Biomarkers For Detection Of Lung Cancer Research Articles

Early detection of lung cancer biomarkers in exhaled breath by modified armchair stanene nanoribbons.

In this study, we analyze armchair stanene nanoribbons as excellent sensing substances for the early diagnosis of lung cancer using density functional theory and the non-equilibrium Green function. Four modified configurations of surface- and edge-defected armchair stanene nanoribbons were studied to improve the sensing performance. Our probes indicated that the adsorption energy of armchair stanene nanoribbons is at least five times greater than that of other previously reported substances, such as single-wall carbon nanotubes, phosphorene, and silicene. A noticeable reduction in the current was observed, implying the high sensitivity of our sensing configurations. The adsorption energy and current results suggest that configurations with a single vacancy and edge defects improve the sensitivity and selectivity of the system because of their free dangling bonds. The calculated results demonstrate that the both-side edge defected armchair stanene nanoribbons reduce the adsorption energy to -8.35 eV and increase the sensitivity up to 45% for toluene detection. This reduction in adsorption energy and the surge of sensitivity shows ultra-high sensing performance, yielding a more efficient structure for the future design of early-diagnosis lung cancer sensing applications, thus improving lung cancer patients' survival and life expectancy.

Looking for the Genes Related to Lung Cancer From Nasal Epithelial Cells by Network and Pathway Analysis.

Previous studies have indicated that the airway epithelia of lung cancer-associated injury can extend to the nose and it was associated with abnormal gene expression. The aim of this study was to find the possible lung cancer-related genes from the nasal epithelium as bio-markers for lung cancer detection. WGCNA was performed to calculate the module–trait correlations of lung cancer based on the public microarray dataset, and their data were processed by statistics of RMA and t-test. Four specific modules associated with clinical features of lung cancer were constructed, including blue, brown, yellow, and light blue. Of which blue or brown module showed strong connection to genetic connectivity. From the brown module, it was found that HCK, NCF1, TLR8, EMR3, CSF2RB, and DYSF are the hub genes, and from the blue module, it was found that SPEF2, ANKFN1, HYDIN, DNAH5, C12orf55, and CCDC113 are the pivotal genes corresponding to the grade. These genes can be taken as the bio-markers to develop a noninvasive method of diagnosing early lung cancer.

Abstract 1184: Early-stage lung cancer detection via circulating microbial DNA biomarkers and machine learning classification

Abstract Introduction: Human tissues, including tumors, are extensively colonized by taxonomically diverse microbes. Intra-tumoral microbial activity and events of cellular turnover and trafficking contribute to shedding of microbial nucleic acids into the blood stream. Here we characterized microbial signatures (mbDNA) present in primary-tumor tissue and in the blood of patients affected with different cancer types, with particular focus on lung cancer, and we demonstrated the discriminatory power of such microbial signatures for the identification and classification of lung cancer versus other cancer types. We further validated our findings using plasma-derived cell-free microbial DNA (cf-mbDNA) to discriminate between lung cancer and cancer-free control samples. Methods: We examined The Cancer Genome Atlas (TCGA) compendium of treatment-naïve, whole genome and transcriptomic sequencing datasets to extrapolate genetic signatures of microbial origin associated with 33 different tumor types collected from 10,481 patients, which included non-neoplastic tumor-adjacent tissue and blood samples. 7.2% of TCGA sequencing reads were classified as non-human, of which 35.2% could be taxonomically classified using a reference database containing 59,974 total microbial genomes. These taxonomically assigned data sets were then used to train machine learning models (using a 70/30 train/test split for all cancers) to discriminate between and within types and stages of cancer. Results: We demonstrated that mbDNA signatures from whole blood can be used to accurately classify the tissue of origin of 20 unique cancer types, including lung adenocarcinoma and lung squamous cell carcinoma. For lung adenocarcinoma we reported high discrimination between paired tumor tissue and normal-adjacent tissue (Avg. {AUROC,AUPR}={0.85,0.95}) and between primary tumor tissue and all-other cancer types (Avg. {AUROC,AUPR}={0.96,0.69}, n=32 cancer-types). We also demonstrated the high performance of blood-derived mbDNA when discriminating among TCGA cancer types: Avg. {AUROC,AUPR}={0.97,0.80}. Subsequent liquid biopsy results using plasma-derived cf-mbDNA offer compelling evidence that cf-mbDNA signatures can robustly discriminate adenocarcinoma lung-cancer samples from non-cancer controls. Conclusion: mbDNA holds considerable promise as a truly orthogonal means of detecting and classifying lung cancer independently from host genomic alternations. Using only mbDNA signatures we have demonstrated robust discrimination between cancer-free controls and lung cancer samples and have provided early evidence of the applicability of this approach to liquid biopsy. Our present efforts analyzing plasma cf-mbDNA with an expanded sample cohort will serve to fully validate this new class of liquid biopsy biomarkers for lung cancer detection. Citation Format: Gregory D. Sepich-Poore, Serena Fraraccio, Stephen Wandro, Rob Knight, Sandrine Miller-Montgomery, Eddie Adams. Early-stage lung cancer detection via circulating microbial DNA biomarkers and machine learning classification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1184.

Electrochemical biosensor utilizing dual-mode output for detection of lung cancer biomarker based on reduced graphene oxide-modified reduced-molybdenum disulfide multi-layered nanosheets

Electrochemical biosensor utilizing dual-mode output for detection of lung cancer biomarker based on reduced graphene oxide-modified reduced-molybdenum disulfide multi-layered nanosheets

A Sight of the Diagnostic Value of Aberrant Cell-Free DNA Methylation in Lung Cancer.

Background Lung cancer is one of the most commonly diagnosed cancer worldwide. As one of the liquid biopsy analytes, alternations in cell-free DNA (cfDNA) methylation could function as promising biomarkers for lung cancer detection. Methods In this study, differential methylation analysis was performed to identify candidate markers, and lasso regression with 10-fold cross-validation (CV) was used to establish the diagnostic marker panel. The performance of the binary classifier was evaluated using the receiver operating characteristic (ROC) curve and the precision-recall (PR) curve. Results We identified 4072 differentially methylated regions (DMRs) based on cfDNA methylation data, and then a 10-DMR marker panel was established. The panel achieved an area under the ROC curve (AUROC) of 0.922 and an area under the PR curve (AUPR) of 0.899 in a cfDNA cohort containing 29 lung cancer and 74 normal samples, showing outstanding performance. Besides, the cfDNA-derived markers also performed well in primary tissue datasets, which were more robust than the tissue-derived markers. Conclusion Our study suggested that the 10-DMR marker panel attained high accuracy and robustness and may function as a novel and promising target for lung cancer detection.

Electrochemical studies on doped SnO2 nanocomposite for selective detection of lung cancer biomarkers

Lung cancer (LC) is one of the major disease causes for cancer-related mortality. The detection of volatile organic compounds (VOCs) as lung cancer biomarkers will be useful for early stage detection. Hence, the development of electrochemical sensors to detect acetone and toluene as biomarkers below the allowed permissible limit in a sensitive and selective manner is essential. In this study, transition metal ion doped SnO2 nanocomposites have been developed by the hydrothermal method and used for the selective detection of LC biomarkers. The morphologies, structures, and chemical compositions of synthesized materials were studied using field-emission scanning electron microscopy (FESEM), x-ray diffraction, UV–visible spectroscopy, and Fourier transform infrared spectroscopy. The UV–visible study revealed that the doping of metal ions reduces the bandgap, and FESEM analysis showed a spherical like morphology that improves the adsorption sites on materials. Furthermore, cyclic voltammetry and electrochemical impedance spectroscopy revealed that the doping of transition metal ions improves the charge transfer ability and electrochemical activity of nanocomposites. The selective chemisorption of lung cancer biomarkers on metal-doped SnO2 nanocomposites helps in achieving a superior response with a broad linear detection range (20–100 ppb for toluene and 1–1000 ppb for acetone). In addition, the limit of detection achieved for toluene (1 ppb) and acetone (0.1 ppb) is well below the permissible limit for lung cancer patients. The fabricated nanocomposite is found to be highly selective toward acetone and toluene with a selectivity factor ranging from 1.8 to 12 and 6.6 to 10, respectively, as compared with other VOCs.

Plasmonic Biosensors for the Detection of Lung Cancer Biomarkers: A Review

Lung cancer is the most common and deadliest cancer type globally. Its early diagnosis can guarantee a five-year survival rate. Unfortunately, application of the available diagnosis methods such as computed tomography, chest radiograph, magnetic resonance imaging (MRI), ultrasound, low-dose CT scan, bone scans, positron emission tomography (PET), and biopsy is hindered due to one or more problems, such as phenotypic properties of tumours that prevent early detection, invasiveness, expensiveness, and time consumption. Detection of lung cancer biomarkers using a biosensor is reported to solve the problems. Among biosensors, optical biosensors attract greater attention due to being ultra-sensitive, free from electromagnetic interference, capable of wide dynamic range detection, free from the requirement of a reference electrode, free from electrical hazards, highly stable, capable of multiplexing detection, and having the potential for more information content than electrical transducers. Inspired by promising features of plasmonic sensors, including surface plasmon resonance (SPR), localised surface plasmon resonance (LSPR), and surface enhanced Raman scattering (SERS) such as ultra-sensitivity, single particle/molecular level detection capability, multiplexing capability, photostability, real-time measurement, label-free measurement, room temperature operation, naked-eye readability, and the ease of miniaturisation without sophisticated sensor chip fabrication and instrumentation, numerous plasmonic sensors for the detection of lung cancer biomarkers have been investigated. In this review, the principle plasmonic sensor is explained. In addition, novel strategies and modifications adopted for the detection of lung cancer biomarkers such as miRNA, carcinoembryonic antigen (CEA), cytokeratins, and volatile organic compounds (VOCs) using plasmonic sensors are also reported. Furthermore, the challenges and prospects of the plasmonic biosensors for the detection of lung cancer biomarkers are highlighted.

Current Biosensors Used for Early Detection of Lung Cancer Biomarkers

Cancer, the abnormal and uncontrolled cell growth due to an accumulation of specific genetic and epigenetic defects, has posed a particular threat to human health which makes it to be ranked third in Kenya as a cause of death after infectious and cardiovascular diseases. The unregulated cell growth leads to the formation of a tumor, which after some period of time spreads beyond the site of origin and metastasizes to other body organs and systems, making it incurable. Early detection of cancer (long before the tumor is perceived) greatly increases the chance of curability, and is the point of focus for many cancer researchers. Lung cancer is the primary cancer threatening human life worldwide and its mortality rate has not decreased for a number of years. The mortality rate can be reduced by early diagnosis and subsequent treatment, for example if the serum proteins, microRNA, tumor-associated antigens, are detected in elevated amounts in the blood of potential lung cancer patients using a highly sensitive biosensor. Throughout the years, researchers worldwide have extensively investigated many screening modalities for lung cancer detection, including chest radiograph (CRG), computed tomography (CT), low-dose CT (LDCT), magnetic resonance imaging (MRI), and positron emission tomography (PET) and biopsy. However, these techniques are not suitable for patients with other pathologies. Developing a rapid and sensitive technique for early diagnosis of lung cancer plays an important role in early diagnosis and subsequent treatment. The typical methodologies limit the applications due to the low selectivity, low sensitivity, high cost, low stability and invasive procedure. Tumor markers as biochemical parameters can reveal cancer occurrence and progression, which show selectivity, stability sensitivity, convenience, and low cost in developing biosensors, and act as good prospect for fabricating biosensors of detecting lung cancer. This review portrays several biosensors (2010-2019) for detection of lung cancer biomarkers. To start with, cancer in general and Lung cancer has been briefly described focusing on the screening methods that have been used for lung cancer. The currently used diagnostic tools for lung cancer with their advantages and disadvantages is also portrayed. overview of the application of transducers used, including optical, mass-based, electrochemical and calorimetric biosensors, in lung cancer detection, together with the advantages and disadvantages of the development of new biosensors. Finally, the recent challenges and further opportunities for developing effective biosensors for early diagnosis of lung cancer are discussed.

A panel of DNA methylation biomarkers for detection and improving diagnostic efficiency of lung cancer

Lung cancer remains the leading cause of cancer deaths worldwide. Although low-dose spiral computed tomography (LDCT) screening is used for the detection of lung cancer in a high-risk population, false-positive results of LDCT remain a clinical problem. Here, we developed a blood test of a novel panel of three established lung cancer methylation biomarkers for lung cancer detection. Short stature homeobox 2 gene (SHOX2), ras association domain family 1A gene (RASSF1A), and prostaglandin E receptor 4 gene (PTGER4) methylation was analyzed in a training cohort of 351 individuals (197 controls, 154 cases) and validated from an independent cohort of 149 subjects (89 controls, 60 cases). The novel panel biomarkers distinguished between malignant and benign lung disease at high sensitivity and specificity: 87.0% sensitivity [95% CI 80.2–91.5%], 98.0% specificity [95% CI 94.9–99.4%]. Sensitivity in adenocarcinoma, squamous cell carcinoma, small cell lung cancer, and other lung cancer was 89.0%, 87.5%, 85.7%, and 77.8%, respectively. Notably, cancer patients in stage I and II showed high diagnostic sensitivity at 82.5% and 90.5%, respectively. Moreover, the diagnostic efficiency did not show bias toward age, gender, smoking, and the presence of other (nonlung) cancers. The performance of the panel in the validation cohort confirmed the diagnostic value. These findings clearly showed that this panel of DNA methylation biomarkers was effective in detecting lung cancer noninvasively and may provide clinical utility in stand-alone or in combination with current imaging techniques to improve the diagnosis of lung cancer.

Ultrasensitive electrochemical immunosensor via RAFT polymerization signal amplification for the detection of lung cancer biomarker

Abstract Cytokeratin fragment antigen 21–1 (CYFRA21-1), a characteristic biomarker of non-small cell lung cancer (NSCLC), its abundance is closely related to the diagnosis and prognosis of the disease. Thus, an ultrasensitive electrochemical immunosensor for the detection of CYFRA21-1 was designed by exploiting reversible addition-fragmentation chain transfer (RAFT) polymerization signal amplification strategy. Briefly, graphene oxide (GO) was immobilized on a glassy carbon electrode (GCE) surface to bind a CYFRA21-1 primary antibody (Ab1) via an amide bond, allowing the analyte CYFRA21-1 to be selectively anchored to Ab1 by specific antigen-antibody interactions. Then, the chain transfer agent-conjugated a CYFRA21-1 secondary antibody was attached to the CYFRA21-1, forming a sandwich-like immunocomplex that can be linked to numerous monomers through RAFT polymerization. Accordingly, a high-density electroactive polymer chain was then grafted onto the immunocomplex, dramatically amplifying the electrochemical signal generated by the immobilized CYFRA21-1. Using the strategy, the current response increased linearly with the logarithm of the CYFRA21-1 concentration in the range of 0.5 fg mL−1 to 10 pg mL−1 (R2 = 0.998), with a low detection limit of 0.14 fg mL−1 (S/N = 3). Furthermore, the CYFRA21-1 concentrations in clinical serum samples analyzed by our method were consistent with those obtained by magnetic particle chemiluminescence assay (MPCA) method. Thus, owing to its high sensitivity, environmental benignity, and low cost, this strategy shows great potential for clinical diagnosis and analysis.

A Panel of Platelet circRNAs Serve as Biomarker for Lung Cancer Detection

Background: Early stages of lung cancer have a better prognosis than later stages. The accuracy of current lung cancer early detection methods is relatively poor. We firstly try to use circRNAs of platelets enable blood-based early detection of lung cancer. Methods: We did a three-stage study that included healthy controls, benign lung nodule, and patients with diagnosed lung cancer from two hospitals in China. We used High-throughput Illumina sequencing analysis to profile circRNAs expression in the 8 patients with lung cancer and 8 non-cancer controls (including healthy controls and benign lung nodule patients). Using a training cohort of patients with lung cancer and non-cancer controls, we built a circRNAs classifier to detect lung cancer. Then, the classifiers’ ability was validated in two independent cohorts of patients and non-cancer controls. We used the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) to evaluate diagnostic performance. Findings: Between Aug 1, 2018, and September 31, 2019, we recruited 88 participants to the training cohort, and 694 and 605 participants to the two independent validation cohorts. A signature comprising 23 circRNAs was identified by RNA-Seq analysis in the discovery stage. Then, four circRNAs (CD274, TIMP1, FLNA, ITGA2B) was found to have the possibility to serve as biomarkers for LC detection in the training stage by qRT-PCR. These selected four circRNAs from platelets was identified and subsequently assessed in the two independent validation cohorts. The area under the curve (AUC) for the five circRNAs from platelets (Pcs) were 0.975 in the validation cohort. In the following blinded test including the platelets from benign pulmonary nodule, LC patients and LC patients after surgery, Pcs correctly discriminated the patients with 90.6% sensitivity and 96.3% specificity. Interpretation: CircRNAs from platelets are very promising biomarkers for early stages of lung cancer diagnosis with very high sensitivity and specificity. This novel technology could serve as a Non-invasive, highly accurate diagnostic tool for lung nodule carriers that difficult to distinguish malignance from benign ones. Funding Statement: This work was supported by grants from National Natural Science Foundation of China (81572262), Jiangsu Province’s Key provincial Talents Program (ZDRCA2016028), 333 high class Talented Man Project (BRA2016516) and The Natural Science Foundation of the Jiangsu Higher Education Institution of China (18KJB320006). Declaration of Interests: None declared. Ethics Approval Statement: The study was approved by institutional ethics committee in each study center.

Urinary fluorescent metabolite O-aminohippuric acid is a useful biomarker for lung cancer detection.

Urine contains diagnostically important metabolites that can act as natural fluorophores. However, whether these fluorescent metabolites can be used in lung cancer diagnosis is unknown. This study was conducted to determine whether fluorescent urinary metabolites could be useful biomarkers for lung cancer detection. A total of 46 lung cancer patients and 185 volunteers without cancer were evaluated between November 2013 and November 2014. Samples of the first urine of the day were collected from lung cancer patients and diagnosed at the Hamamatsu University School of Medicine and the Hamamatsu Medical Center prior to cancer treatment, and from volunteers without cancer at the Hamamatsu Medical Imaging Center. Fluorescent urinary metabolites were screened by high-performance liquid chromatography and select effective fluorescent substances for distinguishing cancer from non-cancer status. The fraction of patients at each stage of cancer severity were: 41.3% stage I, 8.7% stage II, 19.6% stage III, and 30.4% stage IV. A robust predictive biomarker for lung cancer was selected by the multivariate logistic analysis of fluorescent metabolites and identified to be O-aminohippuric acid (OAH). The area under the curve (AUC) data for OAH was 0.837 (95% CI 0.769-0.898, P < 0.001). We identified a fluorescent urinary metabolite that can predict lung cancer. OAH exceeds the AUC (0.817) of lung cancer detection by AminoIndex® cancer screening, can be analyzed non-invasively without additional sample processing, and may be a valuable addition to existing lung cancer prediction models.

An analytical investigation on the effect of porous conductive cellulose acetate composite morphology on the detection of organic compounds

AbstractA smart porous conductive polymer composite (CPC) consisting of cellulose acetate as matrix and multiwalled carbon nanotubes as conductive filler was prepared to detect a set of lung cancer biomarkers. The solvent evaporation‐induced phase separation was used to introduce porosity into the conductive composite. The prepared sensitive layers exhibited high response intensity, low response time, and good recovery behavior toward the mentioned analytes. A thorough investigation was conducted on the morphology, response behavior, sensitivity, and selectivity of the prepared CPC transducer. The selectivity of responses was considered based on the thermodynamic and kinetic characteristics of polymer and analytes such as Hansen solubility parameters, Flory‐Huggins interaction parameter, and diffusion coefficient of analytes into the polymer membrane. Moreover, the microstructure of porous layers was characterized by using SEM, contact angle, and BET. The volume porosity and specific surface area of the sensitive layers were increased by the introduction of porosity into the polymer composite, causing the improvement of sensing parameters. The obtained responses further confirmed the promising potential of the prepared porous CPC structure, for the detection of lung cancer biomarkers, from exhaled breath as an inexpensive, repeatable, accurate, and noninvasive method.

Selective and ultrasensitive electrochemical immunosensing of NSE cancer biomarker in human serum using epoxy-substituted poly(pyrrole) polymer modified disposable ITO electrode

An ultrasensitive, specific and label-free immunosensor was developed to determine the lowest alteration of neuron specific enolase antigen (NSE), the standard biomarker of lung cancer patients. This immunosensor was the first biosensor which was fabricated by utilizing epoxy-substituted-polypyrrole P(Pyr-Epx) polymer as an immobilization platform. The fabrication steps of the immunosensor were followed by using electrochemical studies (electrochemical impedance spectroscopy and cyclic voltammetry) and these techniques were applied to characterize the binding interactions. Scanning electron microscopy and atomic force microscopy analyses were performed to investigate the changes formed on the indium tin oxide electrode surface morphology and electrode surface structure. The optimization of the experimental parameters and the analytical performance of the proposed immunosensor were widely evaluated. Under optimum experimental conditions, the change in impedimetric signal was determined to follow the specific biointeraction between anti-NSE antibody and NSE antigen. The sensing ability of the proposed immunosensor for NSE detection showed a wide linear detection range of 0.02–7.5 pg/mL, with a low detection limit of 6.1 fg/mL. This impedimetric immunosensor also demonstrated highly repeatable and reproducible responses, and reliable results in the analysis of human serum samples with recoveries between 98.29 % and 102.81 %. In addition, the designed immunosensor could be used for detection of lung cancer biomarkers after simple dilution of serum samples with phosphate buffer.

Sputum long non-coding RNA biomarkers for diagnosis of lung cancer.

Analysis of molecular changes in sputum may help diagnose lung cancer. Long non-coding RNAs (lncRNAs) play vital roles in various biological processes, and their dysregulations contribute to the development and progression of lung tumorigenesis. Herein, we determine whether aberrant lncRNAs could be used as potential sputum biomarkers for lung cancer. Using reverse transcription PCR, we measure expressions of lung cancer-associated lncRNAs in sputum of a discovery cohort of 67 lung cancer patients and 65 cancer-free smokers with benign diseases and a validation cohort of 59 lung cancer patients and 60 cancer-free smokers with benign diseases. In the discovery cohort, four of the lncRNAs displayed a significantly different level in sputum of lung cancer patients vs.cancer-free smokers with benign diseases (all P< 0.001). From the four lncRNAs, three lncRNAs (SNHG1, H19, and HOTAIR) are identified as a biomarker panel, producing 82.09% sensitivity and 89.23% specificity for diagnosis of lung cancer. Furthermore, the biomarker panel has a higher sensitivity (82.09% vs. 52.24%, P= 0.02) and a similar specificity compared with sputum cytology (89.23% vs. 90.77%, P= 0.45). In addition, the lncRNA biomarker panel had a higher sensitivity (87.50% vs. 70.07%, p= 0.03) for diagnosis of squamous cell carcinoma compared with adenocarcinoma of the lung, while maintaining the same specificity (89.23%). The potential of the sputum lncRNA biomarkers for lung cancer detection is confirmed in the validation cohort. We have for the first time shown that the analysis of lncRNAs in sputum might be a noninvasive approach for diagnosis of lung cancer.

The Biological and Clinical Relevance of Inhibitor of Growth (ING) Genes in Non-Small Cell Lung Cancer.

Carcinogenic mutations allow cells to escape governing mechanisms that commonly inhibit uncontrolled cell proliferation and maintain tightly regulated homeostasis between cell death and survival. Members of the inhibition of growth (ING) family act as tumor suppressors, governing cell cycle, apoptosis and cellular senescence. The molecular mechanism of action of ING genes, as well as their anchor points in pathways commonly linked to malignant transformation of cells, have been studied with respect to a variety of cancer specimens. This review of the current literature focuses specifically on the action mode of ING family members in lung cancer. We have summarized data from in vitro and in vivo studies, highlighting the effects of varying levels of ING expression in cancer cells. Based on the increasing insight into the function of these proteins, the use of ING family members as clinically useful biomarkers for lung cancer detection and prognosis will probably become routine in everyday clinical practice.

Sensitive detection of lung cancer biomarkers using an aptameric graphene-based nanosensor with enhanced stability.

We present an electrolyte-gated graphene field effect transistor (GFET) nanosensor using aptamer for rapid, highly sensitive and specific detection of a lung cancer biomarker interleukin-6 (IL-6) with enhanced stability. The negatively charged aptamer folds into a compact secondary conformation upon binding with IL-6, thus altering the carrier concentration of graphene and yielding a detectable change in the drain-source current Ids. Aptamer has smaller size than other receptors (e.g. antibodies), making it possible to bring the charged IL-6 more closely to the graphene surface upon affinity binding, thereby enhancing the sensitivity of the detection. Thanks to the higher stability of aptamer over antibodies, which degrade easily with increasing storage time, consistent sensing performance was obtained by our nanosensor over extended-time (>24h) storage at 25°C. Additionally, due to the GFET-enabled rapid transduction of the affinity recognition to IL-6, detection of IL-6 can be achieved in several minutes (<10min). Experimental results indicate that this nanosensor can rapidly and specifically respond to the change in IL-6 levels with high consistency after extended-time storage and a detection limit (DL) down to 139 fM. Therefore, our nanosensor holds great potential for lung cancer diagnosis at its early stage.

Abstract 2048: Stat3 downstream transthyretin stimulates tumor growth through regulation of tumor, immune and endothelial cells

Abstract Lung cancer is a very aggressive malignant form of cancer, and is one of the biggest public health challenges facing the United States and many other countries. Although incidence rates have been stabilized, an estimated 154,050 Americans are expected to die from lung &amp; bronchus cancer in 2018, accounting for approximately 25.3 percent of all cancer deaths. Lung cancer is a difficult disease to detect in its early stages, with greater than 50% of patients diagnosed with lung cancer presenting with metastatic disease. Early detection of lung cancer is an important opportunity for decreasing mortality while it is still treatable and curable. The overall 5-year survival rate is ~15 percent. Thus, it is essential to better understand the mechanisms that initiate lung carcinogenesis and find easy-use biomarkers for more accurate lung cancer detection. Due to heterogeneity of lung cancers, a panel of biomarkers should be used for more accurate lung cancer detection and classification. Signal transducer and activator of transcription 3 (Stat3) is well known for its lung cancer-promoting activity. To assess the consequences of STAT3 persistent activation in the lung, a doxycycline-controlled CCSP-rtTA/(tetO)7-Stat3C bitransgenic mouse model was generated that over-expresses STAT3C (a constitutively active form of STAT3) in alveolar type II (AT II) epithelial cells. In sequential steps, Stat3C over-expression up-regulated pro-inflammatory molecules, increased inflammatory cell infiltration and caused adenocarcinomas in the lung. Using this lung tumor model, thirteen secretory proteins that are Stat3 downstream gene products were identified as a panel of biomarkers for lung cancer detection in human sera. This panel of biomarkers potentially differentiates different types of lung cancer for classification. Among them, the transthyretin (TTR) concentration was highly increased in human serum of lung cancer patients. TTR concentration was also induced in the serum, bronchoalveolar lavage fluid, alveolar type II epithelial cells and alveolar myeloid cells of the CCSP-rtTA/(tetO)7-Stat3C lung tumor mouse model. Recombinant TTR stimulated lung tumor cell proliferation and growth, which were mediated by activation of mitogenic and oncogenic molecules. TTR possesses cytokine functions to stimulate myeloid cell differentiation, which are known to play roles in tumor environment. TTR demonstrated a great influence on a wide spectrum of endothelial cell functions to control tumor and immune cell migration and infiltration. TTR-treated endothelial cells suppressed T cell proliferation. Taken together, Stat3 downstream inducible secretory protein biomarkers potentially can be used as clinical targets for lung cancer personalized treatment if their expression levels are increased in a given lung cancer patient in the blood. Citation Format: Xinchun Ding. Stat3 downstream transthyretin stimulates tumor growth through regulation of tumor, immune and endothelial cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2048.

Circulating MicroRNA Biomarkers for Lung Cancer Detection in East Asian Populations.

Background: Lung cancer (LC) is the leading cause of cancer-related death in Eastern Asia. The prognosis of LC highly depends on tumor stages and early detection could substantially reduce LC mortality. Accumulating evidence suggested that circulating miRNAs in plasma or serum may have applications in early LC detection. We thus conducted a systematic literature review on the diagnostic value of miRNAs markers for LC in East Asian populations. Methods: PubMed and ISI Web of Knowledge were searched to retrieve relevant articles published up to 17 September 2018. Information on study design, population characteristics, investigated miRNAs and diagnostic accuracy (including sensitivity, specificity and area under the curve (AUC)) were independently extracted by two reviewers. Results: Overall, 46 studies that evaluated a total of 88 miRNA markers for LC diagnosis in East Asian populations were identified. Sixteen of the 46 studies have incorporated individual miRNA markers as panels (with 2–20 markers). Three promising miRNA panels with ≥90% sensitivity and ≥90% specificity were discovered, two of which were externally validated. Diagnostic performance of circulating miRNAs in East Asian populations was comparable to previously summarized performance in Western populations. Forty-four miRNAs were reported in both populations. No major differences in diagnostic performance by ethnicity of the same miRNA was observed. Conclusions: Circulating miRNAs or miRNA panels, possibly in combination with other promising molecular markers including epigenetic and genetic markers, may be promising candidates for noninvasive LC early detection. However, large studies with samples collected prospectively in true screening settings are required to validate the promising markers or marker panels.

Photochemical Synthesis of Porous CuFeSe2/Au Heterostructured Nanospheres as SERS Sensor for Ultrasensitive Detection of Lung Cancer Cells and Their Biomarkers

Rapid and sensitive identification of tumor biomarker or cancer cells in their nascent stage based on surface-enhanced Raman scattering (SERS) is still an attractive challenge due to the low molecular affinity for the metal surface, the complexity of the sample, and low-efficiency use of hot spots in one- or two-dimensional geometries. Here, we demonstrated a novel kind of renewable CuFeSe2/Au heterostructured nanospheres that are hierarchically porous for specific and sensitive detection of lung cancer biomarkers of aldehydes and lung cancer cells. The heterostructured nanospheres were constructed by loading an Au shell formed by photoreduction on the CuFeSe2 frameworks. P-aminothiophenol (4-ATP) as a Raman-active probe molecule was first grafted on CuFeSe2/Au nanospheres, and then the gaseous aldehyde molecules were sensitively bonded onto the nanospheres by formation of a C═N bond with a detection limit of 1.0 ppb. Moreover, the resulting folic acid (FA)-conjugated nanospheres have a high SERS activity...