A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia

Structural Features and Physiological Associations of Human 14-3-3ζ Pseudogenes

There are about 14,000 pseudogenes that are mutated or truncated sequences resembling functional parent genes. About two-thirds of pseudogenes are processed, while others are duplicated. Although initially thought dead, emerging studies indicate they have functional and regulatory roles. We study 14-3-3ζ, an adaptor protein that regulates cytokine signaling and inflammatory diseases, including rheumatoid arthritis, cancer, and neurological disorders. To understand how 14-3-3ζ (gene symbol YWHAZ) performs diverse functions, we examined the human genome and identified nine YWHAZ pseudogenes spread across many chromosomes. Unlike the 32 kb exon-to-exon sequence in YWHAZ, all pseudogenes are much shorter and lack introns. Out of six, four YWHAZ exons are highly conserved, but the untranslated region (UTR) shows significant diversity. The putative amino acid sequence of pseudogenes is 78-97% homologous, resulting in striking structural similarities with the parent protein. The OMIM and Decipher database searches revealed chromosomal loci containing pseudogenes are associated with human diseases that overlap with the parent gene. To the best of our knowledge, this is the first report on pseudogenes of the 14-3-3 family protein and their implications for human health. This bioinformatics-based study introduces a new insight into the complexity of 14-3-3ζ's functions in biology.

Union With Recursive Feature Elimination: A Feature Selection Framework to Improve the Classification Performance of Multicategory Causes of Death in Colorectal Cancer

Despite the use of machine learning tools, it is challenging to properly model cause-specific deaths in colorectal cancer (CRC) patients and choose appropriate treatments. Here, we propose an interesting feature selection framework, namely union with recursive feature elimination (U-RFE), to select the union feature sets that are crucial in CRC progression-specific mortality using The Cancer Genome Atlas (TCGA) dataset. Based on the union feature sets, we compared the performance of 5 classification algorithms, including logistic regression (LR), support vector machines (SVM), random forest (RF), eXtreme gradient boosting (XGBoost), and Stacking, to identify the best model for classifying 4-category deaths. In the first stage of U-RFE, LR, SVM, and RF were used as base estimators to obtain subsets containing the same number of features but not exactly the same specific features. Union analysis of the subsets was then performed to determine the final union feature set, effectively combining the advantages of different algorithms. We found that the U-RFE framework could improve various models' performance. Stacking outperformed LR, SVM, RF, and XGBoost in most scenarios. When the target feature number of the RFE was set to 50 and the union feature set contained 298 deterministic features, the Stacking model achieved F1_weighted, Recall_weighted, Precision_weighted, Accuracy, and Matthews correlation coefficient of 0.851, 0.864, 0.854, 0.864, and 0.717, respectively. The performance of the minority categories was also significantly improved. Therefore, this recursive feature elimination-based approach of feature selection improves performances of classifying CRC deaths using clinical and omics data or those using other data with high feature redundancy and imbalance.

Novel Biomarkers for Diagnosis and Monitoring of Immune Thrombocytopenia

Lower-than-normal platelet counts are a hallmark of the acquired autoimmune illness known as immune thrombocytopenia, which can affect both adults and children. Immune thrombocytopenia patients' care has evolved significantly in recent years, but the disease's diagnosis has not, and it is still only clinically achievable with the elimination of other causes of thrombocytopenia. The lack of a valid biomarker or gold-standard diagnostic test, despite ongoing efforts to find one, adds to the high rate of disease misdiagnosis. However, in recent years, several studies have helped to elucidate a number of features of the disease's etiology, highlighting how the platelet loss is not only caused by an increase in peripheral platelet destruction but also involves a number of humoral and cellular immune system effectors. This made it possible to identify the role of immune-activating substances such cytokines and chemokines, complement, non-coding genetic material, the microbiome, and gene mutations. Furthermore, platelet and megakaryocyte immaturity indices have been emphasized as new disease markers, and prognostic signs and responses to particular types of therapy have been suggested. Our review's goal was to compile information from the literature on novel immune thrombocytopenia biomarkers, markers that will help us improve the management of these patients.