Early Diagnosis Of Non-small Cell 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.

Circulating serum exosomes i-tRF-AspGTC and tRF-1-SerCGA as diagnostic indicators for non-small cell lung cancer.

tRF-RNA-a representative of non-coding RNA (ncRNA)-is a precursor or fragment of mature tRNA and plays a crucial regulatory role in the occurrence and development of cancer. There is currently little research on tRF-RNA as a diagnostic marker in cancer, especially for NSCLC from serum exosomes. Serum exosomes were successfully extracted from serum; their physical morphology was captured by transmission electron microscopy (TEM); appropriate particle size detection was performed using qNano; surface labeling was verified through western blotting. Serum exosomes i-tRF-AspGTC and tRF-1-SerCGA were selected through gene microarray, and qPCR was used to validate their significance in 242 patients and 201 healthy individuals. The area under the curve (AUC) was used to evaluate the diagnostic indicators of non-small cell lung cancer (NSCLC). Compared with 201 healthy individuals, i-tRF-AspGTC and tRF-1-SerCGA were significantly downregulated in 242 NSCLC patients and 95 early-stage patients. For tRF-AspGTC and tRF-1-SerCGA, the predictive diagnostic efficiency rates of AUC were 0.690 and 0.680, respectively, whereas the early diagnostic efficiency rates were 0.656 and 0.688, respectively. The result of combined diagnosis with CEA and CYFRA21-1 was 0.928, and the early diagnostic efficiency was 0.843, which is a very high biological predictive factor for NSCLC. The expression of serum exosomes i-tRF-AspGTC and tRF-1-SerCGA was significantly downregulated in NSCLC patients. These exosomes could be used as predictive indicators for diagnosis or early diagnosis of NSCLC.

Abstract C028: Using novel multiomic plasma profiling from liquid biopsies to identify potential signatures for disease diagnostics in late-stage non-small cell lung cancer (NSCLC) in Trinidad and Tobago

Abstract Lung cancer is the leading cause of cancer-associated deaths in North America, with the vast majority being non-small cell lung cancer (NSCLC) with a five-year survival rate of only 24%. Non-invasive discovery of biomarkers associated with early-diagnosis of NSCLC can enable precision oncology efforts using liquid biopsy-based multiomic profiling of plasma cell-free DNA. Although tissue biopsies are currently the gold standard for tumor profiling, this method presents many limitations since these are invasive, risky, and sometimes hard to obtain as well as only giving a limited tumor profile. Blood-based, biomarker directed liquid biopsies provide a less-invasive, more robust approach to interrogate both tumor- and non-tumor-derived signals. Liquid biopsies can delineate complex tumoral genomic and proteomic profiles throughout tumor evolution and are increasingly used for cancer diagnostics that enables a precision oncology approach. We intend to examine 30 stage III-IV NSCLC pre-surgical patients and collect plasma samples. Cell-free DNA (cfDNA) will be extracted from plasma, and next-generation sequencing (NGS) performed as well as comparative traditional diagnostics. Through the analysis of tumor-specific alterations used as biomarkers, including single nucleotide variants (SNVs), insertions, deletions, copy number variations (CNVs), and methylation alterations we intend to identify tumor-derived DNA—ctDNA, among the total pool of cfDNA. Analysis of cancer genotyping for diagnostic purposes will also be compared to accuracy of traditional, imaging and tissue-based techniques. Using liquid biopsies offer opportunities to improve the surveillance of cancer patients during treatment and would supplement current diagnosis and tumor profiling strategies previously not readily available in Trinidad and Tobago. There are a vast number of advantages to use molecular biomarkers like liquid biopsies in diagnosis and tumor profiling as well as to monitor cancer patients, providing early information regarding disease evolution and treatment efficacy, and reorient treatment strategies rapidly, thereby improving clinical oncology outcomes. With the development of both experimental protocols and computational methods dedicated to liquid biopsy, the implementation of multi-omics strategies into the clinical workflow, especially in an under-performing clinical diagnostic system, will benefit the clinical management of cancers including decision-making guidance and patient outcome improvement in the underserved communities of Trinidad and Tobago. Citation Format: Samuel M. West, Nicole Ramlachan. Using novel multiomic plasma profiling from liquid biopsies to identify potential signatures for disease diagnostics in late-stage non-small cell lung cancer (NSCLC) in Trinidad and Tobago [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C028.

Combinations of plasma cfDNA concentration, integrity and tumor markers are promising biomarkers for early diagnosis of non-small cell lung cancer

BackgroundCirculating cell-free DNA (cfDNA) concentration and integrity as noninvasive biomarkers play an important role in cancer diagnosis, prognosis and therapy monitoring. However, few studies have been conducted on the combination of plasma cfDNA concentration, integrity and tumor markers (CEA, CA125, NSE and CYFRA21-1) for cancer detection. Thus, the purpose of this study was to investigate the diagnostic value of combining plasma cfDNA concentration, integrity and tumor markers in early detection of non-small cell lung cancer (NSCLC). MethodsPlasma cfDNA concentration from 50 healthy controls and 84 NSCLC patients were assessed by quantitative real-time PCR of ALU repeated sequence. Plasma cfDNA integrity was calculated as the ratio of long to short fragments (ALU115/60). ResultsPlasma cfDNA concentration (ALU60 and ALU115) and integrity ALU115/60 were significantly higher in NSCLC patients with stage III/IV than in healthy controls (p = 0.0002, p < 0.0001, and p = 0.0093, respectively). The receiver operating characteristic (ROC) curve for discriminating NSCLC patients from healthy controls had an area under the curve (AUC) of 0.936 (95 % CI, 0.939–0.996). Moreover, the combination of plasma cfDNA concentration, integrity and tumor markers (CEA, CA125, NSE and CYFRA21-1) had higher diagnostic performance than either plasma cfDNA concentration alone, integrity alone or tumor markers alone, with sensitivity, specificity and AUC value of 94.05%, 90.00% and 0.968, respectively. These results demonstrated that the combination of plasma cfDNA concentration, integrity and tumor markers could significantly improve the diagnostic accuracy of NSCLC. ConclusionCombination of plasma cfDNA concentration, integrity and tumor markers is a promising biomarker for early diagnosis of NSCLC.

Estimation of disability free life expectancy in non small cell lung cancer based on real world data

To quantify the societal impact of disability in patients with non-small cell lung cancer (NSCLC), this study estimated the disability-free life expectancy (DFLE), loss-of-DFLE and explored their associations with quality-adjusted life expectancy (QALE) and loss-of-QALE. We interlinked national databases and applied a rolling-over algorithm to estimate the lifetime survival function for patients with NSCLC. Using the EuroQOL-5 Dimension (EQ-5D) and Barthel index (BI), we repeatedly measured the quality-of-life and disability functions of NSCLC patients who visited our hospital from 2011 to 2020. Age-, sex-matched referents were simulated from lifetables of the same calendar year of diagnosis. We categorized BI scores ≤ 70 as in need of long-term care and constructed linear mixed models to estimate the utility values and disability scores. We collected 960 cases and 3088 measurements. The proportions of measurements without disability at age 50–64 and in stage I–IIIa, 50–64 and stage IIIb–IV, 65–89 and stage I–IIIa and 65–89 and stage IIIb–IV were 97.3%, 89.3%, 94.8%,78.3%, corresponding to DFLEs of 15.3, 2.4, 6.8, 1.2 years and losses-of-DFLE of 8.1, 20.7, 4.0, 8.6 years, respectively, indicating that advanced stage had a stronger effect than old age. Survivors in advanced stages showed increased demands for assistance in almost all subitems. The DFLEs seemed to be approximate to the QALEs and the latter were shorter than the former due to discomfort and depression. From a societal perspective, future health technology assessment should consider the impact of lifetime duration of functional disability. Early diagnosis of NSCLC may decrease the burden of long-term care.

Comprehensive serum N-glycan profiling identifies a biomarker panel for early diagnosis of non-small-cell lung cancer.

Aberrant serum N-glycan profiles have been observed in multiple cancers including non-small-cell lung cancer (NSCLC), yet the potential of N-glycans in the early diagnosis of NSCLC remains to be determined. In this study, serum N-glycan profiles of 275 NSCLC patients and 309 healthy controls were characterized by MALDI-TOF-MS. The levels of serum N-glycans and N-glycosylation patterns were compared between NSCLC and control groups. In addition, a panel of N-glycan biomarkers for NSCLC diagnosis was established and validated using machine learning algorithms. As a result, a total of 54 N-glycan structures were identified in human serum. Compared with healthy controls, 29 serum N-glycans were increased or decreased in NSCLC patients. N-glycan abundance in different histological types or clinical stages of NSCLC presented differentiated changes. Furthermore, an optimal biomarker panel of eight N-glycans was constructed based on logistic regression, with an AUC of 0.86 in the validation set. Notably, this model also showed a desirable capacity in distinguishing early-stage patients from healthy controls (AUC=0.88). In conclusion, our work highlights the abnormal N-glycan profiles in NSCLC and provides supports potential application of N-glycan biomarker panel in clinical NSCLC detection.

Analysis of Diagnostic Value of CTC and CTDNA in Early Lung Cancer.

This paper is the exploration and analysis of the diagnostic value of circulating tumor cells (CTC) and circulating tumor deoxyribonucleic acid (ctDNA) in early non-small cell lung cancer (NSCLC).A total of 125 NSCLC patients that hospitalized from March 2019 to December 2020 were selected, of which 64 subjects with stage I-II were listed as the early-stage group, and 61 patients with stage III-IV were classified as the intermediate-advanced group; In addition, 47 patients with benign pulmonary nodules hospitalized within same period were selected as a group of benign pulmonary nodules, and 50 healthy subjects were enrolled as a control group. The levels of CTC and ctDNA, serum tumor markers carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin 21-1 (CYFRA21-1) and gastrin-releasing peptide precursor (pro-GRP) were detected. The diagnostic value of CTC and ctDNA in early NSCLC patients was analyzed by ROC curve. The area under the curve of CTC count for early-stage lung cancer was 0. 949 (P=0. 000, 95%CI: 0. 918-0. 980), and ctDNA was 0. 914 (P=0. 000, 95%CI: 0. 866~0. 963). There were significant differences in CTC count and ctDNA among subjects in each group (P<0. 05). CTC count and ctDNA of the early and intermediate-advanced group were remarkably higher than those of the control group and group of benign pulmonary nodules (P<0. 05), and CTC count and ctDNA of patients in the middle-late group were critically higher than those in the early-stage group (P<0. 05). There were significant differences in serum CEA, NSE, CYFRA21-1 and pro-GRP levels among all groups (P<0. 05). The results showed that CTC and ctDNA in NSCLC patients were significantly positively correlated with CEA, NSE, CYFRA21-1 and pro-GRP (P<0. 05). Peripheral blood CTC and ctDNA levels are significantly increased in NSCLC patients. They are positively correlated with serum tumor markers CEA, NSE, CYFRA21-1 and pro-GRP levels, and are related to tumor progression. CTC and ctDNA have high value in the clinical diagnosis of early NSCLC.

Microporous PdCuB nanotag-based electrochemical aptasensor with Au@CuCl2 nanowires interface for ultrasensitive detection of PD-L1-positive exosomes in the serum of lung cancer patients

Programmed cell death ligand 1 protein-positive (PD-L1+) exosomes have been found to be a potential biomarker for the diagnosis of non-small cell lung cancer (NSCLC). However, the development of highly sensitive detection technique for PD-L1+ exosomes is still a challenge in clinical applications. Herein, a sandwich electrochemical aptasensor based on ternary metal-metalloid palladium-copper-boron alloy microporous nanospheres (PdCuB MNs) and Au@CuCl2 nanowires (NWs) was designed for the detection of PD-L1+ exosomes. The excellent peroxidase-like catalytic activity of PdCuB MNs and the high conductivity of Au@CuCl2 NWs endow the fabricated aptasensor with intense electrochemical signal, thus enabling the detection of low abundance exosomes. The analytical results revealed that the aptasensor maintained favorable linearity over a wide concentration range of 6 orders of magnitude and reached a low detection limit of 36 particles/mL. The aptasensor is successfully applied to the analysis of complex serum samples and achieves the accurate identification of clinical NSCLC patients. Overall, the developed electrochemical aptasensor provides a powerful tool for early diagnosis of NSCLC.

Comprehensive analysis of circulating cell-free RNAs in blood for diagnosing non-small cell lung cancer

Early screening and detection of non-small cell lung cancer (NSCLC) is crucial due to the significantly low survival rate in advanced stages. Blood-based liquid biopsy is non-invasive test to assistant disease diagnosis, while cell-free RNA is one of the promising biomarkers in blood. However, the disease related signatures have not been explored completely for most cell-free RNA transcriptome sequencing (cfRNA-Seq) datasets. To address this gap, we developed a comprehensive cfRNA-Seq pipeline for data analysis and constructed a machine learning model to facilitate noninvasive early diagnosis of NSCLC. The results of our study have demonstrated the identification of differential mRNA, lncRNAs and miRNAs from cfRNA-Seq, which have exhibited significant association with development and progression of lung cancer. The classifier based on gene expression signatures achieved an impressive area under the curve (AUC) of up to 0.9, indicating high specificity and sensitivity in both cross-validation and independent test. Furthermore, the analysis of T cell and B cell immune repertoire extracted from cfRNA-Seq have provided insights into the immune status of cancer patients, while the microbiome analysis has revealed distinct bacterial and viral profiles between NSCLC and normal samples. In our future work, we aim to validate the existence of cancer associated T cell receptors (TCR)/B cell receptors (BCR) and microorganisms, and subsequently integrate all identified signatures into diagnostic model to improve the prediction accuracy. This study not only provided a comprehensive analysis pipeline for cfRNA-Seq dataset but also highlights the potential of cfRNAs as promising biomarkers and models for early NSCLC diagnosis, emphasizing their importance in clinical settings.

A Novel Signal-On Electrochemiluminescence Immunosensor for the Detection of NSCLC Antigen Biomarker Based on New Co-Reaction Accelerators.

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer with substantial morbidity and mortality. Herein, a new signal-on electrochemiluminescence (ECL) immunosensor based on multiple amplification strategies is constructed for ultrasensitive detection of cytokeratin 19 fragment antigen 21-1 (CYFRA21-1) biomarker related to NSCLC. Polyethyleneimine (PEI) functionalized MXene is decorated with NiMn layer double hydroxide (NiMn LDH) to form MXene-PEI-NiMn LDH composite. Specially, the La-MOF@ZIF-67 bimetallic organic framework (named as LZBM) and MXene-PEI-NiMn LDH both served as coreaction accelerators to improve the ECL emission of the luminol-H2 O2 system. To be specific, Au nanoparticles (AuNPs) coated MXene-PEI-NiMn LDH is applied to immobilize primary CYFRA21-1 antibody (Ab1 ), while AuNPs decorated LZBM was used for the loading of luminol and secondary CYFRA21-1 antibody (Ab2 ) to form tracer label. Therefore, the ECL signal of the sandwich-type immunosensor is significantly enhanced due to the high loading capability for luminol and the synergistic catalytic ability for the decomposition of H2 O2 into reactive oxygen species (ROS). Under the optimal experimental conditions, the ECL immunosensor exhibited good analytical performances for CYFRA21-1 detection with a wide linear range (100 fg mL-1 -100 ng mL-1 ) and a low limit of detection (85.20 fg mL-1 ), providing a promising method for early diagnosis of NSCLC.

A tumor-associated autoantibody panel for the detection of non-small cell lung cancer.

Lung cancer is the second most frequent malignancy and the leading cause of cancer-associated death worldwide. Compared with patients diagnosed at advanced disease stages, early detection of lung cancer significantly improved the 5-year survival rate from 3.3% to 48.8%, which highlights the importance of early detection. Although multiple technologies have been applied to the screening and early diagnosis of lung cancer so far, some limitations still exist so they could not fully suit the needs for clinical application. Evidence show that autoantibodies targeting tumor-associated antigens(TAAs) could be found in the sera of early-stage patients, and they are of great value in diagnosis. Methods, we identified and screened TAAs in early-stage non-small cell lung cancer(NSCLC) samples using the serological analysis of recombinant cDNA expression libraries(SEREX). We measured the levels of the 36 autoantibodies targeting TAAs obtained by preliminary screening via liquid chip technique in the training set(332 serum samples from early-stage NSCLC patients, 167 samples from patients with benign lung lesions, and 208 samples from patients with no obvious abnormalities in lungs), and established a binary logistic regression model based on the levels of 8 autoantibodies to distinguish NSCLC samples. Results, We validated the diagnostic efficacy of this model in an independent test set(163 serum samples from early-stage NSCLC patients, and 183 samples from patients with benign lung lesions), the model performed well in distinguishing NSCLC samples with an AUC of 0.8194. After joining the levels of 4 serum tumor markers into its independent variables, the final model reached an AUC of 0.8568, this was better than just using the 8 autoantibodies (AUC:0.8194) or the 4 serum tumor markers alone(AUC: 0.6948). In conclusion, we screened and identified a set of autoantibodies in the sera of early-stage NSCLC patients through SEREX and liquid chip technique. Based on the levels of 8 autoantibodies, we established a binary logistic regression model that could diagnose early-stage NSCLC with high sensitivity and specificity, and the 4 conventional serum tumor markers were also suggested to be effective supplements for the 8 autoantibodies in the early diagnosis of NSCLC.

Serum-derived piR-hsa-164586 of extracellular vesicles as a novel biomarker for early diagnosis of non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is a major cause of death in those with malignant tumors. To achieve the early diagnosis of NSCLC, we investigated serum-derived Piwi-interacting RNA (piRNA) of extracellular vesicles to filter diagnostic biomarkers for NSCLC. High-throughput sequencing from cancerous tissues and adjacent noncancerous tissues in patients with NSCLC was first applied to recognize candidate piRNAs as diagnostic biomarkers. These screened piRNAs were further validated in 115 patients (including 95 cases in stage I) and 47 healthy individuals using quantitative real-time PCR (qRT-PCR). We showed that piR-hsa-164586 was significantly upregulated compared with paracancerous tissues and extracellular vesicles from the serum samples of healthy individuals. Moreover, the area under the curve (AUC) value of piR-hsa-164586 was 0.623 and 0.624 to distinguish patients with all stages or stage I of NSCLC, respectively, from healthy individuals. The diagnostic performance of piR-hsa-164586 was greatly improved compared with the cytokeratin-19-fragment (CYFRA21-1). Additionally, piR-hs-164586 was associated with the clinical characteristics of patients with NSCLC. Its expression was associated with the age and TNM stage of patients with NSCLC, indicating that it can serve as an effective and promising biomarker for the early diagnosis of NSCLC.

Enzyme-free sandwich-type electrochemical immunosensor based on high catalytic binary PdCu mesoporous metal nanoparticles and conductive black phosphorous nanosheets for ultrasensitive detection of pro-SFTPB in non-small cell lung cancer

Pro-surfactant protein B (pro-SFTPB) has been proven to be a powerful biomarker for the early diagnosis of non-small-cell lung cancer (NSCLC). In this study, a novel enzyme-free electrochemical immunosensor is developed for the ultrasensitive detection of pro-SFTPB using binary PdCu mesoporous metal nanoparticles (MMNs) and few-layer black phosphorous (BP nanosheets). Concretely, PdCu MMNs, as novel essential signal labels, were successfully synthesized, offering excellent outstanding peroxidase-like catalytic activity toward hydrogen peroxide (H2O2). Meanwhile, the combination of BP nanosheets with biocompatible gold nanoparticles (AuNPs) served as the substrate on the electrode. With the aid of binary PdCu MMNs nanozymes and BP nanosheets, the enzyme-free electrochemical immunosensor revealed ultrasensitivity detection of pro-SFTPB ranging from 10 pg/mL to 100 ng/mL with an ultralow limit of detection of 5.3 pg/mL. In addition, the immunosensor enables accurate detection of pro-SFTPB in serum samples. Altogether, the developed electrochemical immunosensor provides an enzyme-free platform for pro-SFTPB detection, providing a new method for the early diagnosis of NSCLC.

MicroRNA-200a-3p and GATA6 are abnormally expressed in patients with non-small cell lung cancer and exhibit high clinical diagnostic efficacy.

Lung cancer is one of the main threats to human health. Survival of patients with lung cancer depends on timely detection and diagnosis. Among the genetic irregularities that control cancer development and progression, there are microRNAs (miRNAs/miRs). The present study aimed to investigate the expression patterns of miR-200a-3p and transcription factor GATA-6 (GATA6) in peripheral blood of patients with non-small cell lung cancer (NSCLC) and their clinical significance. The expression patterns of miR-200a-3p and GATA6 in the peripheral blood of patients with NSCLC and healthy subjects were measured via reverse transcription-quantitative PCR. The correlation between GATA6/miR-200a-3p expression and their diagnostic efficacy were analyzed by receiver operating characteristic curve analysis. The association between miR-200a-3p/GATA6 expression with the patient clinicopathological characteristics, and their correlation with carcinoembryonic antigen (CEA), neuron specific enolase (NSE) and squamous cell carcinoma antigen (SCCAg) were evaluated. The cumulative survival rate was examined, and whether miR-200a-3p and GATA6 expression levels were independently correlated with the prognosis of NSCLC was analyzed using multivariate logistic regression model. The results demonstrated that the expression of miR-200a-3p was high and that of GATA6 was low in the peripheral blood of patients with NSCLC, and both exhibited high clinical diagnostic efficacy. miR-200a-3p was revealed to target GATA6 by dual-luciferase assay. miR-200a-3p in the peripheral blood was correlated with TNM stage, lymph node metastasis and distal metastasis, while GATA6 in the peripheral blood was correlated with TNM stage and lymph node metastasis. miR-200a-3p and GATA6 were positively correlated with CEA and SCCAg, but not with NSE. High expression of miR-200a-3p and low expression of GATA6 predicted poor prognosis in patients with NSCLC. After adjusting for TNM stage, lymph node metastasis, distance metastasis, GATA6, CEA, NSE and SCCAg in the logistic regression model, it was indicated that the high expression of miR-200a-3p increased the risk of death in patients with NSCLC. Collectively, it was revealed that miR-200a-3p and GATA6 were abnormally expressed in the peripheral blood of patients with NSCLC. Serum levels of miR-200a-3p >1.475 and GATA6 <1.195 may assist the early diagnosis of NSCLC. GATA6 may function in NSCLC as a miR-200a-3p target, which may provide a future reference for NSCLC early diagnosis and treatment.

A comprehensive profiling of soluble immune checkpoints from the sera of patients with non-small cell lung cancer.

BackgroundImmunotherapy was widely used for the treatment of non‐small cell lung cancer (NSCLC). However, whether inhibition of immune checkpoints individually or simultaneously could improve the therapeutic efficacy of NSCLC remains to be investigated. Here, we explored the aberrant levels of several checkpoints and evaluated their potential diagnostic values for NSCLC.MethodsSerum samples of 89 NSCLC patients and 57 healthy donors were collected from Nanjing Drum Tower Hospital between November 2019 and July 2020. Fourteen human immune checkpoints were quantified by Procarta‐Plex Human Immuno‐Oncology Checkpoint Panel.ResultsThe expression levels of sTIM‐3, sCD137, sCD27, sLAG‐3, sIDO, sPD‐L2, sCD152, sCD80, and sPD‐1 were all significantly increased in serum of NSCLC patients. Especially, sLAG‐3 was significantly elevated in serum of NSCLC patients at early‐stage (stages I and II), TIM‐3, CD137, and CD27 were significantly higher in the advanced NSCLC patients (stages III and IV) than in the early‐stage groups. Receiver operating characteristics (ROC) results showed that except for PD‐1, all the other immune checkpoint proteins had potential diagnostic values for NSCLC. sTIM‐3 had the highest diagnostic accuracy, followed by sLAG‐3. Combining sTIM‐3, sLAG‐3, and sCD137 could increase the accuracy to a higher level. Moreover, sCD27 was correlated with NSCLC cancer type, age, sex, and disease stage, while sCD137 was correlated with age and disease stage. sTIM‐3 and sIDO were correlated with stage and age, respectively.ConclusionsTIM‐3 and LAG‐3 were independent biomarkers for the early diagnosis of NSCLC. The combination of TIM‐3, LAG‐3, and CD137 could increase the diagnostic accuracy.

Circulating CD15+ LOX-1+ PMN-MDSCs are a potential biomarker for the early diagnosis of non-small-cell lung cancer.

Non-small-cell lung cancer (NSCLC) is the most common clinical lung cancer. Polymorphonuclear-myeloid derived suppressor cells (PMN-MDSCs), which are the major population of MDSCs, are involved in NSCLC progression. Recently, it was found that lectin-type oxidized LDL receptor 1 (LOX-1) could identify human PMN-MDSCs. However, the role of CD15+ LOX-1+ PMN-MDSCs in NSCLC early diagnosis has not been revealed. Here, we tried to confirm the application of the newly identified CD15+ LOX-1+ PMN-MDSCs in the early diagnosis of NSCLC. Flow cytometry (FCM) was used to detect the proportion of CD15+ LOX-1+ PMN-MDSCs in the peripheral blood (PB) of healthy controls (HC) and NSCLC patients. The correlation of CD15+ LOX-1+ PMN-MDSC frequency with levels of cytokeratin 19-fragments (CYFRA21-1), carcinoembryonic antigen (CEA), and carbohydrate antigen 125 (CA125) was analysed. Receiver operating characteristic (ROC) curve was used to estimate the diagnostic efficacy of CD15+ LOX-1+ PMN-MDSCs for NSCLC. Additionally, the association of CD15+ LOX-1+ PMN-MDSC frequency with NSCLC prognosis/recurrence after surgery was explored. The proportion of CD15+ LOX-1+ PMN-MDSCs increased in PB of NSCLC patients. CD15+ LOX-1+ PMN-MDSC proportion was positively correlated with levels of CEA, CA125 and CYFRA21-1. Detection of PMN-MDSC percentage in PB owed high sensitivity and specificity for NSCLC diagnosis. The proportion of CD15+ LOX-1+ PMN-MDSCs decreased in patients after surgery. The frequency of CD15+ LOX-1+ PMN-MDSCs was lower in NSCLC patients without recurrence compared to those with recurrence after surgery. Circulating CD15+ LOX-1+ PMN-MDSCs are a potential diagnostic marker for NSCLC, and are associated with NSCLC prognosis and recurrence after surgery.

Urinary malate dehydrogenase 2 is a new biomarker for early detection of non-small-cell lung cancer.

Reliable and noninvasive biomarkers for the early diagnosis of non‐small‐cell lung cancer (NSCLC) are an unmet need. This study aimed to screen and validate potential urinary biomarkers for the early diagnosis of NSCLC. Using protein mass spectrometry, urinary MDH2 was found to be abundant both in patients with lung cancer and lung cancer model mice compared with controls. Urine samples obtained as retrospective and prospective cohorts including 1091 NSCLC patients and 736 healthy controls were measured using ELISA. Patients with stage I NSCLC had higher urinary MDH2 compared with healthy controls. The area under the receiver‐operating characteristic curve (AUC) for the urinary MDH2 was 0.7679 and 0.7234 in retrospective and prospective cohorts to distinguish stage I cases from controls. Urinary MDH2 levels correlated with gender and smoking history. MDH2 expression levels were elevated in lung cancer tissues. MDH2 knockdown using shRNA inhibited the proliferation of lung cancer cells. Our study demonstrated that urinary MDH2 concentration was higher in early‐stage NSCLC patients compared with that in controls and that MDH2 could serve as a potential biomarker for early detection of NSCLC.

Circulating Biomarkers for Early Stage Non-Small Cell Lung Carcinoma Detection: Supplementation to Low‐Dose Computed Tomography

Lung cancer is currently the leading cause of cancer death in both developing and developed countries. Given that lung cancer has poor prognosis in later stages, it is essential to achieve an early diagnosis to maximize patients’ overall survival. Non-small cell lung cancer (NSCLC) is the most common form of primary lung cancer in both smokers and non-smokers. The current standard screening method, low‐dose computed tomography (LDCT), is the only radiological method that demonstrates to have mortality benefits across multiple large randomized clinical trials (RCT). However, these RCTs also found LDCT to have a significant false positive rate that results in unnecessary invasive biopsies being performed. Due to the lack of both sensitive and specific screening methods for the early detection of lung cancer, there is an urgent need for alternative minimally or non-invasive biomarkers that may provide diagnostic, and/or prognostic information. This has led to the identification of circulating biomarkers that can be readily detectable in blood and have been extensively studied as prognosis markers. Circulating microRNA (miRNA) in particular has been investigated for these purposes as an augmentation to LDCT, or as direct diagnosis of lung cancer. There is, however, a lack of consensus across the studies on which miRNAs are the most clinically useful. Besides miRNA, other potential circulating biomarkers include circulating tumor cells (CTCs), circulating tumor DNA (ctDNAs) and non-coding RNAs (ncRNAs). In this review, we provide the current outlook of several of these biomarkers for the early diagnosis of NSCLC.

Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data.

Rationale: Most lung cancers are diagnosed at an advanced stage. Presymptomatic identification of high-risk individuals can prompt earlier intervention and improve long-term outcomes. Objectives: To develop a model to predict a future diagnosis of lung cancer on the basis of routine clinical and laboratory data by using machine learning. Methods: We assembled data from 6,505 case patients with non-small cell lung cancer (NSCLC) and 189,597 contemporaneous control subjects and compared the accuracy of a novel machine learning model with a modified version of the well-validated 2012 Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial risk model (mPLCOm2012), by using the area under the receiver operating characteristic curve (AUC), sensitivity, and diagnostic odds ratio (OR) as measures of model performance. Measurements and Main Results: Among ever-smokers in the test set, a machine learning model was more accurate than the mPLCOm2012 for identifying NSCLC 9-12 months before clinical diagnosis (P < 0.00001) and demonstrated an AUC of 0.86, a diagnostic OR of 12.3, and a sensitivity of 40.1% at a predefined specificity of 95%. In comparison, the mPLCOm2012 demonstrated an AUC of 0.79, an OR of 7.4, and a sensitivity of 27.9% at the same specificity. The machine learning model was more accurate than standard eligibility criteria for lung cancer screening and more accurate than the mPLCOm2012 when applied to a screening-eligible population. Influential model variables included known risk factors and novel predictors such as white blood cell and platelet counts. Conclusions: A machine learning model was more accurate for early diagnosis of NSCLC than either standard eligibility criteria for screening or the mPLCOm2012, demonstrating the potential to help prevent lung cancer deaths through early detection.

Discovery and validation of extracellular vesicle-associated miRNAs as noninvasive detection biomarkers for early-stage non-small-cell lung cancer.

miRNAs in circulating extracellular vesicles (EVs) are promising biomarkers for cancer. However, their diagnostic ability for early‐stage non‐small‐cell lung cancer (NSCLC) is not well known. In this study, the circulating EV miRNAs profiling was initially performed in 36 untreated NSCLC patients and 36 healthy controls by TaqMan Low Density Array (TLDA). Subsequently, we performed quantitative reverse‐transcription PCR assay (RT‐qPCR) validation in several independent cohorts that included 159 NSCLC patients, 120 age/sex‐matched healthy controls and 31 benign nodule patients enrolled from three different clinical centres. In addition, 38 cases of NSCLC were analysed before and after surgery. We demonstrated that miR‐520c‐3p and miR‐1274b were significantly and steadily increased in NSCLC patients in comparison with healthy controls and benign nodule patients (P < 0.001) and decreased markedly after tumour resection (P < 0.001). The areas under the curve (AUCs) of the ROC curve of the two‐miRNA panel were 0.857 (95% CI, 0813–0.901; P < 0.0001) and 0.845 (95% CI, 0.793–0.896; P < 0.0001) for NSCLC and NSCLC stage I, respectively. Furthermore, the panel was able to differentiate NSCLC from benign nodules with an AUC of 0.823 (95% CI, 0.730–0.915; P < 0.0001). Furthermore, logistic regression analysis revealed the two‐miRNA panel as an independent risk factor for NSCLC (OR = 16.128, P < 0.0001). In conclusion, miR‐520c‐3p and miR‐1274b have biomarker potential for early diagnosis of NSCLC in multiple centres.