中国肺癌杂志最新文献

Immune checkpoint blockade therapy has demonstrated remarkable efficacy in treating various malignancies; however, its clinical application remains challenged by low response rates and immune-related adverse events. Immunoglobulin superfamily member 11 (IGSF11), an inhibitory immune checkpoint molecule, serves as a specific ligand for the V-domain immunoglobulin suppressor of T cell activation (VISTA). Through the IGSF11/VISTA axis, it suppresses T cell function and represents a promising novel target for cancer immunotherapy. IGSF11 is widely expressed across multiple tumor types, though its regulatory mechanisms vary depending on the malignancy. Studies have confirmed that blocking the IGSF11-VISTA interaction or specifically inhibiting IGSF11 exerts antitumor effects. While IGSF11 is closely associated with patient prognosis, its prognostic significance differs among cancer types. This review systematically summarizes the structural characteristics of IGSF11, its regulatory mechanisms, interaction with VISTA, and functional role within the tumor microenvironment.
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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.

Immune checkpoint inhibitors (ICIs) have become the cornerstone of treatment for driver gene-negative advanced non-small cell lung cancer (NSCLC). However, resistance is inevitable, and the underlying mechanisms remain incompletely understood. Histological transformation is a rare but emerging cause of acquired resistance to immunotherapy, with only sporadic case reports documented to date. Here, we report the first case of lung adenocarcinoma that underwent histological transformation to atypical carcinoid following first-line therapy with ICIs combined with chemotherapy, highlighting the critical role of histological lineage switching in mediating NSCLC resistance to ICIs. Notably, the patient harbored a rearranged during transfection (RET) fusion mutation. Subsequent targeted therapy with Selpercatinib after histological transformation demonstrated favorable efficacy, suggesting a potential therapeutic strategy for atypical carcinoid patients with co-occurring rare driver mutations. This case provides a potential therapeutic option for atypical carcinoid patients with rare mutations.
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In epidemiological statistics, the incidence rate and mortality rate of malignant lung tumors rank among the top. Non-small cell lung cancer (NSCLC) constitutes an important part of lung cancer and has become a key focus of clinical research and treatment. Among the genomic characteristics of NSCLC, the Kirsten rat sarcoma viral oncogene homolog (KRAS) mutation is one of the main tumor drivers, accounting for approximately 25% of all NSCLC cases. The existence of this mutation is closely related to the treatment response and prognosis of patients. Therefore, the treatment strategy for KRAS-mutated NSCLC is an important topic in the field of tumor research. In the current era, immunomodulatory therapy has rapidly gained popularity and developed rapidly in oncology due to its unique mechanism of action and remarkable clinical efficacy. The treatment strategies targeting the KRAS-mutated of NSCLC have gradually become a research hotspot. The advent of immune checkpoint inhibitors (ICIs) has opened up a new therapeutic avenue for patients with such cancers, and clinical studies have shown significant effects in improving survival rates. Nevertheless, there are still many challenges in the application of immunotherapy, such as the complexity of the tumor microenvironment, individual differences among patients, and drug resistance mechanisms. This article reviews the progress of immunotherapy for KRAS-mutated NSCLC, focusing on the specific application of immunotherapy, the exploration of combination therapies, and the results of related clinical trials. At the same time, it discusses the possible future development directions of KRAS-mutated NSCLC treatment, providing a reference for clinical treatment practice.
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In recent years, the widespread application of chest computed tomography (CT) screening has led to a significant increase in the detection rate of pulmonary nodules. As a critical diagnostic tool for early-stage lung cancer, video-assisted thoracic surgery (VATS) has emerged as the preferred therapeutic approach for pulmonary nodules. Clinical evidence demonstrates that precise preoperative localization significantly enhances surgical success rates (reducing conversion to thoracotomy), minimizes complications, and shortens operation time. This comprehensive review systematically evaluates six cutting-edge localization techniques: percutaneous puncture-assisted localization, electromagnetic navigation bronchoscopy (ENB) localization, 3D-printed auxiliary localization, basin-analysis-based localization, robotic navigation system localization, and mixed reality (MR)-guided localization. By critically analyzing their operational principles, efficacy, safety profiles, and clinical applicability, this paper aims to provide evidence-based recommendations for optimizing clinical decision-making in pulmonary nodule management.
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Lung cancer has the highest incidence and mortality of any cancer in the world. In recent years, with the development of microbial detection technology, the intratumor microbiota has gradually become a hot spot and frontier in the field of lung cancer research. Studies have found that the microbiota present in tumors can influence the development of lung cancer in a variety of ways. In addition, the intratumor microbiota can be used as a potential biomarker for the diagnosis and prognosis assessment of lung cancer, and the regulation of the intratumor microbiota of lung cancer is expected to become a new type of lung cancer treatment. In this paper, we reviewed the latest research progress on the relationship between intratumor microbiota and lung cancer, summarized the origin and characteristics of intratumor microbiota, discussed the mechanism of its influence on the occurrence and development of lung cancer, and explored its potential applications in the diagnosis, treatment and prognosis of lung cancer.
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Background: Lung adenocarcinoma (LUAD) is the predominant subtype of non-small cell lung cancer (NSCLC). Damage-specific DNA binding protein 1 (DDB1), as a core protein of the CUL4-DDB1 ubiquitin ligase complex, is involved in the regulation of DNA damage repair, epigenetic modification, and cell cycle checkpoint activation. While the involvement of DDB1 in tumour progression through DNA repair and RNA transcriptional regulation has been reported, its expression and role in LUAD remain to be elucidated. This study aims to investigate the expression and role of DDB1 in LUAD.

Methods: The expression, clinicopathological features and prognosis of DDB1 in LUAD were analysed using databases such as UALCAN, Kaplan-Meier Plotter and GEPIA; The interaction network and enriched functional pathways were constructed by GeneMANIA and Metascape; the correlation between DDB1 and immune cells by combining with TISIDB infiltration was evaluated, and the clustering results of cell subtypes and the expression of DDB1 in different immune cell subpopulations were analysed by single-cell sequencing; finally, tissue microarrays were used to further verify the expression and prognostic value of DDB1 in LUAD.

Results: The mRNA and protein expression of DDB1 in LUAD tissues were significantly higher than those in normal tissues (P<0.01), and the high expression correlated with later clinical stage (P<0.001), lymph node metastasis (P<0.001) and poor prognosis (P<0.001). Functional enrichment showed that DDB1 was involved in DNA repair and RNA transcriptional regulation, and TISIDB evaluation revealed that DDB1 was negatively correlated with the expression level of immune cells, suggesting the potential regulation of the immune microenvironment. Single cell analysis showed that DDB1 was mainly expressed in T cells, alveolar macrophages and dendritic cells. Tissue microarrays confirmed that overall survival was shorter in the DDB1 high expression group (P<0.001), and Cox multifactorial analysis showed that DDB1 was an independent predictor of LUAD prognosis.

Conclusions: DDB1 is highly expressed in LUAD, which is associated with poor prognosis, and is closely related to tumor immune cell infiltration, and is involved in tumourigenesis and development through DNA repair and RNA transcriptional regulation. DDB1 can be used as a potential prognostic marker and therapeutic target for LUAD.

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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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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