M6A-related lncRNAs predict clinical outcome and regulate the tumor immune microenvironment in hepatocellular carcinoma

Abstract

LncRNA N6-methylandenosine (m6A) modification has been shown to be associated with the constitution of the tumor microenvironment (TME) and tumorigenesis. It’s essential to understand the mechanisms of lncRNA m6A modification in hepatocellular carcinoma (HCC) and identify relative prognostic predictors to guide therapy and explore potential therapeutic targets. Pearson correlation analysis was performed to identify m6A-related lncRNAs in 374 patients with HCC. Unsupervised cluster analysis of the potential m6A-related lncRNA-based HCC subtypes was conducted, followed by the concurrent analysis of their relationship with TME characteristics, immune checkpoints, immune features, and prognosis through single sample gene set enrichment analysis and ESTIMATE algorithm. Cox regression analyses were performed to screen prognostic m6A-related lncRNA, construct an m6A-related lncRNA signature (m6A-RLRS), and establish an integrated nomogram for the prognosis of patients with HCC. We identified 61 m6A-related lncRNAs and two HCC subtypes defined by consensus cluster of m6A-related lncRNAs with distinct clinical features. Progression-free survival (PFS), three TME-related scores, 15 immune-associated gene sets, and two immune checkpoints expression were found to be significantly different among the two subtypes. Twenty-five prognostic m6A-related lncRNAs were determined, four of which were included to establish an m6A-RLRS with favorable discrimination, and the signature was validated in the validation set and an independent FAHWMU cohort (n = 60). Furthermore, a novel nomogram combining signature and clinical predictors was generated with a C-index of 0.703, and an original ceRNA regulatory network consisting of 9 lncRNAs, 28 miRNAs, and 75 target mRNAs was constructed. Finally, the differential expression of four m6A-related lncRNA was verified by qRT-PCR. In conclusion, m6A-related lncRNA prognostic signature and molecular subtype contributes to accurately predict the prognosis of HCC and provide potential novel therapeutic targets.

The m6A reader YTHDC1-mediated lncRNA CTBP1-AS2 m6A modification accelerates cholangiocarcinoma progression

“…m6A modifications affect the function of lncRNAs through multiple regulatory mechanisms. On the one hand, m6A modification acts on the RNA-DNA triple helix structure to regulate the relationship between lncRNAs and specific DNA loci [ [35] , [36] , [37] ]. On the other hand, m6A modifications provide binding sites for methylated readers (readers) or regulate the structure of local RNAs, thereby inducing the binding of RNA-binding proteins (RBPs) and regulating the function of lncRNAs [ 38 , 39 ].…”

Beta2-Microglobulin as Predictive Biomarkers in the Prognosis of Hepatocellular Carcinoma and Development of a New Nomogram

“… 47 Our model achieved a C-index of 0.712 (95% CI, 0.698–0.726), indicating higher predictive accuracy compared to these previously published models. In recent years, several scholars, including Long et al, 48 Tang et al, 49 and Chen et al, 50 have developed prognostic nomograms for predicting OS in HCC patients using costly genomic data. However, it is worth noting that their respective nomogram models exhibited lower C-index values and incurred higher costs compared to our developed nomogram.…”

From past to future: Bibliometric analysis of global research productivity on nomogram (2000–2021)

“…Over time, nomogram is no longer limited to the study of oncology, but more applied to the study of diagnosis and prediction of non-tumor-related diseases. For example, nomograms were used to analyze the correlation between factors in genomics or proteomics and the development and progression of diseases ( 32 – 34 ); Nomograms were used to predict adverse outcomes in obstetrics or reproductive medicine ( 35 – 38 ); it can also be combined with radiomics to analyze the relationship between different image features or image segmentation factors and disease diagnosis ( 39 , 40 ), predict chronic disease incidence in orthopedics ( 41 – 43 ), or analyze and detect the incidence of chronic diseases combined with metabolomics ( 37 ). In addition, the display modes of nomogram are gradually diversified.…”