Bioinformatics identification of key genes correlating NOD1 and Endoplasmic Reticulum stress in Hepatitis B virus-induced acute liver failure

TL;DR

This study identifies key genes linked to NOD1 and endoplasmic reticulum stress in acute liver failure caused by Hepatitis B virus.

Contribution

The study introduces a novel approach combining bioinformatics and machine learning to identify key genes in HBV-ALF.

Findings

Five key genes (SEL1L, DNAJB9, DERL3, NOD1, and CFTR) showed high diagnostic accuracy for HBV-ALF.

The genes were associated with retinol metabolism and peroxisome signaling pathways.

The identified genes are significantly linked to immune cell types like M1 macrophages and neutrophils.

Abstract

Endoplasmic reticulum stress (ERS) has been implicated in a range of biological processes, yet its specific involvement in Hepatitis B virus-associated acute liver failure (HBV-ALF) remains poorly understood. This study aimed to identify key ERS-related genes (ERGs) and elucidate their underlying mechanisms in HBV-ALF. Publicly available HBV-ALF-related datasets (GSE38941, GSE62029) and ERGs were analyzed. Intersection genes were determined by overlapping differentially expressed genes from both datasets with ERGs, and genes showing strong correlation with NOD1 were selected as candidates. The BottleNeck algorithm in the Cytohubba plugin and machine learning-based screening were subsequently applied to refine key gene selection. Diagnostic performance was assessed using ROC curves, while a nomogram was constructed to evaluate the predictive value for HBV-ALF. Functional enrichment and immune-related analyses were also conducted on the identified key genes. The results revealed that among 5,699 intersection genes, 265 overlapped with ERGs, resulting in 97 key intersection genes. Of these, 86 showed strong correlation with NOD1. From the top 20 genes identified by the BottleNeck algorithm, five key genes—SEL1L, DNAJB9, DERL3, NOD1, and CFTR—were ultimately selected through machine learning. ROC analysis demonstrated that all five genes exhibited high diagnostic accuracy, with AUC values exceeding 0.8, effectively distinguishing HBV-ALF samples from normal controls. The nomogram displayed strong predictive performance for HBV-ALF development. Gene set enrichment analysis indicated that these genes were involved in retinol metabolism and peroxisome signaling pathways, and were significantly associated with immune cell types including M1 macrophages, plasma cells, and neutrophils. These findings provide novel insights into the molecular mechanisms of HBV-ALF and highlight potential targets for future diagnostic and therapeutic strategies.

The online version contains supplementary material available at 10.1038/s41598-025-19813-x.

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