tetano
Editor, Senior Moderator
Transl Pediatr
. 2026 Jan 31;15(1):12.
doi: 10.21037/tp-2025-246. Epub 2026 Jan 23.
Molecular subtyping and immune microenvironment heterogeneity in pediatric influenza-associated prolonged multiple organ dysfunction syndrome
Ming Chi[SUP] #[/SUP][SUP] 1 [/SUP], Lei Wang[SUP] #[/SUP][SUP] 2 [/SUP], Wenliang Bi[SUP] 3 [/SUP], Fei Liu[SUP] 4 [/SUP], Shulei Liu[SUP] 5 [/SUP], Dawei Zhang[SUP] 6 [/SUP]
Affiliations
Background: Pediatric influenza infections that progress to prolonged multiple organ dysfunction syndrome (PMODS) carry a high mortality rate. The underlying molecular heterogeneity, particularly involving dysregulated autophagy pathways and the immune microenvironment, remains poorly characterized, hindering the development of targeted interventions. This study aimed to integrate transcriptomic profiling and machine learning to dissect autophagy-related gene (ARG) dysregulation, characterize the immune microenvironment, and identify clinical biomarkers associated with PMODS severity.
Methods: We analyzed the publicly available transcriptomic dataset GSE236877, comprising 191 pediatric samples from influenza patients: 38 with PMODS or who died, 27 who recovered from MODS (RM), and 126 who never developed MODS (NM). Differential expression analysis of ARGs was performed. Unsupervised consensus clustering was used to identify molecular subtypes within the PMODS group. Immune cell infiltration was quantified using CIBERSORT. A Random Forest (RF) machine learning algorithm was employed to prioritize key discriminatory genes, whose correlations with clinical parameters were subsequently assessed.
Results: Compared to NM samples, PMODS cases exhibited significant upregulation of CCL2, CTSB, HIF1A, and NFKB1, alongside downregulation of CASP1, CASP8, TNFSF10, and EIF2AK2. Consensus clustering stratified PMODS patients into two distinct molecular subtypes (C1 and C2). Subtype C1 was characterized by a hyperinflammatory signature, marked by elevated expression of CCL2 and increased infiltration of Macrophages M0. In contrast, subtype C2 displayed a profile of apoptotic activation, with upregulated TNFSF10 and significantly reduced Macrophages M0 infiltration. RF analysis identified CCL2, TNFSF10, and HIF1A as the top three genes for discriminating disease states. Their expression levels showed significant correlations with leukocyte counts and clinical disease severity scores.
Conclusions: This study reveals significant molecular heterogeneity within pediatric influenza-associated PMODS, delineating two distinct subtype-specific mechanisms: C1 hyperinflammation vs. C2 apoptotic activation. It identifies CCL2, TNFSF10, and HIF1A as key biomarkers linked to immune dysregulation and clinical severity. These findings provide a foundational framework for the development of subtype-stratified, precision management strategies for this critical condition.
Keywords: Pediatric influenza; autophagy-related genes (ARGs); immune microenvironment; multiple organ dysfunction syndrome (MODS); transcriptomic profiling.
. 2026 Jan 31;15(1):12.
doi: 10.21037/tp-2025-246. Epub 2026 Jan 23.
Molecular subtyping and immune microenvironment heterogeneity in pediatric influenza-associated prolonged multiple organ dysfunction syndrome
Ming Chi[SUP] #[/SUP][SUP] 1 [/SUP], Lei Wang[SUP] #[/SUP][SUP] 2 [/SUP], Wenliang Bi[SUP] 3 [/SUP], Fei Liu[SUP] 4 [/SUP], Shulei Liu[SUP] 5 [/SUP], Dawei Zhang[SUP] 6 [/SUP]
Affiliations
- PMID: 41657442
- PMCID: PMC12877861
- DOI: 10.21037/tp-2025-246
Background: Pediatric influenza infections that progress to prolonged multiple organ dysfunction syndrome (PMODS) carry a high mortality rate. The underlying molecular heterogeneity, particularly involving dysregulated autophagy pathways and the immune microenvironment, remains poorly characterized, hindering the development of targeted interventions. This study aimed to integrate transcriptomic profiling and machine learning to dissect autophagy-related gene (ARG) dysregulation, characterize the immune microenvironment, and identify clinical biomarkers associated with PMODS severity.
Methods: We analyzed the publicly available transcriptomic dataset GSE236877, comprising 191 pediatric samples from influenza patients: 38 with PMODS or who died, 27 who recovered from MODS (RM), and 126 who never developed MODS (NM). Differential expression analysis of ARGs was performed. Unsupervised consensus clustering was used to identify molecular subtypes within the PMODS group. Immune cell infiltration was quantified using CIBERSORT. A Random Forest (RF) machine learning algorithm was employed to prioritize key discriminatory genes, whose correlations with clinical parameters were subsequently assessed.
Results: Compared to NM samples, PMODS cases exhibited significant upregulation of CCL2, CTSB, HIF1A, and NFKB1, alongside downregulation of CASP1, CASP8, TNFSF10, and EIF2AK2. Consensus clustering stratified PMODS patients into two distinct molecular subtypes (C1 and C2). Subtype C1 was characterized by a hyperinflammatory signature, marked by elevated expression of CCL2 and increased infiltration of Macrophages M0. In contrast, subtype C2 displayed a profile of apoptotic activation, with upregulated TNFSF10 and significantly reduced Macrophages M0 infiltration. RF analysis identified CCL2, TNFSF10, and HIF1A as the top three genes for discriminating disease states. Their expression levels showed significant correlations with leukocyte counts and clinical disease severity scores.
Conclusions: This study reveals significant molecular heterogeneity within pediatric influenza-associated PMODS, delineating two distinct subtype-specific mechanisms: C1 hyperinflammation vs. C2 apoptotic activation. It identifies CCL2, TNFSF10, and HIF1A as key biomarkers linked to immune dysregulation and clinical severity. These findings provide a foundational framework for the development of subtype-stratified, precision management strategies for this critical condition.
Keywords: Pediatric influenza; autophagy-related genes (ARGs); immune microenvironment; multiple organ dysfunction syndrome (MODS); transcriptomic profiling.