Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach

Knockdown of PTEN promotes colon cancer progression and induces M2 macrophage polarization in the colon cancer cell environment

Objective

This article aims to study the effect of phosphate and tension homolog deleted on chromosome ten (PTEN) knockdown on colon cancer progression and macrophage polarization in the cancer environment.

Materials and Methods and Results

The expression of PTEN in colon cancer tissues and colon cancer cells was significantly lower than in precancerous tissues or CCD-18Co cells, and the decrease was most evident in SW620 cells. The expressions of phosphate (p)-p38, c-Jun N-terminal kinase (JNK), activator protein 1 (AP-1), B-cell lymphoma-2 (Bcl-2) protein in colon cancer tissues and cells were significantly higher than in precancerous tissues or CCD-18Co cells (P-values < 0.05). Bcl-2-associated X (Bax) and Caspase-3 expressions in colon cancer tissues and cells were significantly lower than in precancerous tissues or CCD-18Co cells (P-values < 0.05). 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) was applied to measure cell viability. Transwell evaluated the cell migration and invasion ability. Si-PTEN improved the proliferation, migration, and invasion of SW620 cells (P-values < 0.05). The expression levels of arginase-1 (Arg-1), CD163, CD206 in colon cancer tissues were significantly higher than in precancerous tissues (P-values < 0.05). The cell cycle, the number of M1 and M2 double-positive cells were assessed by flow cytometry. Si-PTEN reduced the expression of tumor necrosis factor-alpha (TNF-α), interleukin-1beta (IL-1β), and inducible nitric oxide synthase (iNOS), which upregulated the expression of Arg-1, CD206, CD163, p-p38, JNK, and AP-1 (P-values < 0.05).

Conclusion

Si-PTEN promoted colon cancer progression and induced the polarization of M2 tumor-associated macrophages in the colon cancer cell environment.

A hitchhiker's guide to cancer models

Cancer is a complex and uniquely personal disease. More than 1.7 million people in the United States are diagnosed with cancer every year. As the burden of cancer grows, so does the need for new, more effective therapeutics and for predictive tools to identify optimal, personalized treatment options for every patient. Cancer models that recapitulate various aspects of the disease are fundamental to making advances along the continuum of cancer treatment from benchside discoveries to bedside delivery. In this review, we use a thought experiment as a vehicle to arrive at four broad categories of cancer models and explore the strengths, weaknesses, opportunities, and threats for each category in advancing our understanding of the disease and improving treatment strategies.

Routine blood biomarkers for the detection of multiple myeloma using machine learning

INTRODUCTION

Primary laboratory tests performed in the diagnosis of multiple myeloma (MM) include bone marrow examination and free light chain assay; however, these may only be ordered after clinical suspicion of disease. In contrast, routine blood test results are readily available.

METHODS

Machine learning algorithms (ML) combined with routine blood tests were used to detect MM. Feature selection was performed to achieve improved classification performance. The robustness of the classification models was assessed in an internal and external validation data set. To minimize the divergence, the training and validation data sets were combined and used to assess the performance of the ML algorithms.

RESULTS

The AdaBoost-DecisionTable produced the best performance (accuracy =94.75%, sensitivity =87.70%, positive predictive value (PPV) =92.50%, F-measure =90.00%, and areas under the receiver operating characteristic curves (AUC) =97.50%) in the training data set using a 10-fold cross-validation. Performance in the validation data sets was affected by the divergence of the data sets, with accuracy greater than 85% and AUC greater than 90% in the validation data sets. The ML algorithm achieved a high accuracy of 92.61%, high AUC (96.80%), a sensitivity value of 85.20%, a PPV value of 88.50%, and an F-measure of 86.80% in a test set that was randomly selected from the combined data set.

CONCLUSIONS

Combining ML and routine serum biomarkers hold a potential benefit in MM diagnosis.

Quantitative predictive approaches for Dupuytren disease: a brief review and future perspectives

In this study we review the current state of the art for Dupuytren's disease (DD), while emphasising the need for a better integration of clinical, experimental and quantitative predictive approaches to understand the evolution of the disease and improve current treatments. We start with a brief review of the biology of this disease and current treatment approaches. Then, since certain aspects in the pathogenesis of this disorder have been compared to various biological aspects of wound healing and malignant processes, next we review some in silico (mathematical modelling and simulations) predictive approaches for complex multi-scale biological interactions occurring in wound healing and cancer. We also review the very few in silico approaches for DD, and emphasise the applicability of these approaches to address more biological questions related to this disease. We conclude by proposing new mathematical modelling and computational approaches for DD, which could be used in the absence of animal models to make qualitative and quantitative predictions about the evolution of this disease that could be further tested in vitro.

Microfluidic devices for glycobiomarker detection in cancer

During oncogenesis, several alterations occur within cells, one of them being the abnormal glycosylation of proteins, resulting in the formation of glycoproteins with aberrant glycan structures, which can be secreted into the blood stream. Their specific association to tumour cells makes them useful indicators (biomarkers) of the oncogenic process and their detection in blood can be employed in different stages of tumour development for early detection, prognosis and therapeutic drug monitoring. Due to the importance of detecting cancer-associated glycoproteins with aberrant glycosylation in blood or serum, analytical methodologies with improved performance are required to ameliorate the laboratorial tests currently used for the detection of these analytes. Microfluidics was created to facilitate the implementation of simple and point-of-care analysis, away from a centralized laboratory. The massive use of microfluidic systems in clinical settings can be seen in pregnancy tests and diabetes control, for example. But what about other clinical domains, such as the detection of glycoproteins with aberrant glycans secreted by tumour cells? Are microfluidic systems helpful in this case? This review analyses the requirements of a microfluidic assay for the detection of low-abundant blood/serum cancer-associated glycoproteins with abnormal glycans and the progresses that have been made in the last years to develop integrated microfluidic devices for this particular application. The diverse microfluidic systems found in literature present, in general, the same analytical performance as the conventional assays but have additional advantages, namely a reduction in assay times, a decrease of sample and reagent consumption and lower costs. The review will also focus on the improvements that are still needed for better biosensing of this type of cancer biomarkers using microfluidic devices.

Oncological Ligand-Target Binding Systems and Developmental Approaches for Cancer Theranostics

Targeted treatment of cancer hinges on the identification of specific intracellular molecular receptors on cancer cells to stimulate apoptosis for eventually inhibiting growth; the development of novel ligands to target biomarkers expressed by the cancer cells; and the creation of novel multifunctional carrier systems for targeted delivery of anticancer drugs to specific malignant sites. There are numerous receptors, antigens, and biomarkers that have been discovered as oncological targets (oncotargets) for cancer diagnosis and treatment applications. Oncotargets are critically important to navigate active anticancer drug ingredients to specific disease sites with no/minimal effect on surrounding normal cells. In silico techniques relating to genomics, proteomics, and bioinformatics have catalyzed the discovery of oncotargets for various cancer types. Effective oncotargeting requires high-affinity probes engineered for specific binding of receptors associated with the malignancy. Computational methods such as structural modeling and molecular dynamic (MD) simulations offer opportunities to structurally design novel ligands and optimize binding affinity for specific oncotargets. This article proposes a streamlined approach for the development of ligand-oncotarget bioaffinity systems via integrated structural modeling and MD simulations, making use of proteomics, genomic, and X-ray crystallographic resources, to support targeted diagnosis and treatment of cancers and tumors.

A high-performance microfluidic detection platform to conduct a novel multiple-biomarker panel for ovarian cancer screening

Ovarian cancer is an important leading cause of cancer-related deaths among females, and a single biomarker does not have the sensitivity and specificity required for an effective ovarian cancer screening. Herein, we investigate a high-performance microfluidic detection platform to conduct a novel panel of multiple biomarkers for the early detection of ovarian carcinoma, which include CA125, HE4, OPN, MSLN, Hsp70, CA153, AFP, IL-6, and IL-8 using a microfluidic chip. High-throughput microfluidic chips and graphene oxide-assembled substrate are used to microprint repeatable capture antibody arrays and conduct multiple biomarkers in microscale volume samples. The proposed microfluidic platform achieves an ultralow detection limit of ∼1 pg mL⁻¹ and 0.01 U mL⁻¹ with excellent detection selectivity and a short detection time of 30 min. The analysis of serum biomarkers in 18 ovarian cancer patients and 4 healthy persons indicates a clear subgroup sorting between the high-grade serous ovarian carcinoma, borderline, and benign tumor patients, and healthy persons. The proposed detection platform and the biomarker panel are promising to conduct an early detection of ovarian cancer.

A high-performance microfluidic detection platform is developed to conduct a novel panel of multiple biomarkers for the early detection of ovarian carcinoma, which is promising for the early detection of ovarian cancer.

Modeling EV Kinetics for Use in Early Cancer Detection

Tumor-derived extracellular vesicles (EVs) represent promising biomarkers for monitoring cancers. Technological advances have improved the ability to measure EV reliably in blood using protein, RNA, or lipid detection methods. However, it is less clear how efficacious current EV assays are for the early detection of small and thus curable tumors. Here, a mathematical model is developed to estimate key parameter values and future requirements for EV testing. Tumor volumes in mice correlate well with increases in total number of circulating EV allowing the researchers to calculate EV shed rates for four different published cancer models. Model extrapolations to human physiology show good agreement with published clinical data. Specifically, it is shown that current bulk EV detection systems are ≈104 -fold too insensitive to detect human cancers of ≈1 cm3 . Conversely, it is predicted that emerging single EV methods will allow blood-based detection of cancers of <1 mm3 in humans.

The emergence of new trends in clinical laboratory diagnosis

Diagnostic processes typically rely on traditional and laborious methods, that are prone to human error, resulting in frequent misdiagnosis of diseases. Computational approaches are being increasingly used for more precise diagnosis of the clinical pathology, diagnosis of genetic and microbial diseases, and analysis of clinical chemistry data. These approaches are progressively used for improving the reliability of testing, resulting in reduced diagnostic errors. Artificial intelligence (AI)-based computational approaches mostly rely on training sets obtained from patient data stored in clinical databases. However, the use of AI is associated with several ethical issues, including patient privacy and data ownership. The capacity of AI-based mathematical models to interpret complex clinical data frequently leads to data bias and reporting of erroneous results based on patient data. In order to improve the reliability of computational approaches in clinical diagnostics, strategies to reduce data bias and analyzing real-life patient data need to be further refined.

Genetic predisposition and prediction protocol for epithelial neoplasms in disease-free individuals: A systematic review

Background

Epithelial neoplasm is an important global health-care problem, with high morbidity and mortality rates. Early diagnosis and appropriate treatment are essential for increased life survival. Prediction of occurrence of malignancy in a disease-free individual by any means will be a great breakthrough for healthy living.

Aims and Objectives

The aims and objectives were to predict the genetic predisposition and propose a prediction protocol for epithelial malignancy of various systems in our body, in a disease-free individual.

Methods

We have searched databases both manually and electronically, published in English language in Cochrane group, Google search, MEDLINE and PubMed from 2000 to 2019. We have included all the published, peer-reviewed, narrative reviews; randomized controlled trials; case-control studies; and cohort studies and excluded the abstract-only articles and duplicates. Specific words such as "etiological factors," "pathology and mutations," "signs and symptoms," "genetics and IHC marker," and "treatment outcome" were used for the search. A total of 1032 citations were taken, and only 141 citations met the inclusion criteria and were analyzed.

Results

After analyzing various articles, the etiological factors, clinical signs and symptoms, genes and the pathology involved and the commonly used blood and tissue markers were analyzed. A basic investigation strategy using immunohistochemistry markers was established.

Conclusion

The set of proposed biomarkers should be studied in future to predict genetic predisposition in disease-free individuals.

World Workshop on Oral Medicine VII: Prognostic biomarkers in oral leukoplakia and proliferative verrucous leukoplakia-A systematic review of retrospective studies

OBJECTIVE

To systematically review retrospective studies examining prognostic potentials of candidate biomarkers to stratify malignant progression of oral leukoplakia (OL) and proliferative verrucous leukoplakia (PVL).

MATERIALS AND METHODS

A systematic literature search of PubMed, EMBASE, Evidence-Based Medicine and Web of Science databases targeted literature published through 29 March 2018. Inter-rater agreement was ascertained during title, abstract and full-text reviews. Eligibility evaluation and data abstraction from eligible studies were guided by predefined PICO questions and bias assessment by the Quality in Prognosis Studies tool. Reporting followed Preferred Reporting Items for Systematic Review and Meta-Analysis criteria. Biomarkers were stratified based on cancer hallmarks.

RESULTS

Eligible studies (n = 54/3,415) evaluated 109 unique biomarkers in tissue specimens from 2,762 cases (2,713 OL, 49 PVL). No biomarker achieved benchmarks for clinical application to detect malignant transformation. Inter-rater reliability was high, but 65% of included studies had high "Study Confounding" bias risk.

CONCLUSION

There was no evidence to support translation of candidate biomarkers predictive of malignant transformation of OL and PVL. Systematically designed, large, optimally controlled, collaborative, prospective and longitudinal studies with a priori-specified methods to identify, recruit, prospectively follow and test for malignant transformation are needed to enhance feasibility of prognostic biomarkers predicting malignant OL or PVL transformation.

Porcine seminal protein-I and II mRNA expression in boar spermatozoa is significantly correlated with fertility

In recent years, genomic and proteomic biomarkers have been identified for the diagnosis of male fertility to overcome the limitations of conventional semen analysis. Owing to the limited genes available so far, the single gene approach is commonly adopted for analyzing the phenotype of interest. However, the single-gene approach is less effective than multiple-gene strategies for diagnosing a desirable phenotype. Herein, we investigate the ability of two fertility-related genomic markers (porcine seminal protein (PSP)-I and PSP-II) in spermatozoa to predict boar litter size in addition to conventional semen parameters. First, we examined different semen parameters (motility, motion kinematics, and capacitation status) and gene expression in high- and low-litter size boar spermatozoa. Then, we evaluated the correlation of these parameters with the fertility of 21 Yorkshire boars. Finally, we investigated the efficacy of single/combined markers to predict male fertility using a comprehensive statistical model. Our result showed that there were no significant differences in sperm motility, motion kinematics, or capacitation status, however, the mRNA expression of PSP-I and PSP-II in spermatozoa was significantly different in high- and low-litter size boars. In the individual screening test, the expression of both genes was negatively correlated with boar fertility (r = 0-0.578 and -0.456, respectively), whereas only hyperactivation (HYP) showed a positive correlation (r = 0.444) among the tested semen parameters. As single markers, PSP-I and PSP-II have a better diagnostic power to predict boar fertility, regardless of HYP, in quality assessment analyses. In addition, when these markers were combined, the positive predictive value, negative predictive value, and overall test effectiveness for fertility detection were improved. Surprisingly, when PSP-I and PSP-II were considered together, the deviation of the predicted average litter size between high- and low-litter size boars was 1.77. Based on the findings, we suggest that the use of genomic markers in spermatozoa rather than commonly analyzed semen parameters may be more accurate for evaluating male fertility. Moreover, using a combination of markers could increase the overall accuracy of (in)fertility predictions, and thus, could be considered for field application.