Apelin (APLN) is a biomarker contributing to the diagnosis and prognosis of hepatocellular carcinoma

Sep 21, 2026

Apelin (APLN) is investigated as a biomarker for hepatocellular carcinoma (HCC), where diagnosis and prognosis remain challenging. Diagnostic and prognostic models are built from TCGA data using traditional binary classification and machine learning methods, while APLN-related mechanisms are examined with GEO single-cell sequencing (GSE149614). Multiple algorithms distinguish HCC from normal effectively. A four-gene prognostic model using LASSO and Cox regression predicts three-year survival well. APLN is upregulated in tumor endothelial contexts and serves as an independent predictor of endothelial malignancy and rapid progression after T2.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Disease background and unmet needs

Diagnostic modeling approach

Prognostic modeling approach

Methods

Data sources (TCGA, GEO)

Machine learning classification

LASSO and Cox prognostic model

Results

Diagnostic discrimination performance

Four-gene prognostic performance

Biological role of APLN in endothelial malignancy

Discussion

Clinical implications and model reliability

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Apelin (APLN) is a biomarker contributing to the diagnosis and prognosis of hepatocellular carcinoma Xi Mao1,2,4, Xiaoya Zhu1,4, Tong Pan1, Zehui Liu1, Pingping Shangguan1, Yi Zhang1, Yingle Liu1,2*, Xiwen Jiang3* & Qi Zhang1* Liver cancer, classified as a malignant hepatic tumor, can be divided into two categories: primary, originating within the liver, and secondary, resulting from metastasis to the liver from other organs. Hepatocellular carcinoma (HCC) is the main form of primary liver cancer and the third leading cause of cancer-related deaths. The diagnosis and prognosis of HCC using current methods still face numerous challenges. This study aims to develop novel diagnostic and prognostic models while identifying new biomarkers for improved HCC treatment. Diagnostic and prognostic models for HCC were constructed using traditional binary classification methods and machine learning algorithms based on theTCGA database (Downloaded in August 2023). The mechanisms by which APLN (Apelin) affects HCC were investigated using single-cell sequencing data sourced from the GEO database (GSE149614). The diagnostic models yielded by various algorithms could effectively distinguished HCC samples from normal ones. The prognostic model, composed of four genes, was constructed using LASSO and Cox regression algorithms, demonstrating good performance in predicting the three-year survival rate of HCC patients. The HCC biomarker Apelin (APLN) was identified in this study. APLN in liver cancer tissues mainly comes from endothelial cells and is associated with the carcinogenesis of these cells. APLN expression is significantly upregulated in liver cancer tissues, marking it as a viable indicator of endothelial cell malignancy in HCC. Furthermore, APLN expression was determined to be an independent predictor of tumor endothelial cell carcinogenesis, unaffected by its modifications such as single nucleotide variation, copy number variation, and methylation. Additionally, liver cancers characterized by high APLN expression are likely to progress rapidly after T2 stage. Our study presents diagnostic and prognostic models for HCC with appreciably improved accuracy and reliability compared to previous reports. APLN is a reliable HCC biomarker and contributes to the establishment of our models. Keywords Biomarker, Hepatocellular carcinoma, Diagnosis, Prognosis Abbreviations AFP Alpha-fetoprotein APLN Apelin DEG Differentially expressed genes GEO Gene expression omnibus GO Gene ontology GSCA Gene set cancer analysis GTEx Genotype tissue expression HCC Hepatocellular carcinoma ICGC International cancer genome consortium KEGG Kyoto encyclopedia of genes and genomes 1State Key Laboratory of Virology, College of Life Sciences, Wuhan University, Wuhan, China. 2Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, China. 3School of Life Sciences and Biopharmaceuticals, Guangdong Pharmaceutical University, Guangzhou, China. 4These authors contributed equally: Xi Mao and Xiaoya Zhu.*[email: mvlwu@whu.edu.cn](email: mvlwu@whu.edu.cn); [yuanyecat@vip.sina.com](yuanyecat@vip.sina.com); [gracetey@whu.edu.cn](gracetey@whu.edu.cn) [www. nature.com/scientificreports/](www. nature.com/scientificreports/) KIRP LASSO LUAD LUSCp-GSK3β PCA PPI PVTT TCGA Kidney renal papillary cell carcinoma Least absolute shrinkage and selection operator Lung adenocarcinoma Lung squamous cell carcinoma Phospho-glycogen synthase kinase 3β Principal component analysis Protein–protein interaction Portal vein tumor thrombus The cancer genome atlas Hepatocellular carcinoma (HCC), the major form of primary liver cancer, is the third leading cause of cancerrelated mortality, with a 5-year relative survival rate of approximately 18%1. The major risk factors for HCC involve chronic alcohol consumption, hepatitis B or C virus infection, and non-alc

FAQs

What is the main goal of the study on Apelin (APLN) in HCC?

To develop improved diagnostic and prognostic models for hepatocellular carcinoma and to identify new biomarkers, specifically focusing on APLN.

How are diagnostic and prognostic models constructed in the study?

Diagnostic models use traditional binary classification and machine learning algorithms based on TCGA data. The prognostic model is built from four genes using LASSO and Cox regression.

What does the study conclude about APLN’s role in hepatocellular carcinoma?

APLN is significantly upregulated in liver cancer tissues, mainly from endothelial cells, and it independently predicts tumor endothelial cell carcinogenesis and rapid progression after T2 stage.

Sep 21, 2026

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