Diagnosis Of Lung Cancer Research Articles

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection.

As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions

The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with efficient feature extraction methods to reduce the volume of data and extract meaningful information to enhance the classification accuracy and interpretability. In this research, we discover the uniqueness of applying STFT (Short Term Fourier Transform), LASSO (Least Absolute Shrinkage and Selection Operator), and EHO (Elephant Herding Optimisation) for extracting significant features from lung cancer and reducing the dimensionality of the microarray gene expression database. The classification of lung cancer is performed using the following classifiers: Gaussian Mixture Model (GMM), Particle Swarm Optimization (PSO) with GMM, Detrended Fluctuation Analysis (DFA), Naive Bayes classifier (NBC), Firefly with GMM, Support Vector Machine with Radial Basis Kernel (SVM-RBF) and Flower Pollination Optimization (FPO) with GMM. The EHO feature extraction with the FPO-GMM classifier attained the highest accuracy in the range of 96.77, with an F1 score of 97.5, MCC of 0.92 and Kappa of 0.92. The reported results underline the significance of utilizing STFT, LASSO, and EHO for feature extraction in reducing the dimensionality of microarray gene expression data. These methodologies also help in improved and early diagnosis of lung cancer with enhanced classification accuracy and interpretability.

Allostatic Load/Chronic Stress and Cardiovascular Outcomes in Patients Diagnosed With Breast, Lung, or Colorectal Cancer.

Cardiovascular disease and cancer share a common risk factor: chronic stress/allostatic load (AL). A 1-point increase in AL is linked to up to a 30% higher risk of major cardiac events (MACE) in patients with prostate cancer. However, AL's role in MACE in breast cancer, lung cancer, or colorectal cancer remains unknown. Patients ≥18 years of age diagnosed with the mentioned 3 cancers of interest (2010-2019) and followed up at a large, hybrid academic-community practice were included in this retrospective cohort study. AL was modeled as an ordinal measure (0-11). Adjusted Fine-Gray competing risks regressions estimated the impact of AL precancer diagnosis on 2-year MACE (a composite of heart failure, ischemic stroke, acute coronary syndrome, and atrial fibrillation). The effect of AL changes over time on MACE was calculated via piecewise Cox regression (before, and 2 months, 6 months, and 1 year after cancer diagnosis). Among 16 467 patients, 50.5% had breast cancer, 27.9% had lung cancer, and 21.4% had colorectal cancer. A 1-point elevation in AL before breast cancer diagnosis corresponded to a 10% heightened associated risk of MACE (adjusted hazard ratio, 1.10 [95% CI, 1.06-1.13]). Similar findings were noted in lung cancer (adjusted hazard ratio, 1.16 [95% CI, 1.12-1.20]) and colorectal cancer (adjusted hazard ratio, 1.13 [95% CI, 1.08-1.19]). When considering AL as a time-varying exposure, the peak associated MACE risk occurred with a 1-point AL rise between 6 and 12 months post- breast cancer, lung cancer, and colorectal cancer diagnosis. AL warrants investigation as a potential marker in these patients to identify those at elevated cardiovascular risk and intervene accordingly.

Perioperative Challenges: Analysis of Surgical Complications in Screening Lung Carcinoma Patient.

Introduction In September 2020, the Institute for Pulmonary Diseases of Vojvodina (IPBV) started a lung cancer screening program using low-dose computed tomography (LDCT). Video-assisted thoracic surgery (VATS) lobectomy is the most effective treatment for early-stage lung cancer. However, the frequency of postoperative complications in VATS anatomical lung resections among patients enrolled in the screening program has not been adequately studied. This study aims to compare the frequency of surgical complications and length of hospital stay between patients enrolled in the screening program and a control group. Methods Retrospective, observational, monocentric, non-randomized study was conducted at the IPBV in Sremska Kamenica. The study included patients with a confirmed diagnosis of lung cancer who underwent anatomic pulmonary resection with mediastinal lymphadenectomy for therapeutic purposes. The patients were divided into two groups: the first group consisted of 34 patients who participated in the lung carcinoma screening program, while the second control group consisted of 102 patients. Over the past three years, all patients identified with nodules suspicious of malignancy during the screening program were sequentially enrolled in the screening group. For the control group, patients were selected based on a matching process to ensure valid statistical comparisons with the screening group. They were matched in a 3:1 ratio with patients from the screening group based on criteria including gender, disease stage, pathohistological type of cancer, tumor, node, and metastasis (TNM) stage of the disease, and degree of surgical resection. Patients were monitored for demographic parameters, smoking status, presence of comorbidities and prior oncological diseases, pulmonary function parameters, level of pre-operational risk, the number of lymph nodes removed by biopsies, spread through alveolar spaces (STAS), and the occurrence of complications after surgery (infection, bleeding, air leak, presence of adhesions), re-drainage, and length of hospital stay. Results The patients in the screening group had a higher incidence of infections, bleeding, prolonged air leak, and required re-drainage after surgery compared to the control group. Patients from the screening program with a high operative risk, prolonged air leak, and pleural adhesions had a statistically significant higher hospital stay longer than the control group. Conclusions This research emphasizes the importance of screening programs for detecting lung cancer in the early stages. However, it also highlights the need for further research to reduce surgical complications and improve therapeutic interventions for patients in the screening program.

DFA-UNet: dual-stream feature-fusion attention U-Net for lymph node segmentation in lung cancer diagnosis.

In bronchial ultrasound elastography, accurately segmenting mediastinal lymph nodes is of great significance for diagnosing whether lung cancer has metastasized. However, due to the ill-defined margin of ultrasound images and the complexity of lymph node structure, accurate segmentation of fine contours is still challenging. Therefore, we propose a dual-stream feature-fusion attention U-Net (DFA-UNet). Firstly, a dual-stream encoder (DSE) is designed by combining ConvNext with a lightweight vision transformer (ViT) to extract the local information and global information of images; Secondly, we propose a hybrid attention module (HAM) at the bottleneck, which incorporates spatial and channel attention to optimize the features transmission process by optimizing high-dimensional features at the bottom of the network. Finally, the feature-enhanced residual decoder (FRD) is developed to improve the fusion of features obtained from the encoder and decoder, ensuring a more comprehensive integration. Extensive experiments on the ultrasound elasticity image dataset show the superiority of our DFA-UNet over 9 state-of-the-art image segmentation models. Additionally, visual analysis, ablation studies, and generalization assessments highlight the significant enhancement effects of DFA-UNet. Comprehensive experiments confirm the excellent segmentation effectiveness of the DFA-UNet combined attention mechanism for ultrasound images, underscoring its important significance for future research on medical images.

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Fractional order cancer model infection in human with CD8+ T cells and anti-PD-L1 therapy: simulations and control strategy

In order to comprehend the dynamics of disease propagation within a society, mathematical formulations are essential. The purpose of this work is to investigate the diagnosis and treatment of lung cancer in persons with weakened immune systems by introducing cytokines (IL2&IL12\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ IL_{2} \\& IL_{12}$$\\end{document}) and anti-PD-L1 inhibitors. To find the stable position of a recently built system TCDIL2IL12\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$IL_{2} IL_{12}$$\\end{document}Z, a qualitative and quantitative analysis are taken under sensitive parameters. Reliable bounded findings are ensured by examining the generated system’s boundedness, positivity, uniqueness, and local stability analysis, which are the crucial characteristics of epidemic models. The positive solutions with linear growth are shown to be verified by the global derivative, and the rate of impact across every sub-compartment is determined using Lipschitz criteria. Using Lyapunov functions with first derivative, the system’s global stability is examined in order to evaluate the combined effects of cytokines and anti-PD-L1 inhibitors on people with weakened immune systems. Reliability is achieved by employing the Mittag-Leffler kernel in conjunction with a fractal-fractional operator because FFO provide continuous monitoring of lung cancer in multidimensional way. The symptomatic and asymptomatic effects of lung cancer sickness are investigated using simulations in order to validate the relationship between anti-PD-L1 inhibitors, cytokines, and the immune system. Also, identify the actual state of lung cancer control with early diagnosis and therapy by introducing cytokines and anti-PD-L1 inhibitors, which aid in the patients’ production of anti-cancer cells. Investigating the transmission of illness and creating control methods based on our validated results will both benefit from this kind of research.

Calls to action on lung cancer management and research.

Lung cancer, the leading cause of cancer-related deaths globally, remains a pressing health issue despite significant medical advances. The New York Lung Cancer Foundation brought together experts from academia, the pharmaceutical and biotech industries as well as organizational leaders and patient advocates, to thoroughly examine the current state of lung cancer diagnosis, treatment, and research. The goal was to identify areas where our understanding is incomplete and to develop collaborative public health and scientific strategies to generate better patient outcomes, as highlighted in our "Calls to Action." The consortium prioritized 8 different calls to action. These include (1) develop strategies to cure more patients with early-stage lung cancer, (2) investigate carcinogenesis leading to lung cancers in patients without a history of smoking, (3) harness precision medicine for disease interception and prevention, (4) implement solutions to deliver prevention measures and effective therapies to individuals in under-resourced countries, (5) facilitate collaborations with industry to collect and share data and samples, (6) create and maintain open access to big data repositories, (7) develop new immunotherapeutic agents for lung cancer treatment and prevention, and (8) invest in research in both the academic and community settings. These calls to action provide guidance to representatives from academia, the pharmaceutical and biotech industries, organizational and regulatory leaders, and patient advocates to guide ongoing and planned initiatives.

DNA methylation analysis in plasma for early diagnosis in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) represents the most prevalent type of lung cancer. SHOX2 and RASSF1A methylation have been identified as important biomarkers for diagnosis and prognosis of lung cancer. Bronchoalveolar lavage fluid (BALF) exhibits good specificity and sensitivity in diagnosing pulmonary diseases, but its acquisition is challenging and may cause discomfort to patients. In clinical, plasma samples are more convenient to obtain than BALF; however, there is little research on the concurrent detection of SHOX2 and RASSF1A methylation in plasma. This study aims to assess the diagnostic value of a combined promoter methylation assay for SHOX2 and RASSF1A in early-stage LUAD using plasma samples. BALF and blood samples were obtained from 36 early-stage LUAD patients, with a control group of nineteen non-tumor individuals. The promoter methylation levels of SHOX2 and RASSF1A in all subjects were assessed using the human SHOX2 and RASSF1A gene methylation kit. The methylation detection rate of SHOX2 and RASSF1A in plasma was 61.11%, slightly lower than that in BALF (66.7%). The Chi-square test revealed no significant difference in the methylation rate between BALF and plasma (P > 0.05). The area under the receiver operating characteristic (ROC) curve analysis for blood was 0.806 (95% CI, 0.677 to 0.900), while for BALF it was 0.781 (95% CI, 0.649 to 0.881). Additionally, we conducted an analysis on the correlation between SHOX2 and RASSF1A methylation levels in plasma with gender, age, tumor differentiation, pathologic classification, and other clinicopathological variables; however, no significant correlations were observed. Measurement of SHOX2 and RASSF1A methylation for early diagnosis of LUAD can be achieved with high sensitivity and specificity by using plasma as a substitute for BALF samples.

Advancing lung cancer diagnosis with bio-inspired algorithms: a comprehensive assessment

Advancing lung cancer diagnosis with bio-inspired algorithms: a comprehensive assessment

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature. This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information. After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient. This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research. Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility. Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

A human-in-the-loop method for pulmonary nodule detection in CT scans

Automated pulmonary nodule detection using computed tomography scans is vital in the early diagnosis of lung cancer. Although extensive well-performed methods have been proposed for this task, they suffer from the domain shift issue between training and test images. Unsupervised domain adaptation (UDA) methods provide a promising means to mitigate the domain variance; however, their performance is still limited since no target domain supervision is introduced. To make the pulmonary nodule detection algorithm more applicable in clinical practice and further boost the performance across domains, we propose a human-in-the-loop method in a semi-supervised fashion to enhance the model generalization ability when transferred from source domain to target domain. Specifically, we first train a detector model on source domain, and then the pre-trained detector is utilized with our proposed uncertainty-guided sample selection scheme (USSS) to find a few target domain samples worth annotating most and obtain their human annotations. Finally, the annotated and the rest unlabeled target domain samples are used together to refine the pre-trained model via our proposed zoom-in and zoom-out constraint (ZZC) strategy. We evaluate our method on the Nodule Analysis 2016 (LUNA16) and TianChi datasets. Experimental results show that our method surpasses recent competitive methods on source domain and also achieves surprising performance on target domain.

Asbestos-Related lung Cancer: An underappreciated oncological issue

Asbestos, a group of class I (WHO) carcinogenic fibers, is the main cause of mesothelioma. Asbestos inhalation also increases the risk to develop other solid tumours with lung cancer as the most prominent example [91]. The incidence of asbestos-related lung cancer (ARLC) is estimated to be to six times larger than the mesothelioma incidence thereby becoming an important health issue [86]. Although the pivotal role of asbestos in inducing lung cancer is well established, the precise causal relationships between exposures to asbestos, tobacco smoke, radon and ‘particulate’ (PM2.5) air pollution remain obscure and new knowledge is needed to establish appropriate preventive measures and to tailor existing screening practices[22,61,65]. We hypothesize that a part of the increasing numbers of lung cancer diagnoses in never-smokers can be explained by (historic and current) exposures to asbestos as well as combinations of different forms of air pollution (PM2.5, asbestos and silica).

Early stage lung cancer detection from speech sounds in natural environments

In the diagnosis of early-stage lung cancer, conventional methods often rely on periodic imaging techniques using medical devices. However, recent studies suggest that speech sounds could offer valuable insights into the diagnosis of disease. This study investigates the different characteristics of speech sounds recorded in natural environments between individuals diagnosed with lung cancer and those who are healthy. Using signal processing techniques and a self-supervised contrastive learning approach, we investigate the classification of these speech sounds for the diagnosis of early-stage lung cancer. Our results show that it is possible to utilize naturally recorded speech sounds. Using the Graph Attention Transformer Fine-Tuning Contrastive Learning (GAT-ftCL) model, which leverages graph neural networks to capture complex relationships in data and fine-tunes the learning process through contrastive learning, we achieve an accuracy of 90.90% in distinguishing individuals diagnosed with lung cancer from healthy individuals. Furthermore, the model achieves a remarkable accuracy of 92.85% in specifically identifying individuals with early stage (stage 1) lung cancer within the stage 1 group and healthy individuals. These results underline the diagnostic potential of natural speech sounds, especially in the detection of early-stage lung cancer.

Bioengineered Abiotic Nanomaterials Through Cell Membrane-Camouflaging: Advancements and Challenges in Lung Cancer.

Lung cancer remains a major global health concern with high mortality rates and poor prognosis. Bridging the gap between the chemical and cellular understanding of cell-decorated biomimetic nanocomposites and their clinical translation is crucial for developing effective therapies. Nanocomposites show promise in targeted drug delivery and diagnostics, but their clinical application is hindered by biocompatibility and clearance issues. To overcome these challenges, biomimetic approaches utilizing cell membrane-coated nanomaterials emerge. By camouflaging nanomaterials with cell membranes, the biointerfaces are enhanced, and the inherent properties of the donor cell membranes are acquired. This review provides an overview of recent advancements on cell membrane-coated nanocomposites for lung cancer diagnosis and treatment. It discusses fabrication techniques, biomedical applications, challenges, and future prospects. The incorporation of cell membranes into nanocomposites holds potential for improved lung cancer therapy, but further development and refinement are needed for precise tumor targeting. Addressing the identified challenges will pave the way for clinical translation of these biomimetic nanoplatforms and advance lung cancer diagnosis and treatment.

Jmjd2c maintains the ALDHbri+ cancer stemness with transcription factor SOX2 in lung squamous cell carcinoma

ABSTRACT Lung squamous cell carcinoma (LSCC) is a deadly cancer in the world. Histone demethylase Jmjd2c is a key epigenetic regulator in various tumors, while the molecular mechanism underlying Jmjd2c regulatory in LSCC is still unclear. We used the aldehyde dehydrogenasebright (ALDHbri+) subtype as a research model for cancer stem cells (CSCs) in LSCC and detected the sphere formation ability and the proportion of ALDHbri+ CSCs with Jmjd2c interference and caffeic acid (CA) treatment. Additionally, we carried out bioinformatic analysis on the expression file of Jmjd2c RNAi mice and performed western blotting, qRT-PCR, Co-IP and GST pull-down assays to confirm the bioinformatic findings. Moreover, we generated Jmjd2c-silenced and Jmjd2c-SOX2-silenced ALDHbri+ tumor-bearing BALB/c nude mice to detect the effects on tumor progression. The results showed that Jmjd2c downregulation inhibited the sphere formation and the proportion of ALDHbri+ CSCs. The SOX2 decreased expression significantly in Jmjd2c RNAi mice, and they were positively co-expressed according to the bioinformatic analysis. In addition, SOX2 expression decreased in Jmjd2c shRNA ALDHbri+ CSCs, Jmjd2c and SOX2 proteins interacted with each other. Furthermore, Jmjd2c interference revealed significant blocking effect, and Jmjd2c-SOX2 interference contributed even stronger inhibition on ALDHbri+ tumor progression. The Jmjd2c and SOX2 levels were closely related to the development and prognosis of LSCC patients. This study indicated that Jmjd2c played key roles on maintaining ALDHbri+ CSC activity in LSCC by interacting with transcription factor SOX2. Jmjd2c might be a novel molecule for therapeutic targets and biomarkers in the diagnosis and clinical treatment of lung cancer.

Deep learning for lungs cancer detection: a review

Although lung cancer has been recognized to be the deadliest type of cancer, a good prognosis and efficient treatment depend on early detection. Medical practitioners’ burden is reduced by deep learning techniques, especially Deep Convolutional Neural Networks (DCNN), which are essential in automating the diagnosis and classification of diseases. In this study, we use a variety of medical imaging modalities, including X-rays, WSI, CT scans, and MRI, to thoroughly investigate the use of deep learning techniques in the field of lung cancer diagnosis and classification. This study conducts a comprehensive Systematic Literature Review (SLR) using deep learning techniques for lung cancer research, providing a comprehensive overview of the methodology, cutting-edge developments, quality assessments, and customized deep learning approaches. It presents data from reputable journals and concentrates on the years 2015–2024. Deep learning techniques solve the difficulty of manually identifying and selecting abstract features from lung cancer images. This study includes a wide range of deep learning methods for classifying lung cancer but focuses especially on the most popular method, the Convolutional Neural Network (CNN). CNN can achieve maximum accuracy because of its multi-layer structure, automatic learning of weights, and capacity to communicate local weights. Various algorithms are shown with performance measures like precision, accuracy, specificity, sensitivity, and AUC; CNN consistently shows the greatest accuracy. The findings highlight the important contributions of DCNN in improving lung cancer detection and classification, making them an invaluable resource for researchers looking to gain a greater knowledge of deep learning’s function in medical applications.

Methylation of a group of microRNA genes as a marker for the diagnosis and prognosis of non-small cell lung cancer

Objectives. Lung cancer, representing a difficult-to-diagnose heterogeneous malignant neoplasm, is characterized by an asymptomatic course up to late stages, a high incidence of adverse outcomes, and a high probability of metastasis. Its most common form is non-small cell lung cancer (NSCLC). Recent studies have demonstrated a significant role of non-coding RNAs—in particular, microRNAs—in the development of NSCLC. MicroRNAs, which function as post-transcriptional regulators of the expression of protein-coding genes, including those associated with oncogenesis, are involved in the processes of cell proliferation, differentiation, and apoptosis. One of the approaches for regulating the expression of microRNAs themselves is to change the methylation of the CpG island adjacent to the microRNA gene or overlapping it. It has been shown that microRNA genes are several times more likely to undergo methylation than protein-coding genes. The aim of the present work is to study changes in the level of methylation of a number of microRNA genes and compile a potential panel of markers for the diagnosis and prognosis of NSCLC.Methods. Samples of NSCLC tumors were collected and clinically characterized at the Blokhin National Medical Research Center of Oncology, Ministry of Health of the Russian Federation, Moscow, Russia. High-molecular-weight DNA was isolated from tissues using a standard method. The level of methylation was analyzed using bisulfite conversion of DNA and quantitative methyl-specific polymerase chain reaction with real-time detection. The significance of differences between the studied groups was assessed by the nonparametric Mann–Whitney U test for independent samples. Differences were considered significant at p < 0.05.Results. The analysis of methylation levels of microRNA genes revealed a significant (p < 0.05) increase in the methylation level of eight microRNA genes: MIR124-1/2/3, MIR125В-1, MIR129-2, MIR137, MIR375, MIR1258, and MIR339 (p < 0.01, false discovery rate ≤ 0.25). On the basis of receiver operating characteristic analysis, a panel of markers is proposed for the diagnosis of NSCLC according to the nature of methylation of the studied microRNA genes in the tumor and in the normal tissue.Conclusions. Our results, which contribute to the understanding of molecular mechanisms involved in NSCLC development, can be used in the development of new diagnostic and prognostic approaches in clinical oncology.

Evaluating the efficacy of percutaneous puncture biopsy guided by contrast-enhanced ultrasound for peripheral pulmonary lesions.

The incidence and mortality of lung cancer have increased annually. Accurate diagnosis can help improve therapeutic efficacy of interventions and prognosis. Percutaneous lung biopsy is a reliable method for the clinical diagnosis of lung cancer. Ultrasound-guided percutaneous lung biopsy technology has been widely promoted and applied in recent years. To investigate the diagnostic value of contrast-enhanced ultrasound (CEUS)-guided percutaneous biopsy in peripheral pulmonary lesions. We retrospectively collected data on 237 patients with peripheral thoracic focal lesions who underwent puncture biopsy at Wuxi People's Hospital. The patients were randomly divided into two groups: The CEUS-guided before lesion puncture group (contrast group) and conventional ultrasound-guided group (control group). Analyze the diagnostic efficacy of the puncture biopsy, impact of tumor size, and number of puncture needles and complications were analyzed and compared between the two groups. Accurate pathological results were obtained for 92.83% (220/237) of peripheral lung lesions during the first biopsy, with an accuracy rate of 95.8% (113/118) in the contrast group and 89.9% (107/119) in the control group. The difference in the area under the curve (AUC) between the contrast and the control groups was not statistically significant (0.952 vs 0.902, respectively; P > 0.05). However, when the lesion diameter ≥ 5 cm, the diagnostic AUC of the contrast group was higher than that of the control group (0.952 vs 0.902, respectively; P < 0.05). In addition, the average number of puncture needles in the contrast group was lower than that in the control group (2.58 ± 0.53 vs 2.90 ± 0.56, respectively; P < 0.05). CEUS guidance can enhance the efficiency of puncture biopsy of peripheral pulmonary lesions, especially for lesions with a diameter ≥ 5 cm. Therefore, CEUS guidance has high clinical diagnostic value in puncture biopsy of peripheral focal lung lesions.

Polygenic inheritance and its interplay with smoking history in predicting lung cancer diagnosis: a French-Canadian case-control cohort

The most near-term clinical application of genome-wide association studies in lung cancer is a polygenic risk score (PRS). A case-control dataset was generated consisting of 4002 lung cancer cases from the LORD project and 20,010 ethnically matched controls from CARTaGENE. A genome-wide PRS including >1.1 million genetic variants was derived and validated in UK Biobank (n=5419 lung cancer cases). The predictive ability and diagnostic discrimination performance of the PRS was tested in LORD/CARTaGENE and benchmarked against previous PRSs from the literature. Stratified analyses were performed by smoking status and genetic risk groups defined as low (<20th percentile), intermediate (20-80th percentile) and high (>80th percentile) PRS. The phenotypic variance explained and the effect size of the genome-wide PRS numerically outperformed previous PRSs. Individuals with high genetic risk had a 2-fold odds of lung cancer compared to low genetic risk. The PRS was an independent predictor of lung cancer beyond conventional clinical risk factors, but its diagnostic discrimination performance was incremental in an integrated risk model. Smoking increased the odds of lung cancer by 7.7-fold in low genetic risk and by 11.3-fold in high genetic risk. Smoking with high genetic risk was associated with a 17-fold increase in the odds of lung cancer compared to individuals who never smoked and with low genetic risk. Individuals at low genetic risk are not protected against the smoking-related risk of lung cancer. The joint multiplicative effect of PRS and smoking increases the odds of lung cancer by nearly 20-fold. This work was supported by the CQDM and the IUCPQ Foundation owing to a generous donation from Mr. Normand Lord.