Genome-wide analysis and expression profiling of the HD-ZIP gene family in kiwifruit.

The homeodomain-leucine zipper (HD-Zip) gene family plays a pivotal role in plant development and stress responses. Nevertheless, a comprehensive characterization of the HD-Zip gene family in kiwifruit has been lacking. In this study, we have systematically identified 70 HD-Zip genes in the Actinidia chinensis (Ac) genome and 55 in the Actinidia eriantha (Ae) genome. These genes have been categorized into four subfamilies (HD-Zip I, II, III, and IV) through rigorous phylogenetic analysis. Analysis of synteny patterns and selection pressures has provided insights into how whole-genome duplication (WGD) or segmental may have contributed to the divergence in gene numbers between these two kiwifruit species, with duplicated gene pairs undergoing purifying selection. Furthermore, our study has unveiled tissue-specific expression patterns among kiwifruit HD-Zip genes, with some genes identified as key regulators of kiwifruit responses to bacterial canker disease and postharvest processes. These findings not only offer valuable insights into the evolutionary and functional characteristics of kiwifruit HD-Zips but also shed light on their potential roles in plant growth and development.

Predicting the Lung Adenocarcinoma and Its Biomarkers by Integrating Gene Expression and DNA Methylation Data.

The early symptoms of lung adenocarcinoma patients are inapparent, and the clinical diagnosis of lung adenocarcinoma is primarily through X-ray examination and pathological section examination, whereas the discovery of biomarkers points out another direction for the diagnosis of lung adenocarcinoma with the development of bioinformatics technology. However, it is not accurate and trustworthy to diagnose lung adenocarcinoma due to omics data with high-dimension and low-sample size (HDLSS) features or biomarkers produced by utilizing only single omics data. To address the above problems, the feature selection methods of biological analysis are used to reduce the dimension of gene expression data (GSE19188) and DNA methylation data (GSE139032, GSE49996). In addition, the Cartesian product method is used to expand the sample set and integrate gene expression data and DNA methylation data. The classification is built by using a deep neural network and is evaluated on K-fold cross validation. Moreover, gene ontology analysis and literature retrieving are used to analyze the biological relevance of selected genes, TCGA database is used for survival analysis of these potential genes through Kaplan-Meier estimates to discover the detailed molecular mechanism of lung adenocarcinoma. Survival analysis shows that COL5A2 and SERPINB5 are significant for identifying lung adenocarcinoma and are considered biomarkers of lung adenocarcinoma.