An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data

Zhonglie Wang; Rui Yan; Jie Liu; Yudong Liu; Fei Ren; Chunhou Zheng; Fa Zhang

doi:10.1007/978-3-030-60796-8_21

An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data

Zhonglie Wang, Rui Yan, Jie Liu, Yudong Liu, Fei Ren, Chunhou Zheng^*, Fa Zhang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Accurate prediction is helpful to the treatment of liver cancer. In this paper, we propose a method based on a combination of deep learning and network fusion to predict the survival subtype of liver cancer, of which Univariate Cox-PH regression model was used twice. We integrated RNA sequencing, miRNA sequencing, DNA methylation data and clinical data of liver cancer from TCGA to infer two survival subtypes. We then also constructed an XGBoost supervised classification model to predict the survival subtype of the new sample. Experimental results show that our model gives two subgroups with significant survival differences and Concordance index. We also use two additional confirmation cohorts downloaded from the GEO database to verify our multi-omics model. We found highly expressed stemness marker genes CD24, KRT19 and EPCAM and the tumor marker gene BIRC5 in two survival subgroups. Our method has great clinical significance for the prediction of HCC prognosis.

Original language	English
Title of host publication	Intelligent Computing Methodologies - 16th International Conference, ICIC 2020, Proceedings
Editors	De-Shuang Huang, Prashan Premaratne
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	247-257
Number of pages	11
ISBN (Print)	9783030607951
DOIs	https://doi.org/10.1007/978-3-030-60796-8_21
Publication status	Published - 2020
Externally published	Yes
Event	16th International Conference on Intelligent Computing, ICIC 2020 - Bari , Italy Duration: 2 Oct 2020 → 5 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12465 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Intelligent Computing, ICIC 2020
Country/Territory	Italy
City	Bari
Period	2/10/20 → 5/10/20

Keywords

Deep learning
Multi-omics data
Network fusion
Supervised classification model
Survival subtype

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-60796-8_21

Cite this

Wang, Z., Yan, R., Liu, J., Liu, Y., Ren, F., Zheng, C., & Zhang, F. (2020). An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data. In D.-S. Huang, & P. Premaratne (Eds.), Intelligent Computing Methodologies - 16th International Conference, ICIC 2020, Proceedings (pp. 247-257). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12465 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60796-8_21

Wang, Zhonglie ; Yan, Rui ; Liu, Jie et al. / An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data. Intelligent Computing Methodologies - 16th International Conference, ICIC 2020, Proceedings. editor / De-Shuang Huang ; Prashan Premaratne. Springer Science and Business Media Deutschland GmbH, 2020. pp. 247-257 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{82f0530c25e5416693348b7574d7eba6,

title = "An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data",

abstract = "Accurate prediction is helpful to the treatment of liver cancer. In this paper, we propose a method based on a combination of deep learning and network fusion to predict the survival subtype of liver cancer, of which Univariate Cox-PH regression model was used twice. We integrated RNA sequencing, miRNA sequencing, DNA methylation data and clinical data of liver cancer from TCGA to infer two survival subtypes. We then also constructed an XGBoost supervised classification model to predict the survival subtype of the new sample. Experimental results show that our model gives two subgroups with significant survival differences and Concordance index. We also use two additional confirmation cohorts downloaded from the GEO database to verify our multi-omics model. We found highly expressed stemness marker genes CD24, KRT19 and EPCAM and the tumor marker gene BIRC5 in two survival subgroups. Our method has great clinical significance for the prediction of HCC prognosis.",

keywords = "Deep learning, Multi-omics data, Network fusion, Supervised classification model, Survival subtype",

author = "Zhonglie Wang and Rui Yan and Jie Liu and Yudong Liu and Fei Ren and Chunhou Zheng and Fa Zhang",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th International Conference on Intelligent Computing, ICIC 2020 ; Conference date: 02-10-2020 Through 05-10-2020",

year = "2020",

doi = "10.1007/978-3-030-60796-8_21",

language = "English",

isbn = "9783030607951",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "247--257",

editor = "De-Shuang Huang and Prashan Premaratne",

booktitle = "Intelligent Computing Methodologies - 16th International Conference, ICIC 2020, Proceedings",

address = "Germany",

}

Wang, Z, Yan, R, Liu, J, Liu, Y, Ren, F, Zheng, C & Zhang, F 2020, An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data. in D-S Huang & P Premaratne (eds), Intelligent Computing Methodologies - 16th International Conference, ICIC 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12465 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 247-257, 16th International Conference on Intelligent Computing, ICIC 2020, Bari , Italy, 2/10/20. https://doi.org/10.1007/978-3-030-60796-8_21

An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data. / Wang, Zhonglie; Yan, Rui; Liu, Jie et al.
Intelligent Computing Methodologies - 16th International Conference, ICIC 2020, Proceedings. ed. / De-Shuang Huang; Prashan Premaratne. Springer Science and Business Media Deutschland GmbH, 2020. p. 247-257 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12465 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data

AU - Wang, Zhonglie

AU - Yan, Rui

AU - Liu, Jie

AU - Liu, Yudong

AU - Ren, Fei

AU - Zheng, Chunhou

AU - Zhang, Fa

PY - 2020

Y1 - 2020

N2 - Accurate prediction is helpful to the treatment of liver cancer. In this paper, we propose a method based on a combination of deep learning and network fusion to predict the survival subtype of liver cancer, of which Univariate Cox-PH regression model was used twice. We integrated RNA sequencing, miRNA sequencing, DNA methylation data and clinical data of liver cancer from TCGA to infer two survival subtypes. We then also constructed an XGBoost supervised classification model to predict the survival subtype of the new sample. Experimental results show that our model gives two subgroups with significant survival differences and Concordance index. We also use two additional confirmation cohorts downloaded from the GEO database to verify our multi-omics model. We found highly expressed stemness marker genes CD24, KRT19 and EPCAM and the tumor marker gene BIRC5 in two survival subgroups. Our method has great clinical significance for the prediction of HCC prognosis.

AB - Accurate prediction is helpful to the treatment of liver cancer. In this paper, we propose a method based on a combination of deep learning and network fusion to predict the survival subtype of liver cancer, of which Univariate Cox-PH regression model was used twice. We integrated RNA sequencing, miRNA sequencing, DNA methylation data and clinical data of liver cancer from TCGA to infer two survival subtypes. We then also constructed an XGBoost supervised classification model to predict the survival subtype of the new sample. Experimental results show that our model gives two subgroups with significant survival differences and Concordance index. We also use two additional confirmation cohorts downloaded from the GEO database to verify our multi-omics model. We found highly expressed stemness marker genes CD24, KRT19 and EPCAM and the tumor marker gene BIRC5 in two survival subgroups. Our method has great clinical significance for the prediction of HCC prognosis.

KW - Deep learning

KW - Multi-omics data

KW - Network fusion

KW - Supervised classification model

KW - Survival subtype

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U2 - 10.1007/978-3-030-60796-8_21

DO - 10.1007/978-3-030-60796-8_21

M3 - Conference contribution

AN - SCOPUS:85093952439

SN - 9783030607951

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 247

EP - 257

BT - Intelligent Computing Methodologies - 16th International Conference, ICIC 2020, Proceedings

A2 - Huang, De-Shuang

A2 - Premaratne, Prashan

PB - Springer Science and Business Media Deutschland GmbH

T2 - 16th International Conference on Intelligent Computing, ICIC 2020

Y2 - 2 October 2020 through 5 October 2020

ER -

Wang Z, Yan R, Liu J, Liu Y, Ren F, Zheng C et al. An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data. In Huang DS, Premaratne P, editors, Intelligent Computing Methodologies - 16th International Conference, ICIC 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 247-257. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60796-8_21

An Integration Framework for Liver Cancer Subtype Classification and Survival Prediction Based on Multi-omics Data

Abstract

Publication series

Conference

Keywords

UN SDGs

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