FAM81A identified as a stemness-related gene by screening DNA methylation sites based on machine learning-accessed stemness in pancreatic cancer

Alteration in DNA methylation patterns: Epigenetic signatures in gastrointestinal cancers

Abnormalities in epigenetic modifications can cause malignant transformations in cells, leading to cancers of the gastrointestinal (GI) tract, which accounts for 20% of all cancers worldwide. Among the epigenetic alterations, DNA hypomethylation is associated with genomic instability. In addition, CpG methylation and promoter hypermethylation have been recognized as biomarkers for different malignancies. In GI cancers, epigenetic alterations affect genes responsible for cell cycle control, DNA repair, apoptosis, and tumorigenic-specific signaling pathways. Understanding the pattern of alterations in DNA methylation in GI cancers could help scientists discover new molecular-based pharmaceutical treatments. This study highlights alterations in DNA methylation in GI cancers. Understanding epigenetic differences among GI cancers may improve targeted therapies and lead to the discovery of new diagnostic biomarkers.

Identification and validation of stemness-based and ferroptosis-related molecular clusters in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with an extremely poor prognosis. Cancer stem cells (CSCs) are considered to be responsible for the poor survival, recurrence and therapy resistance of PDAC. Ferroptosis plays a crucial role in the sustain and survival of CSCs. Here, we employed a rigorous evaluation of multiple datasets to identify a novel stemness-based and ferroptosis-related genes (SFRGs) signature to access the potential prognostic application. This work we retrieved RNA-sequencing and clinical annotation data from the TCGA, ICGC, GTEx and GEO database, and acquired 26 stem cell gene sets and 259 ferroptosis genes from StemChecker database and FerrDb database, respectively. Based on consensus clustering and ssGSEA analysis, we identified two expression patterns of CSCs traits (C1 and C2). Then, WGCNA analysis was implemented to screen out hub module genes correlated with stemness. Furthermore, differential expression analysis, Pearson correlation analysis, and the Least absolute shrinkage and selection operator (LASSO) and Cox regression were performed to identify the SFRGs and to construct model. In addition, the differences in prognosis, tumor microenvironment (TME) components and therapy responses were evaluated between two risk groups. Finally, we verified the most influential marker ARNTL2 experimentally by western blot, qRT-PCR, sphere formation assay, mitoscreen assay, intracellular iron concentration determination and MDA determination assays. In conclusion, we developed a stemness-based and ferroptosis-related prognostic model, which could help predict overall survival for PDAC patients. Targeting ferroptosis may be a promising therapeutic strategy to inhibit PDAC progression by suppressing CSCs.