ObjectiveTo investigate the relationship between the expression of programmed cell death ligand-1 (PD-L1) and the maximal standardized uptake value (SUVmax) in 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and the correlation of clinical factors between SUVmax values and PD-L1.MethodsThe clinical data of 84 patients with invasive lung adenocarcinoma diagnosed pathologically in West China Hospital, Sichuan University from August 2016 to November 2018 were analyzed retrospectively, including 38 males and 46 females, aged 60 (32-85) years. The tumor was acinar-predominant in 37 patients, papillary in 20, lepidic in 19, solid in 5 and micropapillary in 3. Multivariate analysis of the relationship between SUVmax value and other clinicopathological features was performed by linear regression. Logistic regression analysis was used to analyze the relationship between PD-L1 protein expression and other pathological features.ResultsThe SUVmax of the PD-L1 expression group was significantly higher than that of the non-PD-L1 expression group in the whole invasive lung adenocarcinoma group (P=0.002) and intermediate-grade histologic subtype (P=0.016). The SUVmax cut-off value of PD-L1 expression in the whole invasive lung adenocarcinoma group and intermediate-grade histologic subtype was 5.34 (AUC: 0.732, P=0.002) and 5.34 (AUC: 0.720, P=0.017), respectively. Multivariate analysis showed that pleura involvement, vascular tumor thrombus and the increase of tumor diameter could cause the increase of the SUVmax value, while the SUVmax value decreased in the moderately differentiated tumor compared with the poorly differentiated tumor. The SUVmax cut-off value between low-grade histologic subtype and intermediate-grade histologic subtype, intermediate-grade histologic subtype and high-grade histologic subtypes was 1.54 (AUC: 0.854, P<0.001) and 5.79 (AUC: 0.889, P<0.001), respectively. Multivariate analysis of PD-L1 expression showed pleura involvement (P=0.021, OR=0.022, 95%CI 0.001 to 0.558) and moderate differentiation (opposite to poor differentiation) (P=0.004, OR=0.053, 95%CI 0.007 to 0.042) decreased the expression of PD-L1.ConclusionThe SUVmax of the PD-L1 expression group is significantly higher than that of the non-PD-L1 expression group in the whole invasive lung adenocarcinoma group and intermediate-grade histologic subtype. The level of SUVmax and the expression of PD-L1 in invasive lung adenocarcinoma are related to many clinical factors.
Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which was obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), GO analysis, KEGG analysis, and GSEA, to analyze the data were employed. Our objective was to establish a five-gene panel risk assessment model using Cox regression and LASSO regression. Based on this model, we constructed a Nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, ROC curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as Metabolism of xenobiotics by cytochrome P450, Natural killer cell-mediated cytotoxicity, Antigen processing and presentation, and Regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the ROC curve (AUC) of 0.675, as well as in time-dependent ROC curves. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a Nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.