Occurrence Of Lung Cancer Research Articles

Immune cells mediated the causal relationship between the gut microbiota and lung cancer: a Mendelian randomization study.

The gut microbiota (GM) influences the occurrence and progression of lung cancer (LC), with potential involvement of immune cells (IC). We aimed to investigate the causal impact of GM on LC and identify potential immune cell mediators. The utilized data for the Genome-Wide Association Studies (GWAS) were summarized as follows: gut microbiota data from the Dutch Microbiome Project (DMP) (N = 7,738), lung cancer data from the Transdisciplinary Research in Cancer of the Lung (TRICL) and International Lung Cancer Consortium (ILCCO) (Ncase = 29,266, Ncontrol = 56,450) included four types of cancer: NSCLC, LUAD, LUSC, and SCLC, and immune cell data from European populations (N = 3,757). We employed bi-directional two-sample univariable Mendelian randomization (UVMR), multivariable Mendelian randomization (MVMR), and mediation analysis to assess the causal relationship between GM and LC and potential immune cell mediators. Bi-directional UVMR analysis revealed that 24 gut microbiota species can affect LC, while LC can affect the abundance of 17 gut microbiota species. Mediation analysis demonstrated that six immune cells mediated the causal relationships of seven gut microbiota species on LC: "CCR7 on naive CD8+ T cell" mediated the causal relationship between s_Alistipes_putredinis and LUAD, with a mediation proportion of 9.5% and P = 0.018; "IgD- CD27- B cell %lymphocyte" mediated the causal relationships between g_Gordonibacter and s_Gordonibacter_pamelaeae with LUSC, with mediation proportions of 11.8% and 11.9%, respectively and P = 0.029; "CD20- CD38- B cell %lymphocyte" mediated the causal relationship between s_Bacteroides_clarus and SCLC, with a mediation proportion of 13.8% and P = 0.005; "CD20 on IgD+ CD38- unswitched memory B cell" mediated the causal relationship between s_Streptococcus_thermophilus and SCLC, with a mediation proportion of 14.1% and P = 0.023; "HLA DR on CD14- CD16+ monocyte" mediated the causal relationship between s_Bifidobacterium_bifidum and SCLC, with a mediation proportion of 8.7% and P = 0.012; "CD45 on Granulocytic Myeloid-Derived Suppressor Cells" mediated the causal relationship between f_Lactobacillaceae and SCLC, with a mediation proportion of 4.0% and P = 0.021. This Mendelian randomization study identified several specific gut microbiotas that exhibit causal relationships with lung cancer and potentially mediate immune cells.

Role of pyroptosis-related cytokines in the prediction of lung cancer

ObjectivesLung cancer is the leading cause to induce cancer-related mortality. Effective biomarkers for prediction the occurrence of lung cancer is urgently needed. Our previous studies indicated that pyroptosis-related cytokines TNF-α, IFN-γ, MIP-1α, MIP-1β, MIP-2 and IP-10 is important to influence the efficacy of chemotherapy drug in lung cancer tissues. But the role of pyroptosis-related cytokines in prediction the occurrence of lung cancer is still unknown. MethodsBlood samples were collected from 258 lung cancer patients at different stage and 80 healthy volunteers. Serum levels of pyroptosis-related cytokines including TNF-α, IFN-γ, MIP-1α, MIP-1β, MIP-2 and IP-10 were measured by Cytometric Bead Array (CBA). ROC curve was performed to evaluate the cut-off value and diagnosis value for prediction and diagnosis of lung cancer. ResultsCompared with control group, the levels of IP-10, MIP-1α, MIP-1β, MIP-2 and TNF-α were significantly higher in lung cancer patients (45.5 (37.1–56.7): 57.2 (43.0–76.5), 34.4 (21.8–75.2): 115.4 (96.6–191.2), 49.3 (25.6–78.7): 160.5 (124.9–218.6), 22.6 (17.8–31.2): 77.9 (50.1–186.5), 3.80 (2.3–6.2): 10.3 (5.7–16.6)), but the level of IFN-γ was decreased in the patients (12.38 (9.1–27.8): 5.9 (3.5–9.7)). All the above cytokines were significantly associated with the diagnosis of lung cancer, and the AUC values of IFN-γ, IP-10, MIP-1α, MIP-1β, MIP-2, and TNF-α were 0.800, 0.656, 0.905, 0.921, 0.914, and 0.824. And the AUC can rise to 0.986 after combining the above factors, and the sensitivity and specificity also up to 96.7 % and 93.7 %, respectively. Additionally, TNF-α (r = 0.400, P < 0.01), MIP-2 (r = 0.343, P < 0.01), MIP-1α (r = 0.551, P < 0.01) and MIP-1β (r = 0.403, p < 0.01) were positively associated with occurrence of lung cancer, but IFN-γ (r = −0.483, p < 0.01) was negatively associated with occurrence of lung cancer. As far as the potential of early diagnosis of lung cancer, TNF-α (AUC = 0.577), MIP-1α (AUC = 0.804) and MIP-1β (AUC = 0.791) can predict the early stage of lung cancer, and combination of the above three cytokines has a better predictive efficiency (AUC = 0.854). ConclusionOur study establishes a link between the levels of IP-10, MIP-1α, MIP-1β, MIP-2, TNF-α and IFN-γ and diagnosis of lung cancer. Besides, we observed a synergistic effect of these five pyroptosis-related cytokines in diagnosing lung cancer patient, suggesting their potential as biomarkers for lung cancer diagnosis. Moreover, the combination of TNF-α, MIP-1α and MIP-1β are also potential predictors for the early diagnosis of lung cancer.

The Biological Role of Macrophage in Lung and Its Implications in Lung Cancer Immunotherapy.

The lungs are the largest surface of the body and the most important organ in the respiratory system, which are constantly exposed to the external environment. Tissue Resident Macrophages in lung constitutes the important defense against external pathogens. Macrophages connects the innate and adaptive immune system, and also plays important roles in carcinogenesis and cancer immunotherapy. Lung cancer is the leading cause of cancer-related death worldwide, with an overall five-year survival rate of only 21%. Macrophages that infiltrate or aggregate in lung tumor microenvironment are defined as tumor-associated macrophages (TAMs). TAMs are the main components of immune cells in the lung tumor microenvironment. The differentiation and maturation process of TAMs can be roughly divided into two different types: classical activation pathway produces M1 tumor-associated macrophages, and bypass activation pathway produces M2 tumor-associated macrophages. Studies have found that TAMs are related to tumor invasion, metastasis, and treatment resistance, and show potential as a new target for tumor immunotherapy. Therefore, the biological function of macrophages in lung and the role of TAMs in the occurrence, development, and treatment of lung cancer are discussed in this paper.

Research Progress of the Effect of Mediterranean Diet on the Correlation of Lung Cancer

Lung cancer is one of the top 10 causes of death in the world today, and it is a great concern worldwide for its high mortality rate. Currently, the researchers are digging into various factors influencing the occurrence and development of lung cancer in order to increase the odds for curing lung cancer, improve the prognosis of lung cancer patients as well as reduce its morbidity. The Mediterranean diet (MD) is a special dietary structure that is based on eating vegetables, fruits, coarse grains, legumes and low-fat fish, which have anti-inflammatory, antioxidant and lipid-lowering effects. Recent studies have revealed that the MD may prevent lung cancer occurrence to some extent and inhibit its development. The purpose of this paper is to summarize and analytically discuss the effects of the MD on the oncogenesis and development of lung cancer through a review of the relevant literatures, thus to provide references for MD to prevent and treat lung cancer. .

MAPK4 facilitates angiogenesis by inhibiting the ERK pathway in non-small cell lung cancer.

Angiogenesis plays an important role in the occurrence and development of non-small cell lung cancer (NSCLC). The atypical mitogen-activated protein kinase 4 (MAPK4) has been shown to be involved in the pathogenesis of various diseases. However, the potential role of MAPK4 in the tumor angiogenesis of NSCLC remains unclear. Adult male C57BL/6 wild-type mice were randomly divided into the control group and p-siMAPK4 intervention group, respectively. The cell proliferation was analyzed withflow cytometry and immunofluorescence staining. The vascular density in tumor mass was analyzed by immunofluorescence staining. The expressions of MAPK4 and related signaling molecules were detected by western blot analysis and immunofluorescence staining, and so on. We found that the expression of MAPK4, which was dominantly expressed in local endothelial cells (ECs), was correlated with tumor angiogenesis of NSCLC. Furthermore, MAPK4 silencing inhibited the proliferation and migration abilities of human umbilical vein ECs (HUVECs). Global gene analysis showed that MAPK4 silencing altered the expression of multiple genes related to cell cycle and angiogenesis pathways, and that MAPK4 silencing increased transduction of the extracellular regulated protein kinases 1/2 (ERK1/2) pathwaybut not Akt and c-Jun n-terminal kinase pathways. Further analysis showed that MAPK4 silencing inhibited the proliferation and migration abilities of HUVECs cultured in tumor cell supernatant, which was accompanied with increased transduction of the ERK1/2 pathway. Clinical data analysis suggested that the higher expression of MAPK4 and CD34 were associated with poor prognosis ofpatients with NSCLC. Targeted silencing of MAPK4 in ECs using small interfering RNA driven by the CD34 promoter effectively inhibited tumor angiogenesis and growth of NSCLC in vivo. Our results reveal that MAPK4 plays an important role in the angiogenesis and development of NSCLC. MAPK4 may thus represent a new target for NSCLC.

Label-Free Multiplex Profiling of Exosomal Proteins with a Deep Learning-Driven 3D Surround-Enhancing SERS Platform for Early Cancer Diagnosis.

Identification of protein profiling on plasma exosomes by SERS can be a promising strategy for early cancer diagnosis. However, it is still challenging to detect multiple exosomal proteins simultaneously by SERS since the Raman signals of exosomes detected by conventional colloidal nanocrystals or two-dimensional SERS substrates are incomplete and complex. Herein, we develop a novel three-dimensional (3D) surround-enhancing SERS platform, named 3D se-SERS, for the multiplex detection of exosomal proteins. In this 3D se-SERS, proteins and exosomes are covered with "hotspots" generated by the gold nanoparticles, which surround the analytes densely and three-dimensionally, providing sensitive and comprehensive SERS signals. Combining this 3D se-SERS with a deep learning model, we successfully quantitatively profiled seven proteins including CD63, CD81, CD9, CD151, CD171, TSPAN8, and PD-L1 on the surface of plasma exosomes from patients, which can predict the occurrence and advancement of lung cancer. This 3D se-SERS integrating deep learning technique benefits from high sensitivity and significant multiplexing ability for comprehensive analysis of proteins and exosomes, demonstrating the potential of deep learning-driven 3D se-SERS technology for plasma exosome-based early cancer diagnosis.

Bioinformatics Study and Experimental Evaluation of miR-182, and miR-34 Expression Profiles in Tuberculosis and Lung Cancer.

Lung cancer is one of the most dangerous cancers and tuberculosis is one of the deadliest infectious diseases in the world. Many studies have confirmed the connection between lung cancer and tuberculosis, and also the microRNAs (miRNAs) that play a major role in the development of these two diseases. This study aims to use different databases to find effective miRNAs and their role in different genes in lung and tuberculosis diseases. It also aims to determine the role of miR-34a and miR-182 in lung cancer and tuberculosis. Using the Gene Expression Omnibus (GEO) database, the influential miRNA databases were studied in the two diseases. Finally, considering bioinformatics results and literature studies, two miR-34a and miR-182 were selected. The role of these miRNAs and their target genes was carefully evaluated using bioinformatics. The expression of miRNAs in the plasma of patients with lung cancer and tuberculosis and healthy individuals was investigated. According to the GEO database, miR-34a and miR-182 are miRNAs that affect tuberculosis and lung cancer. By checking the miRBase, miRcode, DIANA, miRDB, galaxy, Kyoto Encyclopedia of Genes and Genomes databases, the role of these miRNAs on genes and different molecular pathways and their effect on these miRNAs were mentioned. The results of the present study showed that the expression of miR-34a and miR-182 was lower than that of healthy people. The p-value for miR-182 was &lt;0.0001 and for miR-34a was 0.3380. Reducing the expression pattern of these miRNAs indicates their role in lung cancer and tuberculosis occurrence. Therefore, these miRNAs can be used as a biomarker for prognosis, diagnosis, and treatment methods.

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.

A review of obstructive sleep apnea and lung cancer: epidemiology, pathogenesis, and therapeutic options.

Despite undeniable advances in modern medicine, lung cancer still has high morbidity and mortality rates. Lung cancer is preventable and treatable, and it is important to identify new risk factors for lung cancer, especially those that can be treated or reversed. Obstructive sleep apnea (OSA) is a very common sleep-breathing disorder that is grossly underestimated in clinical practice. It can cause, exacerbate, and worsen adverse outcomes, including death and various diseases, but its relationship with lung cancer is unclear. A possible causal relationship between OSA and the onset and progression of lung cancer has been established biologically. The pathophysiological processes associated with OSA, such as sleep fragmentation, intermittent hypoxia, and increased sympathetic nervous excitation, may affect normal neuroendocrine regulation, impair immune function (especially innate and cellular immunity), and ultimately contribute to the occurrence of lung cancer, accelerate progression, and induce treatment resistance. OSA may be a contributor to but a preventable cause of the progression of lung cancer. However, whether this effect exists independently of other risk factors is unclear. Therefore, by reviewing the literature on the epidemiology, pathogenesis, and treatment of lung cancer and OSA, we hope to understand the relationships between the two and promote the interdisciplinary exchange of ideas between basic medicine, clinical medicine, respiratory medicine, sleep medicine, and oncology.

Different Influence Pattern of Conventional and Alternative Sources of Smoking on Adhesion Molecules and Cytokine Secretion in THP-1 Monocytes.

Tobacco is a carcinogen that is closely associated with the occurrence of lung cancer and head and neck squamous cell carcinoma (HNSCC). The consumption of tobacco is also leading to alterations in different immune cell subtypes. However, the impact of different conventional and alternative smoking sources on human monocytes remains elusive. In this study, we investigated the influence of aqueous extracts of different sources of smoking (cigarettes; heated tobacco product IQOS; e-cigarettes with and without nicotine; nicotine pouches) on different monocytic adhesion molecules, chemokine receptors and checkpoint molecule PD-L1 by flow cytometry. Cytokine expression patterns were evaluated using human cytokine arrays and the human monocyte leukemia cell line THP-1 as a model. Data revealed differential effects of the analyzed conventional and alternative smoking devices on monocyte adhesion molecules and cytokine secretion. The examined smoking devices can be assigned to two differential monocyte activation patterns. Monocytes stimulated with aqueous extracts of cigarettes, e-cigarette without nicotine, and heat not burn product IQOS revealed distinct alterations of surface markers and cytokines compared to the monocyte activation pattern in response to aqueous extracts of nicotine, nicotine pouches, and e-cigarette with nicotine. Our data indicate differential immunological consequences of different conventional and alternative smoking sources with and without nicotine. Further comprehensive analysis as well as in vivo investigations on peripheral blood monocyte subsets from smoking individuals using different smoking sources are required to better understand the impact on monocyte characteristics, especially with regard to the development of cancer.

Abstract 5515: Low body mass index (BMI) induces neuronal NPY and promotes brain metastasis of lung cancer

Abstract Brain metastases (BrMs) are a common occurrence in lung cancer with a dismal outcome. To develop a more effective therapeutic strategy, it is crucial to understand the underlying pathological mechanism of brain metastasis. Recent clinical data has unveiled a significant association between low body mass index (BMI) and an increased risk of brain metastasis in lung cancer patients, leading to reduced survival rates compared to other primary tumors. However, the molecular mechanism underlying this paradoxical relationship, often referred to as the "obesity paradox," in the context of lung cancer mortality presents a complex and intriguing puzzle and warrants in-depth investigation. Here, we examined biological mechanism of how low BMI promotes lung cancer brain metastasis. In this study, we conducted a retrospective pan-analysis of 5,516 patient cohorts with brain metastasis originating from lung, breast, melanoma, and renal cancers with their BMI status. We found that lung cancer patients with low BMI have significantly higher brain metastatic incidence compared to the high BMI patients, in contrast to breast, melanoma, and renal cancers. Furthermore, we found that low BMI plays a pivotal role in mediating neuron activation within the brain and that the secretion of neuronal neuropeptide Y (NPY), a protein involved in appetite and energy homeostasis via GHSR-receptor manner, promotes metabolic switching through NPY/NPY5R axis in the tumor cells in the brain thereby enabling metastasis. Elevated levels of neuronal NPY in the brains of cancer-free subjects with low BMI suggest its potential utility as a promising prognostic biomarker for identifying the increased risk of metastatic disease in lung cancer patients with low BMI. We also demonstrated that reversing low BMI or blocking the NPY/Y5R interactions effectively abrogated brain metastasis in our animal model. Our findings suggest a novel pro-metastatic role of low BMI-induced NPY in the progression of brain metastasis. We emphasize the importance of elucidating the clinical implications of this relationship to develop an updated intervention strategy for the clinical care of patients with lung cancer and low BMI. Citation Format: Abhishek Tyagi, Shih-Ying Wu, Ravindra Deshpande, Kerui Wu, Liang Liu, Kounosuke Watabe. Low body mass index (BMI) induces neuronal NPY and promotes brain metastasis of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5515.

Changing smoking habits and the occurrence of lung cancer in Sweden-a population analysis.

The objective is to estimate the importance of the decrease of smoking habits in Sweden for the occurrence of lung cancer. The change in smoking habits in the general population was retrieved from surveys and on taxation of sale of cigarettes. We used data from the Swedish Cancer Register on incidence of lung cancer between 1970 and 2021, stratified for sex, age and cell type, and compared the occurrence overtime in ages between 40 and 84 years. The sale of cigarettes peaked in 1980 to 1800 cigarettes per person and decreased to 600 per person in 2021. The change in incidence rates of squamous cell cancer and other cell types varied over time, sex, and age in a pattern that partly seems to be explained by change in the prevalence of daily smokers. The incidence of adenocarcinoma was similar in men and women 1970-2021 and increased, e.g. for women and men 75-79 years of age from around 20 cases in early 1970s to around 120 cases per 100 000 person-years in the 2020s. Our data indicate that the risk of lung cancer several years after smoking cessation is less favourable than previously studies have indicated. There is a similar increase in the incidence of adenocarcinoma in men and women which is hard to explain only with changing smoking habits. The change from non-filter to filter cigarettes in the 1960s-1970s may be a contributing factor.

Roles of IL-4, IL-13, and Their Receptors in Lung Cancer.

Interleukin (IL)-4 and IL-13 are the main effectors of innate lymphoid cells (ILC2) of the type 2 innate immune response, which can carry out specific signal transmission between multiple cells in the tumor immune microenvironment. IL-4 and IL-13 mediate signal transduction and regulate cellular functions in a variety of solid tumors through their shared receptor chain, the transmembrane heterodimer interleukin-4 receptor alpha/interleukin-13 receptor alpha-1 (type II IL-4 receptor). IL-4, IL-13, and their receptors can induce the formation of a variety of malignant tumors and play an important role in their progression, growth, and tumor immunity. In order to explore possible targets for lung cancer prediction and treatment, this review summarizes the characteristics and signal transduction pathways of IL-4 and IL-13, and their respective receptors, and discusses in depth their possible role in the occurrence and development of lung cancer.

Abstract 2749: DHX36 may promote the growth and metastasis of lung cancer

Abstract Introduction: Helicase is a kind of protein molecular motor that widely exists in eukaryotic cells, viruses and bacterial cells, and participates in DNA replication, transcription and other life activities. DHX36, a kind of helicase, is a new homologous gene of human DNA/RNA helicase cloned by our laboratory by homologous molecular cloning which used the sequence of the maleless gene (mle) of Drosophila. Studies have shown that DHX36 upregulates the expression of proto oncogene and promotes the occurrence of cancer. At present, there are few studies on DHX36 in lung cancer. This study aims to clarify the relationship between DHX36 and lung cancer and provide a new molecular target for the diagnosis, treatment and prognosis of lung cancer. Methods: Twenty-eight pairs of clinical tumor samples from lung adenocarcinoma and adjacent tissues were collected, and the protein expression of DHX36 in different tissues was detected by western blot. Correlation between DHX36 expression and survival prognosis of lung cancer patients was analyzed by online database. Over-expression of DHX36 in A549 and H460 cells and knockdown of DHX36 in A549 and H1975 cell lines, then the migration, invasion and growth index of cells were analyzed by real-time cell analyzer. After DHX36 stable knock-down cell line was established in H1975 cells, they were injected into nude mice by subcutaneous injection and tail vein injection respectively at the same time as the control cells, and the growth of subcutaneous tumors and the metastasis of lung tissues in nude mice were recorded and collected respectively. The tissues were analyzed by histology, immunohistochemistry, and western blot. Results: In 28 pairs of clinical lung cancer tumor samples, we found that DHX36 was higher in cancer tissues than in adjacent tissues in 23 pairs, which indicated that DHX36 might be a kind of proto oncogene to promote the occurrence and development of lung cancer. Bioinformatics analysis revealed that the high expression of DHX36 was related to the low survival of lung cancer patients. The results of RTCA showed that the abilities of growth, invasion and migration of cells in the over-expressed DHX36 groups were higher than those in the control groups. After DHX36 was knocked down, the growth, invasion and invasion of lung cancer cells were inhibited. Conclusion: DHX36 is expected to be a new molecular target of lung cancer. Next, we will knock out DHX36 in lung cancer cells to observe the tumor growth and lung metastasis in mice. Citation Format: Ting Li, Na Luo, Jiewen Fu, Chunli Wei, Junjiang Fu. DHX36 may promote the growth and metastasis of lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2749.

Effect of CircCCND1 on the Malignant Biological Behaviors of H446 Lung Cancer Cells by Regulating the MiR-340-5p/TGIF1 Axis

Lung cancer is a common malignant tumor of the lung. To explore the molecular mechanism of the occurrence and development of lung cancer is a hot topic in current research. Cyclic RNA D1 (CircCCND1) is highly expressed in lung cancer and may be a potential target for the treatment of lung cancer. The aim of this study was to investigate the effect of CircCCND1 on the malignant biological behaviors of lung cancer cells by regulating the miR-340-5p/transforming growth factor β-induced factor homeobox 1 (TGIF1) axis. The expression of CircCCND1, miR-340-5p, and TGIF1 mRNA in human normal lung epithelial cells BEAS-2B and human lung cancer H446 cells were detected. H446 cells cultured in vitro were randomly divided into control group, CircCCND1 siRNA group, miR-340-5p mimics group, negative control group, and CircCCND1 siRNA+miR-340-5p inhibitor group. Cell proliferation, mitochondrial membrane potential, apoptosis, migration, and invasion were detected, and the expressions of CircCCND1, miR-340-5p, TGIF1 mRNA, BCL2-associated X protein (Bax), cleaved Caspase-3, N-cadherin, E-cadherin, and TGIF1 proteins in each group were detected. The targeting relationship of miR-340-5p with CircCCND1 and TGIF1 was verified. Compared with BEAS-2B cells, CircCCND1 and TGIF1 mRNA were increased in H446 cells, and miR-340-5p expression was decreased (P<0.05). Knocking down CircCCND1 or up-regulating the expression of miR-340-5p inhibited the proliferation, migration and invasion of H446 cells, decreased the expression of TGIF1 mRNA and TGIF1 protein, and promoted cell apoptosis. Down-regulation of miR-340-5p could antagonize the inhibitory effect of CircCCND1 knockdown on the malignant biological behavior of H446 lung cancer cells. CircCCND1 may target the down-regulation of miR-340-5p, and miR-340-5p may target the down-regulation of TGIF1. Knocking down CircCCND1 can inhibit the malignant behaviors of lung cancer H446 cells, which may be achieved through the regulation of miR-340-5p/TGIF1 axis.

A unique circ_0067716/EIF4A3 double-negative feedback loop impacts malignant transformation of human bronchial epithelial cells induced by benzo(a)pyrene

Benzo(a)pyrene as a pervasive environmental contaminant is characterized by its substantial genotoxicity, and epidemiological investigations have established a correlation between benzo(a)pyrene exposure and the susceptibility to human lung cancer. Notably, much research has focused on the link between epigenetic alterations and lung cancer induced by chemicals, although circRNAs are also emerging as relevant contributors to the carcinogenic process of benzo(a)pyrene. In this study, we identified circ_0067716 as being significantly upregulated in response to stress injury and downregulated during malignant transformation induced by benzo(a)pyrene-7,8-diol-9,10-epoxide (BPDE) in human bronchial epithelial cells. The observed differential expression of circ_0067716 in cells treated with BPDE for varying durations suggests a strong correlation between this circRNA and BPDE exposure. The tissue samples of lung cancer patients also suggest that a lower circ_0067716 expression is associated with BPDE-DNA adduct levels. Remarkably, we demonstrate that EIF4A3, located in the nucleus, interacts with the flanking sequences of circ_0067716 and inhibits its biogenesis. Conversely, circ_0067716 is capable of sequestering EIF4A3 in the cytoplasm, thereby preventing its translocation into the nucleus. EIF4A3 and circ_0067716 can form a double-negative feedback loop that could be affected by BPDE. During the initial phase of BPDE exposure, the expression of circ_0067716 was increased in response to stress injury, resulting in cell apoptosis through the involvement of miR-324-5p/DRAM1/BAX axis. Subsequently, as cellular adaptation progressed, long-term induction due to BPDE exposure led to an elevated EIF4A3 and a reduced circ_0067716 expression, which facilitated the proliferation of cells by stabilizing the PI3K/AKT pathway. Thus, our current study describes the effects of circ_0067716 on the genotoxicity and carcinogenesis induced by benzo(a)pyrene and puts forwards to the possible regulatory mechanism on the occurrence of smoking-related lung cancer, providing a unique insight based on epigenetics.

Screening of immune related gene and survival prediction of lung adenocarcinoma patients based on LightGBM model

Lung cancer is one of the malignant tumors with the greatest threat to human health, and studies have shown that some genes play an important regulatory role in the occurrence and development of lung cancer. In this paper, a LightGBM ensemble learning method is proposed to construct a prognostic model based on immune relate gene (IRG) profile data and clinical data to predict the prognostic survival rate of lung adenocarcinoma patients. First, this method used the Limma package for differential gene expression, used CoxPH regression analysis to screen the IRG to prognosis, and then used XGBoost algorithm to score the importance of the IRG features. Finally, the LASSO regression analysis was used to select IRG that could be used to construct a prognostic model, and a total of 17 IRG features were obtained that could be used to construct model. LightGBM was trained according to the IRG screened. The K-means algorithm was used to divide the patients into three groups, and the area under curve (AUC) of receiver operating characteristic (ROC) of the model output showed that the accuracy of the model in predicting the survival rates of the three groups of patients was 96%, 98% and 96%, respectively. The experimental results show that the model proposed in this paper can divide patients with lung adenocarcinoma into three groups [5-year survival rate higher than 65% (group 1), lower than 65% but higher than 30% (group 2) and lower than 30% (group 3)] and can accurately predict the 5-year survival rate of lung adenocarcinoma patients.

G protein-coupled estrogen receptor activates PI3K/AKT/mTOR signaling to suppress ferroptosis via SREBP1/SCD1-mediated lipogenesis.

Lung cancer is the leading cause of cancer-related death worldwide. The sex differences in the occurrence and fatality rates of non-small cell lung cancer (NSCLC), along with its association with estrogen dependence, suggest that estrogen receptors (ERs) contribute to the development of NSCLC. However, the influence of G protein-coupled estrogen receptor (GPER1) on NSCLC remains to be determined. Escape from ferroptosis is one of the hallmarks of tumor discovered in recent years. In this context, the present study evaluated whether GPER1 promotes NSCLC progression by preventing ferroptosis, and the underlying mechanism through which GPER1 protects against ferroptosis was also explored. The effects of GPER1 on the cytotoxicity of H2O2, the ferroptosis inducer RSL3, and Erastin were assessed using the CCK8 assay and plate cloning. Lipid peroxidation levels were measured based on the levels of MDA and BODIPY™581/591C11. GPER1 overexpression and knockdown were performed and G1 was used, and the expression of SCD1 and PI3K/AKT/mTOR signaling factors was measured. Immunofluorescence analysis and immunohistochemistry were performed on paired specimens to measure the correlation between the expression of GPER1 and SCD1 in NSCLC tissues. The effect of GPER1 on the cytotoxicity of cisplatin was measured in vitro using the CCK8 assay and in vivo using xenograft tumor models. GPER1 and G1 alleviated the cytotoxicity of H2O2, reduced sensitivity to RSL3, and impaired lipid peroxidation in NSCLC tissues. In addition, GPER1 and G1 promoted the protein and mRNA expression of SCD1 and the activation of PI3K/AKT/mTOR signaling. GPER1 and SCD1 expression were elevated and positively correlated in NSCLC tissues, and high GPER1 expression predicted a poor prognosis. GPER1 knockdown enhanced the antitumor activity of cisplatin in vitro and in vivo. GPER1 prevents ferroptosis in NSCLC by promoting the activation of PI3K/AKT/mTOR signaling, thereby inducing SCD1 expression. Therefore, treatments targeting GPER1 combined with cisplatin would exhibit better antitumor effects.

Hsa_circ_0008133 contributes to lung cancer progression by promoting glycolysis metabolism through the miR-760/MEX3A axis.

Lung cancer is a very common cancer with poor prognosis and high mortality. Circular RNAs (circRNAs) have been confirmed to be related to the occurrence of lung cancer, and circ_0008133 has been found to be possibly related to lung cancer. Expression of circ_0008133, miR-760, and mex-3 RNA binding family member A (MEX3A) messenger RNA (mRNA) was detected using quantitative real-time polymerase chain reaction (qRT-PCR). Cell viability, colony number, migration, and invasion were assessed using cell counting kit-8 (CCK8), colony formation, wound healing, and transwell assays. Glucose consumption and lactate production were detected using commercial kits. Protein expression was measured using western blot. Dual-luciferase reporter assay and RNA pull-down assay were used to analyze the relationships between miR-760 and circ_0008133 or MEX3A. The effects of circ_0008133 knockdown on tumor growth in vivo were examined by the nude mice expriment. Immunohistochemistry (IHC) assay analyzed Ki-67 expression. Circ_0008133 and MEX3A were markedly boosted in lung cancer tissues and cells. Circ_0008133 knockdown decreased lung cancer cell viability, glucose consumption, lactate production, colony formation, migration, and invasion. In mechanism, circ_0008133 might positively regulate MEX3A expression by sponging miR-760. Additionally, knockdown of circ_0008133 inhibited tumor growth in vivo. Circ_0008133 accelerated the progression of lung cancer by promoting glycolysis metabolism through the miR-760/MEX3A axis.

Robust explanation supervision for false positive reduction in pulmonary nodule detection.

Lung cancer is the deadliest and second most common cancer in the United States due to the lack of symptoms for early diagnosis. Pulmonary nodules are small abnormal regions that can be potentially correlated to the occurrence of lung cancer. Early detection of these nodules is critical because it can significantly improve the patient's survival rates. Thoracic thin-sliced computed tomography (CT) scanning has emerged as a widely used method for diagnosing and prognosis lung abnormalities. The standard clinical workflow of detecting pulmonary nodules relies on radiologists to analyze CT images to assess the risk factors of cancerous nodules. However, this approach can be error-prone due to the various nodule formation causes, such as pollutants and infections. Deep learning (DL) algorithms have recently demonstrated remarkable success in medical image classification and segmentation. As an ever more important assistant to radiologists in nodule detection, it is imperative ensure the DL algorithm and radiologist to better understand the decisions from each other. This study aims to develop a framework integrating explainable AI methods to achieve accurate pulmonary nodule detection. A robust and explainable detection (RXD) framework is proposed, focusing on reducing false positives in pulmonary nodule detection. Its implementation is based on an explanation supervision method, which uses nodule contours of radiologists as supervision signals to force the model to learn nodule morphologies, enabling improved learning ability on small dataset, and enable small dataset learning ability. In addition, two imputation methods are applied to the nodule region annotations to reduce the noise within human annotations and allow the model to have robust attributions that meet human expectations. The 480, 265, and 265 CT image sets from the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset are used for training, validation, and testing. Using only 10, 30, 50, and 100 training samples sequentially, our method constantly improves the classification performance and explanation quality of baseline in terms of Area Under the Curve (AUC) and Intersection over Union (IoU). In particular, our framework with a learnable imputation kernel improves IoU from baseline by 24.0% to 80.0%. A pre-defined Gaussian imputation kernel achieves an even greater improvement, from 38.4% to 118.8% from baseline. Compared to the baseline trained on 100 samples, our method shows less drop in AUC when trained on fewer samples. A comprehensive comparison of interpretability shows that our method aligns better with expert opinions. A pulmonary nodule detection framework was demonstrated using public thoracic CT image datasets. The framework integrates the robust explanation supervision (RES) technique to ensure the performance of nodule classification and morphology. The method can reduce the workload of radiologists and enable them to focus on the diagnosis and prognosis of the potential cancerous pulmonary nodules at the early stage to improve the outcomes for lung cancer patients.