Transcription factors linked to the molecular signatures in the development of hepatocellular carcinoma on a cirrhotic background

NF-YAl drives EMT in Claudinlow tumours

NF-Y is a trimeric transcription factor whose binding site -the CCAAT box- is enriched in cancer-promoting genes. The regulatory subunit, the sequence-specificity conferring NF-YA, comes in two major isoforms, NF-YA long (NF-YAl) and short (NF-YAs). Extensive expression analysis in epithelial cancers determined two features: widespread overexpression and changes in NF-YAl/NF-YAs ratios (NF-YAr) in tumours with EMT features. We performed wet and in silico experiments to explore the role of the isoforms in breast -BRCA- and gastric -STAD- cancers. We generated clones of two Claudin^low BRCA lines SUM159PT and BT549 ablated of exon-3, thus shifting expression from NF-YAl to NF-YAs. Edited clones show normal growth but reduced migratory capacities in vitro and ability to metastatize in vivo. Using TCGA, including upon deconvolution of scRNA-seq data, we formalize the clinical importance of high NF-YAr, associated to EMT genes and cell populations. We derive a novel, prognostic 158 genes signature common to BRCA and STAD Claudin^low tumours. Finally, we identify splicing factors associated to high NF-YAr, validating RBFOX2 as promoting expression of NF-YAl. These data bring three relevant results: (i) the definition and clinical implications of NF-YAr and the 158 genes signature in Claudin^low tumours; (ii) genetic evidence of 28 amino acids in NF-YAl with EMT-promoting capacity; (iii) the definition of selected splicing factors associated to NF-YA isoforms.

To investigate the internal association between SARS-CoV-2 infections and cancer through bioinformatics

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), also known as COVID-19, is currently prevalent worldwide and poses a significant threat to human health. Individuals with cancer may have an elevated risk for SARS-CoV-2 infections and adverse outcomes. Therefore, it is necessary to explore the internal relationship between these two diseases. In this study, transcriptome analyses were performed to detect mutual pathways and molecular biomarkers in three types of common cancers of the breast, liver, colon, and COVID-19. Such analyses could offer a valuable understanding of the association between COVID-19 and cancer patients. In an analysis of RNA sequencing datasets for three types of cancers and COVID-19, we identified a sum of 38 common differentially expressed genes (DEGs). A variety of combinational statistical approaches and bioinformatics techniques were utilized to generate the protein-protein interaction (PPI) network. Subsequently, hub genes and critical modules were found using this network. In addition, a functional analysis was conducted using ontologies keywords, and pathway analysis was also performed. Some common associations between cancer and the risk and prognosis of COVID-19 were discovered. The datasets also revealed transcriptional factors-gene interplay, protein-drug interaction, and a DEGs-miRNAs coregulatory network with common DEGs. The potential medications discovered in this investigation could be useful in treating cancer and COVID-19.

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.