Highly sensitive detection of melanoma based on serum proteomic profiling

A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer

Lung cancer is a high-risk disease that causes mortality worldwide; nevertheless, lung nodules are the main manifestation that can help to diagnose lung cancer at an early stage, lowering the workload of radiologists and boosting the rate of diagnosis. Artificial intelligence-based neural networks are promising technologies for automatically detecting lung nodules employing patient monitoring data acquired from sensor technology through an Internet-of-Things (IoT)-based patient monitoring system. However, the standard neural networks rely on manually acquired features, which reduces the effectiveness of detection. In this paper, we provide a novel IoT-enabled healthcare monitoring platform and an improved grey-wolf optimization (IGWO)-based deep convulution neural network (DCNN) model for lung cancer detection. The Tasmanian Devil Optimization (TDO) algorithm is utilized to select the most pertinent features for diagnosing lung nodules, and the convergence rate of the standard grey wolf optimization (GWO) algorithm is modified, resulting in an improved GWO algorithm. Consequently, an IGWO-based DCNN is trained on the optimal features obtained from the IoT platform, and the findings are saved in the cloud for the doctor's judgment. The model is built on an Android platform with DCNN-enabled Python libraries, and the findings are evaluated against cutting-edge lung cancer detection models.

Novel multiple markers to distinguish melanoma from dysplastic nevi

Background

Distinguishing melanoma from dysplastic nevi can be challenging.

Objective

To assess which putative molecular biomarkers can be optimally combined to aid in the clinical diagnosis of melanoma from dysplastic nevi.

Methods

Immunohistochemical expressions of 12 promising biomarkers (pAkt, Bim, BRG1, BRMS1, CTHRC1, Cul1, ING4, MCL1, NQO1, SKP2, SNF5 and SOX4) were studied in 122 melanomas and 33 dysplastic nevi on tissue microarrays. The expression difference between melanoma and dysplastic nevi was performed by univariate and multiple logistic regression analysis, diagnostic accuracy of single marker and optimal combinations were performed by receiver operating characteristic (ROC) curve and artificial neural network (ANN) analysis. Classification and regression tree (CART) was used to examine markers simultaneous optimizing the accuracy of melanoma. Ten-fold cross-validation was analyzed for estimating generalization error for classification.

Results

Four (Bim, BRG1, Cul1 and ING4) of 12 markers were significantly differentially expressed in melanoma compared with dysplastic nevi by both univariate and multiple logistic regression analysis (p < 0.01). These four combined markers achieved 94.3% sensitivity, 81.8% specificity and attained 84.3% area under the ROC curve (AUC) and the ANN classified accuracy with training of 83.2% and testing of 81.2% for distinguishing melanoma from dysplastic nevi. The classification trees identified ING4, Cul1 and BRG1 were the most important classification parameters in ranking top-performing biomarkers with cross-validation error of 0.03.

Conclusions

The multiple biomarkers ING4, Cul1, BRG1 and Bim described here can aid in the discrimination of melanoma from dysplastic nevi and provide a new insight to help clinicians recognize melanoma.

Blood biomarkers for uveal melanoma

Uveal melanoma disseminates hematogenously, and blood biomarkers may be useful for prognosis and for monitoring disease progression. Melanoma-associated, metastatic and immune factors have been measured in the blood of patients with uveal melanoma, as have circulating melanoma cells. Most of the biomarkers were derived from studies in cutaneous melanoma. For various biological and/or technical reasons, these assessments have not demonstrated the accuracy required for effective prognostic or monitoring assays. Advances in uveal melanoma genomics and proteomics have generated many candidate biomarkers that are potentially measurable in blood. Measuring circulating nucleic acids may also be possible. Improvements in molecular profiling techniques that accurately predict metastatic risk in uveal melanoma patients should facilitate biomarker discovery and aid implementation in clinical testing. The stage is set to translate the advances made in understanding the molecular characteristics of uveal melanoma in order to identify and test clinically useful blood biomarkers of tumor dissemination and/or progression.

Proteomics in melanoma biomarker discovery: great potential, many obstacles

The present clinical staging of melanoma stratifies patients into heterogeneous groups, resulting in the application of aggressive therapies to large populations, diluting impact and increasing toxicity. To move to a new era of therapeutic decisions based on highly specific tumor profiling, the discovery and validation of new prognostic and predictive biomarkers in melanoma is critical. Genomic profiling, which is showing promise in other solid tumors, requires fresh tissue from a large number of primary tumors, and thus faces a unique challenge in melanoma. For this and other reasons, proteomics appears to be an ideal choice for the discovery of new melanoma biomarkers. Several approaches to proteomics have been utilized in the search for clinically relevant biomarkers, but to date the results have been relatively limited. This article will review the present work using both tissue and serum proteomics in the search for melanoma biomarkers, highlighting both the relative advantages and disadvantages of each approach. In addition, we review several of the major obstacles that need to be overcome in order to advance the field.

Mass spectrometry of peptides and proteins from human blood

It is difficult to convey the accelerating rate and growing importance of mass spectrometry applications to human blood proteins and peptides. Mass spectrometry can rapidly detect and identify the ionizable peptides from the proteins in a simple mixture and reveal many of their post-translational modifications. However, blood is a complex mixture that may contain many proteins first expressed in cells and tissues. The complete analysis of blood proteins is a daunting task that will rely on a wide range of disciplines from physics, chemistry, biochemistry, genetics, electromagnetic instrumentation, mathematics and computation. Therefore the comprehensive discovery and analysis of blood proteins will rank among the great technical challenges and require the cumulative sum of many of mankind's scientific achievements together. A variety of methods have been used to fractionate, analyze and identify proteins from blood, each yielding a small piece of the whole and throwing the great size of the task into sharp relief. The approaches attempted to date clearly indicate that enumerating the proteins and peptides of blood can be accomplished. There is no doubt that the mass spectrometry of blood will be crucial to the discovery and analysis of proteins, enzyme activities, and post-translational processes that underlay the mechanisms of disease. At present both discovery and quantification of proteins from blood are commonly reaching sensitivities of ∼1 ng/mL.

The effect of artificial neural network model combined with six tumor markers in auxiliary diagnosis of lung cancer

To evaluate the diagnosis potential of artificial neural network (ANN) model combined with six tumor markers in auxiliary diagnosis of lung cancer, to differentiate lung cancer from lung benign disease, normal control, and gastrointestinal cancers. Serum carcino-embryonic antigen (CEA), gastrin, neurone specific enolase (NSE), sialic acid (SA), Cu/Zn, Ca were measured through different experimental procedures in 117 lung cancer patients, 93 lung benign disease patients, 111 normal control, 47 gastric cancer patients, 50 patients with colon cancer and 50 esophagus cancer patients, 19 parameters of basic information were surveyed among lung cancer, lung benign disease and normal control, then developed and evaluated ANN models to distinguish lung cancer. Using the ANN model with the six serum tumor markers and 19 parameters to distinguish lung cancer from benign lung disease and healthy people, the sensitivity was 98.3%, the specificity was 99.5% and the accuracy was 96.9%. Another three ANN models with the six serum tumor markers were employed to differentiate lung cancer from three gastrointestinal cancers, the sensitivity, specificity and accuracy of distinguishing lung cancer from gastric cancer by the ANN model of lung cancer-gastric cancer were 100%, 83.3% and 93.5%, respectively; The sensitivity, specificity and accuracy of discriminating lung cancer by lung cancer-colon cancer ANN model were 90.0%, 90.0%, and 90.0%; And which were 86.7%, 84.6%, and 86.0%, respectively, by lung cancer-esophagus cancer ANN model. ANN model built with the six serum tumor markers could distinguish lung cancer, not only from lung benign disease and normal people, but also from three common gastrointestinal cancers. And our evidence indicates the ANN model maybe is an excellent and intelligent system to discriminate lung cancer.

An empirical assessment of validation practices for molecular classifiers

Proposed molecular classifiers may be overfit to idiosyncrasies of noisy genomic and proteomic data. Cross-validation methods are often used to obtain estimates of classification accuracy, but both simulations and case studies suggest that, when inappropriate methods are used, bias may ensue. Bias can be bypassed and generalizability can be tested by external (independent) validation. We evaluated 35 studies that have reported on external validation of a molecular classifier. We extracted information on study design and methodological features, and compared the performance of molecular classifiers in internal cross-validation versus external validation for 28 studies where both had been performed. We demonstrate that the majority of studies pursued cross-validation practices that are likely to overestimate classifier performance. Most studies were markedly underpowered to detect a 20% decrease in sensitivity or specificity between internal cross-validation and external validation [median power was 36% (IQR, 21-61%) and 29% (IQR, 15-65%), respectively]. The median reported classification performance for sensitivity and specificity was 94% and 98%, respectively, in cross-validation and 88% and 81% for independent validation. The relative diagnostic odds ratio was 3.26 (95% CI 2.04-5.21) for cross-validation versus independent validation. Finally, we reviewed all studies (n = 758) which cited those in our study sample, and identified only one instance of additional subsequent independent validation of these classifiers. In conclusion, these results document that many cross-validation practices employed in the literature are potentially biased and genuine progress in this field will require adoption of routine external validation of molecular classifiers, preferably in much larger studies than in current practice.

Reproducibility of SELDI Spectra Across Time and Laboratories

This is an open access article. Unrestricted non-commercial use is permitted provided the original work is properly cited.The reproducibility of mass spectrometry (MS) data collected using surface enhanced laser desorption/ionization-time of flight (SELDI-TOF) has been questioned. This investigation was designed to test the reproducibility of SELDI data collected over time by multiple users and instruments. Five laboratories prepared arrays once every week for six weeks. Spectra were collected on separate instruments in the individual laboratories. Additionally, all of the arrays produced each week were rescanned on a single instrument in one laboratory. Lab-to-lab and array-to-array variability in alignment parameters were larger than the variability attributable to running samples during different weeks. The coefficient of variance (CV) in spectrum intensity ranged from 25% at baseline, to 80% in the matrix noise region, to about 50% during the exponential drop from the maximum matrix noise. Before normalization, the median CV of the peak heights was 72% and reduced to about 20% after normalization. Additionally, for the spectra from a common instrument, the CV ranged from 5% at baseline, to 50% in the matrix noise region, to 20% during the drop from the maximum matrix noise. Normalization reduced the variability in peak heights to about 18%. With proper processing methods, SELDI instruments produce spectra containing large numbers of reproducibly located peaks, with consistent heights.

Imaging and 'omic' methods for the molecular diagnosis of cancer

Molecular imaging methods can noninvasively detect specific biological processes that are aberrant in cancer, including upregulated glycolytic metabolism, increased cellular proliferation and altered receptor expression. PET using the glucose analogue 18F-fluoro-2-deoxyglucose, which detects the increased glucose uptake that is a characteristic of tumor cells, has been widely used in the clinic to detect tumors and their responses to treatment; however, there are many new PET tracers being developed for a wide range of biological targets. Magnetic resonance spectroscopy (MRS), which can be used to detect cellular metabolites, can also provide prognostic information, particularly in brain, breast and prostate cancers. An emerging technique, which by hyperpolarizing 13C-labeled cell substrates dramatically enhances their sensitivity to detection, could further extend the use of MRS in molecular imaging in the clinic. Molecular diagnostics applied to serum samples or tumor samples obtained by biopsy, can measure changes at the individual cell level and the underlying changes in gene or protein expression. DNA microarrays enable high-throughput gene-expression profiling, while mass spectrometry can detect thousands of proteins that may be used in the future as biomarkers of cancer. Probing molecular changes will aid not only cancer diagnosis, but also provide tumor grading, based on gene-expression analysis and imaging measurements of cell proliferation and changes in metabolism; staging, based on imaging of metastatic spread and elevation of protein biomarkers; and the detection of therapeutic response, using serial molecular imaging measurements or monitoring of serum markers. The present article provides a summary of the molecular diagnostic methods that are currently being trialed in the clinic.

Proteomic analysis of laser microdissected melanoma cells from skin organ cultures

Gaining insights into the molecular events that govern the progression from melanoma in situ to advanced melanoma and understanding how the local microenvironment at the melanoma site influences this progression are two clinically pivotal aspects that to date are largely unexplored. In an effort to identify key regulators of the crosstalk between melanoma cells and the melanoma-skin microenvironment, primary and metastatic human melanoma cells were seeded into skin organ cultures (SOCs) and grown for two weeks. Melanoma cells were recovered from SOCs by laser microdissection and whole-cell tryptic digests were analyzed by nanoflow liquid chromatography-tandem mass spectrometry. The differential protein abundances were calculated by spectral counting, the results of which provides evidence that cell-matrix and cell-adhesion molecules that are upregulated in the presence of these melanoma cells recapitulate proteomic data obtained from comparative analysis of human biopsies of invasive melanoma and a tissue sample of adjacent, noninvolved skin. This concordance demonstrates the value of SOCs for conducting proteomic investigations of the melanoma microenvironment.