基于网络搜索算法鉴定胃癌耐药关键调控网络

Multi-drug resistance (MDR) is the main reason for failure of chemotherapy in gastric cancer patients. We now understand that a combination of multiple mechanisms contribute to MDR development. Recent omics researches demonstrated that long non-coding RNA, especially ceRNA, plays a key role in tumor genesis and chemo resistance in terms of complex gene regulatory networks. An algorithm named ProteinRank which could integrate multi-omics dataset and suggest drug resistancerelated proteins and sub-networks was brought up lately by our research team. Based on this advantage, this project will firstly integrate mRNA, miRNA, lncRNA profile and quantitative proteomics data of adriamycin resistant gastric cancer cell and its parental cell and construct large-scale ceRNA centered complex interaction network. Then, ProteinRank algorithm and network analysis will be adopted to identify drug resistance related molecules and their regulatory network. Finally, NanoString analysis and Western Blot will be used to validated the expression level of candidate molecules derived by the network analysis. Functional experiments will focus on two or three molecules which have significant protein expression alterations. This project will help reveal new drug resistance mechanisms of gastric cancer and potential drug targets, and present important references for clinical research.

胃癌耐药是导致患者化疗失败的主要原因，且存在多种复杂调控机制。已有研究表明，以ceRNA为代表的非编码RNA在肿瘤及其耐药中起到关键作用，且与其他基因及编码蛋白形成复杂调控网络。申请人近期开发的ProteinRank算法可以有效整合多组学数据，鉴定潜在耐药相关蛋白及其子网。本课题将在此基础上:1.率先系统整合胃癌耐药mRNA、miRNA、lncRNA表达谱和蛋白质谱数据，建立以ceRNA为核心的大规模分子互作网络；2.利用ProteinRank等网络分析算法鉴定胃癌耐药核心分子及其复杂调控网络；3.利用NanoString和WesternBlot等实验大规模验证鉴定到的耐药分子的表达水平，进而从中挑选2-3个分子做蛋白水平表达验证和体外功能验证。该研究有助于发现胃癌耐药的关键调控模块，系统揭示胃癌耐药机制，并提供潜在的耐药逆转靶点。

胃癌是世界范围内发病率和致死率最高的癌症之一。因此，全面掌握胃癌的调控机制，对胃癌诊治有着极为重要的意义。lncRNA可作为肿瘤标志物应用于肿瘤的临床诊断、预后判断、药物治疗靶点等过程，为肿瘤调控机制的进一步揭示、肿瘤患者的诊断与治疗带来了希望。.目前在胃癌的研究中，受限于数据的来源、数据处理方式、实验条件等因素，lncRNA在胃癌中的功能研究往往没有将生物信息学预测与生物实验进行有效整合，导致lncRNA在胃癌中的调控功能不能得到确认。因此，胃癌lncRNA功能预测领域仍存在诸多问题亟待解决。.本课题组在研究中，引入基因间显著共表达特征用于胃癌ceRNA调控网络的构建，提出复杂网络节点重要性评价策略和加权基因共表达网络分析方法等调控网络分析方法揭示非编码RNA调控功能，并结合机器学习、基因表达分析、生存分析、临床病理特征分析等方法，首次发现并设计生物实验初步揭示了DLEU2、DDX11-AS1、MYOSLID、LINC00941等非编码RNA在胃癌中的调控功能。在项目支持下，相关生物信息学挖掘、生物实验技术也在胃癌耐药、结直肠癌、神经母细胞瘤等研究领域也取得了一定成果。受基金支持，本项目共发表论文SCI论文9篇，培养博士研究生2名，已完成计划书上的各种指标。

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