Combined microRNA and mRNA microfluidic TaqMan array cards for the diagnosis of malignancy of multiple types of pancreatico-biliary tumors in fine-needle aspiration material

Incorporation of explainable artificial intelligence in ensemble machine learning-driven pancreatic cancer diagnosis

Despite the strides made in medical science, pancreatic cancer continues to be a threat, highlighting the urgent need for creative strategies to address this concern. Recently, a potential approach that has attracted significant attention is using machine learning in clinical decision-making. This research aims to analyze six machine learning algorithms, and an ensemble voting classifier, develop hybrid models for the early detection of pancreatic cancer based on several clinical characteristics and interpret their performance with Shapley Additive Explanations (SHAP). A publicly available dataset composed of 590 patient urine samples was utilized to develop six conventional models for the classification of cancerous from non-cancerous pancreatic cases through the analysis of specific attributes. An ensemble voting classifier was developed from the best-performed single models, which were later hybridized to form six novel hybrid models. The ensemble voting classifier outperformed all stand-alone models with an accuracy of 96.61% and a precision of 98.72%. The six novel hybrid models exhibited higher performance than single models with voting classifier random forest hybridized model outperforming others with an AUC of 99.05% (95% confidence interval (CI): 0.93-1.00) and an interpretation was given by SHAP showing top influential features in pancreatic cancer diagnosis that exhibited the greatest positive SHAP values. Employing rapid sophisticated models with high accuracy and precision holds significant promise in facilitating the effective detection of various diseases, including pancreatic cancer.

Combined analysis of a serum mRNA/miRNA marker signature and CA 19-9 for timely and accurate diagnosis of recurrence after resection of pancreatic ductal adenocarcinoma: A prospective multicenter cohort study

BACKGROUND AND AIMS

Timely and accurate detection of tumor recurrence in pancreatic ductal adenocarcinoma (PDAC) patients is an urgent and unmet medical need. This study aimed to develop a noninvasive molecular diagnostic procedure for the detection of recurrence after PDAC resection based on quantification of circulating mRNA and miRNA biomarkers in serum samples.

METHODS

In a multicentric study, serum samples from a total of 146 patients were prospectively collected after resection. Samples were classified into a "No Evidence of Disease" and a "Recurrence" group based on clinical follow-up data. A multianalyte biomarker panel was composed of mRNAs and miRNA markers and simultaneously analyzed in serum samples using custom microfluidic qPCR arrays (TaqMan array cards). A diagnostic algorithm was developed combining a 7-gene marker signature with CA19-9 data.

RESULTS

The best-performing marker combination achieved 90% diagnostic accuracy in predicting the presence of tumor recurrence (98% sensitivity; 84% specificity), clearly outperforming the singular CA 19-9 analysis. Moreover, time series data obtained by analyzing successively collected samples from 5 patients during extended follow-up suggested that molecular diagnosis has the potential to detect recurrence earlier than routine clinical procedures.

CONCLUSIONS

TaqMan array card measurements were found to be biologically valid and technically reproducible. The BioPac multianalyte marker panel is capable of sensitive and accurate detection of recurrence in patients resected for PDAC using a simple blood test. This could allow a closer follow-up using shorter time intervals than currently used for imaging, thus potentially prompting an earlier work-up with additional modalities to allow for earlier therapeutic intervention. This study provides a promising approach for improved postoperative monitoring of resected PDAC patients, which is an urgent and unmet clinical need.

Comprehensive multi-omics profiling identifies novel molecular subtypes of pancreatic ductal adenocarcinoma

Pancreatic cancer, a highly fatal malignancy, is predicted to rank as the second leading cause of cancer-related death in the next decade. This highlights the urgent need for new insights into personalized diagnosis and treatment. Although molecular subtypes of pancreatic cancer were well established in genomics and transcriptomics, few known molecular classifications are translated to guide clinical strategies and require a paradigm shift. Notably, chronically developing and continuously improving high-throughput technologies and systems serve as an important driving force to further portray the molecular landscape of pancreatic cancer in terms of epigenomics, proteomics, metabonomics, and metagenomics. Therefore, a more comprehensive understanding of molecular classifications at multiple levels using an integrated multi-omics approach holds great promise to exploit more potential therapeutic options. In this review, we recapitulated the molecular spectrum from different omics levels, discussed various subtypes on multi-omics means to move one step forward towards bench-to-beside translation of pancreatic cancer with clinical impact, and proposed some methodological and scientific challenges in store.

Machine learning algorithms and biomarkers identification for pancreatic cancer diagnosis using multi-omics data integration

PURPOSE

Pancreatic cancer is a lethal type of cancer with most of the cases being diagnosed in an advanced stage and poor prognosis. Developing new diagnostic and prognostic markers for pancreatic cancer can significantly improve early detection and patient outcomes. These biomarkers can potentially revolutionize medical practice by enabling personalized, more effective, targeted treatments, ultimately improving patient outcomes.

METHODS

The search strategy was developed following PRISMA guidelines. A comprehensive search was performed across four electronic databases: PubMed, Scopus, EMBASE, and Web of Science, covering all English publications up to September 2022. The Newcastle-Ottawa Scale (NOS) was utilized to assess bias, categorizing studies as "good," "fair," or "poor" quality based on their NOS scores. Descriptive statistics for all included studies were compiled and reviewed, along with the NOS scores for each study to indicate their quality assessment.

RESULTS

Our results showed that SVM and RF are the most widely used algorithms in machine learning and data analysis, particularly for biomarker identification. SVM, a supervised learning algorithm, is employed for both classification and regression by mapping data points in high-dimensional space to identify the optimal separating hyperplane between classes.

CONCLUSIONS

The application of machine-learning algorithms in the search for novel biomarkers in pancreatic cancer represents a significant advancement in the field. By harnessing the power of artificial intelligence, researchers are poised to make strides towards earlier detection and more effective treatment, ultimately improving patient outcomes in this challenging disease.

Ferroptosis in pancreatic diseases: potential opportunities and challenges that require attention

The pancreas is an abdominal organ with both endocrine and exocrine functions, and patients with pancreatic diseases suffer tremendously. The regulated cell death of various cells in the pancreas is thought to play a key role in disease development. As one of the newly discovered regulated cell death modalities, ferroptosis has the potential for therapeutic applications in the study of multiple diseases. Ferroptosis has been observed in several pancreatic diseases, but its role in pancreatic diseases has not been systematically elucidated or reviewed. Understanding the occurrence of ferroptosis in various pancreatic diseases after damage to the different cell types is crucial in determining disease progression, evaluating targeted therapies, and predicting disease prognosis. Herein, we summarize the research progress associated with ferroptosis in four common pancreatic diseases, namely acute pancreatitis, chronic pancreatitis, pancreatic ductal adenocarcinoma, and diabetes mellitus. Furthermore, the elucidation of ferroptosis in rare pancreatic diseases may provide sociological benefits in the future.

Efficient cross-validation traversals in feature subset selection

Sparse and robust classification models have the potential for revealing common predictive patterns that not only allow for categorizing objects into classes but also for generating mechanistic hypotheses. Identifying a small and informative subset of features is their main ingredient. However, the exponential search space of feature subsets and the heuristic nature of selection algorithms limit the coverage of these analyses, even for low-dimensional datasets. We present methods for reducing the computational complexity of feature selection criteria allowing for higher efficiency and coverage of screenings. We achieve this by reducing the preparation costs of high-dimensional subsets [Formula: see text] to those of one-dimensional ones [Formula: see text]. Our methods are based on a tight interaction between a parallelizable cross-validation traversal strategy and distance-based classification algorithms and can be used with any product distance or kernel. We evaluate the traversal strategy exemplarily in exhaustive feature subset selection experiments (perfect coverage). Its runtime, fitness landscape, and predictive performance are analyzed on publicly available datasets. Even in low-dimensional settings, we achieve approximately a 15-fold increase in exhaustively generating distance matrices for feature combinations bringing a new level of evaluations into reach.

Microfluidics for detection of exosomes and microRNAs in cancer: State of the art

Exosomes are small extracellular vesicles with sizes ranging from 30-150 nanometers that contain proteins, lipids, mRNAs, microRNAs, and double-stranded DNA derived from the cells of origin. Exosomes can be taken up by target cells, acting as a means of cell-to-cell communication. The discovery of these vesicles in body fluids and their participation in cell communication has led to major breakthroughs in diagnosis, prognosis, and treatment of several conditions (e.g., cancer). However, conventional isolation and evaluation of exosomes and their microRNA content suffers from high cost, lengthy processes, difficult standardization, low purity, and poor yield. The emergence of microfluidics devices with increased efficiency in sieving, trapping, and immunological separation of small volumes could provide improved detection and monitoring of exosomes involved in cancer. Microfluidics techniques hold promise for advances in development of diagnostic and prognostic devices. This review covers ongoing research on microfluidics devices for detection of microRNAs and exosomes as biomarkers and their translation to point-of-care and clinical applications.

Design and evaluation of Taqman low density array for monitoring post-transplant viral infections

BACKGROUND

The majority of transplant recipients undergo immunosuppressive treatment to prevent organ or tissue rejection. Consequently, they are more susceptible to infection agents including a number of viruses causing a significant morbidity and mortality. Only a limited number of viruses are currently tested for in transplant donors and recipients due to the cost and complexity. Taqman low density array (TLDA) may provide a suitable format to address more systematic testing approach.

METHODS

One hundred and one liver transplant recipient samples were retrospectively tested for 48 viral targets including two controls (bovine viral diarrhea virus and MS2) and two common viruses (TTV and HPgV), using a custom designed TLDA. Eight samples were analysed simultaneously on 384-well TLDA. Samples giving a signal considered positive/indeterminant were re-tested by different individual confirmatory assays.

RESULTS

Infections with six previously untested for viruses-EBV, HPIV3, HuPuV9, KIV, HMPV and HPV-were detected in fourteen patients. Previously detected HCV infections were also confirmed. These infections did not seem have an effect on 5 year post-transplant outcome. 55 of 79 and 17 of 87 samples available for confirmatory assays were positive for TTV and HPgV, included for the evaluation of the TLDA performance.

CONCLUSIONS

The custom viral TLDA can be successfully used for simultaneous detection of a range of post-transplant viral infections. To fully exploit its potential for monitoring and intervention, a whole blood testing should be applied in a prospective setting.

A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data

Visualising data as diagrams using visual attributes such as colour, shape, size, and orientation is challenging. In particular, large data sets demand graphical display as an essential step in the analysis. In order to achieve comprehension often different attributes need to be displayed simultaneously. In this work a comprehensible bivariate, perceptually optimised visualisation scheme for high-dimensional data is proposed and evaluated. It can be used to show fold changes together with confidence values within a single diagram. The visualisation scheme consists of two parts: a uniform, symmetric, two-sided colour scale and a patch grid representation. Evaluation of uniformity and symmetry of the two-sided colour scale was performed in comparison to a standard RGB scale by twenty-five observers. Furthermore, the readability of the generated map was validated and compared to a bivariate heat map scheme.

Epigenomics of Pancreatic Cancer: A Critical Role for Epigenome-Wide Studies

Several challenges present themselves when discussing current approaches to the prevention or treatment of pancreatic cancer. Up to 45% of the risk of pancreatic cancer is attributed to unknown causes, making effective prevention programs difficult to design. The most common type of pancreatic cancer, pancreatic ductal adenocarcinoma (PDAC), is generally diagnosed at a late stage, leading to a poor prognosis and 5-year survival estimate. PDAC tumors are heterogeneous, leading to many identified cell subtypes within one patient’s primary tumor. This explains why there is a high frequency of tumors that are resistant to standard treatments, leading to high relapse rates. This review will discuss how epigenetic technologies and epigenome-wide association studies have been used to address some of these challenges and the future promises these approaches hold.

Semantic Multi-Classifier Systems Identify Predictive Processes in Heart Failure Models across Species

Genetic model organisms have the potential of removing blind spots from the underlying gene regulatory networks of human diseases. Allowing analyses under experimental conditions they complement the insights gained from observational data. An inevitable requirement for a successful trans-species transfer is an abstract but precise high-level characterization of experimental findings. In this work, we provide a large-scale analysis of seven weak contractility/heart failure genotypes of the model organism zebrafish which all share a weak contractility phenotype. In supervised classification experiments, we screen for discriminative patterns that distinguish between observable phenotypes (homozygous mutant individuals) as well as wild-type (homozygous wild-types) and carriers (heterozygous individuals). As the method of choice we use semantic multi-classifier systems, a knowledge-based approach which constructs hypotheses from a predefined vocabulary of high-level terms (e.g., Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways or Gene Ontology (GO) terms). Evaluating these models leads to a compact description of the underlying processes and guides the screening for new molecular markers of heart failure. Furthermore, we were able to independently corroborate the identified processes in Wistar rats.