Use of 6 m6A-relevant lncRNA genes as prognostic markers of primary liver hepatocellular carcinoma based on TCGA database

The role of long non-coding RNA in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a prevalent liver malignancy with complex etiology and generally poor prognosis. Recently, long non-coding RNAs (lncRNAs), non-protein-coding RNA molecules exceeding 200 nucleotides, have emerged as pivotal players in HCC, influencing its initiation, progression, invasion, and metastasis. These lncRNAs modulate gene expression at epigenetic, transcriptional, and post-transcriptional levels, actively participating in the pathological and physiological processes of HCC. Understanding the intricate relationship between lncRNAs and HCC is important for improving prognosis and reducing mortality. This review summarizes advancements in elucidating the role of lncRNAs in HCC pathogenesis.

Prognostic Signature and Tumor Immune Landscape of N7-Methylguanosine-Related lncRNAs in Hepatocellular Carcinoma

Despite great advances in the treatment of liver hepatocellular carcinoma (LIHC), such as immunotherapy, the prognosis remains extremely poor, and there is an urgent need to develop novel diagnostic and prognostic markers. Recently, RNA methylation-related long non-coding RNAs (lncRNAs) have been demonstrated to be novel potential biomarkers for tumor diagnosis and prognosis as well as immunotherapy response, such as N6-methyladenine (m6A) and 5-methylcytosine (m5C). N7-Methylguanosine (m7G) is a widespread RNA modification in eukaryotes, but the relationship between m7G-related lncRNAs and prognosis of LIHC patients as well as tumor immunotherapy response is still unknown. In this study, based on the LIHC patients’ clinical and transcriptomic data from TCGA database, a total of 992 m7G-related lncRNAs that co-expressed with 22 m7G regulatory genes were identified using Pearson correlation analysis. Univariate regression analysis was used to screen prognostic m7G-related lncRNAs, and the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to construct a 9-m7G-related-lncRNA risk model. The m7G-related lncRNA risk model was validated to exhibit good prognostic performance through Kaplan–Meier analysis and ROC analysis. Together with the clinicopathological features, the m7G-related lncRNA risk score was found to be an independent prognostic factor for LIHC. Furthermore, the high-risk group of LIHC patients was unveiled to have a higher tumor mutation burden (TMB), and their tumor microenvironment was more prone to the immunosuppressive state and exhibited a lower response rate to immunotherapy. In addition, 47 anti-cancer drugs were identified to exhibit a difference in drug sensitivity between the high-risk and low-risk groups. Taken together, the m7G-related lncRNA risk model might display potential value in predicting prognosis, immunotherapy response, and drug sensitivity in LIHC patients.

Identification of m6A-Related lncRNA to Predict the Prognosis of Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. In the past decades, HCC treatment has achieved great progress; however, the overall prognosis remains poor. Therefore, it is the need of the hour to identify new prognostic biomarkers which can advance our understanding related to the underlying molecular mechanism of adverse prognosis and apply them to clinical work in prognosis prediction. In the present study, data of 576 HCC patients and 292 normal control cases from TCGA and ICGC databases were enrolled to our bioinformatic analysis. SNHG1 and SNHG3 were identified as overlapping genes in TCGA and ICGC databases using Pearson correlation analysis and univariate Cox regression analysis. Further, we used the median of the SNHG1 and SNHG3 expression values as the cutoff values to define the HCC patient groups with high or low expression level. The subsequent analysis revealed that abnormal high expression of SNHG1 or SNHG3 affected the immune infiltration patterns and the crosstalk among immune cells. Moreover, high expression of SNHG1 or SNHG3 resulted in drug resistant to AKT inhibitor VII, bexarotene, bicalutamide, dasatinib, erlotinib, and gefitinib. In addition, lower tumor neoantigen burden was observed in high SNHG1 or SNHG3 group. Further, we found significant relation between the aberrant upregulation of SNHG1 and SNHG3 in tumor grade and stage. We established a nomogram to systematically predict the 5- and 8-year overall survival of liver cancer patients with good accuracy. Finally, the in vitro assays suggest that SNHG1 and SNHG3 promote the proliferative, migratory, and invasive abilities of HCC cells.

Identification of Five m6A-Related lncRNA Genes as Prognostic Markers for Endometrial Cancer Based on TCGA Database

Background. Due to difficulties involved in its early diagnosis and adequate prognostication, uterine corpus endometrial carcinoma (UCEC) is one of the most serious threats to human health, with the five-year survival rate being as low as roughly 60%. The discovery of specific biomarkers that serve as prognosticators of UCEC is of great significance. The role of N6-methyladenosine- (m6A-) related long noncoding RNAs (lncRNAs) in the pathogenesis of UCEC remains undefined. In this study, we explored the expression profiles of m6A-related lncRNAs of patients with UCEC and identified novel prognostic markers for UCEC. Methods. Gene expression and clinical data were extracted from The Cancer Genome Atlas. Coexpression analysis was performed to identify m6A-related lncRNAs, which were entered into univariate Cox regression models for evaluating the prognosis of UCEC. Clusters of UCEC patients and enrichment pathways were identified using consistent data clustering and gene set enrichment analysis (GSEA). A risk score model was established, and Kaplan–Meier analysis was conducted for investigating overall survival (OS) across two patient groups (high risk and low risk). Lastly, the relationship between the risk score and the cell content of 22 types of immune cells, clusters, age, programmed cell death 1 ligand-1 (PD-L1) expression level, immune score, and pathological grade was analyzed. Results. We identified a total of 2084 lncRNAs associated with m6A, of which 32 lncRNAs were prognostically relevant. Two clusters (clusters 1 and 2) of patients with UCEC were defined; patients in cluster 1 were found to have significantly higher pathological grades and shorter overall survival time compared to those in cluster 2. GSEA showed that “MITOTIC SPINDLE and other pathways” were more enriched in cluster 1. Five major lncRNAs associated with m6A were screened out, and risk score modeling was used for UCEC prognosis prediction. High risk scores were associated with a shorter OS. The risk score was also verified as an independent prognostic indicator for UCEC and was related to immune cell infiltration levels. Finally, we observed a higher pathological grade and greater levels of PD-L1 in the high-risk group than in the low-risk group of patients. Conclusions. m6A-related lncRNAs play an important role in UCEC progression. The risk-based model constructed from the five key m6A-related lncRNAs was implicated in immune cell infiltration and can potentially be an accurate prognosticator for UCEC.