Background: Early diagnosis of liver metastasis is of great importance for enhancing the survival of colorectal adenocarcinoma (CAD) patients, and the combined use of a single biomarker in a classier model has shown great improvement in predicting the metastasis of several types of cancers. However, it is little reported for CAD. This study therefore aimed to screen an optimal classier model of CAD with liver metastasis and explore the metastatic mechanisms of genes when applying this classier model.Methods: The differentially expressed genes between primary CAD samples and CAD with metastasis samples were screened from the Moffitt Cancer Center (MCC) dataset GSE131418. The classification performances of six selected algorithms, namely, LR, RF, SVM, GBDT, NN, and CatBoost, for classification of CAD with liver metastasis samples were compared using the MCC dataset GSE131418 by detecting their classification test accuracy. In addition, the consortium datasets of GSE131418 and GSE81558 were used as internal and external validation sets to screen the optimal method. Subsequently, functional analyses and a drug‐targeted network construction of the feature genes when applying the optimal method were conducted.Results: The optimal CatBoost model with the highest accuracy of 99%, and an area under the curve of 1, was screened, which consisted of 33 feature genes. A functional analysis showed that the feature genes were closely associated with a “steroid metabolic process” and “lipoprotein particle receptor binding” (eg APOB and APOC3). In addition, the feature genes were significantly enriched in the “complement and coagulation cascade” pathways (eg FGA, F2, and F9). In a drug‐target interaction network, F2 and F9 were predicted as targets of menadione.Conclusion: The CatBoost model constructed using 33 feature genes showed the optimal classification performance for identifying CAD with liver metastasis.

Objective. Asthma (AS) is a chronic inflammatory disease of the airway, and macrophages contribute to AS remodeling. Our study aims at screening macrophage‐related gene signatures to build a risk prediction model and explore its predictive abilities in AS diagnosis. Methods. Three microarray datasets were downloaded from the GEO database. The Limma package was used to screen differentially expressed genes (DEGs) between AS and controls. The ssGSEA algorithm was used to determine immune cell proportions. The Pearson correlation coefficient was computed to select the macrophage‐related DEGs. The LASSO and RFE algorithms were implemented to filter the macrophage‐related DEG signatures to establish a risk prediction model. Receiver operating characteristic (ROC) curves were used to assess the diagnostic ability of the prediction model. Finally, the qPCR was used to detect the expression of selected differential genes in sputum from healthy people and asthmatic patients. Results. We obtained 1,189 DEGs between AS and controls from the combined datasets. By evaluating immune cell proportions, macrophages showed a significant difference between the two groups, and 439 DEGs were found to be associated with macrophages. These genes were mainly enriched in the gene ontology‐biological process of immune and inflammatory responses, as well as in the KEGG pathways of cytokine‐cytokine receptor interaction and biosynthesis of antibiotics. Finally, 10 macrophage‐related DEG signatures (EARS2, ATP2A2, COLGALT1, GART, WNT5A, AK5, ZBTB16, CCL17, ADORA3, and CXCR4) were screened as an optimized gene set to predict AS diagnosis, and they showed diagnostic abilities with AUCs of 0.968 and 0.875 in ROC curves of combined and validation datasets, respectively. The mRNA expressions of EARS2, ATP2A2, COLGALT1, and GART in the control group were higher than in AS group, while the expressions of WNT5A, AK5, ZBTB16, CCL17, ADORA3, and CXCR4 in the control group were lower than that in the AS group. Conclusion. We proposed a diagnostic model based on 10 macrophage‐related genes to predict AS risk.\.

Colorectal cancer (CRC) is a common type of malignant cancer worldwide. Recent studies have identified the gut microbiota as the origin of CRC, and T lymphocyte-mediated immune functions have been ...