Machine-Learning Algorithm-Based Prediction of Diagnostic Gene Biomarkers Related to Immune Infiltration in Patients With Chronic Obstructive Pulmonary Disease.

Objective: This study aims to identify clinically relevant diagnostic biomarkers in chronic obstructive pulmonary disease (COPD) while exploring how immune cell infiltration contributes towards COPD pathogenesis. Methods: The GEO database provided two human COPD gene expression datasets (GSE38974 and GSE76925; n=134) along with the relevant controls (n=49) for differentially expressed gene (DEG) analyses. Candidate biomarkers were identified using the support vector machine recursive feature elimination (SVM-RFE) analysis and the LASSO regression model. The discriminatory ability was determined using the area under the receiver operating characteristic curve (AUC) values. These candidate biomarkers were characterized in the GSE106986 dataset (14 COPD patients and 5 controls) in terms of their respective diagnostic values and expression levels. The CIBERSORT program was used to estimate patterns of tissue infiltration of 22 types of immune cells. Furthermore, the in vivo and in vitro model of COPD was established using cigarette smoke extract (CSE) to validated the bioinformatics results. Results: 80 genes were identified via DEG analysis that were primarily involved in cellular amino acid and metabolic processes, regulation of telomerase activity and phagocytosis, antigen processing and MHC class I-mediated peptide antigen presentation, and other biological processes. LASSO and SVM-RFE were used to further characterize the candidate diagnostic markers for COPD, SLC27A3, and STAU1. SLC27A3 and STAU1 were found to be diagnostic markers of COPD in the metadata cohort (AUC=0.734, AUC=0.745). Their relevance in COPD were validated in the GSE106986 dataset (AUC=0.900 AUC=0.971). Subsequent analysis of immune cell infiltration discovered an association between SLC27A3 and STAU1 with resting NK cells, plasma cells, eosinophils, activated mast cells, memory B cells, CD8+, CD4+, and helper follicular T-cells. The expressions of SLC27A3 and STAU1 were upregulated in COPD models both in vivo and in vitro. Immune infiltration activation was observed in COPD models, accompanied by the enhanced expression of SLC27A3 and STAU1. Whereas, the knockdown of SLC27A3 or STAU1 attenuated the effect of CSE on BEAS-2B cells. Conclusion: STUA1 and SLC27A3 are valuable diagnostic biomarkers of COPD. COPD pathogenesis is heavily influenced by patterns of immune cell infiltration. This study provides a molecular biology insight into COPD occurrence and in exploring new therapeutic means useful in COPD.

The complete chloroplast genome of an endangered plant-Nomocharis aperta.

Nomocharis aperta is an endangered and endemic species with high ornamental value in China. In this study, we reported a complete chloroplast genome of N. aperta, which was de novo assembled using the next-generation sequencing data. The complete chloroplast genome is 152,845 in length, including a large single copy region of 70,506 bp and a small single copy region of 17,468 bp and two inverted repeat regions of 26,520 bp. A total of 130 functional genes were encoded, consisting of 84 protein-coding genes, 36 transfer RNA genes, and 8 ribosomal RNA genes. The overall AT content of the chloroplast genome is 63.00%. In addition, phylogenetic analysis with the reported chloroplast genomes showed that N. aperta is nested within Lilium and close to L. henricii, L. bakerianum and L. taliense. It indicates that the study on the relationship between Nomocharis and Lilium needs more Nomocharis and Lilium complete chloroplast genome, especially some key species like N. aperta.