BACKGROUND AND OBJECTIVES

Cell lines serve as invaluable tools in studying lung cancer biology and developing new therapies to combat the disease. However, commercially available cell lines are typically of Caucasian origin and may be less representative of the local genetic background. To address this, our lab previously immortalized cells from pleural fluid of a Filipino non-small cell lung cancer (NSCLC) patient via CDK4 transduction. Copy number variations (CNVs) are a type of genetic variation which may affect physiology and disease by disrupting gene function or altering gene expression, and in cancer, these may be associated with patient outcomes. CNV profiling can be valuable for understanding the biology of our immortalized cells and identifying genes that could serve as potential targets for diagnostic, prognostic, and therapeutic interventions. This study aimed to characterize previously immortalized NSCLC-derived cells, GL01, in comparison with an established lung adenocarcinoma (LUAD) cell line, A549, through whole-genome microarray-based copy number profiling.

DNA was extracted from GL01 and A549 cells using a commercially-available silica-based DNA extraction kit. DNA extracts were quantified and normalized for microarray analysis. Whole-genome copy number profiling was done using the OncoScan CNV Plus Assay following the manufacturer’s protocols, and data was analyzed using the Chromosome Analysis Suite software. Functional analysis of genes identified to be involved in copy number aberrations was done using the PANTHER Classification System.

Copy number aberrations span 1,592,737,105 bp in GL01 and 1,715,708,552 bp in A549, with a high degree of concordance between the two. Large-scale and focal copy number aberrations previously identified to be recurrent in various LUAD cohorts were present in both GL01 and A549. Focal copy number aberrations associated with previously described lung cancer-related genes involve the PDE4D gene in GL01 and the SKIL and CDKN2A/CDKN2B genes in both GL01 and A549. PANTHER Pathway analysis of genes positively correlated with mRNA expression showed that the ubiquitin proteasome pathway was significantly overrepresented in both GL01 (FDR p = 0.000074) and A549 (FDR p = 0.000075), with 20 genes involved. Additionally, the KRAS:p.G12C/S:c.34G>T/A somatic mutation variant was detected in both GL01 and A549.

This study provides a method for identifying potentially clinically-relevant genes associated with a sample’s copy number aberrations and the pathways they represent, providing personalized mechanistic, prognostic, and therapeutic insights into the cancer biology of our cells.

OBJECTIVE: To explore the key target molecules and potential mechanisms of oridonin against non-small cell lung cancer (NSCLC). METHODS: The target molecules of oridonin were retrieved from SEA, STITCH, SuperPred and TargetPred databases; target genes associated with the treatment of NSCLC were retrieved from GeneCards, DisGeNET and TTD databases. Then, the overlapping target molecules between the drug and the disease were identified. The protein-protein interaction (PPI) was constructed using the STRING database according to overlapping targets, and Cytoscape was used to screen for key targets. Molecular docking verification were performed using AutoDockTools and PyMOL software. Using the DAVID database, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted. The impact of oridonin on the proliferation and apoptosis of NSCLC cells was assessed using cell counting kit-8, cell proliferation EdU image kit, and Annexin V-FITC/PI apoptosis kit respectively. Moreover, real-time quantitative PCR and Western blot were used to verify the potential mechanisms. RESULTS: Fifty-six target molecules and 12 key target molecules of oridonin involved in NSCLC treatment were identified, including tumor protein 53 (TP53), Caspase-3, signal transducer and activator of transcription 3 (STAT3), mitogen-activated protein kinase kinase 8 (MAPK8), and mammalian target of rapamycin (mTOR). Molecular docking showed that oridonin and its key target molecules bind spontaneously. GO and KEGG enrichment analyses revealed cancer, apoptosis, phosphoinositide-3 kinase/protein kinase B (PI3K/Akt), and other signaling pathways. In vitro experiments showed that oridonin inhibited the proliferation, induced apoptosis, downregulated the expression of Bcl-2 and Akt, and upregulated the expression of Caspase-3. CONCLUSION Oridonin can act on multiple targets and pathways to exert its inhibitory effects on NSCLC, and its mechanism may be related to upregulating the expression of Caspase-3 and downregulating the expressions of Akt and Bcl-2.
Diterpenes, Kaurane/chemistry* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Humans ; Network Pharmacology ; Lung Neoplasms/pathology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Molecular Docking Simulation ; Protein Interaction Maps/drug effects* ; Cell Line, Tumor ; Signal Transduction/drug effects* ; Gene Expression Regulation, Neoplastic/drug effects* ; Reproducibility of Results ; Gene Ontology

OBJECTIVES: Esophageal squamous cell carcinoma (ESCC) is characterized by complex pathogenesis and poor prognosis. In recent years, epithelial-mesenchymal transition (EMT) in tumor initiation and progression has attracted increasing attention. Enhancer of zeste homolog 2 (EZH2), which is aberrantly expressed in various tumors, may be closely related to the EMT process. This study aims to examine the expression and correlation of EZH2 and EMT markers in ESCC cells and tissues, evaluate the effects of EZH2 knockdown on ESCC cell proliferation, invasion, and migration, and explore how EZH2 contributes to the malignant biological behavior of ESCC. METHODS: Bioinformatics analyses were used to assess EZH2 expression levels in ESCC. Small interfering RNA was used to knock down EZH2 in ESCC cell lines EC109 and EC9706. Cell proliferation, invasion, and migration were evaluated using cell counting kit-8 (CCK-8), wound healing, and Transwell assays. Protein and mRNA expression levels of EZH2, E-cadherin (E-cad), and vimentin (Vim) were detected by Western blotting and real time fluorogenic quantitative PCR (RT-qPCR), respectively. Immunohistochemical (IHC) staining was performed on 70 ESCC tissue samples and 40 paired adjacent normal tissues collected from the First Affiliated Hospital of Shihezi University between 2010 and 2016 to assess the expression of EZH2, E-cad, and Vim, and to analyze their associations with clinicopathological feature and patient prognosis. RESULTS: Bioinformatics analysis showed that EZH2 was highly expressed in ESCC (P<0.001), and high EZH2 expression was associated with worse prognosis (P<0.001). CCK-8, wound healing, and Transwell assays demonstrated that EZH2 knockdown significantly suppressed the proliferation, invasion, and migration of ESCC cells (P<0.001). In addition, Vim expression was significantly reduced, while E-cad expression was significantly increased at both protein and mRNA levels in EZH2-silenced cells (all P<0.05). IHC staining analysis revealed higher expression of EZH2 and Vim and lower expression of E-cad in ESCC tissues compared to adjacent normal tissues. Kaplan-Meier survival analysis showed that low expression of EZH2 and Vim and high expression of E-cad were associated with longer survival (all P<0.05). CONCLUSIONS EZH2 promotes malignant biological behavior in ESCC by mediating EMT. Elevated EZH2 expression is associated with poor prognosis in ESCC patients.
Humans ; Enhancer of Zeste Homolog 2 Protein/physiology* ; Esophageal Squamous Cell Carcinoma/pathology* ; Epithelial-Mesenchymal Transition/genetics* ; Esophageal Neoplasms/metabolism* ; Cell Proliferation ; Cell Line, Tumor ; Cell Movement ; Cadherins/genetics* ; Vimentin/genetics* ; Male ; Female ; Middle Aged ; Neoplasm Invasiveness ; Prognosis ; RNA, Small Interfering/genetics* ; Gene Expression Regulation, Neoplastic

OBJECTIVES: Non-small cell lung cancer (NSCLC) is associated with poor prognosis, with 30% of patients diagnosed at an advanced stage. Mutations in the EGFR and KRAS genes are important prognostic factors for NSCLC, and targeted therapies can significantly improve survival in these patients. Although tissue biopsy remains the gold standard for detecting gene mutations, it has limitations, including invasiveness, sampling errors due to tumor heterogeneity, and poor reproducibility. This study aims to develop machine learning models based on radiomic features to predict EGFR and KRAS gene mutation status in NSCLC patients, thereby providing a reference for precision oncology. METHODS: Imaging and mutation data from eligible NSCLC patients were obtained from the publicly available Lung-PET-CT-Dx dataset in The Cancer Imaging Archive (TCIA). A three-dimensional-convolutional neural network (3D-CNN) was used to extract imaging features from the regions of interest (ROI). The LightGBM algorithm was employed to build classification models for predicting EGFR and KRAS gene mutation status. Model performance was evaluated using 5-fold cross-validation, with receiver operator characteristic (ROC) curves, area under the curve (AUC), accuracy, sensitivity, and specificity used for validation. RESULTS: The models effectively predicted EGFR and KRAS mutations in NSCLC patients, achieving an AUC of 0.95 for EGFR mutations and 0.90 for KRAS. The models also demonstrated high accuracy (EGFR 89.66%; KRAS 87.10%), sensitivity (EGFR 93.33%; KRAS 87.50%), and specificity (EGFR 85.71%; KRAS 86.67%). CONCLUSIONS A radiogenomics-machine learning predictive model can serve as a non-invasive tool for anticipating EGFR and KRAS gene mutation status in NSCLC patients.
Humans ; Carcinoma, Non-Small-Cell Lung/diagnostic imaging* ; Lung Neoplasms/diagnostic imaging* ; Mutation ; Proto-Oncogene Proteins p21(ras)/genetics* ; ErbB Receptors/genetics* ; Machine Learning ; Positron Emission Tomography Computed Tomography ; Female ; Male ; Neural Networks, Computer ; Middle Aged ; Aged

OBJECTIVES: To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk. METHODS: A mixed fluence map optimization objective was constructed, incorporating the outcome-based objective and the physical dose constraints. The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure, radiation-induced cardiac toxicity, and radiation pneumonitis considering clinical risk factors. These models were derived using Cox regression analysis or Logistic regression. The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines. The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method (clinical plan), and the dosimetric indicators and predicted prognostic outcomes were compared between different plans. RESULTS: In terms of the dosemetric indicators, D_95% of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% vs 102.57%, P=0.056), and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy (t=4.537, P<0.05) and 8.40 Gy (t=4.104, P<0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% vs 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan. CONCLUSIONS The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Prognosis ; Radiotherapy, Intensity-Modulated/methods* ; Carcinoma, Non-Small-Cell Lung/radiotherapy* ; Radiotherapy Dosage ; Female ; Male ; Middle Aged

OBJECTIVES: To investigate the effect of curcumin on lipid metabolism in non-small cell lung cancer (NSCLC) and its molecular mechanism. METHODS: The inhibitory effect of curcumin (0-70 μmol/L) on proliferation of A549 and H1299 cells was assessed using MTT assay, and 20 and 40 μmol/L curcumin was used in the subsequent experiments. The effect of curcumin on lipid metabolism was evaluated using cellular uptake assay, wound healing assay, triglyceride (TG)/free fatty acid (NEFA) measurements, and Oil Red O staining. Western blotting was performed to detect the expressions of PGC-1α, PPAR-α, and HIF-1α in curcumin-treated cells. Network pharmacology was used to predict the metabolic pathways, and the results were validated by Western blotting. In a nude mouse model bearing A549 cell xenograft, the effects of curcumin (20 mg/kg) on tumor growth and lipid metabolism were assessed by measuring tumor weight and observing the changes in intracellular lipid droplets. RESULTS: Curcumin concentration-dependently inhibited the proliferation of A549 and H1299 cells and significantly reduced TG and NEFA levels and intracellular lipid droplets. Western blotting revealed that curcumin significantly upregulated PGC-1α and PPAR‑α expressions in the cells. KEGG pathway enrichment analysis predicted significant involvement of the HIF-1 signaling pathway in curcumin-treated NSCLC, suggesting a potential interaction between HIF-1α and PPAR‑α. Western blotting confirmed that curcumin downregulated the expression of HIF-1α. In the tumor-bearing mice, curcumin treatment caused significant reduction of the tumor weight and the number of lipid droplets in the tumor cells. CONCLUSIONS Curcumin inhibits NSCLC cell proliferation and lipid metabolism by downregulating the HIF-1α pathway.
Curcumin/pharmacology* ; Humans ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Animals ; Lipid Metabolism/drug effects* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Lung Neoplasms/pathology* ; Mice, Nude ; Down-Regulation ; Mice ; Cell Proliferation/drug effects* ; Cell Line, Tumor ; Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha ; PPAR alpha/metabolism* ; Signal Transduction/drug effects* ; A549 Cells

OBJECTIVES: To investigate the inhibitory effect of Fuzheng Huaji Decoction against non-small cell lung cancer (NSCLC) cells in vitro and explore the underlying mechanism. METHODS: The active ingredients and targets of Fuzheng Huaji Decoction were identified using TCMSP and SwissTargetPrediction databases. NSCLC-related targets from GeneCards and PharmGKB were intersected with the targets of the Decoction, and a protein-protein interaction (PPI) network was constructed to identify the core targets, which were analyzed with GO and KEGG pathway enrichment analysis. Cultured A549 cells were treated with different concentrations of Fuzheng Huaji Decoction-medicated serum, and the changes in cell proliferation, apoptosis, and protein expressions were examined using CCK-8 assay, annexin V-FITC/PI staining and Western blotting. RESULTS: Fuzheng Huaji Decoction contained 140 active ingredients, and 707 drug-disease intersecting targets were identified. Among these targets, TP53, AKT1, HIF1A, GAPDH, ALB, EGFR, CTNNB1, and TNF were identified as the core targets which were involved in the biological processes related to kinases and receptors and the PI3K-AKT, Ras, calcium, and MAPK pathways. Molecular docking studies indicated strong binding affinity of the active ingredients with TP53, AKT1, and HIF1A. In cultured A549 cells, treatment with 2.5%, 5%, and 10% Fuzheng Huaji Decoction-medicated serum significantly inhibited cell proliferation, promoted cell apoptosis, and downregulated the expression levels of HIF1A, p-AKT (Thr308), and TP53 proteins. CONCLUSIONS Fuzheng Huaji Decoction inhibits proliferation of NSCLC cells possibly by downregulating the expressions of HIF1A, p-AKT (Thr308), and TP53.
Humans ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Drugs, Chinese Herbal/pharmacology* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Lung Neoplasms/metabolism* ; A549 Cells ; Proto-Oncogene Proteins c-akt/metabolism* ; Protein Interaction Maps ; Signal Transduction/drug effects* ; Cell Line, Tumor

OBJECTIVES: To construct a nomogram for predicting the efficacy of immune checkpoint inhibitors (ICIs) in advanced non-small cell lung cancer (aNSCLC) by integrating chest CT radiomics signature that reflects the tumor microenvironment (TME) and clinical parameters of the patients. METHODS: Transcriptomic and CT imaging data from TCGA, GEO and TCIA databases were integrated for weighted gene co-expression network analysis (WGCNA) of the GEO cohort to identify the immunotherapy-related genes (IRGs) associated with ICIs response. A prognostic model was built using these IRGs in the TCGA cohort to assess immune microenvironment features across different risk groups. Radiomics features were extracted from TCIA lung_3 cohort using PyRadiomics, and 94 features showing strong association with IRGs (|r|>0.4) were selected. A retrospective cohort consisting of 210 aNSCLC patients receiving first-line ICIs at Guangdong Provincial People's Hospital was analyzed and divided into training (n=147) and validation (n=63) groups. Least absolute shrinkage and selection operator was used for radiomic features selection, and logistic regression was applied to construct a combined clinical-radiomic model and nomogram for predicting ICIs therapy response. The performance of the model was evaluated using ROC curve, calibration curve, and decision curve analysis. RESULTS: WGCNA identified 84 IRGs enriched in immune activation pathways. The combined model outperformed individual models in both the training (AUC=0.725, 95% CI: 0.644-0.807) and validation cohorts (AUC=0.706, 95% CI: 0.577-0.836). Calibration curve and decision curve analyses confirmed the clinical efficacy of the nomogram for predicting ICIs therapy response in aNSCLC patients. CONCLUSIONS The genomic-radiomic-clinical multidimensional predictive framework established in this study provides an interpretable biomarker combination and clinical decision-making tool for evaluating ICIs efficacy in aNSCLC, potentially facilitating personalized immunotherapy decision-making.
Humans ; Carcinoma, Non-Small-Cell Lung/therapy* ; Tumor Microenvironment ; Lung Neoplasms/therapy* ; Immunotherapy ; Tomography, X-Ray Computed ; Nomograms ; Retrospective Studies ; Immune Checkpoint Inhibitors/therapeutic use* ; Prognosis ; Male ; Female ; Radiomics

Mitochondria are the convergence point of multiple pathways that trigger programmed cell death (PCD). Mitochondrial-associated PCD (mtPCD) is involved in the pathogenesis of several diseases. However, the role of mtPCD in the prognostic prediction of cancers including non-small-cell lung cancer (NSCLC) remains to be investigated. Here, 12 mtPCD patterns were analyzed in transcriptomics, genomics, and clinical data collected from 4 datasets containing 977 patients. A risk-score assessment system containing 18 genes was established. We found that NSCLC patients with a high-risk score had a poorer prognosis. A nomogram was constructed by incorporating the risk score with clinical features. The risk score was further associated with clinicopathological information, tumor-mutation frequency, and immunotherapy responses. NSCLC patients with a high risk score had more Treg cells infiltration. However, these patients had higher tumor-mutation burden scores and may be more sensitive to immunotherapy. Moreover, receptor-interacting serine/threonine protein kinase 2 (RIPK2) was selected from mtPCD gene model for validation. We found that RIPK2 exhibited oncogenic function, and its expression level was inversely associated with the overall survival of NSCLC. Taken together, our results indicated the accuracy and practicability of the mtPCD gene model and RIPK2 in predicting the prognosis of NSCLC.
Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Lung Neoplasms/pathology* ; Prognosis ; Male ; Female ; Nomograms ; Middle Aged ; Mitochondria/metabolism* ; Apoptosis/genetics* ; Mutation ; Biomarkers, Tumor/genetics* ; Aged

SMARCA4-deficient non small cell lung cancer (SMARCA4-dNSCLC) has recently garnered increasing attention due to its high malignancy and poor prognosis. The literature suggests that in non small cell lung cancer (NSCLC), the loss of SMARCA4 frequently co-occurs with mutations in KRAS, KEAP1, and STK11 rather than in EGFR, ALK, and ROS1. Herein, we present the first documented case of SMARCA4-dNSCLC accompanied with rare mutations of EGFR exon 20 S768I and exon 18 G719X. The patient achieved partial response with afatinib for 17 months. Our case highlights the importance of EGFR mutations in the precision targeted treatment of SMARCA4-dNSCLC.
Humans ; Afatinib/therapeutic use* ; Antineoplastic Agents/therapeutic use* ; Carcinoma, Non-Small-Cell Lung/pathology* ; DNA Helicases/genetics* ; ErbB Receptors/genetics* ; Lung Neoplasms/pathology* ; Mutation ; Nuclear Proteins/genetics* ; Transcription Factors/genetics*