Quantitative proteomic analysis of bile in extrahepatic cholangiocarcinoma patients

Comparative Analysis of Digestion Methods for Bile Proteomics: The Key to Unlocking Biliary Biomarker Potential

BACKGROUND

Bile's potential to reflect the health of the biliary system has led to increased attention, with proteomic analysis offering deeper understanding of biliary diseases and potential biomarkers. With the emergence of normothermic machine perfusion (NMP), bile can be easily collected and analyzed. However, the composition of bile can make the application of proteomics challenging. This study systematically evaluated various trypsin digestion methods to optimize proteomics of bile from human NMP livers.

METHODS

Bile was collected from 12 human donor livers that were accepted for transplantation after the NMP viability assessment. We performed tryptic digestion using six different methods: in-gel, in-solution, S-Trap, SMART, EasyPep, and filter-aided sample purification, with or without additional precipitation before digestion. Proteins were analyzed using untargeted proteomics. Methods were assessed for total protein IDs, variation, and protein characteristics to determine the most optimal method.

RESULTS

Methods involving precipitation surpassed crude methods in protein identifications (4500 vs 3815) except for in-gel digestion. Filtered data (40%) resulted in 3192 versus 2469 for precipitated and crude methods, respectively. We found minimal differences in mass, cellular components, or hydrophobicity of proteins between methods. Intermethod variability was notably diverse, with in-gel, in-solution, and EasyPep outperforming others. Age-related biological comparisons revealed upregulation of metabolic-related processes in younger donors and immune response and cell cycle-related processes in older donors.

CONCLUSIONS

Variability between methods emphasizes the importance of cross-validation across multiple analytical approaches to ensure robust analysis. We recommend the in-gel crude method for its simplicity and efficiency, avoiding additional precipitation steps. Sample processing speed, cost, cleanliness, and reproducibility should be considered when a digestion method is selected for bile proteomics.

Small duct and large duct type intrahepatic cholangiocarcinoma reveal distinct patterns of immune signatures

PURPOSE

Dedicated gene signatures in small (SD-iCCA) and large (LD-iCCA) duct type intrahepatic cholangiocarcinoma remain unknown. We performed immune profiling in SD- and LD-iCCA to identify novel biomarker candidates for personalized medicine.

METHODS

Retrospectively, 19 iCCA patients with either SD-iCCA (n = 10, median age, 63.1 years (45-86); men, 4) or LD-iCCA (n = 9, median age, 69.7 years (62-85); men, 5)) were included. All patients were diagnosed and histologically confirmed between 04/2009 and 01/2021. Tumor tissue samples were processed for differential expression profiling using NanoString nCounter® PanCancer Immune Profiling Panel.

RESULTS

With the exception of complement signatures, immune-related pathways were broadly downregulated in SD-iCCA vs. LD-iCCA. A total of 20 immune-related genes were strongly downregulated in SD-iCCA with DMBT1 (log2fc = -5.39, p = 0.01) and CEACAM6 (log2fc = -6.38, p = 0.01) showing the strongest downregulation. Among 7 strongly (log2fc > 2, p ≤ 0.02) upregulated genes, CRP (log2fc = 5.06, p = 0.02) ranked first, and four others were associated with complement (C5, C4BPA, C8A, C8B). Total tumor-infiltrating lymphocytes (TIL) signature was decreased in SD-iCCA with elevated ratios of exhausted-CD8/TILs, NK/TILs, and cytotoxic cells/TILs while having decreased ratios of B-cells/TILs, mast cells/TILs and dendritic cells/TILs. The immune profiling signatures in SD-iCCA revealed downregulation in chemokine signaling pathways inclulding JAK2/3 and ERK1/2 as well as nearly all cytokine-cytokine receptor interaction pathways with the exception of the CXCL1/CXCR1-axis.

CONCLUSION

Immune patterns differed in SD-iCCA versus LD-iCCA. We identified potential biomarker candidate genes, including CRP, CEACAM6, DMBT1, and various complement factors that could be explored for augmented diagnostics and treatment decision-making.

Refinement of paramagnetic bead-based digestion protocol for automatic sample preparation using an artificial neural network

Despite technological advances in the proteomics field, sample preparation still represents the main bottleneck in mass spectrometry (MS) analysis. Bead-based protein aggregation techniques have recently emerged as an efficient, reproducible, and high-throughput alternative for protein extraction and digestion. Here, a refined paramagnetic bead-based digestion protocol is described for Opentrons® OT-2 platform (OT-2) as a versatile, reproducible, and affordable alternative for the automatic sample preparation for MS analysis. For this purpose, an artificial neural network (ANN) was applied to maximize the number of peptides without missed cleavages identified in HeLa extract by combining factors such as the quantity (μg) of trypsin/Lys-C and beads (MagReSyn® Amine), % (w/v) SDS, % (v/v) acetonitrile, and time of digestion (h). ANN model predicted the optimal conditions for the digestion of 50 μg of HeLa extract, pointing to the use of 2.5% (w/v) SDS and 300 μg of beads for sample preparation and long-term digestion (16h) with 0.15 μg Lys-C and 2.5 μg trypsin (≈1:17 ratio). Based on the results of the ANN model, the manual protocol was automated in OT-2. The performance of the automatic protocol was evaluated with different sample types, including human plasma, Arabidopsis thaliana leaves, Escherichia coli cells, and mouse tissue cortex, showing great reproducibility and low sample-to-sample variability in all cases. In addition, we tested the performance of this method in the preparation of a challenging biological fluid such as rat bile, a proximal fluid that is rich in bile salts, bilirubin, cholesterol, and fatty acids, among other MS interferents. Compared to other protocols described in the literature for the extraction and digestion of bile proteins, the method described here allowed identify 385 unique proteins, thus contributing to improving the coverage of the bile proteome.

Bile metabolic fingerprints distinguish biliary tract cancer from benign biliary diseases

BACKGROUND AND AIMS

Biliary tract cancers are aggressive gastrointestinal malignancies characterized by a dismal 5-year overall survival rate <20%. Current diagnostic modalities suffer from limitations regarding sensitivity and specificity. This study aimed to develop a bile metabolite-based platform for precise discrimination between malignant and benign biliary diseases.

APPROACH AND RESULTS

Samples were collected from 336 patients with biliary tract cancer or benign biliary diseases across 3 independent cohorts. Untargeted metabolic fingerprinting was performed on 300 bile samples using novel nanoparticle-enhanced laser desorption/ionization mass spectrometry. Subsequently, a diagnostic assay was developed based on the exploratory cohort using a selected bile metabolic biomarker panel, with performance evaluated in the validation cohort. Further external validation of disease-specific metabolites from bile samples was conducted in a prospective cohort (n = 36) using quantitative analysis. As a result, we established a novel bile-based assay, BileMet, for the rapid and precise detection of malignancies in the biliary tract system with an AUC of 0.891. We identified 6-metabolite biomarker candidates and discovered the critical role of the chenodeoxycholic acid glycine conjugate as a protective metabolite associated with biliary tract cancer.

CONCLUSIONS

Our findings confirmed the improved diagnostic capabilities of BileMet assay in a clinical setting. If applied, the BileMet assay enables intraoperative testing and fast medical decision-making for cases with suspected malignancy where brush cytology detection fails to support malignancy, ultimately reducing the economic burden by over 90%.

Insight into the mechanism of gallstone disease by proteomic and metaproteomic characterization of human bile

Introduction

Cholesterol gallstone disease is a prevalent condition that has a significant economic impact. However, the role of the bile microbiome in its development and the host's responses to it remain poorly understood.

Methods

In this study, we conducted a comprehensive analysis of microbial and human bile proteins in 40 individuals with either gallstone disease or gallbladder polyps. We employed a combined proteomic and metaproteomic approach, as well as meta-taxonomic analysis, functional pathway enrichment, and Western blot analyses.

Results

Our metaproteomic analysis, utilizing the lowest common ancestor algorithm, identified 158 microbial taxa in the bile samples. We discovered microbial taxa that may contribute to gallstone formation, including β-glucuronidase-producing bacteria such as Streptococcus, Staphylococcus, and Clostridium, as well as those involved in biofilm formation like Helicobacter, Cyanobacteria, Pseudomonas, Escherichia coli, and Clostridium. Furthermore, we identified 2,749 human proteins and 87 microbial proteins with a protein false discovery rate (FDR) of 1% and at least 2 distinct peptides. Among these proteins, we found microbial proteins crucial to biofilm formation, such as QDR3, ompA, ndk, pstS, nanA, pfIB, and dnaK. Notably, QDR3 showed a gradual upregulation from chronic to acute cholesterol gallstone disease when compared to polyp samples. Additionally, we discovered other microbial proteins that enhance bacterial virulence and gallstone formation by counteracting host oxidative stress, including sodB, katG, rbr, htrA, and ahpC. We also identified microbial proteins like lepA, rtxA, pckA, tuf, and tpiA that are linked to bacterial virulence and potential gallstone formation, with lepA being upregulated in gallstone bile compared to polyp bile. Furthermore, our analysis of the host proteome in gallstone bile revealed enhanced inflammatory molecular profiles, including innate immune molecules against microbial infections. Gallstone bile exhibited overrepresented pathways related to blood coagulation, folate metabolism, and the IL-17 pathway. However, we observed suppressed metabolic activities, particularly catabolic metabolism and transport activities, in gallstone bile compared to polyp bile. Notably, acute cholelithiasis bile demonstrated significantly impaired metabolic activities compared to chronic cholelithiasis bile.

Conclusion

Our study provides a comprehensive metaproteomic analysis of bile samples related to gallstone disease, offering new insights into the microbiome-host interaction and gallstone formation mechanism.

Silencing of peroxiredoxin 2 suppresses proliferation and Wnt/β-catenin pathway, and induces senescence in hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a common malignancy worldwide, still lacks effective clinical treatment. The study aimed to investigate the oncogenes that affect the progression of HCC and their possible mechanisms. In our study, we initially confirmed a higher level of PRDX2 in the bile of HCC patients compared to those with choledocholithiasis by 2-DE, LC-MS, and ELISA. Subsequently, we demonstrated the high expression of peroxiredoxin 2 (PRDX2) in HCC based on the TCGA database and clinical sample analysis. Furthermore, PRDX2 overexpression enhanced the viability of HCC cells. And PRDX2 silencing induced senescence of HCC cells. In vivo, knockdown of PRDX2 significantly reduced the weight of xenograft tumors. PRDX2 also was found to activate the Wnt/β-catenin pathway by inducing β-catenin nuclear translocation. Consequently, we proved that silencing PRDX2 could inhibit proliferation and Wnt/β-catenin pathway while promoting senescence in HCC cells.

Bile liquid biopsy in biliary tract cancer

Biliary tract cancers are heterogeneous in etiology, morphology and molecular characteristics thus impacting disease management. Diagnosis is complex and prognosis poor. The advent of liquid biopsy has provided a unique approach to more thoroughly understand tumor biology in general and biliary tract cancers specifically. Due to their minimally invasive nature, liquid biopsy can be used to serially monitor disease progression and allow real-time monitoring of tumor genetic profiles as well as therapeutic response. Due to the unique anatomic location of biliary tract cancer, bile provides a promising biologic fluid for this purpose. This review focuses on the composition of bile and the use of these various components, ie, cells, extracellular vesicles, nucleic acids, proteins and metabolites as potential biomarkers. Based on the disease characteristics and research status of biliary tract cancer, considerable effort should be made to increase understanding of this disease, promote research and development into early diagnosis, develop efficient diagnostic, therapeutic and prognostic markers.

Identification of a novel bile marker clusterin and a public online prediction platform based on deep learning for cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor, and its diagnosis is still a challenge. This study aimed to identify a novel bile marker for CCA diagnosis based on proteomics and establish a diagnostic model with deep learning.

METHODS

A total of 644 subjects (236 CCA and 408 non-CCA) from two independent centers were divided into discovery, cross-validation, and external validation sets for the study. Candidate bile markers were identified by three proteomics data and validated on 635 clinical humoral specimens and 121 tissue specimens. A diagnostic multi-analyte model containing bile and serum biomarkers was established in cross-validation set by deep learning and validated in an independent external cohort.

RESULTS

The results of proteomics analysis and clinical specimen verification showed that bile clusterin (CLU) was significantly higher in CCA body fluids. Based on 376 subjects in the cross-validation set, ROC analysis indicated that bile CLU had a satisfactory diagnostic power (AUC: 0.852, sensitivity: 73.6%, specificity: 90.1%). Building on bile CLU and 63 serum markers, deep learning established a diagnostic model incorporating seven factors (CLU, CA19-9, IBIL, GGT, LDL-C, TG, and TBA), which showed a high diagnostic utility (AUC: 0.947, sensitivity: 90.3%, specificity: 84.9%). External validation in an independent cohort (n = 259) resulted in a similar accuracy for the detection of CCA. Finally, for the convenience of operation, a user-friendly prediction platform was built online for CCA.

CONCLUSIONS

This is the largest and most comprehensive study combining bile and serum biomarkers to differentiate CCA. This diagnostic model may potentially be used to detect CCA.

Percutaneous transhepatic cholangial drainage/percutaneous transhepatic biliary stent implantation for treatment of extrahepatic cholangiocarcinoma with obstructive jaundice

Extrahepatic cholangiocarcinoma is a malignant tumor originating from the extrahepatic bile duct including the hilar region to the lower bile duct of the common bile duct. With the development of interventional techniques and medical materials, percutaneous transhepatic cholangial drainage (PTCD) or percutaneous transhepatic biliary stent implantation (PTBS) has become the main treatment to relieve biliary obstruction. However, the occurrence of postoperative complications seriously affects the prognosis of patients, and the combination of biliary stenting with local treatment has been found to significantly prolong the time to biliary patency. This article reviews the progress of PTCD/PTBS in the treatment of extrahepatic cholangiocarcinoma with obstructive jaundice, evaluates its clinical efficacy, and points out the current problems and posible solutions to provide more reference for subsequent related studies.

Plasmon-Enhanced Single Extracellular Vesicle Analysis for Cholangiocarcinoma Diagnosis

Cholangiocarcinoma (CCA) is a fatal disease often detected late in unresectable stages. Currently, there are no effective diagnostic methods or biomarkers to detect CCA early with high confidence. Analysis of tumor-derived extracellular vesicles (tEVs) harvested from liquid biopsies can provide a new opportunity to achieve this goal. Here, an advanced nanoplasmonic sensing technology is reported, termed FLEX (fluorescence-amplified extracellular vesicle sensing technology), for sensitive and robust single EV analysis. In the FLEX assay, EVs are captured on a plasmonic gold nanowell surface and immunolabeled for cancer-associated biomarkers to identify tEVs. The underlying plasmonic gold nanowell structures then amplify EVs' fluorescence signals, an effective amplification process at the single EV level. The FLEX EV analysis revealed a wide heterogeneity of tEVs and their marker levels. FLEX also detected small tEVs not detected by conventional EV fluorescence imaging due to weak signals. Tumor markers (MUC1, EGFR, and EPCAM) are identified in CCA, and this marker combination is applied to detect tEVs in clinical bile samples. The FLEX assay detected CCA with an area under the curve of 0.93, significantly better than current clinical markers. The sensitive and accurate nanoplasmonic EV sensing technology can aid in early CCA diagnosis.

New molecular mechanisms in cholangiocarcinoma: signals triggering interleukin-6 production in tumor cells and KRAS co-opted epigenetic mediators driving metabolic reprogramming

Background

Cholangiocarcinoma (CCA) is still a deadly tumour. Histological and molecular aspects of thioacetamide (TAA)-induced intrahepatic CCA (iCCA) in rats mimic those of human iCCA. Carcinogenic changes and therapeutic vulnerabilities in CCA may be captured by molecular investigations in bile, where we performed bile proteomic and metabolomic analyses that help discovery yet unknown pathways relevant to human iCCA.

Methods

Cholangiocarcinogenesis was induced in rats (TAA) and mice (Jnk^Δhepa + CCl₄ + DEN model). We performed proteomic and metabolomic analyses in bile from control and CCA-bearing rats. Differential expression was validated in rat and human CCAs. Mechanisms were addressed in human CCA cells, including Huh28-KRAS^G12D cells. Cell signaling, growth, gene regulation and [U-¹³C]-D-glucose-serine fluxomics analyses were performed. In vivo studies were performed in the clinically-relevant iCCA mouse model.

Results

Pathways related to inflammation, oxidative stress and glucose metabolism were identified by proteomic analysis. Oxidative stress and high amounts of the oncogenesis-supporting amino acids serine and glycine were discovered by metabolomic studies. Most relevant hits were confirmed in rat and human CCAs (TCGA). Activation of interleukin-6 (IL6) and epidermal growth factor receptor (EGFR) pathways, and key genes in cancer-related glucose metabolic reprogramming, were validated in TAA-CCAs. In TAA-CCAs, G9a, an epigenetic pro-tumorigenic writer, was also increased. We show that EGFR signaling and mutant KRAS^G12D can both activate IL6 production in CCA cells. Furthermore, phosphoglycerate dehydrogenase (PHGDH), the rate-limiting enzyme in serine-glycine pathway, was upregulated in human iCCA correlating with G9a expression. In a G9a activity-dependent manner, KRAS^G12D promoted PHGDH expression, glucose flow towards serine synthesis, and increased CCA cell viability. KRAS^G12D CAA cells were more sensitive to PHGDH and G9a inhibition than controls. In mouse iCCA, G9a pharmacological targeting reduced PHGDH expression.

Conclusions

In CCA, we identified new pro-tumorigenic mechanisms: Activation of EGFR signaling or KRAS mutation drives IL6 expression in tumour cells; Glucose metabolism reprogramming in iCCA includes activation of the serine-glycine pathway; Mutant KRAS drives PHGDH expression in a G9a-dependent manner; PHGDH and G9a emerge as therapeutic targets in iCCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02386-2.

Serum α2,6-sialylated glycoform of serotransferrin as a glycobiomarker for diagnosis and prediction of clinical severity in cholangiocarcinoma

BACKGROUND

Glycoprotein sialylation changes are associated with severe development of various cancers. We previously discovered the sialylation of serotransferrin (TF) in cholangiocarcinoma (CCA) using glycoproteomics approach. However, a simple and reliable method for validating sialylation of a specific glycobiomarker is urgently needed.

METHODS

We identified the altered glycosylation in CCA tissues by glycoproteomics approach using mass spectrometry. An enzyme-linked lectin assay (ELLA) was developed for determining the serum levels of sialylated TF in CCA, hepatocellular carcinoma (HCC) and healthy controls in training and validation cohorts.

RESULTS

The nine highly sialylated glycoforms of TF were markedly abundant in CCA tumor tissues than in control. Serum SNA-TF and MAL1-TF were significantly higher in CCA patients. Under receiver operating characteristic curve, serum SNA-TF concentrations significantly differentiated CCA from healthy control. Higher SNA-TF were significantly correlated with severe tumor stages and lymph node metastasis. The combined SNA-TF, MAL1-TF, and CA19-9 as a novel glycobiomarkers panel demonstrated the highest specificity (96.2%) for distinguishing CCA from HCC patients. In CCA patients with low CA19-9 levels, SNA-TF in combination with CA19-9 achieved in 97% diagnostic accuracy.

CONCLUSIONS

Sialylated serotransferrin glycoforms could be used as a novel glycobiomarker for diagnosis and prediction of clinical severity in CCA patients.

Molecular diagnostics and biomarkers in cholangiocarcinoma

Regardless of anatomic origin, cholangiocarcinoma is generally an aggressive malignancy with a relatively high case fatality. Surgical resection with curative intent remains the best opportunity to achieve meaningful long-term survival. Most patients present, however, with advanced disease and less than 20% of patients are candidates for surgical resection. Unfortunately, even patients who undergo resection have a 5-year survival that ranges from 20 to 40%. Biomarkers are indicators of normal, pathologic, or biologic responses to an intervention and can range from a characteristic (i.e., blood pressure reading which can detect hypertension) to specific genetic mutations or proteins (i.e., carcinoembryonic antigen level). Novel biomarkers and improved molecular diagnostics represent an attractive opportunity to improve detection as well as to identify novel therapeutic targets for patients with cholangiocarcinoma. We herein review the latest advances in molecular diagnostics and biomarkers related to the early detection and treatment of patients with cholangiocarcinoma.

Diagnosis Biomarkers of Cholangiocarcinoma in Human Bile: An Evidence-Based Study

Simple Summary

A liquid biopsy has the characteristics of low trauma and easy acquisition in the diagnosis of cholangiocarcinoma. Many researchers try to find diagnostic or prognostic biomarkers of CCA through blood, urine, bile and other body fluids. Due to the close proximity of bile to the lesion and the stable nature, bile gradually comes into people’s view. The evaluation of human bile diagnostic biomarkers is not only to the benefit of screening more suitable clinical markers but also of exploring the pathological changes of the disease.

Abstract

Cholangiocarcinoma (CCA) is a multifactorial malignant tumor of the biliary tract, and the incidence of CCA is increasing in recent years. At present, the diagnosis of CCA mainly depends on imaging and invasive examination, with limited specificity and sensitivity and late detection. The early diagnosis of CCA always faces the dilemma of lacking specific diagnostic biomarkers. Non-invasive methods to assess the degree of CAA have been developed throughout the last decades. Among the many specimens looking for CCA biomarkers, bile has gotten a lot of attention lately. This paper mainly summarizes the recent developments in the current research on the diagnostic biomarkers for CCA in human bile at the levels of the gene, protein, metabolite, extracellular vesicles and volatile organic compounds.

DIA-Based Proteomic Analysis of Plasma Protein Profiles in Patients with Severe Acute Pancreatitis

Acute pancreatitis (AP) is a pancreatic inflammatory disease that varies greatly in course and severity. To further the understanding of the pathology of AP, we carried out data-independent acquisition-based proteomic analyses using proteins extracted from the plasma of patients with severe acute pancreatitis (SAP) (experimental group) and healthy volunteers (control group). Compared to the control group, there were 35 differentially expressed proteins (DEPs) in the plasma of patients with SAP. Of those, the expression levels for 6 proteins were significantly increased, and 29 proteins were significantly decreased. Moreover, six candidate biomarkers—VWF, ORM2, CD5L, CAT, IGLV3-10, and LTF—were matched as candidate biomarkers of the disease severity of AP. The area under the receiver operating characteristic of 0.903 (95% CI: 0.839, 0.967) indicated that this combination of these six candidate biomarkers had a good prediction accuracy for predicting the severity of AP. Our study provides specific DEPs that may be useful in the diagnosis and prognosis of SAP, which suggests new theoretical bases for the occurrence and development of SAP and offers potential novel treatment strategies for SAP.

Bile Processing Protocol for Improved Proteomic Analysis

One of the critical issues to warrant the success of a proteome-wide analysis is sample preparation. Efficient protein extraction in the absence of interferent material is mandatory to achieve an ample proteome coverage by mass spectrometry. The study of biological fluids is always challenging due to their specific biochemical composition. However, there is increasing interest in their characterization as it will provide proteins that may advice disease setting, state, and progression. In particular, bile is proximal to liver and pancreas, and its study is especially attractive since it might provide valuable information for the clinical management of severe diseases afflicting these organs, which are at an urgent need of new biomarkers. Though previous efforts have been made to optimize protocols to analyze bile proteome, only partial descriptions were achieved due to its complex composition, where proteins represent less than 5% of the organic components. Here we describe a new method that significantly increases the bile proteome coverage while reducing by a factor of six the amount of sample required for the proteomic analysis.

Matrix-assisted laser desorption ionization mass spectrometry profiling of plasma exosomes evaluates osteosarcoma metastasis

Osteosarcoma is the most common primary sarcoma of bone among adolescents, often characterized by early lung metastasis resulting in high mortality. Recently, exosomes have been used in liquid biopsy to monitor tumors. Herein, we used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to profile human plasma exosomes for the evaluation of osteosarcoma lung metastasis. Forty patients with osteosarcoma with (n = 20) or without (n = 20) lung metastasis as well as 12 heathy controls were recruited. Exosomes were isolated from human plasma for MALDI-TOF MS analysis. Multivariate statistical analyses were performed based on the MALDI-TOF mass spectra. The strategy can efficiently differentiate osteosarcomas from healthy controls and further discriminate osteosarcoma lung metastasis from non-lung metastasis. We identified seven exosomal proteins as potential biomarkers of osteosarcoma lung metastasis. The proposed method holds great promise to clinically diagnose osteosarcoma and monitor osteosarcoma lung metastasis.

Omics-Based Platforms: Current Status and Potential Use for Cholangiocarcinoma

Cholangiocarcinoma (CCA) has been identified as a highly malignant cancer that can be transformed from epithelial cells of the bile duct, including intrahepatic, perihilar and extrahepatic. High-resolution imaging tools (abdominal ultrasound, computed tomography and percutaneous transhepatic cholangial drainage) are recruited for diagnosis. However, the lack of early diagnostic biomarkers and treatment evaluation can lead to serious outcomes and poor prognosis (i.e., CA19-9, MUC5AC). In recent years, scientists have established a large number of omics profiles to reveal underlying mechanisms and networks (i.e., IL-6/STAT3, NOTCH). With these results, we achieved several genomic alteration events (i.e., TP53_mut, KRAS_mut) and epigenetic modifications (i.e., DNA methylation, histone modification) in CCA cells and clinical patients. Moreover, we reviewed candidate gene (such as NF-kB, YAP1) that drive gene transcription factors and canonical pathways through transcriptomics profiles (including microarrays and next-generation sequencing). In addition, the proteomics database also indicates which molecules and their directly binding status could trigger dysfunction signatures in tumorigenesis (carbohydrate antigen 19-9, mucins). Most importantly, we collected metabolomics datasets and pivotal metabolites. These results reflect the pharmacotherapeutic options and evaluate pharmacokinetic/pharmacodynamics in vitro and in vivo. We reversed the panels and selected many potentially small compounds from the connectivity map and L1000CDS² system. In this paper, we summarize the prognostic value of each candidate gene and correlate this information with clinical events in CCA. This review can serve as a reference for further research to clearly investigate the complex characteristics of CCA, which may lead to better prognosis, drug repurposing and treatment strategies.

Digging deeper into bile proteome

The analysis of biological fluids to identify proteins that may indicate a disease setting, state and progression, is an increasingly explored field. Despite the expectatives created, there are several hurdles that must be solved to reach an extensive proteome coverage using mass spectrometry, mainly due to the complex composition of the matrices. In this regard, bile is specially challenging and yet, very attractive, as a proximal fluid that might provide valuable information for the management of liver and pancreas associated diseases. Proteins account for less than 5% of bile organic components and, although optimized protocols for protein extraction have been developed, only partial descriptions of bile proteome have been achieved. In this manuscript a new procedure is described that significantly improves protein recovery from rat bile, which reduces by a factor of six the sample amount required for a typical proteomics analysis. Moreover, the number of proteins reliably identified in a single nanoLC-MS/MS run from 1 μg protein was increased by three-fold. This procedure provides a valuable resource to dig deeper into the molecular composition of bile and open new avenues to identify new hallmarks of disease such as cholangiocarcinoma, hepatocellular carcinoma and pancreatic cancer for their better clinical management.

Pilot Multi-Omic Analysis of Human Bile from Benign and Malignant Biliary Strictures: A Machine-Learning Approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (¹H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accuracy.