Frontiers in genetics · 2024
Predicting novel biomarkers for early diagnosis and dynamic severity monitoring of human ulcerative colitis.
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Chen Yu, Chen Li, Huang Sheng, Yang Li, Wang Li, Yang Feiyun, Huang Jinxiu, Ding Xiuliang
Аннотация
BACKGROUND: Ulcerative colitis is an emerging global health concern that poses a significant threat to human health and can progress to colorectal cancer if not diagnosed and treated promptly. Currently, the biomarkers used clinically for diagnosis and dynamic severity monitoring lack disease specificity.
METHODS: Mouse models induced with 2%, 2.5%, and 3% DSS were utilized to simulate human UC with varying severities of inflammation. Transcriptome sequencing technology was employed to identify differentially expressed genes (DEGs) between the control group and each treatment group. Functional enrichment analysis of the KEGG database was performed for shared DEGs among the three treatment groups. DEGs that were significantly and strongly correlated with DSS concentrations were identified using Spearman correlation analysis. Human homologous genes of the interested DEGs were searched in the HomoloGene database, and their regulation patterns in UC patients were validated using the GSE224758 dataset. These genes were then submitted to the DisGeNET database to identify their known associations with human diseases. Online tools, including SignalP 6.0 and DeepTMHMM 1.0, were used to predict signal peptides and transmembrane helices in the amino acid sequences of human genes homologous to the DEGs of interest.
RESULTS: A total of 1,230, 995, and 2,214 DEGs were identified in the 2%, 2.5%, and 3% DSS-induced groups, respectively, with 668 DEGs common across all three groups. These shared DEGs were primarily associated with signaling transport, pathogenesis, and immune response. Through extensive screening, LGI2 and PRSS22 were identified as potentially novel biomarkers with higher specificity and ease of detection for the early diagnosis and dynamic severity monitoring of human UC, respectively.
CONCLUSION: We have identified two potentially novel biomarkers, LGI2 and PRSS22, which are easy of detection and more specific for human UC. These findings provide new insights into the accurate diagnosis and dynamic monitoring of this persistent disease.
METHODS: Mouse models induced with 2%, 2.5%, and 3% DSS were utilized to simulate human UC with varying severities of inflammation. Transcriptome sequencing technology was employed to identify differentially expressed genes (DEGs) between the control group and each treatment group. Functional enrichment analysis of the KEGG database was performed for shared DEGs among the three treatment groups. DEGs that were significantly and strongly correlated with DSS concentrations were identified using Spearman correlation analysis. Human homologous genes of the interested DEGs were searched in the HomoloGene database, and their regulation patterns in UC patients were validated using the GSE224758 dataset. These genes were then submitted to the DisGeNET database to identify their known associations with human diseases. Online tools, including SignalP 6.0 and DeepTMHMM 1.0, were used to predict signal peptides and transmembrane helices in the amino acid sequences of human genes homologous to the DEGs of interest.
RESULTS: A total of 1,230, 995, and 2,214 DEGs were identified in the 2%, 2.5%, and 3% DSS-induced groups, respectively, with 668 DEGs common across all three groups. These shared DEGs were primarily associated with signaling transport, pathogenesis, and immune response. Through extensive screening, LGI2 and PRSS22 were identified as potentially novel biomarkers with higher specificity and ease of detection for the early diagnosis and dynamic severity monitoring of human UC, respectively.
CONCLUSION: We have identified two potentially novel biomarkers, LGI2 and PRSS22, which are easy of detection and more specific for human UC. These findings provide new insights into the accurate diagnosis and dynamic monitoring of this persistent disease.