BACKGROUND: Molecular subtyping is an essential complementarity after pathological analyses for targeted therapy. This study aimed to investigate the consistency of next-generation sequencing (NGS) results between circulating tumor DNA (ctDNA)-based and tissue-based in non-small cell lung cancer (NSCLC) and identify the patient characteristics that favor ctDNA testing. METHODS: Patients who diagnosed with NSCLC and received both ctDNA- and cancer tissue-based NGS before surgery or systemic treatment in Lung Cancer Center, Sichuan University West China Hospital between December 2017 and August 2022 were enrolled. A 425-cancer panel with a HiSeq 4000 NGS platform was used for NGS. The unweighted Cohen's kappa coefficient was employed to discriminate the high-concordance group from the low-concordance group with a cutoff value of 0.6. Six machine learning models were used to identify patient characteristics that relate to high concordance between ctDNA-based and tissue-based NGS. RESULTS: A total of 85 patients were enrolled, of which 22.4% (19/85) had stage III disease and 56.5% (48/85) had stage IV disease. Forty-four patients (51.8%) showed consistent gene mutation types between ctDNA-based and tissue-based NGS, while one patient (1.2%) tested negative in both approaches. Patients with advanced diseases and metastases to other organs would be suitable for the ctDNA-based NGS, and the generalized linear model showed that T stage, M stage, and tumor mutation burden were the critical discriminators to predict the consistency of results between ctDNA-based and tissue-based NGS. CONCLUSION ctDNA-based NGS showed comparable detection performance in the targeted gene mutations compared with tissue-based NGS, and it could be considered in advanced or metastatic NSCLC.
Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Circulating Tumor DNA/blood* ; High-Throughput Nucleotide Sequencing/methods* ; Female ; Male ; Lung Neoplasms/pathology* ; Middle Aged ; Mutation/genetics* ; Aged ; Adult ; Aged, 80 and over

Lung cancer is one of the most lethal tumors in the world with a 5-year overall survival rate of less than 20%, mainly including lung adenocarcinoma (LUAD). Tumor microenvironment (TME) has become a new research focus in the treatment of lung cancer. The TME is heterogeneous in composition and consists of cellular components, growth factors, proteases, and extracellular matrix. The various cellular components exert a different role in apoptosis, metastasis, or proliferation of lung cancer cells through different pathways, thus contributing to the treatment of adenocarcinoma and potentially facilitating novel therapeutic methods. This review summarizes the research progress on different cellular components with cell-cell interactions in the TME of LUAD, along with their corresponding drug candidates, suggesting that targeting cellular components in the TME of LUAD holds great promise for future theraputic development.
Humans ; Tumor Microenvironment/drug effects* ; Adenocarcinoma of Lung/drug therapy* ; Lung Neoplasms/pathology* ; Adenocarcinoma/metabolism* ; Animals ; Apoptosis/physiology*

Lung cancer is one of the most common and deadliest cancers globally, with its incidence and mortality rates rising each year. Therefore, finding new, safe, and effective alternative therapies poses a significant research challenge in this field. Programmed cell death refers to the process by which cells actively self-destruct in response to specific stimuli, regulated by genetic mechanisms. Modern research indicates that dysregulation of programmed cell death is widespread in the occurrence and progression of lung cancer, allowing cancer cells to evade death while continuing to proliferate and metastasize. Thus, inducing the death of lung cancer cells can be considered a novel therapeutic strategy for treating the disease. In recent years, research on traditional Chinese medicine(TCM) in the field of oncology has gained widespread attention, becoming a focal point. An increasing number of studies have demonstrated that TCM can inhibit the progression of lung cancer and exert anti-cancer effects by inducing apoptosis, necroptosis, pyroptosis, autophagy, and ferroptosis. This paper provided a comprehensive review of the molecular mechanisms of programmed cell death in lung cancer, along with the potential mechanisms and research advancements related to the regulation of these processes by TCM, so as to establish a theoretical foundation and direction for future basic and clinical research on lung cancer.
Humans ; Lung Neoplasms/pathology* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use* ; Apoptosis/drug effects* ; Animals ; Autophagy/drug effects*

Brain metastasis has emerged as a significant challenge in the comprehensive management of patients with non-small cell lung cancer (NSCLC), particularly in those harboring driver gene mutations. Traditional treatments such as radiotherapy and surgery offer limited clinical benefits and are often accompanied by cognitive dysfunction and a decline in quality of life. In recent years, novel small molecule tyrosine kinase inhibitors targeting epidermal growth factor receptor (EGFR), anaplastic lymphoma kinase (ALK), and other pathways have been developed, effectively penetrating the blood-brain barrier while enhancing intracranial drug concentrations and improving patient outcomes. This advancement has transformed the treatment landscape for brain metastases in NSCLC. Consequently, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association and the Brain Metastasis Collaboration Group of the Lung Cancer Youth Expert Committee of the Beijing Medical Reward Foundation have jointly initiated and formulated the Clinical Practice Guidelines for the Management of Brain Metastases from Non-small Cell Lung Cancer with Actionable Gene Alterations in China (2025 Edition). This guideline integrates the latest research findings with clinical experience, adhering to multidisciplinary treatment principles, and encompasses aspects such as diagnosis, timing of intervention, and systemic and local treatment options for driver gene positive NSCLC brain metastases. Additionally, it proposes individualized treatment strategies tailored to different driver gene types, aiming to provide clinicians with a reference to enhance the overall diagnostic and therapeutic standards for NSCLC brain metastases in China.
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Humans ; Brain Neoplasms/drug therapy* ; Carcinoma, Non-Small-Cell Lung/pathology* ; China ; Lung Neoplasms/genetics*

BACKGROUND: Ferroptosis-related genes play a crucial role in regulating intracellular iron homeostasis and lipid peroxidation, and they are involved in the regulation of tumor growth and drug resistance. The expression of ferroptosis-related genes in tumor tissues can be used to predict patients' future survival times, aiding doctors and patients in anticipating disease progression. Based on the sequencing data of lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) database, this study identified genes involved in the regulation of ferroptosis, constructed a prognostic model, and evaluated the predictive performance of the model. METHODS: A total of 1467 ferroptosis-related genes were obtained from the GeneCards database. Gene expression profiles and clinical data from 541 LUAD patients were collected from the TCGA database. The expression data of all ferroptosis-related genes were extracted, and differentially expressed genes were identified using R software. Survival analysis was performed on these genes to screen for those with prognostic value. Subsequently, a prognostic risk scoring model for ferroptosis-related genes was constructed using LASSO regression model. Each LUAD patient sample was scored, and the patients were divided into high-risk and low-risk groups based on the median score. Receiver operating characteristic (ROC) curves were plotted, and the area under the curve (AUC) was calculated. Kaplan-Meier survival curves were generated to assess model performance, followed by validation in an external dataset. Finally, univariate and multivariate Cox regression analyses were conducted to evaluate the independent prognostic value and clinical relevance of the model. RESULTS: Through survival analysis, 121 ferroptosis-related genes associated with prognosis were initially identified. Based on this, a LUAD prognostic risk scoring model was constructed using 12 ferroptosis-related genes (ALG3, C1QTNF6, CCT6A, GLS2, KRT6A, LDHA, NUPR1, OGFRP1, PCSK9, TRIM6, IGF2BP1 and MIR31HG). The results indicated that patients in the high-risk group had significantly shorter survival time than those in the low-risk group (P<0.001), and the model demonstrated good predictive performance in both the training set (1-yr AUC=0.721) and the external validation set (1-yr AUC=0.768). Risk scores were significantly associated with the prognosis of LUAD patients in both univariate and multivariate Cox regression analyses (P<0.001), suggesting that this score is an important prognostic factor for LUAD patients. CONCLUSIONS This study successfully established a LUAD risk scoring model composed of 12 ferroptosis-related genes. In the future, this model is expected to be used in conjunction with the tumor-node-metastasis (TNM) staging system for prognostic predictions in LUAD patients.
Humans ; Ferroptosis/genetics* ; Prognosis ; Adenocarcinoma of Lung/pathology* ; Lung Neoplasms/pathology* ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Middle Aged ; ROC Curve

Lung cancer is the malignant tumor with the highest mortality rate worldwide. The tumor microenvironment (TME) is a key factor in the progression of lung cancer, composed of tumor cells, signaling molecules, fibroblasts, immune cells, etc. Among them, tumor associated neutrophil (TAN), as an important component of immune cells in the TME, plays multiple roles in tumor cell proliferation, invasion, angiogenesis, and metastasis due to its aberrant function, and is closely associated with poor prognosis. However, there are limited researches on the mechanism of TAN in lung cancer. This review aims to provide more scientific basis for studying the therapeutic targets of lung cancer and developing new drugs by elucidating the different subtypes of TAN and their mechanisms of action in the occurrence and development of lung cancer.
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Humans ; Neutrophils/pathology* ; Lung Neoplasms/pathology* ; Tumor Microenvironment ; Animals

BACKGROUND: Currently, lung cancer is one of the malignant tumors with a high morbidity and mortality all over the world. However, the exact mechanisms underlying lung cancer progression remain unclear. The tumor necrosis factor receptor associated factor (TRAF) family members are cytoplasmic adaptor proteins, which function as both adaptor proteins and ubiquitin ligases to regulate diverse receptor signalings, leading to the activation of nuclear factor kappa-B (NF-κB), mitogen-activated protein kinase (MAPK) and interferon regulatory factor (IRF) signaling. The aim of this study was to investigate the expression of TRAFs in different tissues and cancer types, as well as its mRNA expression, protein expression, prognostic significance and functional enrichment analysis in non-small cell lung cancer (NSCLC), in order to provide new strategies for the diagnosis and treatment of NSCLC. METHODS: RNA sequencing data from the The Genotype-Tissue Expression database was used to analyze the expression patterns of TRAF family members in different human tissues. RNA sequencing data from the Cancer Cell Line Encyclopedia database was used to analyze the expression patterns of TRAF family members in different types of cancer cell lines. RNA sequencing data from the The Cancer Genome Atlas (TCGA) database was used to analyze the mRNA levels of TRAF family members across different types of human cancers. Immunohistochemistry (IHC) analyses from HPA database were used to analyze the TRAF protein levels in NSCLC [lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC)]. Overall survival analysis was performed by Log-rank test using original data from Kaplan-Meier Plotter database to evaluate the correlation between TRAF expressions and prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on the TRAF family-related genes using RNA sequencing data from the TCGA database for NSCLC. The correlation between the expression levels of TRAF family members and the tumor immune microenvironment was analyzed using the ESTIMATE algorithm based on RNA sequencing data from the TCGA database. RESULTS: The TRAF family members exhibited significant tissue-specific expression heterogeneity. TRAF2, TRAF3, TRAF6 and TRAF7 were widely expressed in most tissues, while the expressions of TRAF1, TRAF4 and TRAF5 were restricted to specific tissues. The expressions of TRAF family members were highly specific among different types of cancer cell lines. In mRNA database of LUAD and LUSC, the expressions of TRAF2, TRAF4, TRAF5 and TRAF7 were significantly upregulated; while TRAF6 did the opposite; moveover, TRAF1 and TRAF3 only displayed a significant upregulation in LUAD and LUSC, respectively. Except for TRAF3, TRAF4 and TRAF7, other TRAF proteins displayed an obviously deeper IHC staining in LUAD and LUSC tissues compared with normal tissues. Additionally, patients with higher expression levels of TRAF2, TRAF4 and TRAF7 had shorter overall survival; while patients with higher expression levels of TRAF3, TRAF5 and TRAF6 had significantly longer overall survival; however, no significant difference had been observed between TRAF1 expression and the overall survival. TRAF family members differentially regulated multiple pathways, including NF-κB, immune response, cell adhesion and RNA splicing. The expression levels of TRAF family members were closely associated with immune cell infiltration and stromal cell content in the tumor immune microenvironment, with varying positive and negative correlations among different members. CONCLUSIONS TRAF family members exhibit highly specific expression differences across different tissues and cancer types. Most TRAF proteins exhibit upregulation at both mRNA and protein levels in NSCLC, whereas, only upregulated expressions of TRAF2, TRAF4 and TRAF7 predict worse prognosis. The TRAF family members regulate processes such as inflammation, immunity, adhesion and splicing, and influence the tumor immune microenvironment.
Humans ; Carcinoma, Non-Small-Cell Lung/pathology* ; Lung Neoplasms/mortality* ; Prognosis ; Gene Expression Regulation, Neoplastic ; Tumor Necrosis Factor Receptor-Associated Peptides and Proteins/metabolism*

Neuroendocrine carcinoma (NEC), a subtype of neuroendocrine tumors with high proliferative activity, is characterized by strong invasiveness and poor prognosis. This article reports a previously healthy female non-smoker who developed NEC occurring sequentially in different lobes of both lungs. The lesions were pathologically diagnosed by hematoxylin-eosin (HE) staining as large cell neuroendocrine carcinoma (LCNEC) and small cell lung cancer (SCLC), respectively. Next-generation sequencing (NGS) performed on both lesions revealed the presence of echinoderm microtubule-associated protein-like 4-anaplastic lymphoma kinase (EML4-ALK) fusion mutations in both lesions. Notably, the patient achieved a significant therapeutic response to ALK-tyrosine kinase inhibitors (TKIs) targeted therapy.
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Humans ; Oncogene Proteins, Fusion/metabolism* ; Female ; Lung Neoplasms/enzymology* ; Carcinoma, Neuroendocrine/pathology* ; Middle Aged

Lung cancer is still one of the most common malignant tumors in the world. With the increase of its incidence and the development of medical technology, the overall survival of lung cancer patients has significantly extended compared to before. The incidence of brain and meningeal metastases from lung cancer has also been rising year by year, but patients with brain and meningeal metastases from lung cancer have a poor prognosis and a very high mortality rate, and the diagnosis is mainly based on computed tomography (CT), magnetic resonance imaging (MRI) and other imaging examinations. However, the imaging features are diverse and the specificity is low, which makes it easy to be misdiagnosed and missed. Therefore, accurately identifying brain and meningeal metastases and timely targeted treatment is crucial for improving patient prognosis. This paper analyzed the diagnosis and treatment of a case of lung cancer with no obvious recurrence and metastasis in nearly 7-year long-term follow-up after radical lung cancer surgery, but the patient with abnormal behavior, impaired consciousness and epilepsy in the past 5 months, and multiple punctate calcifications in the brain found by head CT and MRI. This paper consider that the patient's mental and behavioral symptoms were caused by brain and meningeal metastasis of lung cancer after excluding infectious disease and ineffective treatment of autoimmune encephalitis, and further pathological biopsy and genetic detection confirmed the diagnosis of metastatic lung adenocarcinoma with epidermal growth factor receptor (EGFR) L858R gene mutation, and the patient's symptoms were significantly improved after targeted therapy by Osimertinib. This paper also searched the relevant literatures of brain calcifications in databases such as China National Knowledge Infrastructure (CNKI), Wanfang, UpToDate, PubMed, etc., and found that intracerebral calcifications exist in a variety of diseases, including infectious, genetic and neurodegenerative diseases, vascular diseases, metabolic diseases and tumors. However, brain calcification in brain and meningeal metastases are often underestimated, and the consequent risk is misdiagnosis and delayed treatment. Therefore, brain and meningeal metastases manifested as brain calcification should not be ignored in patients with a history of previous tumors.
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Humans ; Lung Neoplasms/pathology* ; Brain Neoplasms/diagnostic imaging* ; Meningeal Neoplasms/diagnostic imaging* ; Calcinosis/diagnostic imaging* ; Male ; Middle Aged ; Tomography, X-Ray Computed ; Magnetic Resonance Imaging

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