Ahmed, Fowsiyo

Abstract Background and Aims Primary sclerosing cholangitis (PSC) increases the risk of developing cholangiocarcinoma (CCA), which is the leading cause of premature death in these patients. Current diagnostic methods for early detection of either condition remain largely suboptimal. This study investigates serum metabolites as potential non-invasive diagnostic biomarkers. Methods This multicenter international study analyzed 459 serum samples from 13 international centers, including patients with PSC (n = 216), PSC who developed CCA during follow-up (PSC to CCA; n = 24), PSC with concomitant CCA (PSC-CCA; n = 88), ulcerative colitis (UC; n = 14), and healthy individuals (n = 79). Serum metabolomics was evaluated by ultra-high-performance liquid chromatography-mass spectrometry (UHPLC-MS) and the accuracy of the single candidate metabolite biomarkers was further assessed. Machine learning was used to generate the best diagnostic and predictive algorithms. Results Fifty-two serum metabolites were identified as markers of PSC, independent of age, biological sex, cirrhosis, UC, or ursodeoxycholic acid (UDCA) treatment. A diagnostic model based on only six lipids distinguished PSC patients from healthy controls with high accuracy (AUC 0.952 discovery, 0.918 validation), independently of the levels of transaminases or cholestasis markers (i.e., GGT, AP, bilirubin) in blood. Subgroup analyses were performed in PSC patients with and without inflammatory bowel disease (IBD), confirming the model’s consistent performance across both subgroups when compared with their respective control groups – UC and healthy controls (AUC 0.959 and 0.939, respectively). The PSC model also confirmed its diagnostic performance in patients with established PSC-CCA or progression to CCA within 2 years, when compared to UC and healthy controls, showing AUC values ranging from 0.911 to 0.976. Additionally, 20 metabolites were significantly altered in PSC patients with CCA (PSC-CCA), regardless of demographics, cirrhosis, UDCA treatment, IBD status, or CCA subtype, and independently of serum transaminases and cholestatic markers levels. A model incorporating six lipids accurately identified PSC-CCA cases compared to PSC patients (AUC = 0.912 discovery; 0.896 validation), showing excellent performance in early-stage tumor detection (AUC 0.921) and outperforming CA19-9 (AUC 0.708). Importantly, the model retained high accuracy even in CCA patients with low CA19-9 levels (AUC 0.905) and in PSC patients who developed CCA within a year of clinical diagnosis (AUC 0.792). Conclusion The PSC and PSC-CCA blood tests provide a non-invasive approach for diagnosing PSC and PSC-CCA, addressing an important need in this population. Their integration into clinical practice could enhance risk stratification, early detection, personalized surveillance, and treatment decision-making in PSC patients.

Abstract Background and Aims Cholangiocarcinoma (CCA) is a rare and aggressive malignancy originating from the bile ducts, characterized by a poor prognosis and limited treatment options. Potentially curative interventions include surgical resection with negative margins (R0) and liver transplantation. However, despite these treatments, the likelihood of post-surgical recurrence remains high, with 50–70% of patients experiencing disease recurrence within five years. This highlights the urgent need for reliable prognostic biomarkers to guide clinical decision-making and personalize postoperative care. Numerous prognostic biomarkers identified in tumor tissue for CCA have been proposed, but many studies suffer from limitations such as single-center designs, small patient cohorts, and semi-quantitative techniques such as immunohistochemistry. These methods often rely on observer interpretation and antibody specificity, which can compromise objectivity and reproducibility. To overcome these limitations, we aimed to identify and validate robust quantitative prognostic biomarkers for CCA by analyzing tumor mRNA expression, integrating them using machine learning (ML), and developing a web-based tool to predict tumor recurrence and overall survival (OS). Method A systematic literature review was conducted to identify candidate prognostic tissue biomarkers for CCA, which were further validated using Cox proportional hazards (PH) OS analyses in five independent transcriptomic cohorts. An international multicenter cohort of 221 patients undergoing tumor resection was assembled, and tumor mRNA expression was analyzed using high-throughput TaqMan OpenArray RT-qPCR. Associations between clinical and molecular features and recurrence-free survival (RFS) and OS were assessed using Cox PH analyses. Six ML survival models were trained and evaluated using nested cross-validation, followed by feature reduction. Shapley Additive exPlanations (SHAP) values were applied to assess feature importance and enhance model interpretability. Results Among 496 candidate CCA prognostic biomarkers identified from the literature, 52 showed prognostic value in Cox PH analyses across transcriptomic cohorts. In the multicenter validation cohort, integrative Cox PH analyses incorporating clinical variables and gene expression identified 11 genes significantly associated with RFS and 25 with OS. The 11 RFS-associated genes, combined with five clinical variables, were used to train six ML survival models, among which the random survival forest (RSF) model demonstrated the highest predictive accuracy. Following training of RSF models across all combinations of four genes and five clinical variables, and subsequent feature importance–based selection, a final model incorporating three genes (FSCN1, LDHA, and SLC2A1) and four clinical variables (CCA subtype, resection margin status, biological sex, and age at surgery) achieved a testing time-dependent AUC of 0.82. This model significantly stratified patients into high- and low recurrence-risk groups, with a median RFS (mRFS) of 8 months and 70 months, respectively. In addition, an OS prediction model using the same feature set achieved a testing AUC of 0.775 and significantly stratified patients according to 5-year OS risk, with a median OS (mOS) of 23 months in the high-risk group and 77 months in the low-risk group. Conclusion We identified and validated a robust panel of prognostic tissue biomarkers in patients with resected CCA. Integration of these biomarkers with clinical variables into ML-based prognostic models enabled accurate prediction of RFS and OS. The resulting web-based tool allows clinicians to input RT-qPCR results for the validated biomarkers and obtain precise, individualized prognostic predictions for postoperative recurrence and survival.

Background and Aims: Cholangiocarcinoma (CCA) is a rare but aggressive malignancy with poorly understood genetic susceptibility. To date, genome-wide association studies (GWAS) investigating germline variants associated with CCA risk remain limited. We aimed to identify genetic risk loci for CCA and its clinical subtypes through comprehensive GWAS and post-GWAS analyses. Approach and Results: We conducted a GWAS of 2366 CCA cases and 11,750 controls of European ancestry. Genome-wide significant loci ( p <5×10 −8 ) were identified and further examined through fine-mapping, functional annotation, and HLA imputation. Subgroup analyses were conducted by CCA subtypes and primary sclerosing cholangitis (PSC) status. Cross-trait linkage disequilibrium score regression and Mendelian randomization were employed to investigate the shared genetic architecture and potential causal relationships with a diverse range of traits. We identified 1 new genome-wide significant variant, rs535777 (OR=1.44), near HLA-DRB1/DQA1 associated with CCA, and 2 variants associated with extrahepatic CCA: rs116224263 (OR=0.17) in LINC02506 at 4p15.1 and rs6914950 (OR=1.63) near HLA-DRB1/DQB1 . Stratified analyses revealed rs2395184 (OR=3.51) near HLA-DRA/DRB5 associated with PSC-related CCA, and rs142674434 (OR=2.98) in THSD7A at 7p21.3 associated with non-PSC-related CCA. HLA imputation uncovered new amino acid residues associated with disease risk. Cross-trait analyses identified shared genetic signals between CCA and anthropometric, lipidemic, lifestyle, and medical traits. Mendelian randomization supported putative causal associations for 12 traits with CCA or its subtypes. Conclusions: Our large-scale GWAS highlights new genetic variants and HLA-linked mechanisms underlying CCA susceptibility. Integrating multi-step post-GWAS approaches enhances understanding of CCA pathogenesis and may facilitate the development of risk biomarkers for early detection and precision prevention strategies.

ABSTRACT Introduction Hepatocellular carcinoma (HCC) development in patients with metabolic dysfunction‐associated steatotic liver disease (MASLD) is a growing health concern, but the underlying mechanisms are not fully understood. Epigenetic aging biomarkers, reflecting cellular and tissue aging, have been linked to various age‐related pathologies, but their association with MASLD‐HCC is unknown. We investigated associations between five epigenetic aging biomarkers and MASLD‐HCC risk. Methods We performed whole blood DNA methylation assay (Infinium 850k array) and calculated principal components‐based (PC) versions of HorvathAge, HannumAge, PhenoAge and GrimAge and the DunedinPACE aging rate. We further calculated relative age accelerations for PCHorvathAge, PCHannumAge, PCPhenoAge and PCGrimAge. The aging biomarkers were modelled as continuous variables and categorised into tertiles based on distributions among controls. Associations between each aging biomarker and MASLD‐HCC were examined using logistic regression, calculating odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for covariates. Results Data on 272 MASLD‐HCC cases and 316 cancer‐free MASLD controls recruited from six sites and matched on chronological age, sex and study site were analysed. Higher relative age accelerations of PCPhenoAge (OR T3 vs. T1 = 2.25, 95% CI: 1.45–3.50; OR continuous = 1.04, 95% CI: 1.02–1.07, p = 0.009), PCGrimAge (OR T3 vs. T1 = 3.97, 95% CI: 2.41–6.64; OR continuous = 1.16, 95% CI: 1.10–1.24, p = 8.76 × 10 −07 ) and DunedinPACE (OR T3 vs. T1 = 3.45, 95% CI: 2.17–5.55; OR continuous = 1.72, 95% CI: 1.43–2.10, p = 2.58 × 10 −08 ) were associated with MASLD‐HCC, but not PCHorvathAge or PCHannumAge. Conclusion Higher relative age accelerations of PCPhenoAge, PCGrimAge and DunedinPACE aging rate are associated with risk of MASLD‐HCC. These aging biomarkers could improve HCC risk assessment and facilitate risk stratification in patients with MASLD.

Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, increasingly arising in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Epigenetic dysregulation, particularly DNA methylation, has been implicated in MASLD-HCC development, yet the roles that the principal DNA methylation precursor metabolites, S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), play in this association are unclear. Objective: We investigated associations of circulating SAM, SAH, the SAM/SAH ratio, with MASLD-HCC. Methods: In a multi-center pilot case–control study, we evaluated 69 MASLD-HCC cases and 136 cancer-free MASLD controls. Plasma SAM and SAH levels were quantified by liquid chromatography–tandem mass spectrometry. Metabolite levels were categorized as greater than or less than the median based on distribution in controls. Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for age, sex, body mass index, smoking status, and type 2 diabetes. Results: MASLD-HCC cases had significantly higher plasma SAM levels (mean 121 vs. 96 nmol/L; p = 0.001) and SAM/SAH ratios (2.09 vs. 1.48; p = 6.42 × 10−7) than MASLD controls. In multivariable-adjusted models, elevated SAM levels (OR≥median vs. <median = 2.76; 95% CI: 1.38–5.72) and higher SAM/SAH ratio (OR≥median vs. <median = 2.30; 95% CI: 1.15–4.73) were associated with higher odds of MASLD-HCC. SAH alone was associated with MASLD-HCC. Conclusions: Higher plasma SAM levels and SAM/SAH ratios are independently linked to MASLD-HCC development. These metabolites might serve as noninvasive markers for HCC risk stratification in patients with MASLD and improve early detection efforts for MASLD-HCC.

Cholangiocarcinoma (CCA), the second most common primary liver tumor, is associated with a dismal outcome, and useful prognostic markers are not currently available in clinical practice. SerpinB3, a serine protease inhibitor, was recently found to play a relevant role in malignant transformation in different cancers. The aim of the present study was to determine the expression of SerpinB3/4 in tissue and serum samples of patients with CCA in relation to clinical outcomes. SerpinB3/4 was assessed in the tissue microarrays (TMAs) of 123 surgically resected CCAs. ELISA assays were carried out in 188 patients with CCA to detect the free and IgM-linked forms of SerpinB3/4. Overall survival was analyzed in relation to SerpinB3/4 expression, and Cox models were used to identify the variables associated with survival. High levels of SerpinB3/4 (TMA score 2+/3+) were detected in 15 tumors (12.2%), characterized by a more advanced TNM stage (III/IV: 64.3% vs. 31.3%; p = 0.031) and lower overall patient survival, independently of CCA subclass (intrahepatic CCA: median 1.1 (0.8—Not Estimable, NE) vs. 2.4 (1.8–3.4) years; p = 0.0007; extrahepatic CCA: median 0.8 (0.2—NE) vs. 2.2 (1.5–5.4) years; p = 0.011). Vascular invasion (p = 0.027) and SerpinB3/4 scores (p = 0.0016) were independently associated with mortality in multivariate analysis. Patients who had detectable free or IgM-linked SerpinB3/4 in their serum showed poorer survival (1 vs. 2.4 years, p = 0.015, for free SerpinB3/4, and 1 vs. 2.6 years, p = 0.0026, for SerpinB3/4–IgM). In conclusion, high levels of SerpinB3/4 in tissue and serum in CCA are associated with poor outcomes after surgery, regardless of tumor subclass.

Abstract Animal studies implicate one-carbon metabolism and DNA methylation genes in hepatocellular carcinoma (HCC) development in the setting of metabolic perturbations. Using human samples, we investigated the associations between common and rare variants in these closely related biochemical pathways and risk for metabolic HCC development in a multicenter international study. We performed targeted exome sequencing of 64 genes among 556 metabolic HCC cases and 643 cancer-free controls with metabolic conditions. Multivariable logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for multiple comparisons. Gene-burden tests were used for rare variant associations. Analyses were performed in the overall sample and among non-Hispanic whites. The results show that among non-Hispanic whites, presence of rare functional variants in ABCC2 was associated with 7-fold higher risk of metabolic HCC (OR = 6.92, 95% CI: 2.38–20.15, P = 0.0004), and this association remained significant when analyses were restricted to functional rare variants observed in ≥2 participants (cases 3.2% versus controls 0.0%, P = 1.02 × 10−5). In the overall multiethnic sample, presence of rare functional variants in ABCC2 was nominally associated with metabolic HCC (OR = 3.60, 95% CI: 1.52–8.58, P = 0.004), with similar nominal association when analyses were restricted to functional rare variants observed in ≥2 participants (cases 2.9% versus controls 0.2%, P = 0.006). A common variant in PNPLA3 (rs738409[G]) was associated with higher HCC risk in the overall sample (P = 6.36 × 10−6) and in non-Hispanic whites (P = 0.0002). Our findings indicate that rare functional variants in ABCC2 are associated with susceptibility to metabolic HCC in non-Hispanic whites. PNPLA3-rs738409 is also associated with metabolic HCC risk.

Diabetes increases the risk of liver disease progression and cirrhosis development in patients with nonalcoholic steatohepatitis (NASH). The association between diabetes and the risk of hepatocellular carcinoma (HCC) in NASH patients with cirrhosis is not well quantified. All patients with the diagnosis of NASH cirrhosis seen at Mayo Clinic Rochester between January 2006 and December 2015 were identified. All adult liver transplant registrants with NASH between 2004 and 2017 were identified using the United Network for Organ Sharing (UNOS)/Organ Procurement and Transplantation registry for external validation. Cox proportional hazard analysis was performed to investigate the association between diabetes and HCC risk. Among 354 Mayo Clinic patients with NASH cirrhosis, 253 (71%) had diabetes and 145 (41%) were male. Mean age at cirrhosis evaluation was 62. During a median follow‐up of 47 months, 30 patients developed HCC. Diabetes was associated with an increased risk of developing HCC in univariate (hazard ratio [HR] = 3.6; 95% confidence interval [CI] = 1.1‐11.9; P = 0.04) and multivariable analysis (HR = 4.2; 95% CI = 1.2‐14.2; P = 0.02). In addition, age (per decade, HR = 1.8; 95% CI = 1.2‐2.6; P < 0.01) and low serum albumin (HR = 2.1; 95% CI = 1.5‐2.9; P < 0.01) were significantly associated with an increased risk of developing HCC in multivariable analysis. Other metabolic risk factors, including body mass index, hyperlipidemia, and hypertension, were not associated with HCC risk. Among UNOS NASH registrants (N = 6,630), 58% had diabetes. Diabetes was associated with an increased risk of developing HCC in univariate (HR = 1.4; 95% CI = 1.1‐1.8; P < 0.01) and multivariable (HR = 1.3; 95% CI = 1.0‐1.7; P = 0.03) analysis. Conclusion: Diabetes is associated with an increased risk of HCC in patients with NASH cirrhosis.