北京生物醫(yī)學(xué)工程

基于差異代謝網(wǎng)絡(luò)的泛癌代謝分析

Pan-cancer analysis based on differential metabolic network

作者：熊宇峰鄭浩然

單位：中國(guó)科學(xué)技術(shù)大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院(合肥 230027)

關(guān)鍵詞：基因表達(dá)數(shù)據(jù)；代謝網(wǎng)絡(luò)；基因敲除；藥物靶標(biāo)；泛癌

分類號(hào)：R318.04

出版年·卷·期（頁(yè)碼）：2020·39·5（506-512）

摘要：

目的基因組規(guī)模代謝網(wǎng)絡(luò)模型（genome-scale metabolic model, GEM）是當(dāng)前癌癥代謝研究常用的一種分析工具，高質(zhì)量的癌癥代謝網(wǎng)絡(luò)有助于人們了解癌癥內(nèi)部的代謝特征。泛癌代謝分析需要為多種癌癥構(gòu)造代謝網(wǎng)絡(luò)模型，然而傳統(tǒng)的直接基于癌癥基因表達(dá)或蛋白質(zhì)數(shù)據(jù)進(jìn)行的網(wǎng)絡(luò)重構(gòu)方法，無(wú)法避免引入癌癥所在組織的代謝特征，從而影響探尋疾病本身的代謝特點(diǎn)。為了削弱組織代謝特征對(duì)研究的影響，更加準(zhǔn)確地提取癌癥相對(duì)正常組織細(xì)胞在代謝層面上發(fā)生的變化，提出一種基于差異代謝網(wǎng)絡(luò)的泛癌代謝分析方法。方法以癌癥基因組圖譜（The Cancer Genome Atlas, TCGA）數(shù)據(jù)庫(kù)中17種癌癥作為分析對(duì)象，通過(guò)分析癌癥及癌旁組織的基因表達(dá)數(shù)據(jù)，采用基因芯片線性模型（linear models for microarray analysis, Limma）工具包分別提取這17種癌癥的差異表達(dá)基因，并將人類全基因組規(guī)模代謝網(wǎng)絡(luò)Recon3D作為背景網(wǎng)絡(luò)，結(jié)合代謝網(wǎng)絡(luò)重構(gòu)算法FASTCORE為每種癌癥構(gòu)建差異代謝網(wǎng)絡(luò)。最后聯(lián)合多種癌癥網(wǎng)絡(luò)利用流量平衡分析（flux balance analysis, FBA）方法進(jìn)行基因敲除以及毒性測(cè)試，分析癌癥代謝共性，發(fā)現(xiàn)潛在藥物治療靶點(diǎn)。結(jié)果得到了每種癌癥的差異表達(dá)基因、差異代謝網(wǎng)絡(luò)模型，以及潛在的藥物靶點(diǎn)，通過(guò)聯(lián)合所有癌癥進(jìn)行分析，最后得到了7個(gè)潛在的泛癌藥物靶點(diǎn)，其中包括CRLS1、CTH、PTDSS1、SLC6A14、SGMS1、PKM以及PLD2。7個(gè)藥物靶標(biāo)中已經(jīng)有5個(gè)用于不同的癌癥治療策略中，而其余結(jié)果代表了潛在的藥物靶點(diǎn)。結(jié)論本文提出的差異代謝網(wǎng)絡(luò)模型可以有效削弱組織代謝特征對(duì)癌癥本身特征的干擾，有助于泛癌代謝分析。

Objective Genome-scale metabolic model (GEM) is currently a commonly used tools for cancer metabolism research. High-quality cancer metabolism models can help us understand the metabolic characteristics of cancer. In pan-cancer analysis we need to reconstructed connected genome-scale metabolic models for each cancer. However, the traditional way based on gene expression or protein data to construct metabolic network lead to the metabolic characteristics of cancer tissue, which influence understanding the metabolic characteristics of disease. In order to eliminate the influence of tissue metabolism on the research and accurately extract the metabolic changes of cancer relative to normal tissues, we propose a pan-cancer metabolic analysis method based on differential metabolic networks. Methods Taking 17 cancers in The Cancer Genome Atlas (TCGA) as the analysis objects, we used the linear models for microarray analysis (Limma) toolkit to determine genes that are differentially expressed between cancer and benign tissue. Then we combined differential genes, reference generic human GEM named Recon3D and metabolic network reconstruction algorithm FASTCORE to build differential metabolic networks for each cancer. Flux balance analysis (FBA) was used for metabolic flux analysis and gene knockout on a variety of cancer networks to study the common features of cancer metabolism and potential drug targets. Results In the experiments, we obtained differentially expressed genes, differential metabolic network models, and potential drug targets for each type of cancer. Through the analysis of all cancers, seven potential pan-cancer drug targets were obtained, including CRLS1, CTH, PTDSS1, SLC6A14, SGMS1, PKM, and PLD2. Five of the seven drug targets had already been used in different cancer treatment strategies, while the remaining drug targets represent new potential drugs. Conclusion The differential metabolic network model proposed in this paper can effectively eliminate the differences between different tissues, which is helpful to studying the metabolic characteristics of pan-cancer.

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