This study aimed to investigate the immune landscape in hepatoblastoma (HB) based on deconvolution methods and identify a biomarkers panel for diagnosis based on a machine learning algorithm. Firstly, we identified 277 differentially expressed genes (DEGs) and differentiated and functionally identified the modules in DEGs. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and GO (gene ontology) were used to annotate these DEGs, and the results suggested that the occurrence of HB was related to DNA adducts, bile secretion, and metabolism of xenobiotics by cytochrome P450. We selected the top 10 genes for our final diagnostic panel based on the random forest tree method. Interestingly, TNFRSF19 and TOP2A were significantly down-regulated in normal samples, while other genes (TRIB1, MAT1A, SAA2-SAA4, NAT2, HABP2, CYP2CB, APOF, and CFHR3) were significantly down-regulated in HB samples. Finally, we constructed a neural network model based on the above hub genes for diagnosis. After cross-validation, the area under the ROC curve was close to 1 (AUC = 0.972), and the AUC of the validation set was 0.870. In addition, the results of single-sample gene-set enrichment analysis (ssGSEA) and deconvolution methods revealed a more active immune responses in the HB tissue. In conclusion, we have developed a robust biomarkers panel for HB patients.
High-titanium (high-Ti, more than 1 wt % Ti) magnetite, commonly containing ilmenite exsolution, has long been attributed to an igneous origin and has been used as the most critical factor in previously developed discriminant diagrams. However, recent studies have shown that high-Ti magnetite can be present in high-temperature hydrothermal deposits, suggesting a probable hydrothermal origin. This also calls for reconsideration and necessary modification of the currently available discriminant diagrams. This high-Ti magnetite issue is particularly acute in iron oxide-apatite (IOA) deposits and raises controversy in the discussion of the origin of the high-Ti magnetite. With statistical analysis and machine learning techniques, this study applies two unsupervised dimensionality reduction methods—principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE)—on a compiled data set consisting of 876 laser ablation-inductively coupled plasma-mass spectrometry analyses of primary high-Ti magnetite from high-temperature ore-forming systems worldwide. Three models are built with different element combinations to identify magnetite of different origins. The models were further evaluated by the support vectors machine (SVM) and receiver operating characteristic (ROC) curves and proved to be able to describe the characteristics of trace element compositions of high-Ti magnetite of different origins. Our models suggest that Mg, Mn, Al, Ti, V, and Co from 59 analyzed trace elements show promising properties as effective discriminators, and on this basis, a new discrimination diagram of lg(Al) + lg(Ti) + lg(V) versus lg(Mn)/[lg(Co) + lg(Mg)] is developed for distinguishing high-Ti magnetite of igneous and hydrothermal origin. Our results also show that the high-Ti magnetite in the IOA deposits has chemical compositions similar to those of high-temperature hydrothermal deposits, including the iron oxide copper-gold and porphyry deposits, but significantly distinct from the igneous magnetite. Our study, hence, implies a magmatic-hydrothermal origin for the magnetite in IOA deposits.
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