MicroRNA-126-3p as a predictive biomarker for patients with primary biliary cholangitis refractory to ursodeoxycholic acid

Abstract

BACKGROUND

Ursodeoxycholic acid (UDCA) is the first-line therapeutic agent for primary biliary cholangitis (PBC). However, a subset of patients exhibit a suboptimal response to UDCA, and reliable predictive biomarkers remain elusive. Studies have implicated plasma microRNAs (miRNAs) in the pathophysiological progression of PBC, with certain miRNAs demonstrating potential as diagnostic and disease progression biomarkers. However, biomarkers capable of predicting the therapeutic efficacy of UDCA have not yet been identified.

AIM

To investigate differentially expressed miRNAs in PBC patients with divergent UDCA treatment responses and to explore potential biomarkers that predict treatment response in PBC.

METHODS

Plasma samples from treatment-naive PBC patients receiving ≥ 1 year of standard UDCA treatment were collected. Efficacy was evaluated using the Paris I criteria. Patient samples were divided into discovery group (n = 10) and validation group (n = 30), with further stratification of patients into drug-resistant and drug-sensitive (DS) cohorts. Next-generation sequencing and quantitative real-time polymerase chain reaction were used to screen, functionally analyze, and validate the pre-treatment miRNA profiles of the treatment groups.

RESULTS

Forty-nine miRNAs were differentially expressed between the two groups before UDCA treatment (N = 40). MiR-22-5p and miR-126-3p were highly expressed in the DS group before treatment (P < 0.001), whereas miR-7706 exhibited a low expression (P = 0.017). Post-treatment, miR-126-3p maintained low expression in the drug-resistant group (P = 0.003), but showed elevated levels in the DS group (P < 0.001). Logistic regression analysis identified miR-126-3p expression (odds ratio = 34.32, 95% confidence interval: 1.95-605.40, P = 0.016) as a significant factor influencing UDCA treatment response, while miR-22-5p (P = 0.990) and miR-7706 (P = 0.157) showed no significant association. MiR-126-3p levels were negatively correlated with total bilirubin (r = -0.356, P = 0.005) and immunoglobulin G levels (r = -0.311, P = 0.015). The area under the receiver operating characteristic curve was 0.891 (P = 0.0003, 95% confidence interval: 0.772-1.000) with a sensitivity of 82.4% and a specificity of 84.6%.

CONCLUSION

Plasma miRNA expression profiles are heterogenous in patients with PBC with differential responses to UDCA therapy. MiR-126-3p demonstrates predictive potential for a suboptimal response to UDCA in patients with PBC.

Key Words: Primary biliary cholangitis; MicroRNA; Ursodeoxycholic acid; Efficacy; Early prediction

Core Tip: Patients with primary biliary cholangitis exhibit variable responses to ursodeoxycholic acid (UDCA) therapy, and reliable predictive biomarkers before therapy remain elusive. This study identified 49 differentially expressed microRNAs in patients with different response and explore their potential as predictive biomarkers. MiR-126-3p maintained low expression in the drug-resistant group while showing elevated levels in the sensitive group after therapy. Its expression was significantly positively correlated with UDCA efficacy and negatively associated with total bilirubin and immunoglobulin G levels. The receiver operating characteristic curve analysis demonstrated an area under the curve of 0.891 with a sensitivity of 82.4% and specificity of 84.6%, demonstrating high predictive potential value in patient’s refractory to UDCA.

INTRODUCTION

Primary biliary cholangitis (PBC) is an autoimmune liver disease characterized by the destruction of small intrahepatic bile ducts, progressive cholestasis, and the presence of highly specific serum anti-mitochondrial antibodies (AMAs)[1]. Characterized by insidious onset and chronic progression, PBC predominantly affects females[2], with a peak incidence between 40-60 years of age, manifesting as fatigue, pruritus, jaundice, and right upper quadrant discomfort[2-5].

Ursodeoxycholic acid (UDCA) is the first-line therapy for PBC[1]. Although UDCA can slow early stage hepatic fibrosis[6], approximately 40% of PBC patients exhibit suboptimal therapeutic responses to UDCA[7]. Obeticholic acid can be used as a second-line treatment for patients with poor response to UDCA[8], yet obeticholic acid treatment can lead to pruritus and an increased incidence of serious adverse events. Recent phase III clinical trials have demonstrated that seladelpar significantly reduced alkaline phosphatase (ALP) to normal levels in UDCA non-responders compared with placebo[9]. Meanwhile, a phase III clinical trial of elafibranor found that, compared with placebo, elafibranor significantly improved cholestasis-related biochemical markers; however, adverse events occurred more frequently, including abdominal pain, diarrhea, nausea, and vomiting[10].

The evaluation criteria for the therapeutic efficacy of UDCA in PBC have evolved over the decades, with the biochemical marker-based Barcelona criteria first proposed in 2006[11]. Subsequently, the international research consortia established multiple criteria, including Paris I[12], Rotterdam[13], Ehime[14], Toronto[15], and Paris II[16]. Zhang et al[17] developed the Beijing criteria, which innovatively shortened the therapeutic evaluation duration to six months, providing earlier and regionally tailored evaluation benchmarks for Asian populations. However, the current assessment of therapeutic response in PBC requires a 6-12-month observation period, creating a critical treatment window delay. This time gap hinders the administration of other effective treatments, leading to the need for liver transplantation and even mortality. Therefore, predicting the criteria outcomes based on blood sample analysis before the start of treatment can improve prognosis. Recent studies have identified biomarkers relevant to other biliary diseases. Serum matrix metalloproteinase-7 has been shown to effectively differentiate cholestatic cholangitis from non-cholestatic forms[18]. Multiplex proteomic analysis combining carbohydrate antigen 19-9 (CA19-9) and chemokine ligand 20 (CCL20) measurements demonstrated improved discriminatory power between biliary tract cancer and benign biliary conditions[19]. Therefore, it appears theoretically plausible for biomarkers to enable the prediction of a poor response to UCDA in patients with PBC.

MicroRNA (miRNA or miR) is a 19-25 nucleotide non-coding RNA molecule that suppresses protein production by complementary binding to the 3’ untranslated region of target genes and interfering with mRNA translation[20]. MiRNAs play crucial roles in a number of biological processes, including tissue development and differentiation, immunity, cell proliferation, apoptosis, and oncogenesis[21]. An increasing number of studies have reported the miRNA profiles of patients with PBC and their potential value in the diagnosis and prediction of therapeutic efficacy. Thirty-five miRNAs were found to be differentially expressed in patients with end-stage PBC compared to healthy subjects[22]. In diagnostic applications, a miRNA panel (miR-122-5p, miR-141-3p, and miR-26b-5p) demonstrated exceptional diagnostic accuracy for PBC [area under the curve (AUC) = 0.905], suggesting its potential as a biomarker for early disease detection[23]. Differential miRNA expression has also been observed in CD4+ T and B lymphocytes from patients with PBC[22,24-26]. Additionally, changes in serum miRNA profiles might reflect underlying liver injury or inflammation and could be a valuable approach for early diagnosis and prognosis in many diseases[27,28]. A study identified miRNA disparities between patients with UDCA-resistant PBC and healthy controls through therapeutic efficacy analysis[29]. A previous study revealed that serum overexpression of miR-299-5p correlated with UDCA treatment non-response, underscoring the potential of miRNAs as biomarkers for predicting therapeutic outcomes in PBC[30]. Meta-analyses have shown that miRNAs, such as miR-26a, miR-150, and miR-192, enhance the diagnostic accuracy for cholangiocarcinoma, particularly when integrated with CA19-9 Levels[31]. However, there remains a critical gap in longitudinal dynamic studies of miRNAs in patients with PBC.

Previous investigations have delineated baseline miRNA disparities between patients with UDCA-resistant PBC and healthy controls, as well as post-treatment differential expression patterns. However, the effect of UDCA therapy on miRNA dynamics and the longitudinal variation between treatment responders and non-responders remain unclear. This study systematically profiled serum miRNA expression in PBC cohorts by comparatively analyzing baseline expression patterns and therapeutic trajectory variations to identify pretreatment predictive biomarkers of UDCA responsiveness.

MATERIALS AND METHODS

Human samples

We enrolled patients with PBC with complete follow-up data (> 1 year) from the Fifth Medical Center of PLA General Hospital between January 2017 and June 2020. Comprehensive clinical data, including medical history, baseline characteristics, laboratory parameters, and serum samples pre-treatment and after ≥ 48 weeks of standardized therapy, were collected. All serum specimens were cryopreserved at -80 °C in the biorepository of the Fifth Medical Center. The inclusion criteria were: (1) Age 18-75 years with PBC diagnosis per the American Association for the Study of Liver Diseases criteria[32]; (2) Treatment-naive patients completing > 48 weeks of UDCA therapy (13-15 mg/kg/day); and (3) Availability of paired baseline and 48-week serum samples. The exclusion criteria were: (1) Concurrent viral infections; (2) Coexisting hepatic disorders; (3) Major organ failure; (4) Pregnancy; (5) Malignancy history; and (6) Immunosuppressant/experimental drug use within 6 months. Therapeutic response was evaluated using Paris I criteria[12]: Total bilirubin (TBIL) ≤ 0.01 g/L, ALP ≤ 3 × upper limit of normal, aspartate aminotransferase (AST) ≤ 2 × upper limit of normal after 1 year of UDCA therapy. Patients meeting the criteria were considered as the drug-sensitive (DS) group, with others the drug-resistant (DR) group. Patient samples were randomly stratified into discovery and test cohorts to screen for candidate miRNAs with therapeutic-prediction potential. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki (6^th revision, 2008) as reflected by a priori approval by the Medical Ethics Committee of the 302 Clinical Medical School (Approval No. 2016174D). Informed consent was obtained from all patients included in the study.

Extraction and sequencing of miRNA

Total RNA was isolated from serum samples using the Plasma/Serum Circulating and Exosomal RNA Purification Mini Kit (Norgen Biotek Corp., Canada; NGB-51000) according to the manufacturer’s protocol. The RNA integrity was assessed using an Agilent 2100 Bioanalyzer and an RNA 6000 Nano LabChip Kit (Agilent, CA, United States; 5065-4476). Electropherogram analysis confirmed the quality of serum RNA. Small RNA sequencing libraries were constructed using TruSeq Small RNA Sample Prep Kits (Illumina Inc., CA, United States; RS-200-0012) per the established workflow. The libraries were subjected to single-end sequencing (1 × 50 bp) on an Illumina HiSeq 2000/2500 platform. The experimental procedures strictly followed Illumina standardized protocols.

Quantitative real-time polymerase chain reaction

Total RNA was isolated from patient serum using Qiagen’s miRNeasy Serum/Plasma Kit (Qiagen, Germany; 217184), followed by reverse transcription of 2 μg RNA to cDNA with TaKaRa’s First Strand cDNA Synthesis Kit (TaKaRa, Janpan; RR036B). Amplification was performed using the ABI Step one plus Real time-PCR System (ABI, United States). All procedures were performed according to manufacturer’s instructions. The quantitative real-time polymerase chain reaction (qRT-PCR) primers are listed in Supplementary Table 1. The -ΔΔCt method was applied for relative quantification using U6 as a housekeeping gene and normalized baseline expression.

Statistical analysis

Statistical analyses were performed for demographic and clinical characteristic comparisons between the UDCA-responsive and non-responsive PBC cohorts using IBM SPSS Statistics software (IBM Corp., United States; version 26.0). Continuous variables with normal distribution were expressed as mean ± SD and analyzed by Student’s t-test. Non-normally distributed data were reported as median (interquartile range) and evaluated using the Mann-Whitney U test for intergroup comparisons and the Wilcoxon rank-sum test for intragroup analyses. Categorical variables were presented as frequencies and assessed by χ² test or Fisher’s exact test.

For miRNA profiling, ACGT101-miR software was used for data processing and normalization. Receiver operating characteristic curves were constructed to evaluate the predictive efficacy of the candidate miRNAs, with diagnostic accuracy quantified by the AUC. Binary logistic regression was used to assess the significance of predictive variables. Spearman’s rank correlation coefficient was used to analyze the association between miRNA expression and clinical parameters. A two-tailed P < 0.05 was considered statistically significant. The statistical methods of this study were reviewed by Dr. Bi of the Phase I Clinical Trial Ward of the Fifth Medical Center of PLA General Hospital.

RESULTS

Patient characteristics

The study cohort comprised 40 patients. The discovery cohort comprised 5 DS and 5 DR specimens, whereas the test cohort contained 17 DS and 13 DR samples (Figure 1A). Among the patients in discovery cohort, all were women, the average age of the DR patients was 49.00 ± 5.39 years, and the average age of the DS patients was 43.80 ± 10.45 years (P = 0.352). Both cohorts showed improvements in biochemical parameters after standard UDCA therapy. The DS cohort had significantly lower post-treatment levels of AST [79.00 (66.00, 90.50) vs 24.00 (17.00, 50.50), P = 0.008], ALP [381.00 (256.50, 519.50) vs 139.00 (75.50, 252.00) , P = 0.032], TBIL [52.80 (26.25, 57.50) vs 11.00 (10.45, 12.55), P = 0.008], and lactate dehydrogenase [238.00 (185.00, 387.50) vs 162.00 (126.50, 168.00), P = 0.016] than the DR group. Comparative analysis revealed a downward trend in immunoglobulin G (IgG), IgA, and IgM levels post-treatment in the DS group, in contrast to an upward trend observed in the DR cohort. No statistically significant intergroup differences in immunoglobulin levels (IgG/IgA/IgM) were detected before or after treatment (Table 1). The test cohort comprised 30 patients with clinical characteristics comparable to those of the discovery cohort and minimal differences in laboratory indices (Supplementary Table 2).

Figure 1 Significant differences in microRNA expression and functional enrichment of primary biliary cholangitis with different ursodeoxycholic acid efficacy. A: Scheme of overall study design; B: The number of differentially expressed microRNAs (miRNAs) before ursodeoxycholic acid treatment in the sensitive group and the resistance group. Up-regulated miRNAs are colored in red and down-regulated miRNAs are colored in blue; C: Volcano plot shows miRNAs that differ significantly between the two groups, based on fold change (drug sensitive pre-treatment vs drug resistance pre-treatment; drug sensitive post-treatment vs drug resistance pre-treatment) and P-value. In particular, blue (fold change < -1.25; P < 0.05) or red dots (fold change > 1.25; P < 0.05) indicate the presence of significantly downregulated or upregulated miRNAs, respectively. Gray dots were non-significantly different miRNAs; D: Clustering heatmap of pre-treatment differential miRNAs expression between resistant and sensitive groups. Red indicates upregulation and blue indicates downregulation. The columns and rows represent experimental SP samples and miRNAs, respectively; E: Clustering heatmap of differential miRNAs expression before and after treatment in the sensitive group; F: Gene Ontology enrichment analysis of differential miRNA target genes before ursodeoxycholic acid treatment in sensitive and resistance groups; G: Kyoto Encyclopedia of Genes and Genomes enrichment analysis of the differential miRNAs target genes. UDCA: Ursodeoxycholic acid; PBC: Primary biliary cholangitis; miRNA-seq: MicroRNA sequencing; RT-qRCR: Real-time quantitative polymerase chain reaction; DRpre: Drug resistance pre-treatment; DSpre: Drug sensitive pre-treatment; DSpost: Drug sensitive post-treatment.

Table 1 Biochemical characteristics of discovery cohort patients.

Characteristic	Baseline			Post-therapy			Baseline vs post-therapy
	DR (n = 5)	DS (n = 5)	P value	DR (n = 5)	DS (n = 5)	P value	P value
Sex (female/male)	5/0	5/0	1	5/0	5/0	1	-
Age (year)	49.00 (5.39)	43.80 (10.45)	0.352	49.00 (5.39)	43.80 (10.45)	0.352	-
Biochemical indicators
ALT (U/L)	35.00 (21.00, 109.00)	34.00 (20.00, 74.50)	0.738	23.00 (21.50, 73.00)	23.00 (10.00, 47.50)	0.444	0.169
AST (U/L)	74.00 (64.00, 168.00)	80.00 (26.00, 159.50)	0.69	79.00 (66.00, 90.50)	24.00 (17.00, 50.50)	0.008	0.083
ALP (U/L)	377.00 (189.50, 727.00)	212.00 (158.50, 467.50)	0.421	381.00 (256.50, 519.50)	139.00 (75.50, 252.00)	0.032	0.074
GGT (U/L)	310.00 (121.50, 590.00)	238.00 (152.50, 502.00)	1	198.00 (82.50, 358.00)	41.00 (20.00, 205.50)	0.31	0.028
TBIL (μmol/L)	27.80 (24.40, 456.10)	19.00 (9.45, 34.65)	0.421	52.80 (26.25, 57.50)	11.00 (10.45, 12.55)	0.008	0.859
LDH (U/L)	287.00 (166.50, 434.50)	163.00 (131.00, 185.00)	0.095	238.00 (185.00, 387.50)	162.00 (126.50, 168.00)	0.016	0.093
IgG (U/L)	13.49 (11.37, 19.51)	14.83 (13.55, 15.76)	0.841	13.68 (13.43, 17.98)	13.69 (12.35, 16.93)	0.841	0.878
IgA (U/L)	2.20 (1.60, 2.62)	3.37 (2.09, 4.06)	0.151	2.53 (1.69, 3.24)	2.95 (2.03, 4.09)	0.579	0.374
IgM (U/L)	3.34 (2.17, 4.37)	2.38 (2.18, 4.48)	0.841	3.70 (2.36, 4.15)	1.81 (1.30, 2.37)	0.095	0.114
Autoantibody
AMA (+/-)	5/0	2/3	0.167	5/0	2/3	0.167	-
Gp210 (+/-)	3/2	5/0	0.444	3/2	5/0	0.444	-
Sp100 (+/-)	3/2	1/4	0.524	3/2	1/4	0.524	-

DR: Drug-resistant; DS: Drug-sensitive; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; ALP: Alkaline phosphatase; GGT: Gamma-glutamyl transferase; TBIL: Total bilirubin; LDH: Lactate dehydrogenase; IgG: Immunoglobulin G; IgA: Immunoglobulin A; IgM: Immunoglobulin M; AMA: Anti-mitochondrial antibody.

Differential expression of serum miRNAs in PBC patients with varied therapeutic responses to UDCA treatment

RNA-sequencing analysis revealed 40 differentially expressed miRNAs in the DS group pre- and post-UDCA treatment (DS_post vs DS_pre: 11 up-regulated and 29 downregulated miRNAs, P < 0.05, Figure 1B), indicating that UDCA treatment induced significant alterations in the miRNA expression profiles of PBC patients. A baseline comparison between the DS and DR groups prior to treatment identified 69 differentially expressed miRNAs (DS_pre vs DR_pre: 47 upregulated and 22 downregulated, P < 0.05, Figure 1B), suggesting that miRNA signatures could serve as predictive biomarkers for UDCA therapeutic efficacy. Volcano plot visualization demonstrates the magnitude and statistical significance of the differences in miRNA expression across the groups (Figure 1C).

The heatmap in Figure 1D demonstrates both the relative expression patterns of miRNAs and the clear segregation between the DR and DS cohorts. Pre-treatment miRNA profiling identified 34 human-specific miRNAs that were upregulated and 15 were downregulated in the DS group compared to those in the DR group (P < 0.05), reinforcing their biomarker potential. Post-treatment analysis within the DS cohort revealed UDCA induced modulation of 21 human-specific miRNAs (9 upregulated and 12 downregulated, P < 0.05; Figure 1E), reflecting dynamic regulatory responses to therapy. Baseline miRNA expression disparities between the DS and DR groups before intervention are prospective biomarkers for predicting UDCA responsiveness.

The differentially expressed miRNAs were primarily involved in biological processes, such as signal transduction, regulation of transcription, DNA-templating, and positive regulation of transcription by RNA polymerase II. These miRNAs are also involved in the composition of cell membrane, cytoplasm, and nuclei. More importantly, these genes were highly enriched in protein binding (Figure 1F). Moreover, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that the miRNA targeting genes were enriched in the following pathways: Ubiquitin-mediated proteolysis, actin cytoskeletal regulation, and the Ras/Rap1 signaling (Figure 1G).

Expression and prognostic value of miR-22-5p, miR-126-3p, and miR-7706 in the test set

Eleven human-specific miRNAs with stable differential expression between the DS and DR groups at baseline were identified through significance-ranking analysis: MiR-22-5p, miR-10a-5p, miR-126-3p, miR-7706, miR-223-3p, miR-556-5p, miR-130a-3p, miR-3909, miR-6513-3p, miR-219a-5p, and miR-146-3p (Table 2). qRT-PCR results corroborated the sequencing data, revealing elevated expression of miR-22-5p, miR-126-3p, miR-223-3p, miR-556-5p, miR-130a-3p, and miR-219a-5p in the DS group compared to the DR group, with reduced expression of miR-10a-5p, miR-7706, miR-3909, miR-6513-3p, and miR-146-3p in the same group comparison (Figure 2A).

Figure 2 Primary biliary cholangitis patients with high baseline miR-126-3p expression are sensitive to ursodeoxycholic acid, miR-126-3p expression was consistently upregulated after receiving ursodeoxycholic acid treatment and correlated with liver function. A: Differentially expressed microRNAs (miRNAs) screened by sequencing were validated by quantitative real-time polymerase chain reaction in the discovery cohort; B: The top three differentially expressed folds of miRNAs from sequencing were selected in the test cohort for quantitative real-time polymerase chain reaction validation; C: Receiver operating characteristic curve analysis of miR-126-3p expression as a predictor of ursodeoxycholic acid efficacy; D: Comparison of miR-126-3p expression before and after treatment in primary biliary cholangitis patients in the test cohort; E: Correlation between miR-126-3p expression and liver function indexes. ^aP < 0.05; ^bP < 0.01; ^cP < 0.005; ^dP < 0.001. DRpre: Drug resistance pre-treatment; DRpost: Drug resistance post-treatment; DSpre: Drug sensitive pre-treatment; DSpost: Drug sensitive post-treatment; AUC: Area under the curve; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; ALP: Alkaline phosphatase; GGT: Gamma-glutamyl transferase; TBIL: Total bilirubin; IgG: Immunoglobulin G.

Table 2 Preliminary screening of differentially expressed microRNAs in the drug-sensitive and drug-resistant groups before ursodeoxycholic acid treatment.

Groups	MiRNAs	MiR_sequencing		Log2FC	FC	P value	Expression level
Differential expression in DS group before and after treatment	Hsa-miR-126-3p	TCGTACCGTGAGTAATAATGC	Up	0.686818109	1.61	0.002	High
	Hsa-miR-223-3p	TGTCAGTTTGTCAAATACCCCAA	Up	0.36848971	1.29	0.008	High
	Hsa-miR-22-5p	AGTTCTTCAGTGGCAAGCTTTA	Up	0.348412695	1.27	0	Middle
	Hsa-miR-556-5p	GATGAGCTCATTGTAATATGA	Up	0.587162181	1.5	0.008	Middle
	Hsa-miR-130a-3p	CAGTGCAATGTTAAAAGGGCAT	Up	0.5645536	1.48	0.009	Middle
	Hsa-miR-10a-5p	TACCCTGTAGATCCGAATTTGT	Down	-0.50035024	0.71	0.001	Middle
	Hsa-miR-7706	TGAAGCGCCTGTGCTCTGCCGAG	Down	-1.27927924	0.41	0.007	Middle
Stable expression in DS group before and after treatment	Hsa-miR-219a-5p	TGATTGTCCAAACGCAATTCTCG	Up	2.968566339	7.83	0.003	Middle
	Hsa-miR-3909	TGTCCTCTAGGGCCTGCAGTCT	Down	-1.39892689	0.38	0.012	Middle
	Hsa-miR-6513-3p	TCAAGTGTCATCTGTCCCTAGA	Down	-0.95445711	0.52	0.035	Middle
	Hsa-miR-146b-3p	GCCCTGTGGACTCAGTTCTGGT	Down	-0.48419167	0.71	0.012	Middle

MiRNAs: MicroRNAs; FC: Fold change; DS: Drug-sensitive.

Three miRNAs (miR-22-5p, miR-126-3p, and miR-7706) were selected for further validation based on their PCR concordance, expression trajectory patterns, sequencing fold-change differences, and significance rankings. In pre- and post-treatment serum samples from the test cohort, qRT-PCR analysis revealed that, prior to treatment, the DS group had higher expression of miR-22-5p and miR-126-3p and lower expression of miR-7706 than the DR group. MiR-7706 expression was upregulated in the DR group after therapy (P = 0.003), in contrast to the pronounced downregulation in the DS cohort (P < 0.001). MiR-126-3p was downregulated in the DR group (P = 0.003) but robustly upregulated in the DS group post-treatment (P < 0.001), with divergent expression trajectories between the cohorts (Figure 2B).

Logistic regression analysis indicated no association between miR-22-5p (P = 0.990), miR-7706 (P = 0.157), and therapeutic response. Conversely, miR-126-3p expression positively correlated with UDCA sensitivity (P = 0.016), suggesting that elevated miR-126-3p levels were associated with an increased probability of UDCA responsiveness. Further receiver operating characteristic analysis of miR-126-3p (Figure 2C) demonstrated an AUC of 0.891 (P = 0.0003, 95% confidence interval: 0.772-1.000), with 82.4% sensitivity and 84.6% specificity. These results confirm that miR-126-3p is a robust predictor of therapeutic outcomes. Youden index analysis identified an optimal cutoff value of 0.786 for miR-126-3p (maximum index = 0.67), indicating UDCA sensitivity when the expression exceeded this threshold.

Correlation between miR-126-3p expression and liver function parameter levels

Comparative analysis of the miR-126-3p expression trajectories before and after treatment revealed crucial intergroup variations (Figure 2D). Strong intragroup correlations were observed between pre- and post-treatment miR-126-3p levels in both the DS (r = 0.953, P < 0.001) and DR (r = 0.984, P < 0.001) cohorts. Post-treatment upregulation of miR-126-3p was observed in the DS group, whereas downregulation occurred in the DR cohort, with a statistically significant divergence (Z = 4.625, P < 0.001). Inverse correlations were identified between levels of miR-126-3p and TBIL (r = -0.356, P = 0.005), as well as IgG (r = -0.311, P = 0.015); however, no associations were detected with ALT, AST, ALP, and gamma-glutamyl transferase (GGT) parameters (Figure 2E).

DISCUSSION

Currently, validated biomarkers for predicting disease progression and prognosis in patients with PBC are lacking. Most assessments of therapeutic efficacy are based on changes in ALP, GGT, ALB, and TBIL levels after UDCA treatment. Two multicenter studies evaluated the prognostic value of the Mayo Risk, UK-PBC, and GLOBE scores by comparing the predicted and actual survival rates using Kaplan-Meier analyses. However, all of these tools exhibit inherent delays and limitations in evaluating UDCA response efficacy[33,34]. A targeted, multiplex-validated proteomic study revealed a reduction in the inflammatory proteome following UDCA treatment and identified C-X-C motif chemokine ligand 11 and CCL20 Levels as markers of treatment response with an AUC of 0.91[35]. However, their utility as predictive biomarkers requires further validation in prospective studies. In addition, a stratification model incorporating age, TBIL, and ALP has been shown to aid risk identification among younger patients[36]. However, the predictive capability of these models for the response to UDCA remains limited.

Previous studies have revealed differential miRNA expression profiles in the liver tissues of patients with PBC compared to healthy controls, suggesting their potential as predictors of disease progression[22,37]. In this study, we performed comprehensive serum miRNA profiling and identified distinct expression patterns between UDCA-sensitive and UDCA-resistant groups through a test cohort analysis. Pre-treatment serum analysis revealed multiple differentially expressed miRNAs between the response groups, with dynamic expression changes following UDCA intervention. MiR-126-3p emerged as a robust predictor of therapeutic response, showing higher baseline levels in UCDA-sensitive patients with further upregulation post-treatment, whereas it is decreased in UCDA-resistant cohorts.

The profiling of miRNA expression landscapes contrasts with prior research by Padgett et al[22], who identified 35 differentially expressed miRNAs between patients with PBC and healthy controls, including 24 miRNAs downregulated including miR-126 and 11 miRNAs upregulated. Liang et al[38] reported 16 differentially expressed plasma miRNAs in PBC cohorts, with a notable downregulation of miR-92a that was inversely correlated with the T helper type 17 cell population. We suggested that these discrepancies may stem from divergent control group selection strategies used in the study designs. Our approach minimized the confounding effects of disease-associated miRNA alterations by comparing UDCA-responsive and -refractory PBC cohorts without healthy controls.

Divergent findings were observed between our study and the existing reports regarding therapeutic predictive miRNAs for PBC treatment efficacy. Sakamoto et al[39] documented post-UDCA miRNA alterations in refractory PBC and identified 58 differentially expressed miRNAs between responders and non-responders. Key molecules, including miR-122, miR-378f, miR-4311, and miR-4714-3p, correlated with biochemical fluctuations (direct bilirubin, AST, ALT, GGT, TBIL, and lactate dehydrogenase) in past studies[39]. Previous studies have exclusively focused on post-therapeutic miRNA profiles without assessing baseline expression or UDCA-induced dynamics[39]. Our longitudinal analysis revealed divergent miR-126-3p expression trajectories during UDCA therapy, demonstrating its dual utility for preliminary efficacy prediction and mechanistic involvement in treatment resistance; however, the precise regulatory pathways require elucidation. Clinical biochemical markers have been used to predict the therapeutic efficacy of UDCA. For instance, one study demonstrated that patients with PBC with a high neutrophil-to-lymphocyte ratio exhibited a poorer response to UDCA therapy[40], although standardized evaluation criteria for this prognostic indicator remain to be established. Notably, a predictive model was developed to stratify treatment responses in patients with PBC prior to UDCA initiation. This model showed that elevated levels of total cholesterol, neutrophil-to-lymphocyte ratio, and ALP were significantly associated with UDCA nonresponse[41].

MiR-126-3p has potential as a novel biomarker for early stage treatment response assessment. This study pioneered the identification of miR-126-3p’s therapeutic predictive role in PBC, diverging from prior research that focused on its involvement in other hepatobiliary disorders. Comparative analysis revealed significant downregulation of miR-126-3p in patients with biliary atresia compared to healthy controls, although no differential expression was observed in cholestatic disease[42]. Furthermore, exosomal miR-126-3p exhibits elevated expression in malignant biliary obstruction, with serum quantification achieving 74% diagnostic accuracy, surpassing that of CA19-9 in a validation cohort[43]. Longitudinal monitoring revealed marked miR-126-3p downregulation following initial sorafenib administration in patients with hepatocellular carcinoma. Significant reductions in serum miR-126-3p levels have been observed in patients with treatment-responsive metastatic cancer patients receiving bevacizumab-based first-line regimens[44]. Collectively, these findings suggest that miR-126-3p is a pivotal modulator of therapeutic response across multiple disease contexts.

Although the precise mechanisms remain unknown, several studies have provided evidence supporting the role of miR-126-3p in modulating the UDCA response[45]. MiR-126-3p directly targets inflammatory mediators and attenuates chemokine-driven tissue injury by downregulating CCL2 expression[46], potentially enhancing the anti-cholestatic and anti-inflammatory effects of UDCA. Using extracellular vesicle-mediated signaling within hepatic stellate and hepatocellular carcinoma cells, miR-126-3p suppresses the expression of ADAM9 and collagen type I alpha 1, thereby reducing fibrosis and extracellular matrix deposition[47]. This antifibrotic effect may contribute to improved therapeutic outcomes. Furthermore, miR-126 has been reported to regulate the tuberous sclerosis complex 1/mammalian target of rapamycin/vascular endothelial growth factor receptor 2 axis in dendritic cells, supporting innate immune function[48] and suggesting a potential hepatoprotective mechanism that could enhance the response to UDCA. These potential mechanisms may act synergistically to improve therapeutic efficacy. Nonetheless, further studies are warranted to clarify the underlying molecular pathways.

This study had several limitations. Primarily, the therapeutic response assessment relied solely on the Paris I criteria, rather than incorporating additional validated evaluation metrics. The Paris I and II criteria have been widely applied as criteria for assessing UDCA response across both European and Asian cohorts, including Chinese populations, and are recommended in clinical practice guidelines[32]. Compared to other response criteria, the Paris I criteria demonstrate greater specificity in predicting long-term outcomes, providing superior prognostic value across all stages of PBC[49]. Moreover, most patients in the discovery cohort underwent histological evaluation at baseline and were in advanced disease stages, making the Paris I criteria particularly suitable for accurately assessing the response to UDCA therapy. Future studies should adopt more robust evaluation criteria to enhance the methodological rigor. Second, the absence of histopathological liver specimens precluded the confirmation of ductopenia in non-responders, necessitating subsequent histological verification. Third, discordant autoantibody profiles were observed between the cohorts, with AMA-negative status predominating in the DS group (60%). To mitigate the potential confounding effects of AMA heterogeneity, AMA-associated miRNAs (e.g., miR-21 and miR-150) were excluded from biomarker screening[50]. Finally, as this was an observational study, selection bias may have been inevitable. Large-scale prospective studies and additional multicenter validation studies are required to confirm our findings.

CONCLUSION

MiR-126-3p is a non-invasive biomarker predictive of UDCA-refractory PBC progression. Early stage evaluation of UDCA responsiveness facilitates personalized therapeutic optimization and improves clinical outcomes. These findings may be helpful in elucidating the mechanisms involved in the pathogenesis of PBC and developing therapeutic strategies.

ACKNOWLEDGEMENTS

We acknowledge all patients who participated in this study and their families.