Lung Cancer Gene Regulatory Network of Transcription Factors Related to the Hallmarks of Cancer.

The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target genes in order to analyze transcription factor complex formation, as well as its synchronized co-expression patterns in every type of lung cancer. The regulatory function of the most frequently dysregulated transcription factors over lung cancer deregulated genes was validated with ChEA3 enrichment analysis. A Kaplan-Meier plotter analysis linked the dysregulation of the top transcription factors with lung cancer patients' survival. Our results indicate that lung cancer has unique and common deregulated genes and transcription factors with pulmonary arterial hypertension, co-expressed and regulated in a coordinated and cooperative manner by the transcriptional regulatory network that might be associated with critical biological processes and signaling pathways related to the acquisition of the hallmarks of cancer, making them potentially relevant tumor biomarkers for lung cancer early diagnosis and targets for the development of personalized therapies against lung cancer.

Computational identification of biomarker genes for lung cancer considering treatment and non-treatment studies.

BACKGROUND: Lung cancer is the number one cancer killer in the world with more than 142,670 deaths estimated in the United States alone in the year 2019. Consequently, there is an overreaching need to identify the key biomarkers for lung cancer. The aim of this study is to computationally identify biomarker genes for lung cancer that can aid in its diagnosis and treatment. The gene expression profiles of two different types of studies, namely non-treatment and treatment, are considered for discovering biomarker genes. In non-treatment studies healthy samples are control and cancer samples are cases. Whereas, in treatment studies, controls are cancer cell lines without treatment and cases are cancer cell lines with treatment. RESULTS: The Differentially Expressed Genes (DEGs) for lung cancer were isolated from Gene Expression Omnibus (GEO) database using R software tool GEO2R. A total of 407 DEGs (254 upregulated and 153 downregulated) from non-treatment studies and 547 DEGs (133 upregulated and 414 downregulated) from treatment studies were isolated. Two Cytoscape apps, namely, CytoHubba and MCODE, were used for identifying biomarker genes from functional networks developed using DEG genes. This study discovered two distinct sets of biomarker genes - one from non-treatment studies and the other from treatment studies, each set containing 16 genes. Survival analysis results show that most non-treatment biomarker genes have prognostic capability by indicating low-expression groups have higher chance of survival compare to high-expression groups. Whereas, most treatment biomarkers have prognostic capability by indicating high-expression groups have higher chance of survival compare to low-expression groups. CONCLUSION: A computational framework is developed to identify biomarker genes for lung cancer using gene expression profiles. Two different types of studies - non-treatment and treatment - are considered for experiment. Most of the biomarker genes from non-treatment studies are part of mitosis and play vital role in DNA repair and cell-cycle regulation. Whereas, most of the biomarker genes from treatment studies are associated to ubiquitination and cellular response to stress. This study discovered a list of biomarkers, which would help experimental scientists to design a lab experiment for further exploration of detail dynamics of lung cancer development.

Effective voltammetric tool for simultaneous detection of MMP-1, MMP-2, and MMP-9; important non-small cell lung cancer biomarkers.

Simultaneous detection of multiple biomarkers can allow to reduce the costs of medical diagnostics, and thus improve the accuracy and effectiveness of disease diagnosis and prognosis. Here, for the first time, we present a low-cost, simple, and rapid method for simultaneous detection of three matrix metalloproteinases (MMP-1, MMP-2, and MMP-9) that play important roles in the progression of lung cancer. The sensor matrix was constructed using a G2 polyamidoamine dendrimer (PAMAM) containing amino, carboxyl, and sulfhydryl groups. The recognition process was based on specific enzymatic cleavage of the Gly-Ile peptide bond by MMP-1, Gly-Leu bond by MMP-2, and Gly-Met bond by MMP-9, and monitoring was done by square wave voltammetry. The activity of metalloproteinases was detected based on the change of current signals of redox receptors (dipeptides labeled with electroactive compounds) covalently anchored onto the electrode surface. The conditions of the biosensor construction, including the concentration of receptors on the sensor surface and the time of interaction of the receptor with the analyte, were carefully optimized. Under optimal conditions, the linear response of the developed method ranged from 1.0⋅10-8 to 1.0 mg⋅L-1, and the limit of detection for MMP-1, MMP-2, and MMP-9 was 0.35, 0.62, and 1.10 fg⋅mL-1, respectively. The constructed biosensor enabled us to efficiently profile the levels of active forms of MMP-1, MMP-2, and MMP-9 in tissue samples (plasma and lung and tumor extracts). Thus, the developed biosensor can aid in the early detection and diagnosis of lung cancer.

Emerging Biosensing Methods to Monitor Lung Cancer Biomarkers in Biological Samples: A Comprehensive Review.

Lung cancer is the most commonly diagnosed of all cancers and one of the leading causes of cancer deaths among men and women worldwide, causing 1.5 million deaths every year. Despite developments in cancer treatment technologies and new pharmaceutical products, high mortality and morbidity remain major challenges for researchers. More than 75% of lung cancer patients are diagnosed in advanced stages, leading to poor prognosis. Lung cancer is a multistep process associated with genetic and epigenetic abnormalities. Rapid, accurate, precise, and reliable detection of lung cancer biomarkers in biological fluids is essential for risk assessment for a given individual and mortality reduction. Traditional diagnostic tools are not sensitive enough to detect and diagnose lung cancer in the early stages. Therefore, the development of novel bioanalytical methods for early-stage screening and diagnosis is extremely important. Recently, biosensors have gained tremendous attention as an alternative to conventional methods because of their robustness, high sensitivity, inexpensiveness, and easy handling and deployment in point-of-care testing. This review provides an overview of the conventional methods currently used for lung cancer screening, classification, diagnosis, and prognosis, providing updates on research and developments in biosensor technology for the detection of lung cancer biomarkers in biological samples. Finally, it comments on recent advances and potential future challenges in the field of biosensors in the context of lung cancer diagnosis and point-of-care applications.

Dual-template rectangular nanotube molecularly imprinted polypyrrole for label-free impedimetric sensing of AFP and CEA as lung cancer biomarkers.

A high-performance sensing layer based on dual-template molecularly imprinted polymer (DMIP) was fabricated and successfully applied for one-by-one detection of carcinoembryonic antigen (CEA) and alpha-fetoprotein (AFP) as lung cancer biomarkers. The plastic antibodies of AFP and CEA were created into the electropolymerized polypyrrole (PPy) on a fluorine-doped tin oxide (FTO) electrode. Raman spectroscopy, field emission scanning electron microscopy (FE-SEM), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS) tests were performed to pursue the formation and characterization of the sensing layer. Methyl orange (MO) increased the conductivity of PPy and induced the formation of MO doped PPy (PPy-MO) rectangular-shaped nanotubes. Using impedimetric detection, the rebinding of the template antigens was evaluated, the charge transfer resistance increased as the concentration of AFP and CEA increased. The linear dynamic ranges of 5-104 and 10-104 pg mL-1 and detection limits of 1.6 and 3.3 pg mL-1 were obtained for CEA and AFP, respectively. Given satisfactory results in the determination of AFP and CEA in the human serum samples, high sensitivity, and good stability of DMIP sensor made it a promising method for sensing of AFP and CEA in serum samples.

13-Plex UHPLC-MS/MS Analysis of Hexanal and Heptanal Using Multiplex Tags Chemical Isotope Labeling Technology.

In this work, a new series of chemical isotope labeling reagents, levofloxacin-hydrazide-based mass tags (LHMTs) named as LHMT359/360/361/362/363/364/365/366/373/375/376/378/379/381 were first designed and synthesized for the high-throughput analysis of potential biomarkers containing hexanal and heptanal of lung cancer. We exploited a new core structure of levofloxacin-d3, which significantly enhanced the multiplexing capability. Among them, LHMT359 was used for labeling standard compounds as internal standards for quantification. Using LHMT373-heptanal as dummy template, dummy magnetic molecularly imprinted polymers (DMMIPs) were prepared for magnetic dispersive solid-phase extraction after derivatization procedure. Other 12 LHMTs were established for high-throughput labeling hexanal and heptanal in human serum samples. The presynthesized DMMIPs can selectively extract LHMTs-derivatives of hexanal and heptanal from equally mixed derivatization solutions. The enriched derivatives of hexanal and heptanal were quantified by ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). A single UHPLC-MS/MS run enabled simultaneously quantifying hexanal and heptanal from 12 serum samples only within 2 min. The limits of detection were all 0.5 pM for hexanal and heptanal. The accuracies from human serum samples ranged from -10.2% to +11.0% with the intra- and interday precisions less than 11.3%. Meanwhile, this method was successfully applied for the analysis of hexanal and heptanal in serum samples from healthy people and lung cancer patients. The results show that this method has the significant advantages of high sensitivity, accuracy, selectivity, and analysis-throughput. The method application indicates that the developed method is promising in the screening of suspected lung cancer patients.

A National Assessment of Diagnostic Test Use for Patients with Advanced NSCLC and Factors Influencing Physician Decision-Making.

BACKGROUND: Diagnostic tests, including US Food and Drug Administration (FDA)-approved tests and laboratory-developed tests, are frequently used to guide care for patients with cancer, and, recently, have been the subject of several policy discussions and insurance coverage determinations. As the use of diagnostic testing has evolved, stakeholders have raised questions about the lack of standardized test performance metrics and the risk this poses to patients. OBJECTIVES: To describe the use of diagnostic testing for patients with advanced non-small-cell lung cancer (NSCLC), to analyze the utilization of FDA-approved versus laboratory-developed diagnostic tests, and to evaluate the impact of existing regulatory and coverage frameworks on diagnostic test ordering and physician treatment decision-making for patients with advanced NSCLC. METHODS: We conducted a 2-part study consisting of an online survey and patient chart review from March 1, 2019, to March 25, 2019, of physicians managing patients with advanced NSCLC. Respondents qualified for this study if they managed at least 5 patients with advanced NSCLC per month and had diagnosed at least 1 patient with advanced NSCLC in the 12 months before the survey. A total of 150 physicians completed the survey; before completing the survey, they were instructed to review between 4 and 8 charts of patients with stage IV NSCLC from their list of active patients. RESULTS: A total of 150 practicing oncologists who manage patients with advanced NSCLC responded to the survey and reviewed a total of 815 patient charts. Of these 815 patients, 812 (99.6%) were tested for at least 1 biomarker, including 73% of patients who were tested for EGFR, 70% tested for ALK, 58% tested for BRAF V600E, and 38% of patients tested for ROS1, by FDA-approved diagnostic tests. In all, 185 (83%) patients who tested positive for EGFR and 60 (83%) patients who tested positive for ALK received an FDA-approved targeted therapy for their biomarker. A total of 98 (65%) physicians responded that the patient's insurance coverage factored into their decision to order diagnostic tests and 69 (45%) physicians responded that cost or the patient's insurance coverage could influence them not to prescribe an indicated targeted therapy. CONCLUSION: The survey results indicate that diagnostic testing has become routine in the treatment of patients with advanced NSCLC, the use of FDA-approved diagnostic tests has increased, and insurance coverage and cost influence patient access to diagnostic testing as well as to targeted treatment options.

Lung Cancer Biomarkers.

Lung cancer is the most frequently occurring cancer in the world and continually leads in mortality among cancers. The overall 5-year survival rate for lung cancer has risen only 4% (from 12% to 16%) over the past 4 decades, and late diagnosis is a major obstacle in improving lung cancer prognosis. Survival of patients undergoing lung resection is greater than 80%, suggesting that early detection and diagnosis of cancers before they become inoperable and lethal will greatly improve mortality. Lung cancer biomarkers can be used for screening, detection, diagnosis, prognosis, prediction, stratification, therapy response monitoring, and so on. This review focuses on noninvasive diagnostic and prognostic biomarkers. For that purpose, our discussion in this review will focus on biological fluid-based biomarkers. The body fluids include blood (serum or plasma), sputum, saliva, BAL, pleural effusion, and VOC. Since it is rich in different cellular and molecular elements and is one of the most convenient and routine clinical procedures, serum or plasma is the main source for the development and validation of many noninvasive biomarkers. In terms of molecular aspects, the most widely validated ones are proteins, some of which are used in the clinical sector, though in limited accessory purposes. We will also discuss the lung cancer (protein) biomarkers in clinical trials and currently in the validation phase with hundreds of samples. After proteins, we will discuss microRNAs, methylated DNA, and circulating tumor cells, which are being vigorously developed and validated as potential lung cancer biomarkers. The main aim of this review is to provide researchers and clinicians with an understanding of the potential noninvasive lung cancer biomarkers in biological fluids that have recently been discovered.

Network-based identification of biomarkers coexpressed with multiple pathways.

Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.