Identification of a glioma functional network from gene fitness data using machine learning

Uses of artificial intelligence in glioma: A systematic review

Glioma is the most prevalent type of primary brain tumor in adults. The use of artificial intelligence (AI) in glioma is increasing and has exhibited promising results. The present study performed a systematic review of the applications of AI in glioma as regards diagnosis, grading, prediction of genotype, progression and treatment response using different databases. The aim of the present study was to demonstrate the trends (main directions) of the recent applications of AI within the field of glioma, and to highlight emerging challenges in integrating AI within clinical practice. A search in four databases (Scopus, PubMed, Wiley and Google Scholar) yielded a total of 42 articles specifically using AI in glioma and glioblastoma. The articles were retrieved and reviewed, and the data were summarized and analyzed. The majority of the articles were from the USA (n=18) followed by China (n=11). The number of articles increased by year reaching the maximum number in 2022. The majority of the articles studied glioma as opposed to glioblastoma. In terms of grading, the majority of the articles were about both low-grade glioma (LGG) and high-grade glioma (HGG) (n=23), followed by HGG/glioblastoma (n=13). Additionally, three articles were about LGG only; two articles did not specify the grade. It was found that one article had the highest sample size among the other studies, reaching 897 samples. Despite the limitations and challenges that face AI, the use of AI in glioma has increased in recent years with promising results, with a variety of applications ranging from diagnosis, grading, prognosis prediction, and reaching to treatment and post-operative care.

Identification and validation of the role of ZNF281 in 5-fluorouracil chemotherapy of gastric cancer

BACKGROUND

The early diagnosis of gastric cancer (GC) and overcoming chemotherapy resistance is challenging. The aberrant expression of zinc finger protein 281 (ZNF281) and the over-activation of the Wnt/β-catenin pathway are oncogenic factors and confer tumor chemoresistance. ZNF281 modulates the Wnt/β-catenin pathway to influence malignant tumor behavior. However, the role of ZNF281 in GC chemotherapy and the relationship with the Wnt/β-catenin pathway have not been elucidated by researchers.

METHODS

We explored differences in ZNF281 expression in Pan-cancer and normal tissues, the effect of its expression on prognosis of patients treated with 5-fluorouracil (5-FU). Cox regression was utilized to determine whether ZNF281 is an independent prognostic factor. Enrichment analysis was performed to explore the mechanism underlying ZNF281's role in 5-FU treatment. We assessed the relationship between ZNF281 and the tumour microenvironment (TME) and combined bulk-RNA and single-cell RNA data to analyse the relationship between ZNF281 and immune infiltration. In vitro experiments verified the effects of ZNF281 knockdown on proliferation, invasion, migration, apoptosis, DNA damage of GC cells with 5-FU treated and the Wnt/β-catenin pathway proteins.

RESULTS

ZNF281 was highly expressed in seven cancers and correlates with the prognosis. It is an independent prognostic factor in 5-FU treatment. ZNF281 correlates with TME score, CD8T cell abundance. ZNF281 is primarily associated with DNA repair and the Wnt/β-catenin pathway. ZNF281 knockdown enhanced the effect of 5-FU on phenotypes of GC cells.

CONCLUSION

We identified and verified ZNF281 as one of the potential influencing factors of 5-FU treatment in GC and may be associated with the Wnt/β-catenin pathway. Low ZNF281 may contribute to improved 5-FU sensitivity in GC patients.

Three's a crowd - why did three N-terminal methyltransferases evolve for one job?

N-terminal methylation of the α-amine group (Nα-methylation) is a post-translational modification (PTM) that was discovered over 40 years ago. Although it is not the most abundant of the Nα-PTMs, there are more than 300 predicted substrates of the three known mammalian Nα-methyltransferases, METTL11A and METTL11B (also known as NTMT1 and NTMT2, respectively) and METTL13. Of these ∼300 targets, the bulk are acted upon by METTL11A. Only one substrate is known to be Nα-methylated by METTL13, and METTL11B has no proven in vivo targets or predicted targets that are not also methylated by METTL11A. Given that METTL11A could clearly handle the entire substrate burden of Nα-methylation, it is unclear why three distinct Nα-methyltransferases have evolved. However, recent evidence suggests that many methyltransferases perform important biological functions outside of their catalytic activity, and the Nα-methyltransferases might be part of this emerging group. Here, we describe the distinct expression, localization and physiological roles of each Nα-methyltransferase, and compare these characteristics to other methyltransferases with non-catalytic functions, as well as to methyltransferases with both catalytic and non-catalytic functions, to give a better understanding of the global roles of these proteins. Based on these comparisons, we hypothesize that these three enzymes do not just have one common function but are actually performing three unique jobs in the cell.