# Identification and validation of tissue-based gene biomarkers for acute intestinal graft-versus-host disease(AIGVHD)

## TL;DR

This study identifies three genes as potential biomarkers for diagnosing acute intestinal graft-versus-host disease, a serious complication in stem cell transplants.

## Contribution

The study introduces FCGR3A, SERPING1, and IFITM3 as novel tissue-based gene biomarkers for AIGVHD with strong diagnostic potential.

## Key findings

- Three key genes (FCGR3A, SERPING1, IFITM3) are positively associated with immune cell infiltration in AIGVHD.
- A machine learning model using these genes achieved an AUC of 0.9802 for predicting AIGVHD.
- Mouse model experiments confirmed higher mRNA expression of these genes in AIGVHD.

## Abstract

Acute intestinal graft-versus-host disease (AIGVHD) is a common complication of allogeneic hematopoietic stem cell transplantation (allo HSCT) with a high mortality rate. The primary aim of the present study is to identify tissue-based gene biomarkers pertinent to AIGVHD, thereby facilitating early diagnosis and exploration of potential therapeutic targets.

The dataset was obtained from the GEO database. DEGs were identified, followed by GO and KEGG pathways analysis for the common DEGs. PPI networks and WGCNA analysis were used to identify essential genes, and correlations between critical genes and immune cell infiltration were also examined. The diagnostic efficacy of these essential genes was evaluated using ROC curves, leading to the development of 11 machine learning models based on this gene set. Furthermore, we established a mouse model of aGVHD, which was identified by clinical score, pathological analysis, flow cytometry detection of implantation rate, and immunohistochemical detection of CD4 expression. Finally, we measured the mRNA expression levels of the key genes in the mice’s intestinal tissue using real-time PCR.

DEGs showed a marked enrichment in immune and inflammatory response pathways. Our analysis identified three key genes, FCGR3A, SERPING1, and IFITM3, which were positively associated with M1 macrophage and neutrophil infiltration. Subsequently, we developed machine learning models utilizing these three genes and found that the RF model exhibited a robust predictive capacity for AIGVHD occurrence, achieving an AUC of 0.9802 (95% CI: 0.966–0.9945). An aGVHD mouse model was also successfully created, and we discovered that the aGVHD group’s mRNA expression levels of three key genes were noticeably higher than the control group’s.

In this study, we identified FCGR3A, SERPING1, and IFITM3 as tissue-based gene biomarkers for AIGVHD, highlighting their diagnostic efficacy. Furthermore, we confirmed the association of these genes with AIGVHD through investigations conducted in aGVHD mouse models.