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*

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

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

BACKGROUND: Lung cancer currently ranks first globally in both incidence and mortality. Pemetrexed (PMX) serves as a first-line treatment for lung adenocarcinoma (LUAD), but the patients often develop drug resistance during therapy. Milk exosome (mEXO) have the advantages of low immunogenicity, high tissue affinity, and low cost, and mEXO itself has anti-tumor effects. Hyaluronan (HA) naturally bind to CD44, a receptor which is highly expressed in LUAD tissues. This study aims to construct hyaluronan-modified milk exosome (HA-mEXO) and preliminarily investigate their molecular mechanisms for reversing PMX resistance through cellular experiments. METHODS: Exosomes were extracted from milk using high-speed centrifugation, and HA-mEXO was constructed. PMX-resistant A549 and PC-9 cell lines were treated with mEXO and HA-mEXO, respectively. CCK-8 assays, colony formation assays, Transwell assays, and flow cytometry were performed to evaluate proliferation, colony formation, migration, invasion, and apoptosis phenotypes in the treated resistant cell lines. Finally, transcriptomic sequencing, analysis, and cellular functional recovery experiments were conducted to investigate the mechanism by which HA-mEXO reverses PMX resistance in LUAD cells. RESULTS: The expression of CD44 in A549 and PC-9 LUAD drug-resistant cell lines was significantly higher than that in parental cells, and the uptake rate of HA-mEXO by drug-resistant cell lines was significantly higher than that of mEXO. Compared to the mEXO group, HA-mEXO-treated A549 and PC-9 resistant cells exhibited significantly reduced half maximal inhibitory concentration (IC50) values for PMX, markedly diminished clonogenic, migratory, and invasive capabilities, and a significantly increased proportion of apoptotic cells. Western blot analysis revealed that, compared to parental cells, A549 and PC-9 drug-resistant cells exhibited downregulated ZNF516 expression and upregulated ABCC5 expression. Immunofluorescence analysis revealed that HA-mEXO treatment downregulated ABCC5 expression in A549 and PC-9 drug-resistant cells compared to the PBS group, whereas co-treatment with HA-mEXO and ZNF516 knockdown showed no significant change in ABCC5 expression. CONCLUSIONS HA-mEXO carrying ZNF516 suppress ABCC5 expression, thereby enhancing the sensitivity of A549 and PC-9 LAUD drug-resistant cells to PMX.
Humans ; Hyaluronic Acid/chemistry* ; Drug Resistance, Neoplasm/drug effects* ; Exosomes/chemistry* ; Adenocarcinoma of Lung/genetics* ; Pemetrexed/pharmacology* ; Animals ; Lung Neoplasms/pathology* ; Milk/chemistry* ; Cell Proliferation/drug effects* ; Apoptosis/drug effects* ; Cell Line, Tumor ; Hyaluronan Receptors/metabolism*

Ground-glass nodule (GGN) lung cancer often progresses slowly in clinical and there are few clinical studies on long-term follow-up of patients with operable GGN lung cancer treated with stereotactic body radiation therapy (SBRT). We present a successful case of GGN lung cancer treated with SBRT, but a new GGN was found in the lung adjacent to the SBRT target during follow-up. The nodule progressed rapidly and was confirmed as lung adenocarcinoma by surgical resection. No significant risk factors and related driving genes were found in molecular pathological findings and genetic tests. It deserves further study whether new GGN is related to the SBRT. This case suggests that the follow-up after SBRT should be vigilant against the occurrence of new rapidly progressive lung cancer in the target area and adjacent lung tissue.
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Humans ; Lung Neoplasms/pathology* ; Radiosurgery ; Retrospective Studies ; Adenocarcinoma of Lung/surgery* ; Lung/pathology*

BACKGROUND: Lung cancer prevails and induces high mortality around the world. This study provided real-world information on the evolution of clinicopathological profiles and survival outcomes of lung cancer, and provided survival information within stage I subtypes. METHODS: Patients pathologically confirmed with lung cancer between January 2009 and December 2018 were identified with complete clinicopathological information, molecular testing results, and follow-up data. Shifts in clinical characteristics were evaluated using χ2 tests. Overall survival (OS) was calculated through the Kaplan-Meier method. RESULTS: A total of 26,226 eligible lung cancer patients were included, among whom 62.55% were male and 52.89% were smokers. Non-smokers and elderly patients took increasingly larger proportions in the whole patient population. The proportion of adenocarcinoma increased from 51.63% to 71.80%, while that of squamous carcinoma decreased from 28.43% to 17.60%. Gene mutations including EGFR (52.14%), KRAS (12.14%), and ALK (8.12%) were observed. Female, younger, non-smoking, adenocarcinoma patients and those with mutated EGFR had better survival prognoses. Importantly, this study validated that early detection of early-stage lung cancer patients had contributed to pronounced survival benefits during the decade. Patients with stage I lung cancer, accounted for an increasingly considerable proportion, increasing from 15.28% to 40.25%, coinciding with the surgery rate increasing from 38.14% to 54.25%. Overall, period survival analyses found that 42.69% of patients survived 5 years, and stage I patients had a 5-year OS of 84.20%. Compared with that in 2009-2013, the prognosis of stage I patients in 2014-2018 was dramatically better, with 5-year OS increasing from 73.26% to 87.68%. Regarding the specific survival benefits among stage I patients, the 5-year survival rates were 95.28%, 93.25%, 82.08%, and 74.50% for stage IA1, IA2, IA3, and IB, respectively, far more promising than previous reports. CONCLUSIONS Crucial clinical and pathological changes have been observed in the past decade. Notably, the increased incidence of stage I lung cancer coincided with an improved prognosis, indicating actual benefits of early detection and management of lung cancer.
Humans ; Male ; Female ; Aged ; Lung Neoplasms/genetics* ; Adenocarcinoma/pathology* ; Prognosis ; Survival Rate ; Mutation ; ErbB Receptors/genetics* ; Neoplasm Staging ; Retrospective Studies

Humans ; Pleura/pathology* ; Solitary Fibrous Tumor, Pleural/pathology* ; Adenocarcinoma of Lung ; Lung Neoplasms ; Pleural Neoplasms/pathology*

Medical studies have found that tumor mutation burden (TMB) is positively correlated with the efficacy of immunotherapy for non-small cell lung cancer (NSCLC), and TMB value can be used to predict the efficacy of targeted therapy and chemotherapy. However, the calculation of TMB value mainly depends on the whole exon sequencing (WES) technology, which usually costs too much time and expenses. To deal with above problem, this paper studies the correlation between TMB and slice images by taking advantage of digital pathological slices commonly used in clinic and then predicts the patient TMB level accordingly. This paper proposes a deep learning model (RCA-MSAG) based on residual coordinate attention (RCA) structure and combined with multi-scale attention guidance (MSAG) module. The model takes ResNet-50 as the basic model and integrates coordinate attention (CA) into bottleneck module to capture the direction-aware and position-sensitive information, which makes the model able to locate and identify the interesting positions more accurately. And then, MSAG module is embedded into the network, which makes the model able to extract the deep features of lung cancer pathological sections and the interactive information between channels. The cancer genome map (TCGA) open dataset is adopted in the experiment, which consists of 200 pathological sections of lung adenocarcinoma, including 80 data samples with high TMB value, 77 data samples with medium TMB value and 43 data samples with low TMB value. Experimental results demonstrate that the accuracy, precision, recall and F1 score of the proposed model are 96.2%, 96.4%, 96.2% and 96.3%, respectively, which are superior to the existing mainstream deep learning models. The model proposed in this paper can promote clinical auxiliary diagnosis and has certain theoretical guiding significance for TMB prediction.
Humans ; Lung Neoplasms/pathology* ; Carcinoma, Non-Small-Cell Lung/genetics* ; Mutation ; Adenocarcinoma of Lung/genetics* ; Biomarkers, Tumor/genetics*

We explored clinicopathological features and treatment strategies for thoracic SMARCA4-deficient undifferentiated tumor (SMARCA4-UT). Thoracic SMARCA4-UT is a new entity recently acknowledged in the 2021 edition of World Health Organization Classification of Thoracic Tumors, and doctors are relatively unfamiliar with its diagnosis, treatment, and prognosis. Taking a case of SMARCA4-UT treated in Peking University First Hospital as an example, this multi-disciplinary discussion covered several hot issues on diagnosing and treating thoracic SMARCA4-UT, including histological features, immu- nohistochemical and molecular phenotype, immune checkpoint inhibitor (ICI) therapy, and pathological assessment of neoadjuvant therapy response. The patient was an older man with a long history of smoking and was admitted due to a rapidly progressing solid tumor in the lower lobe of the right lung. Histologically, tumor cells were epithelioid, undifferentiated, diffusely positive for CD34, and partially positive for SALL4.The expression of BRG1 protein encoded by SMARCA4 gene was lost in all of tumor cells, and next-generation sequencing(NGS)confirmed SMARCA4 gene mutation (c.2196T>G, p.Y732Ter). The pathological diagnosis reached as thoracic SMARCA4-UT, and the preoperative TNM stage was T1N2M0 (ⅢA). Tumor proportion score (TPS) detected by immunohistochemistry of programmed cell death 1-ligand 1 (PD-L1, clone SP263) was 2%. Tumor mutation burden (TMB) detected by NGS of 1 021 genes was 16. 3/Mb. Microsatellite detection showed the tumor was microsatellite stable (MSS). Neo-adjuvant therapy was implemented with the combined regimen of chemotherapy and ICI. Right lower lobectomy was performed through thoracoscopy after the two weeks' neoadjuvant. The pathologic assessment of lung tumor specimens after neoadjuvant therapy revealed a complete pathological response (CPR). The post-neoadjuvant tumor TNM stage was ypT0N0M0. Then, five cycles of adjuvant therapy were completed. Until October 2022, neither tumor recurrence nor metastasis was detected, and minimal residual disease (MRD) detection was negative. At present, it is believed that if BRG1 immunohistochemical staining is negative, regardless of whether SMARCA4 gene mutation is detected, it should be classified as SMARCA4-deficient tumors. SMARCA4-deficient tumors include a variety of carcinomas and sarcomas. The essential criteria for diagnosing SMARCA4-UT includes loss of BRG1 expression, speci-fic histological morphology, and exclude other common thoracic malignant tumors with SMARCA4-deficiency, such as squamous cell carcinoma, adenocarcinoma and large cell carcinoma. SMARCA4-UT is a very aggressive malignant tumor with a poor prognosis. It has almost no targeted therapy mutations, and little response to chemotherapy, but ICI is currently the only effective drug. The successful diagnosis and treatment for this case of SMARCA4-UT should enlighten significance for various kinds of SMARCA4-deficient tumors.
Humans ; Immune Checkpoint Inhibitors ; Neoplasm Recurrence, Local ; Lung Neoplasms/genetics* ; Thoracic Neoplasms/pathology* ; Adenocarcinoma ; DNA Helicases ; Nuclear Proteins ; Transcription Factors

OBJECTIVE: To investigate the effects of expression levels of S100 calcium-binding protein A10 (S100A10) in lung adenocarcinoma (LUAD) on patient prognosis and the regulatory role of S100A10 in lung cancer cell proliferation and metastasis. METHODS: Immunohistochemistry was used to detect the expression levels of S100A10 in LUAD and adjacent tissues, and the relationship between S100A10 expression and clinicopathological parameters and prognosis of the patients was statistically analyzed. The lung adenocarcinoma expression dataset in TCGA database was analyzed using gene enrichment analysis (GSEA) to predict the possible regulatory pathways of S100A10 in the development of lung adenocarcinoma. Lactate production and glucose consumption of lung cancer cells with S100A10 knockdown or overexpression were analyzed to assess the level of glycolysis. Western blotting, CCK-8 assay, EdU-594 assay, and Transwell assays were performed to determine the expression level of S100A10 protein, proliferation and invasion ability of lung cancer cells. A549 cells with S100A10 knockdown and H1299 cells with S100A10 overexpression were injected subcutaneously in nude mice, and tumor growth was observed. RESULTS: The expression level of S100A10 was significantly upregulated in LUAD tissues as compared with the adjacent tissues, and an elevated S100A10 expression level was associated with lymph node metastasis, advanced tumor stage and distant organ metastasis (P < 0.05), but not with tumor differentiation or the patients' age or gender (P > 0.05). Survival analysis showed that elevated S100A10 expressions in the tumor tissue was associated with a poor outcome of the patients (P < 0.001). In the lung cancer cells, S100A10 overexpression significantly promoted cell proliferation and invasion in vitro (P < 0.001). GSEA showed that the gene sets of glucose metabolism, glycolysis and mTOR signaling pathway were significantly enriched in high expressions of S100A10. In the tumor-bearing nude mice, S100A10 overexpression significantly promoted tumor growth, while S100A10 knockdown obviously suppressed tumor cell proliferation (P < 0.001). CONCLUSION S100A10 overexpression promotes glycolysis by activating the Akt-mTOR signaling pathway to promote proliferation and invasion of lung adenocarcinoma cells.
Animals ; Mice ; Adenocarcinoma of Lung/pathology* ; Cell Proliferation ; Lung Neoplasms/pathology* ; Mice, Nude ; Proto-Oncogene Proteins c-akt ; Signal Transduction ; TOR Serine-Threonine Kinases ; Humans ; S100 Proteins/genetics*