Expression level of CEBPA gene in acute lymphoblastic leukemia individuals

Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes

Identifying promising targets is a critical step in modern drug discovery, with causative genes of diseases that are an important source of successful targets. Previous studies have found that the pathogeneses of various diseases are closely related to the evolutionary events of organisms. Accordingly, evolutionary knowledge can facilitate the prediction of causative genes and further accelerate target identification. With the development of modern biotechnology, massive biomedical data have been accumulated, and knowledge graphs (KGs) have emerged as a powerful approach for integrating and utilizing vast amounts of data. In this study, we constructed an evolution-strengthened knowledge graph (ESKG) and validated applications of ESKG in the identification of causative genes. More importantly, we developed an ESKG-based machine learning model named GraphEvo, which can effectively predict the targetability and the druggability of genes. We further investigated the explainability of the ESKG in druggability prediction by dissecting the evolutionary hallmarks of successful targets. Our study highlights the importance of evolutionary knowledge in biomedical research and demonstrates the potential power of ESKG in promising target identification. The data set of ESKG and the code of GraphEvo can be downloaded from https://github.com/Zhankun-Xiong/GraphEvo.

Notch-associated lncRNAs profiling circuiting epigenetic modification in colorectal cancer

Background

Colorectal cancer (CRC) is one of the most prevalent digestive cancers, ranking the 2nd cause of cancer-related fatality worldwide. The worldwide burden of CRC is predicted to rise by 60% by 2030. Environmental factors drive, first, inflammation and hence, cancer incidence increase.

Main

The Notch-signaling system is an evolutionarily conserved cascade, has role in the biological normal developmental processes as well as malignancies. Long non-coding RNAs (LncRNAs) have become major contributors in the advancement of cancer by serving as signal pathways regulators. They can control gene expression through post-translational changes, interactions with micro-RNAs or down-stream effector proteins. Recent emerging evidence has emphasized the role of lncRNAs in controlling Notch-signaling activity, regulating development of several cancers including CRC.

Conclusion

Notch-associated lncRNAs might be useful prognostic biomarkers or promising potential therapeutic targets for CRC treatment.

Therefore, here-in we will focus on the role of “Notch-associated lncRNAs in CRC” highlighting “the impact of Notch-associated lncRNAs as player for cancer induction and/or progression.”

Graphical Abstract

Association between the CEBPA and c-MYC genes expression levels and acute myeloid leukemia pathogenesis and development

CEBPA and c-MYC genes belong to TF and play an essential role in hematologic malignancies development. Furthermore, these genes also co-regulate with RUNX1 and lead to bone marrow differentiation and may contribute to the leukemic transformation. Understanding the function and full characteristics of selected genes in the group of patients with AML can be helpful in assessing prognosis, and their usefulness as prognostic factors can be revealed. The aim of the study was to evaluate CEBPA and c-MYC mRNA expression level and to seek their association with demographical and clinical features of AML patients such as: age, gender, FAB classification, mortality or leukemia cell karyotype. Obtained results were also correlated with the expression level of the RUNX gene family. To assess of relative gene expression level the qPCR method was used. The expression levels of CEBPA and c-MYC gene varied among patients. Neither CEBPA nor c-MYC expression levels differed significantly between women and men (p=0.8325 and p=0.1698, respectively). No statistically significant correlation between age at the time of diagnosis and expression of CEBPA (p=0.4314) or c-MYC (p=0.9524) was stated. There were no significant associations between relative CEBPA (p=0.4247) or c-MYC (p=0.4655) expression level and FAB subtype and mortality among the enrolled patients (p=0.5858 and p=0.8437, respectively). However, it was observed that c-MYC and RUNX1 expression levels were significantly positively correlated (rS=0.328, p=0.0411). Overall, AML pathogenesis involves a complex interaction among CEBPA, c-MYC and RUNX family genes.