Potentially useful biomarkers for the diagnosis, treatment and prognosis of lung cancer

Prospects, advances and biological applications of MOF-based platform for the treatment of lung cancer

Nowadays in our society, lung cancer is exhibiting a high mortality rate and threat to human health. Conventional diagnostic techniques used in the field of lung cancer often necessitate the use of extensive instrumentation, exhibit a tendency for false positives, and are not suitable for widespread early screening purposes. Conventional approaches to treat lung cancer primarily involve surgery, chemotherapy, and radiotherapy. However, these broad-spectrum treatments suffer from drawbacks such as imprecise targeting and significant side effects, which restrict their widespread use. Metal-organic frameworks (MOFs) have attracted significant attention in the diagnosis and treatment of lung cancer owing to their tunable electronic properties and structures and potential applications. These porous nanomaterials are formed through the intricate assembly of metal centers and organic ligands, resulting in highly versatile frameworks. Compared to traditional diagnostic and therapeutic modalities, MOFs can improve the sensitivity of lung cancer biomarker detection in the diagnosis of lung cancer. In terms of treatment, they can significantly reduce side effects and improve therapeutic efficacy. Hence, this perspective provides an overview concerning the advancements made in the field of MOFs as potent biosensors for lung cancer biomarkers. It also delves into the latest research dealing with the use of MOFs as carriers for drug delivery. Additionally, it explores the applications of MOFs in various therapeutic approaches, including chemodynamic therapy, photodynamic therapy, photothermal therapy, and immunotherapy. Furthermore, this review comprehensively analyses potential applications of MOFs as biosensors in the field of lung cancer diagnosis and combines different therapeutic approaches aiming for enhanced therapeutic efficacy. It also presents a concise overview of the existing obstacles, aiming to pave the way for future advancements in lung cancer diagnosis and treatment.

Diagnostic potential of protein serum biomarkers for distinguishing small and non-small cell lung cancer in patients with suspicious lung lesions

BACKGROUND

Biomarkers play a role in identifying, managing, and predicting cancer outcomes. In lung cancer, they are used at various time points. Doubts remain regarding their accuracy for differential diagnosis and histological subtyping. A diagnostic test study was conducted. It included malignant lesions and controls with benign lesions. Before lung biopsy, all patients had the following biomarkers measured in serum (Pro-GRP,NSE,CYFRA21-1,SCC-Ag,CEA).

METHODS

The predictive capacity of serum biomarkers was evaluated to discriminate between lung cancer and benign pathology. The accuracy was also assessed for distinguishing between SCLC and NSCLC and explored their ability to perform histological subtyping.

RESULTS

93 patients were included, 60 with lung cancer, 33 with benign pathology. Pro-GRP and NSE were elevated in SCLC compared with NSCLC or nonmalignant disease. The most accurate for differentiating between malignant and benign pathology were CEA and CYFRA21-1. Pro-GRP had a poor predictive capacity for distinguishing NSCLC from SCLC. However, combined with CEA and CYFRA21-1, performance improved. For SCLC, the diagnostic capacity of Pro-GRP increased by combining with biomarkers, such as NSE/CYFRA21-1.

CONCLUSIONS

Biomarkers lacked the sensitivity and specificity for independent differential diagnosis or histological subtyping. However, the observed patterns in biomarker levels associated with specific histological subtypes suggest potential utility in a multi-biomarker approach or in conjunction with other diagnostic tools. This insight could guide future research to improve diagnostic accuracy and personalized treatment strategies in lung cancer.

Application of Monocyte-to-Albumin Ratio and Neutrophil Percentage-to-Hemoglobin Ratio on Distinguishing Non-Small Cell Lung Cancer Patients from Healthy Subjects

Objective

This study aims at assessing the potential benefits of observation of monocyte-to-albumin ratio (MAR) and neutrophil percentage-to-hemoglobin ratio (NPHR) in the detection of non-small cell lung cancer (NSCLC).

Methods

This study retrospectively involved 195 NSCLC patients and 204 healthy volunteers. The correlations between the clinicopathological characteristics of NSCLC and the two ratios including MAR and NPHR were assessed. The diagnostic efficiency of NSCLC patients by MAR and NPHR, alone or in combination with carcinoembryonic antigen (CEA), was assessed by receiver operating characteristic (ROC) curve. The risk factors for NSCLC were analyzed with binary logistic regression.

Results

Compared to healthy controls, the levels of MAR and NPHR in NSCLC patients were elevated. MAR and NPHR were related to clinicopathologic characteristics and increased significantly along with the progression of NSCLC. The area under the curve (AUC) for 95% confidence interval (95% CI) of MAR and NPHR in the diagnosis of NSCLC was 0.812 (0.769-0.854) and 0.724 (0.675-0.774), respectively. The combination of MAR, NPHR, and CEA achieved the highest diagnostic utility compared to each individually or combined markers (AUC, 0.86; 95% CI, 0.824-0.896; sensitivity, 72.8%; specificity, 87.3%). Further analysis showed that MAR combined with NPHR presented the potential to detect early-stage (IA-IIB) NSCLC (AUC, 0.794; 95% CI, 0.743-0.845; sensitivity, 55.1%; specificity, 87.7%). The result indicated that MAR and NPHR might be risk factors for NSCLC.

Conclusion

MAR and NPHR could be novel and effective auxiliary indexes in the detection of NSCLC, especially when combined with CEA.

Nanoparticle Biomolecular Corona-Based Enrichment of Plasma Glycoproteins for N-Glycan Profiling and Application in Biomarker Discovery

Biomolecular corona formation has emerged as a recurring and important phenomenon in nanomedicine that has been investigated for potential applications in disease diagnosis. In this study, we have combined the "personalized protein corona" with the N-glycosylation profiling that has recently gained considerable interest in human plasma biomarker discovery as a powerful early warning diagnostic and patient stratification tool. We envisioned that the protein corona formation could be exploited as an enrichment step that is critically important in both proteomic and proteoglycomic workflows. By using silica nanoparticles, plasma fibrinogen was enriched to a level in which its proteomic and glycomic "fingerprints" could be traced with confidence. Despite being a more simplified glycan profile compared to full plasma, the corona glycan profile revealed a fibrinogen-derived glycan peak that was found to potentially distinguish lung cancer patients from controls in a pilot study.

Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

Current and Future Development in Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related deaths in North America and other developed countries. One of the reasons lung cancer is at the top of the list is that it is often not diagnosed until the cancer is at an advanced stage. Thus, the earliest diagnosis of lung cancer is crucial, especially in screening high-risk populations, such as smokers, exposure to fumes, oil fields, toxic occupational places, etc. Based on the current knowledge, it looks that there is an urgent need to identify novel biomarkers. The current diagnosis of lung cancer includes different types of imaging complemented with pathological assessment of biopsies, but these techniques can still not detect early lung cancer developments. In this review, we described the advantages and disadvantages of current methods used in diagnosing lung cancer, and we provide an analysis of the potential use of body fluids as carriers of biomarkers as predictors of cancer development and progression.

Identification of potential serum biomarkers for simultaneously classifying lung adenocarcinoma, squamous cell carcinoma and small cell carcinoma

BACKGROUND

Histological subtypes of lung cancer are crucial for making treatment decisions. However, multi-subtype classifications including adenocarcinoma (AC), squamous cell carcinoma (SqCC) and small cell carcinoma (SCLC) were rare in the previous studies. This study aimed at identifying and screening potential serum biomarkers for the simultaneous classification of AC, SqCC and SCLC.

PATIENTS AND METHODS

A total of 143 serum samples of AC, SqCC and SCLC were analyzed by 1HNMR and UPLC-MS/MS. The stepwise discriminant analysis (DA) and multilayer perceptron (MLP) were employed to screen the most efficient combinations of markers for classification.

RESULTS

The results of non-targeted metabolomics analysis showed that the changes of metabolites of choline, lipid or amino acid might contribute to the classification of lung cancer subtypes. 17 metabolites in those pathways were further quantified by UPLC-MS/MS. DA screened out that serum xanthine, S-adenosyl methionine (SAM), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE) and squamous cell carcinoma antigen (SCC) contributed significantly to the classification of AC, SqCC and SCLC. The average accuracy of 92.3% and the area under the receiver operating characteristic curve of 0.97 would be achieved by MLP model when a combination of those five variables as input parameters.

CONCLUSION

Our findings suggested that metabolomics was helpful in screening potential serum markers for lung cancer classification. The MLP model established can be used for the simultaneous diagnosis of AC, SqCC and SCLC with high accuracy, which is worthy of further study.

The Evaluation of Serum Biomarkers for Non-small Cell Lung Cancer (NSCLC) Diagnosis

Introduction: Lung cancer ranks top in the cause of cancer death globally. The identification of effective biomarkers is essential for non-small cell lung cancer (NSCLC) diagnosis.

Methods: The expression levels of prolactin (PRL), CEA, and CYFR21 in serum were assayed by ELISA. The blood samples were attained from 44 NSCLC cases and 44 healthy controls. Logistic regression and receiver operating characteristic (ROC) analyses were applied to evaluate the diagnostic efficacy and create diagnostic mathematical models.

Results: Serum PRL, CEA, and CYFR21 levels were significantly higher in patients with NSCLC than the healthy controls (all P-values <0.001). According to the model to predict NSCLC patients from the healthy controls, a combination of PRL, CEA, and CYFR21 biomarkers was more effective than individual biomarker alone, with AUC = 0.960 (95% CI: 0.921–0.999), sensitivity = 0.909, specificity = 0.955, positive predicted value = 0.952, and negative predicted value = 0.913.

Conclusion: Prolactin can be used as a potential serum biomarker for the diagnosis of NSCLC. A panel of PRL, CEA, and CYFRA21 was found as promising serum biomarkers for the diagnosis of NSCLC with relatively high sensitivity and specificity.

Targeted next-generation sequencing reveals recurrence-associated genomic alterations in early-stage non-small cell lung cancer

Purpose

The identification of genomic alterations related to recurrence in early-stage non-small cell lung cancer (NSCLC) patients may help better stratify high-risk individuals and guide treatment strategies. This study aimed to identify the molecular biomarkers of recurrence in early-stage NSCLC.

Results

Of the 42 tumors evaluable for genomic alterations, TP53 and EGFR were the most frequent alterations with population frequency 52.4% and 50.0%, respectively. Fusion genes were detected in four patients, which had lower mutational burden and relatively better genomic stability. EGFR mutation and fusion gene were mutually exclusive in this study. CDKN2A, FAS, SUFU and SMARCA4 genomic alterations were only observed in the relapsed patients. Increased copy number alteration index was observed in early relapsed patients. Among these genomic alterations, early-stage NSCLCs harboring CDKN2A, FAS, SUFU and SMARCA4 genomic alterations were found to be significantly associated with recurrence. Some of these new findings were validated using The Cancer Genome Atlas (TCGA) dataset.

Conclusions

The genomic alterations of CDKN2A, FAS, SUFU and SMARCA4 in early-stage NSCLC are found to be associated with recurrence, but confirmation in a larger independent cohort is required to define the clinical impact.

Materials and Methods

Paired primary tumor and normal lung tissue samples were collected for targeted next-generation sequencing analysis. A panel targets exons for 440 genes was used to assess the mutational and copy number status of selected genes in three clinically relevant groups of stage I/II NSCLC patients: 1) Early relapse; 2) Late relapse; and 3) No relapse.

Early detection of lung cancer biomarkers through biosensor technology: A review

Lung cancer is undoubtedly one of the most serious health issues of the 21 st century. It is the second leading cause of cancer-related deaths in both men and women　worldwide, accounting for about 1.5 million deaths annually. Despite advances in the treatment of lung cancer with new pharmaceutical products and technological improvements, morbidity and mortality rates remains a significant challenge for the cancer biologists and oncologists. The vast majority of lung cancer patients present with advanced-stage of pathological process that ultimately leads to poor prognosis and a five-year survival rate less than 20%. Early and accurate screening and analysis using cost-effective means are urgently needed to effectively diagnose the disease, improve the survival rate or to reduce mortality and morbidity associated with lung cancer patients. Thus, the only hope for early recognition of risk factors and timely diagnosis and treatment of lung cancer is biosensors technology. Novel biosensing based diagnostics approaches for predicting metastatic risks are likely to have significant therapeutic and clinical impact in the near future. This article systematically provides a brief overview of various biosensing platforms for identification of lung cancer disease biomarkers, with a specific focus on recent advancements in electrochemical and optical biosensors, analytical performances of different biosensors, challenges and further research opportunities for routine clinical analysis.

How Nanotechnology and Biomedical Engineering Are Supporting the Identification of Predictive Biomarkers in Neuro-Oncology

The field of neuro-oncology is rapidly progressing and internalizing many of the recent discoveries coming from research conducted in basic science laboratories worldwide. This systematic review aims to summarize the impact of nanotechnology and biomedical engineering in defining clinically meaningful predictive biomarkers with a potential application in the management of patients with brain tumors. Data were collected through a review of the existing English literature performed on Scopus, MEDLINE, MEDLINE in Process, EMBASE, and/or Cochrane Central Register of Controlled Trials: all available basic science and clinical papers relevant to address the above-stated research question were included and analyzed in this study. Based on the results of this systematic review we can conclude that: (1) the advances in nanotechnology and bioengineering are supporting tremendous efforts in optimizing the methods for genomic, epigenomic and proteomic profiling; (2) a successful translational approach is attempting to identify a growing number of biomarkers, some of which appear to be promising candidates in many areas of neuro-oncology; (3) the designing of Randomized Controlled Trials will be warranted to better define the prognostic value of those biomarkers and biosignatures.

Understanding the Pathological Basis of Neurological Diseases Through Diagnostic Platforms Based on Innovations in Biomedical Engineering: New Concepts and Theranostics Perspectives

The pace of advancement of genomics and proteomics together with the recent understanding of the molecular basis behind rare diseases could lead in the near future to significant advances in the diagnosing and treating of many pathological conditions. Innovative diagnostic platforms based on biomedical engineering (microdialysis and proteomics, biochip analysis, non-invasive impedance spectroscopy, etc.) are introduced at a rapid speed in clinical practice: this article primarily aims to highlight how such platforms will advance our understanding of the pathological basis of neurological diseases. An overview of the clinical challenges and regulatory hurdles facing the introduction of such platforms in clinical practice, as well as their potential impact on patient management, will complement the discussion on foreseeable theranostic perspectives. Indeed, the techniques outlined in this article are revolutionizing how we (1) identify biomarkers that better define the diagnostic criteria of any given disease, (2) develop research models, and (3) exploit the externalities coming from innovative pharmacological protocols (i.e., those based on monoclonal antibodies, nanodrugs, etc.) meant to tackle the molecular cascade so far identified.

Neuron-Specific Enolase Predicts Poor Outcome After Cardiac Arrest and Targeted Temperature Management: A Multicenter Study on 1,053 Patients

OBJECTIVE

Outcome prediction after cardiac arrest is important to decide on continuation or withdrawal of intensive care. Neuron-specific enolase is an easily available, observer-independent prognostic biomarker. Recent studies have yielded conflicting results on its prognostic value after targeted temperature management.

DESIGN, SETTING, AND PATIENTS

We analyzed neuron-specific enolase serum concentrations 3 days after nontraumatic in-hospital cardiac arrest and out-of-hospital cardiac arrest and outcome of patients from five hospitals in Germany, Austria, and Italy. Patients were treated at 33°C for 24 hours. Cerebral Performance Category was evaluated upon ICU discharge. We performed case reviews of good outcome patients with neuron-specific enolase greater than 90 μg/L and poor outcome patients with neuron-specific enolase less than or equal to 17 μg/L (upper limit of normal).

MEASUREMENTS AND MAIN RESULTS

A neuron-specific enolase serum concentration greater than 90 μg/L predicted Cerebral Performance Category 4-5 with a positive predictive value of 99%, false positive rate of 0.5%, and a sensitivity of 48%. All three patients with neuron-specific enolase greater than 90 μg/L and Cerebral Performance Category 1-2 had confounders for neuron-specific enolase elevation. An neuron-specific enolase serum concentration less than or equal to 17 μg/L excluded Cerebral Performance Category 4-5 with a negative predictive value of 92%. The majority of 14 patients with neuron-specific enolase less than or equal to 17 μg/L who died had a cause of death other than hypoxic-ischemic encephalopathy. Specificity and sensitivity for prediction of poor outcome were independent of age, sex, and initial rhythm but higher for out-of-hospital cardiac arrest than for in-hospital cardiac arrest patients.

CONCLUSION

High neuron-specific enolase serum concentrations reliably predicted poor outcome at ICU discharge. Prediction accuracy differed and was better for out-of-hospital cardiac arrest than for in-hospital cardiac arrest patients. Our "in-the-field" data indicate 90 μg/L as a threshold associated with almost no false positives at acceptable sensitivity. Confounders of neuron-specific enolase elevation should be actively considered: neuron-specific enolase-producing tumors, acute brain diseases, and hemolysis. We strongly recommend routine hemolysis quantification. Neuron-specific enolase serum concentrations less than or equal to 17 μg/L argue against hypoxic-ischemic encephalopathy incompatible with reawakening.

Potential circulating miRNA signature for early detection of NSCLC

Circulating microRNAs (c-miRNAs) are promising biomarkers for screening, early detection and prognosis of cancer. The purpose of this investigation was to identify a panel of c-miRNAs in plasma that could contribute to early detection of non-small cell lung cancer (NSCLC). We profiled the expression of 44 unique plasma miRNAs in training set of 34 NSCLC patients and 20 matched healthy individuals by miRCURY LNA™ Universal RT microRNA PCR Panel and calculated dysregulation fold changes using the 2-ΔΔCt equation. Selected plasma miRNAs were then validated by SYBR green q-RT PCR using an independent validation set of plasma samples from NSCLC patients (n: 72) and NC (n: 50). In the validation set, the receiver operating characteristic (ROC) curves were generated for four miRNAs. In the training set, 17 miRNAs were significantly up-regulated and nine were down-regulated in the plasma from NSCLC patients versus matched normal controls. Four miRNAs (miR-21, miR-328, miR-375 and miR-141) were selected for validating their diagnostic value in the testing set. ROC plot analysis showed that a high specificity (98%) and sensitivity (82.7%) in miR-141 in comparing early NSCLC patient and controls. So among these four plasma miRNAs only miR-141 could be promising biomarkers for early detection of NSCLC. In addition to, we found a significant positive correlation between stage and miR-21 expression level (95% CI: 0.687-0.863; p-value < 0.0001). Considering the accessibility and stability of circulating miRNAs, plasma miR-141 is a useful biomarker early detection of NSCLC as a supplement in future screening studies.

Meta-markers for the differential diagnosis of lung cancer and lung disease

Misdiagnosis of lung cancer remains a serious problem due to the difficulty of distinguishing lung cancer from other respiratory lung diseases. As a result, the development of serum-based differential diagnostic biomarkers is in high demand. In this study, 198 clinical serum samples from non-cancer lung disease and lung cancer patients were analyzed using nLC-MRM-MS for the levels of seven lung cancer biomarker candidates. When the candidates were assessed individually, only SERPINEA4 showed statistically significant changes in the serum levels. The MRM results and clinical information were analyzed using a logistic regression analysis to select model for the best 'meta-marker', or combination of biomarkers for differential diagnosis. Also, under consideration of statistical interaction, variables having low significance as a single factor but statistically influencing on meta-marker model were selected. Using this probabilistic classification, the best meta-marker was determined to be made up of two proteins SERPINA4 and PON1 with age factor. This meta-marker showed an enhanced differential diagnostic capability (AUC=0.915) for distinguishing the two patient groups. Our results suggest that a statistical model can determine optimal meta-markers, which may have better specificity and sensitivity than a single biomarker and thus improve the differential diagnosis of lung cancer and lung disease patients.

BIOLOGICAL SIGNIFICANCE

Diagnosing lung cancer commonly involves the use of radiographic methods. However, an imaging-based diagnosis may fail to differentiate lung cancer from non-cancerous lung disease. In this study, we examined several serum proteins in the sera of 198 lung cancer and non-cancerous lung disease patients by multiple-reaction monitoring. We then used a combination of variables to generate a meta-marker model that is useful as a differential diagnostic biomarker.

Robust ultrasensitive tunneling-FET biosensor for point-of-care diagnostics

For point-of-care (POC) applications, robust, ultrasensitive, small, rapid, low-power, and low-cost sensors are highly desirable. Here, we present a novel biosensor based on a complementary metal oxide semiconductor (CMOS)-compatible silicon nanowire tunneling field-effect transistor (SiNW-TFET). They were fabricated “top-down” with a low-cost anisotropic self-stop etching technique. Notably, the SiNW-TFET device provided strong anti-interference capacity by applying the inherent ambipolarity via both pH and CYFRA21-1 sensing. This offered a more robust and portable general protocol. The specific label-free detection of CYFRA21-1 down to 0.5 fgml⁻¹ or ~12.5 aM was achieved using a highly responsive SiNW-TFET device with a minimum sub-threshold slope (SS) of 37 mVdec⁻¹. Furthermore, real-time measurements highlighted the ability to use clinically relevant samples such as serum. The developed high performance diagnostic system is expected to provide a generic platform for numerous POC applications.

Tumour-associated antigens and their anti-cancer applications

So far, a number of tumour-associated antigens (TAAs), such as heat shock proteins, alpha-fetoprotein, carcino-embryonic antigen and others have been identified in a variety of malignant tumours. Differences in the expression levels of TAAs in cancers compared with normal cells have led to these antigens being investigated as diagnostic and prognostic biomarkers or exciting targets in cancer treatment. Here, we systematically list the current representative TAAs to shed some light on current approaches and challenges for their anti-cancer application in cancer therapy. In this review, we discuss the ongoing pre-clinical studies and clinical development of TAAs in human cancers, and the potential application of these TAAs in the diagnosis and prognosis for cancer treatment.

Ultrasensitive Detection of Dual Cancer Biomarkers with Integrated CMOS-Compatible Nanowire Arrays

A direct, rapid, highly sensitive and specific biosensor for detection of cancer biomarkers is desirable in early diagnosis and prognosis of cancer. However, the existing methods of detecting cancer biomarkers suffer from poor sensitivity as well as the requirement of enzymatic labeling or nanoparticle conjugations. Here, we proposed a two-channel PDMS microfluidic integrated CMOS-compatible silicon nanowire (SiNW) field-effect transistor arrays with potentially single use for label-free and ultrasensitive electrical detection of cancer biomarkers. The integrated nanowire arrays showed not only ultrahigh sensitivity of cytokeratin 19 fragment (CYFRA21-1) and prostate specific antigen (PSA) with detection to at least 1 fg/mL in buffer solution but also highly selectivity of discrimination from other similar cancer biomarkers. In addition, this method was used to detect both CYFRA21-1 and PSA real samples as low as 10 fg/mL in undiluted human serums. With its excellent properties and miniaturization, the integrated SiNW-FET device opens up great opportunities for a point-of-care test (POCT) for quick screening and early diagnosis of cancer and other complex diseases.

Blood-sampling collection prior to surgery may have a significant influence upon biomarker concentrations measured

BACKGROUND

Biomarkers can be subtle tools to aid the diagnosis, prognosis and monitoring of therapy and disease progression. The validation of biomarkers is a cumbersome process involving many steps. Serum samples from lung cancer patients were collected in the framework of a larger study for evaluation of biomarkers for early detection of lung cancer. The analysis of biomarker levels measured revealed a noticeable difference in certain biomarker values that exhibited a dependence of the time point and setting of the sampling. Biomarker concentrations differed significantly if taken before or after the induction of anesthesia and if sampled via venipuncture or arterial catheter.

METHODS

To investigate this observation, blood samples from 13 patients were drawn 1-2 days prior to surgery (T1), on the same day by venipuncture (T2) and after induction of anesthesia via arterial catheter (T3). The biomarkers Squamous Cell Carcinoma antigen (CanAG SCC EIA, Fujirebio Diagnostics, Malvern, USA), Carcinoembrionic Antigen (CEA), and CYFRA 21-1 (Roche Diagnostics GmbH, Mannheim, Germany) were analyzed.

RESULTS

SCC showed a very strong effect in relation to the sampling time and procedure. While the first two points in time (T1; T2) were highly comparable (median fold-change: 0.84; p = 0.7354; correlation ρ = 0.883), patients showed a significant increase (median fold-change: 4.96; p = 0.0017; correlation ρ = -0.036) in concentration when comparing T1 with the sample time subsequent to anesthesia induction (T3). A much weaker increase was found for CYFRA 21-1 at T3 (median fold-change: 1.40; p = 0.0479). The concentration of CEA showed a very small, but systematic decrease (median fold-change: 0.72; p = 0.0039).

CONCLUSIONS

In this study we show the unexpectedly marked influence of blood withdrawal timing (before vs. after anesthesia) and procedure (venous versus arterial vessel puncture) has on the concentration of the protein biomarker SCC and to a less extent upon CYFRA21-1. The potential causes for these effects remain to be elucidated in subsequent studies, however these findings highlight the importance of a standardized, controlled blood collection protocol for biomarker detection.

Expression and function of GSTA1 in lung cancer cells

Glutathione S-transferase A1 (GSTA1) appears to be primarily involved in detoxification processes, but possible roles in lung cancer remain unclear. The objective of this study was to investigate the expression and function of GSTA1 in lung cancer cells. Real-time PCR and Western blotting were performed to assess expression in cancer cell lines and the normal lung cells, then verify the A549 cells line with stable overexpression. Localization of GSTA1 proteins was assessed by cytoimmunofluorescence. Three double-strand DNA oligoRNAs (SiRNAs) were synthesized prior to being transfected into A549 cells with Lipofectamine 2000, and then the most efficient SiRNA was selected. Expression of the GSTA1 gene in the transfected cells was determined by real-time PCR and Western blotting. The viability of the transfected cells were assessed by MTT. Results showed that the mRNA and protein expression of A549 cancer cells was higher than in MRC-5 normal cells. Cytoimmunofluorescence demonstrated GSTA1 localization in the cell cytoplasm and/or membranes. Transfection into A549 cells demonstrated that down-regulated expression could inhibit cell viability. Our data indicated that GSTA1 expression may be a target molecule in early diagnosis and treatment of lung cancer.

A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer

BACKGROUND

Lung cancer is an important and common cancer that constitutes a major public health problem, but early detection of small cell lung cancer can significantly improve the survival rate of cancer patients. A number of serum biomarkers have been used in the diagnosis of lung cancers; however, they exhibit low sensitivity and specificity.

METHODS

We used biochemical methods to measure blood levels of lactate dehydrogenase (LDH), C-reactive protein (CRP), Na+, Cl-, carcino-embryonic antigen (CEA), and neuron specific enolase (NSE) in 145 small cell lung cancer (SCLC) patients and 155 non-small cell lung cancer and 155 normal controls. A gene expression programming (GEP) model and Receiver Operating Characteristic (ROC) curves incorporating these biomarkers was developed for the auxiliary diagnosis of SCLC.

RESULTS

After appropriate modification of the parameters, the GEP model was initially set up based on a training set of 115 SCLC patients and 125 normal controls for GEP model generation. Then the GEP was applied to the remaining 60 subjects (the test set) for model validation. GEP successfully discriminated 281 out of 300 cases, showing a correct classification rate for lung cancer patients of 93.75% (225/240) and 93.33% (56/60) for the training and test sets, respectively. Another GEP model incorporating four biomarkers, including CEA, NSE, LDH, and CRP, exhibited slightly lower detection sensitivity than the GEP model, including six biomarkers. We repeat the models on artificial neural network (ANN), and our results showed that the accuracy of GEP models were higher than that in ANN. GEP model incorporating six serum biomarkers performed by NSCLC patients and normal controls showed low accuracy than SCLC patients and was enough to prove that the GEP model is suitable for the SCLC patients.

CONCLUSION

We have developed a GEP model with high sensitivity and specificity for the auxiliary diagnosis of SCLC. This GEP model has the potential for the wide use for detection of SCLC in less developed regions.

Quantification of cell-free mSHOX2 Plasma DNA for therapy monitoring in advanced stage non-small cell (NSCLC) and small-cell lung cancer (SCLC) patients

Purpose

Most patients suffering from advanced lung cancer die within a few months. To exploit new therapy regimens we need better methods for the assessment of a therapy response.

Material and Methods

In a pilot study we prospectively enrolled 36 patients with advanced NSCLC and SCLC (34 stage IV, 2 stage IIIB) of whom 34 received standard platinum-based chemo/radiotherapy and two were treated with a tyrosine kinase inhibitor. We measured the levels of extracellular methylated SHOX2 DNA (mSHOX2) in plasma before and during therapy until re-staging. The mSHOX2 analysis was blinded with respect to the clinical data making it an observational study.

Results

According to the re-staging of 31 first-line patients, 19 patients were classified as non-responders while 12 patients were in the responder group. We observed a tight correlation between radiological data and the change of plasma mSHOX2 level as the equivalent for a therapy response. A ROC analysis showed a high discriminatory power for both patient groups already one week after therapy start (AUC 0.844). Additionally, a Kaplan-Meier and Cox Proportional Hazards analyses revealed a strong relationship between survival and plasma mSHOX2 value p≤0.001 (hazard ratio 11.08) providing some evidence for mSHOX2 also being a predictive marker.

Conclusion

The longitudinal measurement of extracellular plasma mSHOX2 DNA yields information about the response to cytotoxic treatment and allows an early assessment of treatment response for lung cancer patients. If confirmed in a larger study this would be a valuable tool for selecting and guiding a cytotoxic treatment.

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.

Comparative gene expression profiling analysis of lymphoblastoid cells reveals neuron-specific enolase gene (ENO2) as a susceptibility gene of heroin dependence

Heroin dependence is a complex mental disorder resulting from interactions between genetic and environmental factors. Identifying the susceptibility genes of heroin dependence is the basis for understanding the pathogenesis of heroin dependence. Using a total gene expression microarray, we detected 924 differentially expressed gene transcripts in lymphoblastoid cell lines (LCLs) between 19 male heroin-dependent individuals and 20 male control subjects, including 279 upregulated and 645 downregulated gene transcripts in heroin-dependent individuals. We verified the reduced expression of the neuron-specific enolase gene (ENO2) in heroin-dependent individuals using real-time quantitative polymerase chain reaction and Western blot analysis. We further compared the allele and genotype frequencies of three single nucleotide polymorphisms (SNPs, rs11064464, rs3213433 and rs10849541) of the ENO2 gene between 532 male heroin-dependent individuals and 369 male controls. No significant differences in the allele or genotype frequencies of these three SNPs were detected between these two groups. Nevertheless, we identified a haplotype (T-C-G) derived from these three SNPs significantly underrepresented in heroin-dependent individuals compared with the control group (72.7% versus 75.9%, P<0.032), while two other rare haplotypes (C-A-G and T-C-A) significantly overrepresented in heroin-dependent individuals compared with the control group (P<0.001). Further study, however, did not detect significant differences of the plasma concentration of neuron-specific enolase between these two groups. Our data suggest that the ENO2 gene might be associated with heroin dependence, and reduced ENO2 gene expression may confer increased risk to heroin dependence.

High cofilin-1 levels correlate with cisplatin resistance in lung adenocarcinomas

High cofilin-1 levels have been shown to be an accurate prognostic biomarker in non-small cell lung cancer (NSCLC) and a predictive factor in drug resistance. Herein we explore the role of cofilin-1 in cis-diamminedichloroplatinum(II) (cisplatin) resistance. We evaluated cofilin-1 levels in intrinsically cisplatin-resistant A549 (ICR-A549) cells and determined the cisplatin toxicity in A549 cells transiently transfected and overexpressing CFL1 plasmid. Moreover, expression levels (activity) of the CFL1 gene network were analyzed in a cisplatin-resistant human lung adenocarcinoma cell panel. ICR-A549 cells, selected by challenging parental cells with 10-fold drug GI50 value, presented a sixfold increase in cisplatin GI50 value and an increased cofilin-1 immunocontent (P < 0.01). In addition, cells transfected with cofilin-1 became more resistant to cisplatin (P < 0.01). High activity of the CFL1 gene network was found in a cisplatin-resistant adenocarcinoma cell panel (P < 0.01). In vitro evidences suggest that cofilin-1 is a biological predictor of cisplatin resistance, supporting new treatment initiatives based on cofilin-1 levels to guide chemotherapeutic interventions in NSCLC patients.

Serum markers in small cell lung cancer: opportunities for improvement

Lung cancer is one of the leading causes of death from malignancy worldwide. In particular small cell lung cancers, which comprise about 15-20% of all lung cancers, are extremely aggressive and cure rates are extremely low. Therefore, new treatment modalities are needed and detection at an early stage of disease, as well as adequate monitoring of treatment response is essential in order to improve outcome. In this respect, the use of non-invasive tools for screening and monitoring has gained increasing interest and the clinical applicability of reliable, tumor-related substances that can be detected as tumor markers in easily accessible body fluids is subject of intense investigation. Some of these indicators, such as high LDH levels in serum as a reflection of the disease, have been in use for a long time as a general tumor marker. To allow for improved monitoring of the efficacy of new therapeutic modalities and for accurate subtyping, there is a strong need for specific and sensitive markers that are more directly related to the biology and behavior of small cell lung cancer. In this review the current status of these potential markers, like CEA, NSE, ProGRP, CK-BB, SCC, CgA, NCAM and several cytokeratins will be critically analyzed with respect to their performance in blood based assays. Based on known cleavage sites for cytoplasmic and extracellular proteases, a prediction of stable fragments can be obtained and used for optimal test design. Furthermore, insight into the synthesis of specific splice variants and neo-epitopes resulting from protein modification and cleavage, offers further opportunities for improvement of tumor assays. Finally, we discuss the possibility that detection of SCLC related autoantibodies in paraneoplastic disease can be used as a very early indicator of SCLC.

Individualized correction of neuron-specific enolase (NSE) measurement in hemolyzed serum samples

BACKGROUND

Accuracy of serum neuron-specific enolase (NSE) measurement is paramount, particularly in the context of neurological outcome prognostication. However, NSE measurements are compromised by even slight hemolysis, as it is abundant in red blood cells (RBCs). We derived and validated an individualized hemolysis correction equation in an attempt to reduce the current rejection rate of 14% at our institution.

METHODS

Intracellular NSE was measured in RBC lysates to determine concentration variability. A correction equation was derived, accounting for both RBC-derived NSE false-elevation and hemoglobin-derived signal quenching. The performance of this individualized correction was evaluated in intentionally hemolyzed samples and accuracy was compared to a generalized correction.

RESULTS

Significant inter-individual variability of RBC NSE was observed, with an almost two-fold range (15.7-28.5 ng NSE/mg Hb, p<0.001); intra-individual variability was insignificant. The individualized hemolysis correction equation derived: NSE(corr)=NSE(meas)-(Hb(serum))(NSE(RBCs/Hb))+0.0844(Hb(serum))+1.1 corrected 95% of the intentionally hemolyzed samples to within ±5 ng/ml of corresponding baseline NSE concentrations, compared to 74% using a generalized formula.

CONCLUSIONS

The individualized hemolysis correction provides increased accuracy in the estimation of true serum NSE concentrations for hemolyzed samples, compared to a generalized approach, by accounting for inter-individual RBC NSE variability. Incorporating this correction should reduce sample rejection rates and overall health care costs.

Prognostic value of tumor markers, NSE, CA125 and SCC, in operable NSCLC Patients

The aim of this study was to investigate the prognostic value of tumor markers in operable non-small cell lung cancer (NSCLC) patients. A total of 481 NSCLC patients were enrolled in the present study. High levels of neuron-specific enolase (NSE), carbohydrate antigen 125 (CA125) and squamous cell carcinoma antigen (SCC) were detected in 306 (63.6%), 89 (18.5%) and 125 (26.0%) patients, respectively. Seventy-eight of 481 patients died of disease progression, and the median disease-free survival (DFS) and overall survival (OS) were 16.0 and 21.0 months, respectively. The three-year DFS rate was 56.7%, and the OS rate was 75.3%. For serum NSE, the three-year cumulative DFS rate for the normal and elevated group was 67.7% and 51.8% (p = 0.007). The OS in patients with high and normal levels of NSE was 34.0 months and 48.0 months, respectively. The median DFS was 46.0 months versus 32.0 months (p = 0.001), and the OS was 48.0 months versus 44.0 months (p = 0.001) in patients with normal and high levels of CA125. For patients with squamous cell carcinoma, the overall survival was significantly shorter in patients with elevated levels of SCC (p = 0.041). In the multivariate analysis high levels of NSE, CA125 and clinical stage were significantly correlated with worse prognosis (p < 0.05). Patients with all three tumor markers elevated presented the worst prognosis (p < 0.05). In our analysis, high levels of preoperative serum NSE and CA125 are correlated with worse survival in operable NSCLC patients.

An omics strategy for discovering pulmonary biomarkers potentially relevant to the evaluation of tobacco products

Smoking is known to cause serious lung diseases including chronic bronchitis, chronic obstructive lung disease, obstruction of small airways, emphysema and cancer. Tobacco smoke is a complex chemical aerosol containing at least 8000 chemical constituents, either tobacco derived or added by tobacco product manufacturers. Identification of all of the toxic agents in tobacco smoke is challenging, and efforts to understand the mechanisms by which tobacco use causes disease will be informed by new biomarkers of exposure and harm. In 2009, President Obama signed into law the Family Smoking Prevention and Tobacco Control Act granting the US FDA the authority to regulate tobacco products to protect public health. This perspective article presents the background, rationale and strategy for using omics technologies to develop new biomarkers, which may be of interest to the FDA when implementing the Family Smoking Prevention and Tobacco Control Act.

Synaptonemal complex protein 3 as a novel prognostic marker in early stage non-small cell lung cancer

Synaptonemal complex protein 3 is a marker for cell transformation that has prognostic significance in various cancers. However, the prognostic significance of synaptonemal complex protein 3 has not been studied in non-small cell lung cancer. To investigate the potential correlation between synaptonemal complex protein 3 and various clinicopathologic parameters, we assessed the expression of synaptonemal complex protein 3 in archival tumor tissues from 258 patients with non-small cell lung cancer by immunohistochemical staining. By immunofluorescence, synaptonemal complex protein 3 was detected in both the cytoplasmic and nuclear fractions of NCI-H1299 cell. In tumor samples, synaptonemal complex protein 3 is detected as cytoplasmic expression pattern and observed in 50 clinical samples (19.4%) by immunohistochemical staining. Synaptonemal complex protein 3 expression was correlated with T status (P = .008), lymph node metastasis (P = .010), tumor types (P = .019), and pleural invasion (P = .005). In multivariate analysis of patients with early stage disease, increased synaptonemal complex protein 3 expression predicted worse overall survival in early stage (stage I and II) with pT1 status (P = .041). These results suggest that positive synaptonemal complex protein 3 expression is a portent of poor outcome and may be a potential biomarker in the early stages of the non-small cell lung cancer for survival and may provide clues in the identification of patients for adjuvant therapy.

Integrating genomics and proteomics-oriented biomarkers to comprehend lung cancer

BACKGROUND

Lung cancer is the leading cause of cancer deaths worldwide. Recent years have brought tremendous progress in the development of genomic and proteomic platforms to study lung cancer progression and biomarker identification.

OBJECTIVE

To evaluate and integrate potential innovations of 'omics' (e.g., genomics and proteomics) technologies in dissecting biomarkers for lung cancer.

METHODS

Omics technologies permit simultaneous monitoring of many hundreds or thousands of macro and small molecules, as well as functional monitoring of multiple pivotal cellular pathways. Discussion follows to explore the principal challenges in the development of cancer biomarkers integrating genomics with proteomics data sets with their functional counterparts in conjunction with clinical data.

RESULTS/CONCLUSION

Sets of genes and gene interactions affecting different subsets of cancers can be determined using genomics in lung cancer. Proteomic studies have generated numerous functional data sets of potential diagnostic, prognostic and therapeutic significance in lung cancer. It is likely that omics will take a central place in the understanding, diagnosis, monitoring and treatment of lung cancer. Here the potential benefits and pitfalls of these methodologies are reviewed for the faster discovery of therapeutically valuable biomarkers for lung cancer.

Primary pleural squamous cell carcinoma misdiagnosed as localized mesothelioma: a case report and review of the literature

Primary pleural squamous cell carcinoma is very rare, and there is a lack of experience in the diagnosis and treatment of this condition. An asymptomatic 75-year-old man was referred to us after a right pleural nodule was found on computed tomography during a routine health examination. He underwent surgery for his pleural tumor twice over the following 2 years. Histopathological examination eventually led to a diagnosis of primary pleural squamous cell carcinoma.

XAGE-1a and XAGE-1d are potential biomarkers of lung squamous cell carcinoma

BACKGROUND

Lung cancer is the leading cause of cancer deaths worldwide. We evaluated the diagnostic potential of sera XAGE-1a and XAGE-1d in lung cancer, both of which are variants of the X antigen family, member 1.

METHODS

The expression levels of XAGE-1a and XAGE-1d in cell lines were determined using western blot analysis. Competitive ELISA was used to analyze XAGE-1a and XAGE-1d levels in culture supernatants and sera from 194 lung cancer patients and 194 healthy sex- and age-group-matched controls. To evaluate the diagnostic performance of these proteins, we also analyzed carcinoembryonic antigen (CEA) and cytokeratin 19 fragment (CYFRA 21-1) in culture supernatants and 388 sera using commercial ELISA kits.

RESULTS

XAGE-1a and XAGE-1d proteins were expressed in both breast cancer and lung cancer cell lines, but they were only secreted by the latter. The areas under the curves (AUCs) for XAGE-1a and XAGE-1d were 0.787 and 0.806, respectively. The cutoff values (sensitivity, specificity) for XAGE-1a and XAGE-1d were 1.62 ng/ml (0.866, 0.572) and 2.51 ng/ml (0.871, 0.613), respectively. The diagnostic performance was improved for patients with squamous cell carcinoma. The AUC values for XAGE-1a and XAGE-1d for patients with squamous cell carcinoma versus a group containing all healthy participants and patients with any illness other than squamous cell carcinoma were similar to those for CEA and CYFRA 21-1. Better performance (AUC: 0.914) for all patients was obtained when using a combination of four markers (Random Forest).

CONCLUSIONS

Sera XAGE-1a and XAGE-1d are potential biomarkers for lung cancer; they display a diagnostic performance comparable to that of CEA or CYFRA 21-1. Further studies are needed to evaluate the diagnostic and prognostic potential of XAGE-1a and XAGE-1d in lung cancer.

Overexpression of CARMA3 in non-small-cell lung cancer is linked for tumor progression

We aimed to investigate the clinical significance of the expression of novel scaffold protein CARMA3 in non-small-cell lung cancer (NSCLC) and the biological function of CARMA3 in NSCLC cell lines. We observed moderate to high CARMA3 staining in 68.8% of 141 NSCLC specimens compared to corresponding normal tissues. The overexpression of CARMA3 was significantly correlated with TNM stage (P = 0.022) and tumor status (P = 0.013). CARMA3 upregulation also correlated with a shorter survival rate of patients of nodal status N0 (P = 0.042)as well as the expression of epidermal growth factor receptor (EGFR) (P = 0.009). In EGFR mutation positive cases, CARMA3 expression was much higher (87.5%) compared to non-mutation cases (66.1%). In addition, we observed that knockdown of CARMA3 inhibits tumor cell proliferation and invasion, and induces cell cycle arrest at the boundary between the G1 and S phase. We further demonstrated a direct link between CARMA3 and NF-κB activation. The change of biological behavior in CARMA3 knockdown cells may be NF-κB-related. Our findings demonstrated, for the first time, that CARMA3 was overexpressed in NSCLC and correlated with lung cancer progression, EGFR expression, and EGFR mutation. CARMA3 could serve as a potential companion drug target, along with NF-kB and EGFR in EGFR-mutant lung cancers.

Can plasma DNA monitoring be employed in personalized chemotherapy for patients with advanced lung cancer?

Personalized chemotherapy is the ideal treatment usually chosen to help improve the survival chances of patients with advanced lung cancer. However, there is no short-term evaluation protocol for predicting the efficacy of the therapy. The aim of this study was to determine the value of using plasma DNA to monitor chemotherapeutic efficacy and to select most appropriate chemotherapeutic regimen for patients with advanced lung cancer. Eighty-eight lung cancer patients and 200 healthy controls were included in this study. Plasma DNA was extracted from plasma samples with internal controls by using the BILATEST DNA Kit. The quantity of plasma DNA was determined by using duplex real-time quantitative PCR. After first-line chemotherapy, plasma DNA levels of partial response patients were significantly different from those of stable disease patients or progressive disease patients, but with no statistical difference from healthy controls (P=0.014, P<0.001 and P=0.418, respectively). Survival analysis showed a statistically better survival time in patients who had lower levels of plasma DNA after the third cycle chemotherapy (P=0.031). In this study, the correlation of the kinetics of DNA concentrations with chemotherapeutic efficacy during the whole therapy was also observed. The quantification of plasma DNA is a sensitive indicator of chemotherapeutic efficacy in advanced lung cancer patients, and it can be useful in predicting response to therapy and guiding medication.

Fucosylated glycoproteomic approach to identify a complement component 9 associated with squamous cell lung cancer (SQLC)

Human lung cancer is a major cause of cancer mortality worldwide. Understanding the pathophysiological features and the development of novel biomarkers for diagnosis as well as treatment are major tasks. In the present study, sera from ten SQLC patients and healthy control (HEC) were collected and pooled, respectively. The pooled sera were depleted via an immunoaffinity method and further subjected to fucosylation enrichment. Enriched fucosylated glycoproteins were resolved by SDS-PAGE and subsequently analyzed by LC-ESI-MS/MS. From comparative proteomic analysis, we selected the C9 protein. C9 protein levels were validated by Western blot, protein arrays and the fucosylation levels of C9 by hybrid lectin ELISA (HLE) in the sera of 120 HEC and 118 SQLC patients. The C9 protein level was 6.4-fold higher in SQLC patients compared to HEC, as determined by Western blot analysis. The results were concurrently confirmed by a protein array that showed a C9 level significantly higher in SQLC patients, as compared to HEC, with a sensitivity of 53% and a specificity of 89%. C9 fucosylation levels were significantly higher in SQLC patients compared to HEC (p<0.05) when tested by HLE. These findings suggest that C9 and fucosylated form could serve as a useful marker for SQLC.

Validation of cofilin-1 as a biomarker in non-small cell lung cancer: application of quantitative method in a retrospective cohort

PURPOSE

Cofilin is a cytoskeletal protein whose overexpression has been associated with aggressiveness in several types of malignancies. Here, we established and optimized a simple semi-quantitative immunohistochemistry (SQ-IHC) method for cofilin quantification in tumor biopsies, and applied it in a retrospective cohort of NSCLC patients aiming at validating the use of cofilin-1 as a prognostic biomarker.

METHODS

The SQ-IHC method for cofilin-1 quantification was established and applied in a NSCLC cohort. An archival collection of biopsies from 50 patients with clinicopathological information and 5 years follow-up was accessed. Association between cofilin-1 immunocontent and clinical outcome was assessed using standard Kaplan-Meier mortality curves and the log-rank test. To evaluate the robustness of our findings, three different partitional clustering strategies were used to stratify patients into two groups according to the biomarker expression level (hierarchical clustering, Kmeans and median cutoff).

RESULTS

In all the three different partitional clustering we used, survival analysis showed that patient with high cofilin-1 immunocontent had a lower overall survival rate (P < 0.05), and could be used to discriminate between good and bad prognosis. No other correlation was found when the variables age, sex or histological type were tested in association with patients outcome or with cofilin immunocontent.

CONCLUSIONS

Our method showed good sensitivity/specificity to indicate the outcome of patients according to their cofilin immunocontent in biological samples. Its application in a retrospective cohort and the results presented here are an important step toward the validation process of cofilin-1 as a prognostic biomarker.

Antibody purification-independent microarrays (PIM) by direct bacteria spotting on TiO2-treated slides

The preparation of effective conventional antibody microarrays depends on the availability of high quality material and on the correct accessibility of the antibody active moieties following their immobilization on the support slide. We show that spotting bacteria that expose recombinant antibodies on their external surface directly on nanostructured-TiO(2) or epoxy slides (purification-independent microarray - PIM) is a simple and reliable alternative for preparing sensitive and specific microarrays for antigen detection. Variable domains of single heavy-chain antibodies (VHHs) against fibroblast growth factor receptor 1 (FGFR1) were used to capture the antigen diluted in serum or BSA solution. The FGFR1 detection was performed by either direct antigen labeling or using a sandwich system in which FGFR1 was first bound to its antibody and successively identified using a labeled FGF. In both cases the signal distribution within each spot was uniform and spot morphology regular. The signal-to-noise ratio of the signal was extremely elevated and the specificity of the system was proved statistically. The LOD of the system for the antigen was calculated being 0.4ng/mL and the dynamic range between 0.4ng/mL and 10μg/mL. The microarrays prepared with bacteria exposing antibodies remain fully functional for at least 31 days after spotting. We finally demonstrated that the method is suitable for other antigen-antibody pairs and expect that it could be easily adapted to further applications such as the display of scFv and IgG antibodies or the autoantibody detection using protein PIMs.

Identification and validation of SAA as a potential lung cancer biomarker and its involvement in metastatic pathogenesis of lung cancer

Lung cancer is recently regarded as an overhealed inflammatory disease. Serum amyloid A (SAA) is known as an acute phase protein, but it is likely involved in the cancer pathogenesis. We identified both SAA1 and SAA2 in the pooled sera of lung cancer patients but not in the healthy control, by LC-MS/MS analysis. We found that about 14-fold higher levels of SAA in lung cancer patients' sera and plasma compared to healthy controls by ELISA using total 350 samples (13.89 ± 37.18 vs 190.49 ± 234.70 ug/mL). The SAA levels were also significantly higher than in other pulmonary disease or other cancers. An immunohistochemical study using tissue microarray showed that, unlike other cancer tissues, lung cancer tissues highly express SAA. Further in vitro experiments showed that SAA is induced from lung cancer cells by the interaction with THP-1 monocytes and this, in return, induces MMP-9 from THP-1. In in vivo animal models, overexpressed SAA promoted Lewis lung carcinoma (LLC) cells to metastasize and colonize in the lung. Our data suggest that a higher concentration of SAA can serve as an indicator of lung adenocarcinoma and represents a therapeutic target for the inhibition of lung cancer metastasis.

The Haptoglobin β chain as a supportive biomarker for human lung cancers

Haptoglobin (Hp) is produced as an acute phase reactant during inflammation, infection, malignant diseases, and several cancers. In proteomics analysis using human blood samples, the Hp peptide levels were about 3-fold higher in lung cancer patients versus normal individuals. This study is aimed at analyzing the elevation of which chain of Hp is closely related to lung cancers and can be a serum biomarker for lung cancers. In Western blot (WB) analysis, we found that the Hp β chain can be a better diagnostic biomarker for lung cancers. In the result of the Hp β chain ELISA developed by us, the concentrations of the Hp β chain in the sera increased about 4-fold in 190 lung adenocarcinoma patients versus 190 healthy controls (8.0 ± 3.8 μg ml(-1)vs. 1.9 ± 1.2 μg ml(-1)). ELISA data showed that the serum levels of the Hp β chain in breast cancer (1.5 ± 0.5 μg ml(-1)) and hepatocellular carcinoma (HCC) (1.4 ± 1.0 μg ml(-1)) patients remained similar to those of healthy controls. Compared to lung adenocarcinoma, the Hp β chain levels in the plasma of patients with other respiratory diseases such as tuberculosis (TBC), idiopathic pulmonary fibrosis (IPF) and bronchial asthma (BA) were closer to those of healthy controls. Our data suggest that an increase of the Hp β chain can be a potential serum biomarker for lung cancers.

Collagen XXIII: a potential biomarker for the detection of primary and recurrent non-small cell lung cancer

BACKGROUND

Collagen XXIII is a transmembrane collagen previously shown to be upregulated in metastatic prostate cancer. The purpose of this study was to determine the protein expression of collagen XXIII in tumor tissues from a variety of cancers and to assess the utility of collagen XXIII as a biomarker for non-small cell lung cancer (NSCLC).

METHODS

A multicancer tissue microarray was used for the immunohistochemical examination of collagen XXIII protein expression in a variety of cancers. Subsequently, collagen XXIII expression was analyzed in three separate cohorts using tissue microarrays with representative tumor and control lung tissues from NSCLC patients. In addition, NSCLC patient urine samples were analyzed for the presence of collagen XXIII through Western blot.

RESULTS

Collagen XXIII was present in tissue samples from a variety of cancers. Within lung cancer tissues, collagen XXIII staining was enriched in NSCLC subtypes. Collagen XXIII was present in 294 of 333 (88%) lung adenocarcinomas and 97 of 133 (73%) squamous cell carcinomas. In urine, collagen XXIII was present in 23 of 29 (79%) NSCLC patient samples but only in 15 of 54 (28%) control samples. High collagen XXIII staining intensity correlated with shorter recurrence-free survival in NSCLC patients.

CONCLUSIONS

We show the capability of collagen XXIII as a tissue and urinary biomarker for NSCLC, in which positivity in tissue or urine significantly correlates with the presence of NSCLC and high staining intensity is a significant recurrence predictor.

IMPACT

Inclusion of collagen XXIII in a tissue- or urine-based cancer biomarker panel could inform NSCLC patient treatment decisions.

Serum amyloid A is elevated in the serum of lung cancer patients with poor prognosis

Background:

Lung cancer is known as the top cancer killer in most developed countries. However, there is currently no promising diagnostic or prognostic biomarker for lung cancer. This study aims to discover non-invasive differential markers in the serum of lung cancer patients, to determine the protein identity of the candidate biomarker(s), and to investigate any clinical implication of the biomarker(s) concerned.

Methods:

Blood specimens were collected from 154 pre-operative patients with lung cancer and 35 healthy blood donors with no evidence of lung cancer. Fractionated serum samples were processed by surface-enhanced laser desorption/ionisation time-of-flight mass spectrometry (MS). Candidate biomarker was identified using sodium dodecyl sulphate polyacrylamide gel electrophoresis and tryptic digestion followed by tandem MS fragmentation analysis, which was subsequently validated with immunoassay.

Results:

A differential protein with m/z 11.6 kDa was detected and identified as an isoform of human serum amyloid A (SAA). It was significantly increased by 1822% in lung cancer patients when compared with the healthy controls, which gave an area under the receiver operator characteristic curve of 0.88. In addition, the protein was also significantly elevated by 77% in lung cancer patients with survival <5 years when compared with patients with survival ⩾5 years.

Conclusion:

There are several functions of the SAA protein, described in the context of inflammation, that are compatible with the mechanism of tumour invasion and metastasis. Our study not only detected increased SAA level in the serum of lung cancer patients but also identified that elevated SAA level may be a non-invasive biomarker useful for the prediction of lung cancer prognosis.