Development of a Protein Biomarker Panel to Detect Non-Small-Cell Lung Cancer in Korea

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

Proteomic analysis of multiple organ dysfunction induced by rhabdomyolysis

Rhabdomyolysis (RM) leads to dysfunction in the core organs of kidney, lung and heart, which is an important reason for the high mortality and disability rate of this disease. However, there is a lack of systematic research on the characteristics of rhabdomyolysis-induced injury in various organs and the underlying pathogenetic mechanisms, and especially the interaction between organs. We established a rhabdomyolysis model, observed the structural and functional changes in kidney, heart, and lung. It is observed that rhabdomyolysis results in significant damage in kidney, lung and heart of rats, among which the pathological damage of kidney and lung was significant, and of heart was relatively light. Meanwhile, we analyzed the differentially expressed proteins (DEPs) in the kidney, heart and lung between the RM group and the sham group based on liquid chromatography-tandem mass spectrometry (LC-MS/MS). In our study, Serpina3n was significantly up-regulated in the kidney, heart and lung. Serpina3n is a secreted protein and specifically inhibits a variety of proteases and participates in multiple physiological processes such as complement activation, inflammatory responses, apoptosis pathways, and extracellular matrix metabolism. It is inferred that Serpina3n may play an important role in multiple organ damage caused by rhabdomyolysis and could be used as a potential biomarker. This study comprehensively describes the functional and structural changes of kidney, heart and lung in rats after rhabdomyolysis, analyzes the DEPs of kidney, heart and lung, and determines the key role of Serpina3n in multiple organ injury caused by rhabdomyolysis. SIGNIFICANCE: This study comprehensively describes the functional and structural changes of kidney, heart and lung in rats after rhabdomyolysis, analyzes the DEPs of kidney, heart and lung, and determines the key role of Serpina3n in multiple organ injury caused by rhabdomyolysis.

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

Lung cancer (LC) causes few symptoms in the earliest stages, leading to one of the highest mortality rates among cancers. Low-dose computerised tomography (LDCT) is used to screen high-risk individuals, reducing the mortality rate by 20%. However, LDCT results in a high number of false positives and is associated with unnecessary follow-up and cost. Biomarkers with high sensitivities and specificities could assist in the early detection of LC, especially in patients with high-risk features. Carcinoembryonic antigen (CEA), cytokeratin 19 fragments and cancer antigen 125 have been found to be highly expressed during the later stages of LC but have low sensitivity in the earliest stages. We determined the best biomarkers for the early diagnosis of LC, using a systematic review of eight databases. We identified 98 articles that focussed on the identification and assessment of diagnostic biomarkers and achieved a pooled area under curve of 0.85 (95% CI 0.82-0.088), indicating that the diagnostic performance of these biomarkers when combined was excellent. Of the studies, 30 focussed on single/antigen panels, 22 on autoantibodies, 31 on miRNA and RNA panels, and 15 suggested the use of circulating DNA combined with CEA or neuron-specific enolase (NSE) for early LC detection. Verification of blood biomarkers with high sensitivities (Ciz1, exoGCC2, ITGA2B), high specificities (CYFR21-1, antiHE4, OPNV) or both (HSP90α, CEA) along with miR-15b and miR-27b/miR-21 from sputum may improve early LC detection. Further assessment is needed using appropriate sample sizes, control groups that include patients with non-malignant conditions, and standardised cut-off levels for each biomarker.

Identification of protein signatures for lung cancer subtypes based on BPSO method

The objective of this study was to identify protein biomarkers that can distinguish between LUAD and LUSC, critical for personalized treatment plans. The proteomic profiling data of LUAD and LUSC samples from TCPA database, along with phenotype and survival information from TCGA database were downloaded and preprocessed for analysis. We used BPSO feature selection method and identified 10 candidate protein biomarkers that have better classifying performance, as analyzed by t-SNE and PCA algorithms. To explore the causalities among these proteins and their associations with tumor subtypes, we conducted the PCStable algorithm to construct a regulatory network. Results indicated that 4 proteins, MIG6, CD26, NF2, and INPP4B, were directly linked to the lung cancer subtypes and may be useful in guiding therapeutic decision-making. Besides, spearman correlation, Cox proportional hazard model and Kaplan-Meier curve was employed to validate the biological significance of the candidate proteins. In summary, our study highlights the importance of protein biomarkers in the classification of lung cancer subtypes and the potential of computational methods for identifying key biomarkers and understanding their underlying biological mechanisms.

Conducting polymer composite-based biosensing materials for the diagnosis of lung cancer: A review

The development of a simple and fast cancer detection method is crucial since early diagnosis is a key factor in increasing survival rates for lung cancer patients. Among several diagnosis methods, the electrochemical sensor is the most promising one due to its outstanding performance, portability, real-time analysis, robustness, amenability, and cost-effectiveness. Conducting polymer (CP) composites have been frequently used to fabricate a robust sensor device, owing to their excellent physical and electrochemical properties as well as biocompatibility with nontoxic effects on the biological system. This review brings up a brief overview of the importance of electrochemical biosensors for the early detection of lung cancer, with a detailed discussion on the design and development of CP composite materials for biosensor applications. The review covers the electrochemical sensing of numerous lung cancer markers employing composite electrodes based on the conducting polyterthiophene, poly(3,4-ethylenedioxythiophene), polyaniline, polypyrrole, molecularly imprinted polymers, and others. In addition, a hybrid of the electrochemical biosensors and other techniques was highlighted. The outlook was also briefly discussed for the development of CP composite-based electrochemical biosensors for POC diagnostic devices.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD)

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.

Patient-derived xenograft (PDX) models, applications and challenges in cancer research

The establishing of the first cancer models created a new perspective on the identification and evaluation of new anti-cancer therapies in preclinical studies. Patient-derived xenograft models are created by tumor tissue engraftment. These models accurately represent the biology and heterogeneity of different cancers and recapitulate tumor microenvironment. These features have made it a reliable model along with the development of humanized models. Therefore, they are used in many studies, such as the development of anti-cancer drugs, co-clinical trials, personalized medicine, immunotherapy, and PDX biobanks. This review summarizes patient-derived xenograft models development procedures, drug development applications in various cancers, challenges and limitations.

Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications

The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.

Translation of aptamers toward clinical diagnosis and commercialization

The dominance of antibodies in diagnostics has gradually changed following the discovery of aptamers in the early 1990s. Aptamers offer inherent advantages over traditional antibodies, including higher specificity, higher affinity, smaller size, greater stability, ease of manufacture, and low immunogenicity, rendering them the best candidates for point-of-care testing (POCT). In the past 20 years, the research community and pharmaceutical companies have made great efforts to promote the development of aptamer technology. Macugen® (pegaptanib) was the first aptamer drug approved by the US Food and Drug Administration (FDA), and various aptamer-based diagnostics show great promise in preclinical research and clinical trials. In this review, we introduce recent literature, ongoing clinical trials, commercial reagents of aptamer-based diagnostics, discuss the FDA regulatory mechanisms, and highlight the prospects and challenges in translating these studies into viable clinical diagnostic tools.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Acute Proteomic Changes in Lung after Radiation: Toward Identifying Initiating Events of Delayed Effects of Acute Radiation Exposure in Non-human Primate after Partial Body Irradiation with Minimal Bone Marrow Sparing

ABSTRACT

Radiation-induced lung injury is a delayed effect of acute radiation exposure resulting in pulmonary pneumonitis and fibrosis. Molecular mechanisms that lead to radiation-induced lung injury remain incompletely understood. Using a non-human primate model of partial body irradiation with minimal bone marrow sparing, lung was analyzed from animals irradiated with 12 Gy at timepoints every 4 d up to 21 d after irradiation and compared to non-irradiated (sham) controls. Tryptic digests of lung tissues were analyzed by liquid chromatography-tandem mass spectrometry followed by pathway analysis. Out of the 3,101 unique proteins that were identified, we found that 252 proteins showed significant and consistent responses across at least three time points post-irradiation, of which 215 proteins showed strong up-regulation while 37 proteins showed down-regulation. Canonical pathways affected by irradiation, changes in proteins that serve as upstream regulators, and proteins involved in key processes including inflammation, fibrosis, and retinoic acid signaling were identified. The proteomic profiling of lung conducted here represents an untargeted systems biology approach to identify acute molecular events in the non-human primate lung that could potentially be initiating events for radiation-induced lung injury.

Aptamers: Novel Therapeutics and Potential Role in Neuro-Oncology

A relatively new paradigm in cancer therapeutics is the use of cancer cell-specific aptamers, both as therapeutic agents and for targeted delivery of anticancer drugs. After the first therapeutic aptamer was described nearly 25 years ago, and the subsequent first aptamer drug approved, many efforts have been made to translate preclinical research into clinical oncology settings. Studies of aptamer-based technology have unveiled the vast potential of aptamers in therapeutic and diagnostic applications. Among pediatric solid cancers, brain tumors are the leading cause of death. Although a few aptamer-related translational studies have been performed in adult glioblastoma, the use of aptamers in pediatric neuro-oncology remains unexplored. This review will discuss the biology of aptamers, including mechanisms of targeting cell surface proteins, various modifications of aptamer structure to enhance therapeutic efficacy, the current state and challenges of aptamer use in neuro-oncology, and the potential therapeutic role of aptamers in pediatric brain tumors.

Accelerating protein biomarker discovery and translation from proteomics research for clinical utility

Discovery proteomics research has made significant progress in the past several years; however, the number of protein biomarkers deployed in clinical practice remains rather limited. There are several scientific and procedural gaps between discovery proteomics research and clinical implementation, which have contributed to poor biomarker validity and few clinical applications. The complexity and low throughput of proteomics approaches have added additional barriers for biomarker assay translation to clinical applications. Recently, targeted proteomics have become a powerful tool to bridge the biomarker discovery to clinical validation. In this perspective, we discuss the challenges and strategies in proteomics research from a clinical perspective, and propose several recommendations for discovery proteomics research to accelerate protein biomarker discovery and translation for future clinical applications.

Advances in Oligonucleotide Aptamers for NSCLC Targeting

Non-small-cell lung cancer (NSCLC) is the most common type of lung cancer worldwide, with the highest incidence in developed countries. NSCLC patients often face resistance to currently available therapies, accounting for frequent relapses and poor prognosis. Indeed, despite great recent advancements in the field of NSCLC diagnosis and multimodal therapy, most patients are diagnosed at advanced metastatic stage, with a very low overall survival. Thus, the identification of new effective diagnostic and therapeutic options for NSCLC patients is a crucial challenge in oncology. A promising class of targeting molecules is represented by nucleic-acid aptamers, short single-stranded oligonucleotides that upon folding in particular three dimensional (3D) structures, serve as high affinity ligands towards disease-associated proteins. They are produced in vitro by SELEX (systematic evolution of ligands by exponential enrichment), a combinatorial chemistry procedure, representing an important tool for novel targetable biomarker discovery of both diagnostic and therapeutic interest. Aptamer-based approaches are promising options for NSCLC early diagnosis and targeted therapy and may overcome the key obstacles of currently used therapeutic modalities, such as the high toxicity and patients’ resistance. In this review, we highlight the most important applications of SELEX technology and aptamers for NSCLC handling.

POLE2 Serves as a Prognostic Biomarker and Is Associated with Immune Infiltration in Squamous Cell Lung Cancer

Background

Squamous cell lung cancer is the main cause of cancer-associated mortality. The discovery of promising prognostic biomarkers for predicting the survival of patients with squamous cell lung cancer remains a challenge.

Material/Methods

Gene expression profiles of GSE33479 and GSE51855, including 42 squamous cell lung cancer tissues and 17 normal tissues, from the GEO database were assessed to find common differentially expressed genes (DEGs) via the GEO2R online tool and Venn diagram software. Then, gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analyses were conducted. The key protein-protein interaction (PPI) network within those common DEGs was subsequently illustrated through a combination of Search Tool for Retrieval of Interacting Genes (STRING) and Cytoscape software. Finally, core genes associated with survival and levels of immune infiltration were demonstrated by the Kaplan-Meier plotter and Tumor Immune Estimation Resource (TIMER) online database, respectively.

Results

In total, 483 DEGs were involved, including 216 upregulated genes enriched in “cell division”, “DNA replication”, and “DNA repair pathway” and 267 downregulated genes enriched in “cell adhesion”, “oxidation-reduction process”, and “cell-cell signaling”. The 75 core genes were selected by Molecular Complex Detection applied in Cytoscape. Four genes – MND1, FOXM1, CDC6, and POLE2 – were found to be significantly associated with survival. Further analysis of the KEEG pathway and TIMER database revealed that only POLE2 was enriched in “DNA replication” and its higher expression was negatively associated with survival and immune infiltration.

Conclusions

Higher expression of POLE2 is a prognosis-related biomarker for worse survival and is negatively associated with immune infiltration in squamous cell lung cancer.

Label-Free Quantitative Proteomics Identifies Novel Biomarkers for Distinguishing Tuberculosis Pleural Effusion from Malignant Pleural Effusion

PURPOSE

To identify potential protein biomarkers for distinguishing tuberculosis plural effusion (TBPE) from malignant plural effusion (MPE).

EXPERIMENTAL DESIGN

Five independent samples from each group (TBPE and MPE) are enrolled for label-free quantitative proteomics analyses. The differentially expressed proteins are validated by western blot and ELISA. Logistic regression analysis is used to obtain the optimal diagnostic model.

RESULTS

In total, 14 proteins with significant difference are identified between TBPE and MPE. Seven differentially expressed proteins are validated using western blot, and the expression patterns of these seven proteins are similar with those in proteomics analysis. Statistically significant differences in four proteins (AGP1, ORM2, C9, and SERPING1) are noted between TBPE and MPE in the training set (n = 230). Logistic regression analysis shows the combination of AGP1-ORM2-C9 presents a sensitivity of 73.0% (92/126) and specificity of 89.4% (93/104) in discriminating TBPE from MPE. Additional validation is performed to evaluate the diagnostic model in an independent blind testing set (n = 80), and yielded a sensitivity of 74.4% (32/43) and specificity of 91.9% (34/37) in discriminating TBPE from MPE.

CONCLUSION

The study uncovers the proteomic profiles of TBPE and MPE, and provides new potential diagnostic biomarkers for distinguishing TBPE from MPE.

Identification of protein changes in the blood plasma of lung cancer patients subjected to chemotherapy using a 2D-DIGE approach

A comparative analysis of blood samples (depleted of albumin and IgG) obtained from lung cancer patients before chemotherapy versus after a second cycle of chemotherapy was performed using two-dimensional difference gel electrophoresis (2D-DIGE). The control group consisted of eight patients with non-cancerous lung diseases, and the experimental group consisted of four adenocarcinoma (ADC) and four squamous cell carcinoma (SCC) patients. Analyses of gels revealed significant changes in proteins and/or their proteoforms between control patients and lung cancer patients, both before and after a second cycle of chemotherapy. Most of these proteins were related to inflammation, including acute phase proteins (APPs) such as forms of haptoglobin and transferrin, complement component C3, and clusterin. The variable expression of APPs can potentially be used for profiling lung cancer. The greatest changes observed after chemotherapy were in transferrin and serotransferrin, which likely reflect disturbances in iron turnover after chemotherapy-induced anaemia. Significant changes in plasma proteins between ADC and SCC patients were also revealed, suggesting use of plasma vitronectin as a potential marker of SCC.

Graphene-based biosensors for the detection of prostate cancer protein biomarkers: a review

Prostate cancer (PC) is the sixth most common cancer type in the world, which causes approximately 10% of total cancer fatalities. The detection of protein biomarkers in body fluids is the key topic for the diagnosis and prognosis of PC. Highly sensitive screening of PC is the most effective approach for reducing mortality. Thus, there are a growing number of literature that recognizes the importance of new technologies for early diagnosis of PC. Graphene is playing an important role in the biosensor field with remarkable physical, optical, electrochemical and magnetic properties. Many recent studies demonstrated the potential of graphene materials for sensitive detection of protein biomarkers. In this review, the graphene-based biosensors toward PC analysis are mainly discussed in two groups: Firstly, novel biosensor interfaces were constructed through the modification of graphene materials onto sensor surfaces. Secondly, ingenious signal amplification strategies were developed using graphene materials as catalysts or carriers. Graphene-based biosensors have exhibited remarkable performance with high sensitivities, wide detection ranges, and long-term stabilities.

Elevated Circulating CD4+CD25-Foxp3+ Regulatory T Cells in Patients with Nonsmall Cell Lung Cancer

Purpose: CD4⁺CD25⁺Foxp3⁺ regulatory T (Treg) cell-mediated immunosuppression has been implicated as a crucial mechanism of tumor immune cell escape in nonsmall cell lung cancer (NSCLC). However, little is known concerning the specific role of CD4⁺CD25^-Foxp3⁺ Treg cells in NSCLC. The aim of this study was to investigate the frequency of circulating CD4⁺CD25^-Foxp3⁺ Treg cells and their role in NSCLC. Methods: The frequencies of Treg, T helper (Th)1, Th2, and Th17 cells in peripheral blood were separately measured in 36 NSCLC patients and 20 healthy controls (HCs) using flow cytometry. Serum cytokine concentrations were determined using cytometric bead arrays. Results: The frequencies of circulating CD4⁺CD25⁺ T cells and CD4⁺CD25⁺Foxp3⁺ and CD4⁺CD25^-Foxp3⁺ Treg cells were significantly higher in advanced-stage NSCLC patients compared with patients with limited-stage NSCLC. The frequencies of circulating CD4⁺CD25⁺Foxp3⁺ and CD4⁺CD25^-Foxp3⁺ Treg cells were negatively correlated with interleukin (IL)-17, but positively correlated with serum IL-10 levels. In addition, the Th17/CD4⁺CD25^-Foxp3⁺ Treg cell ratios were negatively correlated with serum cytokeratin 19 fragment (CYFRA 21-1) concentrations in patients with NSCLC. Moreover, coculturing CD4⁺CD25^-Foxp3⁺ Treg cells and CD14⁺ monocytes in vitro resulted in a higher frequency of CD206⁺CD14⁺ M2-like monocytes compared with CD14⁺ monocytes. Conclusions: Elevated circulating CD4⁺CD25^-Foxp3⁺ Treg cells may be involved in the pathogenesis of NSCLC.

Clinical Validation of a Protein Biomarker Panel for Non-Small Cell Lung Cancer

We validated the diagnostic performance of a previously developed blood-based 7-protein biomarker panel, AptoDetect™-Lung (Aptamer Sciences Inc., Pohang, Korea) using modified aptamer-based proteomic technology for lung cancer detection. Non-small cell lung cancer (NSCLC), 200 patients and benign nodule controls, 200 participants were enrolled. In a high-risk population corresponding to ≥ 55 years of age and ≥ 30 pack-years, the diagnostic performance was improved, showing 73.3% sensitivity and 90.5% specificity with an area under the curve of 0.88. AptoDetect™-Lung (Aptamer Sciences Inc.) offers the best validated performance to discriminate NSCLC from benign nodule controls in a high-risk population and could play a complementary role in lung cancer screening.

Single Cell Proteomics for Molecular Targets in Lung Cancer: High-Dimensional Data Acquisition and Analysis

In the proteomic and genomic era, lung cancer researchers are increasingly under challenge with traditional protein analyzing tools. High output, multiplexed analytical procedures are in demand for disclosing the post-translational modification, molecular interactions and signaling pathways of proteins precisely, specifically, dynamically and systematically, as well as for identifying novel proteins and their functions. This could be better realized by single-cell proteomic methods than conventional proteomic methods. Using single-cell proteomic tools including flow cytometry, mass cytometry, microfluidics and chip technologies, chemical cytometry, single-cell western blotting, the quantity and functions of proteins are analyzed simultaneously. Aside from deciphering disease mechanisms, single-cell proteomic techniques facilitate the identification and screening of biomarkers, molecular targets and promising compounds as well. This review summarized single-cell proteomic tools and their use in lung cancer.

Aptamers as Diagnostic Tools in Cancer

Cancer is the second leading cause of death worldwide. Researchers have been working hard on investigating not only improved therapeutics but also on early detection methods, both critical to increasing treatment efficacy, and developing methods for disease prevention. The use of nucleic acids, or aptamers, has emerged as more specific and accurate cancer diagnostic and therapeutic tools. Aptamers are single-stranded DNA or RNA molecules that recognize specific targets based on unique three-dimensional conformations. Despite the fact aptamer development has been mainly restricted to laboratory settings, the unique attributes of these molecules suggest their high potential for clinical advances in cancer detection. Aptamers can be selected for a wide range of targets, and also linked with an extensive variety of diagnostic agents, via physical or chemical conjugation, to improve previously-established detection methods or to be used as novel biosensors for cancer diagnosis. Consequently, herein we review the principal considerations and recent updates in cancer detection and imaging through aptamer-based molecules.

SERPINA3 Silencing Inhibits the Migration, Invasion, and Liver Metastasis of Colon Cancer Cells

OBJECTIVE

To investigate the impact of SERPINA3 on the migration, invasion, and liver metastasis of colon cancer cells.

METHODS

Immunohistochemical staining was conducted to determine SERPINA3 expression in the cancer and adjacent normal tissues of 131 patients suffering from colon cancer. In vitro experiment, colon cancer cells with low (HT-29P), intermediate (KM-12C), and high (HT-29LMM, KM-12L4) metastatic potential were obtained to examine SERPINA3 expression levels. Besides, quantitative real-time PCR (qRT-PCR) and Western Blot were performed to detect SERPINA3 expression in HT-29LMM and KM-12L4 cells transfected with SERPINA3 siRNA; Wound-healing and Transwell assays to measure cell migration and invasion, respectively; and ELISA to detect MMP-2 and MMP-9 levels. In vivo experiment, mice with liver metastasis of colon cancer were established to observe the effect of SERPINA3 silencing on liver metastasis. Immunohistochemical assay was applied to evaluate the expressions of Serpina3, Mmp-2, Mmp-9, and proliferating cell nuclear antigen (Pcna) in liver metastasis tissues.

RESULTS

SERPINA3 in colon cancer tissues was higher than in adjacent normal tissues, which was associated with patients' clinicopathological features. Besides, SERPINA3 expression showed a rising trend in low, intermediate, and high metastatic potential colon cancer cells. After KM-12L4 and HT-29LMM cells transfected with SERPINA3 siRNA, the migration and invasive ability of cells, as well as the expression levels of MMP-2 and MMP-9 were all decreased. Moreover, SERPINA3 siRNA could not only reduce live metastasis of mice, but also down-regulate the expression of Mmp-2 and Mmp-9 in liver metastasis tissues.

CONCLUSION

SERPINA3 silencing could inhibit the migration, invasion, and liver metastasis of colon cancer cells.

Aptamers in the Therapeutics and Diagnostics Pipelines

Aptamers are short single-stranded DNA or RNA oligonucleotides that can selectively bind to small molecular ligands or protein targets with high affinity and specificity, by acquiring unique three-dimensional structures. Aptamers have the advantage of being highly specific, relatively small in size, non-immunogenic and can be easily stabilized by chemical modifications, thus allowing expansion of their diagnostic and therapeutic potential. Since the invention of aptamers in the early 1990s, great efforts have been made to make them clinically relevant for diseases like macular degeneration, cancer, thrombosis and inflammatory diseases. Furthermore, owing to the aforementioned advantages and unique adaptability of aptamers to point-of-care platforms, aptamer technology has created a stable niche in the field of in vitro diagnostics by enhancing the speed and accuracy of diagnoses. The aim of this review is to give an overview on aptamers, highlight the inherent therapeutic and diagnostic opportunities and challenges associated with them and present various aptamers that have reached therapeutic clinical trials, diagnostic markets or that have immediate translational potential for therapeutics and diagnostics applications.

Screening for early stage lung cancer and its correlation with lung nodule detection

Currently, the most effective way of reducing lung cancer mortality is early diagnosis of lung cancer. The National Lung Screening Trial has proved the efficacy of lung cancer screening using low-dose computed tomography to reduce lung cancer mortality. However, many questions remain surrounding lung cancer screening implementation, among which include how to select the optimal risk population, the personalized screening interval based different levels of risk, methods to improve diagnostic discrimination between malignant and benign disease in detected lung nodules, and the roles of biomolecular markers in stratifying risk and in guiding the management of indeterminate nodules. This review concentrates on the latest developments of lung cancer screening and provides an overview of the main unanswered questions on lung nodule detection.

Recent insights into human bronchial proteomics - how are we progressing and what is next?

The human respiratory system is highly prone to diseases and complications. Many lung diseases, including lung cancer (LC), tuberculosis (TB), and chronic obstructive pulmonary disease (COPD) have been among the most common causes of death worldwide. Cystic fibrosis (CF), the most common genetic disease in Caucasians, has adverse impacts on the lungs. Bronchial proteomics plays a significant role in understanding the underlying mechanisms and pathogenicity of lung diseases and provides insights for biomarker and therapeutic target discoveries. Areas covered: We overview the recent achievements and discoveries in human bronchial proteomics by outlining how some of the different proteomic techniques/strategies are developed and applied in LC, TB, COPD, and CF. Also, the future roles of bronchial proteomics in predictive proteomics and precision medicine are discussed. Expert commentary: Much progress has been made in bronchial proteomics. Owing to the advances in proteomics, we now have better ability to isolate proteins from desired cellular compartments, greater protein separation methods, more powerful protein detection technologies, and more sophisticated bioinformatic techniques. These all contributed to our further understanding of lung diseases and for biomarker and therapeutic target discoveries.

Current Advances in Aptamers for Cancer Diagnosis and Therapy

Nucleic acid aptamers are single-stranded oligonucleotides that interact with target molecules with high affinity and specificity in unique three-dimensional structures. Aptamers are generally isolated by a simple selection process called systematic evolution of ligands by exponential enrichment (SELEX) and then can be chemically synthesized and modified. Because of their high affinity and specificity, aptamers are promising agents for biomarker discovery, as well as cancer diagnosis and therapy. In this review, we present recent progress and challenges in aptamer and SELEX technology and highlight some representative applications of aptamers in cancer therapy.

Discovery and Validation of a Six-Marker Serum Protein Signature for the Diagnosis of Active Pulmonary Tuberculosis

New non-sputum biomarker tests for active tuberculosis (TB) diagnostics are of the highest priority for global TB control. We performed in-depth proteomic analysis using the 4,000-plex SOMAscan assay on 1,470 serum samples from seven countries where TB is endemic. All samples were from patients with symptoms and signs suggestive of active pulmonary TB that were systematically confirmed or ruled out for TB by culture and clinical follow-up. HIV coinfection was present in 34% of samples, and 25% were sputum smear negative. Serum protein biomarkers were identified by stability selection using L1-regularized logistic regression and by Kolmogorov-Smirnov (KS) statistics. A naive Bayes classifier using six host response markers (HR6 model), including SYWC, kallistatin, complement C9, gelsolin, testican-2, and aldolase C, performed well in a training set (area under the sensitivity-specificity curve [AUC] of 0.94) and in a blinded verification set (AUC of 0.92) to distinguish TB and non-TB samples. Differential expression was also highly significant (P < 10⁻²⁰) for previously described TB markers, such as IP-10, LBP, FCG3B, and TSP4, and for many novel proteins not previously associated with TB. Proteins with the largest median fold changes were SAA (serum amyloid protein A), NPS-PLA2 (secreted phospholipase A2), and CA6 (carbonic anhydrase 6). Target product profiles (TPPs) for a non-sputum biomarker test to diagnose active TB for treatment initiation (TPP#1) and for a community-based triage or referral test (TPP#2) have been published by the WHO. With 90% sensitivity and 80% specificity, the HR6 model fell short of TPP#1 but reached TPP#2 performance criteria. In conclusion, we identified and validated a six-marker signature for active TB that warrants diagnostic development on a patient-near platform.

Clinical validation of a blood-based classifier for diagnostic evaluation of asymptomatic individuals with pulmonary nodules

BACKGROUND

The number of pulmonary nodules detected in the US is expected to increase substantially following recent recommendations for nationwide CT-based lung cancer screening. Given the low specificity of CT screening, non-invasive adjuvant methods are needed to differentiate cancerous lesions from benign nodules to help avoid unnecessary invasive procedures in the asymptomatic population. We have constructed a serum-based multi-biomarker panel and assessed its clinical accuracy in a retrospective analysis of samples collected from participants with suspicious radiographic findings in the Prostate, Lung, Chest and Ovarian (PLCO) cancer screening trial.

METHODS

Starting with a set of 9 candidate biomarkers, we identified 8 that exhibited limited pre-analytical variability with increasing clotting time, a key pre-analytical variable associated with the collection of serum. These 8 biomarkers were evaluated in a training study consisting of 95 stage I NSCLC patients and 186 smoker controls where a 5-biomarker pulmonary nodule classifier (PNC) was selected. The clinical accuracy of the PNC was determined in a blinded study of asymptomatic individuals comprising 119 confirmed malignant nodule cases and 119 benign nodule controls selected from the PLCO screening trial.

RESULTS

A PNC comprising 5 biomarkers: CEA, CYFRA 21-1, OPN, SCC, and TFPI, was selected in the training study. In an independent validation study, the PNC resolved lung cancer cases from benign nodule controls with an AUC of 0.653 (p < 0.0001). CEA and CYFRA 21-1, two of the markers included in the PNC, also accurately distinguished malignant lesions from benign controls.

CONCLUSIONS

A 5-biomarker blood test has been developed for the diagnostic evaluation of asymptomatic individuals with solitary pulmonary nodules.

Serpin peptidase inhibitor, clade A member 3 (SERPINA3), is overexpressed in glioma and associated with poor prognosis in glioma patients

Glioma is the most common and aggressive human primary tumor in the central nervous system. Despite present clinical advancements, median survival time remains poor in this malignant tumor. Serpin peptidase inhibitor, clade A member 3 (SERPINA3), is a member of the serpin superfamily of protease inhibitors. Its aberrant expression has been observed in various tumors. However, its clinical significance and biological function in glioma remain unclear, especially for the prognosis of glioma patients. In this study, we investigated SERPINA3 expression in glioma tissue samples and its significance in predicting the prognosis of glioma patients. SERPINA3 protein expression was studied by immunohistochemistry, while real-time polymerase chain reaction was used to study SERPINA3 mRNA expression. We found that SERPINA3 was upregulated in glioma tissue at both mRNA and protein levels, compared with noncancerous brain tissues. We also found that high SERPINA3 expression in glioma tissues correlated significantly with advanced World Health Organization grade. Univariate and multivariate analyses revealed that high SERPINA3 expression was an independent prognostic factor for poor overall survival of glioma patients. Moreover, our findings were further validated by online Oncomine database. Taken together, our results suggest that SERPINA3 plays an oncogenic role in glioma progression and provide an insight into the application of SERPINA3 as a novel predictor of clinical outcomes and a potential biomarker of glioma.