Early Diagnosis Of Lung Cancer Research Articles

Genetic profile of ferroptosis in non-small cell lung carcinoma and pharmaceutical options for ferroptosis induction.

Lung cancer (LC) is the leading cause of cancer-related deaths and the second most commonly diagnosed malignancy worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell LC (LUSCC) are the most common subtypes of non-small cell LC (NSCLC). Early diagnosis of LC can be challenging due to a lack of biomarkers. The overall survival (OS) of patients with NSCLC is still poor despite the enormous efforts that have been made to develop novel treatments. Understanding fundamental molecular and genetic mechanisms is necessary to develop new therapeutic approaches for NSCLC. A recently identified type of programmed cell death known as ferroptosis is one potential approach. Ferroptosis causes oxidative damage and the death of cancerous cells by peroxidizing unsaturated phospholipids and accumulating reactive oxygen species (ROS) in an iron-dependent manner. Ferroptosis-related gene (FRG) signatures have recently been evaluated for their ability to predict patient OS and prognosis. These analyses show FRGs are involved in cancer progression, and may serve as promising biomarkers for tumor diagnosis and therapy. Moreover, we summarize the current pharmaceutical options of ferroptosis induction and their underlying molecular mechanism in LC. Therefore, this review aims to provide a comprehensive summary of FRG-based prognostic models, their associated metabolic and signaling pathways, and promising therapeutic options for ferroptosis induction in NSCLC.

Bedside to bench and back again-translational research in interventional pulmonology.

Translational research in Interventional Pulmonology has made significant advances in recent years, ranging from novel biomarkers and imaging to practice-changing clinical trials in lung cancer and pleural disease. This review article aims to summarize key research studies in the field to understand the latest published evidence and to highlight areas of growing academic interest. In lung cancer, the role of novel imaging and biomarkers and their potential utility in early lung cancer diagnosis will be highlighted. In pleural disease, less invasive/conservative treatment in pneumothorax, early aggressive treatment in pleural infection along with novel biomarkers, and the shift beyond drainage strategies in malignant pleural effusion and mesothelioma will be discussed. This overview of translational research in the field of interventional pulmonology will ultimately help to highlight the gaps in current evidence to promote research in areas of clinical significance.

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in-house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in-house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor-immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T-cell infiltration, indicative of enhanced antitumor-immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator-activated receptor (PPAR), and Toll-like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning-derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor-immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor-immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.

Potential of Chest Radiography in Primary Diagnosis of Lung Cancer

Objective: to evaluate the effectiveness of diagnostics for non-small cell lung cancer (LC) based on chest radiography/fluorography data for LC screening diagnostics during preventive medical examination and check-ups.Material and methods. The work was organized as a retrospective cohort study. The starting point of the study was chest radiography, the final point was the diagnosis of LC based on the results of low-dose computed tomography (LDCT) and tumor morphological examination. The patient sample was composed using inclusion and exclusion criteria and initially included 800 patients, then narrowed down to 788. Patients were divided into groups according to LC stage and depending on whether radiography during preventive measures made it possible to suspect LC verified within 3 months via LDCT for any reasons not related to the suspicion of a tumor process. The diagnostic coefficient and informative value of radiography were calculated for each stage of the established LC diagnosis.Results. The study showed that chest radiography does not allow establishing stage I LC diagnosis (J=0.00; p<0.05), it is low informative for stage II LC (J=0.25; p<0.05), and medium informative for stage III LC (J=0.80; p<0.05). Chest radiography does not reliably detect LC signs at early stages in 84.3% of cases.Conclusion. The obtained results indicate an insufficient sensitivity of radiography as a screening method, primarily for early LC diagnosis. The introduction of chest LDCT instead of radiography into the procedure for preventive medical examination and check-ups of the adult population approved by the Ministry of Health of the Russian Federation will increase LC detection at early stages by 5.37 times (95% confidence interval 3,77–7,64).

Comparative Analysis of Mutation in the Buccal Epithelium and Blood in Patients with Lung Cancer and Healthy People

Lung cancer is one of the leading causes of cancer death. Finding new methods for the early and accurate diagnosis of lung cancer is critical for effective treatment. We have shown that patients with lung cancer have more mutations in the FLT3, PDGFRA, KDR, PIK3CA, HRAS, FGFR3 genes in the buccal epithelium than people without diagnosed lung cancer. Thus, study of molecular alterations may be used as a method for the accurate diagnosis of lung cancer in the early stages of investigational procedure.

A clinically applicable model more suitable for predicting malignancy or benignity of pulmonary ground glass nodules in women patients

BackgroundIn recent years, clinicians often encounter patients with multiple pulmonary nodules in their clinical practices. As most of these ground glass nodules (GGNs) are small in volume and show no spicule sign, it is difficult to use Mayo Clinic Model to make early diagnosis of lung cancer accurately, especially in large numbers of nonsmoking women who have no tumor history. Other clinical models are disadvantaged by a relatively high false-positive or false-negative rate. Therefore, there is an urgent need to establish a new model of predicting malignancy or benignity of pulmonary GGNs for the sake of making accurate and early diagnosis of lung cancer.MethodsIncluded in this study were GGNs surgically resected from patients who were admitted to Yiwu Central Hospital from January 2018 to March 2024, including both male and female patients, there is no gender specific issue. The nature of all these GGN tissues was confirmed pathologically. The case data were statistically analyzed to establish a mathematical prediction model, the prediction performance of which was verified by the pathological results.ResultsAltogether 261 GGN patients met the inclusion criteria. Using the results of logistic regression analysis, a mathematical prediction equation was established as follows: Malignant probability (mP) = ex/ (1 + ex); when mP was > 0.5, the GGN was considered as malignant, and when mP was ≤ 0.5, it was considered as benign. x= -2.46 + 1.032*gender + 1.85*mGGN + 1.40*VCS-0.0027*mean CT value of the nodule + 0.078*maximum diameter of the nodule, where e represents the natural logarithm; if the patient was a female, gender = 1 (otherwise = 0); if the pulmonary nodule was a mixed GGN, mGGN = 1 (otherwise = 0); if the pulmonary nodule had vascular convergence sign, VCS = 1 (otherwise = 0). The prediction performance of the mathematical prediction model was verified as follows: the negative prediction value was 0.97156 and the positive prediction value was 0.3800 in the model group versus 1 and 0.25 in the verification group.ConclusionIn this study, we identified female gender, mGGN, VCS, mean CT value and maximum nodule diameter as five key factors for predicting malignancy or benignity of pulmonary nodules, based on which we established a mathematical prediction model. This novel innovation may provide a useful auxiliary tool for predicting malignancy and benignity of pulmonary nodules, especially in women patients.

Rapid Detection of Explicit Volatile Organic Compounds for Early Diagnosis of Lung Cancer Using MoSi2N4 Monolayer.

In this study, we investigate the adsorption of MoSi2N4) and MoSi2N4-VNtowards five potential lung cancer volatile organic compounds (VOCs).Density functional theory calculations reveal that MoSi2N4 weakly adsorb the mentioned VOCs, whereas introduction of nitrogen vacancies significantly enhances the adsorption energies ([[EQUATION]]), both in gas phase and aqueous medium. The MoSi2N4-VN monolayers exhibit a reduced bandgap and facilitate charge transfer upon VOCs adsorption, resulting in enhanced [[EQUATION]] values of -0.83, -0.76, -0.49, -0.61, and -0.50 eV for 2,3,4-trimethyl hexane, 4-methyl octane, o-toluidine, Aniline, and Ethylbenzene, respectively. Bader charge analysis and spin-polarized density of states (SPDOS) elucidate the charge redistribution and hybridization between MoSi2N4-VN and the adsorbed VOCs. The work function of MoSi2N4-VN is significantly reduced upon VOCs adsorption due to induced dipole moments, enabling smooth charge transfer and selective VOCs sensing. Notably, MoSi2N4-VN monolayers exhibit sensor responses ranging from 16.2% to 26.6% towards the VOCs, with discernible selectivity. Importantly, the recovery times of the VOCs desorption is minimal, reinforcing the suitability of MoSi2N4-VN as a rapid, and reusable biosensor platform for efficient detection of lung cancer biomarkers. Thermodynamic analysis based on Langmuir adsorption model shows improved adsorption and detection capabilities MoSi2N4-VN under diverse operating conditions of temperatures and pressures.

FocalNeXt: A ConvNeXt augmented FocalNet architecture for lung cancer classification from CT-scan images

Early and accurate diagnosis of lung cancer, a life-threatening disease, is critical to the successful treatment of patients with the disease. On the other hand, it is well known that the integration of computer-aided diagnosis (CAD) systems into the diagnostic workflow facilitates the process and improves the diagnostic results of automated systems by reducing the difficulties associated with human observation. In this paper, we present FocalNeXt, a new ConvNeXt-augmented FocalNet architecture specifically designed for automated lung cancer detection from computed tomography (CT) scan images. By combining the strong attention mechanism of FocalNet and the feature extraction mechanism of ConvNeXt within the vision transformer paradigm, FocalNeXt aims to improve diagnostic accuracy. Our comprehensive evaluation of a publicly available Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) CT-scan dataset includes rigorous comparisons and an ablation study. FocalNeXt achieved an accuracy of 99.81%, outperforming the state-of-the-art methods. The model also excelled in sensitivity (99.78%), recall (99.36%), and F1-score (99.56%), positioning FocalNeXt as a leading model for lung cancer detection. The ablation study further demonstrated its efficacy and underscored the robustness of FocalNeXt in different configurations. The results underline its potential to contribute to advances in medical imaging and personalized healthcare.

EP.04D.03 Shifting Stages: Implications of a Global Pandemic on Cancer Care and A Multipronged Approach to Early Diagnosis for Non-Small Cell Lung Cancer

EP.04D.03 Shifting Stages: Implications of a Global Pandemic on Cancer Care and A Multipronged Approach to Early Diagnosis for Non-Small Cell Lung Cancer

Gold Nanocluster-Based Capture-Detection Platform for Clinical Diagnosis of Early Lung Cancer

Gold Nanocluster-Based Capture-Detection Platform for Clinical Diagnosis of Early Lung Cancer

Impact of the Lung Microbiota on Development and Progression of Lung Cancer.

The past several years have provided a more profound understanding of the role of microbial species in the lung. The respiratory tract is a delicate ecosystem of bacteria, fungi, parasites, and viruses. Detecting microbial DNA, pathogen-associated molecular patterns (PAMPs), and metabolites in sputum is poised to revolutionize the early diagnosis of lung cancer. The longitudinal monitoring of the lung microbiome holds the potential to predict treatment response and side effects, enabling more personalized and effective treatment options. However, most studies into the lung microbiota have been observational and have not adequately considered the impact of dietary intake and air pollutants. This gap makes it challenging to establish a direct causal relationship between environmental exposure, changes in the composition of the microbiota, lung carcinogenesis, and tumor progression. A holistic understanding of the lung microbiota that considers both diet and air pollutants may pave the way to improved prevention and management strategies for lung cancer.

Precision patient selection for improved detection of circulating genetically abnormal cells in pulmonary nodules.

Circulating genetically abnormal cells (CACs) have emerged as a promising biomarker for the early diagnosis of lung cancer, particularly in patients with pulmonary nodules. However, their performance may be suboptimal in certain patient populations. This study aimed to refine patient selection to improve the detection of CACs in pulmonary nodules. A retrospective analysis was conducted on 241 patients with pulmonary nodules who had undergone pathological diagnosis through surgical tissue specimens. Utilizing consensus clustering analysis, the patients were categorized into three distinct clusters. Cluster 1 was characterized by older age, larger nodule size, and a higher prevalence of hypertension and diabetes. Notably, the diagnostic efficacy of CACs in Cluster 1 surpassed that of the overall patient population (AUC: 0.855 vs. 0.689, P = 0.044). Moreover, for Cluster 1, an integrated diagnostic model was developed, incorporating CACs, sex, maximum nodule type, and maximum nodule size, resulting in a further improved AUC of 0.925 (95% CI 0.846-1.000). In conclusion, our study demonstrates that CACs detection shows better diagnostic performance in aiding the differentiation between benign and malignant nodules in older patients with larger pulmonary nodules and comorbidities such as diabetes and hypertension. Further research and validation are needed to explore how to better integrate CACs detection into clinical practice.

Cross-omics strategies and personalised options for lung cancer immunotherapy.

Lung cancer is one of the most common malignant tumours worldwide and its high mortality rate makes it a leading cause of cancer-related deaths. To address this daunting challenge, we need a comprehensive understanding of the pathogenesis and progression of lung cancer in order to adopt more effective therapeutic strategies. In this regard, integrating multi-omics data of the lung provides a highly promising avenue. Multi-omics approaches such as genomics, transcriptomics, proteomics, and metabolomics have become key tools in the study of lung cancer. The application of these methods not only helps to resolve the immunotherapeutic mechanisms of lung cancer, but also provides a theoretical basis for the development of personalised treatment plans. By integrating multi-omics, we have gained a more comprehensive understanding of the process of lung cancer development and progression, and discovered potential immunotherapy targets. This review summarises the studies on multi-omics and immunology in lung cancer, and explores the application of these studies in early diagnosis, treatment selection and prognostic assessment of lung cancer, with the aim of providing more personalised and effective treatment options for lung cancer patients.

Potential for trans-pulmonary tumor markers in the early diagnosis of lung cancer: a case report

BackgroundMeasurement of tumor markers from peripheral venous blood is an emerging tool to assist in the early diagnosis of lung cancer. Samples from the pulmonary artery and pulmonary artery wedge position (trans-pulmonary samples) are accessible via right-heart catheterization and, by virtue of their proximity to lung tumors, may increase diagnostic yield.Case presentationWe report a case of a 64 year-old woman from whom trans-pulmonary samples were obtained and who was diagnosed 16 months later with recurrent metastatic small cell lung cancer. Carcinoembryonic antigen, cytokeratin fragment 21 − 1 (CYFRA), and human epididymis protein 4 (HE4) levels demonstrated increasing concentrations across the pulmonary circulation. These gradients exceeded the assays’ coefficient of variation by several-fold. For CYFRA and HE4, pulmonary artery wedge concentrations exceeded peripheral venous levels by more than 10% and peripheral arterial levels were up to 8% higher than peripheral venous levels.ConclusionsEvaluating the feasibility and utility of trans-pulmonary tumor markers for lung cancer diagnosis in a larger cohort should be considered. The addition of a peripheral arterial sample to standard peripheral venous samples may be a more practical alternative.

Advancing Lung Cancer Diagnosis through NH2-MON-SPME-GC-MS/MS: Enhanced Sensitivity in Aldehyde Biomarker Detection from Exhaled Breath.

The sensitive detection of trace biomarkers in exhaled breath for lung cancer diagnosis represents a critical area of research in life analytical chemistry, with profound implications for early disease detection, therapeutic intervention, and prognosis monitoring. Despite its potential, the analytical process faces significant challenges due to the ultratrace levels of disease biomarkers present and the complex, high-humidity composition of exhaled breath. This study introduces a highly sensitive method for detecting aldehyde biomarkers in exhaled breath by integrating the use of amino-functionalized microporous organic networks (NH2-MON) as a solid-phase microextraction (SPME) fiber coating with gas chromatography-triple quadrupole mass spectrometry (GC-MS/MS) analysis. The method innovatively combines sample collection and extraction, achieving a dual-step enrichment process that significantly enhances both the enrichment efficiency and reproducibility of biomarker detection while effectively mitigating the interference caused by water vapor in exhaled breath. The NH2-MON, utilized as an SPME fiber coating, demonstrates exceptional enrichment capacity for five key aldehyde biomarkers, facilitating the development of a highly sensitive detection approach for these biomarkers in exhaled breath. Compared to previously reported methods, the proposed technique exhibits significantly lower limits of quantification, ranging from 0.77 to 11.89 pg mL-1, and achieves substantially higher enrichment factors, ranging from 9156- to 35723-fold. The practicality and feasibility of the method were validated through the analysis of exhaled breath samples from lung cancer patients, underscoring its potential application in the early diagnosis and monitoring of lung cancer.

CCDC113 promotes colorectal cancer tumorigenesis and metastasis via TGF-β signaling pathway

Colorectal cancer (CRC) is the second leading cause of cancer-related mortality worldwide. Although CRC patients’ survival is improved with surgical resection and immunotherapy, metastasis and recurrence remain major problems leading to poor prognosis. Therefore, exploring pathogenesis and identifying specific biomarkers are crucial for CRC early diagnosis and targeted therapy. CCDC113, a member of CCDC families, has been reported to play roles in ciliary assembly, ciliary activity, PSCI, asthma and early lung cancer diagnosis. However, the functions of CCDC113 in CRC still remain unclear. In this study, we find that CCDC113 is significantly highly expressed in CRC. High expression of CCDC113 is significantly correlated with CRC patients’ poor prognosis. CCDC113 is required for CRC tumorigenesis and metastasis. RNA-seq and TCGA database analysis indicate that CCDC113 is positively correlated with TGF-β signaling pathway. TGF-β signaling pathway inhibitor galunisertib could reverse the increased proliferation and migration ability of CRC cells caused by CCDC113 overexpression in vitro and in vivo. These results indicate that CCDC113 promotes CRC tumorigenesis and metastasis via TGF-β signaling pathway. In conclusion, it is the first time to explore the functions and mechanisms of CCDC113 in CRC tumorigenesis and metastasis. And CCDC113 may be a potential biomarker and therapeutic target for CRC intervention.

Breathomics: may it become an affordable, new tool for early diagnosis of non-small-cell lung cancer? An exploratory study on a cohort of 60 patients.

Analysis of breath, specifically the patterns of volatile organic compounds (VOCs), has shown the potential to distinguish between patients with lung cancer (LC) and healthy individuals (HC). However, the current technology relies on complex, expensive and low throughput analytical platforms, which provide an offline response, making it unsuitable for mass screening. A new portable device has been developed to enable fast and on-site LC diagnosis, and its reliability is being tested. Breath samples were collected from patients with histologically proven non-small-cell lung cancer (NSCLC) and healthy controls using Tedlar bags and a Nafion filter attached to a one-way mouthpiece. These samples were then analysed using an automated micro portable gas chromatography device that was developed in-house. The device consisted of a thermal desorption tube, thermal injector, separation column, photoionization detector, as well as other accessories such as pumps, valves and a helium cartridge. The resulting chromatograms were analysed using both chemometrics and machine learning techniques. Thirty NSCLC patients and 30 HC entered the study. After a training set (20 NSCLC and 20 HC) and a testing set (10 NSCLC and 10 HC), an overall specificity of 83.3%, a sensitivity of 86.7% and an accuracy of 85.0% to identify NSCLC patients were found based on 3 VOCs. These results are a significant step towards creating a low-cost, user-friendly and accessible tool for rapid on-site LC screening. ClinicalTrials.gov Identifier: NCT06034730.

Preoperative localization of pulmonary nodules by electromagnetic navigation bronchoscopy combined with methylene blue injection.

Electromagnetic navigation bronchoscopy (ENB) can help to accurately locate pulmonary nodules using a minimally invasive approach. This study sought to evaluate the clinical efficacy and safety of dye marking localization under the guidance of ENB followed by surgery. A retrospective analysis was performed of 61 patients who underwent ENB localization using methylene blue dye marking before surgery at Shanghai General Hospital from October 2021 to February 2022. The clinical efficacy and safety of ENB localization and the related factors affecting the navigation time of ENB location were analyzed. ENB was performed on 170 pulmonary nodules in 61 patients with a median age of 60 [interquartile range (IQR), 18] years. The majority of patients (70.69%) had more than two pulmonary nodules. The median maximum nodule diameter was 10 (IQR, 8) mm, and 48.21% of the nodules were mixed ground-glass nodules. Median time for ENB navigation was 10.5 (IQR, 6) min. The navigation success rate was 92.96%, and the ENB location success rate was 95.89%. The rate of complications related to ENB localization was 1.64% (there was only one case of pulmonary hemorrhage). The multivariate analysis showed that the factors related to the navigation time included the node location (P=0.001) and location mode (P=0.04). ENB-guided methylene blue injection is an effective and safe tool for localizing and marking pulmonary nodules, and can be used to assist the diagnosis and treatment of early lung cancer. The node location and location mode had significant effects on navigation time.

Preoperative early-stage lung cancer patients and local brain area changes: a cross-sectional observational descriptive study.

Lung cancer is a major global health concern. Patients undergo a substantial process of emotional transformation following a lung cancer diagnosis, during which subtle changes in brain function and/or structure may occur. As such, the present study aimed to investigate the neuroplastic changes induced by negative emotions in patients with early-stage lung cancer. This cross-sectional study recruited 35 patients with early-stage lung cancer and 33 age- and sex-matched healthy control patients. All participants completed the Hamilton Anxiety Rating Scale (HAMA), Hamilton Depression Rating Scale (HAMD), and functional magnetic resonance imaging (fMRI). Amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) were used as the fMRI indices. Correlations between the clinical assessments and ALFF and ReHo values were calculated. Our analysis revealed no significant differences in HAMD and HAMA scores between patients and control patients (p > 0.05). However, significant alterations in ALFF and ReHo were observed in multiple brain regions in patients with early-stage lung cancer compared to healthy controls (P FalseDiscoveryRate < 0.05). Specifically, ALFF values were decreased in the right postcentral gyrus, calcarine, and left middle cingulate, while ReHo values increased in the right angular gyrus and decreased in the bilateral postcentral gyrus, insula, left calcarine, putamen, superior temporal gyrus, middle cingulate, and right Rolandic gyrus. The HAMD score was significantly correlated with the ALFF value in the right postcentral gyrus (P = 0.007). This study provides valuable insights into the adaptive responses of the brain following the early diagnosis of lung cancer, revealing potential disturbances in negative emotional processing. Harnessing neuroplasticity may open new avenues for the establishment of personalized treatment strategies and targeted interventions to support the emotional and mental health of patients with lung cancer.

Dumbbell probe initiated multi-rolling circle amplification assisted CRISPR/Cas12a for highly sensitive detection of clinical microRNA

A novel miRNA detection technique named Dumbbell probe initiated multi-Rolling Circle Amplification assisted CRISPR/Cas12a (DBmRCA) was developed relying on the ligation-free dumbbell probe and the high-sensitivity CRISPR/Cas12a signal out strategy. This DBmRCA assay streamlines miRNA quantification within a mere 30-min timeframe and with exceptional analytical precision. The efficacy of this method was validated by assessing miRNA levels in clinical samples, revealing distinct expression panel of miR-200a and miR-126 in lung cancer/adjacent/normal tissue specimens. Moreover, a predictive model was established to classify benign and malignant tumor. Due to its time efficiency, enhanced sensitivity, and streamlined workflow, this assay would be a reliable tool for miRNA analysis in clinical settings, offering potential guidance for early diagnosis and treatment of lung cancer.