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Growing evidence has revealed that N6-Methyladenosine (m6A) modification is crucial in cancer development, yet its role in hepatocellular carcinoma (HCC) remains unclear. To address this, we developed a novel m6A regulator-based prognostic signature (m6A-RPS) using comprehensive bioinformatics analysis of TCGA, GEO, and ICGC datasets. Our analysis revealed widespread dysregulation of m6A regulators in HCC tissues. Unsupervised consensus clustering further revealed distinct m6A methylation subtypes with significant survival differences, indicating the potential of m6A modification patterns in prognostic stratification for HCC. Using TCGA-LIHC cohort, LASSO Cox regression selected five key hub regulators (VIRMA, YTHDF1, YTHDF2, YTHDC1, IGF2BP3) to construct the m6A-RPS model. This model proved to be a powerful and independent prognostic indicator (HR = 2.849 (1.819-4.461), P < 0.001), and validated in external cohort (ICGC-LIRI-JP). Patients with high m6A-RPS scores exhibited significantly poorer overall survival and progression-free interval, and the scores were positively correlated with adverse clinical characteristics (e.g., advanced stage, vascular invasion). To facilitate clinical translation, we developed a nomogram that integrated the m6A-RPS with key clinical variables for individualized survival prediction. Genomically, the high-risk group exhibited higher tumor mutation burden and mutation rates in hub regulators. Functional enrichment analyses implicated dysregulation in critical pathways like Wnt signaling, DNA replication, and cell cycle. Crucially, m6A-RPS stratified the tumor immune microenvironment: high-risk patients displayed an immunosuppressive phenotype characterized by enriched Th2 cells and higher potential for immune escape, whereas low-risk patients showed enhanced cytotoxic immune infiltration and elevated immunophenoscores, suggesting greater potential responsiveness to immune checkpoint inhibitors. Differential sensitivity to chemotherapy agents was also predicted. Finally, we constructed a regulatory network linking miRNAs, hub regulators, and 2 downstream target genes. Our study establishes m6A-RPS as a robust tool for prognosis prediction and immune landscape assessment in HCC, offering significant potential to guide personalized therapeutic strategies, particularly immunotherapy selection.
Pubmed ID: 41120498
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Software package for interpreting gene expression data. Used for interpretation of a large-scale experiment by identifying pathways and processes.
View all literature mentionsAn algorithm for the identification of microRNA targets. Details are provided (3' UTR alignments with predicted sites, links to various public databases etc) regarding: # microRNA target predictions in vertebrates (Krek et al, Nature Genetics 37:495-500 (2005)) # microRNA target predictions in seven Drosophila species (Grn et al, PLoS Comp. Biol. 1:e13 (2005)) # microRNA targets in three nematode species (Lall et al, Current Biology 16, 1-12 (2006)) # human microRNA targets that are not conserved but co-expressed (i.e. the microRNA and mRNA are expressed in the same tissue) (Chen and Rajewsky, Nat Genet 38, 1452-1456 (2006)) co-expressed targets
View all literature mentionsCollection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.
View all literature mentionsDatabase of known and predicted protein interactions. The interactions include direct (physical) and indirect (functional) associations and are derived from four sources: Genomic Context, High-throughput experiments, (Conserved) Coexpression, and previous knowledge. STRING quantitatively integrates interaction data from these sources for a large number of organisms, and transfers information between these organisms where applicable. The database currently covers 5''214''234 proteins from 1133 organisms. (2013)
View all literature mentionsCatalogs of predicted microRNA targets in worm (based on ce6 genome assembly), fly (dm3), mouse (mm9) and human (hg18). We follow standard seed parameter settings and consider seeds of length 6-8 bases, beginning at position 2 of the microRNA. No mismatches or loops are allowed, but a single G:U wobble is allowed in 7- or 8-mers. In genes missing a 3' UTR annotation, 500 bp (fly), 800 bp (human and mouse) or 300 bp (worm) downstream of the annotated end of the coding sequence were used as the predicted UTR. For each organism, a catalog with zero flank and with a flank of 3 and 15 bases upstream and downstream.
View all literature mentionsA genomics database project is an academic research program to identify molecular features of cancers that predict response to anti-cancer drugs.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsA unified data repository of the National Cancer Institute (NCI)'s Genomic Data Commons (GDC) that enables data sharing across cancer genomic studies in support of precision medicine. The GDC supports several cancer genome programs at the NCI Center for Cancer Genomics (CCG), including The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and the Cancer Genome Characterization Initiative (CGCI). The GDC Data Portal provides a platform for efficiently querying and downloading high quality and complete data. The GDC also provides a GDC Data Transfer Tool and a GDC API for programmatic access.
View all literature mentionsA portal that provides visualization, analysis and download of large-scale cancer genomics data sets.
View all literature mentionsOpen source software package for statistical programming language R to create plots based on grammar of graphics. Used for data visualization to break up graphs into semantic components such as scales and layers.
View all literature mentionsPortal provides tools for visualizing, querying, and downloading cancer data, which is released on quarterly schedule.
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