Research Progress of RNA Methylation Modification in Colorectal Cancer

Influence of RNA Methylation on Cancerous Cells: A Prospective Approach for Alteration of In Vivo Cellular Composition

RNA methylation is a dynamic and ubiquitous post-transcriptional modification that plays a pivotal role in regulating gene expression in various conditions like cancer, neurological disorders, cardiovascular diseases, viral infections, metabolic disorders, and autoimmune diseases. RNA methylation manifests across diverse RNA species including messenger RNA (mRNA), ribosomal RNA (rRNA), and transfer RNA (tRNA), exerting pivotal roles in gene expression regulation and various biological phenomena. Aberrant activity of writer, eraser, and reader proteins enables dysregulated methylation landscape across diverse malignancy transcriptomes, frequently promoting cancer pathogenesis. Numerous oncogenic drivers, tumour suppressors, invasion/metastasis factors, and signalling cascade components undergo methylation changes that modulate respective mRNA stability, translation, splicing, transport, and protein-RNA interactions accordingly. Functional studies confirm methylation-dependent alterations drive proliferation, survival, motility, angiogenesis, stemness, metabolism, and therapeutic evasion programs systemically. Methyltransferase overexpression typifies certain breast, liver, gastric, and other carcinomas correlating with adverse clinical outcomes like diminished overall survival. Mapping efforts uncover nodal transcripts for targeted drug development against hyperactivated regulators including METTL3. Some erasers and readers also suitable lead candidates based on apparent synthetic lethality. Proteomic screens additionally highlight relevant methylation-sensitive effector pathways amenable to combinatorial blockade, reversing compensatory signalling mechanisms that facilitate solid tumour progression. Quantifying global methylation burdens and responsible enzymes clinically predicts patient prognosis, risk stratification for adjuvant therapy, and overall therapeutic responsiveness.

A novel m6A/m5C/m1A/m7G-related classification and risk signature predicts prognosis and reveals immunotherapy inclination in gastric cancer

Background

Gastric cancer (GC) is characterized by high morbidity and mortality rates, and the prognosis is not optimistic. Therefore, the search for new biomarkers is crucial. Methylation modifications in RNA modifications play a crucial role in tumors. However, the role of methylation modification of integrated m6A/m5C/m1A/m7G, in GC and its related analysis have not been reported. It still needs to be studied in depth. Our study aims to deepen our understanding of m6A/m5C/m1A/m7G methylation and potentially provide new strategies for GC treatment.

Methods

We used TCGA-STAD (The Cancer Genome Atlas-Stomach Adenocarcinoma) as a training set and GSE84433 as a validation set to analyze and determine potential associations between m6A/m5C/m1A/m7G-related genes and clinical risk of GC. In addition, we explored the prognostic value and potential biological mechanisms of m6A/m5C/m1A/m7G-related genes in GC through consistent clustering, differential expression gene identification, enrichment analysis, and immune infiltration analysis. Finally, we constructed m6A/m5C/m1A/m7G-related risk signature (MRRS) to evaluate the correlation between risk grade and survival prognosis, drug sensitivity, and immune infiltration, and validated the validity by immunohistochemical staining.

Results

We identified subgroups of C1, C2, and C3 patients by consensus clustering using data from 45 m6A/m5C/m1A/m7G-related genes. The three groups showed significant differences in survival, immune scores, and immune cell infiltration. We then constructed MRRS using least absolute shrinkage and selection operator (LASSO) regression analysis, including SLC5A6, FKBP10, GPC3, and GGH, which could accurately differentiate between high-/low-risk populations. Its accuracy was further validated in the validation set and immunohistochemical staining. These results suggest that m6A/m5C/m1A/m7G are closely related to the GC tumor immune microenvironment, and MRRS has good performance in predicting the survival of GC patients.

Conclusions

In this study, we highlighted the association of m6A/m5C/m1A/m7G subtypes with changes in the GC immunotumor microenvironment. We constructed and validated MRRS, which is valuable in predicting survival, immune infiltration and drug sensitivity in GC patients. This helps to deepen our understanding of m6A/m5C/m1A/m7G methylation and potentially provides new strategies for GC treatment.

Investigating gene signatures associated with immunity in colon adenocarcinoma to predict the immunotherapy effectiveness using NFM and WGCNA algorithms

Colon adenocarcinoma (COAD), a frequently encountered and highly lethal malignancy of the digestive system, has been the focus of intensive research regarding its prognosis. The intricate immune microenvironment plays a pivotal role in the pathological progression of COAD; nevertheless, the underlying molecular mechanisms remain incompletely understood. This study aims to explore the immune gene expression patterns in COAD, construct a robust prognostic model, and delve into the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby providing critical support for individualized treatment strategies and prognostic evaluation. Initially, we curated a comprehensive dataset by screening 2600 immune-related genes (IRGs) from the ImmPort and InnateDB databases, successfully obtaining a rich data resource. Subsequently, the COAD patient cohort was classified using the non-negative matrix factorization (NMF) algorithm, enabling accurate categorization. Continuing on, utilizing the weighted gene co-expression network analysis (WGCNA) method, we analyzed the top 5000 genes with the smallest p-values among the differentially expressed genes (DEGs) between immune subtypes. Through this rigorous screening process, we identified the gene modules with the strongest correlation to the COAD subpopulation, and the intersection of genes in these modules with DEGs (COAD vs COAD vs Normal colon tissue) is referred to as Differentially Expressed Immune Genes Associated with COAD (DEIGRC). Employing diverse bioinformatics methodologies, we successfully developed a prognostic model (DPM) consisting of six genes derived from the DEIGRC, which was further validated across multiple independent datasets. Not only does this predictive model accurately forecast the prognosis of COAD patients, but it also provides valuable insights for formulating personalized treatment regimens. Within the constructed DPM, we observed a downregulation of CALB2 expression levels in COAD tissues, whereas NOXA1, KDF1, LARS2, GSR, and TIMP1 exhibited upregulated expression levels. These genes likely play indispensable roles in the initiation and progression of COAD and thus represent potential therapeutic targets for patient management. Furthermore, our investigation into the molecular mechanisms and therapeutic targets for COAD liver metastasis revealed associations with relevant processes such as fat digestion and absorption, cancer gene protein polysaccharides, and nitrogen metabolism. Consequently, genes including CAV1, ANXA1, CPS1, EDNRA, and GC emerge as promising candidates as therapeutic targets for COAD liver metastasis, thereby providing crucial insights for future clinical practices and drug development. In summary, this study uncovers the immune gene expression patterns in COAD, establishes a robust prognostic model, and elucidates the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby possessing significant theoretical and clinical implications. These findings are anticipated to offer substantial support for both the treatment and prognosis management of COAD patients.

Bringing to Light the Importance of the miRNA Methylome in Colorectal Cancer Prognosis Through Electrochemical Bioplatforms

This work reports the first electrochemical bioplatforms developed for the determination of the total contents of either target miRNA or methylated target miRNA. The bioplatforms are based on the hybridization of the target miRNA with a synthetic biotinylated DNA probe, the capture of the formed DNA/miRNA heterohybrids on the surface of magnetic microcarriers, and their recognition with an antibody selective to these heterohybrids or to the N6-methyladenosine (m6A) epimark. The determination of the total or methylated target miRNA was accomplished by labeling such secondary antibodies with the horseradish peroxidase (HRP) enzyme. In both cases, amperometric transduction was performed on the surface of disposable electrodes after capturing the resulting HRP-tagged magnetic bioconjugates. Because of their increasing relevance in colorectal cancer (CRC) diagnosis and prognosis, miRNA let-7a and m6A methylation were selected. The proposed electrochemical bioplatforms showed attractive analytical and operational characteristics for the determination of the total and m6A-methylated target miRNA in less than 75 min. These bioplatforms, innovative in design and application, were applied to the analysis of total RNA samples extracted from cultured cancer cells with different metastatic profiles and from paired healthy and tumor tissues of patients diagnosed with CRC at different stages. The obtained results demonstrated, for the first time using electrochemical platforms, the potential of interrogating the target miRNA methylation level to discriminate the metastatic capacities of cancer cells and to identify tumor tissues and, in a pioneering way, the potential of the m6A methylation in miRNA let-7a to serve as a prognostic biomarker for CRC.

Decoding Ribosome Heterogeneity: A New Horizon in Cancer Therapy

The traditional perception of ribosomes as uniform molecular machines has been revolutionized by recent discoveries, revealing a complex landscape of ribosomal heterogeneity. Opposing the conventional belief in interchangeable ribosomal entities, emerging studies underscore the existence of specialized ribosomes, each possessing unique compositions and functions. Factors such as cellular and tissue specificity, developmental and physiological states, and external stimuli, including circadian rhythms, significantly influence ribosome compositions. For instance, muscle cells and neurons are characterized by distinct ribosomal protein sets and dynamic behaviors, respectively. Furthermore, alternative forms of ribosomal RNA (rRNAs) and their post-transcriptional modifications add another dimension to this heterogeneity. These variations, orchestrated by spatial, temporal, and conditional factors, enable the manifestation of a broad spectrum of specialized ribosomes, each tailored for potentially distinct functions. Such specialization not only impacts mRNA translation and gene expression but also holds significant implications for broader biological contexts, notably in the realm of cancer research. As the understanding of ribosomal diversity deepens, it also paves the way for exploring novel avenues in cellular function and offers a fresh perspective on the molecular intricacies of translation.

Plasma metabolomic profiles associated with mortality and longevity in a prospective analysis of 13,512 individuals

Experimental studies reported biochemical actions underpinning aging processes and mortality, but the relevant metabolic alterations in humans are not well understood. Here we examine the associations of 243 plasma metabolites with mortality and longevity (attaining age 85 years) in 11,634 US (median follow-up of 22.6 years, with 4288 deaths) and 1878 Spanish participants (median follow-up of 14.5 years, with 525 deaths). We find that, higher levels of N2,N2-dimethylguanosine, pseudouridine, N4-acetylcytidine, 4-acetamidobutanoic acid, N1-acetylspermidine, and lipids with fewer double bonds are associated with increased risk of all-cause mortality and reduced odds of longevity; whereas L-serine and lipids with more double bonds are associated with lower mortality risk and a higher likelihood of longevity. We further develop a multi-metabolite profile score that is associated with higher mortality risk. Our findings suggest that differences in levels of nucleosides, amino acids, and several lipid subclasses can predict mortality. The underlying mechanisms remain to be determined.