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

Hong Chen^1,2,3,4,5,6Liyu Fu^2,3Liying Liu^2,3Yunyan He^1,3,4,5,6*

• ¹Department of Pediatrics, First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi Zhuang Region, China
• ²Graduate School, Guangxi Medical University, Nanning, Guangxi Zhuang Region, China
• ³The Key Laboratory of Children’s Disease Research in Guangxi’s Colleges and Universities, Education Department of Guangxi Zhuang Autonomous Region, Nanning, Guangxi Zhuang Region, China
• ⁴The first afflicted hospital of Guangxi medical university/Difficult and Criticaillness Center, Pediatric Clinical Medical Research Center of Guangxi, Nanning, Guangxi Zhuang Region, China
• ⁵NHC Key Laboratory of Thalassemia Medicine, Nanning, China
• ⁶Guangxi Key laboratory of Thalassemia Research, Nanning, China

Background: 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.Method: The dataset was obtained from the GEO database. DEGs were identified, followed by GO and KEGG pathway 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.Result: 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.Conclusion: 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.