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《天津医科大学学报》[ISSN:1006-8147/CN:12-1259/R]

卷:

30卷

期数:

2024年03期

页码:

205-211,254

栏目:

肿瘤研究专题

出版日期:

2024-05-20

文章信息/Info

Title:

A liver cancer prediction model based on multi-omics Lasso regression analysis

文章编号:

1006-8147(2024)03-0205-08

作者:

罗焱瑞; 赵倩

（天津医科大学基础医学院细胞生物学系，天津300070）

Author(s):

LUO Yanrui;  ZHAO Qian

（Department of Cell Biology， School of Basic Medical Sciences， Tianjin Medical University， Tianjin 300070， China）

关键词:

肝癌; 差异表达基因; 差异甲基化基因; Lasso回归分析

Keywords:

liver cancer;  differentially expressed genes;  differentially methylated genes;  Lasso regression analysis

分类号:

R735.7

DOI:

10.20135/j.issn.1006-8147.2024.03.0205

文献标志码:

A

摘要:

目的：通过生物信息学方法整合肝癌转录组和甲基化组数据，利用Lasso回归分析筛选肝癌特异性标志物，并构建肿瘤预测模型。方法：在GEO（gene expression omnibus）中下载GSE70091、GSE77314数据集，共 53 例肝癌患者的转录组测序（RNA-seq）和全基因组甲基化测序（WGBS）数据，分别将肝癌与癌旁对照间的转录组、甲基化组数据进行差异分析，并对差异表达基因（DEG）和差异甲基化基因（DMG）进行整合，以筛选出候选肝癌标志基因。对候选肝癌标志基因进行GO（gene ontology）和Reactome通路富集分析，使用Lasso回归分析筛选标志基因，构建肝癌预测模型，并在其他队列中进行性能验证。结果：共筛选出 288 个DEG（｜log2FC｜>1，P.adj<0.05）和 28 528 个DMG（P<0.05），通过DEG和DMG的交叉分析找到 51 个高甲基化下调（Hyper-Down）基因和 111 个低甲基化上调（Hypo-Up）基因。GO和Reactome通路富集分析显示，Hypo-Up基因主要富集在细胞有丝分裂通路上（FDR<0.05，P<0.05），Hyper-Down基因主要与转录激活的功能相关（FDR<0.05，P<0.05）。使用Lasso回归分析筛选出 11 个具有非零系数的基因并构建肝癌预测模型。最后在GSE77314、外部验证队列（TCGA-LIHC）中验证出模型曲线下面积（AUC）分别为1、0.998。结论：通过整合肝癌多组学数据，使用Lasso回归分析筛选出 11 个基因标志物，并构建肝癌预测模型。

Abstract:

Objective：To integrate hepatocellular carcinoma transcriptomic and methylationomic data by bioinformatics methods， screen hepatocellular carcinoma-specific markers and construct tumor prediction models using Lasso regression analysis. Methods： The transcriptome sequencing （RNA-seq） and whole genome methylation sequencing （WGBS） data of a total of 53 hepatocellular carcinoma patients from the GSE70091 and GSE77314 datasets were downloaded from the GEO （gene expression omnibus） database， and the transcriptome and methylation data between hepatocellular carcinomas and adjacent normal tissues were analyzed for differences， respectively. Differentially expressed genes （DEGs） and differentially methylated genes （DMGs） were integrated to screen out hepatocellular carcinoma candidate marker genes. The GO （gene ontology） and Reactome pathway enrichment analyses were performed on the candidate marker genes， and Lasso regression analysis was used to screen the marker genes and construct the liver cancer prediction model， and the performance was validated in other cohorts. Results： Totally， 288 DEGs （|log2FC|>1， P.adj<0.05） and 28 528 DMGs （P<0.05） were screened out， and 51 Hyper-Down genes and 111 Hypo-Up genes were identified by cross-analysis of DEG and DMG. GO and Reactome results showed that Hypo-Up genes were enriched in the cellular mitotic pathway （FDR<0.05， P<0.05） and Hyper-Down genes were mainly associated with transcriptional activation functions （FDR<0.05， P<0.05）. A liver cancer prediction model was created by screening 11 genes with non-zero coefficients using Lasso regression analysis. Finally， in GSE77314 and TCGA-LIHC cohorts， the model′s area under curve （AUC） was validated as 1 and 0.998， respectively. Conclusion： A total of 11 gene markers and construct a hepatocellular carcinoma prediction model by are screened integrating the multi-omics data of hepatocellular carcinoma and using Lasso regression analysis.

参考文献/References:

[1] XIA C，DONG X，LI H，et al. Cancer statistics in China and United States，2022： profiles，trends，and determinants[J]. Chin Med J （Engl），2022，135（5）：584-590.
[2] LOUD J T，MURPHY J. Cancer screening and early detection in the 21（st） century[J]. Semin Oncol Nurs，2017，33（2）：121-128.
[3] LEE C W，TSAI H I，LEE W C，et al. Normal alpha-fetoprotein hepatocellular carcinoma： are they really normal[J]. J Clin Med，2019，8（10）：1736-1751.
[4] BOUATTOUR M，MEHTA N，HE A R，et al. Systemic treatment for advanced hepatocellular carcinoma[J]. Liver Cancer，2019，8（5）： 341-358.
[5] BRUIX J，GORES G J，MAZZAFERRO V. Hepatocellular carcinoma： clinical frontiers and perspectives[J]. Gut，2014，63（5）：844-855.
[6] WORLAND T，HARRISON B，DELMENICO L，et al. Hepatocellular carcinoma screening utilising serum alpha-fetoprotein measurement and abdominal ultrasound is more effective than ultrasound alone in patients with non-viral cirrhosis[J]. J Gastrointest Cancer，2018，49（4）： 476-480.
[7] LI X，LIU Y，SALZ T，et al. Whole-genome analysis of the methylome and hydroxymethylome in normal and malignant lung and liv-er[J]. Genome Res，2016，26（12）：1730-1741.
[8] LIU G，HOU G，LI L，et al. Potential diagnostic and prognostic marker dimethylglycine dehydrogenase （DMGDH） suppresses hepatocellular carcinoma metastasis in vitro and in vitro[J]. Oncotarget，2016，7（22）：32607-32616.
[9] LIU J，LICHTENBERG T，HOADLEY K A，et al. An integrated tcga pan-cancer clinical data resource to drive high-quality survival outcome analytics[J]. Cell，2018，173（2）：400-416.
[10] KIM D，PAGGI J M，PARK C，et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype[J]. Nat Biotechnol，2019，37（8）： 907-915.
[11] KOVAKA S，ZIMIN A V，PERTEA G M，et al. Transcriptome assembly from long-read RNA-seq alignments with StringTie2[J]. Genome Biol，2019，20（1）： 278.
[12] WU T，HU E，XU S，et al. ClusterProfiler 4.0： a universal enrichment tool for interpreting omics data[J]. Innovation（Camb），2021，2（3）： 100141.
[13] GILLESPIE M，JASSAL B，STEPHAN R，et al. The reactome pathway knowledge base 2022[J]. Nucleic Acids Res，2022，50（D1）： 687-692.
[14] LEE H. Analysis of bisulfite sequencing data using bismark and dmrcaller to identify differentially methylated regions[J]. Methods Mol Biol，2022，2443： 451-463.
[15] WU H，XU T，FENG H，et al. Detection of differentially methylated regions from whole-genome bisulfite sequencing data without replicates[J]. Nucleic Acids Res，2015，43（21）： e141.
[16] FISHILEVICH S，NUDEL R，RAPPAPORT N，et al. GeneHancer： genome-wide integration of enhancers and target genes in Gene-Cards[J]. Database （Oxford），2017，2017.
[17] YU G，WANG L G，HE Q Y. ChIPseeker： an R/Bioconductor package for ChIP peak annotation，comparison and visualization[J]. Bio-informatics，2015，31（14）： 2382-2383.
[18] CAVALCANTE R G，SARTOR M A. Annotatr： genomic regions in context[J]. Bioinformatics，2017，33（15）： 2381-2383.
[19] LI Y，LU F，YIN Y. Applying logistic LASSO regression for the diagnosis of atypical Crohn′s disease[J]. Sci Rep，2022，12（1）： 11340.
[20] HOADLEY K A，YAU C，HINOUE T，et al. Cell-of-origin patterns dominate the molecular classification of 10，000 tumors from 33 types of cancer[J]. Cell，2018，173（2）： 291-304.
[21] FORNER A，REIG M，BRUIX J. Hepatocellular carcinoma[J]. Lan-cet，2018，391（10127）： 1301-1314.
[22] LEE S，HWANG K S，LEE H J，et al. Aberrant CpG island hypermethylation of multiple genes in colorectal neoplasia[J]. Lab Invest，2004，84（7）： 884-893.
[23] LICCHESI J D，WESTRA W H，HOOKER C M，et al. Promoter hypermethylation of hallmark cancer genes in atypical adenomatous hyperplasia of the lung[J]. Clin Cancer Res，2008，14（9）： 2570-2578.
[24] LAKSHMINARASIMHAN R，LIANG G. The role of DNA methylation in cancer[J]. Adv Exp Med Biol，2016，945： 151-172.
[25] WOUTERS J，KALENDER ATAK Z，AERTS S. Decoding transcriptional states in cancer[J]. Curr Opin Genet Dev，2017，43： 82-92.
[26] LEAL-ESTEBAN L C，FAJAS L. Cell cycle regulators in cancer cell metabolism[J]. Biochim Biophys Acta Mol Basis Dis，2020，1866（5）： 165715.
[27] LIU Z，SUN Q，WANG X. PLK1，a potential target for cancer therapy[J]. Transl Oncol，2017，10（1）： 22-32.
[28] ZIEGLER Y，LAWS M J，SANABRIA GUILLEN V，et al. Suppression of FOXM1 activities and breast cancer growth in vitro and in vivo by a new class of compounds[J]. NPJ Breast Cancer，2019，5： 45.

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备注/Memo

备注/Memo:

作者简介 罗焱瑞（1994-），男，硕士，研究方向：计算基因组学；通信作者：赵倩，E-mail： zhaoq@tmu.edu.cn。

常用功能

更新日期/Last Update: 2024-05-20