RNA m6A modifications regulate crosstalk between tumor metabolism and immunity.

In recent years, m6A modifications in RNA transcripts have arisen as a hot topic in cancer research. Indeed, a number of independent studies have elaborated that the m6A modification impacts the behavior of tumor cells and tumor-infiltrating immune cells, altering tumor cell metabolism along with the differentiation and functional activity of immune cells. This review elaborates on the links between RNA m6A modifications, tumor cell metabolism, and immune cell behavior, discussing this topic from the viewpoint of reciprocal regulation through "RNA m6A-tumor cell metabolism-immune cell behavior" and "RNA m6A-immune cell behavior-tumor cell metabolism" axes. In addition, we discuss the various factors affecting RNA m6A modifications in the tumor microenvironment, particularly the effects of hypoxia associated with cancer cell metabolism along with immune cell-secreted cytokines. Our analysis proposes the conclusion that RNA m6A modifications support widespread interactions between tumor metabolism and tumor immunity. With the current viewpoint that long-term cancer control must tackle cancer cell malignant behavior while strengthening anti-tumor immunity, the recognition of RNA m6A modifications as a key factor provides a new direction for the targeted therapy of tumors. This article is categorized under: RNA Processing > RNA Editing and Modification RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

Nuclear mitochondria-related genes-based molecular classification and prognostic signature reveal immune landscape, somatic mutation, and prognosis for glioma.

Background: Glioma is the most frequent malignant primary brain tumor, and mitochondria may influence the progression of glioma. The aim of this study was to analyze the role of nuclear mitochondria related genes (MTRGs) in glioma, identify subtypes and construct a prognostic model based on nuclear MTRGs and machine learning algorithms. Methods: Samples containing both gene expression profiles and clinical information were retrieved from the TCGA database, CGGA database, and GEO database. We selected 16 nuclear MTRGs and identified two clusters of glioma. Prognostic features, microenvironment, mutation landscape, and drug sensitivity were compared between the clusters. A prognostic model based on multiple machine learning algorithms was then constructed and validated by multiple datasets. Results: We observed significant discrepancies between the two clusters. Cluster One had higher nuclear MTRG expression, a lower survival rate, and higher immune infiltration than Cluster Two. For the two clusters, we found distinct predictive drug sensitivities and responses to immune therapy, and the infiltration of immune cells was significantly different. Among the 22 combinations of machine learning algorithms we tested, LASSO was the most effective in constructing the prognostic model. The model's accuracy was further verified in three independent glioma datasets. We identified MGME1 as a vital gene associated with infiltrating immune cells in multiple types of tumors. Conclusion: In short, our research identified two clusters of glioma and developed a dependable prognostic model based on machine learning methods. MGME1 was identified as a potential biomarker for multiple tumors. Our results will contribute to precise medicine and glioma management.

Decoding the role of long non-coding RNAs in periodontitis: A comprehensive review.

Periodontitis is an inflammatory disease characterized by the pathological loss of alveolar bone and the adjacent periodontal ligament. It is considered a disease that imposes a substantial health burden, with an incidence rate of 20-50%. The etiology of periodontitis is multifactorial, with genetic factors accounting for approximately half of severe cases. Studies have revealed that long non-coding RNAs (lncRNAs) play a pivotal role in periodontitis pathogenesis. Accumulating evidence suggests that lncRNAs have distinct regulatory mechanisms, enabling them to control numerous vital processes in periodontal cells, including osteogenic differentiation, inflammation, proliferation, apoptosis, and autophagy. In this review, we summarize the diverse roles of lncRNAs in the pathogenesis of periodontitis, shedding light on the underlying mechanisms of disease development. By highlighting the potential of lncRNAs as biomarkers and therapeutic targets, this review offers a new perspective on the diagnosis and treatment of periodontitis, paving the way for further investigation into the field of lncRNA-based therapeutics.

Reliability and validity of the hidden hunger assessment scale in China-revised for high school students / 《全球健康杂志（英文）》

Objective:Hidden hunger remains a severe public health problem that affects millions of people worldwide.In China,challenges related to dietary imbalance and hidden hunger persist.Micronutrient inadequacy deserves more attention among adolescents,given its vital role in their growth and development;however,this problem appears to have been largely ignored.High school students,in particular,are often at a high risk of hidden hunger but have limited assessment tools available.Therefore,this study aims to revise the hidden hunger assessment scale for high school students(HHAS-HSS)in China and assess its reliability and validity.Methods:Based on a literature review,expert consultation,pre-experiment,and formal survey,a hidden hunger assessment scale was revised for high school students.The formal survey involved 9 336 high school students in 11 of the 16 cities in Anhui Province,China,and 9038 valid questionnaires were collected and included in the analysis.The item analysis,internal consistency reliability,test-retest reliability,content validity,exploratory factor analysis,and confirmatory factor analysis of the HHAS-HSS were examined.Results:The HHAS-HSS included a total of 4 dimensions and 12 items:"vegetables and food diversity"(three items),"fruits and dairy products"(three items),"micronutrient-dense foods"(four items),and"health condition and eating habits"(two items).The results showed a Cronbach's alpha of 0.758,a split-half reliability of 0.829,and a test-retest reliability of 0.793,indicating good internal consistency.Using the Bartlett's test and Kaiser-Meyer-Olkin test(KMO)to test the exploratory factor analysis presented a four-factor model of the HHAS-HSS,the KMO value was 0.820(P＜0.001),which indicated the possibility for factor confirmatory factor analysis.Using the maximum variance rotation method,four factors were obtained,and the cumulative variance explained rate was 57.974％.Confirmatory factor analysis also supported the division of the scale into four dimensions,and the fitting indices were x2=1417.656,x2/df=29.534,goodness-of-fit index=0.974,adjusted goodnesss-of-fit index=0.958,parsimonious goodness-of-fit index=0.600,normed fit index=0.938,incremental fit index=0.940,Tucker-Lewis index=0.917,comparative fit index=0.939,and root mean square error of approximation=0.056.Except for x2/df,all the indices reached the fitting standard,and the above results showed that the construct validity of the scale reached an acceptable level.Conclusions:The HHAS-HSS has good validity and reliability for Chinese high school students.It is a convenient self-report measure of hidden hunger risk.

Progress of CRISPR-Cas13 Mediated Live-Cell RNA Imaging and Detection of RNA-Protein Interactions.

Ribonucleic acid (RNA) and proteins play critical roles in gene expression and regulation. The relevant study increases the understanding of various life processes and contributes to the diagnosis and treatment of different diseases. RNA imaging and mapping RNA-protein interactions expand the understanding of RNA biology. However, the existing methods have some limitations. Recently, precise RNA targeting of CRISPR-Cas13 in cells has been reported, which is considered a new promising platform for RNA imaging in living cells and recognition of RNA-protein interactions. In this review, we first described the current findings on Cas13. Furthermore, we introduced current tools of RNA real-time imaging and mapping RNA-protein interactions and highlighted the latest advances in Cas13-mediated tools. Finally, we discussed the advantages and disadvantages of Cas13-based methods, providing a set of new ideas for the optimization of Cas13-mediated methods.