Heliyon · 2023
Molecular subtypes based on N6-methyladenosine RNA methylation demonstrate the heterogeneity of immune and biological functions in pediatric septic shock.
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Wang Huabin, Huang Junbin, Guo Cheng, Wu Jingfang, Zhang Liyuan, Ren Xueyun, Gan Lijun
Аннотация
INTRODUCTION: Septic shock in children is a highly heterogeneous syndrome involving different immune states and biological processes. We used a bioinformatics approach to explore the relationship between N6-methyladenosine (m6A) methylation and septic shock in children.
METHODS: A gene expression dataset including information on 98 children with septic shock was selected. To construct and evaluate a risk prediction model, machine learning was used to screen marker m6A regulators. Based on differentially expressed m6A regulators, molecular subtypes for paediatric septic shock were constructed. Subsequently, the differences in the m6Ascore, heterogeneity of immune cell infiltration, and heterogeneity of biological functions between the different subtypes were analyzed. Finally, real-time quantitative PCR (RT-qPCR) was performed to validate the expression of the marker m6A regulators.
RESULTS: Fifteen differentially expressed m6A regulators were identified. Six marker m6A regulators, including LRPPRC, ELAVL1, RBM15, CBLL1, FTO, and RBM15B, were screened using the random forest method. The risk prediction model for paediatric septic shock constructed using m6A markers had strong consistency and high clinical practicability. Two subtypes of paediatric septic shock have been identified based on the differential expression pattern of m6A regulators. Significant differences were observed in RNA epigenetics, immune statuses, and biological processes between the two m6A subtypes. Differentially expressed genes between the two subtypes were enriched in cell number homeostasis, redox responses, and innate immune system responses. Finally, the six marker m6A regulators were verified in additional samples.
CONCLUSIONS: Based on the heterogeneity of m6A methylation-regulated genes, two different subtypes of septic shock in children with different RNA epigenetics, immune statuses, and biological processes were identified, revealing the heterogeneity of the disease largely attributable to differential m6A methylation. The findings will help explore and establish appropriate individualized treatments.
METHODS: A gene expression dataset including information on 98 children with septic shock was selected. To construct and evaluate a risk prediction model, machine learning was used to screen marker m6A regulators. Based on differentially expressed m6A regulators, molecular subtypes for paediatric septic shock were constructed. Subsequently, the differences in the m6Ascore, heterogeneity of immune cell infiltration, and heterogeneity of biological functions between the different subtypes were analyzed. Finally, real-time quantitative PCR (RT-qPCR) was performed to validate the expression of the marker m6A regulators.
RESULTS: Fifteen differentially expressed m6A regulators were identified. Six marker m6A regulators, including LRPPRC, ELAVL1, RBM15, CBLL1, FTO, and RBM15B, were screened using the random forest method. The risk prediction model for paediatric septic shock constructed using m6A markers had strong consistency and high clinical practicability. Two subtypes of paediatric septic shock have been identified based on the differential expression pattern of m6A regulators. Significant differences were observed in RNA epigenetics, immune statuses, and biological processes between the two m6A subtypes. Differentially expressed genes between the two subtypes were enriched in cell number homeostasis, redox responses, and innate immune system responses. Finally, the six marker m6A regulators were verified in additional samples.
CONCLUSIONS: Based on the heterogeneity of m6A methylation-regulated genes, two different subtypes of septic shock in children with different RNA epigenetics, immune statuses, and biological processes were identified, revealing the heterogeneity of the disease largely attributable to differential m6A methylation. The findings will help explore and establish appropriate individualized treatments.