Pancreatic ductal adenocarcinoma (PDAC) is the most common and among the deadliest of pancreatic cancers. Its 5-year survival is only ∼8%. Pancreatic cancers are a heterogeneous group of diseases, of which PDAC is particularly aggressive. Like many other cancers, PDAC also starts as a pre-invasive precursor lesion (known as pancreatic intraepithelial neoplasia, PanIN), which offers an opportunity for both early detection and early treatment. Even advanced PDAC can benefit from prognostic biomarkers. However, reliable biomarkers for early diagnosis or those for prognosis of therapy remain an unfulfilled goal for PDAC. In this study, we selected 153 PDAC patients from the TCGA database and used their clinical, DNA methylation, gene expression, and micro-RNA (miRNA) and long non-coding RNA (lncRNA) expression data for multi-omics analysis. Differential methylations at about 12,000 CpG sites were observed in PDAC tumor genomes, with about 61% of them hypermethylated, predominantly in the promoter regions and in CpG-islands. We correlated promoter methylation and gene expression for mRNAs and identified 17 genes that were previously recognized as PDAC biomarkers. Similarly, several genes (B3GNT3, DMBT1, DEPDC1B) and lncRNAs (PVT1, and GATA6-AS) are strongly correlated with survival, which have not been reported in PDAC before. Other genes such as EFR3B, whose biological roles are not well known in mammals are also found to strongly associated with survival. We further identified 406 promoter methylation target loci associated with patients survival, including known esophageal squamous cell carcinoma biomarkers, cg03234186 (ZNF154), and cg02587316, cg18630667, and cg05020604 (ZNF382). Overall, this is one of the first studies that identified survival associated genes using multi-omics data from PDAC patients.

DNA cytosine nucleotide methylation (epigenomics and epigenetics) is an important mechanism for controlling gene expression in cardiac development. Combined artificial intelligence and whole-genome epigenomic analysis of circulating cell-free DNA in maternal blood has the potential for the detection of fetal congenital heart defects. This study aimed to use genome-wide DNA cytosine methylation and artificial intelligence analyses of circulating cell-free DNA for the minimally invasive detection of fetal congenital heart defects. In this prospective study, whole-genome cytosine nucleotide methylation analysis was performed on circulating cell-free DNA using the Illumina Infinium MethylationEPIC BeadChip array. Multiple artificial intelligence approaches were evaluated for the detection of congenital hearts. The Ingenuity Pathway Analysis program was used to identify gene pathways that were epigenetically altered and important in congenital heart defect pathogenesis to further elucidate the pathogenesis of isolated congenital heart defects. There were 12 cases of isolated nonsyndromic congenital heart defects and 26 matched controls. A total of 5918 cytosine nucleotides involving 4976 genes had significantly altered methylation, that is, a P value of <.05 along with ≥5% whole-genome cytosine nucleotide methylation difference, in congenital heart defect cases vs controls. Artificial intelligence analysis of the methylation data achieved excellent congenital heart defect predictive accuracy (areas under the receiver operating characteristic curve, ≥0.92). For example, an artificial intelligence model using a combination of 5 whole-genome cytosine nucleotide markers achieved an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.87-1.0) with 98% sensitivity and 94% specificity. We found epigenetic changes in genes and gene pathways involved in the following important cardiac developmental processes: "cardiovascular system development and function," "cardiac hypertrophy," "congenital heart anomaly," and "cardiovascular disease." This lends biologic plausibility to our findings. This study reported the feasibility of minimally invasive detection of fetal congenital heart defect using artificial intelligence and DNA methylation analysis of circulating cell-free DNA for the prediction of fetal congenital heart defect. Furthermore, the findings supported an important role of epigenetic changes in congenital heart defect development.

The epithelial-to-mesenchymal transition (EMT) is considered a transcriptional process that induces a switch in cells from a polarized state to a migratory phenotype. Here, we show that KSR1 and ERK promote EMT-like phenotype through the preferential translation of Epithelial-Stromal Interaction 1 (EPSTI1), which is required to induce the switch from E- to N-cadherin and coordinate migratory and invasive behavior. EPSTI1 is overexpressed in human colorectal cancer (CRC) cells. Disruption of KSR1 or EPSTI1 significantly impairs cell migration and invasion in vitro, and reverses EMT-like phenotype, in part, by decreasing the expression of N-cadherin and the transcriptional repressors of E-cadherin expression, ZEB1 and Slug. In CRC cells lacking KSR1, ectopic EPSTI1 expression restored the E- to N-cadherin switch, migration, invasion, and anchorage-independent growth. KSR1-dependent induction of EMT-like phenotype via selective translation of mRNAs reveals its underappreciated role in remodeling the translational landscape of CRC cells to promote their migratory and invasive behavior.

Kinases are a group of intracellular signaling molecules that play critical roles in various biological processes. Even though kinases comprise one of the most well-known therapeutic targets, many have been understudied and therefore warrant further investigation. DNA methylation is one of the key epigenetic regulators that modulate gene expression. In this study, the human kinome’s DNA methylation and gene expression patterns were analyzed using the level-3 TCGA data for 32 cancers. Unsupervised clustering based on kinome data revealed the grouping of cancers based on their organ level and tissue type. We further observed significant differences in overall kinase methylation levels (hyper- and hypomethylation) between the tumor and adjacent normal samples from the same tissue. Methylation expression quantitative trait loci (meQTL) analysis using kinase gene expression with the corresponding methylated probes revealed a highly significant and mostly negative association (~92%) within 1.5 kb from the transcription start site (TSS). Several understudied (dark) kinases (PKMYT1, PNCK, BRSK2, ERN2, STK31, STK32A, and MAPK4) were also identified with a significant role in patient survival. This study leverages results from multi-omics data to identify potential kinase markers of prognostic and diagnostic importance and further our understanding of kinases in cancer.

Osteosarcoma (OS) is a common bone malignancy in children and adolescents. Although histological subtyping followed by improved OS treatment regimens have helped achieve favorable outcomes, a lack of understanding of the molecular subtypes remains a challenge to characterize its genetic heterogeneity and subsequently to identify diagnostic and prognostic biomarkers for developing effective treatments. In the present study, global analysis of DNA methylation, and mRNA and miRNA gene expression in OS patient samples were correlated with their clinical characteristics. The mucin family of genes, MUC6, MUC12, and MUC4, were found to be highly mutated in the OS patients. Results revealed the enrichment of molecular pathways including Wnt signaling, Calcium signaling, and PI3K-Akt signaling in the OS tumors. Survival analyses showed that the expression levels of several genes such as RAMP1, CRIP1, CORT, CHST13, and DDX60L, miRNAs and lncRNAs were associated with survival of OS patients. Molecular subtyping using Cluster-Of-Clusters Analysis (COCA) for mRNA, lncRNA, and miRNA expression; DNA methylation; and mutation data from the TARGET dataset revealed two distinct molecular subtypes, each with a distinctive gene expression profile. Between the two subtypes, three upregulated genes, POP4, HEY1, CERKL, and seven downregulated genes, CEACAM1, ABLIM1, LTBP2, ISLR, LRRC32, PTPRF, and GPX3, associated with OS metastasis were found to be differentially regulated. Thus, the molecular subtyping results provide a strong basis for classification of OS patients that could be used to develop better prognostic treatment strategies.