Lung Cancer Risk Research Articles

Patient Lung Cancer Screening Decisions and Environmental and Psychosocial Factors

Screening for lung cancer using low-dose computed tomography is associated with reduced lung cancer-specific mortality, but uptake is low in the US; understanding how patients make decisions to engage with lung cancer screening is critical for increasing uptake. Prior research has focused on individual-level psychosocial factors, but environmental factors (eg, historical contexts that include experiencing racism) and modifying factors-those that can be changed to make it easier or harder to undergo screening-also likely affect screening decisions. To investigate environmental, psychosocial, and modifying factors influencing lung cancer screening decision-making and develop a conceptual framework depicting relationships between these factors. This multimethod qualitative study was conducted from December 2021 to June 2022 using virtual semistructured interviews and 4 focus groups (3-4 participants per group). All participants met US Preventive Services Task Force eligibility criteria for lung cancer screening (ie, age 50-80 years, at least a 20 pack-year smoking history, and either currently smoke or quit within the past 15 years). Screening-eligible US participants were recruited using an online panel. Key factors influencing screening decisions (eg, knowledge, beliefs, barriers, and facilitators) were the main outcome. A theory-informed, iterative inductive-deductive approach was applied to analyze data and develop a conceptual framework summarizing results. Among 34 total participants (interviews, 20 [59%]; focus groups, 14 [41%]), mean (SD) age was 59.1 (4.8) years and 20 (59%) identified as female. Half had a household income below $20 000 (17 [50%]). Participants emphasized historical and present-day racism as critical factors contributing to mistrust of health care practitioners and avoidance of medical procedures like screening. Participants reported that other factors, such as public transportation availability, also influenced decisions. Additionally, participants described psychosocial processes involved in decisions, such as perceived screening benefits, lung cancer risk appraisal, and fear of a cancer diagnosis or harmful encounters with practitioners. In addition, participants identified modifying factors (eg, insurance coverage) that could make receiving screening easier or harder. In this qualitative study of patient lung cancer screening decisions, environmental, psychosocial, and modifying factors influenced screening decisions. The findings suggest that systems-level interventions, such as those that help practitioners understand and discuss patients' prior negative health care experiences, are needed to promote effective screening decision-making.

Investigation and Analysis of Lung Cancer Risk Factors in the Health Check-Up Population

Objective: To investigate and analyze the risk factors of lung cancer in the population of health checkups. Methods: A total of 500 cases of the health check-up population were selected for data study. Surveyed showed that 19 cases of lung cancer were grouped as the lung cancer group and the remaining 481 cases made up the control group, and the risk factors were analyzed. Results: Among men, there was a significant proportion of individuals aged 60–69 years old, and women aged 30–39 years old. Additionally, individuals aged 60 and above were at increased risk of developing lung cancer. The results of the multifactorial analysis were that the risk factors affecting the detection of lung cancer in healthy people were smoking history, family history of lung cancer, secondhand smoke, history of respiratory diseases, psychosomatic factors, living environment, and kitchen fumes. Conclusion: People over 60 years of age were prone to early lung cancer, followed by individuals aged 30–39 years. It is important to identify the risk factors of lung cancer to strengthen the screening of high-risk groups for early detection and treatment.

Association of Matrix Metalloproteinase-9 Genotypes With Lung Cancer Risk in Taiwan.

Matrix metalloproteinase-9 (MMP-9) expression is upregulated in various diseases, including lung cancer. However, the role of MMP-9 genotype in lung cancer susceptibility remains uncertain. This study aimed to clarify the contribution of MMP-9 promoter rs3918242 genotypes to the risk of lung cancer in Taiwan. The MMP-9 rs3918242 genotypes of 358 lung cancer patients and 716 healthy controls were determined using polymerase chain reaction-restriction fragment length polymorphism methodology. Individuals carrying the variant CT or TT genotype of MMP-9 rs3918242 did not demonstrate an increased risk of lung cancer compared to wild-type CC carriers [odds ratio (OR)=1.11 and 1.85, 95% confidence interval (95%CI)=0.82-1.48 and 0.91-3.76; p=0.5541 and 0.1280, respectively]. Moreover, individuals carrying the T allele did not show a higher lung cancer risk compared to those with the C allele (OR=1.21, 95%CI=0.95-1.54, p=0.1444). However, a significant association was observed between the MMP-9 rs3918242 TT genotype and lung cancer risk among non-smokers (OR=5.48, 95%CI=1.31-22.89, p=0.0181). The presence of the TT genotype for MMP-9 rs3918242 may indicate an elevated risk of lung cancer among non-smokers.

Serum lipid levels, genetic risk, and lung cancer incidence: A large prospective cohort study.

Previous studies usually focused on the separate association of metabolism or genetic factors with lung cancer risk and have largely ignored their combined effect. We aimed to examine the associations between serum lipid levels, genetic risk, and lung cancer risk. 426,524 participants of the UK Biobank were included. The Cox proportional hazards models and restricted cubic splines were performed to assess the association between serum lipid and lung cancer risk. Polygenic risk score (PRS) was constructed to assess its joint effect and interaction with serum lipid on lung cancer risk. Higher level of apolipoprotein A were significantly correlated with lower lung cancer risk. A inverse-J-shaped relationship between HDL and incident lung cancer was found. Individuals with low total cholesterol, HDL, LDL, apolipoprotein A, and apolipoprotein B, combined with high PRS, showed significantly elevated lung cancer risks. Compared to those with low PRS and low triglycerides, participants with high PRS and elevated triglyceride levels had a notably higher risk. The interaction effect of high PRS and low LDL (RERI:0.25, 95%CI: 0.04-0.46), as well as the interaction effect of high PRS and low apolipoprotein B (RERI:0.28, 95%CI: 0.07-0.48), were both greater than the sum of their individual effects on lung cancer risk. Serum lipids were associated with lung cancer risk. LDL or apolipoprotein B interacting with genetic risk may affect lung cancer risk. Our findings emphasize the need for individuals with heightened genetic risk should pay more attention to their lipid levels to reduce lung cancer risk.

The Causal Effect of Gut Microbiota and Plasma Metabolome on Lung Cancer and the Heterogeneity across Subtypes: A Mendelian Randomization Study.

The causal effect and pathways of gut microbiota and plasma metabolome on lung cancer have been important topics for personalized medicine; however, the heterogeneity of lung cancer subtypes has not gained enough attention in previous studies. This study sought to employ a Mendelian randomization analysis to screen the specific gut microbiota and plasma metabolome, which may have a causal effect on lung cancer. We further extended our analysis to estimate the effects of these exposures on various pathological subtypes of lung cancer. Furthermore, a mediation analysis was performed to identify the potential pathway underlying the influence of microbiota and metabolites. Our study identified 13 taxa and 15 metabolites with a causal association with the overall risk of lung cancer. Furthermore, we found 8 taxa and 14 plasma metabolites with a causal effect on lung adenocarcinoma, 4 taxa and 10 metabolites with a causal effect on squamous cell lung carcinoma, and 7 taxa and 16 metabolites with a causal effect on SCLC. We also identified seven mediation pathways that could potentially elucidate the influence of these microbiota and metabolites on overall lung cancer or special subtypes. Our study highlighted the heterogeneity of the gut microbiome and plasma metabolome in a lung cancer subtype and elucidated the potential underlying mechanisms. This could pave the way for more personalized lung cancer prevention and treatment.

The Efficacy of Dietary Intake, Supplementation, and Blood Concentrations of Carotenoids in Cancer Prevention: Insights from an Umbrella Meta-Analysis.

Previous meta-analyses of multiple studies have suggested that dietary intake and blood concentrations of carotenoids, as well as dietary supplement of certain carotenoids, play a role in reducing the risk of cancer. However, the conclusions of these studies have been subject to controversy. We conducted an umbrella review of meta-analyses to comprehensively analyze and evaluate the evidence pertaining the association between carotenoids and cancer outcomes. We searched PubMed, Web of Science, Embase, and Cochrane Library databases of meta-analyses and systematic reviews up to June 2023. Our selection criteria encompassed meta-analyses of cohort and case-control studies, as well as randomized controlled clinical trials, which investigated the associations between carotenoids and cancer risk. We also determined the levels of evidence for these associations with AMSTAR 2 criteria. We included 51 eligible articles, including 198 meta-analyses for qualitative synthesis in the umbrella review. Despite the presence of moderate to high heterogeneity among the studies, dietary intake, supplementation, and blood concentrations of carotenoids were inversely associated with the risk of total cancer, and certain specific cancers of lung, digestive system, prostate, breast, head and neck, and others. Subgroup analysis also showed that individual carotenoids (α-carotene, β-carotene, β-cryptoxanthin, lutein, zeaxanthin, and lycopene) offer certain protection against specific types of cancers. However, high doses of carotenoid supplements, especially β-carotene, significantly increased the risk of total cancer, lung cancer, and bladder cancer. Our umbrella meta-analysis supported that high intake of dietary carotenoids as a whole food approach could be more beneficial in reducing cancer risk. Concurrently, the findings suggest that the efficacy of single-carotenoid supplementation in cancer prevention remains a subject of controversy.

Hybrid Machine Learning Models for Fine-grained Air Quality Forecasting

Air pollution has become a major environmental issue that is causing many deaths each year and putting the environment and human health at serious risk. It causes the greenhouse effect, contributes to global warming, and increases the risk of lung cancer and other diseases that affect the respiratory system, including allergies. Setting and upholding strict air quality standards is essential to effectively combating air pollution. The air quality index (AQI) is a measurement used to determine the amount of pollutants in the atmosphere. By utilizing the capabilities of machine learning algorithms, precise forecasting of the fine-grained AQI is made feasible. To predict the AQI, a number of algorithms have been used, including logistic regression, decision tree regression, KNN, SVR, and linear regression. This project's main goal is to create models with machine learning algorithms and determine which model is best for AQI prediction.

Environmental and Genetic Interaction Models for Predicting Lung Cancer Risk Using Machine Learning: A Systematic Review and Meta-Analysis

This systematic review and meta-analysis examined environmental and genetic interaction models for predicting lung cancer risk using machine learning techniques. The findings underscore the importance of considering both genetic and environmental factors to enhance predictive accuracy, with significant clinical implications. Among the models reviewed, the Rotation Forest model demonstrated superior performance, achieving an AUC of 0.993, reflecting excellent predictive capabilities. The mean AUC across all models was approximately 0.789, indicating moderate to good discrimination. These results hold promising implications for personalized medicine and clinical decision-making, potentially improving patient outcomes and reducing the global burden of lung cancer. The meta-analysis further highlighted strong performance, with an average TRI-performance (accuracy, precision, recall) of 88.1%, demonstrating robust predictive abilities. Integrating machine learning with multidimensional data deepens the understanding of the biological mechanisms underlying lung cancer and supports its application in precision oncology, paving the way for individualized interventions and improved clinical management.

Adherence to the low-fat diet pattern reduces the risk of lung cancer in American adults aged 55 years and above: a prospective cohort study

ObjectivesThere is little evidence on the association between low-fat dietary patterns and lung cancer risk among middle-aged and older adults. To fill this gap, we comprehensively investigated the association of adherence to a low-fat diet (LFD) and intake of different fat components including saturated, monounsaturated, and polyunsaturated fatty acids with incidence of lung cancer and its subtypes [non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC)] among adults aged 55 years and older. DesignA prospective cohort study with a mean follow-up time of 8.8 years. Setting and participantsThis study used data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. The study population included 98,459 PLCO participants age 55 and over at baseline who completed food frequency questionnaires providing detailed dietary information and had no history of cancer. MethodsDietary intake was assessed using a validated food frequency questionnaire at baseline. A LFD score was calculated based on fat, protein, and carbohydrate intake as a percentage of total calories. Cox proportional hazards regression models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between LFD score and intake of fat components (in quartiles) and incident lung cancer and its subtypes over follow-up. Restricted cubic spline analyses were conducted to examine possible nonlinear relationships. Subgroup analyses were performed to evaluate potential effect modifiers, and several sensitivity analyses were conducted to assess the stability of the findings. ResultsDuring a follow-up of 869,807.9 person-years, 1,642 cases of lung cancer were observed, consisting of 1,408 (85.75%) cases of NSCLC and 234 (14.25%) cases of SCLC. The highest versus the lowest quartiles of the LFD score were found to be associated with a reduced risk of lung cancer (HR, 0.76; 95% CI, 0.66−0.89), NSCLC (HR, 0.79; 95% CI, 0.67−0.93), and SCLC (HR, 0.59; 95% CI, 0.38−0.92). The restricted cubic spline plots demonstrated a linear dose-response relationship between the LFD score and the risk of lung cancer as well as its subtypes. This risk reduction association for overall lung cancer was more pronounced in smokers (HR, 0.71; 95% CI, 0.60−0.84; P for interaction = 0.003). For fat components, high consumption of saturated fatty acids was associated with an increased lung cancer risk (HR, 1.35; 95% CI, 1.10–1.66), especially for SCLC (HR, 2.05; 95% CI, 1.20–3.53). No significant association was found between consumption of monounsaturated or polyunsaturated fatty acids and incident lung cancer and its subtypes. ConclusionsOur findings suggest that adherence to LFD may reduce the lung cancer risk, particularly in smokers; while high saturated fatty acids consumption may increase lung cancer risk, especially for SCLC, among middle-aged and older adults in the US population.

Genetic correlation and causal associations between psychiatric disorders and lung cancer risk

BackgroundPatients with certain psychiatric disorders have increased lung cancer incidence. However, establishing a causal relationship through traditional epidemiological methods poses challenges. MethodsAvailable summary statistics of genome-wide association studies of cigarette smoking, lung cancer, and eight psychiatric disorders, including attention deficit/hyperactivity disorder (ADHD), autism, depression, major depressive disorder, bipolar disorder, insomnia, neuroticism, and schizophrenia (range N: 46,350-1,331,010) were leveraged to estimate genetic correlations using Linkage Disequilibrium Score Regression and assess causal effect of each psychiatric disorder on lung cancer using two-sample Mendelian randomization (MR) models, comprising inverse-variance weighted (IVW), weighted median, MR-Egger, pleiotropy residual sum and outlier testing (MR-PRESSO), and a constrained maximum likelihood approach (cML-MR). ResultsSignificant positive correlations were observed between each psychiatric disorder and both smoking and lung cancer (all FDR < 0.05), except for the correlation between autism and lung cancer. Both univariable and the cML-MA MR analyses demonstrated that liability to schizophrenia, depression, ADHD, or insomnia was associated with an increased risk of overall lung cancer. Genetic liability to insomnia was linked specifically to squamous cell carcinoma (SCC), while genetic liability to ADHD was associated with an elevated risk of both SCC and small cell lung cancer (all P < 0.05). The later was further supported by multivariable MR analyses, which accounted for smoking. LimitationsParticipants were constrained to European ancestry populations. Causal estimates from binary psychiatric disorders may be biased. ConclusionOur findings suggest appropriate management of several psychiatric disorders, particularly ADHD, may potentially reduce the risk of developing lung cancer.

Updated Analysis of Radon Exposure and Lung Cancer Mortality in the Cohort of Newfoundland Fluorspar Miners (1950-2016).

The commercial mining of fluorspar in St. Lawrence Newfoundland began in 1933. Miners who worked underground were exposed to high levels of radon progeny, especially before ventilation was introduced into the mines in 1960. The mean cumulative radon exposure for underground miners in this cohort was 380.9 working level months (WLM). A series of studies of this cohort have characterized the increased risks of lung cancer mortality due to radon. We have extended the follow-up of this cohort an additional 15 years to provide additional insights on the risks of low levels of radon exposure, and the modifying effects of time since exposure, age at first exposure, attained age, duration of exposure, and cigarette smoking. The cohort consisted of 1,735 underground and 315 male surface miners who, combined, accrued 81,650 person-years of follow-up. The mortality experience of the cohort was determined from 1950-2016 through record linkage to Canadian national death data. Individual-level estimates of exposure to radon progeny, in WLMs, were determined for each year of employment. We compared the mortality experience of the underground miners to Newfoundland men using the standardized mortality ratio (SMR). Poisson regression models were fit to estimate excess relative risks (ERR) per 100 WLM. There were 236 lung cancer deaths identified, and of these, 221 occurred among underground workers. The SMR for lung cancer among underground miners compared to Newfoundland men was 2.67 (95% CI: 2.33, 3.04). The ERR per 100 WLM for lung cancer mortality, assuming a 5-year exposure lag, was 0.41 (95% CI: 0.23, 0.59). Attained age and time since exposure were important modifiers to the radon-lung cancer relationship. The joint relationship between smoking and radon on lung cancer risk was sub-additive, however, the smoking data were limited and available for only half of the cohort.

Lymphangioleiomyomatosis as a potent lung cancer risk factor: Insights from a Japanese large cohort study.

Lymphangioleiomyomatosis (LAM) is a rare neoplastic disease associated with the functional tumour suppressor genes TSC1 and TSC2 and causes structural destruction in the lungs, which could potentially increase the risk of lung cancer. However, this relationship remains unclear because of the rarity of the disease. We investigated the relative risk of developing lung cancer among patients diagnosed with LAM between 2001 and 2022 at a single high-volume centre in Japan, using data from the Japanese Cancer Registry as the reference population. Next-generation sequencing (NGS) was performed in cases where tumour samples were available. Among 642 patients diagnosed with LAM (sporadic LAM, n = 557; tuberous sclerosis complex-LAM, n = 80; unclassified, n = 5), 13 (2.2%) were diagnosed with lung cancer during a median follow-up period of 5.13 years. All patients were female, 61.5% were never smokers, and the median age at lung cancer diagnosis was 53 years. Eight patients developed lung cancer after LAM diagnosis. The estimated incidence of lung cancer was 301.4 cases per 100,000 person-years, and the standardized incidence ratio was 13.6 (95% confidence interval, 6.2-21.0; p = 0.0008). Actionable genetic alterations were identified in 38.5% of the patients (EGFR: 3, ALK: 1 and ERBB2: 1). No findings suggested loss of TSC gene function in the two patients analysed by NGS. Our study revealed that patients diagnosed with LAM had a significantly increased risk of lung cancer. Further research is warranted to clarify the carcinogenesis of lung cancer in patients with LAM.

A nested case-control study of untargeted plasma metabolomics and lung cancer among never-smoking women within the prospective Shanghai Women's Health Study.

The etiology of lung cancer in never-smokers remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Here, we aimed to enhance our understanding of lung cancer pathogenesis among never-smokers using untargeted metabolomics. This nested case-control study included 395 never-smoking women who developed lung cancer and 395 matched never-smoking cancer-free women from the prospective Shanghai Women's Health Study with 15,353 metabolic features quantified in pre-diagnostic plasma using liquid chromatography high-resolution mass spectrometry. Recognizing that metabolites often correlate and seldom act independently in biological processes, we utilized a weighted correlation network analysis to agnostically construct 28 network modules of correlated metabolites. Using conditional logistic regression models, we assessed the associations for both metabolic network modules and individual metabolic features with lung cancer, accounting for multiple testing using a false discovery rate (FDR) < 0.20. We identified a network module of 121 features inversely associated with all lung cancer (p = .001, FDR = 0.028) and lung adenocarcinoma (p = .002, FDR = 0.056), where lyso-glycerophospholipids played a key role driving these associations. Another module of 440 features was inversely associated with lung adenocarcinoma (p = .014, FDR = 0.196). Individual metabolites within these network modules were enriched in biological pathways linked to oxidative stress, and energy metabolism. These pathways have been implicated in previous metabolomics studies involving populations exposed to known lung cancer risk factors such as traffic-related air pollution and polycyclic aromatic hydrocarbons. Our results suggest that untargeted plasma metabolomics could provide novel insights into the etiology and risk factors of lung cancer among never-smokers.

Short Communication: Radon testing via a state tobacco quitline

ObjectiveExposure to radon gas at home is the second largest cause of lung cancer after smoking and dramatically increases smokers’ risk of lung cancer. State tobacco quitlines are uniquely positioned to inform smokers about radon, yet, to our knowledge, none does so. We explored the feasibility of introducing free radon tests via the tobacco quitline in North Dakota, a state with one of the highest radon levels in the U.S. MethodsFive hundred consecutive callers to the ND Quits Tobacco quitline from February 2021 to February 2023 were invited to complete a brief radon questionnaire and receive a free radon test kit. Radon tests were bar-coded so that the return rate of the tests and the radon levels could be determined. ResultsTwo hundred fifty-one (51 %) callers completed the questionnaire and seventy-five radon tests were successfully returned to the laboratory. More than one third of the test results were ≥ 4.0 pCi/L, the action level recommended by the EPA. Only 1 in 5 participants reported knowing that radon caused lung cancer. ConclusionRadon knowledge among ND smokers is poor. Radon test distribution via quitlines is feasible and may be a valuable addition to quitline services, particularly in states with high radon levels.

Predicting the risk of lung cancer using machine learning: A large study based on UK Biobank.

In response to the high incidence and poor prognosis of lung cancer, this study tends to develop a generalizable lung-cancer prediction model by using machine learning to define high-risk groups and realize the early identification and prevention of lung cancer. We included 467,888 participants from UK Biobank, using lung cancer incidence as an outcome variable, including 49 previously known high-risk factors and less studied or unstudied predictors. We developed multivariate prediction models using multiple machine learning models, namely logistic regression, naïve Bayes, random forest, and extreme gradient boosting models. The performance of the models was evaluated by calculating the areas under their receiver operating characteristic curves, Brier loss, log loss, precision, recall, and F1 scores. The Shapley additive explanations interpreter was used to visualize the models. Three were ultimately 4299 cases of lung cancer that were diagnosed in our sample. The model containing all the predictors had good predictive power, and the extreme gradient boosting model had the best performance with an area under curve of 0.998. New important predictive factors for lung cancer were also identified, namely hip circumference, waist circumference, number of cigarettes previously smoked daily, neuroticism score, age, and forced expiratory volume in 1 second. The predictive model established by incorporating novel predictive factors can be of value in the early identification of lung cancer. It may be helpful in stratifying individuals and selecting those at higher risk for inclusion in screening programs.

Oncology: What You May Have Missed in 2023.

Advances in oncology treatment methods have improved outcomes and quality of life for patients with cancer. However, care of these patients can be complex, and the contribution of physicians from different specialties is crucial. This article highlights important publications from 2023 on topics across a wide spectrum relating to the management of oncology patients. The literature was screened for significant new evidence that is relevant to internal medicine specialists and subspecialists whose focus is not oncology. Two articles address the importance of social interventions targeting end-of-life care for low-income and minority patients and the well-being of caregivers. Two additional articles address screening considerations in patients at risk for colorectal and lung cancer. Two more articles address safe use of hormone-related therapies to treat symptoms of menopause and prevent disease recurrence or progression in patients diagnosed with noninvasive breast neoplasia. Finally, several articles were included on topics related to COVID-19 vaccination in patients with cancer, use of cannabinoids for cancer pain control, chronic autoimmune adverse effects related to use of immune checkpoint inhibitors, and the incidence of second primary neoplasms.

Exploring the relationship between Treg-mediated risk in COPD and lung cancer through Mendelian randomization analysis and scRNA-seq data integration

BackgroundEvidence from observational studies suggests an association between chronic obstructive pulmonary disease (COPD) and lung cancer. The potential interactions between the immune system and the lungs may play a causative role in COPD and lung cancer and offer therapeutic prospects. However, the causal association and the immune-mediated mechanisms between COPD and lung cancer remain to be determined.MethodsWe employed a two-sample Mendelian randomization (MR) approach to investigate the causal association between COPD and lung cancer. Additionally, we examined whether immune cell signals were causally related to lung cancer, as well as whether COPD was causally associated with immune cell signals. Furthermore, through two-step Mendelian randomization, we investigated the mediating effects of immune cell signals in the causal association between COPD and lung cancer. Leveraging publicly available genetic data, our analysis included 468,475 individuals of European ancestry with COPD, 492,803 individuals of European ancestry with lung cancer, and 731 immune cell signatures of European ancestry. Additionally, we conducted single-cell transcriptome sequencing analysis on COPD, lung cancer, and control samples to validate our findings.FindingsWe found a causal association between COPD and lung cancer (odds ratio [OR] = 1.63, 95% confidence interval [CI] = 1.31–2.02, P-value < 0.001). We also observed a causal association between COPD and regulatory T cells (odds ratio [OR] = 1.19, 95% confidence interval [CI] = 1.01–1.40, P-value < 0.05), as well as a causal association between regulatory T cells and lung cancer (odds ratio [OR] = 1.02, 95% confidence interval [CI] = 1.002–1.045, P-value < 0.05). Furthermore, our two-step Mendelian randomization analysis demonstrated that COPD is associated with lung cancer through the mediation of regulatory T cells. These findings were further validated through single-cell sequencing analysis, confirming the mediating role of regulatory T cells in the association between COPD and lung cancer.InterpretationAs far as we are aware, we are the first to combine single-celled immune cell data with two-sample Mendelian randomization. Our analysis indicates a causal association between COPD and lung cancer, with regulatory T cells playing an intermediary role.

Visual Insights in Lung Cancer Prediction: Leveraging Data Visualization and Analysis for Building and Tuning Machine Learning Models

Lung cancer stands as a significant global health challenge with its high occurrence rates and fatal lethality. Given lung cancer's severity and complicated symptoms in later stages, healthcare facilities must detect them early. Traditional diagnostic methodologies are often burdened by high cost and specialized expertise, prompting exploration of alternative methods, such as machine Learning. This paper utilizes a dataset from a Nature Medicine study involving 462,000 participants in China and employs machine learning to predict lung cancer risk. Through the application of Exploratory Data Analysis (EDA), this paper explored the correlation between demographic variables, environmental factors, and lifestyle habits with the probability of lung cancer occurrence. The neural network architecture incorporates dynamic layers, rectified linear unit (ReLU) activation, and the Adam optimizer with dropout regularization. Results from EDA reveal correlations between lifestyle factors and lung cancer risk. The machine learning model achieved 70% accuracy on predictions and was refined to 90% through EDA and utilizing the LIME interpreter. This study aimed to advance lung cancer prediction by applying an amalgamation of techniques and technologies, like data visualization, extensive data analysis, and machine learning. This research aimed to contribute to the showcase of applying the latest technological advancements for the era of big data within the medical research and healthcare industry. The success of this model suggests the viability of creating machine learning models targeted for cancer predictions and indicates further advancements, such as personalized prediction models.

Interpretation of global lung cancer statistics

Lung cancer remains one of the leading cause of global cancer-related mortality, posing a significant burden of disease. Tobacco exposure stands as the foremost risk factor for lung cancer. Since the 1960, global efforts have gradually been implemented to control tobacco exposure, consequently reducing tobacco exposure levels within populations. This shift in exposure levels may have altered the epidemiological characteristics of lung cancer globally. This study aims to describe global lung cancer incidence data across five dimensions: age, gender, region, stage at diagnosis, and survival status, using global cancer registry data and relevant research findings. The objective is to elucidate the current epidemiological features of lung cancer worldwide, providing a scientific basis for lung cancer prevention and control. Furthermore, this study offers corresponding measures and recommendations for lung cancer prevention and control, aligning with the three-tiered cancer prevention strategy. Findings indicate that the incidence and mortality burden of lung cancer is significantly higher among the elderly population (aged 65 years and above) compared to the working-age population (aged 15-64 years). The aged-standardized incidence rate of lung cancer remains higher in males than in females, but the overall aged-standardized incidence rate of lung cancer in males shows a declining trend, while that in females shows an increasing trend. Regions with high and very high human development index (HDI) exhibit a substantially higher incidence and mortality burden of lung cancer compared to regions with low and very low HDI. Japan ranks highest in the diagnosis of stage Ⅰ lung cancer, with a diagnosis rate of 38.6%. Its age-standardized 5-year net survival rate is relatively high at 32.9%. Despite improvements in the survival status of lung cancer in certain countries like China and Japan, the overall prognosis for lung cancer remains pessimistic. Given the current epidemiological characteristics of lung cancer, reinforcing tobacco control measures and reducing female-specific lung cancer risk factors stand as significant goals for primary prevention. Promoting low-dose computed tomography screening for high-risk population, minimizing false-positive rates in lung cancer screening, and promoting medical system reforms and standardized treatment constitute principal measures for secondary and tertiary lung cancer prevention, respectively.

Smoking-associated gene expression alterations in nasal epithelium reveal immune impairment linked to lung cancer risk

Background Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to modifiable lifestyle risk in the form of tobacco smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, 40% of which occur more than 15 years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk remain unclear. We thus set out to examine whether risk stratification in the clinic and in the general population can be improved upon by the addition of genetic data and to explore the mechanisms of the persisting risk in former smokers.Methods We analysed transcriptomic data from accessible airway tissues of 487 subjects, including healthy volunteers and clinic patients of different smoking statuses. We developed a computational model to assess smoking-associated gene expression changes and their reversibility after smoking is stopped, comparing healthy subjects to clinic patients with and without lung cancer.ResultsWe find persistent smoking-associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune- and interferon-related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier.ConclusionsOur results provide initial evidence for germline-mediated personalized smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.