Abnormal changes in metabolites caused by m6A methylation modification: The leading factors that induce the formation of immunosuppressive tumor microenvironment and their promising potential for clinical application.

BACKGROUND: N6-methyladenosine (m6A) RNA methylation modifications have been widely implicated in the metabolic reprogramming of various cell types within the tumor microenvironment (TME) and are essential for meeting the demands of cellular growth and maintaining tissue homeostasis, enabling cells to adapt to the specific conditions of the TME. An increasing number of research studies have focused on the role of m6A modifications in glucose, amino acid and lipid metabolism, revealing their capacity to induce aberrant changes in metabolite levels. These changes may in turn trigger oncogenic signaling pathways, leading to substantial alterations within the TME. Notably, certain metabolites, including lactate, succinate, fumarate, 2-hydroxyglutarate (2-HG), glutamate, glutamine, methionine, S-adenosylmethionine, fatty acids and cholesterol, exhibit pronounced deviations from normal levels. These deviations not only foster tumorigenesis, proliferation and angiogenesis but also give rise to an immunosuppressive TME, thereby facilitating immune evasion by the tumor. AIM OF REVIEW: The primary objective of this review is to comprehensively discuss the regulatory role of m6A modifications in the aforementioned metabolites and their potential impact on the development of an immunosuppressive TME through metabolic alterations. KEY SCIENTIFIC CONCEPTS OF REVIEW: This review aims to elaborate on the intricate networks governed by the m6A-metabolite-TME axis and underscores its pivotal role in tumor progression. Furthermore, we delve into the potential implications of the m6A-metabolite-TME axis for the development of novel and targeted therapeutic strategies in cancer research.

Demethylases in tumors and the tumor microenvironment: Key modifiers of N6-methyladenosine methylation.

RNA methylation modifications are widespread in eukaryotes and prokaryotes, with N6-methyladenosine (m6A) the most common among them. Demethylases, including Fat mass and obesity associated gene (FTO) and AlkB homolog 5 (ALKBH5), are important in maintaining the balance between RNA methylation and demethylation. Recent studies have clearly shown that demethylases affect the biological functions of tumors by regulating their m6A levels. However, their effects are complicated, and even opposite results have appeared in different articles. Here, we summarize the complex regulatory networks of demethylases, including the most important and common pathways, to clarify the role of demethylases in tumors. In addition, we describe the relationships between demethylases and the tumor microenvironment, and introduce their regulatory mechanisms. Finally, we discuss evaluation of demethylases for tumor diagnosis and prognosis, as well as the clinical application of demethylase inhibitors, providing a strong basis for their large-scale clinical application in the future.

Long noncoding RNA PVT1 inhibits interferon-α mediated therapy for hepatocellular carcinoma cells by interacting with signal transducer and activator of transcription 1.

Long noncoding RNA (LncRNA) PVT1 has recently been reported to be involved in the development of hepatocellular carcinoma (HCC) and hsigh expression of oncogenic PVT1 is associated with poor prognosis of HCC. Interferon-α (IFN-α) has been used in clinic for HCC therapy. However, whether PVT1 is involved in the IFN-α therapy for HCC is completely unknown. Our study found that high PVT1 expression in HCC cells is associated with high unmethylation in PVT1 promoter region. IFN-α treatment further increases PVT1 expression in HCC cells by enhancing H3K4me3 modification on the promoter. Furthermore, PVT1 knockdown enhances IFN-α-induced HCC cell apoptosis by promoting phosphorylation of signal transducer and activator of transcription 1 (STAT1) and upregulating IFN-stimulated genes expression. Moreover, PVT1 specifically interacts with STAT1 in HCC cells. Taken together, these results for the first time indicate that IFN-α treatment promotes oncogenic PVT1 expression in HCC cells, which interacts with STAT1 to inhibit IFN-α signaling, ultimately blocking IFN-α-induced cells apoptosis, suggesting that lncRNA PVT1 may be a potential target to improve IFN-α-mediated HCC immunotherapies.

Transcriptomic and proteomic responses of sweetpotato whitefly, Bemisia tabaci, to thiamethoxam.

BACKGROUND: The sweetpotato whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae), is one of the most widely distributed agricultural pests. Although it has developed resistance to many registered insecticides including the neonicotinoid insecticide thiamethoxam, the mechanisms that regulate the resistance are poorly understood. To understand the molecular basis of thiamethoxam resistance, "omics" analyses were carried out to examine differences between resistant and susceptible B. tabaci at both transcriptional and translational levels. RESULTS: A total of 1,338 mRNAs and 52 proteins were differentially expressed between resistant and susceptible B. tabaci. Among them, 11 transcripts had concurrent transcription and translation profiles. KEGG analysis mapped 318 and 35 differentially expressed genes and proteins, respectively, to 160 and 59 pathways (p<0.05). Thiamethoxam treatment activated metabolic pathways (e.g., drug metabolism), in which 118 transcripts were putatively linked to insecticide resistance, including up-regulated glutathione-S-transferase, UDP glucuronosyltransferase, glucosyl/glucuronosyl transferase, and cytochrome P450. Gene Ontology analysis placed these genes and proteins into protein complex, metabolic process, cellular process, signaling, and response to stimulus categories. Quantitative real-time PCR analysis validated "omics" response, and suggested a highly overexpressed P450, CYP6CX1, as a candidate molecular basis for the mechanistic study of thiamethoxam resistance in whiteflies. Finally, enzymatic activity assays showed elevated detoxification activities in the resistant B. tabaci. CONCLUSIONS: This study demonstrates the applicability of high-throughput omics tools for identifying molecular candidates related to thiamethoxam resistance in an agricultural important insect pest. In addition, transcriptomic and proteomic analyses provide a solid foundation for future functional investigations into the complex molecular mechanisms governing the neonicotinoid resistance in whiteflies.