Prediction of tissue-of-origin of early stage cancers using serum miRNomes

Environmental factors in gastric carcinogenesis and preventive intervention strategies

Gastric cancer, a significant global health concern, arises from a complex interplay of genetic and environmental factors. Helicobacter pylori (H. pylori) infection is a major risk factor that can be mitigated through eradication strategies. Epstein-Barr virus (EBV) infection causes a distinct subtype of gastric cancer called EBV-associated gastric cancer. The gastric microbiome, a dynamic ecosystem, is also involved in carcinogenesis, particularly dysbiosis and specific bacterial species such as Streptococcus anginosus. Long-term use of proton pump inhibitors and potassium-competitive acid blockers also increases the risk of gastric cancer, whereas non-steroidal anti-inflammatory drugs including aspirin may have a protective effect. Smoking significantly increases the risk, and cessation can reduce it. Dietary factors such as high intake of salt, processed meats, and red meat may increase the risk, whereas a diet rich in fruits and vegetables may be protective. Extracellular vesicles, which are small membrane-bound structures released by cells, modulate the tumor microenvironment and may serve as biomarkers for risk stratification and as therapeutic targets in gastric cancer. This review highlights the multifaceted etiology of gastric cancer and its risk factors and emphasizes the importance of a multi-pronged approach to prevention including H. pylori eradication and modification of lifestyle factors, as well as the potential of microbiome-based and EV-based interventions. Further research is needed to refine risk stratification and to develop personalized prevention strategies.

PCR- and wash-free detection of serum miRNA via signaling probe hybridization

Detection of microRNAs (miRNAs) in the serum is an effective liquid biopsy technique for cancer diagnosis. However, conventional diagnostic methods are time-consuming and complex. Therefore, in this study, we established a signaling probe-based DNA microarray system for miRNA detection. PCR, fluorescence labeling, and washing are not necessary for signaling probes. Four probes were designed using different miRNAs as diagnostic cancer markers. The developed system is useful for various miRNAs, regardless of their target lengths (18-26-mer) and GC content (36%-89%). Here, all the assays were performed within 40 min. Overall, our signaling probe-based DNA hybridization system facilitates the simple and rapid detection of serum miRNAs without the need for gene amplification, fluorescence labeling and washing.

BHBA-GRNet: Cancer detection through improved gene expression profiling using Binary Honey Badger Algorithm and Gene Residual-based Network

Cancer, a pervasive and devastating disease, remains a leading global cause of mortality, emphasizing the growing urgency for effective detection methods. Gene Expression Microarray (GEM) data has emerged as a crucial tool in this context, offering insights into early cancer detection and treatment. While deep learning methods offer promise in detecting various cancers through GEM analysis, they suffer from high dimensionality inherent in gene sequences, preventing optimal detection performance across diverse cancer types. Additionally, existing methods often resort to synthetic features and data augmentation to enhance performance. To address these challenges and enhance accuracy, a novel Binary Honey Badger Algorithm (BHBA) integrated with the Gene Residual Network (GRNet) method has been proposed. Our approach capitalizes on BHBA's feature reduction mechanism, eliminating the need for additional preprocessing steps. Comprehensive evaluations on three well-established datasets representing lung and blood-type cancers demonstrate that our method reduces GEM data size by approximately 40 % and achieves a superior accuracy improvement of around 1 % in lung cancer types compared to state-of-the-art methods.

Prospects for liquid biopsy using microRNA and extracellular vesicles in breast cancer

Among the analytes circulating in body fluids, microRNAs, a type of non-coding RNA and known to exist 2655 in primates, have attracted attention as a novel biomarker for cancer screening. MicroRNAs are signaling molecules with important gene expression regulatory functions that can simultaneously control many gene functions and multiple different pathways in living organisms. These microRNAs are transported in extracellular vesicles (EVs), which are lipid bilayers with 50-150 nm in diameter, and are used as communication tools between cells. Furthermore, the EVs that carry these microRNAs circulate in the bloodstream and have other important implications for understanding the pathogenesis and diagnosis of breast cancer. The greatest benefit from cancer screening is the reduction in breast cancer mortality rate through early detection. Other benefits include reduced incidence of breast cancer, improved quality of life, prognosis prediction, contribution to personalized medicine, and relative healthcare cost containment. This paper outlines the latest developments in liquid biopsy for breast cancer, especially focusing on microRNA and EV diagnostics.

Analysis of the Effects of Short-Term Pterostilbene Intake on Healthy Participants: A Pilot Study

Pterostilbene, a polyphenolic compound and an analog of resveratrol, exerts various biological activities and has higher bioavailability and metabolic stability than resveratrol. However, the effectiveness of pterostilbene intake in humans, particularly its effect on blood microRNA (miRNA) expression levels, has not been evaluated. Accordingly, this pilot study aimed to investigate the effects of pterostilbene on blood biochemistry and blood miRNA expression levels and the safety of continuous intake at doses of 10 or 100 mg/d over 12 wk. A double-blind, placebo-controlled parallel-arm comparison trial was conducted with 30 healthy men. In the analysis of blood miRNA expression levels, miR-34a and miR-193b showed very high increases at week 4 and after week 4 of intake, respectively, suggesting that the responders might be present among participants in the pterostilbene intake group. No adverse events were reported during the trial in any participant, and no abnormalities were observed upon examination by the responsible physician. Thus, pterostilbene intake would regulate blood miRNA expression levels, and the results can be utilized in human studies investigating miRNA expression levels with functional food ingredients.

Evaluation of Plasma microRNA-222 as a Biomarker for Gastric Cancer

Background: The dysregulation of microRNAs (miRNAs) has been detected in patients with gastric cancer (GC), which inspired the use of miRNAs as a novel biomarker for GC. In this study, we investigated the previously reported miRNA dysfunction in cancer tissues as a potential plasma biomarker for GC using quantitative reverse transcriptase polymerase chain reaction (RT-PCR). Methods: The published miRNA abnormalities were searched in the microRNA Cancer Association Database. Plasma samples were collected from patients with GC (n = 26) and controls (n = 17). The sensitivity and specificity of polyadenylation RT-PCR (PA-RT) and stem-loop RT-PCR (SL-RT) were compared. Statistical comparisons between patients with GC and controls were performed to identify miRNA biomarkers, and correlation analyses between the threshold cycle (Ct) values of miRNAs and various blood biochemical parameters were performed to elucidate the confounding factors. Results: mir-17, mir-21, mir-31, mir-99b, mir-222, and U6 were selected. PA-RT showed greater sensitivity and lower specificity than SL-RT (PA-RT vs. SL-RT, mean Ct: 19.6 vs. 29.2; coefficient of variation: 0.42 vs. 0.10). Adopting SL-RT owing to its higher specificity, only mir-222 was significantly upregulated in patients with GC (GC vs. control, miRNA expression: 15.4 vs. 5.27, p = 0.0098). Regarding the correlation between blood biochemical parameters and cells with miRNA expression, mir-31 and mir-99b were correlated with blood urea nitrogen, mir-17, mir-21, and mir-99b were negatively correlated with platelets, and mir-21 was correlated with neutrophils. No obvious correlations were noted between mir-222 expression and blood parameters. Receiver operating characteristic (ROC) curve analysis indicated that mir-222 identified GC patients with a maximum area under the curve (0.73, 95% confidence interval 0.57-0.89). Conclusions: Plasma mir-222 was confirmed to be dysregulated in patients with GC, irrespective of blood biochemical parameters.

Identification and validation of screening models for breast cancer with 3 serum miRNAs in an 11,349 samples mixed cohort

PURPOSE

The study focuses on enhancing breast cancer (BC) prognosis through early detection, aiming to establish a non-invasive, clinically viable BC screening method using specific serum miRNA levels.

METHODS

Involving 11,349 participants across BC, 11 other cancer types, and control groups, the study identified serum biomarkers through feature selection and developed two BC screening models using six machine learning algorithms. These models underwent evaluation across test, internal, and external validation sets, assessing performance metrics like accuracy, sensitivity, specificity, and the area under the curve (AUC). Subgroup analysis was conducted to test model stability.

RESULTS

Based on the three serum miRNA biomarkers (miR-1307-3p, miR-5100, and miR-4745-5p), a BC screening model, SM4BC3miR model, was developed. This model achieved AUC performances of 0.986, 0.986, and 0.939 on the test, internal, and external sets, respectively. Furthermore, the SSM4BC model, utilizing ratio scores of miR-1307-3p/miR-5100 and miR-4745-5p/miR-5100, showed AUCs of 0.973, 0.980, and 0.953, respectively. Subgroup analyses underscored both models' robustness and stability.

CONCLUSION

This research introduced the SM4BC3miR and SSM4BC models, leveraging three specific serum miRNA biomarkers for breast cancer screening. Demonstrating high accuracy and stability, these models present a promising approach for early detection of breast cancer. However, their practical application and effectiveness in clinical settings remain to be further validated.

Early detection of pancreatic cancer by comprehensive serum miRNA sequencing with automated machine learning

BACKGROUND

Pancreatic cancer is often diagnosed at advanced stages, and early-stage diagnosis of pancreatic cancer is difficult because of nonspecific symptoms and lack of available biomarkers.

METHODS

We performed comprehensive serum miRNA sequencing of 212 pancreatic cancer patient samples from 14 hospitals and 213 non-cancerous healthy control samples. We randomly classified the pancreatic cancer and control samples into two cohorts: a training cohort (N = 185) and a validation cohort (N = 240). We created ensemble models that combined automated machine learning with 100 highly expressed miRNAs and their combination with CA19-9 and validated the performance of the models in the independent validation cohort.

RESULTS

The diagnostic model with the combination of the 100 highly expressed miRNAs and CA19-9 could discriminate pancreatic cancer from non-cancer healthy control with high accuracy (area under the curve (AUC), 0.99; sensitivity, 90%; specificity, 98%). We validated high diagnostic accuracy in an independent asymptomatic early-stage (stage 0-I) pancreatic cancer cohort (AUC:0.97; sensitivity, 67%; specificity, 98%).

CONCLUSIONS

We demonstrate that the 100 highly expressed miRNAs and their combination with CA19-9 could be biomarkers for the specific and early detection of pancreatic cancer.

Biomarkers in Cancer Screening: Promises and Challenges in Cancer Early Detection

Cancer continues to be one the leading causes of death worldwide, primarily due to the late detection of the disease. Cancers detected at early stages may enable more effective intervention of the disease. However, most cancers lack well-established screening procedures except for cancers with an established early asymptomatic phase and clinically validated screening tests. There is a critical need to identify and develop assays/tools in conjunction with imaging approaches for precise screening and detection of the aggressive disease at an early stage. New developments in molecular cancer screening and early detection include germline testing, synthetic biomarkers, and liquid biopsy approaches.

Differential miRNA and Protein Expression Reveals miR-1285, Its Targets TGM2 and CDH-1, as Well as CD166 and S100A13 as Potential New Biomarkers in Patients with Diabetes Mellitus and Pancreatic Adenocarcinoma

Early detection of PDAC remains challenging due to the lack of early symptoms and the absence of reliable biomarkers. The aim of the present project was to identify miRNA and proteomics signatures discriminating PDAC patients with DM from nondiabetic PDAC patients. Proteomics analysis and miRNA array were used for protein and miRNA screening. We used Western blotting and Real-Time Quantitative Reverse Transcription polymerase chain reaction (qRT-PCR) for protein and miRNA validation. Comparisons between experimental groups with normal distributions were performed using one-way ANOVA followed by Tukey's post hoc test, and pairwise tests were performed using t-tests. p ≤ 0.05 was considered statistically significant. Protein clusters of differentiation 166 (CD166), glycoprotein CD63 (CD63), S100 calcium-binding protein A13 (S100A13), and tumor necrosis factor-β (TNF-β) were detected in the proteomics screening. The miRNA assay revealed a differential miRNA 1285 regulation. Previously described target proteins of miR-1285 cadherin-1 (CDH-1), cellular Jun (c-Jun), p53, mothers against decapentaplegic homolog 4 (Smad4), human transglutaminase 2 (TGM2) and yes-associated protein (YAP), were validated via Western blotting. miR-1285-3p was successfully validated as differentially regulated in PDAC + DM via qRT-PCR. Overall, our data suggest miRNA1285-3p, TGM2, CDH-1, CD166, and S100A13 as potential meaningful biomarker candidates to characterize patients with PDAC + DM. Data are available via ProteomeXchange with the identifier PXD053169.

MicroRNA expression profiling of urine exosomes in children with congenital cytomegalovirus infection

Congenital cytomegalovirus (cCMV) infection can damage the central nervous system in infants; however, its prognosis cannot be predicted from clinical evaluations at the time of birth. Urinary exosomes can be used to analyze neuronal damage in neuronal diseases. To investigate the extent of neuronal damage in patients with cCMV, exosomal miRNA expression in the urine was investigated in cCMV-infected infants and controls. Microarray analysis of miRNA was performed in a cohort of 30 infants, including 11 symptomatic cCMV (ScCMV), 7 asymptomatic cCMV (AScCMV), and one late-onset ScCMV cases, and 11 healthy controls (HC). Hierarchical clustering analysis revealed the distinct expression profile of ScCMV. The patient with late-onset ScCMV was grouped into the ScCMV cluster. Pathway enrichment analysis of the target mRNAs differed significantly between the ScCMV and HC groups; this analysis also revealed that pathways related to brain development were linked to upregulated pathways. Six miRNAs that significantly different between groups (ScCMV vs. HC and ScCMV vs. AScCMV) were selected for digital PCR in another cohort for further validation. Although these six miRNAs seemed insufficient for predicting ScCMV, expression profiles of urine exosomal miRNAs can reveal neurological damage in patients with ScCMV compared to those with AcCMV or healthy infants.

An evolutionary learning-based method for identifying a circulating miRNA signature for breast cancer diagnosis prediction

Breast cancer (BC) is one of the most commonly diagnosed cancers worldwide. As key regulatory molecules in several biological processes, microRNAs (miRNAs) are potential biomarkers for cancer. Understanding the miRNA markers that can detect BC may improve survival rates and develop new targeted therapeutic strategies. To identify a circulating miRNA signature for diagnostic prediction in patients with BC, we developed an evolutionary learning-based method called BSig. BSig established a compact set of miRNAs as potential markers from 1280 patients with BC and 2686 healthy controls retrieved from the serum miRNA expression profiles for the diagnostic prediction. BSig demonstrated outstanding prediction performance, with an independent test accuracy and area under the receiver operating characteristic curve were 99.90% and 0.99, respectively. We identified 12 miRNAs, including hsa-miR-3185, hsa-miR-3648, hsa-miR-4530, hsa-miR-4763-5p, hsa-miR-5100, hsa-miR-5698, hsa-miR-6124, hsa-miR-6768-5p, hsa-miR-6800-5p, hsa-miR-6807-5p, hsa-miR-642a-3p, and hsa-miR-6836-3p, which significantly contributed towards diagnostic prediction in BC. Moreover, through bioinformatics analysis, this study identified 65 miRNA-target genes specific to BC cell lines. A comprehensive gene-set enrichment analysis was also performed to understand the underlying mechanisms of these target genes. BSig, a tool capable of BC detection and facilitating therapeutic selection, is publicly available at https://github.com/mingjutsai/BSig.

Serum vascular adhesion protein-1 is associated with twelve-year risk of incident cancer, cancer mortality, and all-cause mortality: a community-based cohort study

Background

Vascular adhesion protein-1 (VAP-1), a dual-function glycoprotein, has been reported to play a crucial role in inflammation and tumor progression. We conducted a community-based cohort study to investigate whether serum VAP-1 could be a potential biomarker for predicting incident cancers and mortality.

Method

From 2006 to 2018, we enrolled 889 cancer-free subjects at baseline. Serum VAP-1 levels were measured using a time-resolved immunofluorometric assay. Cancer and vital status of the participants were obtained by linking records with the computerized cancer registry and death certificates in Taiwan.

Results

During a median follow-up of 11.94 years, 69 subjects developed incident cancers and 66 subjects died, including 29 subjects who died from malignancy. Subjects in the highest tertile of serum VAP-1 had a significantly higher risk of cancer incidence (p=0.0006), cancer mortality (p=0.0001), and all-cause mortality (p=0.0002) than subjects in the other tertiles. The adjusted hazard ratios per one standard deviation increase in serum VAP-1 concentrations were 1.28 for cancer incidence (95% CI=1.01-1.62), 1.60 for cancer mortality (95% CI=1.14-2.23), and 1.38 for all-cause mortality (95% CI=1.09-1.75). The predictive performance of serum VAP-1 was better than that of gender, smoking, body mass index, hypertension, diabetes, and estimated glomerular filtration rate but lower than that of age for cancer incidence, cancer mortality, and all-cause mortality, as evidenced by higher increments in concordance statistics and area under the receiver operating characteristic curve.

Conclusion

Serum VAP-1 levels are associated with a 12-year risk of incident cancer, cancer mortality, and all-cause mortality in a general population.

Molecular screening of head neck cancer

Liquid biopsy has become a significant tool in personalized medicine, enabling real-time monitoring of cancer evolution and patient follow-up. This minimally invasive procedure analyzes circulating tumor cells (CTCs) and circulating tumor-derived materials, such as ctDNA, miRNAs, and EVs. CTC analysis significantly impacts prognosis, detection of minimal residual disease (MRD), treatment selection, and monitoring of cancer patients. Liquid biopsy is an attractive option for mouth cancer detection and treatment progress monitoring in many countries. It is not invasive and requires no surgical expertise, making it an attractive option for mouth cancer detection. Liquid biopsy is a diagnostic repeatable test that can profile cancer genomes in real-time with minimal invasiveness and tailor oncological decision-making. It analyzes different blood-circulating biomarkers, with ctDNA being the preferred one. While tissue biopsy remains the gold standard for molecular evaluation of solid tumors, liquid biopsy is a complementary tool in various clinical settings, including treatment selection, monitoring response, cancer clonal evolution, prognostic evaluation, early disease detection, and minimal residual disease (MRD).

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.