Ultrasensitive Method Enables Liquid Biopsy for the Precise Detection of Circulating MicroRNAs.

Due to invasive and serial examinations of bioactive molecules, liquid biopsy (LB) has emerged as a rapid and reliable solution for early disease detection and monitoring. Developing portable devices with high specificity and sensitivity for LB is highly valuable. To realize a generalized approach to increase the sensitivity of LB, we developed an ultrasensitive diagnostic biochip based on the amplification of miRNA by recombinase polymerase amplification and the significant enhancement of fluorescence signals by photonic crystal (PC) materials. The PCs-RPA biochip has a detection limit as low as 0.24 aM, a wide linear range of 8 orders of magnitude, and excellent specificity. Such advantages realize the accurate detection of circulating miRNAs with very low content in clinical serum samples for the precise diagnosis of nonsmall cell lung cancer.

Diagnostic value of serum vascular endothelial growth factor-D in Korean patients with lymphangioleiomyomatosis.

BACKGROUND: Lymphangioleiomyomatosis (LAM) is a rare multisystemic disorder characterized by the proliferation of abnormal smooth muscle-like cells. Although serum vascular endothelial growth factor-D (VEGF-D) is currently used as a diagnostic biomarker for LAM, its diagnostic value in Korean patients is unclear. OBJECTIVES: To evaluate the diagnostic value of serum VEGF-D for LAM in Korean patients. DESIGN: A multicenter prospective cohort study. METHODS: Serum samples were prospectively collected from five medical institutions, from patients with LAM (n = 40) and controls (n = 24; healthy participants = 3, other cystic lung diseases = 13, idiopathic pulmonary fibrosis = 4, idiopathic nonspecific interstitial pneumonia = 4). Serum VEGF-D levels were measured using the enzyme-linked immunosorbent assay, and the diagnostic value was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: The mean age of patients with LAM was 44.5 years, and all were female (controls: 47.8 years; female: 70.8%, p < 0.001). The serum VEGF-D levels were significantly higher in patients with LAM than those in the control group (median: 708.9 pg/mL vs 325.3 pg/mL, p < 0.001). In the ROC curve analysis, serum VEGF-D levels showed good predicting performance for LAM diagnosis (area under the curve = 0.918) with an optimal cut-off value of 432.7 pg/mL (sensitivity = 85.0%, specificity = 87.5%). When 800 pg/mL was used as the cut-off value, the specificity of serum VEGF-D for LAM diagnosis increased to 100.0%. CONCLUSION: Our results suggest that serum VEGF-D may be a useful biomarker for diagnosing LAM in Korean patients, similar to previous reports.

Blood test for diagnosis of lymphangioleiomyomatosis in Korea: role of vascular endothelial growth factor-DIn this study, we discuss a blood test to diagnose a rare lung disease, called lymphangioleiomyomatosis (LAM). LAM primarily affects women, especially during their childbearing years, and can cause serious lung problems such as damage and cyst (air-filled sac) formation. The blood test looks for a special protein in the blood, called vascular endothelial growth factor-D (VEGF-D). If someone has a lot of this protein, it usually means that they have LAM. We have found that when VEGF-D levels are high, the test can effectively separate LAM from other lung diseases. We also found that raising this threshold to higher levels made it much more likely to correctly distinguish a group of people who do not have the disease from patients with LAM. Our study is important because it's the first to show the usefulness of blood VEGF-D testing in Korean LAM patients, and because it suggests an easier and less inconvenient way for physicians to diagnose LAM in Koreans. Our findings are an important step in improving the management of Korean patients with LAM.

Identifying key circulating tumor DNA parameters for predicting clinical outcomes in metastatic non-squamous non-small cell lung cancer after first-line chemoimmunotherapy.

Circulating tumor DNA (ctDNA) provides valuable tumor-related information without invasive biopsies, yet consensus is lacking on optimal parameters for predicting clinical outcomes. Utilizing longitudinal ctDNA data from the large phase 3 IMpower150 study (NCT02366143) of atezolizumab in combination with chemotherapy with or without bevacizumab in patients with stage IV non-squamous Non-Small Cell Lung Cancer (NSCLC), here we report that post-treatment ctDNA response correlates significantly with radiographic response. However, only modest concordance is identified, revealing that ctDNA response is likely not a surrogate for radiographic response; both provide distinct information. Various ctDNA metrics, especially early ctDNA nadirs, emerge as primary predictors for progression-free survival and overall survival, potentially better assessing long-term benefits for chemoimmunotherapy in NSCLC. Integrating radiographic and ctDNA assessments enhances prediction of survival outcomes. We also identify optimal cutoff values for risk stratification and key assessment timepoints, notably Weeks 6-9, for insights into clinical outcomes. Overall, our identified optimal ctDNA parameters can enhance the prediction of clinical outcomes, refine trial designs, and inform therapeutic decisions for first-line NSCLC patients.

Changes in Red Cell Morphology and Haematological Laboratory Parameters Associated With Alectinib.

BACKGROUND: Alectinib is a second-generation anaplastic lymphoma kinase (ALK) inhibitor indicated for ALK-mutated non-small-cell lung cancer. Recently, the association between alectinib and red cell morphological abnormalities has been reported in a few case series. This retrospective observational study aims to determine the frequency of occurrence of acanthocytosis in patients taking alectinib and to evaluate the red cell indices, biochemical markers of haemolysis and eosin-5-maleimide (EMA) binding assay results in patients receiving alectinib. METHODS: Patients who were on alectinib and had a complete blood count test performed in Queen Elizabeth Hospital Haematology Laboratory between 1 May 2021 and 31 August 2021 were included in the study. Haematological investigations that had been performed before and after the commencement of alectinib were reviewed. RESULTS: Fifty patients receiving alectinib were evaluated in this analysis. One hundred per cent of patients showed 3+ acanthocytes on the peripheral blood smears. Compared with the test results before starting alectinib, the post-alectinib blood tests showed a significantly lower haemoglobin concentration, red blood cell count and haematocrit; and a significantly higher mean corpuscular haemoglobin, mean corpuscular haemoglobin concentration and red cell distribution width. All the tested patients showed a marked reduction in EMA mean channel fluorescence compared with normal control. CONCLUSION: Our cohort revealed that alectinib caused significant acanthocytosis in all patients. Alectinib was also associated with changes in red cell indices and biochemical markers of haemolysis, compatible with a spherocytic and anisopoikilocytic morphology with haemolysis. Patients on alectinib had reduced EMA binding.

Imbalance of B-Cell Subpopulations in the Microenvironment of Sarcoidosis or Lung Cancer.

Although the role of T lymphocytes in sarcoidosis (SA) and lung cancer (LC) is quite well reported, the occurrence of B cells in disease microenvironments may suggest their potential role as natural modifiers of the immune response. The aim of this study was to investigate the B-cell profile and lymphocyte-related hematological parameters between patients with SA, LC and healthy controls (HCs). The cells were assessed by flow cytometry and a hematological analyzer in peripheral blood (PB) and material from lymph nodes (LNs) obtained by the EBUS/TBNA method. We showed that in SA patients, there were higher percentages of naïve B and CD21low B cells and a lower percentage of class-switched memory B cells than LC patients in LNs. We observed a higher median proportion of non-switched memory and transitional B cells in the PB of SA patients than in LC patients. We noticed the lowest median proportion of class-switched memory B cells in the PB from SA patients. LC patients had a higher percentage of RE-LYMP and AS-LYMP than SA patients. Our study presented a different profile of B-cell subpopulations in SA and LC patients, distinguishing dominant subpopulations, and showed the relocation from distant compartments of the circulation to the disease microenvironment, thus emphasizing their role.

Plasma extracellular vesicle long RNA profiling identifies a predictive signature for immunochemotherapy efficacy in lung squamous cell carcinoma.

Introduction: The introduction of Immune Checkpoint Inhibitors (ICIs) has marked a paradigm shift in treating Lung Squamous Cell Carcinoma (LUSC), emphasizing the urgent need for precise molecular biomarkers to reliably forecast therapeutic efficacy. This study aims to identify potential biomarkers for immunochemotherapy efficacy by focusing on plasma extracellular vesicle (EV)-derived long RNAs (exLRs). Methods: We enrolled 78 advanced LUSC patients undergoing first-line immunochemotherapy. Plasma samples were collected, and exLR sequencing was conducted to establish baseline profiles. A retrospective analysis was performed on 42 patients to identify differentially expressed exLRs. Further validation of the top differentially expressed exLRs was conducted using quantitative reverse transcription PCR (qRT-PCR). Univariate Cox analysis was applied to determine the prognostic significance of these exLRs. Based on these findings, we developed a predictive signature (p-Signature). Results: In the retrospective analysis of 42 patients, we identified 460 differentially expressed exLRs, with pathways related to leukocyte migration notably enriched among non-responders. Univariate Cox analysis revealed 45 exLRs with prognostic significance. The top 6 protein-coding exLRs were validated using qRT-PCR, identifying CXCL8, SSH3, and SDHAF1 as differentially expressed between responders and non-responders. The p-Signature, comprising these three exLRs, demonstrated high accuracy in distinguishing responders from non-responders, with an Area Under the Curve (AUC) of 0.904 in the retrospective cohort and 0.812 in the prospective cohort. Discussion: This study highlighted the potential of plasma exLR profiles in predicting LUSC treatment efficacy. Intriguingly, lower p-Signature scores were associated with increased abundance of activated CD4+ and CD8+ T cells, indicating a more robust immune environment. These findings suggest that the p-Signature could serve as a valuable tool in guiding personalized and effective therapeutic strategies for LUSC.

An end-to-end deep learning method for mass spectrometry data analysis to reveal disease-specific metabolic profiles.

Untargeted metabolomic analysis using mass spectrometry provides comprehensive metabolic profiling, but its medical application faces challenges of complex data processing, high inter-batch variability, and unidentified metabolites. Here, we present DeepMSProfiler, an explainable deep-learning-based method, enabling end-to-end analysis on raw metabolic signals with output of high accuracy and reliability. Using cross-hospital 859 human serum samples from lung adenocarcinoma, benign lung nodules, and healthy individuals, DeepMSProfiler successfully differentiates the metabolomic profiles of different groups (AUC 0.99) and detects early-stage lung adenocarcinoma (accuracy 0.961). Model flow and ablation experiments demonstrate that DeepMSProfiler overcomes inter-hospital variability and effects of unknown metabolites signals. Our ensemble strategy removes background-category phenomena in multi-classification deep-learning models, and the novel interpretability enables direct access to disease-related metabolite-protein networks. Further applying to lipid metabolomic data unveils correlations of important metabolites and proteins. Overall, DeepMSProfiler offers a straightforward and reliable method for disease diagnosis and mechanism discovery, enhancing its broad applicability.

Association between remnant cholesterol and the risk of 4 site-specific cancers: evidence from a cross-sectional and Mendelian randomization study.

BACKGROUND: Recent studies have implicated remnant cholesterol (RC) in the etiology, progression, and prognosis of cancer. However, very few of them concentrated on the study of the precise relationship between serum RC levels and cancer risk, leaving this subject unexplored. Consequently, this study aims to investigate the association between serum RC levels and 4 site-specific cancers, employing a dual approach that combines observational and mendelian randomization (MR) analysis. METHODS: Based on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2020, this study collected data from18,067 participants. To rule out confounders, this study utilized weighted multivariable logistic regression and assessed non-linear associations using restricted cubic spline (RCS) regression, followed by two-piecewise linear regression. Sensitivity analysis conducted in this study included subgroup analysis, multiple imputation, outlier removal, and propensity score matching. To strengthen causal inference, this study employed univariable and multivariable MR analysis. The robustness and reliability of the findings were estimated by the application of replication and meta-analysis. RESULTS: The results of multivariable logistic regression analysis demonstrated a significant association between serum RC levels and breast cancer, showing that individuals in the higher logRC category had a higher risk of breast cancer compared to those in the lower category (Q3 vs. Q1: OR = 1.71, 95% CI: 1.01-2.88, P = 0.044). Weighted RCS revealed an inverted L-shape association between RC and the risk of breast cancer (P-nonlinear = 0.0386, P-overall = 0.010). Primary MR analysis provided evidence for an increased risk of breast (IVW: OR = 1.08, 95% CI: 1.03-1.12, P = 0.000951) and colorectal cancer (IVW: OR = 1.12, 95% CI: 1.00-1.24, P = 0.0476) associated with RC. However, the results of replication and meta-analysis did not support a significant causal association of RC with the risk of breast cancer (OR = 1.04, 95% CI: 0.95-1.13), lung cancer (OR = 0.95, 95% CI: 0.88-1.03), colorectal cancer (OR = 1.05, 95% CI: 0.92-1.19), and prostate cancer (OR = 1.01, 95% CI: 0.95-1.08). CONCLUSION: Although a non-linear relationship was observed in the cross-sectional study between remnant cholesterol levels and breast cancer risk, MR analyses failed to provide any causal evidence.

Integration of miRNA expression analysis of purified leukocytes and whole blood reveals blood-borne candidate biomarkers for lung cancer.

Changes in leukocyte populations may confound the disease-associated miRNA signals in the blood of cancer patients. We aimed to develop a method to detect differentially expressed miRNAs from lung cancer whole blood samples that are not influenced by variations in leukocyte proportions. The Ref-miREO method identifies differential miRNAs unaffected by changes in leukocyte populations by comparing the within-sample relative expression orderings (REOs) of miRNAs from healthy leukocyte subtypes and those from lung cancer blood samples. Over 77% of the differential miRNAs observed between lung cancer and healthy blood samples overlapped with those between myeloid-derived and lymphoid-derived leukocytes, suggesting the potential impact of changes in leukocyte populations on miRNA profile. Ref-miREO identified 16 differential miRNAs that target 19 lung adenocarcinoma-related genes previously linked to leukocytes. These miRNAs showed enrichment in cancer-related pathways and demonstrated high potential as diagnostic biomarkers, with the LASSO regression models effectively distinguishing between healthy and lung cancer blood or serum samples (all AUC > 0.85). Additionally, 12 of these miRNAs exhibited significant prognostic correlations. The Ref-miREO method offers valuable candidates for circulating biomarker detection in cancer that are not affected by changes in leukocyte populations.

The prevalence and prognostic value of systemic inflammation in good performance status patients with advanced, inoperable non-small cell lung cancer receiving palliative radiotherapy: Comparison of composite ratios and cumulative scores.

INTRODUCTION: The present study sought to examine the relationships between systemic inflammatory composite ratios/cumulative scores, magnitude of systemic inflammatory response (SIR) and survival in good performance status patients (ECOG-PS 0/1) with advanced NSCLC receiving palliative radiotherapy. METHODS: Systemic inflammatory composite ratios/cumulative scores included the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), C-reactive protein, (CRP)-albumin ratio (CAR), neutrophil- lymphocyte score (NLS), platelet-lymphocyte score (PLS), lymphocyte-monocyte score (LMS), neutrophil-platelet score (NPS), modified Glasgow prognostic score (mGPS). The magnitude of SIR was determined by serum CRP concentration, with a median CRP concentration of >10 m mg/L considered to be systemically inflamed. Relationships between systemic inflammatory composite ratios/ cumulative scores and clinicopathological characteristics were examined using chi-square analysis. Relationships between overall survival (OS) and systemic inflammatory composite ratios/ cumulative scores were examined using cox regression analysis. RESULTS: 479 patients were included. 48% (n = 231) of patients were male and 70% (n = 338) were ≥65 years of age. 29% (n = 140) patients were ECOG-PS 0 and 71% (n = 339) were ECOG-PS 1. 98% (n = 469) of patients died during follow-up. The median survival was 5 months (2-11). A similar prevalence of systemic inflammation was noted across the various ratios/scores (NLR >3 68%; LMR <2.4 65%; PLR >150 70%; CAR >0.20 83%; NLS ≥1 66%; LMS ≥1 71%; NPS≥1 50%; PLS≥1 60% and mGPS≥1 75%). Despite not considered to be systemically inflamed, an NLR <3, LMR ≥2.4, PLR ≤150, NLS 0, LMS 0, NPS 0 and PLS 0 all had a median CRP concentration of >10 mg/L. When adjusted for ECOG-PS, CAR>0.40 (p < 0.001) and mGPS 2 (p < 0.05) remained significantly associated with OS. CONCLUSION: Liver-based measures of systemic inflammation (CAR and mGPS) appear more reliable for the quantification of the magnitude of SIR and have prognostic value in patients with advanced NSCLC.

Integrated omics characterization reveals reduced cancer indicators and elevated inflammatory factors after thermal ablation in non-small cell lung cancer patients.

BACKGROUND: Thermal ablation is a minimally invasive treatment for non-small cell lung cancer (NSCLC). Aside from causing an immediate direct tumour cell injury, the effects of thermal ablation on the internal microenvironment are unknown. This study aimed to investigate the effects of thermal ablation on the plasma internal environment in patients with NSCLC. METHODS: 128 plasma samples were collected from 48 NSCLC (pre [LC] and after thermal ablation [LC-T]) patients and 32 healthy controls (HCs). Olink proteomics and metabolomics were utilized to construct an integrated landscape of the cancer-related immune and inflammatory responses after ablation. RESULTS: Compared with HCs, LC patients exhibited 58 differentially expressed proteins (DEPs) and 479 differentially expressed metabolites (DEMs), which might participate in tumour progression and metastasis. Moreover, 75 DEPs were identified among the HC, LC, and LC-T groups. Forty-eight highly expressed DEPs (eg, programmed death-ligand 1 [PD-L1]) in the LC group were found to be downregulated after thermal ablation. These DEPs had significant impacts on pathways such as angiogenesis, immune checkpoint blockade, and pro-tumour chemotaxis. Metabolites involved in tumour cell survival were associated with these proteins at the expression and functional levels. In contrast, 19 elevated proteins (eg, interleukin [IL]-6) were identified after thermal ablation. These proteins were mainly associated with inflammatory response pathways (NF-κB signalling and tumour necrosis factor signalling) and immune cell activation. CONCLUSIONS: Thermal ablation-induced changes in the host plasma microenvironment contribute to anti-tumour immunity in NSCLC, offering new insights into tumour ablation combined with immunotherapy. Trial registration This study was registered on the Chinese Clinical Trial Registry ( https://www.chictr.org.cn/index.html ). ID: ChiCTR2300076517. Registration Date: 2023-10-11.

High red blood cell distribution width attenuates the effectiveness of Immune checkpoint inhibitor therapy: An exploratory study using a clinical data warehouse.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have improved outcomes in cancer treatment but are also associated with adverse events and financial burdens. Identifying accurate biomarkers is crucial for determining which patients are likely to benefit from ICIs. Current markers, such as PD-L1 expression and tumor mutation burden, exhibit limited predictive accuracy. This study utilizes a Clinical Data Warehouse (CDW) to explore the prognostic significance of novel blood-based factors, such as the neutrophil-to-lymphocyte ratio and red cell distribution width (RDW), to enhance the prediction of ICI therapy benefit. METHODS: This retrospective study utilized an exploratory cohort from the CDW that included a variety of cancers to explore factors associated with pembrolizumab treatment duration, validated in a non-small cell lung cancer (NSCLC) patient cohort from electronic medical records (EMR) and CDW. The CDW contained anonymized data on demographics, diagnoses, medications, and tests for cancer patients treated with ICIs between 2017-2022. Logistic regression identified factors predicting ≤2 or ≥5 pembrolizumab doses as proxies for progression-free survival (PFS), and Receiver Operating Characteristic analysis was used to examine their predictive ability. These factors were validated by correlating doses with PFS in the EMR cohort and re-testing their significance in the CDW cohort with other ICIs. This dual approach utilized the CDW for discovery and EMR/CDW cohorts for validating prognostic biomarkers before ICI treatment. RESULTS: A total of 609 cases (428 in the exploratory cohort and 181 in the validation cohort) from CDW and 44 cases from EMR were selected for study. CDW analysis revealed that elevated red cell distribution width (RDW) correlated with receiving ≤2 pembrolizumab doses (p = 0.0008), with an AUC of 0.60 for predicting treatment duration. RDW's correlation with PFS (r = 0.80, p<0.0001) and its weak association with RDW (r = -0.30, p = 0.049) were confirmed in the EMR cohort. RDW also remained significant in predicting short treatment duration across various ICIs (p = 0.0081). This dual methodology verified pretreatment RDW elevation as a prognostic biomarker for shortened ICI therapy. CONCLUSION: This study suggests the utility of CDWs in identifying prognostic biomarkers for ICI therapy in cancer treatment. Elevated RDW before treatment initiation emerged as a potential biomarker of shorter therapy duration.

Promising microRNAs in pre-diagnostic serum associated with lung cancer up to eight years before diagnosis: a HUNT study.

INTRODUCTION: Blood biomarkers for early detection of lung cancer (LC) are in demand. There are few studies of the full microRNome in serum of asymptomatic subjects that later develop LC. Here we searched for novel microRNA biomarkers in blood from non-cancer, ever-smokers populations up to eight years before diagnosis. METHODS: Serum samples from 98,737 subjects from two prospective population studies, HUNT2 and HUNT3, were considered initially. Inclusion criteria for cases were: ever-smokers; no known cancer at study entrance; 0-8 years from blood sampling to LC diagnosis. Each future LC case had one control matched to sex, age at study entrance, pack-years, smoking cessation time, and similar HUNT Lung Cancer Model risk score. A total of 240 and 72 serum samples were included in the discovery (HUNT2) and validation (HUNT3) datasets, respectively, and analysed by next-generation sequencing. The validated serum microRNAs were also tested in two pre-diagnostic plasma datasets from the prospective population studies NOWAC (n = 266) and NSHDS (n = 258). A new model adding clinical variables was also developed and validated. RESULTS: Fifteen unique microRNAs were discovered and validated in the pre-diagnostic serum datasets when all cases were contrasted against all controls, all with AUC > 0.60. In combination as a 15-microRNAs signature, the AUC reached 0.708 (discovery) and 0.703 (validation). A non-small cell lung cancer signature of six microRNAs showed AUC 0.777 (discovery) and 0.806 (validation). Combined with clinical variables of the HUNT Lung Cancer Model (age, gender, pack-years, daily cough parts of the year, hours of indoor smoke exposure, quit time in years, number of cigarettes daily, body mass index (BMI)) the AUC reached 0.790 (discovery) and 0.833 (validation). These results could not be validated in the plasma samples. CONCLUSION: There were a few significantly differential expressed microRNAs in serum up to eight years before diagnosis. These promising microRNAs alone, in concert, or combined with clinical variables have the potential to serve as early diagnostic LC biomarkers. Plasma is not suitable for this analysis. Further validation in larger prospective serum datasets is needed.

'Plasma first' approach for detecting epidermal growth factor receptor mutation in advanced non-small cell lung carcinoma.

INTRODUCTION: The treatment approach for recently diagnosed advanced non-small cell lung cancer (NSCLC) with EGFR mutations primarily relies on confirming the tissue diagnosis as non-squamous NSCLC. This routine clinical practice of tissue diagnosis imposes several barriers and delays in turnaround time (TAT) for biomarker testing, significantly delaying the time to treatment. The objective of this study is to investigate the 'plasma first' approach for detection of EGFR mutation in advanced stage treatment naïve NSCLC patients. METHODS: We prospectively collected blood samples of treatment naïve patients with clinical and radiological suspicion of advanced stage NSCLC prior to obtaining tissue biopsy. Plasma cfDNA was tested for EGFR mutation using two different methods. We compared the sensitivity and TAT of liquid biopsy with tissue biopsy. RESULTS: In total, we analyzed plasma cell-free DNA (cfDNA) of 236 patients suspected of having advanced NSCLC for EGFR mutations. We observed a notably shorter turnaround time (TAT) of 3 days, which was significantly quicker compared to the 12-day TAT for tissue biopsy (p < 0.05). The ddPCR method had a sensitivity of 82.8%, which was higher than 66.34% sensitivity of ARMS-PCR. The current study also highlights that there is no significant difference in the clinical outcome of the patients whether treated based on liquid biopsy only or tissue biopsy (median progression-free survival of 11.56 vs. 11.9 months; p = 0.94). CONCLUSIONS: Utilizing a 'plasma first' strategy, given its shorter turnaround time, strong positive concordance and comparable outcomes to tissue biopsy, emerges as a highly specific and reliable method for detecting EGFR mutations in advanced-stage NSCLC.

Exosomal Thomsen-Friedenreich Glycoantigen: A New Liquid Biopsy Biomarker for Lung and Breast Cancer Diagnoses.

Exosomes are nanosized extracellular vesicles released by cells to transport biomolecules such as proteins and RNAs for intercellular communication. Exosomes play important roles in cancer development and metastasis; therefore, they have emerged as potential liquid biopsy biomarkers for cancer screening, diagnosis, and management. Many exosome cargos, including proteins, RNAs, and lipids, have been extensively investigated as biomarkers for cancer liquid biopsy. However, carbohydrates, an important type of biomolecule, have not yet been explored for this purpose. In this study, we reported a new exosomal carbohydrate biomarker, α-linked Thomsen-Friedenreich glycoantigen (TF-Ag-α; Galß1-3GalNAc-α). To translate our discovery into clinical settings, we developed a surface plasmon resonance-based assay which utilized a unique mAb, JAA-F11, with high specificity to measure the levels of exosomal TF-Ag-α in blood. To the best of our knowledge, we are the first to demonstrate that exosomes carry TF-Ag-α. We detected exosomal TF-Ag-α in as low as 10 µL serum samples from patients with cancer, but in contrast, levels were negligible in those from normal controls. With a total of 233 patients with cancer and normal controls, we showed that exosomal TF-Ag-α detected lung cancer (n = 60) and breast cancer (n = 95) from normal controls (n = 78) with ≥95% and ≥97% accuracy, respectively. These results demonstrated that exosomal TF-Ag-α is a potential liquid biopsy biomarker for cancer diagnosis. SIGNIFICANCE: Exosomes or small extracellular vesicles have emerged as potent biomarkers of cancer liquid biopsy. We discovered a new exosomal carbohydrate marker, TF-Ag-α (Galß1-3GalNAc-α), and showed that exosomal TF-Ag-α detected both lung and breast cancers with >95% accuracy. Our findings demonstrated that exosomal TF-Ag-α is a promising liquid biopsy biomarker for cancer screening and early detection.

Dual-signal output biosensor for the detection of program death-ligand 1 and therapy progress monitoring of cancer.

A disposable dual-output biosensor to detect program death-ligand 1 (PD-L1) was developed for immunotherapy progress monitoring and early cancer detection in a single experimental setup. The aptamer probe was assembled on rGO composited with carboxylated terthiophene polymer (rGO-pTBA) to specifically capture PD-L1 protein labeled with a new redox mediator, ortho-amino phenol para sulphonic acid, for amperometric detection. Each sensing layer was characterized through electrochemical and surface analysis experiments, then confirmed the sensing performance. The calibration plots for the standard PD-L1 protein detection revealed two dynamic ranges of 0.5-100.0 pM and 100.0-500.0 pM, where the detection limit was 0.20 ± 0.001 pM (RSD ≤5.2%) by amperometry. The sensor reliability was evaluated by detecting A549 lung cancer cell-secreted PD-L1 and clinically relevant serum levels of soluble PD-L1 (sPD-L1) using both detection methods. In addition, therapeutic trials were studied through the quantification of sPD-L1 levels for a small cohort of lung cancer patients. A significantly higher level of sPD-L1 was observed for patients (221.6-240.4 pM) compared to healthy individuals (16.2-19.6 pM). After immunotherapy, the patients' PD-L1 level decreased to the range of 126.7-141.2 pM. The results indicated that therapy monitoring was successfully done using both the proposed methods. Additionally, based on a comparative study on immune checkpoint-related proteins, PD-L1 is a more effective biomarker than granzyme B and interferon-gamma.

Proteome Fishing for CRISPR/Cas12a-Based Orthogonal Multiplex Aptamer Sensing.

Detection of serum protein biomarkers is extremely challenging owing to the superior complexity of serum. Here, we report a method of proteome fishing from the serum. It uses a magnetic nanoparticle-protein corona and a multiplexed aptamer panel, which we incubated with the nanoparticle-protein corona for biomarker recognition. To transfer protein biomarker detection to aptamer detection, we established a CRISPR/Cas12a-based orthogonal multiplex aptamer sensing (COMPASS) platform by profiling the aptamers of protein corona with clinical nonsmall cell lung cancer (NSCLC) serum samples. Furthermore, we determined the four out of nine (FOON) panel (including HE4, NSE, AFP, and VEGF165) to be the most cost-effective and accurate panel for COMPASS in NSCLC diagnosis. The diagnostic accuracy of NSCLC by the FOON panel with internal and external cohorts was 95.56% (ROC-AUC = 99.40%) and 89.58% (ROC-AUC = 95.41%), respectively. Our developed COMPASS technology circumvents the otherwise challenging multiplexed serum protein amplification problem and avoids aptamer degradation in serum. Therefore, this novel COMPASS could lead to the development of a facile, cost-effective, intelligent, and high-throughput diagnostic platform for large-cohort cancer screening.

The associations between dysregulation of human blood metabolites and lung cancer risk: evidence from genetic data.

BACKGROUND: Metabolic dysregulation is recognized as a significant hallmark of cancer progression. Although numerous studies have linked specific metabolic pathways to cancer incidence, the causal relationship between blood metabolites and lung cancer risk remains unclear. METHODS: Genomic data from 29,266 lung cancer patients and 56,450 control individuals from the Transdisciplinary Research in Cancer of the Lung and the International Lung Cancer Consortium (TRICL-ILCCO) were utilized, and findings were replicated using additional data from the FinnGen consortium. The analysis focused on the associations between 486 blood metabolites and the susceptibility to overall lung cancer and its three major clinical subtypes. Various Mendelian randomization methods, including inverse-variance weighting, weighted median estimation, and MR-Egger regression, were employed to ensure the robustness of our findings. RESULTS: A total of 19 blood metabolites were identified with significant associations with lung cancer risk. Specifically, oleate (OR per SD = 2.56, 95% CI: 1.51 to 4.36), 1-arachidonoylglyceropholine (OR = 1.79, 95% CI: 1.22 to 2.65), and arachidonate (OR = 1.67, 95% CI: 1.16 to 2.40) were associated with a higher risk of lung cancer. Conversely, 1-linoleoylglycerophosphoethanolamine (OR = 0.57, 95% CI: 0.40 to 0.82), ADpSGEGDFXAEGGGVR, a fibrinogen cleavage peptide (OR = 0.60, 95% CI: 0.47 to 0.77), and isovalerylcarnitine (OR = 0.62, 95% CI: 0.49 to 0.78) were associated with a lower risk of lung cancer. Notably, isoleucine (OR = 9.64, 95% CI: 2.55 to 36.38) was associated with a significantly higher risk of lung squamous cell cancer, while acetyl phosphate (OR = 0.11, 95% CI: 0.01 to 0.89) was associated with a significantly lower risk of small cell lung cancer. CONCLUSION: This study reveals the complex relationships between specific blood metabolites and lung cancer risk, highlighting their potential as biomarkers for lung cancer prevention, screening, and treatment. The findings not only deepen our understanding of the metabolic mechanisms of lung cancer but also provide new insights for future treatment strategies.