Identification of the potential therapeutic target gene UBE2C in human hepatocellular carcinoma: An investigation based on GEO and TCGA databases

A comprehensive identification of potential molecular targets and small drugs candidate for melanoma cancer using bioinformatics and network-based screening approach

Melanoma is the third most common malignant skin tumor and has increased in morbidity and mortality over the previous decade due to its rapid spread into the bloodstream or lymphatic system. This study used integrated bioinformatics and network-based methodologies to reliably identify molecular targets and small molecular medicines that may be more successful for Melanoma diagnosis, prognosis and treatment. The statistical LIMMA approach utilized for bioinformatics analysis in this study found 246 common differentially expressed genes (cDEGs) between case and control samples from two microarray gene-expression datasets (GSE130244 and GSE15605). Protein-protein interaction network study revealed 15 cDEGs (PTK2, STAT1, PNO1, CXCR4, WASL, FN1, RUNX2, SOCS3, ITGA4, GNG2, CDK6, BRAF, AGO2, GTF2H1 and AR) to be critical in the development of melanoma (KGs). According to regulatory network analysis, the most important transcriptional and post-transcriptional regulators of DEGs and hub-DEGs are ten transcription factors and three miRNAs. We discovered the pathogenetic mechanisms of MC by studying DEGs' biological processes, molecular function, cellular components and KEGG pathways. We used molecular docking and dynamics modeling to select the four most expressed genes responsible for melanoma malignancy to identify therapeutic candidates. Then, utilizing the Connectivity Map (CMap) database, we analyzed the top 4-hub-DEGs-guided repurposable drugs. We validated four melanoma cancer drugs (Fisetin, Epicatechin Gallate, 1237586-97-8 and PF 431396) using molecular dynamics simulation with their target proteins. As a result, the results of this study may provide resources to researchers and medical professionals for the wet-lab validation of MC diagnosis, prognosis and treatments.Communicated by Ramaswamy H. Sarma.

Comprehensive analysis of UBE2C expression and its potential roles and mechanisms in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) ranks one of the most common and lethal cancers all over the world. Previous studies suggest that ubiquitin-conjugating enzyme E2C (UBE2C) serves as an oncogene in human cancers. However, its expression, diagnosis, prognosis and potential mechanisms in HCC remain largely unknown. In this study, the expression of UBE2C in HCC was first analyzed by comprehensive bioinformatic analysis. ROC curve analysis and survival analysis were employed to assess the diagnostic and prognostic roles of UBE2C in HCC. UBE2C promoter methylation level and upstream regulatory miRNAs of UBE2C in HCC were explored. The present work demonstrated that UBE2C was significantly upregulated in HCC compared with normal controls. We also found significant diagnostic and prognostic values of UBE2C in HCC. Promoter methylation of UBE2C was obviously decreased in HCC and was negatively correlated with UBE2C mRNA expression. 10 miRNAs were predicted to potentially bind to UBE2C. In vitro assay and bioinformatic correlation analysis together revealed that hsa-miR-193b-3p might be another key upstream regulatory mechanism of UBE2C in HCC. In conclusion, UBE2C is overexpressed in HCC and may serve as a key diagnostic/prognostic biomarker for patients with HCC.

Ubiquitin‑conjugating enzymes as potential biomarkers and therapeutic targets for digestive system cancers (Review)

Digestive system cancers are the leading cause of cancer‑related death worldwide due to their high morbidity and mortality rates. The current treatment methods include surgical treatment, chemotherapy, radiotherapy and endoscopic treatment, and the precisely targeted therapy of digestive system cancers requires to be further studied. The ubiquitin‑proteasome system is the main pathway for protein degradation in cells and the ubiquitin‑conjugating enzymes (E2s) have a decisive role in the specific selection of target proteins for degradation. The E2s have an important physiological role in digestive system cancers, which is related to the clinical tumor stage, differentiation degree and poor prognosis. Furthermore, they are involved in the physiological processes of digestive system tumor cell proliferation, migration, invasion, stemness, drug resistance and autophagy. In the present article, the progress and achievements of the E2s in gastric cancer, hepatocellular carcinoma, pancreatic cancer, colorectal cancer, intrahepatic cholangiocarcinoma, gallbladder cancer and esophageal squamous cell carcinoma were reviewed, which may provide early screening indicators and reliable therapeutic targets for digestive system cancers.

Machine Learning Screens Potential Drugs Targeting a Prognostic Gene Signature Associated With Proliferation in Hepatocellular Carcinoma

Background: This study aimed to screen potential drugs targeting a new prognostic gene signature associated with proliferation in hepatocellular carcinoma (HCC). Methods: CRISPR Library and TCGA datasets were used to explore differentially expressed genes (DEGs) related to the proliferation of HCC cells. Differential gene expression analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct a prognostic gene signature. Then the predictive power of the gene signature was validated in the TCGA and ICGC datasets. Furthermore, potential drugs targeting this gene signature were screened. Results: A total of 640 DEGs related to HCC proliferation were identified. Using univariate Cox analysis and random forest algorithm, 10 hub genes were screened. Subsequently, using multiplex combinatorial screening, five hub genes (FARSB, NOP58, CCT4, DHX37 and YARS) were identified. Taking the median risk score as a cutoff value, HCC patients were divided into high- and low-risk groups. Kaplan-Meier analysis performed in the training set showed that the overall survival of the high-risk group was worse than that of the low-risk group (p < 0.001). The ROC curve showed a good predictive efficiency of the risk score (AUC > 0.699). The risk score was related to gene mutation, cancer cell stemness and immune function changes. Prediction of immunotherapy suggetsted the IC50s of immune checkpoint inhibitors including A-443654, ABT-888, AG-014699, ATRA, AUY-922, and AZ-628 in the high-risk group were lower than those in the low-risk group, while the IC50s of AMG-706, A-770041, AICAR, AKT inhibitor VIII, Axitinib, and AZD-0530 in the high-risk group were higher than those in the low-risk group. Drug sensitivity analysis indicated that FARSB was positively correlated with Hydroxyurea, Vorinostat, Nelarabine, and Lomustine, while negatively correlated with JNJ-42756493. DHX37 was positively correlated with Raltitrexed, Cytarabine, Cisplatin, Tiotepa, and Triethylene Melamine. YARS was positively correlated with Axitinib, Fluphenazine and Megestrol acetate. NOP58 was positively correlated with Vorinostat and 6-thioguanine. CCT4 was positively correlated with Nerabine. Conclusion: The five-gene signature associated with proliferation can be used for survival prediction and risk stratification for HCC patients. Potential drugs targeting this gene signature deserve further attention in the treatment of HCC.

Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

Non-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low- and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment.

Identification of the Potential Prognosis Biomarkers in Hepatocellular Carcinoma: An Analysis Based on WGCNA and PPI

Aim

This study was done to determine biomarkers for the prognostic prediction of hepatocellular carcinoma (HCC).

Materials and Methods

In the Gene Expression Omnibus, the gene expression profiles of HCC were downloaded. Biomarkers were identified by weighted gene co-expression network analysis and protein–protein interaction network analysis.

Results

There were 24 modules, which were characterized by the high correlation with HCC. Meanwhile, through enrichment analysis, differentially expressed genes were largely participated in the ubiquitination and autophagy processes. Moreover, PRC1, TOP2A and CKAP2L may be the hub genes involved in HCC tumorigenesis, and their biomarker roles were further demonstrated via Gene Expression Profiling Interactive Analysis (GEPIA) and Oncomine databases. In addition, the levels of PRC1, TOP2A and CKAP2L were obviously up-regulated in the sera of HCC patients.

Conclusion

PRC1, TOP2A and CKAP2L may serve as biomarkers for the prognostic prediction of HCC patients.

GDI2 is a novel diagnostic and prognostic biomarker in hepatocellular carcinoma

Background: GDP Dissociation inhibitor 2 (GDI2) gene has been correlated with some important biological processes in a variety of cancers, whereas the role of GDI2 in hepatocellular carcinoma (HCC) is ill-defined. We aimed to demonstrate the relationship between GDI2 and HCC based on The Cancer Genome Atlas (TCGA) data mining.

Methods: The expression of GDI2 was compared between cancer and normal tissues of 371 HCC patients collected from TCGA-LIHC, and verified in HCC cell lines. Gene set enrichment analysis (GSEA) was applied to annotate biological function of GDI2. Furthermore, Wilcoxon rank sum test, Logistics regression, as well as Cox regression and Kaplan-Meier survival analysis, were employed to evaluate the association of GDI2 expression with clinicopathological characteristics, and survival status of HCC patients, respectively.

Results: It showed that the expression of GDI2 was much higher in tumor tissues than in normal tissues (P < 0.001) of HCC patients. And the elevated expression of GDI2 was correlated with more aggressive HCC tumor status, including severe primary tumor extent, advanced pathological stage, serious histologic grade, and mutated TP53 status (P < 0.05). Moreover, high GDI2 expression was strongly associated with a poor survival rate (P < 0.001). Both enrichment and immune infiltration analyses implied that GDI2-associated signaling mainly involve lipid metabolism and extracellular matrix (ECM) constructing pathways related to tumor microenvironment (TME) (P < 0.05).

Conclusions: The elevated expression of GDI2 predicts poor prognosis in HCC patients, indicating that GDI2 could be applied as a predictive biomarker for diagnosis and prognosis of HCC.

Identification of SLITRK6 as a Novel Biomarker in hepatocellular carcinoma by comprehensive bioinformatic analysis

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the adult liver and morbidity are increasing in recent years, however, there is still no effective strategy to prevent and diagnose HCC. Therefore, it is urgent to research the effective biomarker to predict clinical outcomes of HCC tumorigenesis. In the current study, differentially expressed genes in HCC and normal tissues were investigated using the Gene Expression Omnibus (GEO) dataset GSE144269 and The Cancer Genome Atlas (TCGA). Gene differential expression analysis and weighted correlation network analysis (WGCNA) methods were used to identify nine and 16 key gene modules from the GEO dataset and TCGA dataset, respectively, in which the green module in the GEO dataset and magenta module in TCGA were significantly correlated with HCC occurrence. Third, the enrichment score of gene function annotation results showed that these two key modules focus on the positive regulation of inflammatory response and cell differentiation, etc. Besides, PPI network analysis, mutation analysis, and survival analysis found that SLITRK6 had high connectivity, and its mutation significantly impacted overall survival. In addition, SLITRK6 was found to be low expressed in tumor cells. To summarize, SLITRK6 mutation was found to significantly affect the occurrence and prognosis of HCC. SLITRK6 was confirmed as a new potential gene target for HCC, which may provide a new theoretical basis for personalized diagnosis and chemotherapy of HCC in the future.

A Five-Gene-Based Prognostic Signature for Hepatocellular Carcinoma

Objective: This study intends to identify potential prognostic marker genes associated with the prognosis of patients suffering from hepatocellular carcinoma (HCC) based on TCGA and GEO analysis.

Methods: TCGA-LIHC cohort was downloaded and the data related to HCC were extracted from The Cancer Genome Atlas (TCGA) database and subjected to differential analysis. HCC-related gene expression datasets were retrieved from the GEO database, followed by differential analysis. After intersection of the results of TCGA and GEO databases, gene interaction analysis was performed to obtain the core genes. To identify the genes related to the prognosis of HCC patients, we conducted univariate and multivariate Cox analyses.

Results: Based on differential analysis of TCGA database, 854 genes were differentially expressed in HCC, any of which might link to the occurrence and progression of HCC. Meanwhile, joint analysis of HCC-related gene expression datasets in the GEO database screened 214 genes. Five core genes CDC20, TOP2A, RRM2, UBE2C and AOX1 were significantly associated with the prognosis of HCC patients and the risk model based on these five genes effectively predicted the prognosis of HCC patients.

Conclusion: Collectively, our data suggest that CDC20, TOP2A, RRM2, UBE2C and AOX1 may be the key genes affecting the prognosis of patients with HCC. The five-gene signature could accurately predict the prognosis of HCC patients.

Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

Fibrinogen-Like Protein 1 Modulates Sorafenib Resistance in Human Hepatocellular Carcinoma Cells

Despite liver cancer being the second-leading cause of cancer-related death worldwide, few systemic drugs have been approved. Sorafenib, the first FDA-approved systemic drug for unresectable hepatocellular carcinoma (HCC), is limited by resistance. However, the precise mechanisms underlying this phenomenon are unknown. Since fibrinogen-like 1 (FGL1) is involved in HCC progression and upregulated after anticancer therapy, we investigated its role in regulating sorafenib resistance in HCC. FGL1 expression was assessed in six HCC cell lines (HepG2, Huh7, Hep3B, SNU387, SNU449, and SNU475) using western blotting. Correlations between FGL1 expression and sorafenib resistance were examined by cell viability, colony formation, and flow cytometry assays. FGL1 was knocked-down to confirm its effects on sorafenib resistance. FGL1 expression was higher in HepG2, Huh7, and Hep3B cells than in SNU387, SNU449, and SNU475 cells; high FGL1-expressing HCC cells showed a lower IC₅₀ and higher sensitivity to sorafenib. In Huh7 and Hep3B cells, FGL1 knockdown significantly increased colony formation by 61% (p = 0.0013) and 99% (p = 0.0002), respectively, compared to that in controls and abolished sorafenib-induced suppression of colony formation, possibly by modulating ERK and autophagy signals. Our findings demonstrate that sorafenib resistance mediated by FGL1 in HCC cells, suggesting FGL1 as a potential sorafenib-resistance biomarker and target for HCC therapy.

The Molecular Basis of Ubiquitin-Conjugating Enzymes (E2s) as a Potential Target for Cancer Therapy

Ubiquitin-conjugating enzymes (E2s) are one of the three enzymes required by the ubiquitin-proteasome pathway to connect activated ubiquitin to target proteins via ubiquitin ligases. E2s determine the connection type of the ubiquitin chains, and different types of ubiquitin chains regulate the stability and activity of substrate proteins. Thus, E2s participate in the regulation of a variety of biological processes. In recent years, the importance of E2s in human health and diseases has been particularly emphasized. Studies have shown that E2s are dysregulated in variety of cancers, thus it might be a potential therapeutic target. However, the molecular basis of E2s as a therapeutic target has not been described systematically. We reviewed this issue from the perspective of the special position and role of E2s in the ubiquitin-proteasome pathway, the structure of E2s and biological processes they are involved in. In addition, the inhibitors and microRNAs targeting E2s are also summarized. This article not only provides a direction for the development of effective drugs but also lays a foundation for further study on this enzyme in the future.

Gene networks and transcriptional regulators associated with liver cancer development and progression

BACKGROUND

Treatment options for hepatocellular carcinoma (HCC) are limited, and overall survival is poor. Despite the high frequency of this malignoma, its basic disease mechanisms are poorly understood. Therefore, the aim of this study was to use different methodological approaches and combine the results to improve our knowledge on the development and progression of HCC.

METHODS

Twenty-three HCC samples were characterized by histological, morphometric and cytogenetic analyses, as well as comparative genomic hybridization (aCGH) and genome-wide gene expression followed by a bioinformatic search for potential transcriptional regulators and master regulatory molecules of gene networks.

RESULTS

Histological evaluation revealed low, intermediate and high-grade HCCs, and gene expression analysis split them into two main sets: GE1-HCC and GE2-HCC, with a low and high proliferation gene expression signature, respectively. Array-based comparative genomic hybridization demonstrated a high level of chromosomal instability, with recurrent chromosomal gains of 1q, 6p, 7q, 8q, 11q, 17q, 19p/q and 20q in both HCC groups and losses of 1p, 4q, 6q, 13q and 18q characteristic for GE2-HCC. Gene expression and bioinformatics analyses revealed that different genes and gene regulatory networks underlie the distinct biological features observed in GE1-HCC and GE2-HCC. Besides previously reported dysregulated genes, the current study identified new candidate genes with a putative role in liver cancer, e.g. C1orf35, PAFAH1B3, ZNF219 and others.

CONCLUSION

Analysis of our findings, in accordance with the available published data, argues in favour of the notion that the activated E2F1 signalling pathway, which can be responsible for both inappropriate cell proliferation and initial chromosomal instability, plays a pivotal role in HCC development and progression. A dedifferentiation switch that manifests in exaggerated gene expression changes might be due to turning on transcriptional co-regulators with broad impact on gene expression, e.g. POU2F1 (OCT1) and NFY, as a response to accumulating cell stress during malignant development. Our findings point towards the necessity of different approaches for the treatment of HCC forms with low and high proliferation signatures and provide new candidates for developing appropriate HCC therapies.

Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

Background

The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC).

Methods

The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA).

Results

A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival.

Conclusion

Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.

NT5DC2 promotes tumor cell proliferation by stabilizing EGFR in hepatocellular carcinoma

Most hepatocellular carcinoma (HCC) patients are diagnosed at an advanced stage; however, the effect of systemic therapy on advanced HCC remains undetermined. Therefore, new treatment targets must be identified. We analyzed Gene Expression Omnibus datasets from two HCC patient cohorts and found that NT5DC2 was associated with vascular invasion and poor survival. In two hepatoma cell lines, NT5DC2 overexpression promoted HCC cell proliferation and clone formation in vitro and promoted tumor growth in vivo. Coimmunoprecipitation assays and liquid chromatography with tandem mass spectrometry analysis revealed that NT5DC2 bound directly to epidermal growth factor receptor (EGFR). NT5DC2 upregulated EGFR expression by downregulating EGFR ubiquitination and preventing its degradation via the ubiquitin-proteasome pathway but did not upregulate its transcription. EGFR upregulation activated downstream signal transduction, which played a critical role in the protumor effects of NT5DC2. Erlotinib, a small-molecule inhibitor of EGFR, blocked the effect of NT5DC2 in promoting HCC cell proliferation. In a cohort of 79 patients who underwent curative resection for HCC, NT5DC2 expression in the tumors was associated with larger tumors and microvascular invasion. NT5DC2 expression was also independently associated with recurrence-free survival. The present study demonstrated for the first time that NT5DC2 promotes tumor cell proliferation in HCC and may serve as a potential molecular target for treating HCC. EGFR blockage could be used to treat selected patients with NT5DC2 upregulation.

Patients with Cholangiocarcinoma Present Specific RNA Profiles in Serum and Urine Extracellular Vesicles Mirroring the Tumor Expression: Novel Liquid Biopsy Biomarkers for Disease Diagnosis

Cholangiocarcinoma (CCA) comprises a group of heterogeneous biliary cancers with dismal prognosis. The etiologies of most CCAs are unknown, but primary sclerosing cholangitis (PSC) is a risk factor. Non-invasive diagnosis of CCA is challenging and accurate biomarkers are lacking. We aimed to characterize the transcriptomic profile of serum and urine extracellular vesicles (EVs) from patients with CCA, PSC, ulcerative colitis (UC), and healthy individuals. Serum and urine EVs were isolated by serial ultracentrifugations and characterized by nanoparticle tracking analysis, transmission electron microscopy, and immunoblotting. EVs transcriptome was determined by Illumina gene expression array [messenger RNAs (mRNA) and non-coding RNAs (ncRNAs)]. Differential RNA profiles were found in serum and urine EVs from patients with CCA compared to control groups (disease and healthy), showing high diagnostic capacity. The comparison of the mRNA profiles of serum or urine EVs from patients with CCA with the transcriptome of tumor tissues from two cohorts of patients, CCA cells in vitro, and CCA cells-derived EVs, identified 105 and 39 commonly-altered transcripts, respectively. Gene ontology analysis indicated that most commonly-altered mRNAs participate in carcinogenic steps. Overall, patients with CCA present specific RNA profiles in EVs mirroring the tumor, and constituting novel promising liquid biopsy biomarkers.