RACGAP1 promotes lung cancer cell proliferation through the PI3K/AKT signaling pathway.

We aimed to investigate the expression and clinic significance of Rac GTPase Activating Protein 1 (RACGAP1) in human lung adenocarcinoma (LUAD). Online database analysis revealed a significant increase in RACGAP1 mRNA expression among 26 types of tumor tissues, including LUAD tissues. Online database and tissue microarray analyses indicated that RACGAP1 expression was significantly upregulated in LUAD tissues. Genetic variation analysis identified four different genetic variations of RACGAPs in LUAD. Moreover, online database analysis showed that RACGAP1 upregulation was correlated with shorter survival in patients with LUAD. After silencing RACGAP1 expression in A549 cells using siRNA and assessing its protein levels via Western blotting, we found that RACGAP1 knockdown inhibited cell growth and induced apoptosis determined using the Cell Counting Kit-8 assay, colony formation assay, and flow cytometry. Mechanistically, western blot analysis indicated that Bax expression increased, whereas Bcl-2 expression decreased. Moreover, RACGAP1 knockdown attenuated PI3K/AKT pathway activation in lung cancer cells. Taken together, our findings showed that RACGAP1 was overexpressed in LUAD tissues and played an important role in lung cancer by increasing cell growth through the PI3K/AKT signaling pathway. This study suggests recommends evaluating RACGAP1 in clinical settings as a novel biomarker and potential therapeutic target for lung cancer.

The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung.

OBJECTIVE: To explore the value of six machine learning models based on PET/CT radiomics combined with EGFR in predicting brain metastases of lung adenocarcinoma. METHODS: Retrospectively collected 204 patients with lung adenocarcinoma who underwent PET/CT examination and EGFR gene detection before treatment from Cancer Hospital Affiliated to Shandong First Medical University in 2020. Using univariate analysis and multivariate logistic regression analysis to find the independent risk factors for brain metastasis. Based on PET/CT imaging combined with EGFR and PET metabolic indexes, established six machine learning models to predict brain metastases of lung adenocarcinoma. Finally, using ten-fold cross-validation to evaluate the predictive effectiveness. RESULTS: In univariate analysis, patients with N2-3, EGFR mutation-positive, LYM%≤20, and elevated tumor markers(P<0.05) were more likely to develop brain metastases. In multivariate Logistic regression analysis, PET metabolic indices revealed that SUVmax, SUVpeak, Volume, and TLG were risk factors for lung adenocarcinoma brain metastasis(P<0.05). The SVM model was the most efficient predictor of brain metastasis with an AUC of 0.82 (PET/CT group),0.70 (CT group),0.76 (PET group). CONCLUSIONS: Radiomics combined with EGFR machine learning model as a new method have higher accuracy than EGFR mutation alone. SVM model is the most effective method for predicting brain metastases of lung adenocarcinoma, and the prediction efficiency of PET/CT group is better than PET group and CT group.

Elucidating the role of EPPK1 in lung adenocarcinoma development.

BACKGROUND: We recently found that epiplakin 1 (EPPK1) alterations were present in 12% of lung adenocarcinoma (LUAD) cases and were associated with a poor prognosis in early-stage LUAD when combined with other molecular alterations. This study aimed to identify a probable crucial role for EPPK1 in cancer development. METHODS: EPPK1 mRNA and protein expression was analyzed with clinical variables. Normal bronchial epithelial cell lines were exposed to cigarette smoke for 16 weeks to determine whether EPPK1 protein expression was altered after exposure. Further, we used CRISPR-Cas9 to knock out (KO) EPPK1 in LUAD cell lines and observed how the cancer cells were altered functionally and genetically. RESULTS: EPPK1 protein expression was associated with smoking and poor prognosis in early-stage LUAD. Moreover, a consequential mesenchymal-to-epithelial transition was observed, subsequently resulting in diminished cell proliferation and invasion after EPPK1 KO. RNA sequencing revealed that EPPK1 KO induced downregulation of 11 oncogenes, 75 anti-apoptosis, and 22 angiogenesis genes while upregulating 8 tumor suppressors and 12 anti-cell growth genes. We also observed the downregulation of MYC and upregulation of p53 expression at both protein and RNA levels following EPPK1 KO. Gene ontology enrichment analysis of molecular functions highlighted the correlation of EPPK1 with the regulation of mesenchymal cell proliferation, mesenchymal differentiation, angiogenesis, and cell growth after EPPK1 KO. CONCLUSIONS: Our data suggest that EPPK1 is linked to smoking, epithelial to mesenchymal transition, and the regulation of cancer progression, indicating its potential as a therapeutic target for LUAD.

A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma.

PURPOSE: Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients. MATERIALS AND METHODS: Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models. RESULTS: In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability. CONCLUSION: Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.

Trametinib sensitizes KRAS-mutant lung adenocarcinoma tumors to PD-1/PD-L1 axis blockade via Id1 downregulation.

BACKGROUND: The identification of novel therapeutic strategies to overcome resistance to the MEK inhibitor trametinib in mutant KRAS lung adenocarcinoma (LUAD) is a challenge. This study analyzes the effects of trametinib on Id1 protein, a key factor involved in the KRAS oncogenic pathway, and investigates the role of Id1 in the acquired resistance to trametinib as well as the synergistic anticancer effect of trametinib combined with immunotherapy in KRAS-mutant LUAD. METHODS: We evaluated the effects of trametinib on KRAS-mutant LUAD by Western blot, RNA-seq and different syngeneic mouse models. Genetic modulation of Id1 expression was performed in KRAS-mutant LUAD cells by lentiviral or retroviral transductions of specific vectors. Cell viability was assessed by cell proliferation and colony formation assays. PD-L1 expression and apoptosis were measured by flow cytometry. The anti-tumor efficacy of the combined treatment with trametinib and PD-1 blockade was investigated in KRAS-mutant LUAD mouse models, and the effects on the tumor immune infiltrate were analyzed by flow cytometry and immunohistochemistry. RESULTS: We found that trametinib activates the proteasome-ubiquitin system to downregulate Id1 in KRAS-mutant LUAD tumors. Moreover, we found that Id1 plays a major role in the acquired resistance to trametinib treatment in KRAS-mutant LUAD cells. Using two preclinical syngeneic KRAS-mutant LUAD mouse models, we found that trametinib synergizes with PD-1/PD-L1 blockade to hamper lung cancer progression and increase survival. This anti-tumor activity depended on trametinib-mediated Id1 reduction and was associated with a less immunosuppressive tumor microenvironment and increased PD-L1 expression on tumor cells. CONCLUSIONS: Our data demonstrate that Id1 expression is involved in the resistance to trametinib and in the synergistic effect of trametinib with anti-PD-1 therapy in KRAS-mutant LUAD tumors. These findings suggest a potential therapeutic approach for immunotherapy-refractory KRAS-mutant lung cancers.

FBXO32-mediated degradation of PTEN promotes lung adenocarcinoma progression.

FBXO32, a member of the F-box protein family, is known to play both oncogenic and tumor-suppressive roles in different cancers. However, the functions and the molecular mechanisms regulated by FBXO32 in lung adenocarcinoma (LUAD) remain unclear. Here, we report that FBXO32 is overexpressed in LUAD compared with normal lung tissues, and high expression of FBXO32 correlates with poor prognosis in LUAD patients. Firstly, we observed with a series of functional experiments that FBXO32 alters the cell cycle and promotes the invasion and metastasis of LUAD cells. We further corroborate our findings using in vivo mouse models of metastasis and confirmed that FBXO32 positively regulates LUAD tumor metastasis. Using a proteomic-based approach combined with computational analyses, we found a positive correlation between FBXO32 and the PI3K/AKT/mTOR pathway, and identified PTEN as a FBXO32 interactor. More important, FBXO32 binds PTEN via its C-terminal substrate binding domain and we also validated PTEN as a bona fide FBXO32 substrate. Finally, we demonstrated that FBXO32 promotes EMT and regulates the cell cycle by targeting PTEN for proteasomal-dependent degradation. In summary, our study highlights the role of FBXO32 in promoting the PI3K/AKT/mTOR pathway via PTEN degradation, thereby fostering lung adenocarcinoma progression.

Synergistic anti-tumor effects of lenalidomide and gefitinib by upregulating ADRB2 and inactivating the mTOR/PI3K/AKT signaling pathway in lung adenocarcinoma.

Gefitinib is commonly used to be the first-line therapy for advanced non-small cell lung cancer (NSCLC). Therapeutic effect of gefitinib is reduced due to acquired resistance, and combined treatment is recommended. In this research, we planned to explore the impacts of combined treatment of lenalidomide and gefitinib on gefitinib-sensitive or -resistant NSCLC cells. The co-treatment results demonstrated that enhanced antitumor impact on NSCLC cell growth, migration, invasion, cell cycle process and apoptosis. The tumor-bearing mouse models were established using PC9/GR cells. In vivo assays also showed that lenalidomide and gefitinib synergistically inhibited mouse tumor growth along increased the survival of mice. ADRB2 was identified as a lowly expressed gene in PC9/GR cells and LUAD tumor tissues. LUAD patients with high ADRB2 expression were indicated with favorable survival outcomes. Moreover, ADRB2 was upregulated in lenalidomide and/or gefitinib-treated PC9/GR cells. ADRB2 deficiency partially offsets the suppressive impacts of lenalidomide and gefitinib co-treatment on the viability and proliferation of PC9/GR cells. Additionally, lenalidomide and gefitinib cotreatment significantly inactivated the mTOR/PI3K/AKT signaling pathway compared with each treatment alone. Rescue assays were performed to explore whether lenalidomide and gefitinib synergistically inhibited the growth of PC9/GR cells via the PI3K/AKT pathway. PI3K activator SC79 significantly restored reduced cell proliferation, migration and invasion along with elevated cell cycle arrest and apoptosis caused by lenalidomide and gefitinib cotreatment. In conclusion, lenalidomide and gefitinib synergistically suppressed LUAD progression and attenuated gefitinib resistance by upregulating ADRB2 and inactivating the mTOR/PI3K/AKT signaling pathway in lung adenocarcinoma.

Significance of Spread Through Air Spaces and Vascular Invasion in Early-stage Adenocarcinoma Survival: A Comprehensive Clinicopathologic Study of 427 Patients for Precision Management.

Spread through air spaces (STAS) is a novel invasive pattern of lung cancer associated with poor prognosis in non-small cell cancer (NSCLC). We aimed to investigate the incidence of STAS in a surgical series of adenocarcinomas (ADCs) resected in our thoracic surgery unit and to identify the association of STAS with other clinicopathological characteristics. We retrospectively enrolled patients with stage cT1a-cT2b who underwent resection between 2016 and 2022. For each case, a comprehensive pathologic report was accessible which included histotype, mitoses, pleural invasion, fibrosis, tumor infiltrating lymphocytes, necrosis, inflammation, vascular and perineural invasion, as well as STAS. PD-L1 expression was also investigated. A total of 427 patients with ADCs underwent surgery. Regarding overall survival (OS), no significant difference was observed between the STAS positive (STAS+) and STAS negative (STAS-) groups ( P =0.44). However, vascular invasion (VI) was associated with a poorer survival probability ( P =0.018). STAS+/VI+ patients had tendentially worse survival compared with STAS+/VI- ( P =0.089). ADCs with pathologic evidence of immune system (IS) activation (TILs>10% and PD-L1≥1) demonstrated significantly increased OS compared with ADCs with no IS and VI. In terms of recurrence rate, no statistical differences were found between the STAS+ and STAS- samples ( P =0.2). VI was also linked to a significantly elevated risk of recurrence ( P =0.0048). Our study suggests that in resected early-stage ADCs, STAS+ does not seem to influence recurrence or mortality. VI was instead an adverse pathologic prognostic factor for both survival and recurrence, whereas IS seemed to be protective.

G2 and S phase-expressed protein 1 is a biomarker for poor prognosis in lung adenocarcinoma.

Studying the regulatory mechanism and clinical application of G2 and S phase-expressed protein 1 (GTSE1) genes in lung adenocarcinoma (LUAD). LUAD data was obtained from The Cancer Genome Atlas (TCGA) database, and differentially expressed genes (DEGs) were derived by analyzing expression data using R software. Survival analysis was performed to identify genes associated with LUAD, and among them, a target gene for LUAD was identified. Further analysis of the gene expression profiling interactive analysis database revealed differences in gene expression between normal and tumor tissues of LUAD patients. Disease free survival (DFS) and overall survival (OS) of the GTSE1 genes in LUAD were compared. The study conducted a GSEA analysis of GTSE1 expression and further investigated the relationships between GTSE1 expression and the survival time of LUAD patients at different pathological stages. The correlations between OS and GTSE1 gene expression were explored based on different treatments. Additionally, the correlation between the GTSE1 gene and immune infiltration was analyzed. The results indicated that the expression of GTSE1 was significantly higher in tumor tissues of LUAD compared to normal tissues. Furthermore, patients with high GTSE1 expression had significantly lower survival rates for OS and DFS compared to patients with low expression of GTSE1. The GSEA analysis of GTSE1 revealed its involvement in LUAD through the Reactome unwinding of DNA and Biocarta ranms pathway. In patients with LUAD at the pathological T2 stage, low expression of GTSE1 was associated with longer survival time. Furthermore, LUAD patients with low GTSE1 expression who underwent surgery without chemotherapy exhibited a longer survival time. The GTST1 gene, identified as a target gene of LUAD, was validated through cell experiments and pathological sections. GTSE1 can be used as a marker and therapeutic target for LUAD. The survival of LUAD patients can be improved by reducing the expression of GTSE1.

DeepCG: A cell graph model for predicting prognosis in lung adenocarcinoma.

Lung cancer is the first leading cause of cancer-related death in the United States, with lung adenocarcinoma as the major subtype accounting for 40% of all cases. To improve patient survival, image-based prognostic models were developed due to the ready availability of pathological images at diagnosis. However, the application of these models is hampered by two main challenges: the lack of publicly available image datasets with high-quality survival information and the poor interpretability of conventional convolutional neural network models. Here, we integrated matched transcriptomic and H&E staining data from TCGA (The Cancer Genome Atlas) to develop an image-based prognostic model, termed Deep-learning based Cell Graph (DeepCG) model. Instead of survival data, we used a gene signature to predict patient prognostic risks, which was then used as labels for training DeepCG. Importantly, by employing graph structures to capture cell patterns, DeepCG can provide cell-level interpretation, which was more biologically relevant than previous region-level insights. We validated the prognostic values of DeepCG in independent datasets and demonstrated its ability to identify prognostically informative cells in images.

Efficacy of Definitive Radiotherapy for Patients with Clinical Stage IIIB or IIIC Lung Adenocarcinoma and Epidermal Growth Factor Receptor (EGFR) Mutations Treated Using First- or Second-Generation EGFR Tyrosine Kinase Inhibitors.

Background: The effectiveness of definitive radiotherapy (RT) for patients with clinical stage IIIB or IIIC lung adenocarcinoma and epidermal growth factor receptor (EGFR) mutations who received first- or second-generation EGFR tyrosine kinase inhibitors (TKIs) is unclear. Methods: Taiwan Cancer Registry data were used in this retrospective cohort study to identify adult patients diagnosed with EGFR-mutated stage IIIB or IIIC lung adenocarcinoma between 2011 and 2020. Patients treated with first- or second-generation EGFR TKIs were classified into RT and non-RT groups. Propensity score (PS) weighting was applied to balance covariates between groups. The primary outcome was overall survival (OS), and the incidence of lung cancer mortality (ILCM) was considered as a supplementary outcome. Additional supplementary analyses were conducted to assess the robustness of the findings. Results: Among 270 eligible patients, 41 received RT and 229 did not. After a median follow-up of 46 months, PS-weighted analysis showed the PS-weighted hazard ratio of death for the RT group compared to the non-RT group was 0.94 (95% CI: 0.61-1.45, p = 0.78). ILCM rates did not differ significantly between the two groups. Supplementary analyses yielded consistent results. Conclusion: The addition of definitive RT to first- or second-generation EGFR TKI treatment does not significantly improve OS of patients with EGFR-mutated stage IIIB or IIIC lung adenocarcinoma. NCT03521154NCT05167851.

[High-grade Fetal Adenocarcinoma of the Lung with Scalp Metastasis: A Case Report].

Fetal adenocarcinoma of the lung (FLAC) is a rare tumor. Due to its different clinicopathological features, biological behavior and clinical outcome, FLAC is classified into low-grade FLAC (L-FLAC) and high-grade FLAC (H-FLAC). Most patients with H-FLAC are middle-aged heavy smokers. Here, we describe an extremely rare case of a young male patient who denies smoking and initially presents with a mass on the top of the head and is eventually diagnosed with H-FLAC. The aim of this article is to improve the understanding and awareness of FLAC, and increase the attention to the disease, so as to prevent the underdiagnosis and misdiagnosis of the disease, strengthen early identification and accurate diagnosis, and promote subsequent effective treatment and improve prognosis.
.

Radiological and clinical features of large consolidative-type pulmonary invasive mucinous adenocarcinoma.

BACKGROUND: This study aimed to investigate the radiological, pathological, and prognostic characteristics of large consolidative-type pulmonary invasive mucinous adenocarcinomas (IMA). METHODS: We retrospectively reviewed 738 patients who confirmed IMA between January 2010 and August 2022, and two radiologists reviewed imaging data to determine subtypes. We included 41 patients with pathologically large consolidative-type IMA. We analyzed their radiological, pathological, and prognostic characteristics. The recurrence-free survival (RFS) and overall survival (OS) were determined using the Kaplan-Meier method. RESULTS: Most lesions were located in the lower lobe, with 46.3% patients showing multiple lesions. Halo, angiogram, vacuole, air bronchogram, and dead branch sign were observed in 97.6%, 73.2%, 63.4%, 61.0%, and 61.0% of the cases, respectively. Unevenly low enhancement was observed in 88.89% of patients. T3 and T4 pathological stages were observed in 50.0% and 30.6% of patients, respectively. Lymph node metastasis was observed in 16.7% patients, with no distant metastasis. Spread-through air spaces and intrapulmonary dissemination were observed in 27.8% and 19.4% patients, respectively. Moreover, Kirsten rat sarcoma viral oncogene mutations were found in 68.6% of cases, and no epidermal growth factor receptor mutations were seen. Among all mutation sites, G12V mutation is the most common, accounting for 40%. The average RFS and OS were 19.4 and 66.4 months, respectively, with 3-year RFS and OS rates of 30.0% and 75.0%, respectively. Pleural invasion and lymph node metastasis were independent risk factors for diagnosis. CONCLUSION: Halo, vacuole, angiogram, and dead branch signs were frequently observed in consolidative-type IMA. Kirsten rat sarcoma viral oncogene mutations are common in consolidative-type IMA, especially site G12V, whereas epidermal growth factor receptor mutations were rare; therefore, gene immunotherapy was more difficult. Most patients were in stage T3-T4; however, lymph node metastasis was rare.

Right Thigh Mass Metastasis from Lung Cancer Mimicking Primary Soft Tissue Sarcoma: A Case Report.

BACKGROUND Soft tissue metastases (STMs) are less common than bone metastases and sometimes misdiagnosed as primary soft tissue malignancies. Skin, lungs, and breast are the most common primary lesions of STMs and rarely the presenting symptoms. We present an STM from lung adenocarcinoma that became a presenting symptom in nonsmoking woman. CASE REPORT A 47-year-old woman presented to our hospital with a painful mass in her right thigh and weight loss of 10 kg for 4 months. Femoral radiograph revealed a lesion suggestive of bone sarcoma. However, magnetic resonance imaging (MRI) showed it was more likely a primary soft tissue sarcoma. A small mediastinal mass was noticed on preoperative chest radiograph, and the patient denied any symptoms except the mass in the right thigh. Our clinicopathological conference team decided to perform a biopsy of mediastinal and right thigh masses. Histopathology examinations confirmed the right thigh mass as soft tissue metastasis from mediastinal mass, confirmed as lung adenocarcinoma. We treated the patient with palliative care with zoledronic acid and gefitinib. At the 6-month follow-up, the patient's symptoms significantly improved, and MRI showed a marked size reduction. CONCLUSIONS Diagnosis of STM can be difficult when presenting as the primary manifestation. Failure to identify promptly can lead to rapid disease progression and unfavorable prognosis. Failure to diagnose primary malignancy during biopsy occurs in approximately 28% of cases. This report has the potential to facilitate the avoidance of unnecessary procedures and highlight the importance of using a multidisciplinary approach in managing cases with malignancy.

TDASD: Generating medically significant fine-grained lung adenocarcinoma nodule CT images based on stable diffusion models with limited sample size.

BACKGROUND AND OBJECTIVES: Spread through air spaces (STAS) is an emerging lung cancer infiltration pattern. Predicting its spread through CT scans is crucial. However, limited STAS data makes this prediction task highly challenging. Stable diffusion is capable of generating more diverse and higher-quality images compared to traditional GAN models, surpassing the dominating GAN family models in image synthesis over the past few years. To alleviate the issue of limited STAS data, we propose a method TDASD based on stable diffusion, which is able to generate high-resolution CT images of pulmonary nodules corresponding to specific nodular signs according to the medical professionals. METHODS: First, we apply the stable diffusion method for fine-tuning training on publicly available lung datasets. Subsequently, we extract nodules from our hospital's lung adenocarcinoma data and apply slight rotations to the original nodule CT slices within a reasonable range before undergoing another round of fine-tuning through stable diffusion. Finally, employing DDIM and Ksample sampling methods, we generate lung adenocarcinoma nodule CT images with signs based on prompts provided by doctors. The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics. RESULTS: Our TDASD method has the capability to generate medically meaningful images by optimizing input prompts based on medical descriptions provided by experts. The images generated by our method can improve the model's classification accuracy. Furthermore, Utilizing solely the data generated by our method for model training, the test results on the original real dataset reveal an accuracy rate that closely aligns with the testing accuracy achieved through training on real data. CONCLUSIONS: The method we propose not only safeguards patient privacy but also enhances the diversity of medical images under limited data conditions. Furthermore, our approach to generating medical images incorporates medical knowledge, resulting in images that exhibit pertinent medical features, thus holding significant value in tumor discrimination diagnostics.

Albumin-to-D-dimer ratios: A novel prognostic factor for evaluating first-line chemotherapy efficacy in advanced lung adenocarcinoma patients.

The prognosis of advanced lung adenocarcinoma (LUAD) remains unfavorable, with chemotherapy constituting a primary treatment modality. Discerning the efficacy of chemotherapy for advanced LUAD is imperative. Prior investigations have demonstrated the prognostic value of albumin and D-dimer individually for malignancies; however, the predictive capacity of albumin-to-D-dimer ratios (ADR) for advanced LUAD subjected to first-line platinum-based chemotherapy remains unexplored. A cohort of 313 patients with advanced LUAD was retrospectively examined in this study, spanning from January 2017 to January 2021. ADR threshold values were ascertained via receiver operating characteristic analysis, followed by the evaluation of the association between pretreatment ADR and clinicopathological characteristics, disease control rate (DCR), and overall response rate (ORR) pertinent to first-line chemotherapy. Prognostic factors for progression-free survival (PFS) were determined employing Cox univariate and multivariate analyses. Subsequently, survival data were illustrated utilizing the Kaplan-Meier method and scrutinized through the log-rank test across the entire and subgroup populations. ADR demonstrated a superior area under the curve (AUC) value relative to albumin and D-dimer individually and exhibited enhanced prognostic predictive capability compared to albumin-to-fibrinogen ratios (AFR) for advanced LUAD (AUC: 0.805 vs. 0.640, DeLong test: p<0.001). ADR yielded a cut-off value of 16.608. A greater proportion of non-smokers was observed within the high-ADR group (ADR>16.608) compared to the low-ADR group (ADR≤16.608). Patients in the high-ADR group displayed elevated BMI and Na+ levels and reduced neutrophil count, monocyte count, globulin, and alkaline phosphatase (all p<0.05). Notably, the high-ADR group exhibited heightened DCR (96.7% vs. 89.2%, p=0.008) and ORR rates (70.1% vs. 51.0%, p=0.001) relative to the low-ADR group. Multivariate analysis outcomes indicated that high ADR constituted an independent risk factor for PFS (hazard ratio: 0.24, p<0.001). Furthermore, patients in the high-ADR cohort displayed a significantly prolonged median PFS (254 vs. 142 days, p<0.0001) compared to their low-ADR counterparts. In subpopulations exhibiting favorable implications for PFS, as determined by multivariate analysis, high-ADR patients consistently demonstrated extended PFS durations relative to the low-ADR group (all p<0.0001). Collectively, our findings suggest that ADR constitutes a novel and promising prognostic indicator for advanced LUAD patients, surpassing the accuracy of albumin and D-dimer individually and AFR. ADR thus serves as a potent instrument for assessing treatment effects and PFS in advanced LUAD patients undergoing first-line chemotherapy.

Systematic identification of a synthetic lethal interaction in brain-metastatic lung adenocarcinoma.

Metastatic lung adenocarcinoma (LuAC) presents a significant clinical challenge due to the short latency and the lack of efficient treatment options. Therefore, identification of molecular vulnerabilities in metastatic LuAC holds great importance in the development of therapeutic drugs against this disease. In this study, we performed a genome-wide siRNA screening using poorly and highly brain-metastatic LuAC cell lines. Using this approach, we discovered that compared to poorly metastatic LuAC (LuAC-Par) cells, brain-metastatic LuAC (LuAC-BrM) cells exhibited a significantly higher vulnerability to c-FLIP (an inhibitor of caspase-8)-depletion-induced apoptosis. Furthermore, in vivo studies demonstrated that c-FLIP knockdown specifically inhibited growth of LuAC-BrM, but not the LuAC-Par, tumors, suggesting the addiction of LuAC-BrM to the function of c-FLIP for their survival. Our in vitro and in vivo analyses also demonstrated that LuAC-BrM is more sensitive to c-FLIP-depletion due to ER stress-induced activation of the c-JUN and subsequent induction of stress genes including ATF4 and DDIT3. Finally, we found that c-JUN not only sensitized LuAC-BrM to c-FLIP-depletion-induced cell death but also promoted brain metastasis in vivo, providing strong evidence for c-JUN's function as a double-edged sword in LuAC-BrM. Collectively, our findings not only reveal a novel link between c-JUN, brain metastasis, and c-FLIP addiction in LuAC-BrM but also present an opportunity for potential therapeutic intervention.

PDGFBB facilitates tumorigenesis and malignancy of lung adenocarcinoma associated with PI3K-AKT/MAPK signaling.

Lung adenocarcinoma (LUAD) remains one of the most aggressive tumors and the efficacy of conventional treatment has been bleak. Nowadays, gene-targeted therapy has become a new favorite in tumor therapy. Herein, we investigated the effect of platelet derived growth factor BB (PDGFBB) on LUAD. Firstly, PDGFBB was upregulated in LUAD patients and closely linked with poor survival. Furthermore, the expression of PDGFBB and PDGFRα/ß in LUAD cells was higher than that in normal lung cells. By loss-of-function with herpes simplex virus (HSV)-PDGFi-shRNA, we found that PDGFBB knockdown caused a significant decrease in proliferation and migration, but evoked apoptosis of LUAD cells in vitro. Conversely, exogenous PDGFBB held adverse effect. Additionally, A549 cells with PDGFBB knockdown had a low probability of tumorigenesis in vivo. Moreover, PDGFBB knockdown restrained the growth of xenografts derived from normal A549 cells. Mechanistically, PDGFBB knockdown suppressed PI3K/AKT and Ras/MAPK signaling, while PDGFBB was the opposite. Therefore, we concluded that PDGFBB might facilitate the tumorigenesis and malignancy of LUAD through its functional downstream nodes-PI3K/AKT and Ras/MAPK signaling, which supported that PDGFBB could serve as a rational therapeutic target for LUAD.

Development and validation of a clinic-radiological model to predict tumor spread through air spaces in stage I lung adenocarcinoma.

OBJECTIVES: Tumor spread through air spaces (STAS) is associated with poor prognosis and impacts surgical options. We aimed to develop a user-friendly model based on 2-[18F] FDG PET/CT to predict STAS in stage I lung adenocarcinoma (LAC). MATERIALS AND METHODS: A total of 466 stage I LAC patients who underwent 2-[18F] FDG PET/CT examination and resection surgery were retrospectively enrolled. They were split into a training cohort (n = 232, 20.3% STAS-positive), a validation cohort (n = 122, 27.0% STAS-positive), and a test cohort (n = 112, 29.5% STAS-positive) according to chronological order. Some commonly used clinical data, visualized CT features, and SUVmax were analyzed to identify independent predictors of STAS. A prediction model was built using the independent predictors and validated using the three chronologically separated cohorts. Model performance was assessed using ROC curves and calculations of AUC. RESULTS: The differences in age (P = 0.009), lesion density subtype (P < 0.001), spiculation sign (P < 0.001), bronchus truncation sign (P = 0.001), and SUVmax (P < 0.001) between the positive and negative groups were statistically significant. Age ≥ 56 years [OR(95%CI):3.310(1.150-9.530), P = 0.027], lesion density subtype (P = 0.004) and SUVmax ≥ 2.5 g/ml [OR(95%CI):3.268(1.021-1.356), P = 0.005] were the independent factors predicting STAS. Logistic regression was used to build the A-D-S (Age-Density-SUVmax) prediction model, and the AUCs were 0.808, 0.786 and 0.806 in the training, validation, and test cohorts, respectively. CONCLUSIONS: STAS was more likely to occur in older patients, in solid lesions and higher SUVmax in stage I LAC. The PET/CT-based A-D-S prediction model is easy to use and has a high level of reliability in diagnosing.