Clinical trial-identified inflammatory biomarkers in breast and pancreatic cancers.

Breast cancer and pancreatic cancer are two common cancer types characterized by high prevalence and high mortality rates, respectively. However, breast cancer has been more well-studied than pancreatic cancer. This narrative review curated inflammation-associated biomarkers from clinical studies that were systematically selected for both breast and pancreatic cancers and discusses some of the common and unique elements between the two endocrine-regulated malignant diseases. Finding common ground between the two cancer types and specifically analyzing breast cancer study results, we hoped to explore potential feasible methods and biomarkers that may be useful also in diagnosing and treating pancreatic cancer. A PubMed MEDLINE search was used to identify articles that were published between 2015-2022 of different kinds of clinical trials that measured immune-modulatory biomarkers and biomarker changes of inflammation defined in diagnosis and treatment of breast cancer and pancreatic cancer patients. A total of 105 papers (pancreatic cancer 23, breast cancer 82) were input into Covidence for the title and abstract screening. The final number of articles included in this review was 73 (pancreatic cancer 19, breast cancer 54). The results showed some of the frequently cited inflammatory biomarkers for breast and pancreatic cancers included IL-6, IL-8, CCL2, CD8+ T cells and VEGF. Regarding unique markers, CA15-3 and TNF-alpha were two of several breast cancer-specific, and CA19 and IL-18 were pancreatic cancer-specific. Moreover, we discussed leptin and MMPs as emerging biomarker targets with potential use for managing pancreatic cancer based on breast cancer studies in the future, based on inflammatory mechanisms. Overall, the similarity in how both types of cancers respond to or result in further disruptive inflammatory signaling, and that point to a list of markers that have been shown useful in diagnosis and/or treatment method response or efficacy in managing breast cancer could potentially provide insights into developing the same or more useful diagnostic and treatment measurement inflammatory biomarkers for pancreatic cancer. More research is needed to investigate the relationship and associated inflammatory markers between the similar immune-associated biological mechanisms that contribute to breast and pancreatic cancer etiology, drive disease progression or that impact treatment response and reflect survival outcomes.

Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction.

Background: Increasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication. Materials and methods: The gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue. Results: Overall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified. Conclusion: For the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine.Graphical abstract.

An explainable machine learning-driven proposal of pulmonary fibrosis biomarkers.

Pulmonary fibrosing diseases are in the very epicenter of biomedical research both due to their increasing prevalence and their association with SARS-CoV-2 infections. Research of idiopathic pulmonary fibrosis, the most lethal among the interstitial lung diseases, is in need for new biomarkers and potential disease targets, a goal that could be accelerated using machine learning techniques. In this study, we have used Shapley values to explain the decisions made by an ensemble learning model trained to classify samples to an either pulmonary fibrosis or steady state based on the expression values of deregulated genes. This process resulted in a full and a laconic set of features capable of separating phenotypes to an at least equal degree as previously published marker sets. Indicatively, a maximum increase of 6% in specificity and 5% in Mathew's correlation coefficient was achieved. Evaluation with an additional independent dataset showed our feature set having a greater generalization potential than the rest. Ultimately, the proposed gene lists are expected not only to serve as new sets of diagnostic marker elements, but also as a target pool for future research initiatives.

Microbial and human transcriptional profiling of coronavirus disease 2019 patients: Potential predictors of disease severity.

The high morbidity of patients with coronavirus disease 2019 (COVID-19) brings on a panic around the world. COVID-19 is associated with sex bias, immune system, and preexisting chronic diseases. We analyzed the gene expression in patients with COVID-19 and in their microbiota in order to identify potential biomarkers to aid in disease management. A total of 129 RNA samples from nasopharyngeal, oropharyngeal, and anal swabs were collected and sequenced in a high-throughput manner. Several microbial strains differed in abundance between patients with mild or severe COVID-19. Microbial genera were more abundant in oropharyngeal swabs than in nasopharyngeal or anal swabs. Oropharyngeal swabs allowed more sensitive detection of the causative SARS-CoV-2. Microbial and human transcriptomes in swabs from patients with mild disease showed enrichment of genes involved in amino acid metabolism, or protein modification via small protein removal, and antibacterial defense responses, respectively, whereas swabs from patients with severe disease showed enrichment of genes involved in drug metabolism, or negative regulation of apoptosis execution, spermatogenesis, and immune system, respectively. Microbial abundance and diversity did not differ significantly between males and females. The expression of several host genes on the X chromosome correlated negatively with disease severity. In this way, our analyses identify host genes whose differential expression could aid in the diagnosis of COVID-19 and prediction of its severity via non-invasive assay.

Diagnostic biomarkers for active tuberculosis: progress and challenges.

Tuberculosis (TB) is a leading cause of morbidity and mortality from a single infectious agent, despite being preventable and curable. Early and accurate diagnosis of active TB is critical to both enhance patient care, improve patient outcomes, and break Mycobacterium tuberculosis (Mtb) transmission cycles. In 2020 an estimated 9.9 million people fell ill from Mtb, but only a little over half (5.8 million) received an active TB diagnosis and treatment. The World Health Organization has proposed target product profiles for biomarker- or biosignature-based diagnostics using point-of-care tests from easily accessible specimens such as urine or blood. Here we review and summarize progress made in the development of pathogen- and host-based biomarkers for active TB diagnosis. We describe several unique patient populations that have posed challenges to development of a universal diagnostic TB biomarker, such as people living with HIV, extrapulmonary TB, and children. We also review additional limitations to widespread validation and utilization of published biomarkers. We conclude with proposed solutions to enhance TB diagnostic biomarker validation and uptake.

Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis.

BACKGROUND: Tuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014-2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients. METHODS: Untargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, "caret" R package, "e1071" R package and "Tensorflow" Python package, respectively. RESULTS: Metabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83-93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent. CONCLUSIONS: Our findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.

Novel Diagnostic Biomarkers in Colorectal Cancer.

Colorectal cancer (CRC) is still a leading cause of cancer death worldwide. Less than half of cases are diagnosed when the cancer is locally advanced. CRC is a heterogenous disease associated with a number of genetic or somatic mutations. Diagnostic markers are used for risk stratification and early detection, which might prolong overall survival. Nowadays, the widespread use of semi-invasive endoscopic methods and feacal blood tests characterised by suboptimal accuracy of diagnostic results has led to the detection of cases at later stages. New molecular noninvasive tests based on the detection of CRC alterations seem to be more sensitive and specific then the current methods. Therefore, research aiming at identifying molecular markers, such as DNA, RNA and proteins, would improve survival rates and contribute to the development of personalized medicine. The identification of "ideal" diagnostic biomarkers, having high sensitivity and specificity, being safe, cheap and easy to measure, remains a challenge. The purpose of this review is to discuss recent advances in novel diagnostic biomarkers for tumor tissue, blood and stool samples in CRC patients.

m6A Regulator-Mediated Methylation Modification Patterns and Characteristics in COVID-19 Patients.

Background: RNA N6-methyladenosine (m6A) regulators may be necessary for diverse viral infectious diseases, and serve pivotal roles in various physiological functions. However, the potential roles of m6A regulators in coronavirus disease 2019 (COVID-19) remain unclear. Methods: The gene expression profile of patients with or without COVID-19 was acquired from Gene Expression Omnibus (GEO) database, and bioinformatics analysis of differentially expressed genes was conducted. Random forest modal and nomogram were established to predict the occurrence of COVID-19. Afterward, the consensus clustering method was utilized to establish two different m6A subtypes, and associations between subtypes and immunity were explored. Results: Based on the transcriptional data from GSE157103, we observed that the m6A modification level was markedly enriched in the COVID-19 patients than those in the non-COVID-19 patients. And 18 essential m6A regulators were identified with differential analysis between patients with or without COVID-19. The random forest model was utilized to determine 8 optimal m6A regulators for predicting the emergence of COVID-19. We then established a nomogram based on these regulators, and its predictive reliability was validated by decision curve analysis. The consensus clustering algorithm was conducted to categorize COVID-19 patients into two m6A subtypes from the identified m6A regulators. The patients in cluster A were correlated with activated T-cell functions and may have a superior prognosis. Conclusions: Collectively, m6A regulators may be involved in the prevalence of COVID-19 patients. Our exploration of m6A subtypes may benefit the development of subsequent treatment modalities for COVID-19.

Exosomal biomarkers: new perspectives for the diagnosis and prognosis of respiratory diseases

Exosomes are small vesicles secreted by host cells after degradation of inert particles or microorganisms. Their size of < 0.1 μm allows them to migrate from the lung to different organs, carrying within them micro RNAs (miRNAs) and other host molecules. It has been reported that in non-infectious lung diseases, such as cancer, asthma, and pulmonary fibrosis, as well as in infectious diseases, such as influenza, COVID-19, pneumonia, and tuberculosis, there is a differential expression of miRNAs, and they have been proposed as biomarkers for diagnosis and/ or as possible therapeutic targets. Additionally, in infectious diseases, both pathogen genetic material and host miRNAs have been found in exosomes with bimodal regulatory functions, that is, they may participate either in promoting infection or controlling disease progression. In infection with influenza viruses and SARS-CoV-2, exosomes can directly block entry into host cells by expressing the receptor on their own surface, thus suggesting them as therapeutic targets. Additionally, another function of exosomal miRNAs is as new vaccines for active and latent tuberculosis. Most of the biomarkers are still in the preclinical phase, so clinical studies are required to evaluate their efficacy as biomarkers for the diagnosis of pulmonary diseases. In this review we will focus on the most relevant information on the role of exosomal RNAs and their function in lung diseases caused by infectious and non-infectious agents, as biomarkers for diagnosis and as prognostic biomarkers, and their possible therapeutic use. © 2021, Instituto Nacional de Enfermedades Respiratorias. All rights reserved.

Infection-Mediated Clinical Biomarkers for a COVID-19 Electrical Biosensing Platform.

The race towards the development of user-friendly, portable, fast-detection, and low-cost devices for healthcare systems has become the focus of effective screening efforts since the pandemic attack in December 2019, which is known as the coronavirus disease 2019 (COVID-19) pandemic. Currently existing techniques such as RT-PCR, antigen-antibody-based detection, and CT scans are prompt solutions for diagnosing infected patients. However, the limitations of currently available indicators have enticed researchers to search for adjunct or additional solutions for COVID-19 diagnosis. Meanwhile, identifying biomarkers or indicators is necessary for understanding the severity of the disease and aids in developing efficient drugs and vaccines. Therefore, clinical studies on infected patients revealed that infection-mediated clinical biomarkers, especially pro-inflammatory cytokines and acute phase proteins, are highly associated with COVID-19. These biomarkers are undermined or overlooked in the context of diagnosis and prognosis evaluation of infected patients. Hence, this review discusses the potential implementation of these biomarkers for COVID-19 electrical biosensing platforms. The secretion range for each biomarker is reviewed based on clinical studies. Currently available electrical biosensors comprising electrochemical and electronic biosensors associated with these biomarkers are discussed, and insights into the use of infection-mediated clinical biomarkers as prognostic and adjunct diagnostic indicators in developing an electrical-based COVID-19 biosensor are provided.

Emerging point-of-care biosensors for rapid diagnosis of COVID-19: current progress, challenges, and future prospects.

Coronavirus disease 2019 (COVID-19) pandemic is currently a serious global health threat. While conventional laboratory tests such as quantitative real-time polymerase chain reaction (qPCR), serology tests, and chest computerized tomography (CT) scan allow diagnosis of COVID-19, these tests are time-consuming and laborious, and are limited in resource-limited settings or developing countries. Point-of-care (POC) biosensors such as chip-based and paper-based biosensors are typically rapid, portable, cost-effective, and user-friendly, which can be used for COVID-19 in remote settings. The escalating demand for rapid diagnosis of COVID-19 presents a strong need for a timely and comprehensive review on the POC biosensors for COVID-19 that meet ASSURED criteria: Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable to end users. In the present review, we discuss the importance of rapid and early diagnosis of COVID-19 and pathogenesis of COVID-19 along with the key diagnostic biomarkers. We critically review the most recent advances in POC biosensors which show great promise for the detection of COVID-19 based on three main categories: chip-based biosensors, paper-based biosensors, and other biosensors. We subsequently discuss the key benefits of these biosensors and their use for the detection of antigen, antibody, and viral nucleic acids. The commercial POC biosensors for COVID-19 are critically compared. Finally, we discuss the key challenges and future perspectives of developing emerging POC biosensors for COVID-19. This review would be very useful for guiding strategies for developing and commercializing rapid POC tests to manage the spread of infections.Graphical abstract.