followed by systematic drug repurposing using dgiDB and PanDrug databases. Top significant 100 DEGs were identified among 27,985 transcripts from the microarray datasets, while RNA-sequencing analysis revealed DEGs from 25,551 transcripts. KEGG pathway analysis highlighted ten major pathways, of which three, malaria, TGF-beta, and PPAR signaling, were common to both datasets and significantly associated with metastatic transformation. Gene literature mining identified eight genes (THBS1, VCAM1, ADIPOQ, LPL, ACKR1, CD36, FABP4, HBA1) overlapping malaria-associated and metastasis gene sets. Network analysis using the STRING database showed strong interactions among 14 of 17 key genes, with VCAM1 emerging as a central hub protein linked to adhesion and immune-related pathways. Subsequent drug-gene interaction screening identified 37 potential repurposed drug candidates, with highest-priority agents including chloroquine (ACKR1, interaction score 0.711), fenofibrate and pioglitazone (ADIPOQ/LPL, 0.622), pirfenidone (THBS1, 0.702), and bevacizumab (THBS1/VCAM1, 0.908), all approved drugs with established clinical safety profiles.These findings emphasize the convergence of TGF-beta, PPAR, and malaria-associated signaling in ovarian cancer metastasis, suggesting that repurposed drugs targeting these pathways, particularly chloroquine and PPAR agonists, may have therapeutic potential in this context. The computational identification of bevacizumab, an agent already approved for ovarian cancer, corroborates the approach. Future work will focus on in vitro validation of prioritized drug candidates, including chloroquine, PPAR agonists, and pirfenidone, against ovarian cancer cell lines to substantiate the in silico predictions and support the development of new treatment strategies derived from existing pharmacologic agents with reduced development timelines.
利益披露 Disclosure
D. Ghafoorzai, None..
A. Riaz, None..
D. F. Khan, None.