Preoperative computed tomography-based tumoral radiomic features prediction for overall survival in resectable non-small cell lung cancer

ObjectivesThe purpose of this study was to evaluate whether preoperative radiomics features could meliorate risk stratification for the overall survival (OS) of non-small cell lung cancer (NSCLC) patients.MethodsAfter rigorous screening, the 208 NSCLC patients without any pre-operative adjuvant therapy were eventually enrolled. We segmented the 3D volume of interest (VOI) based on malignant lesion of computed tomography (CT) imaging and extracted 1542 radiomics features. Interclass correlation coefficients (ICC) and LASSO Cox regression analysis were utilized to perform feature selection and radiomics model building. In the model evaluation phase, we carried out stratified analysis, receiver operating characteristic (ROC) curve, concordance index (C-index), and decision curve analysis (DCA). In addition, integrating the clinicopathological trait and radiomics score, we developed a nomogram to predict the OS at 1 year, 2 years, and 3 years, respectively.ResultsSix radiomics features, including gradient_glcm_InverseVariance, logarithm_firstorder_Median, logarithm_firstorder_RobustMeanAbsoluteDeviation, square_gldm_LargeDependenceEmphasis, wavelet_HLL_firstorder_Kurtosis, and wavelet_LLL_firstorder_Maximum, were selected to construct the radiomics signature, whose areas under the curve (AUCs) for 3-year prediction reached 0.857 in the training set (n=146) and 0.871 in the testing set (n=62). The results of multivariate analysis revealed that the radiomics score, radiological sign, and N stage were independent prognostic factors in NSCLC. Moreover, compared with clinical factors and the separate radiomics model, the established nomogram exhibited a better performance in predicting 3-year OS.ConclusionsOur radiomics model may provide a promising non-invasive approach for preoperative risk stratification and personalized postoperative surveillance for resectable NSCLC patients

Alteration of soil carbon and nitrogen pools and enzyme activities as affected by increased soil coarseness

Soil coarseness decreases ecosystem productivity, ecosystem carbon (C) and nitrogen (N) stocks, and soil nutrient contents in sandy grasslands subjected to desertification. To gain insight into changes in soil C and N pools, microbial biomass, and enzyme activities in response to soil coarseness, a field experiment was conducted by mixing native soil with river sand in different mass proportions: 0, 10, 30, 50, and 70% sand addition. Four years after establishing plots and 2 years after transplanting, soil organic C and total N concentrations decreased with increased soil coarseness down to 32.2 and 53.7% of concentrations in control plots, respectively. Soil microbial biomass C (MBC) and N (MBN) declined with soil coarseness down to 44.1 and 51.9 %, respectively, while microbial biomass phosphorus (MBP) increased by as much as 73.9 %. Soil coarseness significantly decreased the enzyme activities of beta-glucosidase, N-acetylglucosaminidase, and acid phosphomonoesterase by 20.2-57.5 %, 24.5-53.0 %, and 22.2-88.7 %, used for C, N and P cycling, respectively. However, observed values of soil organic C, dissolved organic C, total dissolved N, available P, MBC, MBN, and MBP were often significantly higher than would be predicted from dilution effects caused by the sand addition. Soil coarseness enhanced microbial C and N limitation relative to P, as indicated by the ratios of beta-glucosidase and N-acetyl-glucosaminidase to acid phosphomonoesterase (and MBC: MBP and MBN: MBP ratios). Enhanced microbial recycling of P might alleviate plant P limitation in nutrient-poor grassland ecosystems that are affected by soil coarseness. Soil coarseness is a critical parameter affecting soil C and N storage and increases in soil coarseness can enhance microbial C and N limitation relative to P, potentially posing a threat to plant productivity in sandy grasslands suffering from desertification

Immunogenic cell death-based prognostic model for predicting the response to immunotherapy and common therapy in lung adenocarcinoma

Abstract Lung adenocarcinoma (LUAD) is a malignant tumor in the respiratory system. The efficacy of current treatment modalities varies greatly, and individualization is evident. Therefore, finding biomarkers for predicting treatment prognosis and providing reference and guidance for formulating treatment options is urgent. Cancer immunotherapy has made distinct progress in the past decades and has a significant effect on LUAD. Immunogenic Cell Death (ICD) can reshape the tumor’s immune microenvironment, contributing to immunotherapy. Thus, exploring ICD biomarkers to construct a prognostic model might help individualized treatments. We used a lung adenocarcinoma (LUAD) dataset to identify ICD-related differentially expressed genes (DEGs). Then, these DEGs were clustered and divided into subgroups. We also performed variance analysis in different dimensions. Further, we established and validated a prognostic model by LASSO Cox regression analysis. The risk score in this model was used to evaluate prognostic differences by survival analysis. The treatment prognosis of various therapies were also predicted. LUAD samples were divided into two subgroups. The ICD-high subgroup was related to an immune-hot phenotype more sensitive to immunotherapy. The prognostic model was constructed based on six ICD-related DEGs. We found that high-risk score patients responded better to immunotherapy. The ICD prognostic model was validated as a standalone factor to evaluate the ICD subtype of individual LUAD patients, which might contribute to more effective therapies