BACKGROUND: Lung cancer is one of the most common malignant tumors worldwide and a major cause of cancer-related deaths. Early-stage lung cancer is often manifested as pulmonary nodules, and accurate assessment of the malignancy risk is crucial for prolonging survival and avoiding overtreatment. This study aims to construct a model based on image feature parameters automatically extracted by artificial intelligence (AI) to evaluate its effectiveness in predicting the malignancy of part-solid nodule (PSN). METHODS: This retrospective study analyzed 229 PSN from 222 patients who underwent pulmonary nodule resection at Lanzhou University Second Hospital between October 2020 and February 2025. According to pathological results, 45 cases of benign lesions and precursor glandular lesion were categorized into the non-malignant group, and 184 cases of pulmonary malignancies were categorized into the malignant group. All patients underwent preoperative chest computed tomography (CT), and AI software was used to extract imaging feature parameters. Univariate analysis was used to screen significant variables; variance inflation factor (VIF) was calculated to exclude highly collinear variables, and LASSO regression was further applied to identify key features. Multivariate Logistic regression was used to determine independent risk factors. Based on the selected variables, five models were constructed: Logistic regression, random forest, XGBoost, LightGBM, and support vector machine (SVM). Receiver operating characteristic (ROC) curves were used to assess the performance of the models. RESULTS: The independent risk factors for the malignancy of PSN include roughness (ngtdm), dependence variance (gldm), and short run low gray-level emphasis (glrlm). Logistic regression achieved area under the curves ( AUCs) of 0.86 and 0.89 in the training and testing sets, respectively, showing good performance. XGBoost had AUCs of 0.78 and 0.77, respectively, demonstrating relatively balanced performance, but with lower accuracy. SVM showed an AUC of 0.93 in the training set, which decreased to 0.80 in the testing set, indicating overfitting. LightGBM performed excellently in the training set with an AUC of 0.94, but its performance declined in the testing set, with an AUC of 0.88. In contrast, random forest demonstrated stable performance in both the training and testing sets, with AUCs of 0.89 and 0.91, respectively, exhibiting high stability and excellent generalizability. CONCLUSIONS The random forest model constructed based on independent risk factors demonstrated the best performance in predicting the malignancy of PSN and could provide effective auxiliary predictions for clinicians, supporting individualized treatment decisions.
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Humans ; Male ; Female ; Lung Neoplasms/pathology* ; Middle Aged ; Retrospective Studies ; Artificial Intelligence ; Aged ; Tomography, X-Ray Computed ; Adult ; Solitary Pulmonary Nodule/diagnostic imaging* ; ROC Curve

Non-small cell lung cancer (NSCLC), a leading cause of cancer-related deaths worldwide, remains a significant clinical challenge despite advances in immune checkpoint inhibitors therapy, with drug resistance persisting as a major obstacle. Palmitoylation, a critical post-translational modification (PTM) primarily catalyzed by palmitoyltransferases of the zinc finger DHHC-type (ZDHHC), has recently demonstrated important implications in NSCLC. This review aims to elucidate the mechanisms and clinical potential of ZDHHC-mediated protein palmitoylation in NSCLC progression and immune escape.
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Humans ; Lipoylation ; Lung Neoplasms/pathology* ; Acyltransferases/genetics* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Animals

BACKGROUND: Lung cancer is one of the leading causes of cancer-related mortality worldwide, with above 80% of cases be non-small cell lung cancer (NSCLC), among which lung squamous cell carcinoma (LUSC) occupies a significant proportion. Although comprehensive cancer therapies have considerably improved the overall survival of patients, patients with advanced LUSC have a poorer prognosis. Therefore, there is a need for a biomarker to predict the progress of advanced LUSC in order to improve prognosis through early diagnosis. Previous studies have shown that miRNAs are differentially expressed in lung cancer tissues and play roles as potential oncogenes or tumor suppressors. The aim of this study is to identify differentially expressed miRNAs between early-stage and advanced-stage LUSC, and to establish a set of miRNAs that can predict the progress of advanced LUSC. METHODS: Clinical data and miRNA-related data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Bioinformatic methods were applied to analyze the data. Receiver operating characteristic (ROC) curves were plotted, and various online tools were used to predict target genes, with subsequent analysis of the potential biological mechanisms of these genes. RESULTS: A total of 58 differentially expressed miRNAs were identified between the experiment group and the control group. Seven miRNAs were selected for potential construction of a miRNA biomarker through LASSO regression, and based on the area under the curve (AUC) values of each miRNA, four of these miRNAs (miR-377-3p, miR-4779, miR-6803-5p, miR-3960) were ultimately chosen as biomarkers for predicting advanced LUSC. The AUC under the ROC curve for the combined four miRNAs was 0.865. Enrichment analysis showed that these target genes were involved in several pathways, including cancer-related pathways, mitogen-activated protein kinase (MAPK) signaling pathway, serine/threonine kinase, and tyrosine kinase signaling pathways. CONCLUSIONS The combined use of miR-377-3p, miR-4779, miR-6803-5p and miR-3960 provides a good predictive ability for the progress of advanced LUSC patients, with an AUC of 0.865.
Humans ; MicroRNAs/metabolism* ; Lung Neoplasms/metabolism* ; Biomarkers, Tumor/metabolism* ; Carcinoma, Squamous Cell/pathology* ; Gene Expression Regulation, Neoplastic ; Male ; Female ; Prognosis ; ROC Curve ; Middle Aged

Lung cancer remains one of the most prevalent and deadly malignancies worldwide, with persistently low five-year survival rates. This poor prognosis is primarily attributed to challenges such as difficulties in early diagnosis, high tumor heterogeneity, and strong therapeutic resistance. Although recent advances in targeted therapies and immune checkpoint inhibitors have significantly improved the prognosis of some patients, the majority still encounter primary or secondary resistance. Galectin-3, a multifunctional glycan-binding protein, is constitutively expressed in pulmonary tissues. Its expression encompasses bronchial and alveolar epithelial cells, the pulmonary vasculature, and resident immune cells. Galectin-3 plays a central role in lung cancer progression by regulating tumor cell proliferation, immune evasion, and angiogenesis. The complex immunosuppressive mechanisms within the tumor microenvironment not only facilitate tumor growth and metastasis but also partially limit the efficacy of cancer immunotherapies. Overcoming these barriers requires the exploration of novel regulatory targets to break through therapeutic bottlenecks. This review systematically elucidates the mechanisms by which galectin-3 interacts with immune cells (e.g., T cells, macrophages) in the tumor microenvironment and evaluates its potential as a therapeutic target, including inhibitor development and combination immunotherapy strategies. The findings aim to provide a theoretical foundation for advancing galectin-3 as a novel therapeutic target in lung cancer and offer new perspectives for overcoming current immunotherapy resistance.
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Humans ; Lung Neoplasms/pathology* ; Tumor Microenvironment/immunology* ; Galectin 3/genetics* ; Animals ; Immunomodulation ; Immunotherapy

Small cell lung cancer (SCLC), a highly aggressive subtype of lung cancer that originates from pulmonary neuroendocrine cells, accounts for 10% to 15% of all lung cancers. It is characterized by a high rate of early metastasis and extremely poor prognosis, often accompanied by challenges such as drug resistance and recurrence. Related researches indicates that the Notch signaling pathway plays a crucial role in the occurrence and development of SCLC by regulating processes such as cell proliferation, differentiation, and apoptosis. In SCLC, abnormal Notch signaling may promote tumor malignancy and the occurrence of drug resistance. Additionally, the Notch pathway is involved in the epithelial-mesenchymal transition of SCLC and influences immune escape mechanisms through interactions with the tumor immune microenvironment. This article reviews the molecular mechanisms of Notch signaling in SCLC, including the roles of its receptors and ligands, signal transduction processes, and its role in tumorigenesis. It also discusses the research progress of Notch signaling as a potential therapeutic target and looks forward to future research directions in this field.
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Humans ; Receptors, Notch/genetics* ; Signal Transduction ; Small Cell Lung Carcinoma/pathology* ; Lung Neoplasms/pathology* ; Animals

Lung cancer, particularly non-small cell lung cancer (NSCLC), is a leading cause of cancer-related mortality worldwide. In recent years, metabolomics has emerged as a key systems biology approach for analyzing small-molecule metabolites in cells, tissues and organisms. It provides new strategies for early diagnosis and metabolic profiling. Additionally, metabolomics plays a crucial role in studying resistance mechanisms in lung cancer. Tumor cell metabolic reprogramming is a key driving factor in the initiation and progression of lung cancer. Metabolomics studies have revealed how lung cancer cells regulate critical pathways such as energy metabolism, lipid metabolism, and amino acid metabolism to adapt to the demands of rapid proliferation and invasive metastasis. This review summarizes the latest advances in metabolomics research in lung cancer, focusing on the characteristics of metabolic reprogramming, the identification of potential metabolic biomarkers, and the prospects of metabolomics in early diagnosis and the elucidation of resistance mechanisms in lung cancer.
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Humans ; Metabolomics/methods* ; Lung Neoplasms/pathology* ; Animals ; Biomarkers, Tumor/metabolism*

BACKGROUND: The incidence of leptomeningeal metastasis (LM) in patients with advanced non-small cell lung cancer (NSCLC) is increasing gradually. However, it poses therapeutic challenges due to limited effective interventions. Intrathecal Pemetrexed (IP) holds broad application prospects in the therapeutic domain of LM. This study aims to evaluate the efficacy, safety, and optimal combination strategies of IP in NSCLC-LM patients with epidermal growth factor receptor (EGFR) mutation-positive status, with the aim of providing real-world data support for exploring more precise personalized treatment strategies for these patients. METHODS: 104 EGFR-mutated NSCLC-LM patients who received IP treatment at Affiliated Nanjing Drum Tower Hospital of Nanjing University Medical School from January 2018 to June 2024 were analyzed retrospectively. Clinical parameters, treatment regimens, and survival outcomes were collected. The overall survival (OS), progression-free survival (PFS), clinical response rate and adverse events (AEs) were evaluated. RESULTS: The cohort demonstrated a median PFS of 9.6 months and OS of 13.0 months with 6-month and 1-year OS rates of 80.8% and 56.5%, respectively. Clinical response was observed in 77.9% of patients. The common AEs were myelosuppression (58.7%) and elevation of hepatic aminotransferases (25.0%). Nine (8.7%) patients experienced grade 4 myelosuppression and recovered to normal after receiving symptomatic treatment. Subgroup analyses revealed prolonged OS in patients with Karnofsky performance status (KPS) ≥60 versus <60 (14.4 vs 9.0 months, P=0.0022) and those receiving Bevacizumab therapy versus not (19.2 vs 10.5 months, P=0.0011). CONCLUSIONS IP exhibits promising efficacy and manageable toxicity in EGFR-mutated NSCLC-LM patients. When combined with Bevacizumab, it exerts synergistic antitumor effects with the potential to further improve clinical outcomes.
Humans ; Pemetrexed/therapeutic use* ; Carcinoma, Non-Small-Cell Lung/pathology* ; Male ; Female ; Middle Aged ; Lung Neoplasms/pathology* ; ErbB Receptors/genetics* ; Aged ; Mutation ; Adult ; Retrospective Studies ; Injections, Spinal ; Meningeal Neoplasms/genetics* ; Treatment Outcome ; Aged, 80 and over

BACKGROUND: Lung adenocarcinoma is an important pathohistologic subtype of non-small cell lung cancer (NSCLC). Invasive non-mucinous pulmonary adenocarcinomas (INMA) tend to have a poor prognosis due to their significant heterogeneity and diverse histologic components. Establishing a histologic grading system for INMA is crucial for evaluating its malignancy. In 2021, the International Association for the Study of Lung Cancer (IASLC) proposed that a new histological grading system could better stratify the prognosis of INMA patients. The aim of this study was to establish a visualized nomogram model to predict INMA IASLC grading preoperatively by means of dual-energy computed tomography (DECT), fractal dimension (FD), clinical features and conventional CT parameters. METHODS: A total of 112 patients with INMA who underwent preoperative DECT were retrospectively enrolled from March 2021 to January 2025. Patients were categorized into low-intermediate grade and high grade groups based on IASLC grading. The clinical characteristics and conventional CT parameters, including baseline features, biochemical markers, and serum tumor markers, were collected. DECT-derived parameters, including iodine concentration (IC), effective atomic number (eff-Z), and normalized IC (NIC), were collected and determined as NIC ratio (NICr) and fractal dimension (FD). Univariate analysis was employed to compare differences in conventional characteristics and DECT parameters between the two groups. Variables demonstrating statistical significance were subsequently incorporated into a multivariate Logistic regression analysis. A nomogram model integrating clinical data, conventional CT parameters, and DECT parameters was developed to identify independent predictors for IASLC grading of INMA. The discriminatory performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis. RESULTS: Multivariate analysis identified smoking history [odds ratio (OR)=2.848, P=0.041], lobulation sign (OR=2.163, P=0.004), air bronchogram (OR=7.833, P=0.005), eff-Z in arterial phase (OR=4.266, P<0.001), and IC in arterial phase (OR=1.290, P=0.012) as independent and significant predictors for IASLC grading of INMA. The nomogram model constructed based on these indicators demonstrated optimal predictive performance, achieving an area under the curve (AUC) of 0.804 (95%CI: 0.725-0.883), with specificity and sensitivity of 85.3% and 65.7%, respectively. CONCLUSIONS The nomogram model based on clinical features, imaging features and spectral CT parameters have a large potential for application in the preoperative noninvasive assessment of INMA IASLC grading.
Humans ; Nomograms ; Female ; Male ; Middle Aged ; Tomography, X-Ray Computed/methods* ; Lung Neoplasms/pathology* ; Aged ; Retrospective Studies ; Adenocarcinoma of Lung/pathology* ; Neoplasm Grading ; Adult

Human epidermal growth factor receptor 2 (HER2) mutations play a role as a driver gene in non-small cell lung cancer (NSCLC). Patients with advanced NSCLC harboring HER2 mutations exhibit poor responses to conventional chemotherapy and immunotherapy, hence targeted therapies against HER2 are under extensive investigation. This review analyzes the biological characteristics of HER2, an overview of clinical trials for targeted therapy drugs, including monoclonal antibodies, tyrosine kinase inhibitors (TKIs), and antibody-drug conjugate, and research directions for drug resistance in NSCLC. Currently, Pyrotinib and Trastuzumab deruxtecan have been approved for the treatment of advanced NSCLC with HER2 mutations, suitable for patients who have failed standard therapy, which is far from meeting the clinical demands. Novel selective HER2 TKIs are gradually emerging. Future exploration trends are gradually shifting from single drugs to combination strategies, and are exploring more precise selection strategies as well as research on resistance mechanisms. These studies will provide a theoretical basis for clinical treatment strategies for advanced NSCLC with HER2 mutations, promoting the development of personalized therapy.
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Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Lung Neoplasms/pathology* ; Receptor, ErbB-2/metabolism* ; Mutation ; Molecular Targeted Therapy ; Protein Kinase Inhibitors/therapeutic use* ; Antineoplastic Agents/therapeutic use*

The v-Raf murine sarcoma viral oncogene homolog B (BRAF) gene is one of the most critical proto-oncogenes and functions as a key regulator in the mitogen-activated protein kinase (MAPK)/extracellular signal-regulated kinase (ERK) signaling pathway. The incidence of BRAF mutations in non-small cell lung cancer (NSCLC) patients ranges from 1.5% to 5.5%, with BRAF V600 mutations accounting for approximately 30%-50% of all BRAF mutations, among which BRAF V600E represents the most prevalent mutation type. Currently, the combination of Dabrafenib and Trametinib has been recommended as first-line therapy for BRAF V600-mutant NSCLC by multiple domestic and international guidelines including National Comprehensive Cancer Network (NCCN), European Society of Medical Oncology (ESMO), and Chinese Society of Clinical Oncology (CSCO). However, there are no clear targeted treatment recommendations for BRAF non-V600 mutations. Although case reports suggest that Dabrafenib combined with Trametinib may be effective for patients with BRAF non-V600 mutations, the efficacy and safety require further validation due to limited sample size and lack of large-scale clinical trial data. This article reports a case of NSCLC with a rare BRAF insertion and deletion mutation that responded well to the treatment of Dabrafenib in combination with Trametinib, aiming to enhance clinicians' understanding of such NSCLC cases with extremely rare mutation and provide a reference for future treatment strategies.
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Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Imidazoles/administration & dosage* ; Lung Neoplasms/pathology* ; Mutation ; Neoplasm Metastasis ; Oximes/administration & dosage* ; Proto-Oncogene Mas ; Proto-Oncogene Proteins B-raf/genetics* ; Pyridones/administration & dosage* ; Pyrimidinones/administration & dosage*