基于可變剪接紊亂的乳腺癌亞型預測分析

許鵬; 王兵; 方剛; 石曉龍; 劉文斌

doi:10.11999/JEIT190871

基于可變剪接紊亂的乳腺癌亞型預測分析

doi: 10.11999/JEIT190871 cstr: 32379.14.JEIT190871

許鵬^{1, 3},
王兵²,
方剛¹,
石曉龍¹,
劉文斌^{1, 2, ,}

1.
廣州大學計算科技研究院廣州 510006
2.
溫州大學計算機與人工智能學院溫州 325035
3.
黔南民族師范學院計算機與信息學院都勻 558000

基金項目: 國家重點研發(fā)計劃(2019YFA0706402)，國家自然科學基金(61572367, 61573017, 61972107, 61972109)

詳細信息

作者簡介:
許鵬：男，1986年生，博士后，研究方向為生物信息學

王兵：男，1993年生，碩士生，研究方向為生物信息學

方剛：男，1969年生，教授，研究方向為生物信息學

石曉龍：男，1975年生，教授，研究方向為生物信息學

劉文斌：男，1969年生，教授，研究方向為生物信息學

通訊作者:
劉文斌　wbliu6910@126.com

中圖分類號: TP391
計量
- 文章訪問數: 1785
- HTML全文瀏覽量: 595
- PDF下載量: 72
- 被引次數: 0
出版歷程
- 收稿日期: 2019-11-01
- 修回日期: 2020-05-10
- 網絡出版日期: 2020-05-23
- 刊出日期: 2020-06-22

Analysis of Breast Cancer Subtypes Prediction Based on Alternative Splicing Disorders

Peng XU^{1, 3},
Bing WANG²,
Gang FANG¹,
Xiaolong SHI¹,
Wenbin LIU^{1, 2
, ,}

1.
Institute of Computing Science and Technology, Guangzhou University, Guangzhou 510006, China
2.
College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou 325035, China
3.
School of Computer Science and Information Technology, Qiannan Normal University for Nationalities, Duyun 558000, China

Funds: The National Key R&D Program of China (2019YFA0706402), The National Natural Science Foundation of China (61572367, 61573017, 61972107, 61972109)

摘要

摘要: 可變剪接與多種復雜疾病的發(fā)生、發(fā)展存在密切的聯(lián)系，包括腫瘤在內的多種疾病的產生往往伴隨著可變剪接的紊亂發(fā)生?，F(xiàn)有的乳腺癌亞型分析主要是基于單個剪接異構體出發(fā)，缺少考慮亞型之間由于可變剪接紊亂造成剪接異構體在整體分布上的差異。因此該文提出了基于可變剪接紊亂的乳腺癌亞型預測方法，主要使用Jensen-Shannon(JS)散度來找尋亞型之間的可變剪接紊亂差異較大的基因，并構建反向傳播(BP)神經網絡模型對乳腺癌亞型進行分類。結果表明，該方法不僅能有效發(fā)現(xiàn)腫瘤異質性分子，在乳腺癌亞型分類方面也有較好的識別結果，其平均F1值達到0.89，且能為患者提供個性化乳腺癌亞型藥物推薦。該文的研究將有效促進基于可變剪接紊亂的乳腺癌亞型研究的發(fā)展。
- 乳腺癌亞型預測 /
- 可變剪接 /
- Jensen-Shannon散度 /
- 反向傳播神經網絡 /
- 藥物推薦
Abstract: Alternative splicing is closely related to the occurrence and development of a variety of complex diseases, the emergence of various diseases including tumors is often accompanied by the occurrence of alternative splicing disorders. The existing analysis of breast cancer subtypes is mainly based on single splicing isoform, and the difference in the overall distribution of splicing isoforms caused by alternative splicing disorders among subtypes is not considered. Therefore, a prediction method of breast cancer subtypes based on alternative splicing disorders is proposed, which mainly uses Jensen-Shannon(JS) divergence to find genes with large differences in alternative splicing disorders between subtypes, then constructes Back Propagation(BP) neural network model to classify breast cancer subtypes. The results show that this method could not only effectively detect tumor heterogeneous molecules, but also had good identification results in the classification of breast cancer subtypes, with an average F1-score of 0.89, and could provide personalized drug recommendations for patients with breast cancer subtypes. This study will effectively promote the development of breast cancer subtypes based on alternative splicing disorders.
- Breast cancer subtypes predicion /
- Alternative splicing /
- Jensen-Shannon(JS) divergence /
- Back Propagation(BP) neural network /
- Drug recommendation

HTML全文

圖 1 排名靠前100基因的JS散度分布情況

下載: 全尺寸圖片幻燈片

圖 2 不同乳腺癌亞型與正常型之間差異基因的韋恩圖

下載: 全尺寸圖片幻燈片

圖 3 JS散度大于0.3的基因個數

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圖 4 乳腺癌亞型聚類

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圖 5 乳腺癌亞型分類結果

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表 1 不同乳腺癌亞型的樣本數

乳腺癌亞型	樣本數
Basal	140
Her2	67
LumA	432
LumB	194
Normal	117

下載: 導出CSV

表 2 乳腺癌亞型分類

乳腺癌亞型	精確率	召回率	F1值
Basal	0.97	0.96	0.97
Her2	0.85	0.75	0.79
LumA	0.89	0.92	0.91
LumB	0.81	0.79	0.80
Normal	0.89	0.91	0.90

下載: 導出CSV

表 3 乳腺癌亞型的藥物推薦

靶基因	藥物	Basal	Her2	LumA	LumB
CHEK1	Enzastaurin	0.393	0.369	0.195	0.316
ESR1	Melatonin,Homosalate,Estradiol,2-Amino-1-methyl-6-phenylimidazo(4,5-b)pyridine,Danazol,Fulvestrant,Raloxifene,Custirsen,Tamoxifen,Estrone sulfate,Methyltestosterone,Fluoxymesterone,Afimoxifene	0.305	0.133	0.028	0.025
FOLR2	Folic acid,Methotrexate	0.842	0.025	0.108	0.354
GPER1	Estradiol	0.442	0.438	0.021	0.013
GSN	Latrunculin A	0.443	0.419	0.224	0.476
PPARG	Curcumin,Isoflavone,Valproic acid,Mesalazine,Nabiximols,Cannabidiol	0.668	0.645	0.030	0.637
AURKB	Enzastaurin,AT9283	0.640	0.569	0.352	0.591
ABCC11	Methotrexate,Folic acid	0.431	0.036	0.013	0.040

下載: 導出CSV

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