Highly Sensitive Circulating MicroRNA Panel for Accurate Detection of Hepatocellular Carcinoma in Patients With Liver Disease

Blood circulating miRNAs as pancreatic cancer biomarkers: An evidence from pooled analysis and bioinformatics study

Pancreatic cancer (PC) is one of the deadliest cancers, characterized by a poor prognosis. Currently, there are no screening programs for the early detection of PC, and existing diagnostic methods are primarily limited to high-risk individuals. Biomarkers such as CA19-9 have not significantly improved early diagnosis, making the identification of new potential biomarkers crucial for routine clinical practice. Among the candidate biomarkers, miRNAs have been most extensively studied due to their role in regulating gene expression (either as oncomiRs or tumor suppressor miRNAs) and their potential for minimally invasive analysis through liquid biopsy techniques. This review aims to summarize the current literature on blood-circulating miRNAs and their diagnostic value in PC detection, considering the context of CA19-9 and benign pancreatic diseases. The data from the collected studies were curated through both statistical and bioinformatics analyses to identify the most promising miRNAs with optimal diagnostic accuracy for PC detection and to assess their role in the molecular processes leading to tumor development.

Detection of Circulating mRNA Variants in Hepatocellular Carcinoma Patients Using Targeted RNAseq

Introduction

Mutations in circulating nucleic acids can be used as biomarkers for the early detection and management of hepatocellular carcinoma (HCC). However, while circulating tumor DNA and microRNA have been extensively explored, circulating tumor mRNA and circulating mRNA mutants (ctmutRNA), which may provide advantages over other analytes, remain less well described. We previously reported the identification of 288 HCC selective ctmutRNA variants, called "candidates," from a small cohort of HCC patients using total RNAseq. The objective of the current study was to use targeted RNAseq to validate the specificity and sensitivity of these HCC selective variants in an independent cohort of patients with liver cirrhosis (LC).

Methods

Several methods to isolate small extracellular vesicles and amplify mRNA from the circulation were compared. RNA was isolated, and the primers and probes selective for the 288 regions of interest were used with RNA from HCC (N = 50) and LC and no HCC (N = 35) patients. HCC tumor tissues (N = 11), a normal liver tissue and 3 cell lines were also studied. cDNA synthesis was followed by library construction using QIAseq RNA Fusion XP panel. QC analysis was carried out with an Agilent Bioanalyzer before sequencing on a NextSeq 550 instrument. A GATK HaplotypeCaller was used for variant calling and annotation carried out using snpEff.

Results

Among the test panel of 288 ctmutRNA candidates in the original cohort, 75 were detected in the new cohort of plasma samples. Moreover, 388 other variants in proximity to the original lesions were also found in multiple HCC but not LC plasma samples. A subset of 36 HCC selective variants was able to identify all HCC patients. The most common tumor specific variants were Indels and SNPs. Novel mRNA fusion variants, corresponding to SENP7, HYI, SAR1A, RASA2, TUBA transcripts, etc., were identified in HCC and LC patients.

Conclusion

Circulating RNA could be a robust analyte for noninvasive early detection of HCC and circulating RNA panels could be powerful tools in the entire spectrum of clinical management.

Persisting challenges in the early detection of hepatocellular carcinoma

INTRODUCTION

Prognosis in patients with HCC is largely determined by stage at diagnosis, highlighting the importance of effective early detection strategies. HCC surveillance is associated with increased early detection and reduced HCC-related mortality and is currently recommended in patients with cirrhosis or chronic HBV infection.

AREAS COVERED

We performed a targeted literature review to identify limitations of current HCC surveillance practices and strategies for improvement.

EXPERT OPINION

Semi-annual ultrasound continues as the cornerstone modality for HCC surveillance but has limited sensitivity for detecting early-stage HCC, particularly in patients with obesity and non-viral etiologies. Although sensitivity for early-stage HCC can be improved by using ultrasound with alpha fetoprotein, this strategy misses over one-third of HCC at an early stage. Emerging imaging and biomarker-based surveillance strategies currently remain in varying stages of validation and are not yet ready for routine use in practice. The cost-effectiveness of surveillance in patients with non-cirrhotic liver disease related to hepatitis C or metabolic dysfunction-associated steatotic liver disease continues to be debated, although subgroups with advanced fibrosis may warrant surveillance. Finally, the effectiveness of surveillance is diminished by underuse in clinical practice, particularly in racial minority and low-income groups, highlighting a need for interventions to increase utilization.

DNA-Assembled Size-Permeable Microcapsule Bioreactor Enables Direct and Rapid Sensing of Trace Circulating MicroRNA Biomarkers in Undiluted Cancer Patient Serum

Sensitive and direct detection of circulating microRNAs (miRNAs) in undiluted human serums can significantly facilitate the diagnosis of different diseases in the early stages; however, the achievement of such a goal is highly challenged by the extremely complex sample matrix nature of serums. Here, we show that sensitive and rapid detection of the circulating let-7a miRNA biomarker in undiluted cancer patient serums can be realized by a new size-permeable microcapsule bioreactor. The Cas13a/crRNA-dsDNA@polyethylene glycol core/shell biomicrocapsules are synthesized by simple encapsulation and etching steps. Such biomicrocapsules exhibit high stability, enhanced protein antifouling capability, and size-selective permeability that only allows short miRNA sequences to enter the core cavities, thereby enabling highly sensitive and direct detection of let-7a miRNA in undiluted serums down to 0.21 pM within 20 min via signal amplification by Cas13a/crRNA and near-infrared (NIR) fluorescence signals. The biomicrocapsule also allows unambiguous discrimination of the clear cell renal cell carcinoma patients from the healthy groups via direct detection of let-7a miRNA in the serum samples, thereby highlighting its robust clinical potential for early disease diagnoses.

A combined model of six serum microRNAs as diagnostic markers for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is associated with high morbidity and mortality, and its poor prognosis is mainly due to the lack of an effective means of early diagnosis. This study aimed to identify a group of serum microRNAs (miRNAs) as potential biomarkers for the diagnosis of HCC.

METHODS

We collected 190 HCC cases, 109 benign lesions of the liver, 40 cases of non-HCC tumors, and 130 healthy controls. The 469 participants were divided into training and validation sets. A literature search revealed 12 miRNAs closely associated with HCC. In the training set, significantly differentially expressed miRNAs (DEmiRNAs) were screened using real-time quantitative PCR, and a diagnostic model of HCC was constructed using logistic regression analysis. An independent validation was performed using a validation set. The identified DE miRNAs were subjected to target gene prediction and functional analyses.

RESULTS

Compared to the controls, the levels of miR-21, miR-221, miR-801, and miR-1246 significantly decreased in HCC (P < 0.05), while the levels of miR-26a and miR-122 significantly increased (P < 0.05). A diagnostic model based on the six DE miRNAs was successfully constructed, with AUC values of 0.953 for the training set and 0.952 for the verification set. Finally, 100 target genes of the DE miRNAs were predicted and were significantly enriched in the B cell receptor, neurotrophin, ferroptosis, and EGFR tyrosine kinase inhibitor resistance signaling pathways.

CONCLUSIONS

The constructed diagnostic model based on six DE miRNA combinations has important clinical value for the early diagnosis of HCC.

Diagnostic Value of Circulating microRNAs for Hepatocellular Carcinoma: Results of a Meta-analysis and Validation

Mounting evidence suggests that circulating microRNAs (miRNAs) hold diagnostic value in various malignancies. To identify circulating miRNAs for the early diagnosis of hepatocellular carcinoma (HCC), we conducted a meta-analysis to evaluate the diagnostic utility of miRNAs in HCC and further validated the results of the meta-analysis. English articles published prior to December 2023 were retrieved from databases including PubMed, Embase, and Web of Science. A random-effects or fixed-effects model was applied depending on the heterogeneity among studies. The pooled sensitivity, specificity, and the area under the summary receiver operating characteristic curve (AUC) were calculated to assess diagnostic accuracy. Additionally, RT-qPCR and receiver operating characteristic (ROC) analyses were employed to further validate the findings. A total of 36 studies were included, involving 3362 patients with HCC and 2150 patients with chronic hepatitis. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.79 (95% CI 0.75-0.82), 0.79 (95% CI 0.73-0.84), and 14 (95% CI 9-22), respectively; the positive and negative likelihood ratios were 4.0 and 0.27, respectively; the area under the curve (AUC) in the summary receiver operating characteristic (ROC) was 0.85 (95% CI 0.82-0.88). Validation indicated a significant upregulation of miR-1246, miR-21, and miR-221 in HCC patients compared to those with chronic hepatitis (P < 0.01), while miR-122 and miR-26a were significantly downregulated (P < 0.01). Moreover, the validation results also demonstrated that serum levels of miR-21, miR-26a, miR-122, miR-221, and miR-1246 exhibit high sensitivity and specificity in the diagnosis of HCC. Circulating miRNAs may be promising biomarkers for HCC diagnosis.

Novel biomarkers for monitoring and management of hepatocellular carcinoma

Due to current challenges in the early detection, less than 40% of individuals diagnosed with hepatocellular carcinoma (HCC) are viable candidates for surgical intervention. Therefore, validating and launching of a novel precise diagnostic approach is essential for early diagnosis. Based on developing evidence using circulating tumor cells and their derivatives, circulating miRNAs, and extracellular vesicles (EVs), liquid biopsy may offer a reliable platform for the HCC's early diagnosis. Each liquid biopsy analyte may provide significant areas for diagnosis, prognostic assessment, and treatment monitoring of HCC patients depending on its kind, sensitivity, and specificity. The current review addresses potential clinical applications, current research, and future developments for liquid biopsy in HCC management.

Unveiling miRNA-Gene Regulatory Axes as Promising Biomarkers for Liver Cirrhosis and Hepatocellular Carcinoma

Liver cirrhosis, a severe scarring condition of the liver with the potential to progress to hepatocellular carcinoma (HCC), necessitates the development of reliable biomarkers for early detection due to the asymptomatic nature of its early stages. Recent discoveries in microRNAs (miRNAs) hold promise for a noninvasive test, with the potential to significantly improve patient outcomes. Building upon these promising findings, this study investigates gene expression data, identifying distinct sets of DEGs and DEMs using GEO2R. Subsequently, a gene-miRNA network was constructed using Cytoscape to explore potential interactions between DEMs and their target genes (DEGs). Boxplot analysis was carried out to identify and validate differences in gene expression between healthy and diseased tissues. This analysis revealed four significantly differentially expressed genes: CAV1, PEA15, EMP1, and ENAH. Notably, subsequent survival analysis demonstrated that EMP1 and ENAH significantly impact overall patient survival. Intriguingly, the constructed network identified several potential regulatory axes: hsa-miR-191-5p/ENAH, hsa-miR-3158-3p/ENAH, hsa-miR-371a-5p/ENAH, and hsa-miR-6753-5p/EMP1. Crucially, a direct comparison of DEGs and DEMs between liver cirrhosis and HCC pinpointed AGO3, NCOA3, and TNPO1, along with their regulatory elements, as potential key drivers of HCC development in cirrhotic patients, underscoring their importance as targets for early diagnostic and therapeutic strategies. Finally, immunohistochemical (IHC) analysis not only validates our findings but also reiterates the novelty of the identified genes. Overall, elucidating the role of these novel genes and regulatory elements could pave the way for an earlier and more accurate diagnosis of liver diseases.

Noninvasive multi-cancer detection using blood-based cell-free microRNAs

Patients diagnosed with early-stage cancers have a substantially higher chance of survival than those with late-stage diseases. However, the option for early cancer screening is limited, with most cancer types lacking an effective screening tool. Here we report a miRNA-based blood test for multi-cancer early detection based on examination of serum microRNA microarray data from cancer patients and controls. First, a large multi-cancer training set that included 1,408 patients across 7 cancer types and 1,408 age- and gender-matched non-cancer controls was used to develop a 4-microRNA diagnostic model using 10-fold cross-validation. In three independent validation sets comprising a total of 4,875 cancer patients across 13 cancer types and 3,722 non-cancer participants, the 4-microRNA model achieved greater than 90% sensitivity for 9 cancer types (lung, biliary tract, bladder, colorectal, esophageal, gastric, glioma, pancreatic, and prostate cancers) and 75-84% sensitivity for 3 cancer types (sarcoma, liver, and ovarian cancer), while maintaining greater than 99% specificity. The sensitivity remained to be > 99% for patients with stage 1 lung cancer. Our study provided novel evidence to support the development of an inexpensive and accurate miRNA-based blood test for multi-cancer early detection.

Potential utility of pretreatment serum miRNAs for optimal treatment selection in advanced high-grade serous ovarian cancer

OBJECTIVE

The primary treatment of patients with advanced ovarian cancer is selected from whether primary debulking surgery or neoadjuvant chemotherapy. We investigated whether pretreatment serum microRNA profiles are useful for selecting patients with advanced high-grade serous ovarian cancer who obtain better outcomes from undergoing primary debulking surgery or neoadjuvant chemotherapy.

METHODS

Consecutive patients with clinical stage IIIB-IVB and serum microRNA data were selected. Patients who underwent primary debulking surgery or neoadjuvant chemotherapy were subjected to 1:1 propensity score matching before comparing their progression-free survival using Cox modelling. Progression-free probabilities for the selected microRNA profiles were calculated, and the estimated progression-free survival with the recommended primary treatment was determined and compared with the actual progression-free survival of the patients.

RESULTS

Of the 108 patients with stage IIIB-IVB disease, the data of 24 who underwent primary debulking surgery or neoadjuvant chemotherapy were compared. Eleven and three microRNAs were independent predictors of progression-free survival in patients who underwent primary debulking surgery and neoadjuvant chemotherapy, respectively. Two microRNAs correlated significantly with complete resection of the tumours in primary debulking surgery. No differences were found between the actual and estimated progression-free survival in the primary debulking surgery and neoadjuvant chemotherapy groups (P > 0.05). The recommended and actual primary treatments were identical in 27 (56.3%) of the 48 patients. The median improved survival times between recommended and actual treatment were 11.7 and 32.6 months for patients with actual primary debulking surgery and neoadjuvant chemotherapy, respectively.

CONCLUSIONS

Pretreatment microRNA profiles could be used to select subgroups of patients who benefited more from primary debulking surgery or neoadjuvant chemotherapy and might contribute to selecting the optimal primary treatment modality in advanced high-grade serous ovarian cancer patients.

A unique circulating microRNA pairs signature serves as a superior tool for early diagnosis of pan-cancer

Cancer remains a major burden globally and the critical role of early diagnosis is self-evident. Although various miRNA-based signatures have been developed in past decades, clinical utilization is limited due to a lack of precise cutoff value. Here, we innovatively developed a signature based on pairwise expression of miRNAs (miRPs) for pan-cancer diagnosis using machine learning approach. We analyzed miRNA spectrum of 15832 patients, who were divided into training, validation, test, and external test sets, with 13 different cancers from 10 cohorts. Five different machine-learning (ML) algorithms (XGBoost, SVM, RandomForest, LASSO, and Logistic) were adopted for signature construction. The best ML algorithm and the optimal number of miRPs included were identified using area under the curve (AUC) and youden index in validation set. The AUC of the best model was compared to previously published 25 signatures. Overall, Random Forest approach including 31 miRPs (31-miRP) was developed, proving highly efficient in cancer diagnosis across different datasets and cancer types (AUC range: 0.980-1.000). Regarding diagnosis of cancers at early stage, 31-miRP also exhibited high capacities, with AUC ranging from 0.961 to 0.998. Moreover, 31-miRP exhibited advantages in differentiating cancers from normal tissues (AUC range: 0.976-0.998) as well as differentiating cancers from corresponding benign lesions. Encouragingly, comparing to previously published 25 different signatures, 31-miRP also demonstrated clear advantages. In conclusion, 31-miRP acts as a powerful model for cancer diagnosis, characterized by high specificity and sensitivity as well as a clear cutoff value, thereby holding potential as a reliable tool for cancer diagnosis at early stage.

Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis

PURPOSE

Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment.

METHODS

We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls.

RESULTS

We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC.

CONCLUSION

Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.

Battle of the biopsies: Role of tissue and liquid biopsy in hepatocellular carcinoma

The diagnosis and management of hepatocellular carcinoma (HCC) have improved significantly in recent years. With the introduction of immunotherapy-based combination therapy, there has been a notable expansion in treatment options for patients with unresectable HCC. Simultaneously, innovative molecular tests for early detection and management of HCC are emerging. This progress prompts a key question: as liquid biopsy techniques rise in prominence, will they replace traditional tissue biopsies, or will both techniques remain relevant? Given the ongoing challenges of early HCC detection, including issues with ultrasound sensitivity, accessibility, and patient adherence to surveillance, the evolution of diagnostic techniques is more relevant than ever. Furthermore, the accurate stratification of HCC is limited by the absence of reliable biomarkers which can predict response to therapies. While the advantages of molecular diagnostics are evident, their potential has not yet been fully harnessed, largely because tissue biopsies are not routinely performed for HCC. Liquid biopsies, analysing components such as circulating tumour cells, DNA, and extracellular vesicles, provide a promising alternative, though they are still associated with challenges related to sensitivity, cost, and accessibility. The early results from multi-analyte liquid biopsy panels are promising and suggest they could play a transformative role in HCC detection and management; however, comprehensive clinical validation is still ongoing. In this review, we explore the challenges and potential of both tissue and liquid biopsy, highlighting that these diagnostic methods, while distinct in their approaches, are set to jointly reshape the future of HCC management.

Noncoding RNAs in Hepatocellular Carcinoma: Potential Applications in Combined Therapeutic Strategies and Promising Candidates of Treatment Response

The incidence of hepatocellular carcinoma (HCC) is increasing, and 40% of patients are diagnosed at advanced stages. Over the past 5 years, the number of clinically available treatments has dramatically increased for HCC, making patient management particularly complex. Immune checkpoint inhibitors (ICIs) have improved the overall survival of patients, showing a durable treatment benefit over time and a different response pattern with respect to tyrosine kinase inhibitors (TKIs). Although there is improved survival in responder cases, a sizeable group of patients are primary progressors or are ineligible for immunotherapy. Indeed, patients with nonviral etiologies, such as nonalcoholic steatohepatitis (NASH), and alterations in specific driver genes might be less responsive to immunotherapy. Therefore, improving the comprehension of mechanisms of drug resistance and identifying biomarkers that are informative of the best treatment approach are required actions to improve patient survival. Abundant evidence indicates that noncoding RNAs (ncRNAs) are pivotal players in cancer. Molecular mechanisms through which ncRNAs exert their effects in cancer progression and drug resistance have been widely investigated. Nevertheless, there are no studies summarizing the synergistic effect between ncRNA-based strategies and TKIs or ICIs in the preclinical setting. This review aims to provide up-to-date information regarding the possible use of ncRNAs as therapeutic targets in association with molecular-targeted agents and immunotherapies and as predictive tools for the selection of optimized treatment options in advanced HCCs.

A novel serum m7G-harboring microRNA signature for cancer detection

Background: Emerging evidence points to the exceptional importance and value of m7G alteration in the diagnosis and prognosis of cancers. Nonetheless, a biomarker for precise screening of various cancer types has not yet been developed based on serum m7G-harboring miRNAs. Methods: A total of 20,702 serum samples, covering 12 cancer types and consisting of 7,768 cancer samples and 12,934 cancer-free samples were used in this study. A m7G target miRNA diagnostic signature (m7G-miRDS) was established through the least absolute shrinkage and selection operator (LASSO) analyses in a training dataset (n = 10,351), and validated in a validation dataset (n = 10,351). Results: The m7G-miRDS model, a 12 m7G-target-miRNAs signature, demonstrated high accuracy and was qualified for cancer detection. In the training and validation cohort, the area under the curve (AUC) reached 0.974 (95% CI 0.971-0.977) and 0.972 (95% CI 0.969-0.975), respectively. The m7G-miRDS showed superior sensitivity in each cancer type and had a satisfactory AUC in identifying bladder cancer, lung cancer and esophageal cancer. Additionally, the diagnostic performance of m7G-miRDS was not interfered by the gender, age and benign disease. Conclusion: Our results greatly extended the value of serum circulating miRNAs and m7G in cancer detection, and provided a new direction and strategy for the development of novel biomarkers with high accuracy, low cost and less invasiveness for mass cancer screening, such as ncRNA modification.

Characterization of circulating miRNAs in the treatment of primary liver tumors

BACKGROUND AND AIM

Circulating micro RNAs (miRNAs) indicate clinical pathologies such as inflammation and carcinogenesis. In this study, we aimed to investigate whether miRNA expression level patterns in could be used to diagnose hepatocellular carcinoma (HCC) and biliary tract cancer (BTC), and the relationship miRNA expression patterns and cancer etiology.

METHODS

Patients with HCC and BTC with indications for surgery were selected for the study. Total RNA was extracted from the extracellular vesicle (EV)-rich fraction of the serum and analyzed using Toray miRNA microarray. Samples were divided into two cohorts in order of collection, the first 85 HCC were analyzed using a microarray based on miRBase ver.2.0 (hereafter v20 cohort), and the second 177 HCC and 43 BTC were analyzed using a microarray based on miRBase ver.21 (hereafter v21 cohort).

RESULTS

Using miRNA expression patterns, we found that HCC and BTC could be identified with an area under curve (AUC) 0.754 (v21 cohort). Patients with anti-hepatitis C virus (HCV) treatment (SVR-HCC) and without antiviral treatment (HCV-HCC) could be distinguished by an AUC 0.811 (v20 cohort) and AUC 0.798 (v21 cohort), respectively.

CONCLUSIONS

In this study, we could diagnose primary hepatic malignant tumor using miRNA expression patterns. Moreover, the difference of miRNA expression in SVR-HCC and HCV-HCC can be important information for enclosing cases that are prone to carcinogenesis after being cured with antiviral agents, but also for uncovering the mechanism for some carcinogenic potential remains even after persistent virus infection has disappeared.

Hepatocellular Carcinoma Genetic Classification

ABSTRACT

Hepatocellular carcinoma (HCC) represents a significant global burden, with management complicated by its heterogeneity, varying presentation, and relative resistance to therapy. Recent advances in the understanding of the genetic, molecular, and immunological underpinnings of HCC have allowed a detailed classification of these tumors, with resultant implications for diagnosis, prognostication, and selection of appropriate treatments. Through the correlation of genomic features with histopathology and clinical outcomes, we are moving toward a comprehensive and unifying framework to guide our diagnostic and therapeutic approach to HCC.

Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta

Introduction

In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult.

Methods

By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost.

Results

Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.

Risk stratification and early detection biomarkers for precision HCC screening

Hepatocellular carcinoma (HCC) mortality remains high primarily due to late diagnosis as a consequence of failed early detection. Professional societies recommend semi-annual HCC screening in at-risk patients with chronic liver disease to increase the likelihood of curative treatment receipt and improve survival. However, recent dynamic shift of HCC etiologies from viral to metabolic liver diseases has significantly increased the potential target population for the screening, whereas annual incidence rate has become substantially lower. Thus, with the contemporary HCC etiologies, the traditional screening approach might not be practical and cost-effective. HCC screening consists of (i) definition of rational at-risk population, and subsequent (ii) repeated application of early detection tests to the population at regular intervals. The suboptimal performance of the currently available HCC screening tests highlights an urgent need for new modalities and strategies to improve early HCC detection. In this review, we overview recent developments of clinical, molecular, and imaging-based tools to address the current challenge, and discuss conceptual framework and approaches of their clinical translation and implementation. These encouraging progresses are expected to transform the current "one-size-fits-all" HCC screening into individualized precision approaches to early HCC detection and ultimately improve the poor HCC prognosis in the foreseeable future.

Extracellular Vesicles as Markers of Liver Function: Optimized Workflow for Biomarker Identification in Liver Disease

Liver diseases represent a significant global health burden, necessitating the development of reliable biomarkers for early detection, prognosis, and therapeutic monitoring. Extracellular vesicles (EVs) have emerged as promising candidates for liver disease biomarkers due to their unique cargo composition, stability, and accessibility in various biological fluids. In this study, we present an optimized workflow for the identification of EVs-based biomarkers in liver disease, encompassing EVs isolation, characterization, cargo analysis, and biomarker validation. Here we show that the levels of microRNAs miR-10a, miR-21, miR-142-3p, miR-150, and miR-223 were different among EVs isolated from patients with nonalcoholic fatty liver disease and autoimmune hepatitis. In addition, IL2, IL8, and interferon-gamma were found to be increased in EVs isolated from patients with cholangiocarcinoma compared with healthy controls. By implementing this optimized workflow, researchers and clinicians can improve the identification and utilization of EVs-based biomarkers, ultimately enhancing liver disease diagnosis, prognosis, and personalized treatment strategies.

Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs

BACKGROUND

Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability.

METHODS

We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs.

RESULTS

Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer's disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015).

CONCLUSIONS

The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.

Updating the Clinical Application of Blood Biomarkers and Their Algorithms in the Diagnosis and Surveillance of Hepatocellular Carcinoma: A Critical Review

The most common primary liver cancer is hepatocellular carcinoma (HCC), and its mortality rate is increasing globally. The overall 5-year survival of patients with liver cancer is currently 10-20%. Moreover, because early diagnosis can significantly improve prognosis, which is highly correlated with tumor stage, early detection of HCC is critical. International guidelines advise using α-FP biomarker with/without ultrasonography for HCC surveillance in patients with advanced liver disease. However, traditional biomarkers are sub-optimal for risk stratification of HCC development in high-risk populations, early diagnosis, prognostication, and treatment response prediction. Since about 20% of HCCs do not produce α-FP due to its biological diversity, combining α-FP with novel biomarkers can enhance HCC detection sensitivity. There is a chance to offer promising cancer management methods in high-risk populations by utilizing HCC screening strategies derived from new tumor biomarkers and prognostic scores created by combining biomarkers with distinct clinical parameters. Despite numerous efforts to identify molecules as potential biomarkers, there is no single ideal marker in HCC. When combined with other clinical parameters, the detection of some biomarkers has higher sensitivity and specificity in comparison with a single biomarker. Therefore, newer biomarkers and models, such as the Lens culinaris agglutinin-reactive fraction of Alpha-fetoprotein (α-FP), α-FP-L3, Des-γ-carboxy-prothrombin (DCP or PIVKA-II), and the GALAD score, are being used more frequently in the diagnosis and prognosis of HCC. Notably, the GALAD algorithm was effective in HCC prevention, particularly for cirrhotic patients, regardless of the cause of their liver disease. Although the role of these biomarkers in surveillance is still being researched, they may provide a more practical alternative to traditional imaging-based surveillance. Finally, looking for new diagnostic/surveillance tools may help improve patients' survival. This review discusses the current roles of the most used biomarkers and prognostic scores that may aid in the clinical management of HCC patients.

Liquid Biopsy for Oral Cancer Diagnosis: Recent Advances and Challenges

"Liquid biopsy" is an efficient diagnostic tool used to analyse biomaterials in human body fluids, such as blood, saliva, breast milk, and urine. Various biomaterials derived from a tumour and its microenvironment are released into such body fluids and contain important information for cancer diagnosis. Biomaterial detection can provide "real-time" information about individual tumours, is non-invasive, and is more repeatable than conventional histological analysis. Therefore, over the past two decades, liquid biopsy has been considered an attractive diagnostic tool for malignant tumours. Although biomarkers for oral cancer have not yet been adopted in clinical practice, many molecular candidates have been investigated for liquid biopsies in oral cancer diagnosis, such as the proteome, metabolome, microRNAome, extracellular vesicles, cell-free DNAs, and circulating tumour cells. This review will present recent advances and challenges in liquid biopsy for oral cancer diagnosis.

MicroRNA-217 aggravates breast cancer through activation of NF1-mediated HSF1/ATG7 axis and c-Jun/ATF3/MMP13 axis

Application of microRNA-mediated mRNA expression in treatment of diverse cancers has been documented. The current study was explored to study the role of miR-217 in breast cancer (BC) progression and the related downstream factors. Clinical tissue samples, BC cell lines and the established xenograft models were prepared for ectopic expression and depletion experiments to discern the regulatory roles of miR-217-mediated NF1 in BC cell proliferation, metastasis and chemoresistance as well as tumorigenic ability of BC cells in nude mice. miR-217 was upregulated in BC, which was a predictor of poor prognosis of BC patients. NF1 could be targeted by miR-217. miR-217 promoted malignant characteristics of BC cells through enhancing ATF3-MMP13 interaction by inhibiting NF1. miR-217 repressed sensitivity against anti-cancer drugs by inducing autophagy of BC cells through the NF1/HSF1/ATG7 axis. Also, miR-217 could inhibit NF1 to facilitate tumorigenic ability of BC cells in vivo. Our study emphasized that miR-217 could potentially inhibit NF1 expression to activate the c-Jun, thus enhancing the expression and interaction of ATF3/MMP13 and promoting the malignant features of BC cells. Furthermore, miR-217 conferred chemoresistance on BC by enhancing BC cell autophagy, which was achieved by limiting NF1 expression to induce the HSF1/ATG7 pathway.

Integrated Predictors by Propensity Scoring With Tumor Markers and Plasma Levels of microRNA-21-5p, IL-17, and IL-10 Complement Early Detection of Hepatocellular Carcinoma in Patients With Liver Cirrhosis

Objectives: The clinical usefulness of tumor markers alpha-fetoprotein (AFP) and des-gamma carboxyprothrombin (DCP) in the early detection of hepatocellular carcinoma (HCC) in patients with liver cirrhosis (LC), including those with marker decline after antiviral therapy, is limited. MicroRNAs (miRNAs) are expected to complement detection; however, their details remain unknown. Our prospective pilot study aimed to improve the surveillance of HCC high-risk LC patients by propensity scoring with tumor markers and additional predictors. Methods: Tumor markers and plasma levels of cytokines and miRNAs were observationally measured and statistically evaluated with propensity scoring in 85 eligible patients: 43 with current HCC (cHCC) including 8 with early-HCC, 22 with previous HCC cured (pHCC), and 20 with intact LC (iLC). Results: The analysis of the area under the receiver operating characteristic curve (AUC) showed that the best single predictor was AFP (0.794 for cHCC-discrimination and 0.771 for pHCC-discrimination). AFP-DCP integrated with miR-21-5p for cHCC-discrimination was 0.896; with IL-10 for pHCC-discrimination was 0.872, these were significantly better than those of AFP alone, independently (P < .01). The best single predictor for iLC-discrimination was IL-17 level (0.756). IL-17 integrated with AFP-DCP was 0.882, which was significantly better than that of IL-17 alone (P < .01). The positive likelihood ratio (pLR) for cHCC-discrimination by integration of AFP-DCP and miR-21-5p was 32.2. Preliminary validation analysis of early-HCCs compared to conventional AFP and DCP showed the combinations of AFP-DCP and 3 integrated predictors, miR-21-5p for cHCC-discrimination, IL-10 for pHCC-discrimination, and IL-17 for iLC-discrimination, sensitivity, specificity, and pLR, improved from 37.5% to 62.5%, 55.8% to 83.1%, and 0.85 to 3.70, respectively. Conclusion: The predictors of AFP-DCP combined with iR-21-5p, IL-10, and IL-17 by propensity scoring achieved higher discrimination of cHCCs, pHCCs, and iLCs, may be beneficial for the surveillance of early-HCCs, improving prediction of early-HCCs over conventional methods. However, further validation is required.

Circulating MicroRNAs: Diagnostic Value as Biomarkers in the Detection of Non-alcoholic Fatty Liver Diseases and Hepatocellular Carcinoma

Non-alcoholic fatty liver disease (NAFLD), a metabolic-related disorder, is the most common cause of chronic liver disease which, if left untreated, can progress from simple steatosis to advanced fibrosis and eventually cirrhosis or hepatocellular carcinoma, which is the leading cause of hepatic damage globally. Currently available diagnostic modalities for NAFLD and hepatocellular carcinoma are mostly invasive and of limited precision. A liver biopsy is the most widely used diagnostic tool for hepatic disease. But due to its invasive procedure, it is not practicable for mass screening. Thus, noninvasive biomarkers are needed to diagnose NAFLD and HCC, monitor disease progression, and determine treatment response. Various studies indicated that serum miRNAs could serve as noninvasive biomarkers for both NAFLD and HCC diagnosis because of their association with different histological features of the disease. Although microRNAs are promising and clinically useful biomarkers for hepatic diseases, larger standardization procedures and studies are still required.

Multiomics analyses and machine learning of nuclear receptor coactivator 6 reveal its essential role in hepatocellular carcinoma

Nuclear receptor coactivator 6 (NCOA6), a coactivator of numerous nuclear receptors and transcription factors, regulates multiple critical cellular functions. Nuclear receptor coactivator 6 is dysregulated in various cancers, including hepatocellular carcinoma (HCC); however, its role remains largely unknown. Here we reported that NCOA6 was highly expressed in HCC compared to the adjacent liver tissue, and NCOA6 overexpression was significantly correlated with poor HCC prognosis. Experiments revealed that the knockdown of NCOA6 damaged the proliferation, migration, and invasion of HCC cells. Multiomics and immune infiltration analyses showed a close relationship between NCOA6 expression, multiple cancer-related malignant pathways, and the immunosuppressive microenvironment. Finally, we established an effective NCOA6-related microRNA (miRNA) signature to distinguish HCC from hepatitis\liver cirrhosis patients. To the best of our knowledge, this study is the first to provide a comprehensive analysis of NCOA6 expression in HCC. We found that NCOA6 plays an important role in HCC development and has a potential mechanism of action. Establishing an NCOA6-related miRNA signature will help develop novel diagnostic strategies for HCC patients.

Immunotherapy for nonalcoholic fatty liver disease-related hepatocellular carcinoma: Lights and shadows

About one-fourth of adults globally suffer from nonalcoholic fatty liver disease (NAFLD), which is becoming a leading cause of chronic liver disease worldwide. Its prevalence has rapidly increased in recent years, and is projected to increase even more. NAFLD is a leading cause of hepatocellular carcinoma (HCC), the sixth-most prevalent cancer worldwide and the fourth most common cause of cancer-related death. Although the molecular basis of HCC onset in NAFLD is not completely known, inflammation is a key player. The tumor microenvironment (TME) is heterogeneous in patients with HCC, and is characterized by complex interactions between immune system cells, tumor cells and other stromal and resident liver cells. The etiology of liver disease plays a role in controlling the TME and modulating the immune response. Markers of immune suppression in the TME are associated with a poor prognosis in several solid tumors. Immunotherapy with immune checkpoint inhibitors (ICIs) has become the main option for treating cancers, including HCC. However, meta-analyses have shown that patients with NAFLD-related HCC are less likely to benefit from therapy based on ICIs alone. Conversely, the addition of an angiogenesis inhibitor showed better results regarding the objective response rate and progression-free survival. Adjunctive diagnostic and therapeutic strategies, such as the application of novel biomarkers and the modulation of gut microbiota, should be considered in the future to guide personalized medicine and improve the response to ICIs in patients with NAFLD-related HCC.

A K-nearest Neighbor Model to Predict Early Recurrence of Hepatocellular Carcinoma After Resection

BACKGROUND AND AIMS

Patients with hepatocellular carcinoma (HCC) surgically resected are at risk of recurrence; however, the risk factors of recurrence remain poorly understood. This study intended to establish a novel machine learning model based on clinical data for predicting early recurrence of HCC after resection.

METHODS

A total of 220 HCC patients who underwent resection were enrolled. Classification machine learning models were developed to predict HCC recurrence. The standard deviation, recall, and precision of the model were used to assess the model's accuracy and identify efficiency of the model.

RESULTS

Recurrent HCC developed in 89 (40.45%) patients at a median time of 14 months from primary resection. In principal component analysis, tumor size, tumor grade differentiation, portal vein tumor thrombus, alpha-fetoprotein, protein induced by vitamin K absence or antagonist-II (PIVKA-II), aspartate aminotransferase, platelet count, white blood cell count, and HBsAg were positive prognostic factors of HCC recurrence and were included in the preoperative model. After comparing different machine learning methods, including logistic regression, decision tree, naïve Bayes, deep neural networks, and k-nearest neighbor (K-NN), we choose the K-NN model as the optimal prediction model. The accuracy, recall, precision of the K-NN model were 70.6%, 51.9%, 70.1%, respectively. The standard deviation was 0.020.

CONCLUSIONS

The K-NN classification algorithm model performed better than the other classification models. Estimation of the recurrence rate of early HCC can help to allocate treatment, eventually achieving safe oncological outcomes.

Identification of four serum miRNAs as potential markers to screen for thirteen cancer types

Introduction

Cancer consistently remains one of the top causes of death in the United States every year, with many cancer deaths preventable if detected early. Circulating serum miRNAs are a promising, minimally invasive supplement or even an alternative to many current screening procedures. Many studies have shown that different serum miRNAs can discriminate healthy individuals from those with certain types of cancer. Although many of those miRNAs are often reported to be significant in one cancer type, they are also altered in other cancer types. Currently, very few studies have investigated serum miRNA biomarkers for multiple cancer types for general cancer screening purposes.

Method

To identify serum miRNAs that would be useful in screening multiple types of cancers, microarray cancer datasets were curated, yielding 13 different types of cancer with a total of 3352 cancer samples and 2809 non-cancer samples. The samples were divided into training and validation sets. One hundred random forest models were built using the training set to select candidate miRNAs. The selected miRNAs were then used in the validation set to see how well they differentiate cancer from normal samples in an independent dataset. Furthermore, the interactions between these miRNAs and their target mRNAs were investigated.

Result

The random forest models achieved an average of 97% accuracy in the training set with 95% bootstrap confidence interval of 0.9544 to 0.9778. The selected miRNAs were hsa-miR-663a, hsa-miR-6802-5p, hsa-miR-6784-5p, hsa-miR-3184-5p, and hsa-miR-8073. Each miRNA exhibited high area under the curve (AUC) value using receiver operating characteristic analysis. Moreover, the combination of four out of five miRNAs achieved the highest AUC value of 0.9815 with high sensitivity of 0.9773, indicating that these miRNAs have a high potential for cancer screening. miRNA-mRNA and protein-protein interaction analysis provided insights into how these miRNAs play a role in cancer.

Early detection of hepatocellular carcinoma: roadmap for improvement

INTRODUCTION

Hepatocellular carcinoma (HCC) has a poor prognosis, related, in part, to frequent late-stage diagnosis. Improved implementation of effective HCC surveillance is critical to reduce HCC mortality.

AREAS COVERED

We performed a targeted literature review to identify intervention targets for improving HCC surveillance effectiveness, including enriched risk stratification tools, improved surveillance tools with higher accuracy for early HCC detection, and increasing surveillance adherence.

EXPERT OPINION

HCC surveillance has been demonstrated to be efficacious in several cohort studies but has lower surveillance effectiveness in clinical practice. HCC surveillance is currently recommended in all patients with cirrhosis, and improved risk stratification using clinical risk scores, genetic scores, and novel biomarkers are important to move from a 'one-size-fits-all' strategy to one more aligned with values of precision medicine. Current surveillance modalities, ultrasound, and AFP, miss over one-third of HCC at an early stage and are associated with potential surveillance harms, underscoring a need for alternative surveillance strategies with higher accuracy. MRI- and biomarker-based surveillance strategies have promising early data in phase II studies but require validation in phase III cohorts before routine use in practice. Finally, surveillance is underused in clinical practice, highlighting a need for intervention strategies to increase utilization.

Development and Validation of a Novel Circulating miRNA-Based Diagnostic Score for Early Detection of Hepatocellular Carcinoma

BACKGROUND

With the rise of liquid biopsy in oncology, circulating miRNAs have become one of the most promising noninvasive biomarkers for early detection of hepatocellular carcinoma (HCC). However, a reliable HCC-related circulating miRNA panel and corresponding diagnostic model remain to be explored.

METHODS

Five large public datasets related to intact miRNA profiles in the serum or tumors of HCC patients were included and divided into training cohorts (GSE113740 and TCGA-LIHC) and validation cohorts (GSE112264, GSE113486 and GSE106817). Compared with non-cancer controls and high-risk patients, key miRNAs dysregulated in both the serum and tumors of HCC patients were identified by differential expression analysis and overlapping analysis. The corresponding diagnostic model was constructed by LASSO logistic regression and evaluated by receiver operating characteristic curves and a nomogram with calibration plot.

RESULTS

A distinctive panel of HCC-related circulating miRNAs, including three upregulated miRNAs (miR-184, miR-532-5p, miR-221-3p) and three downregulated miRNAs (miR-5589-5p, let-7b-3p, miR-26b-3p), were rigorously screened out, all of which displayed significant discriminability between HCC patients and controls (all P < 0.05). In addition, a reliable six-circulating miRNA-based diagnostic score was constructed and displayed robust diagnostic ability for HCC (particularly for early-stage HCC) (AUC = 0.9535, P < 0.05) compared with that of the serum α-fetoprotein test. Importantly, its efficacy was sufficiently validated in three independent datasets (AUC = 0.9780/0.9961/0.9681, all P < 0.05). Furthermore, a visual nomogram based on the diagnostic score was correspondingly established to strengthen its clinical applicability.

CONCLUSION

The six-circulating miRNA-based diagnostic score may be a reliable noninvasive biomarker for early-stage HCC screening and dynamic monitoring of postoperative recurrence.

Biomarker Development Using Liquid Biopsy in Hepatocellular Carcinoma

Liver cancer incidence rate continues to increase and currently ranks third in the total number of annual deaths, behind only lung and colorectal cancer. Most patients with hepatocellular carcinoma (HCC) are diagnosed at advanced stages, and they live for less than 2 years after diagnosis on average. This contrasts with those diagnosed at an early stage, who can be cured with surgery. However, even after curative resection, there remains a risk of up to 70% of postoperative HCC recurrence. There have been major changes in the management of HCC in the past 5 years, particularly for patients at advanced stages. Despite this multitude of new therapies, there is a lack of clear biomarkers to guide providers on the best approach to sequence therapies, which would maximize efficacy while minimizing toxicity. There are several areas in clinical management of HCC that are particularly challenging, and would benefit from development and implementation of new biomarkers to improve patient overall survival. Here, we review the major advances in liquid biopsy biomarkers for early detection of HCC, minimum residual disease, and predicting response to treatment.

Fast and robust imputation for miRNA expression data using constrained least squares

BACKGROUND

High dimensional transcriptome profiling, whether through next generation sequencing techniques or high-throughput arrays, may result in scattered variables with missing data. Data imputation is a common strategy to maximize the inclusion of samples by using statistical techniques to fill in missing values. However, many data imputation methods are cumbersome and risk introduction of systematic bias.

RESULTS

We present a new data imputation method using constrained least squares and algorithms from the inverse problems literature and present applications for this technique in miRNA expression analysis. The proposed technique is shown to offer an imputation orders of magnitude faster, with greater than or equal accuracy when compared to similar methods from the literature.

CONCLUSIONS

This study offers a robust and efficient algorithm for data imputation, which can be used, e.g., to improve cancer prediction accuracy in the presence of missing data.

Epigenetic remodelling in human hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer, being the sixth most commonly diagnosed cancer and the fourth leading cause of cancer-related death. As other heterogeneous solid tumours, HCC results from a unique synergistic combination of genetic alterations mixed with epigenetic modifications.In HCC the patterns and frequencies of somatic variations change depending on the nearby chromatin. On the other hand, epigenetic alterations often induce genomic instability prone to mutations. Epigenetics refers to heritable states of gene expression without alteration to the DNA sequence itself and, unlike genetic changes, the epigenetic modifications are reversible and affect gene expression more extensively than genetic changes. Thus, studies of epigenetic regulation and the involved molecular machinery are greatly contributing to the understanding of the mechanisms that underline HCC onset and heterogeneity. Moreover, this knowledge may help to identify biomarkers for HCC diagnosis and prognosis, as well as future new targets for more efficacious therapeutic approaches.In this comprehensive review we will discuss the state-of-the-art knowledge about the epigenetic landscape in hepatocarcinogenesis, including evidence on the diagnostic and prognostic role of non-coding RNAs, modifications occurring at the chromatin level, and their role in the era of precision medicine.Apart from other better-known risk factors that predispose to the development of HCC, characterization of the epigenetic remodelling that occurs during hepatocarcinogenesis could open the way to the identification of personalized biomarkers. It may also enable a more accurate diagnosis and stratification of patients, and the discovery of new targets for more efficient therapeutic approaches.

A Novel Blood-Based microRNA Diagnostic Model with High Accuracy for Multi-Cancer Early Detection

Early detection is critical to reduce cancer deaths as treating early stage cancers is more likely to be successful. However, patients with early stage diseases are often asymptomatic and thus less likely to be diagnosed. Here, we utilized four microarray datasets with a standardized platform to investigate comprehensive microRNA expression profiles from 7536 serum samples. A 4-miRNA diagnostic model was developed from the lung cancer training set (n = 416, 208 lung cancer patients and 208 non-cancer participants). The model showed 99% sensitivity and specificity in the lung cancer validation set (n = 3328, 1358 cancer patients and 1970 non-cancer participants); and the sensitivity remained to be >99% for patients with stage 1 disease. When applied to the additional combined dataset of 3792 participants including 2038 cancer patients across 12 different cancer types and 1754 independent non-cancer controls, the model demonstrated high sensitivities ranging from 83.2 to 100% for biliary tract, bladder, colorectal, esophageal, gastric, glioma, liver, pancreatic, and prostate cancers, and showed reasonable sensitivities of 68.2 and 72.0% for ovarian cancer and sarcoma, respectively, while maintaining 99.3% specificity. Our study provided a proof-of-concept data in demonstrating that the 4-miRNA model has the potential to be developed into a simple, inexpensive and noninvasive blood test for early detection of multiple cancers with high accuracy.

m6A target microRNAs in serum for cancer detection

Recent studies have revealed the significant dysregulation of m⁶A level in peripheral blood in several cancer types and its value in diagnosis. Nonetheless, a biomarker for accurate screening of multiple cancer types has not been established based on the perspective of m⁶A modification. In this study, we aimed to develop a serum diagnostic signature based on the m⁶A target miRNAs for the mass detection of cancer. A total of 14965 serum samples with 12 cancer types were included. Based on training cohort (n=7299), we developed the m6A-miRNAs signature using a support vector machine algorithm for cancer detection. The m6A-miRNAs signature showed high accuracy, and its area under the curve (AUC) in the training, internal validation and external validation cohort reached 0.979 (95%CI 0.976 - 0.982), 0.976 (95%CI 0.973 - 0.979) and 0.936 (95%CI 0.922 - 0.951), respectively. In the performance of distinguishing cancer types, the m6A-miRNAs signature showed superior sensitivity in each cancer type and presented a satisfactory AUC in identifying lung cancer, gastric cancer and hepatocellular carcinoma. Additionally, the diagnostic performance of m6A-miRNAs was not interfered by the gender, age and benign disease. In short, this study revealed the value of serum circulating m⁶A miRNAs in cancer detection and provided a new direction and strategy for the development of novel biomarkers with high accuracy, low cost and less invasiveness for mass cancer screening, such as RNA modification.

Current and Emerging Tools for Hepatocellular Carcinoma Surveillance

Hepatocellular carcinoma (HCC) is a leading cause of cancer‐related mortality worldwide. Early detection of HCC enables patients to avail curative therapies that can improve patient survival. Current international guidelines advocate for the enrollment of patients at high risk for HCC, like those with cirrhosis, in surveillance programs that perform ultrasound every 6 months. In recent years, many studies have further characterized the utility of established screening strategies and have introduced new promising tools for HCC surveillance. In this review, we provide an overview of the most promising new imaging modalities and biomarkers for the detection of HCC. We discuss the role of imaging tools like ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI) in the early detection of HCC, and describe recent innovations which can potentially enhance their applicability, including contrast enhanced ultrasound, low‐dose CT scans, and abbreviated MRI. Next, we outline the data supporting the use of three circulating biomarkers (i.e., alpha‐fetoprotein [AFP], AFP lens culinaris agglutinin‐reactive fraction, and des‐gamma‐carboxy prothrombin) in HCC surveillance, and expand on multiple emerging liquid biopsy biomarkers, including methylated cell‐free DNA (cfDNA), cfDNA mutations, extracellular vesicles, and circulating tumor cells. These promising new imaging modalities and biomarkers have the potential to improve early detection, and thus improve survival, in patients with HCC.

Liquid Biopsy in Hepatocellular Carcinoma: Opportunities and Challenges for Immunotherapy

Hepatocellular carcinoma (HCC) is one of the deadliest cancer types worldwide. HCC is often diagnosed at a late stage when the therapeutic options are very limited. However, even at the earlier stages, the best treatment is liver transplantation, surgical resection or ablation. Surgical resection and ablation may carry a high risk of tumor recurrence. The recent introduction of immunotherapies resulted in clinical responses for a subgroup of patients, but there were still no effective predictive markers for response to immunotherapy or for recurrence after surgical therapy. The identification of biomarkers that could correlate and predict response or recurrence would require close monitoring of the patients throughout and after the completion of treatment. However, this would not be performed efficiently by repeated and invasive tissue biopsies. A better approach would be to use liquid biopsies including circulating tumor DNA (ctDNA), circulating RNA (e.g., microRNAs), circulating tumor cells (CTC) and extracellular vesicles (EVs) (e.g., exosomes) for disease monitoring in a non-invasive manner. In this review, we discuss the currently available technology that can enable the use of liquid biopsy as a diagnostic and prognostic tool. Moreover, we discuss the opportunities and challenges of the clinical application of liquid biopsy for immunotherapy of HCC.

Non-Coding RNA-Based Biosensors for Early Detection of Liver Cancer

Primary liver cancer is an aggressive, lethal malignancy that ranks as the fourth leading cause of cancer-related death worldwide. Its 5-year mortality rate is estimated to be more than 95%. This significant low survival rate is due to poor diagnosis, which can be referred to as the lack of sufficient and early-stage detection methods. Many liver cancer-associated non-coding RNAs (ncRNAs) have been extensively examined to serve as promising biomarkers for precise diagnostics, prognostics, and the evaluation of the therapeutic progress. For the simple, rapid, and selective ncRNA detection, various nanomaterial-enhanced biosensors have been developed based on electrochemical, optical, and electromechanical detection methods. This review presents ncRNAs as the potential biomarkers for the early-stage diagnosis of liver cancer. Moreover, a comprehensive overview of recent developments in nanobiosensors for liver cancer-related ncRNA detection is provided.

Identification of serum prognostic marker miRNAs and construction of microRNA-mRNA networks of esophageal cancer

Esophageal cancer is a common tumor of the digestive system with poor prognosis. This study was to gain a better understanding of the mechanisms involved in esophageal cancer and to identify new prognostic markers. We downloaded the esophageal cancer miRNA expression profile microarray data (GSE113740, GSE112264, GSE122497, GSE113486, and GSE106817) from the GEO database, extracted the esophageal cancer miRNA sequencing data from The Cancer Genome Atlas (TCGA) database, and then used a bioinformatics approach to select common differentially expressed miRNAs (DEMs). Differentially expressed genes (DEGs) were selected by predicting DEM target genes using the miRWalk database and intersecting with differential genes obtained from TCGA database for esophageal cancer. The STRING database was used to obtain protein-protein interaction (PPI) relationships to construct the DEM-DEG network. Furthermore, we selected core genes and core miRNAs associated with esophageal cancer prognosis by performing survival and univariate/multivariate COX analysis on DEMs and DEGs in the network and performed GSEA analysis on core genes alone, and finally the expression of the markers was verified by qPCR in esophageal cancer cell lines Eca109, SKGT-4 and normal esophageal epithelial cells HEEC. Nine DEMs were obtained, of which three were upregulated and six were downregulated, and 326 DEGs were obtained, of which 105 were upregulated and 221 were downregulated. Survival univariate/multivariate COX analysis revealed that five genes, ZBTB16, AQP4, ADCYAP1R1, PDGFD, and VIPR2, and two microRNAs, miR-99a-5p, and miR-508-5p, were related to esophageal cancer prognosis. GSEA analysis showed that the following genes may be involved in esophageal cancer prognosis: ZBTB16 may through the MTOR signaling pathway, AQP4 through the GNRH signaling pathway, ADCYAP1R1 through the PPAR signaling pathway, VIPR2 through the P53 signaling pathway and PDGFD through the PENTOSE-PHOSPHATE signaling pathway.

Liquid Biopsy in Hepatocellular Carcinoma: Where Are We Now?

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer related death worldwide. Diagnostic, prognostic, and predictive biomarkers are urgently needed in order to improve patient survival. Indeed, the most widely used biomarkers, such as alpha-fetoprotein (AFP), have limited accuracy as both diagnostic and prognostic tests. Liver biopsy provides an insight on the biology of the tumor, but it is an invasive procedure, not routinely used, and not representative of the whole neoplasia due to the demonstrated intra-tumoral heterogeneity. In recent years, liquid biopsy, defined as the molecular analysis of cancer by-products, released by the tumor in the bloodstream, emerged as an appealing source of new biomarkers. Several studies focused on evaluating extracellular vesicles, circulating tumor cells, cell-free DNA and non-coding RNA as novel reliable biomarkers. In this review, we aimed to provide a comprehensive overview on the most relevant available evidence on novel circulating biomarkers for early diagnosis, prognostic stratification, and therapeutic monitoring. Liquid biopsy seems to be a very promising instrument and, in the near future, some of these new non-invasive tools will probably change the clinical management of HCC patients.

miR-33a-5p in small extracellular vesicles as non-invasive biomarker for oxaliplatin sensitivity in human colorectal cancer cells

microRNAs (miRNAs) contained in small extracellular vesicles (sEVs) are candidates for non-invasive biomarkers. Oxaliplatin (L-OHP) has been approved for advanced colorectal cancer (CRC) chemotherapy. However, the response to L-OHP differs among CRC patients. In addition, CRC cells often acquire the resistance to L-OHP. This study aimed at the prediction of L-OHP sensitivity by measuring extracellular miRNAs levels. Firstly, we compared intracellular miRNAs expressions in L-OHP-sensitive CRC cells (SW620 and HCT116 cells) with those in acquired and intrinsic L-OHP-resistant cells. In microarray and real-time RT-PCR analyses, the intracellular miR-33a-5p, miR-210–3p, and miR-224–5p expressions were lower in acquired and intrinsic L-OHP-resistant CRC cells than sensitive cells. Furthermore, in SW620 cells, L-OHP sensitivity was decreased by miR-33a-5p inhibitor. On the other hand, miR-210–3p or miR-224–5p inhibitor did not affect L-OHP sensitivity in SW620 cells. Secondly, the amount of miR-33a-5p, miR-210–3p, and miR-224–5p in sEVs was compared. The amount of miR-33a-5p and miR-210–3p in sEVs secreted from acquired and intrinsic L-OHP-resistant cells tended to be small. miR-224–5p was not detected in sEVs secreted from three types of CRC cells examined. To the best of our knowledge, this is the first study demonstrating that miR-33a-5p and/or miR-210–3p in sEVs would be candidates for biomarkers of L-OHP sensitivity. In particular, miR-33a-5p is a promising candidate because it would be directly involved in L-OHP sensitivity.

•

miR-33a-5p was down-regulated in oxaliplatin-resistant colorectal cancer cells.

•

Reduction of intracellular miR-33a-5p expression caused oxaliplatin resistance.

•

miR-33a-5p amount in small extracellular vesicles from resistant cells was small.

•

miR-33a-5p is a candidate for non-invasive biomarker of oxaliplatin sensitivity.

Serum Biomarkers for the Prediction of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of global cancer death. Major etiologies of HCC relate to chronic viral infections as well as metabolic conditions. The survival rate of people with HCC is very low and has been attributed to late diagnosis with limited treatment options. Combining ultrasound and the biomarker alpha-fetoprotein (AFP) is currently one of the most widely used screening combinations for HCC. However, the clinical utility of AFP is controversial, and the frequency and operator-dependence of ultrasound lead to a variable degree of sensitivity and specificity across the globe. In this review, we summarize recent developments in the search for non-invasive serum biomarkers for early detection of HCC to improve prognosis and outcome for patients. We focus on tumor-associated protein markers, immune mediators (cytokines and chemokines), and micro-RNAs in serum or circulating extracellular vesicles and examine their potential for clinical application.

Circulating microRNA as predictors for exercise response in heart failure with reduced ejection fraction

AIMS

Exercise training is a powerful adjunctive therapy in patients with heart failure with reduced ejection fraction (HFrEF), but ca. 55% of patients fail to improve VO2peak. We hypothesize that circulating microRNAs (miRNAs), as epigenetic determinants of VO2peak, can distinguish exercise responders (ER) from exercise non-responders (ENR).

METHODS AND RESULTS

We analysed 377 miRNAs in 18 male HFrEF patients (9 ER and 9 ENR) prior to 15 weeks of exercise training using a miRNA array. ER and ENR were defined as change in VO2peak of >20% or <6%, respectively. First, unsupervised clustering analysis of the miRNA pattern was performed. Second, differential expression of miRNA in ER and ENR was analysed and related to percent change in VO2peak. Third, a gene set enrichment analysis was conducted to detect targeted genes and pathways. Baseline characteristics and training volume were similar between ER and ENR. Unsupervised clustering analysis of miRNAs distinguished ER from ENR with 83% accuracy. A total of 57 miRNAs were differentially expressed in ENR vs. ER. A panel of seven miRNAs up-regulated in ENR (Let-7b, miR-23a, miR-140, miR-146a, miR-191, miR-210, and miR-339-5p) correlated with %changeVO2peak (all P < 0.05) and predicted ENR with area under the receiver operating characteristic curves ≥0.77. Multiple pathways involved in exercise adaptation processes were identified.

CONCLUSION

A fingerprint of seven miRNAs involved in exercise adaptation processes is highly correlated with VO2peak trainability in HFrEF, which holds promise for the prediction of training response and patient-targeted exercise prescription.

Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning

Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction of recurrence after resection is clinically important. However, the pathological characteristics of the early recurrence of HCC have not yet been elucidated. We attempted to predict the early recurrence of HCC after resection based on digital pathologic images of hematoxylin and eosin-stained specimens and machine learning applying a support vector machine (SVM). The 158 HCC patients meeting the Milan criteria who underwent surgical resection were included in this study. The patients were categorized into three groups: Group I, patients with HCC recurrence within 1 year after resection (16 for training and 23 for test); Group II, patients with HCC recurrence between 1 and 2 years after resection (22 and 28); and Group III, patients with no HCC recurrence within 4 years after resection (31 and 38). The SVM-based prediction method separated the three groups with 89.9% (80/89) accuracy. Prediction of Groups I was consistent for all cases, while Group II was predicted to be Group III in one case, and Group III was predicted to be Group II in 8 cases. The use of digital pathology and machine learning could be used for highly accurate prediction of HCC recurrence after surgical resection, especially that for early recurrence. Currently, in most cases after HCC resection, regular blood tests and diagnostic imaging are used for follow-up observation; however, the use of digital pathology coupled with machine learning offers potential as a method for objective postoprative follow-up observation.

Hepatocellular Carcinoma-associated microRNAs Induced by Hepatoma-derived Growth Factor Stimulation

BACKGROUND/AIM

Hepatoma-derived growth factor (HDGF) is involved in the progression of hepatocellular carcinoma (HCC). The present study assessed the epigenomic changes in hepatoma-derived cells through HDGF stimulation.

MATERIALS AND METHODS

We used two hepatoma-derived cell lines (HepG2 and SK-Hep1) and searched for microRNAs whose expression commonly changed in response to HDGF administration. We further explored a genetic database to investigate the association of the candidate microRNAs with the survival of HCC patients.

RESULTS

Despite both HepG2 and SK-Hep1 cells being categorized as hepatoma-derived cells, the microRNA profile differed between these two lines. However, HepG2 and SK-Hep1 cells shared 30 up-regulated and 2 down-regulated microRNAs. Of these, miR-6072 and miR-3137 were significantly associated with a poor prognosis in HCC patients.

CONCLUSION

We identified two candidate microRNAs whose expression increased in response to HDGF stimulation. Both these molecules were associated with a poor prognosis of HCC patients.