北京生物醫(yī)學(xué)工程

一種面向癌癥個性化的代謝分析方法

A metabolic analysis method for cancer individualization

作者：馮昌奇鄭浩然

單位：中國科學(xué)技術(shù)大學(xué)計算機科學(xué)與技術(shù)學(xué)院(合肥 230027)<br />通信作者：鄭浩然,副教授。E-mail: [email protected]

關(guān)鍵詞：癌癥代謝；代謝網(wǎng)絡(luò)模型；個性化；差異表達(dá)基因；靶點基因

分類號：R318

出版年·卷·期（頁碼）：2022·41·4（331-337）

摘要：

目的不同類型的癌癥之間，甚至是同一癌癥類型的不同患者個體之間，在代謝重編程的精確表現(xiàn)上都具有異質(zhì)性，因此導(dǎo)致相同的藥物在不同的癌癥患者體內(nèi)會產(chǎn)生不同的結(jié)果，以致于很難找到一種通用的治療方案。為了探究癌癥的這種異質(zhì)性，本文提出了一種分析癌癥個性化代謝的新方法。方法本文使用來自癌癥基因組圖譜（The Cancer Genome Atlas, TCGA）的轉(zhuǎn)錄組數(shù)據(jù)，采用基于差異表達(dá)基因的代謝網(wǎng)絡(luò)重構(gòu)方法，通過對比每名患者的癌細(xì)胞和鄰近正常細(xì)胞的基因表達(dá)數(shù)據(jù)之間的差異，還原重構(gòu)出癌細(xì)胞相對原發(fā)組織細(xì)胞特有的那部分代謝網(wǎng)絡(luò)。通過模擬基因敲除等手段，尋找潛在的癌癥個性化治療靶點基因。結(jié)果本文對來自TCGA的17種癌癥類型的679對樣本進(jìn)行了分析，發(fā)現(xiàn)了它們在癌癥特異性代謝網(wǎng)絡(luò)上的巨大差異，并得到了大量的個性化治療靶點。特別是其中63個靶點基因，它們只對特定的某個患者有效。結(jié)論不同的患者細(xì)胞癌變后發(fā)生的代謝變化具有很大的差異，需要進(jìn)行個性化治療方案的研究。本文提出的分析方法為癌癥的個性化分析提供了新的思路，找到的個性化靶點也可以進(jìn)行進(jìn)一步的臨床研究。

Objective There is heterogeneity in the precise performance of metabolic reprogramming between different types of cancer, even between different patients of the same cancer type. Therefore, the same drug will produce different results in different cancer patients, so that it is difficult to find a general treatment plan. In order to explore this heterogeneity of cancer, this article proposes a new method to analyze the personalized metabolism of cancer. Methods This article used transcriptome data from The Cancer Genome Atlas (TCGA), and used a metabolic network reconstruction method based on differentially expressed genes. By comparing the difference of the gene expression data between cancer cells and neighboring normal cells in each patient, the metabolic network that is unique to the cancer cell relative to the original tissue cells can be restored and reconstructed. By simulating gene knockout and other means, we can find potential target genes for personalized cancer therapy. Results This article analyzed 679 samples from 17 cancer types from TCGA and found huge differences in cancer-specific metabolic networks, and obtained a large number of personalized treatment targets. In particular, 63 of these target genes are only effective for a specific patient. Conclusion The metabolic abnormalities that occur after different individuals' cells become cancerous are very different, and it is necessary to conduct research on individualized treatment plans. The analysis method proposed in this paper provides new ideas for the personalized analysis of cancer, and the personalized targets found can also be used for further clinical research.

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