RESUMO
Background: This study aimed to establish a comprehensive clinical prognostic risk model based on pulmonary function tests. This model was intended to guide the evaluation and predictive management of patients with resectable stage I-III non-small cell lung cancer (NSCLC) receiving neoadjuvant chemoimmunotherapy. Methods: Clinical pathological characteristics and prognostic survival data for 175 patients were collected. Univariate and multivariate Cox regression analyses, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to identify variables and construct corresponding models. These variables were integrated to develop a ridge regression model. The models' discrimination and calibration were evaluated, and the optimal model was chosen following internal validation. Comparative analyses between the risk scores or groups of the optimal model and clinical factors were conducted to explore the potential clinical application value. Results: Univariate regression analysis identified smoking, complete pathologic response (CPR), and major pathologic response (MPR) as protective factors. Conversely, T staging, D-dimer/white blood cell ratio (DWBCR), D-dimer/fibrinogen ratio (DFR), and D-dimer/minute ventilation volume actual ratio (DMVAR) emerged as risk factors. Evaluation of the models confirmed their capability to accurately predict patient prognosis, exhibiting ideal discrimination and calibration, with the ridge regression model being optimal. Survival analysis demonstrated that the disease-free survival (DFS) in the high-risk group (HRG) was significantly shorter than in the low-risk group (LRG) (P=2.57×10-13). The time-dependent receiver operating characteristic (ROC) curve indicated that the area under the curve (AUC) values at 1 year, 2 years, and 3 years were 0.74, 0.81, and 0.79, respectively. Clinical correlation analysis revealed that men with lung squamous cell carcinoma or comorbid chronic obstructive pulmonary disease (COPD) were predominantly in the LRG, suggesting a better prognosis and potentially identifying a beneficiary population for this treatment combination. Conclusion: The prognostic model developed in this study effectively predicts the prognosis of patients with NSCLC receiving neoadjuvant chemoimmunotherapy. It offers valuable predictive insights for clinicians, aiding in developing treatment plans and monitoring disease progression.
RESUMO
Background: Insulin-like growth factor-1 (IGF-1) display a vital role in in the pathogenesis of lung diseases, however, the relationship between circulating IGF-1 and lung disease remains unclear. Methods: Single nucleotide polymorphisms (SNPs) associated with the serum levels of IGF-1 and the outcomes data of lung diseases including asthma, chronic obstructive pulmonary disease (COPD), lung cancer and idiopathic pulmonary fibrosis (IPF) were screened from the public genome-wide association studies (GWAS). Two-sample Mendelian randomization (MR) analysis was then performed to assess the independent impact of IGF-1 exposure on these lung diseases. Results: Totally, 416 SNPs related to circulating IGF-1 levels among 358,072 participants in UK Biobank. According to a primary casual effects model with MR analyses by the inverse variance weighted (IVW) method, the circulating IGF-1 was demonstrated a significantly related with the risk of asthma (OR, 0.992; 95% CI, 0.985-0.999, P=0.0324), while circulating IGF-1 showed no significant correlation with CODP (OR, 1.000; 95% CI, 0.999-1.001, P=0.758), lung cancer (OR, 0.979, 95% CI, 0.849-1.129, P=0.773), as well as IPIGFF (OR, 1.100, 95% CI, 0.794-1.525, P=0.568). Conclusion: The present study demonstrated that circulating IGF-1 may be causally related to lower risk of asthma.
