Dataset Information

Deciphering the Divergent Gene Expression Landscapes of m6A/m5C/m1A Methylation Regulators in Hepatocellular Carcinoma Through Single-Cell and Bulk RNA Transcriptomic Analysis.

ABSTRACT:

Introduction

RNA modifications mediated by the m6A, m1A, and m5C regulatory genes are crucial for the progression of malignancy. This study aimed to explore the expression of regulator genes for m6A/m5C/m1A methylation at the single-cell level and to validate their expression in cancerous and adjacent para-cancerous liver tissues of adult patients with HCC who underwent tumor resection.

Methods

The bulk sequencing from The Cancer Genome Atlas (TCGA) database and the single-cell RNA sequencing (scRNA-seq) data obtained from the Gene Expression Omnibus (GEO) database were used to identify the dysregulated m6A/m5C/m1A genes for hepatocellular carcinoma (HCC). A real-time polymerase chain reaction (real-time PCR) was used to measure the expression of dysregulated m6A/m5C/m1A genes in collected human HCC tissues and compared with adjacent para-cancerous liver tissues. Immune cell infiltration with these significantly expressed methylation-related genes was evaluated using Timer2.0.

Results

A discrepancy in m6A/m5C/m1A gene expression was observed between bulk sequencing and scRNA-seq. The clustered heatmap of the scRNA-seq-identified dysregulated m6A/m5C/m1A genes in TCGA cohort revealed heterogeneous expression of these methylation regulators within the cancer, whereas their expression in the adjacent liver tissues was more homogeneous. The real-time PCR validated the significant overexpression of DNMT1, NSUN5, TRMT6, IGF2BP1, and IGFBP3, which were identified using scRNA-seq, and IGFBP2, which was identified using bulk sequencing. These dysregulated methylation genes are mainly correlated with the infiltration of natural killer cells.

Discussion

This study suggests that cellular diversity inside tumors contributes to the discrepancy in the expression of methylation regulator genes between traditional bulk sequencing and scRNA-seq. This study identified five regulatory genes that will be the focus of further studies regarding the function of m6A/m5C/m1A in HCC.

SUBMITTER: Liu HT

PROVIDER: S-EPMC10758181 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Deciphering the Divergent Gene Expression Landscapes of m6A/m5C/m1A Methylation Regulators in Hepatocellular Carcinoma Through Single-Cell and Bulk RNA Transcriptomic Analysis.

Liu Hang-Tsung HT Rau Cheng-Shyuan CS Liu Yueh-Wei YW Hsieh Ting-Min TM Huang Chun-Ying CY Chien Peng-Chen PC Lin Hui-Ping HP Wu Chia-Jung CJ Chuang Pei-Chin PC Hsieh Ching-Hua CH

Journal of hepatocellular carcinoma 20231228

<h4>Introduction</h4>RNA modifications mediated by the m6A, m1A, and m5C regulatory genes are crucial for the progression of malignancy. This study aimed to explore the expression of regulator genes for m6A/m5C/m1A methylation at the single-cell level and to validate their expression in cancerous and adjacent para-cancerous liver tissues of adult patients with HCC who underwent tumor resection.<h4>Methods</h4>The bulk sequencing from The Cancer Genome Atlas (TCGA) database and the single-cell RN ...[more]

PMID: 38164510

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Project description:PurposeTo investigate the prognostic value of N6-methyladenosine (m6A)-, 5-methylcytosine (m5C)-, and N1-methyladenosine (m1A)-related genes in cervical cancer (CESC) and predicting immunotherapy response.MethodsWe downloaded cervical cancer mRNA expression profiles, clinical data, and m6A, m5C, m1A-related genes from public databases, and subjected them to serial bioinformatics analysis and clinical sample validation.ResultsDifferential analysis revealed 106 methylation-related differential genes (MEDs), including 44 differentially downregulated and 62 upregulated genes. We then obtained methylation models containing 10 genes by univariate and multifactorial COX analysis. High risk genes with HR > 1 include IQGAP3, PTBP1, STAC3, CUX1, SLC2A1, and CA2, and low risk genes with HR < 1 include IGBP1, DUOX1, CHAF1A, and STAC3. We verified the accuracy of the model from inside TCGA and outside GSE39001 (AUC = 0.729). K-M analysis showed shorter survival times in the High-risk group, and Immunocytic infiltration analysis showed model genes closely associated with six immune cells. The high-risk group may benefit more effectively from immunosuppressive therapy, especially anti-CTLA-4 therapy (p < .05). We also screened nine drugs for potential treatment and verified the expression of three key genes SLC2A1, CUX1, and CA2 using immunohistochemistry and RT-qPCR experiments with clinical samples.ConclusionWe identified a prognostic model using m6A/m5C/m1A-related genes in cervical cancer, which can predict survival time and correlate with immune cell infiltration. Additionally, anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer.KEY MESSAGESCervical cancer still has a high mortality rate, we aim to establish a strong prognostic index and new treatment goals for improving patient survival.The role of three types of RNA methylation modifications, m6A, m5C, and m1A, in cervical cancer, remains unknown. Therefore, it is essential to explore the potential molecular mechanisms of m6A, m5C, and m1A methylation regulation in cervical cancer.We also screened nine drugs for potential treatment and anti-CTLA-4 may be used as an immunotherapeutic agent for cervical cancer. We verified the expression of three key genes SLC2A1, CUX1, and CA2.

Project description:Methylation of RNA plays an important role in cancer. Classical forms of such modifications include N6-methyladenine (m6A), 5-methylcytosine (m5C), and N1-methyladenine (m1A). Methylation-regulated long non-coding (lnc) RNAs are involved in various biological processes, such as tumor proliferation, apoptosis, immune escape, invasion, and metastasis. Therefore, we performed an analysis of transcriptomic and clinical data of pancreatic cancer samples in The Cancer Genome Atlas (TCGA). Using the co-expression method, we summarized 44 m6A/m5C/m1A-related genes and obtained 218 methylation-associated lncRNAs. Next, with COX regression, we screened 39 lncRNAs that are strongly associated with prognosis and found that their expression differed significantly between normal tissues and pancreatic cancer samples (P < 0.001). We then used the least absolute shrinkage and selection operator (LASSO) to construct a risk model comprising seven lncRNAs. In validation set, the nomogram generated by combining clinical characteristics accurately predicted the survival probability of pancreatic cancer patients at 1, 2, and 3 years after diagnosis (AUC = 0.652, 0.686, and 0.740, respectively). Tumor microenvironment analysis showed that the high-risk group had significantly more resting memory CD4 T cells, M0 macrophages, and activated dendritic cells and fewer naïve B cells, plasma cells, and CD8 T cells than the low-risk group (both P < 0.05). Most immune-checkpoint genes were significantly different between the high- and low-risk groups (P < 0.05). The Tumor Immune Dysfunction and Exclusion score showed that high-risk patients benefited more from treatment with immune checkpoint inhibitors (P < 0.001). Overall survival was also lower in high-risk patients with more tumor mutations than in low-risk patients with fewer mutations (P < 0.001). Finally, we explored the sensitivity of the high- and low-risk groups to seven candidate drugs. Our findings indicated that m6A/m5C/m1A-associated lncRNAs are potentially useful biomarkers for the early diagnosis and estimating the prognosis of, and ascertaining the responses to immunotherapy in, patients with pancreatic cancer.

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Project description:BackgroundRNA methylation modification plays an important role in cancers. This study sought to examine the association between m6A/m5C/m1A-related genes and hepatocellular carcinoma (HCC).MethodsGene expression and clinical data of HCC patients were obtained from the TCGA database. Unsupervised consensus clustering was performed according to the expression of m6A/m5C/m1A-related genes in HCC. The relationships among prognosis, clinicopathological features and molecular subtypes were analyzed. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to establish the m6A/m5C/m1A-related gene prognostic signature. Furthermore, the prognostic signature was validated based on the ICGC dataset. RT‒qPCR was used to detect the expression of the model genes in HCC. Clinicopathological features, functional enrichment, gene mutations, immune cell infiltration, and immunotherapy response in different risk groups were analyzed. A nomogram based on risk score and stage was constructed to predict HCC patient prognosis.ResultsTwo m6A/m5C/m1A-related molecular subtypes were identified in HCC, and the prognosis of cluster C1 was worse than that of cluster C2 (p < 0.001). Highly expressed genes in cluster C1 are significantly correlated with G3-4, T3-4, stage III-IV (p < 0.05). An m6A/m5C/m1A-related prognostic signature was established and validated. The RT‒qPCR results showed that the risk signature genes were significantly upregulated in liver cancer tissue (p < 0.05). The prognosis of HCC patients in the high-risk group was worse than that of those in the low-risk group (p < 0.05). Multivariate Cox analysis indicated that the risk score was an independent factor predicting prognosis in HCC patients. ssGSEA revealed that the risk score correlated with the tumor immune microenvironment in HCC. Gene mutation analysis showed that the tumor mutation burden of patients in the high-risk group was much higher (p < 0.05), and the prognosis of HCC patients with high risk scores and high mutation burden was the worst (p = 0.007). A nomogram combining risk scores with clinicopathological features showed performed well in predicting HCC prognosis.ConclusionsThe m6A/m5C/m1A-related genes could predict the prognosis and tumor microenvironment features of HCC and can be important biomarkers relevant to the immunotherapy response.

Project description:RNA modification of m6A/m5C/m1A contributes to the occurrence and development of cancer. Consequently, this study aimed to investigate the functions of m6A/m5C/m1A regulated genes in the prognosis and immune microenvironment of hepatocellular carcinoma (HCC). The expression levels of 45 m6A/m5C/m1A regulated genes in HCC tissues were determined. The functional mechanisms and protein-protein interaction network of m6A/m5C/m1A regulated genes were investigated. The Cancer Genome Atlas (TCGA) HCC gene set was categorized based on 45 m6A/m5C/m1A regulated genes, and survival analysis was used to determine the relationship between the overall survival of HCC patients in subgroups. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct the risk model and nomogram for m6A/m5C/m1A regulated genes. The relationships between m6A/m5C/m1A regulated gene subsets and risk model and immune cell infiltration were analyzed using CIBERSORT. m6A/m5C/m1A regulated genes were involved in mRNA and RNA modifications, mRNA and RNA methylation, mRNA and RNA stability, and other processes. There was a statistically significant difference between cluster1 and cluster2 groups of genes regulated by m6A/m5C/m1A. The prognosis of cluster1 patients was significantly better than that of cluster2 patients. There were statistically significant differences between the two cluster groups in terms of fustat status, grade, clinical stage, and T stage of HCC patients. The risk model comprised the overexpression of YBX1, ZC3H13, YTHDF1, TRMT10C, YTHDF2, RRP8, TRMT6, LRPPRC, and IGF2BP3, which contributed to the poor prognosis of HCC patients. The high-risk score was associated with prognosis, fustat status, grade, clinical stage, T stage, and M stage and was an independent risk factor for poor prognosis in HCC patients. High-risk score mechanisms included spliceosome, RNA degradation, and DNA replication, among others, and high-risk was closely related to stromal score, CD4 memory resting T cells, M0 macrophages, M1 macrophages, resting mast cells, CD4 memory activated T cells, and follicular helper T cells. In conclusion, the cluster subgroup and risk model of m6A/m5C/m1A regulated genes were associated with the poor prognosis and immune microenvironment in HCC and are expected to be the new tools for assessing the prognosis of HCC patients.

Dataset Information

Deciphering the Divergent Gene Expression Landscapes of m6A/m5C/m1A Methylation Regulators in Hepatocellular Carcinoma Through Single-Cell and Bulk RNA Transcriptomic Analysis.

Introduction

Methods

Results

Discussion

Publications

Deciphering the Divergent Gene Expression Landscapes of m6A/m5C/m1A Methylation Regulators in Hepatocellular Carcinoma Through Single-Cell and Bulk RNA Transcriptomic Analysis.

Similar Datasets

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