Multi-omics approaches for drug-response characterization in primary biliary cholangitis and autoimmune hepatitis variant syndrome

Abstract

Background: Primary biliary cholangitis (PBC) and autoimmune hepatitis (AIH) variant syndrome (VS) exhibit a complex overlap of AIH features with PBC, leading to poorer prognoses than those with PBC or AIH alone. The biomarkers associated with drug response and potential molecular mechanisms in this syndrome have not been fully elucidated.

Methods: Whole-transcriptome sequencing was employed to discern differentially expressed (DE) RNAs within good responders (GR) and poor responders (PR) among patients with PBC/AIH VS. Subsequent gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted for the identified DE RNAs. Plasma metabolomics was employed to delineate the metabolic profiles distinguishing PR and GR groups. The quantification of immune cell profiles and associated cytokines was achieved through flow cytometry and immunoassay technology. Uni- and multivariable logistic regression analyses were conducted to construct a predictive model for insufficient biochemical response. The performance of the model was assessed by computing the area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity.

Findings: The analysis identified 224 differentially expressed (DE) mRNAs, 189 DE long non-coding RNAs, 39 DE circular RNAs, and 63 DE microRNAs. Functional pathway analysis revealed enrichment in lipid metabolic pathways and immune response. Metabolomics disclosed dysregulated lipid metabolism and identified PC (18:2/18:2) and PC (16:0/20:3) as predictors. CD4⁺ T helper (Th) cells, including Th2 cells and regulatory T cells (Tregs), were upregulated in the GR group. Pro-inflammatory cytokines (IFN-γ, TNF-α, IL-9, and IL-17) were downregulated in the GR group, while anti-inflammatory cytokines (IL-10, IL-4, IL-5, and IL-22) were elevated. Regulatory networks were constructed, identifying CACNA1H and ACAA1 as target genes. A predictive model based on these indicators demonstrated an AUC of 0.986 in the primary cohort and an AUC of 0.940 in the validation cohort for predicting complete biochemical response.

Conclusion: A combined model integrating genomic, metabolic, and cytokinomic features demonstrated high accuracy in predicting insufficient biochemical response in patients with PBC/AIH VS. Early recognition of individuals at elevated risk for insufficient response allows for the prompt initiation of additional treatments.

Keywords: Autoimmune liver diseases; Cytokinomic; Drug-response; Metabolomic; Whole-transcriptomic.

Fig. 1

Screening of differentially expressed RNAs in GR and PR groups. A The flow chart of the whole-transcriptomic design. B–E The volcano plots of differential expressed mRNAs, lncRNAs, circRNAs and miRNAs. F–I The heatmaps of differential expressed mRNAs, lncRNAs, circRNAs and miRNAs

Fig. 2

GO and KEGG pathway analysis of DE mRNAs, miRNAs, lncRNAs and circRNAs. A GO analysis of DE mRNAs. B Pathway analysis of DE mRNAs. C–E GO analysis of DE miRNAs, lncRNAs and circRNAs. F–H KEGG pathway analysis of DE miRNAs, lncRNAs and circRNAs

Fig. 3

Metabolic profiles of PBC/AIH VS patients between PR and GR groups. A The flow chart of the metabolic design. B PCA analysis. C The annotation of the detected metabolites. D, E The volcano plot and heatmap of differential metabolites. F The enriched pathway analysis of the differential metabolites. G The joint pathway analysis of differential metabolites and genes. H Heatmap of the differential metabolites involved in the joint pathway analysis. I Correlation analysis between the differential metabolites and clinical indictors. J ROC curves of the differential metabolites. K The relative abundance of metabolites with AUC > 0.7. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001

Fig. 4

Immune cells alterations between the PR and GR groups. A, B The flow chart of the study design. C The proportion of CD3⁺CD4⁺ T cells, CD3⁺CD8⁺ T cells, CD3⁻CD56⁺ NK cells and CD3⁺CD56⁺ NKT cells in peripheral blood from PR and GR groups. D Correlation analysis between the immune cells above and clinical indictors. E Multiplex immunofluorescence (MIF) staining of liver tissues from GR and PR. Green represent CD4. Red represent CD8. Orange represent CD56. F, G The percentage of IFN-γ⁺ Th1 cells, IL4⁺ Th2 cells, IL17⁺ Th17 cells, Fxop3⁺ Tregs in between the PR and GR groups. H Correlation analysis between these cells and clinical parameters. ^*P < 0.05, ^**P < 0.01

Fig. 5

Cytokine profiling in plasma from patients with PBC/AIH VS. A Plasma levels of different cytokines between the PR and GR groups. B Correlation analysis between the levels of cytokines and clinical parameters. C ROC curves of the significant differential expressed cytokines. ^* P < 0.05, ^**P < 0.01

Fig. 6

Construction of ceRNA networks related to metabolic and immune pathways. A LncRNA-related ceRNA networks. Pink triangles represent miRNAs. Light blue squares represent lncRNAs. White circles represent mRNAs, including PSMCS, ECl1, ACADS, SHC2, STK11, ACAA1 and CACNA1H. B CircRNA-related ceRNA networks. Pink triangles represent miRNAs. Dark blue prisms represent circRNAs. White circles represent mRNAs, including SLC38A3, RARRES2, PPP1R14B, PSMC3 and GLYCTK. C Expression of the target genes in the lncRNA/circRNA-related networks. D ROC curves of the significant differential expressed genes. ^*P < 0.05

Fig. 7

Performance of the combined model for prediction of biochemical response. A Nomogram based on the combined model to predict biochemical response in PBC/AIH VS patients. B, C ROC curves showing the performance of the combined model in primary cohort and validation cohort. D, E The calibration curve of the combined model in primary cohort and validation cohort