Subtype Of Lung Cancer Research Articles

ADAMTS16 drives epithelial-mesenchymal transition and metastasis through a feedback loop upon TGF-β1 activation in lung adenocarcinoma

AbstractLung adenocarcinoma (LUAD) is the major subtype of lung cancer. The poor prognosis of LUAD patients is attributed primarily to metastasis. ADAMTS16 is a crucial member of the ADAMTS family and is involved in tumor progression. However, its role and regulatory mechanism in LUAD remain unexplored. In this study, ADAMTS16 was identified as a crucial oncogene and survival predictor in LUAD via analyses of public datasets. Clinical specimens and tissue microarrays confirmed the differential expression and prognostic value of ADAMTS16 in LUAD patients. Transcriptome data and in vitro experiments demonstrated that ADAMTS16 was positively associated with epithelial-mesenchymal transition (EMT) and the migration abilities of LUAD cells. Knockdown of ADAMTS16 attenuated lung and pleural metastasis in an animal model. Mechanistically, the results of the enzyme-linked immunosorbent assay (ELISA) and western blot (WB) suggested that ADAMTS16 activated the TGF-β signaling pathway by facilitating the conversion of LAP-TGF-β1 to active TGF-β1. Co-Immunoprecipitation (co-IP) indicated an interaction between ADAMTS16 and LAP-TGF-β1. Inhibition of ADAMTS16 impaired EMT and aggressiveness of LUAD cells, while treatment with recombinant TGF-β1 reversed this inhibition. Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays indicated that SOX4 acted as a transcriptional activator of ADAMTS16 and that TGF-β1 regulated the expression of ADAMTS16 by increasing the binding of SOX4 to the promoter of ADAMTS16. Suppressing the TGF-β signaling pathway inhibited ADAMTS16 expression, EMT, and lung metastasis, whereas overexpressing SOX4 reversed this inhibition. Therefore, ADAMTS16 forms a positive feedback loop with the TGF-β1/SOX4 axis to regulate EMT and metastasis, and disruption of this feedback loop inhibits tumor progression. These findings underscore the potential of ADAMTS16 as a prognostic biomarker and therapeutic target in LUAD and offer novel insight into the mechanism of EMT and metastasis.

Associations of radiotherapy receipt with lung cancer risk by histological subtype among breast cancer survivors in the United States.

Radiotherapy for breast cancer has been associated with an increased risk of secondary malignancies, including primary lung cancer. Whether this association varies by histological subtype of lung cancer remains unknown. Based on the data from 12 Surveillance, Epidemiology, and End Results registries, we examined the association between radiotherapy receipt and the risk of subtype-specific subsequent primary lung cancer (SPLC) among female first primary breast cancer cases diagnosed between ages 20 and 84 from 1992 to 2020. More than half (53%) of the 550,007 breast cancer survivors identified had undergone radiotherapy as part of their initial breast cancer treatment. Over an average follow-up of 9.7 years, 8014 survivors developed SPLCs. For small-cell carcinoma, the standardized incidence ratio (SIR) compared with the general population was higher for survivors who received radiotherapy (SIR = 1.15, 95% confidence interval [CI] = 1.06-1.25) but similar for those who did not receive radiotherapy (SIR = 1.00, 95% CI = 0.91-1.09), with the difference in SIRs being statistically significant (p = .003). Similar associations were found for squamous cell carcinoma (SIRyes = 1.16, 95% CI = 1.08-1.24 vs. SIRno/unknown = 1.06, 95% CI = 0.98-1.15; p = .07). The increased risks were confined to ipsilateral SPLC, with the greatest SIRs for small-cell carcinoma occurring 5-10 years since breast cancer diagnosis (SIR = 1.83, 95% CI = 1.53-2.19) and for squamous cell carcinoma with a latency of 10 years or more (SIR = 1.64, 95% CI = 1.42-1.88). In contrast, the risk of developing adenocarcinoma did not vary by radiotherapy receipt (SIRyes = 1.23, 95% CI = 1.18-1.28 vs. SIRno/unknown = 1.17, 95% CI = 1.12-1.22; p = .18), indicating additional risk factors in play. The findings suggest a distinct carcinogenic pathway of radiation-induced lung cancer across histological subtypes and may inform risk-stratified surveillance guidelines for SPLC.

Abstract 4138519: A Scoping Review Exploring Cardiovascular Risk and Health Metrics and Cancer Prediction

Introduction: Cardiovascular disease (CVD) and cancer are leading causes of morbidity and mortality. Since they share risk factors, it is plausible that CVD risk scores and cardiovascular health (CVH) metrics could also be used to predict cancer. Research Question: What is the relationship between established CVD risk scores and CVH metrics and the incidence of cancer? Methods: A librarian searched four databases (Embase, PubMed, Scopus, Web of Science: Core) in February 2024 without language restriction. Using Covidence, two reviewers independently screened the titles, abstracts, and full texts of the retrieved articles based on the inclusion criteria (prospective observational studies, adults aged ≥18 years, reported incidence of any type of cancer as an outcome, and used at least one CVD risk score or CVH metric as an exposure). A third reviewer resolved conflicts. The characteristics of the studies retained after screening and the summary estimates of the associations were extracted using Excel. Results: Of the 4,165 records screened, 13 studies (reporting 14 CVD or CVH metrics) were included. The heterogeneity in the scales (comparison group) precluded a meta-analysis. Four studies evaluated CVD risk scores (Atherosclerotic Cardiovascular Disease [ASCVD], Framingham Risk Score, Systematic Coronary Risk Evaluation), and 10 reported CVH metrics (American Heart Association [AHA] Life’s Simple 7, AHA Life’s Essential 8). Sample size ranged from 1,880 to 342,226, with median follow-up from 8.1 to 29.6 years. Most of the studies included more women than men (61.5%) and mean age ranged from 44.8 to 72 years. The majority of studies included all cancer types (71.4%), including breast, lung, colorectal, and prostate cancer subtypes, and the total number of cancer events ranged from 387 to 11,643. Studies utilizing CVD risk scores consistently reported that individuals with a higher CVD risk, developed any type of cancer, with hazard ratios (HRs) ranging from 1.02 to 3.71, and studies employing CVH metrics generally indicated an ideal CVH is associated with a lower cancer risk, with HRs ranging from 0.49 to 0.95. Conclusion: Despite heterogeneity in CVD risk metrics and cancer subtypes, most studies reported that higher CVD risk scores or worse CVH metrics may increase future cancer risk. More rigorous studies are needed focusing on specific CVD risk metrics and cancer types of cancer to produce evidence suitable for a meta-analysis.

Abstract 4136755: Associations of Pulmonary Hypertension and Lung Cancer: A Prospective Cohort Study of Half a Million UK Biobank Participants

Background: Pulmonary hypertension (PH) and lung cancer (LC) are both significant public health issues with substantial morbidity and mortality. Recent studies suggest potential associations between pulmonary vascular diseases and LC due to shared risk factors and pathophysiological mechanisms, yet comprehensive research in large populations is scarce. Methods: This prospective cohort study utilized data from the UK Biobank, with baseline assessment between 2006 and 2010 and follow-up until November 30, 2023, for England and Scotland, and May 31, 2022, for Wales. We included 6,425 participants with LC, identified using linked cancer registry data, and followed them for the incidence of PH over a period of 10 years. We adjusted for potential confounders including age, ethnics, sex, smoking status, BMI, and histologic subtypes of LC. Statistical analyses were performed using Time-dependent Cox proportional hazards models to estimate the associations between PH and LC. Results: Of 502,173 participants in this prospective cohort, 6,425 were diagnosed with LC, while 494,451 did not receive the LC diagnosis. The prevalence of PH in the LC cohort and the no-LC cohort were 0.64% (1,494 of 3,142 women [47.55%]) and 2.40% (59 of 154 women [38.31%]), respectively, with significant difference between the two groups (p<0.001). In the LC cohort, the mortality rates of patients without PH and with PH were 69.18% and 77.92% (p=0.020). Among the patients diagnosed with LC and PH, 33.12% were diagnosed with LC after PH (PH-LC), while 66.88% were diagnosed with LC before PH (LC-PH). And the mortality rates of the PH-LC group and the LC-PH group were 78.43% and 77.67%, respectively, with no significant difference between the two groups (p=0.915). Time-dependent Cox regression analysis showed that PH shows an increasing trend in risk (5th year HR: 3.22, 95% CI: 2.09-4.99, p-value < 0.001), highlighting its significant impact on LC long-term prognosis. Conclusion: To conclude, the presence of PH significantly increased the risk of mortality in participants with LC, especially for the long-term LC survivors. And certain percentage of patients were diagnosed with LC before PH, but they had no significant difference between the patients with LC after PH.

Gastroesophageal reflux disease and risk of incident lung cancer: A large prospective cohort study in UK Biobank.

Some pathogenic mechanisms suggest a potential relationship between gastroesophageal reflux disease (GERD) and respiratory diseases. However, evidence regarding the association between GERD and lung cancer is mixed. We aim to explore this relationship based on data from the large-scale UK Biobank study. We performed a cross-sectional and prospective cohort study in 501,569 (45.58% male) individuals included in the UK Biobank at baseline (2006-2010). The Cox proportional hazards model and logistic regression models were used to assess the relationship between GERD and lung cancer, small cell lung cancer (SCLC), lung squamous cell carcinoma (LUSC), and lung adenocarcinoma (LUAD). During a mean follow-up of 11.54 years, 3,863 (0.84%) incident lung cancer cases were identified. In the cross-sectional analysis using logistic models, significant associations were found between GERD and prevalent lung cancer cases (odds ratio [OR] = 1.87, 95% confidence interval [95% CI]: 1.45-2.38) and subtypes of lung cancer, with an OR (95% CI) of 3.19 (1.47-6.79) for SCLC, 2.08 (1.33-3.21) for LUSC, 1.85 (1.15-2.91) for LUAD. In the follow-up analysis using Cox models, GERD was associated with an increased risk of lung cancer (hazard ratio [HR] = 1.24, 95%CI: 1.14-1.34). Similar associations were also observed between GERD and SCLC (HR = 1.39, 95% CI: 1.09-1.78), LUSC (HR = 1.40, 95% CI: 1.18-1.65), and LUAD (HR = 1.17, 95% CI: 1.02-1.33). The risk of lung cancer resulting from GERD was mainly elevated in former smokers (HR = 1.38, 95% CI: 1.23-1.54) and current smokers (HR = 1.18, 95% CI: 1.04-1.34), but not in never-smokers (HR = 0.89, 95% CI: 0.70-1.14). No significant association was observed in former smokers who had quit smoking for at least 25 years. We found that GERD was positively associated with an increased risk of lung cancer, especially among smokers. Awareness of this association may be beneficial for prevention and treatment strategies of both diseases.

Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data

AimsTo develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data.MethodsThis study was conducted retrospectively between March 2018 and December 2019. Patients were randomly assigned to a development cohort (DC, n=179) and a validation cohort (VC, n=77). A total of 7 clinical parameters and 16 ultrasound parameters were collected. Least absolute shrinkage and selection operator regression analysis was employed to identify the most significant predictors utilizing a 10-fold cross-validation. The multivariate logistic regression model was applied to investigate the relevant factors. An individualized nomogram was then developed. Receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were applied for model validation in both DC and VC.ResultsFollowing the final regression analysis, gender, serum carcinoembryonic antigen, lesion size and perfusion defect in contrast-enhanced ultrasound were entered into the nomogram. The model showed moderate predictive ability, with an area under the ROC curve of 0.867 for DC and 0.838 for VC. The calibration curves of the model showed good agreement between actual and predicted probabilities. The ROC and DCA curves demonstrated that the nomogram exhibited a good predictive performance.ConclusionWe developed a nomogram that can predict the histologic subtypes of subpleural NSCLC. Both internal and external validation revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility. This model has the potential to assist clinicians in making treatment recommendations.

A Novel Hybrid Model for Automatic Non-Small Cell Lung Cancer Classification Using Histopathological Images

Background/Objectives: Despite recent advances in research, cancer remains a significant public health concern and a leading cause of death. Among all cancer types, lung cancer is the most common cause of cancer-related deaths, with most cases linked to non-small cell lung cancer (NSCLC). Accurate classification of NSCLC subtypes is essential for developing treatment strategies. Medical professionals regard tissue biopsy as the gold standard for the identification of lung cancer subtypes. However, since biopsy images have very high resolutions, manual examination is time-consuming and depends on the pathologist’s expertise. Methods: In this study, we propose a hybrid model to assist pathologists in the classification of NSCLC subtypes from histopathological images. This model processes deep, textural and contextual features obtained by using EfficientNet-B0, local binary pattern (LBP) and vision transformer (ViT) encoder as feature extractors, respectively. In the proposed method, each feature matrix is flattened separately and then combined to form a comprehensive feature vector. The feature vector is given as input to machine learning classifiers to identify the NSCLC subtype. Results: We set up 13 different training scenarios to test 4 different classifiers: support vector machine (SVM), logistic regression (LR), light gradient boosting machine (LightGBM) and extreme gradient boosting (XGBoost). Among these scenarios, we obtained the highest classification accuracy (99.87%) with the combination of EfficientNet-B0 + LBP + ViT Encoder + SVM. The proposed hybrid model significantly enhanced the classification accuracy of NSCLC subtypes. Conclusions: The integration of deep, textural, and contextual features assisted the model in capturing subtle information from the images, thereby reducing the risk of misdiagnosis and facilitating more effective treatment planning.

Unveiling the distinctive variations in multi-omics triggered by TP53 mutation in lung cancer subtypes: An insight from interaction among intratumoral microbiota, tumor microenvironment, and pathology

The TP53 mutation is one of the most common gene mutations in non-small cell lung cancer (NSCLC) and plays a significant role in the occurrence, development, and prognosis of both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Recent studies have also suggested the predictive value of TP53 mutations in the response to immunotherapy for NSCLC. It is known that intratumoral microbiota, tumor immune microenvironment (TIME) and histology are associated with the roles of TP53 mutation in NSCLC. However, the intrinsic associations among these three factors and their underlying interaction with TP53 mutation are not well understood. Additionally, the potential of predicting TP53 mutations using deep learning methods has not yet been fully evaluated. In this paper, we comprehensively evaluated the tissue microbiome, host gene expression characteristics, and histopathological slides of 992 NSCLC patients obtained from the cancer genome atlas (TCGA) and validated the findings using multi-omics data of 332 NSCLC patients from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Compared to LUSC, LUAD exhibited more substantial differences between patients with and without TP53 mutation in all three aspects. In LUAD, our results imply underlying links between the tissue microbiome and immune cell components in the TIME, and show that the abundance of immune cells is reflected in histology slides. Furthermore, we propose a novel multimodal deep learning model that focuses on histopathology images, which achieves an area under the curve (AUC) of 0.84 in LUAD. In summary, TP53 mutation of LUAD resulted more significant changes in intratumoral microbiota, TIME and histology than that of LUSC. And histopathology images can be used to predict TP53 mutation in LUAD with reasonable accuracy.

Corynoxine suppresses lung adenocarcinoma proliferation and metastasis via inhibiting PI3K/AKT pathway and suppressing Cyclooxygenase-2 expression

BackgroundLung adenocarcinoma (LUAD) is the most common lung cancer subtype, and the prognosis of affected patients is generally poor. The traditional Chinese medicine Uncaria rhychophaylla has been reported to exhibit anti-lung cancer properties. Accordingly, the main bioactive ingredient in Uncaria rhychophaylla, Corynoxine, may hold great value as a treatment for lung cancer.MethodsThe impact of Corynoxine on the viability of LUAD cells was assessed using the Cell Counting Kit-8 (CCK-8) assay. Apoptosis in A549 cells was evaluated via flow cytometry. Migration and invasion capabilities were determined through wound healing and Transwell assays, respectively. The key pathways targeted by Corynoxine in LUAD were identified using a network pharmacology approach. Additionally, Western immunoblotting, quantitative real-time PCR (qRT-PCR), and ELISA assays were conducted to validate the underlying mechanisms. The in vivo anti-tumor efficacy of Corynoxine was assessed in xenograft nude mice.ResultsIn this study, Corynoxine treatment was found to markedly suppress in vitro LUAD cell proliferative, migratory, and invasive activity. It additionally downregulated Vimentin and promoted E-cadherin upregulation consistent with the disruption of epithelial-mesenchymal transition (EMT) induction while also accelerating apoptotic death. Furthermore, network pharmacology analysis revealed that the PI3K/AKT pathway is a potential target of Corynoxine in LUAD. In vitro assays demonstrated that treatment with Corynoxine resulted in the suppression of PI3K/AKT signaling and a consequent drop in cyclooxygenase-2 (COX-2) expression. These findings were further confirmed in vivo in mice harboring A549 tumor xenografts in which Corynoxine was able to interfere with the PI3K/AKT/COX-2 signaling axis.ConclusionThis study elucidated the potential effects of Corynoxine in suppressing proliferation and metastasis in LUAD, along with investigating the underlying mechanisms. These data highlight the promise of Corynoxine as a novel therapeutic tool for the treatment of individuals diagnosed with LUAD.

Case report: A golden tail of immunotherapy: significant tail effect in a chemotherapy-resistant advanced pulmonary sarcomatoid carcinoma patient treated by Sintilimab combined with Anlotinib

Tail effect is a unique phenomenon in immunotherapy characterized by the prolonged maintenance of therapeutic efficacy. It can be observable even after treatment cessation. Immunotherapy has gradually become a vital regimen for the treatment of advanced lung cancer patients, among which immune-combined therapies based on immune checkpoint inhibitors (ICIs) have been applied clinically and demonstrates considerable clinical efficacy. In this case report, the patient was pathologically diagnosed with pulmonary sarcomatoid carcinoma (PSC), a rare and highly aggressive subtype of non-small cell lung cancer (NSCLC) known for its poor prognosis due to high invasiveness and metastatic potential. After developing resistance to chemotherapy, the patient was treated with a combined regimen of sintilimab and anlotinib, leading to initial clinical improvement. Following just three cycles of this regimen, treatment was discontinued, and the patient was discharged. Remarkably, over the subsequent months, the patient exhibited a significant tail effect, evidenced by sustained therapeutic stability, continuous tumor regression, stable low levels of serum carcinoembryonic antigen (CEA), and further improvement in clinical symptoms. Tail effect is a golden tail of immunotherapy. This case illustrates that the tail effect of immunotherapy can offer substantial survival benefits for patients with unresectable advanced lung cancer who have failed chemotherapy.

LW-MorphCNN: a lightweight morphological attention-based subtype classification network for lung cancer

Abstract Lung cancer is generally considered one of the most deadly cancers globally. If it can be identified early and diagnosed correctly, the survival probability of patients can be significantly improved. In this process, histopathological examination is a commonly used method for diagnosing and detecting lung cancer. It is crucial to accurately identify lung cancer subtypes from histopathological images, as this helps doctors formulate effective treatment plans. However, the visual inspection in histopathological diagnosis requires a large amount of time and also depends on the subjective perception of clinicians. Therefore, this paper proposes a lightweight lung cancer subtype classification network based on morphological attention (LW-MorphCNN), which is used to automatically classify the histopathological images of benign lung tumors, ADC (adenocarcinoma), and SCC (squamous cell carcinoma) provided in the public dataset LC25000 (Lung and Colon). This paper takes histopathological images as input and conducts a comparative analysis with classic networks such as VGG16, VGG19, DenseNet121, and ResNet50, as well as existing classification methods proposed in the same work. The network proposed in this paper is superior to other networks in terms of parameter quantity and performance, with an accuracy rate and F1 - score reaching 99.47% and 99.44% respectively. Clinicians can install the provided LW-MorphCNN in the hospital to confirm the diagnosis results.

Combination of grade and spread through air spaces (STAS) predicts recurrence in early stage lung adenocarcinoma: a retrospective cohort study.

Adenocarcinomas, a common subtype of lung cancer, exhibit diverse histological patterns. In 2020, The International Association for the Study of Lung Cancer (IASLC) introduced a grading system emphasizing high-grade components, which has shown prognostic value. Spread through air spaces (STAS) is recognized as a prognostic feature increasing the risk of recurrence in lung cancer. This study evaluates the combination of STAS status and the IASLC-grading system in surgically resected Stage I lung adenocarcinomas. This study is a retrospective analysis of 123 patients with Stage I lung adenocarcinoma who underwent lobectomy between 2011 and 2019. Histological patterns were assessed according to the IASLC criteria, and STAS status was documented. Patients were categorized based on their IASLC Grade and STAS status. Statistical analyses included Kaplan-Meier survival estimates, Cox proportional hazards models, and comparisons using Chi-square and t-tests. The cohort comprised 43 females and 80 males with a mean age of 61.8 ± 7.6years. STAS positivity was noted in 52.8% of patients. STAS positivity correlated significantly with Grade 3 tumors (p < 0.001). The 5-year recurrence-free survival was significantly lower in STAS-positive patients (70.7% vs. 88.7%, p = 0.026). Patients with Grade 3 and STAS positivity had significantly lower recurrence-free survival compared to other groups (p = 0.002). Grade 3 and STAS positivity were independent predictors of poor recurrence-free survival in multivariate analysis. IASLC Grade 3 tumors and STAS positivity are independent prognostic factors for poor recurrence-free survival in Stage I lung adenocarcinomas. Adjuvant treatment strategies should be considered for patients with these characteristics to improve outcomes.

Lymph node metastasis prediction from in-situ lung squamous cell carcinoma histopathology images using deep learning.

Lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer, represents a significant portion of lung cancer cases with distinct histologic patterns impacting prognosis and treatment. The current pathological assessment methods face limitations such as inter-observer variability, necessitating more reliable techniques. This study seeks to predict lymph node metastasis in LUSC using deep learning models applied to histopathology images of primary tumors, offering a more accurate and objective method for diagnosis and prognosis.Whole slide images (WSIs) from the Outdo-LUSC and TCGA-LUSC cohorts were used to train and validate deep-learning models. Multi-instance learning was applied, with patch-level predictions aggregated into WSI-level outcomes. The study employed the ResNet-18 network, transfer learning, and rigorous data preprocessing. To represent WSI features, innovative techniques like patch likelihood histogram (PLH) and bag of words (BoW) were used, followed by training of machine learning classifiers, including the ExtraTrees algorithm.The diagnostic model for lymph node metastasis showed strong performance, particularly using the ExtraTrees algorithm, as demonstrated by receiver operating characteristic (ROC) curves and Grad-CAM visualizations. The signature generated by the ExtraTrees algorithm, named LN_ISLUSCH (lymph node status-related in-situ lung squamous cell carcinoma histopathology), achieved an area under the curve (AUC) of 0.941 (95% CI: 0.926-0.955) in the training set and 0.788 (95% CI: 0.748-0.827) in the test set. Kaplan–Meier analyses confirmed that the LN_ISLUSCH model was a significant prognostic factor (p = 0.02).This study underscores the potential of artificial intelligence in enhancing diagnostic precision in pathology. The LN_ISLUSCH model stands out as a promising tool for predicting lymph node metastasis and prognosis in LUSC. Future studies should focus on larger and more diverse cohorts and explore the integration of additional omics data to further refine predictive accuracy and clinical utility.

Evaluation of Cyclin D1 protein and its association with the clinicopathological characteristics and prognosis of lung cancer: A retrospective study from Southern Kerala, India.

Cyclin D1 is a protein that can enhance the proliferation of cancer cells and has been detected in various malignancies, including lung cancer. However, routine examinations for Cyclin D1 in lung cancer cases have not been conducted in Kerala. This study sought to evaluate the links between cyclin D1 expression, clinicopathological characteristics, and 2-year survival rates in lung cancer. This retrospective cohort study used medical records and paraffin blocks of lung cancer patients at the Regional Cancer Centre in Kerala, India, between 2015 and 2018. The data were collected from 61 subjects, comprising of lung adenocarcinoma (18%), lung squamous cell carcinoma (27.9%), non-small-cell lung carcinoma (18%), poorly differentiated carcinoma (19.7%), and negative for malignant cells (16.4%). Data analysis was conducted using SPSS. The study revealed that 31.10% of the lung cancer patients exhibited overexpression of cyclin D1. A significant correlation was observed between cyclin D1 expression and histopathological results (P = 0.002), indicating that the level of cyclin D1 might be linked to specific histopathological subtypes of lung cancer. Despite this significant finding, cyclin D1 expression did not show any association with the clinical stage of the cancer or other clinical characteristics of the patients. Furthermore, when examining the 2-year survival rates of the patients, the study found no significant difference between those who had overexpression of cyclin D1 and those who did not (P = 0.145). Cyclin D1 expression was associated with histology type of lung cancer with no significant association to prognosis.

MLLT3 knockdown suppresses proliferation and cell mobility in human lung adenocarcinoma.

Lung cancer is emerging as one of the most frequently encountered malignancies around the world that carries high morbidity and mortality. Lung adenocarcinoma (LUAD) has become the most common subtype of lung cancer. MLLT3 or named AF9 was first characterized in acute myeloid leukemia and can downregulate the expression of several critical genes. The aim of this study was to explore the function of MLLT3 in the progression of LUAD and related molecular mechanisms. Immunohistochemistry was employed to assess MLLT3 expression in LUAD tissues, while quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot were utilized to detect MLLT3 expression levels in lung adenocarcinoma cell lines. These results revealed a significant overexpression of MLLT3 in LUAD cell lines and tissues. We further uncovered an association between MLLT3 expression profiles in LUAD tissues and metastasis, as well as TNM stage. Survival analysis showed that elevated MLLT3 correlated with a poorer survival rate. We also found that MLLT3 knockdown repressed LUAD cells invasion, migration, and proliferation in vitro, while inducing cell cycle arrest and apoptosis of LUAD cells. Moreover, knocking down MLLT3 inhibited tumor growth in vivo. Specific markers of apoptosis, cell cycle, epithelial-mesenchymal transition (EMT), and MLLT3-induced signaling were examined by Western blot. We demonstrated that MLLT3 knockdown inhibited the activity of the EGFR-MAPK/ERK signaling pathway, and MLLT3 might be a novel diagnostic biomarker and therapeutic target in lung adenocarcinomas.

Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers

BackgroundRapid on-site evaluation (ROSE) plays an important role during transbronchial sampling, providing an intraoperative cytopathologic evaluation. However, the shortage of cytopathologists limits its wide application. This study aims to develop a deep learning model to automatically analyze ROSE cytological images.MethodsThe hierarchical multi-label lung cancer subtyping (HMLCS) model that combines whole slide images of ROSE slides and serum biological markers was proposed to discriminate between benign and malignant lesions and recognize different subtypes of lung cancer. A dataset of 811 ROSE slides and paired serum biological markers was retrospectively collected between July 2019 and November 2020, and randomly divided to train, validate, and test the HMLCS model. The area under the curve (AUC) and accuracy were calculated to assess the performance of the model, and Cohen’s kappa (κ) was calculated to measure the agreement between the model and the annotation. The HMLCS model was also compared with professional staff.ResultsThe HMLCS model achieved AUC values of 0.9540 (95% confidence interval [CI]: 0.9257–0.9823) in malignant/benign classification, 0.9126 (95% CI: 0.8756–0.9365) in malignancy subtyping (non-small cell lung cancer [NSCLC], small cell lung cancer [SCLC], or other malignancies), and 0.9297 (95% CI: 0.9026–0.9603) in NSCLC subtyping (lung adenocarcinoma [LUAD], lung squamous cell carcinoma [LUSC], or NSCLC not otherwise specified [NSCLC-NOS]), respectively. In total, the model achieved an AUC of 0.8721 (95% CI: 0.7714–0.9258) and an accuracy of 0.7184 in the six-class classification task (benign, LUAD, LUSC, NSCLC-NOS, SCLC, or other malignancies). In addition, the model demonstrated a κ value of 0.6183 with the annotation, which was comparable to cytopathologists and superior to trained bronchoscopists and technicians.ConclusionThe HMLCS model showed promising performance in the multiclassification of lung lesions or intrathoracic lymphadenopathy, with potential application to provide real-time feedback regarding preliminary diagnoses of specimens during transbronchial sampling procedures.Clinical trial numberNot applicable.

Genetic selection for lung adenocarcinoma: a statistical approach based on pathological genetic data

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC), with high morbidity and mortality worldwide, posing a serious threat to human health. Due to the complex molecular mechanism of lung adenocarcinoma, which involves a variety of genetic and environmental factors, this makes early diagnosis and treatment a major challenge. This research aims to identify the key pathogenic genes of lung adenocarcinoma to solve the challenges in early diagnosis and treatment. In this paper, three differential analysis methods, limma, DESeq2 and edgeR, were used to select 3315 differential genes. Then, combined with Lasso regression and XGboost algorithm, the pathological gene data were analyzed in depth, and through these advanced statistical and machine learning techniques, the genes related to lung adenocarcinoma were screened from a large number of gene expression data, and 17 characteristic genes of the highest importance were found GO enrichment analysis and KEGG enrichment analysis were performed on these characteristic genes to clarify the biological functions of the genes. Finally, 8 significant pathogenic genes were identified by Cox survival analysis. The expression levels of these genes are closely related to the prognosis of lung adenocarcinoma patients, providing a new perspective for understanding the disease mechanism. The conclusions of this study not only improve the understanding of the molecular mechanism of lung adenocarcinoma, but also provide potential biomarkers for clinical diagnosis and treatment. The discovery of these genes is expected to promote the development of early diagnosis technology and guide the formulation of personalized treatment plans, thereby improving the treatment efficacy and quality of life of lung adenocarcinoma patients. In the future, these genes may become key targets for the development of new drugs and therapeutic strategies.

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks.

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores-0.98±0.02 for lung cancer subtype differentiation and 0.83±0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

Inhibition of Chk1 with Prexasertib Enhances the Anticancer Activity of Ciclopirox in Non-Small Cell Lung Cancer Cells.

Lung cancer is a leading cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) is the most prevalent lung cancer subtype. Ciclopirox olamine (CPX), an off-patent fungicide, has been identified as a new anticancer agent. Prexasertib (PRE), a Chk1 inhibitor, is in phase 1/2 clinical trials in various tumors. The anticancer effect of the combination of CPX with PRE on NSCLC cells is unknown. Here, we show that CPX is synergistic with PRE in inhibiting cell proliferation and inducing apoptosis of NSCLC (A549 and A427) cells. Combined treatment with CPX and PRE significantly increased the cell population in the G1/G0 and sub-G1 phases, compared to the single treatment with CPX or PRE. Concurrently, the combined treatment downregulated the protein levels of cyclins (A, B1), cyclin-dependent kinases 4, 6, 2 (CDK4, CDK6, CDK2), cell division cycle 25 B, C (Cdc25B, Cdc25C), and upregulated the protein levels of the CDK inhibitors p21 and p27, leading to decreased phosphorylation of Rb. In addition, the combined treatment increased DNA damage, evidenced by increased expression of γH2AX. In line with this, the combined treatment induced more apoptosis than either single treatment. This was associated with increased expression of DR4, DR5, Fas, and FADD and decreased expression of survivin, resulting in activation of caspase 8 and caspase 3 as well as cleavage of poly (ADP ribose) polymerase (PARP). Taken together, the results suggest that inhibition of Chk1 with PRE can enhance the anticancer activity of CPX at least partly by decreasing cell proliferation and increasing apoptosis in NSCLC cells.

Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma

The therapeutic efficacy of lung adenocarcinoma (LUAD), the most prevalent histological subtype of primary lung cancer, remains inadequate, with accurate prognostic assessment posing significant challenges. This study sought to elucidate the prognostic significance of mitochondrial-related genes in LUAD through an integrative multi-omics approach, aimed at developing personalized therapeutic strategies. Utilizing transcriptomic and single-cell RNA sequencing (scRNA-seq) data, alongside clinical information from publicly available databases, we first applied dimensionality reduction and clustering techniques to the LUAD single-cell dataset, focusing on the subclassification of fibroblasts, epithelial cells, and T cells. Mitochondrial-related prognostic genes were subsequently identified using TCGA-LUAD data, and LUAD cases were stratified into distinct molecular subtypes through consensus clustering, allowing for the exploration of gene expression profiles and clinical feature distributions across subtypes. By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. Furthermore, we conducted comprehensive analyses of tumor mutation burden (TMB), immune microenvironment characteristics, and genome-wide association study (GWAS) data, providing additional insights into the mechanistic roles of mitochondrial-related genes in LUAD pathogenesis. This study not only offers a novel approach to improving prognostic assessments in LUAD but also establishes a strong foundation for the development of personalized therapeutic interventions.