The pattern of expression and prognostic value of key regulators for m7G RNA methylation in hepatocellular carcinoma

METTL1 mediated tRNA m7G modification promotes leukaemogenesis of AML via tRNA regulated translational control

BACKGROUND

RNA modifications have been proven to play fundamental roles in regulating cellular biology process. Recently, maladjusted N7-methylguanosine (m7G) modification and its modifiers METTL1/WDR4 have been confirmed an oncogene role in multiple cancers. However, the functions and molecular mechanisms of METTL1/WDR4 in acute myeloid leukemia (AML) remain to be determined.

METHODS

METTL1/WDR4 expression levels were quantified using qRT-PCR, western blot analysis on AML clinical samples, and bioinformatics analysis on publicly available AML datasets. CCK-8 assays and cell count assays were performed to determine cell proliferation. Flow cytometry assays were conducted to assess cell cycle and apoptosis rates. Multiple techniques were used for mechanism studies in vitro assays, such as northern blotting, liquid chromatography-coupled mass spectrometry (LC-MS/MS), tRNA stability analysis, transcriptome sequencing, small non-coding RNA sequencing, quantitative proteomics, and protein synthesis measurements.

RESULTS

METTL1/WDR4 are significantly elevated in AML patients and associated with poor prognosis. METTL1 knockdown resulted in reduced cell proliferation and increased apoptosis in AML cells. Mechanically, METTL1 knockdown leads to significant decrease of m7G modification abundance on tRNA, which further destabilizes tRNAs and facilitates the biogenesis of tsRNAs in AML cells. In addition, profiling of nascent proteins revealed that METTL1 knockdown and transfection of total tRNAs that were isolated from METTL1 knockdown AML cells decreased global translation efficiency in AML cells.

CONCLUSIONS

Taken together, our study demonstrates the important role of METTL1/WDR4 in AML leukaemogenesis, which provides a promising target candidate for AML therapy.

Basement membrane-related regulators for prediction of prognoses and responses to diverse therapies in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains a global health threat. Finding a novel biomarker for assessing the prognosis and new therapeutic targets is vital to treating this patient population. Our study aimed to explore the contribution of basement membrane-related regulators (BMR) to prognostic assessment and therapeutic response prediction in HCC.

MATERIAL AND METHODS

The RNA sequencing and clinical information of HCC were downloaded from TCGA-LIHC, ICGC-JP, GSE14520, GSE104580, and CCLE datasets. The BMR signature was created by the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and used to separate HCC patients into low- and high-risk groups. We conducted analyses using various R 4.1.3 software packages to compare prognoses and responses to immunotherapy, transcatheter arterial chemoembolization (TACE), and chemotherapeutic drugs between the groups. Additionally, stemness indices, molecular functions, and somatic mutation analyses were further explored in these subgroups.

RESULTS

The BMR signature included 3 basement membrane-related genes (CTSA, P3H1, and ADAM9). We revealed that BMR signature was an independent risk contributor to poor prognosis in HCC, and high-risk group patients presented shorter overall survival. We discovered that patients in the high-risk group might be responsive to immunotherapy, while patients in the low-risk group may be susceptible to TACE therapy. Over 300 agents were screened to identify effective drugs for the two subgroups.

CONCLUSION

Overall, basement membrane-related regulators represent novel biomarkers in HCC for assessing prognosis, response to immunotherapy, the effectiveness of TACE therapy, and drug susceptibility.

M7G-related molecular subtypes can predict the prognosis and correlate with immunotherapy and chemotherapy responses in bladder cancer patients

BACKGROUND

N7-methylguanosine (m7G) is closely associated with tumor prognosis and immune response in many cancer types. The correlation between m7G and bladder cancer (BC) needs further study. We aimed to orchestrate molecular subtypes and identify key genes for BC from the perspective of m7G.

METHODS

RNA-seq and clinical data of BC patients were extracted from TCGA and GSE13507 datasets. The patients were subtyped by "ConsensusClusterPlus" and "limma." The clusters were validated by the Kaplan‒Meier curves, univariable and multivariate Cox regression models, the concordance index, and calibration curves. The immunotherapy response was evaluated by immune checkpoints, immune infiltration, TIDE score, and IMvigor210 cohort. Genomics of Drug Sensitivity in Cancer was utilized to predict the chemotherapy response between the clusters.

RESULTS

The m7G-related cluster was ultimately established by EIF4G1, NUDT11, NUDT10, and CCNB1. The independent prognostic value of the m7G-related cluster was validated by the TCGA and GSE13507 datasets. The cluster was involved in immune-associated pathways, such as neutrophil degranulation, antigen processing cross-presentation, and signaling by interleukins pathways. Meanwhile, cluster 2 was positively correlated with many immune checkpoints, such as CD274, CTLA4, HAVCR2, LAG3, PDCD1, and PDCD1LG2. The cluster 2 was significantly correlated with a higher TIDE score than the cluster 1. Furthermore, in the IMvigor210 cohort, patients in the cluster 1 had a higher response rate than those in the cluster 2. Patients in the cluster 2 were sensitive to many chemotherapies.

CONCLUSIONS

We successfully determined molecular subtypes and identified key genes for BC from the perspective of m7G, thereby providing a roadmap for the evolution of immunotherapy and precision medicine.

Genes Modulating Butyrate Metabolism for Assessing Clinical Prognosis and Responses to Systematic Therapies in Hepatocellular Carcinoma

Butyrate, one of the major products of the gut microbiota, has played notable roles in diverse therapies for multiple tumors. Our study aimed to determine the roles of genes that modulate butyrate metabolism (BM) in predicting the clinical prognosis and responses to systemic therapies in hepatocellular carcinoma (HCC). The genes modulating BM were available from the GeneCard database, and gene expression and clinical information were obtained from TCGA-LIHC, GEO, ICGC-JP, and CCLE databases. Candidate genes from these genes that regulate BM were then identified by univariate Cox analysis. According to candidate genes, the patients in TCGA were grouped into distinct subtypes. Moreover, BM- related gene signature (BMGs) was created via the LASSO Cox algorithm. The roles of BMGs in identifying high-risk patients of HCC, assessing the prognoses, and predicting systematic therapies were determined in various datasets. The statistical analyses were fulfilled with R 4.1.3, GraphPad Prism 8.0 and Perl 5.30.0.1 software. In the TCGA cohort, most butyrate-related genes were over-expressed in the B cluster, and patients in the B cluster showed worse prognoses. BMGs constructed by LASSO were composed of eight genes. BMGs exhibited a strong performance in evaluating the prognoses of HCC patients in various datasets, which may be superior to 33 published biomarkers. Furthermore, BMGs may contribute to the early surveillance of HCC, and BMGs could play active roles in assessing the effectiveness of immunotherapy, TACE, ablation therapy, and chemotherapeutic drugs for HCC. BMGs may be served as novel promising biomarkers for early identifying high-risk groups of HCC, as well as assessing prognoses, drug sensitivity, and the responses to immunotherapy, TACE, and ablation therapy in patients with HCC.

Constructing and validating of m7G-related genes prognostic signature for hepatocellular carcinoma and immune infiltration: potential biomarkers for predicting the overall survival

Background

To investigate the prognostic significance of N7-methylguanosine (m7G) regulators and immune infiltration in liver hepatocellular carcinoma (LIHC).

Methods

The research measured predictive m7G genes in LIHC samples from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Data on the stemness index based on mRNA expression (mRNAsi), gene mutations, and corresponding clinical characteristics were obtained from TCGA and ICGC. Lasso regression was used to construct the prediction model to assess the m7G prognostic signals in LIHC. Based on these genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify key biological functions and pathways. The correlation between m7G RNA methylation regulators and the prognosis and immune infiltration of LIHC was evaluated.

Results

There were 21 m7G-related differentially expressed genes (DEGs) in LIHC and healthy tissues, and LIHC patients could be divided into two categories by consensus clustering of these DEGs. A five-gene predictive approach was employed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Patients in the low-risk group showed a significantly higher survival rate compared with those in the high-risk group (P=0.001). Validations using the ICGC database. Also, univariate and multivariate Cox regression analyses suggested that the risk score produced by the predictive model is an independent predictor for LIHC [hazard ratio (HR): 1.848, 95% confidence interval (CI): 1.286-2.656; HR: 2.597, 95% CI: 1.358-4.965]. The ROC curves of the ICGC cohort revealed that the five-gene prediction model performed well [area under the curve (AUC) =0.642 at 1 year, AUC =0.686 at 2 years, and AUC =0.667 at 3 years]. Immuno-oncology scoring revealed that in the high-risk group, among 16 immune cells, the expressions of neutrophils and natural killer (NK) cells were low and that of regulatory T-cells (Tregs) was high.

Conclusions

LIHC occurrence and progression are linked to m7G-related genes. Corresponding prognostic models help forecast the prognosis of LIHC patients. m7G-related genes and associated immune cell infiltration in the TME may serve as potential therapeutic targets in LIHC, which requires further trials. In addition, the m7G-related gene signature offers a viable alternative to predict LIHC, and these m7G-related genes show a prospective research area for LIHC targeted treatment in the future.