Biomarkers For Detection Of Lung Cancer Research Articles

Hypersensitive detection of CYFRA21-1 by SERS dual antibody sandwich method

PurposeAs an important reference index for the early diagnosis of non-small cell lung cancer (NSCLC), CYFRA21-1 still lacks the detection of low equipment cost, wide linear range and high sensitivity. Surface-enhanced Raman scattering (SERS) is a vibration spectrum technology based on the surface plasma of precious metal nanoparticles, which has been effectively applied to the detection of tumor markers. The combination of SERS technology and sensors has great potential in the ultrasensitive detection of tumor markers. The purpose of this study was to develop a sensitive method using SERS to quantitatively detect CYFRA21-1 for early diagnosis of NSCLC. MethodsA double-antibody sandwich immunoassay based on SERS was designed and tested to implement the ultrasensitive detection of CYFRA21-1 in the serum of NSCLC patients. ResultsGold @Ag nanorods (Au @Ag NRs) with higher Raman signals were prepared and used as probes, while magnetic graphene oxide was used as a magnetic substrate. The immunized probe, immune substrate and CYFRA21-1 standard substance in the buffer system formed a double-antibody sandwich structure. The standard curve displayed a liner range from 1pg mL−1 to 10 ng mL−1, and the detection limit (LOD) is 0.8943pg mL−1. The Raman intensity exhibited a wide linear relationship with the logarithm of CYFRA21-1 concentration. ConclusionOur study successfully established a double-antibody sandwich immunoassay based on SERS. This method demonstrated high specificity and sensitivity for detecting CYFRA21-1 protein content in serum. It has the potential to be applied for early detection of lung cancer biomarkers.

Deep Neural Network-Assisted Terahertz Metasurface Sensors for the Detection of Lung Cancer Biomarkers

Deep Neural Network-Assisted Terahertz Metasurface Sensors for the Detection of Lung Cancer Biomarkers

Highly sensitive “off-on” sensor based on MXene and magnetic microspheres for simultaneous detection of lung cancer biomarkers - Neuron specific enolase and carcinoembryonic antigen

In this work, a highly sensitive lung cancer biomarkers detection probe was developed based on Ag and MXene co-functionalized magnetic microspheres. By using carboxyl magnetic microspheres as carrier, MXene was coated repeatedly by Poly (allylamine hydrochloride) (PAH) as interlayer adhesive, and silver particles grown on the surface of MXene in situ can efficiently improve the sensitivity of the probe. The detection of neuron specific enolase (NSE) is mainly through the formation of a specific complex between NSE antigen and antibody, and the release of antibody labeled with amino carbon quantum dots (CQDs) from the surface of Ag nanoparticles (AgNPs), so that the fluorescence is restored and "OFF-ON" is formed. The biosensor exhibits excellently wide linear range (0.0001–1500 ng/mL) and the limit of detection (LOD) is up to 0.03 pg/mL, which is superior to most tumor marker probes based on fluorescence mechanism. Furthermore, we constructed dual detection strategy for NSE and carcinoembryonic antigen (CEA) simultaneously.

Methylated Cell-Free Tumor DNA in Sputum as a Tool for Diagnosing Lung Cancer—A Systematic Review and Meta-Analysis

Lung cancer is the leading cause of cancer-related mortality worldwide. Early diagnosis is pivotal for the prognosis. There is a notable overlap between lung cancer and chronic bronchitis, and the potential use of methylated tumor DNA in sputum as a biomarker for lung cancer detection is appealing. This systematic review and meta-analysis followed the PRISMA 2020 statement. A comprehensive search was conducted in Embase, Medline, Web of Science, and the Cochrane Library, using these search strings: Lung cancer, sputum, and methylated tumor DNA. A total of 15 studies met the eligibility criteria. Studies predominantly utilized a case–control design, with sensitivity ranging from 10 to 93% and specificity from 8 to 100%. A meta-analysis of all genes across studies resulted in a summary sensitivity of 54.3% (95% CI 49.4–59.2%) and specificity of 79.7% (95% CI 75.0–83.7%). Notably, two less explored genes (TAC1, SOX17) demonstrated sensitivity levels surpassing 85%. The study’s findings highlight substantial variations in the sensitivity and specificity of methylated tumor DNA in sputum for lung cancer detection. Challenges in reproducibility could stem from differences in tumor site, sample acquisition, extraction methods, and methylation measurement techniques. This meta-analysis provides a foundation for prioritizing high-performing genes, calling for a standardization and refinement of methodologies before potential application in clinical trials.

Ab initio investigation of functionalization of titanium carbide Ti3C2 MXenes to tune the selective detection of lung cancer biomarkers

Selected volatile organic compounds (VOCs), such as benzene (C6H6), cyclohexane (C6H12), isoprene (C5H8), cyclopropanone (C3H4O), propanol (C3H8O), and butyraldehyde butanal (C4H8O), in exhaled human breath can act as indicators or biomarkers of lung cancer diseases. Detection of such VOCs with low density would pave the way for an early diagnosis of the disease and thus early treatment and cure. In the present investigation, the density-functional theory (DFT) is applied to study the detection of the mentioned VOCs on Ti3C2TX MXenes, saturated with the functional groups Tx = O, F, S, and OH. For selectivity, comparative sensing of other interfering air molecules from exhaled breath, such as O2, N2, CO2, and H2O is further undertaken. Three functionalization (Tx = O, F, and S) are found promising for the selective detection of the studied VOCs, in particular Ti3C2O2 MXenes has shown distinct sensor response toward the C5H8, C6H6, C6H12, and C3H4O. The relatively strong physisorption (Eads≅-0.45to-0.65eV\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{ads}\\cong -0.45 to-0.65 {\ ext{eV}}$$\\end{document}), triggered between VOC and MXene due to an enhancement of van der Waals interaction, is found responsible to affect the near Fermi level states, which in turn controls the conductivity and consequently the sensor response. Meanwhile, such intermediate-strength interactions remain moderate to yield small desorption recovery time (of order τ≅μs-ms)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ au \\cong \\mu {\ ext{s}}-{\ ext{ms}})$$\\end{document} using visible light at room temperature. Thus, Ti3C2O2 MXenes are found promising candidate material for reusable biosensor for the early diagnosis of lung cancer diseases through the VOC detection in exhaled breath.

Investigating the enhancement of lung cancer sensing: the effect of edge halogenation in armchair stanene nanoribbons.

In this research, we explore the impact of edge passivation using halogen atoms on armchair stanene nanoribbon (ASNR) for the early detection of lung cancer biomarkers. We employ non-equilibrium green function (NEGF) and density functional theory (DFT) methods to evaluate sensing characteristics. The edges of ASNR are passivated with fluorine, chlorine, bromine, and iodine atoms. Our findings indicate a significant enhancement in sensing performance upon halogenation of ASNR. Notable changes in adsorption energy and current for edge-halogenated ASNR configurations demonstrate improved sensing behavior. Moreover, current curves exhibit greater distinctiveness of halogenated ASNR in comparison to hydrogenated ASNR. The calculations indicate a change in adsorption energy (Eads) of -7.59 eV, -7.6 eV, -8.3 eV, and -8.6 eV for the adsorption by styrene on I-ASnNR, Br-ASnNR, toluene on Cl-ASnNR, and styrene on F-ASnNR, respectively. The corresponding sensitivity improves up to 37.33%, 38.09%, 38.35%, and 45.5%, respectively. These findings highlight that the most significant change occurs with the edge fluorination of ASnNR. Our findings underscore the effectiveness of halogen atom edge passivation in ASNR for heightened sensing performance, making it a promising choice for the development of early-detection lung cancer sensors.

Electrochemical deposition of Pd on MoS2 supported graphene nanoflakes over functionalised MWCNTs as an immunosensor for detection of lung cancer biomarker

An electrochemical immunosensor was designed to quantify carcinoembryonic antigen (CEA), which is regarded asa tumour marker and prognostic indicator. The glassy carbon electrode (GCE) surface was modified using MoS2, graphene nanoflakes (GNs), fMWCNTs, and electrodeposited PdNPs for the first time to prepare a Pd-MoS2-GNs@fMWCNTs/GCE. The platform was activated with monoclonal anti-CEA. The prepared nanocomposite material helps in improving the surface area and conductivity of the GCE. Moreover, PdNPs act as a linker between nanocomposite materials and anti-CEA, which enhances electron transport. The procedure used to analyse CEA involve sequential addition of CEA into the solution of 5 mM [Fe(CN)6]3-/4- prepared in 0.1 M PBS (pH 7.4) in the presence of fabricated electrode and DPV and EIS methods were used as detection technique. The change in signal response for the [Fe(CN)6]3-/4- redox reaction before and after CEA interaction is considered as the immunosensor response which is directly correlated with CEA concentration. Further, in DPV, the signal inhibition approach was used to measure CEA by monitoring the decrease in oxidation peak current of [Fe(CN)6]3-/4- due to the formation of CEA/anti-CEA immunocomplex. The electrochemical immunosensor demonstrated low LOD of 2 pg/mL and a correlation coefficient of 0.98 within a broad linear range of CEA concentration from 0.002 to 500 ng/mL. The fabricated immunosensor displayed excellent selectivity, repeatability, and stability. Further, the proposed immunosensor effectively measured CEA levels in human blood serum, making it an effective tool for testing CEA in clinical samples.

TIMP1 is an early biomarker for detection and prognosis of lung cancer.

Lung cancer remains the major cause of cancer-related deaths worldwide. Early stages of lung cancer are characterized by long asymptomatic periods that are ineffectively identified with the current screening programs. This deficiency represents a lost opportunity to improve the overall survival of patients. Serum biomarkers are among the most effective strategies for cancer screening and follow up. Using bead-based multiplexing assays we screened plasma and tumours of the KrasG12D/+; Lkb1f/f (KL) mouse model of lung cancer for cytokines that could be used as biomarkers. We identified tissue inhibitor of metalloproteinase 1 (TIMP1) as an early biomarker and validated this finding in the plasma of lung cancer patients. We used immunohistochemistry (IHC), previously published single-cell RNA-seq and bulk RNA-seq data to assess the source and expression of TIMP1in the tumour. The prognostic value of TIMP1 was assessed using publicly available human proteomic and transcriptomic databases. We found that TIMP1 is a tumour-secreted protein with high sensitivity and specificity for aggressive cancer, even at early stages in mice. We showed that TIMP1 levels in the tumour and serum correlate with tumour burden and worse survival in mice. We validated this finding using clinical samples from our institution and publicly available human proteomic and transcriptomic databases. These data support the finding that high tumour expression of TIMP1 correlates with an unfavorable prognosis in lung cancer patients. TIMP1 is a suitable biomarker for lung cancer detection.

Distinct Features of Plasma Ultrashort Single-Stranded Cell-Free DNA as Biomarkers for Lung Cancer Detection.

Using broad range cell-free DNA sequencing (BRcfDNA-Seq), a nontargeted next-generation sequencing (NGS) methodology, we previously identified a novel class of approximately 50 nt ultrashort single-stranded cell-free DNA (uscfDNA) in plasma that is distinctly different from 167 bp mononucleosomal cell-free DNA (mncfDNA). We hypothesize that uscfDNA possesses characteristics that are useful for disease detection. Using BRcfDNA-Seq, we examined both cfDNA populations in the plasma of 18 noncancer controls and 14 patients with late-stage nonsmall cell lung carcinoma (NSCLC). In comparison to mncfDNA, we assessed whether functional element (FE) peaks, fragmentomics, end-motifs, and G-Quadruplex (G-Quad) signatures could be useful features of uscfDNA for NSCLC determination. In noncancer participants, compared to mncfDNA, uscfDNA fragments showed a 45.2-fold increased tendency to form FE peaks (enriched in promoter, intronic, and exonic regions), demonstrated a distinct end-motif-frequency profile, and presented with a 4.9-fold increase in G-Quad signatures. Within NSCLC participants, only the uscfDNA population had discoverable FE peak candidates. Additionally, uscfDNA showcased different end-motif-frequency candidates distinct from mncfDNA. Although both cfDNA populations showed increased fragmentation in NSCLC, the G-Quad signatures were more discriminatory in uscfDNA. Compilation of cfDNA features using principal component analysis revealed that the first 5 principal components of both cfDNA subtypes had a cumulative explained variance of >80%. These observations indicate that the distinct biological processes of uscfDNA and that FE peaks, fragmentomics, end-motifs, and G-Quad signatures are uscfDNA features with promising biomarker potential. These findings further justify its exploration as a distinct class of biomarker to augment pre-existing liquid biopsy approaches.

ZnO quantum dots @ nitrogen and sulfur Co-doped porous carbon nanosheets for the detection of lung cancer biomarkers in exhaled breath

The development of non-selective sensors as an e-nose system for breath analysis application generates unique breath prints delineating the fundamental body metabolism aiding early diagnosis of lung cancer. In this work, size dependent ZnO quantum dots (QDs) were controlled by pH and finely dispersed on nitrogen and sulfur co-doped porous carbon nanosheet (CNS) for exploration of their chemi-resistive gas sensing property. Application of these nanocomposites to different lung cancer biomarkers (toluene, isoprene acetone and benzene) depicts unique responses to each at different operating temperatures. Particularly, the composite with lower ZnO QDs size on sulfur rich porous carbon nanosheet (SC-SQD) exhibited 2.5 times of the response to toluene compared to the composite with higher QDs size and sulfur poor porous carbon nanosheet (NC-LQD) in the concentration range between 1 ppm and 5 ppm. It was demonstrated that for 5 ppm of toluene, a response of 31.4 was observed with a response and recovery time of 18 s and 58 s, respectively. The sensor exhibited an estimated lower limit of detection of 800 ppb. Besides, the sensor exhibited a gradual decrease (∼9.55%) in response to 5 ppm toluene at 90% humidity. This superior performance can be explained by the density of defect states, reactive oxygen species (ROS) and oxidation states of functionalization.

Hypomethylation of DYRK4 in peripheral blood is associated with increased lung cancer risk.

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide. It is urgent to identify new biomarkers for the early detection of LC. DNA methylation in peripheral blood has been reported to be associated with cancers. We conducted two independent case-control studies and a nested case-control study (168 LC cases and 167 controls in study Ⅰ, 677 LC cases and 833 controls in study Ⅱ, 147 precancers and 21 controls in the nested case-control study). The methylation levels of DYRK4 CpG sites were measured using mass spectrometry and their correlations with LC were analyzed by logistic regression and nonparametric tests. Bonferroni correction was used for the multiple comparisons. LC-related decreased DYRK4 methylation was discovered in Study I and validated in Study II (the odds ratios [ORs] for the lowest vs. highest quartile of all three DYRK4 CpG sites ranged from 1.64 to 2.09, all p < 0.001). Combining the two studies, hypomethylation of DYRK4 was observed in stage I cases (ORs per -10% methylation ranged from 1.16 to 1.38, all p < 5.9E-04), and could be enhanced by male gender (ORs ranged from 1.77 to 4.17 via interquartile analyses, all p < 0.017). Hypomethylation of DYRK4_A_CpG_2 was significantly correlated with tumor size, length, and stage (p = 0.034, 0.002, and 0.002, respectively) in LC cases. Our study disclosed the association between DYRK4 hypomethylation in peripheral blood and LC, suggesting the feasibility of blood-based DNA methylation as new biomarker for LC detection.

Cyanocobalamin (VB12) Bionic Enzyme-Assisted Photocatalytic Chain-Growth Polymerization for Detection of Lung Cancer Biomarkers.

Cobalt-mediated radical polymerization is noted for its great level of control over the polymerization of acrylic and vinyl esters monomers, even at high molar mass. Vitamin B12, a natural bionic enzyme cobalt complex, involves the conversion of organic halides to olefins through chain-growth polymerization. In this work, the notion of R-Co(III) free radical persistent free radical effect and vitamin B12 circulation were first reported for the perception of ultralow abundance of microRNA-21, a lung cancer biomarker. Indeed, most Co-containing catalytic reactions can occur under mild conditions due to their minimal bond dissociation of the C-Co bond, with blue light irradiation. Based on the intrinsic stability of the vitamin B12 framework and recycling of the catalyst, it is evident that this natural catalytic scheme has potential applications in medicinal chemistry and biomaterials. In addition, this strategy, combined with highly specific recognition probes and vitamin B12 circulation-mediated chain-growth polymerization, has a detection limit as low as 910 aM. Furthermore, it is sensitive for sensing in serum samples containing biomarkers and shows great potential for RNA selection and amplification sensing in clinical samples.

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

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

Perovskite QD based paper microfluidic device for simultaneous detection of lung cancer biomarkers – Carcinoembryonic antigen and neuron specific enolase

This work reports the microfabricated paper based analytical device (mPADs) for fluorometric detection of lung cancer biomarkers Carcinoembryonic antigen (CEA) and Neuron specific enolase (NSE) simultaneously. The device is patterned by wax printing on the paper that features two closed circular assay zone, connected to one sample zone through the grooved microfluidic channels for analyte flow. CsPbI3 perovskite quantum dots (IPQDs) were phase engineered (PE) for the first time to make it compatible in aqueous and biological medium. As a proof of concept, these PE- IPQDs along with PE CsPbBr3 perovskite quantum dots (BPQDS) were surface modified with streptavidin and conjugated to specific antibodies for demonstration of multiplexing, via sandwich type immunoassay. In addition to offering benefits of being less expensive, portable, enabling robust and facile operations, these developed mPADs exhibit specific and simultaneous detection of CEA and NSE lung cancer biomarkers. A linear response with a detection limit 0.095 ng/mL and 30.0 ng/mL was achieved for CEA and NSE respectively and while it was 0.12 ng/mL and 32 ng/mL for simultaneous detection. This mPAD device (with negligible toxicity of the probe) thus combines the high-throughput capabilities of a microfluidic network with the high sensitivity and multicolour imaging ability offered by perovskite QDs. This can become a promising diagnostic tool if extended for detection of other proteins or biomarkers involved in environmental and food safety research.

Combinatorial Blood Platelets-Derived circRNA and mRNA Signature for Early-Stage Lung Cancer Detection

Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here, we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting an analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an area under the curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 circRNA), enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.

Enhanced sensing performance of armchair stanene nanoribbons for lung cancer early detection using an electric field.

In this study, we analyze the effect of a uniform external electric field on the sensing behavior of armchair stanene nanoribbons (ASnNRs) for early detection of lung cancer biomarkers. The Density functional theory (DFT) and non-equilibrium Green function (NEGF) methods are used to study the sensing behavior. We use Ez = 0.4 V Å-1 and Ez = -0.4 V Å-1 as vertical electric fields and Ey = 0.08 V Å-1 and Ey = -0.08 V Å-1 as transverse electric fields. Our findings demonstrate that applying an electric field in a negative/positive direction considerably increases/decreases the magnitude of the adsorption energy and the transferred charge. In the presence of Ez = 0.4 V Å-1 and Ey = -0.08 V Å-1, a substantial decrease in current was observed. Furthermore, the current curves become more distinguishable compared to the absence of electric fields. The computed results indicate that the negative direction of the applied electric field enhanced the sensitivity and selectivity of ASnNRs for the detection of lung cancer-related biomarkers. The computed results also show that using Ez = -0.4 V Å-1 reduces the adsorption energy to Eads = -8.89 eV and enhances the sensitivity up to 41.83% for styrene detection, demonstrating an improvement in the sensing performance compared to the situation without an electric field. These findings have practical implications, as they can be used to develop highly sensitive early-detection gas sensors, potentially saving human lives.

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