Clinical Respiratory Journal最新文献

SMARCA4-deficient undifferentiated tumor (SMARCA4-UT) in the chest is a high-grade malignant tumor that grows rapidly and often carries a poor prognosis. Unfortunately, there are currently no effective treatment available until now. Here, we report a case of SMARCA4-UT in a patient who showed a swift response to a combination treatment of penpulimab, anlotinib, and chemotherapy. A 55-year-old man was diagnosed with thoracic SMARCA4-UT along with metastases to multiple lymph nodes, the pleura, and bones. Immunohistochemical (IHC) testing indicated the absence of PD-L1 expression in tumor cells. He was given sintilimab and anlotinib as first line treatment. However, a follow-up chest CT revealed progressive disease (PD) after the first cycle treatment. Subsequently, the second line regimen was modified to etoposide and cisplatin (EP) combined with anlotinib and penpulimab. The effectiveness evaluation revealed partial remission (PR) following two cycles of the second-line regimen treatment. Notably, the patient's progress-free survival (PFS) exceeds 7 months and the overall survival up to 12 months. Our case implies that a combination of chemotherapy, anlotinib, and penpulimab might offer a promising therapeutic approach for PD-L1-negative thoracic SMARCA4-UT.

Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are the standard treatment choice for advanced non-small cell lung cancer (NSCLC) patients with EGFR mutations. EGFR-TKIs have made significant progress in the treatment of advanced NSCLC patients, but drug resistance issues still inevitably arise. The mechanism of drug resistance and subsequent treatment has been current research challenge and priority. Immune checkpoint inhibitors (ICIs) are a new choice for late-stage NSCLC patients without druggable molecular alterations. Currently, several studies have applied ICIs therapy for NSCLC patients with EGFR-TKIs resistance and explored the potential efficacy of ICIs. This review elaborates on the current status of immunotherapy after EGFR-TKIs resistance, including ICIs monotherapy, combined with EGFR-TKIs, chemotherapy, antiangiogenic drugs, and other therapies.

Immunotherapy is a pivotal approach in the treatment of lung cancer. Although HLA-E is a potential target for tumor immunotherapy, its role in lung cancer remains unclear. Previous studies have identified the transcription factor IRF5 as a characteristic gene of M1-like macrophages, highlighting its crucial role in promoting antitumor immune responses. In this study, we developed an engineered M1-like macrophage exosomes expressing IRF5 (IRF5 M1-exos) and demonstrated their ability to inhibit proliferation, migration, and invasion of lung cancer cells. Moreover, our experiments using a nude mouse model revealed that IRF5 M1-exos exerted potent therapeutic effects by effectively suppressing tumor growth. Notably, the mechanism by which IRF5 exerts its antitumor function through HLA-E regulation in lung cancer has not been fully elucidated. Here, we identified HLA-E as a downstream target gene of IRF5 and demonstrated that the overexpression of HLA-E can counteract the tumor-promoting effects induced by si-IRF5 M1-exos. These results suggest that M1 macrophage-derived exosomes, enriched with the transcription factor IRF5, exhibit potent antitumor activity by up-regulating HLA-E in lung cancer cells. Therefore, IRF5 M1-exos represent an attractive therapeutic strategy for lung cancer.

Background: As the primary organ of metabolism and detoxification, the liver may contribute to the pathogenesis of lung cancer. We aimed to illuminate the intricate link between liver function biomarkers and lung cancer risk, as well as delineate the role of smoking behavior within this association.

Methods: We investigated the associations of seven liver function biomarkers levels (alkaline phosphatase [ALP], alanine transaminase [ALT], total bilirubin [TBIL], albumin [ALB], gamma-glutamyltransferase [GGT], aspartate transaminase [AST], and total protein [TP]) with lung cancer risk across the UK Biobank (N = 337 499) through restricted cubic splines and Cox proportional hazards models. Moreover, Mendelian randomization (MR) was utilized to evaluate the causal effect of smoking behavior on these biomarkers. Then a lung cancer risk prediction model was developed among smokers by backward stepwise logistic regression.

Results: During a median follow-up of 13.3 years, 3003 lung cancer cases were identified. We found ALP levels positively associated with lung cancer risk, whereas ALT, TBIL, ALB, and AST were inversely correlated; TP exhibited a U-shaped association, whereas GGT displayed a mirrored J-shaped relationship. These associations were amplified among smokers. MR analysis indicated that smoking behavior could increase ALP (odds ratio [OR]: 1.05) and GGT (OR: 1.15) levels while decreasing TBIL (OR: 0.92), ALB (OR: 0.92), and TP (OR: 0.96) levels. The lung cancer risk model incorporating these biomarkers in smokers demonstrated robust discrimination.

Conclusion: Our finding provides perspectives and evidences towards the intricate crosstalk between the hepatic and pulmonary systems, as well as the processes through which tobacco catalyzes lung carcinogenesis.

Purpose: Positive airway pressure (PAP) is the primary treatment for obstructive sleep apnea (OSA). This study aims to predict the optimal PAP pressure in Chinese OSA patients by their polysomnography (PSG) variables and demographic characteristics.

Methods: Patients with an apnea-hypopnea index (AHI) ≥ 15 times/h who received PAP therapy (residual AHI < 5 times/h) and underwent PSG were included in this study. Sex, age, body mass index (BMI), Epworth Sleepiness Scale (ESS), AHI, supine AHI, lowest oxygen saturation (LSaO₂), percentage of total sleep time spent with SaO₂ < 90% (CT90), and PAP pressure were recorded. PAP pressure and other variables were analyzed using univariate correlation and multivariate linear stepwise regression analysis.

Results: A total of 167 patients were enrolled, with 122 in the study group and 45 in the validation group. Univariate correlation analysis revealed a significant correlation between PAP pressure and age, BMI, ESS, AHI, supine AHI, LSaO₂, and CT90. The multivariate linear regression analysis showed that PAP pressure was correlated with gender (b = 1.142, p = 0.032), age (b = -0.039, p = 0.005), AHI (b = 0.047, p = 0.000), and CT90 (b = 0.037, p = 0.000). The final PAP pressure prediction equation was PAPpre (cmH₂O) = 8.548 + 1.142 × sex -0.039 × age + 0.047 × AHI + 0.037 × CT90 (R² = 0.553) (male is defined as 0 and female as 1). This model accounts for 55.3% of the optimal pressure variance, and the area under the ROC curve of PAP prediction pressure is 0.7419.

Conclusion: PSG variables can be used to predict PAP pressure in Chinese OSA patients, but for some individuals, the prediction model is not very good. PAP is correlated with age, BMI, ESS, AHI, supine AHI, LSaO₂, and percentage of total sleep time spent with SaO₂ < 90% (CT90), which can be used to predict the optimal PAP pressure.