Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

Yi Fu; Anfeng Si; Xindong Wei; Xinjie Lin; Yujie Ma; Huimin Qiu; Zhinan Guo; Yong Pan; Yiru Zhang; Xiaoni Kong; Shibo Li; Yanjun Shi; Hailong Wu

doi:10.1186/s12864-023-09194-8

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

BMC Genomics. 2023 Feb 27;24(1):89. doi: 10.1186/s12864-023-09194-8.

Authors

Yi Fu^#^{1

2

3}, Anfeng Si^#⁴, Xindong Wei^#⁵, Xinjie Lin^{1

2}, Yujie Ma^{1

2}, Huimin Qiu^{2

6}, Zhinan Guo^{2

7}, Yong Pan⁸, Yiru Zhang⁸, Xiaoni Kong⁵, Shibo Li⁹, Yanjun Shi¹⁰, Hailong Wu^{11

12

13

14}

Affiliations

¹ Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China.
² Collaborative Innovation Center for Biomedicines, Shanghai University of Medicine and Health Sciences, Shanghai, China.
³ School of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, China.
⁴ Department of Surgical Oncology, Jinling Hospital, Medical School of Nanjing University, Nanjing, China.
⁵ Central Laboratory, Department of Liver Diseases, Shuguang Hospital, Shanghai University of Chinese Traditional Medicine, Shanghai, China.
⁶ School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
⁷ School of Kinesiology, Shanghai University of Sport, Shanghai, China.
⁸ Department of Infectious Disease, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, China.
⁹ Department of Infectious Disease, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, China. lsb0398@126.com.
¹⁰ Abdominal Transplantation Center, General Surgery, School of Medicine, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai, China. shiyanjunss@hotmail.com.
¹¹ Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China. wuhl@sumhs.edu.cn.
¹² Collaborative Innovation Center for Biomedicines, Shanghai University of Medicine and Health Sciences, Shanghai, China. wuhl@sumhs.edu.cn.
¹³ School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China. wuhl@sumhs.edu.cn.
¹⁴ School of Kinesiology, Shanghai University of Sport, Shanghai, China. wuhl@sumhs.edu.cn.

^# Contributed equally.

Abstract

Background: Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.

Methods: Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.

Results: A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.

Conclusion: Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

Keywords: Early recurrence; Hepatocellular carcinoma; Long non-coding RNA signature; Machine learning.

MeSH terms

Carcinoma, Hepatocellular* / genetics
Humans
Liver Neoplasms* / genetics
Machine Learning
Neoplasm Staging
RNA, Long Noncoding* / genetics
alpha-Fetoproteins

Substances

alpha-Fetoproteins
RNA, Long Noncoding

Abstract

MeSH terms

Substances

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