The Prognostic Value and Immune Landscapes of a m6A/m5C/m1A-Related LncRNAs Signature in Head and Neck Squamous Cell Carcinoma.
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SUBMITTER: Wang E
PROVIDER: S-EPMC8670092 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundColon cancer features strong heterogeneity and invasiveness, with high incidence and mortality rates. Recently, RNA modifications involving m6A, m5C, and m1A play a vital part in tumorigenesis and immune cell infiltration. However, integrated analysis among various RNA modifications in colon cancer has not been performed.MethodsRNA-seq profiling, clinical data and mutation data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. We first explored the mutation status and expression levels of m6A/m5C/m1A regulators in colon cancer. Then, different m6A/m5C/m1A clusters and gene clusters were identified by consensus clustering analysis. We further constructed and validated a scoring system, which could be utilized to accurately assess the risk of individuals and guide personalized immunotherapy. Finally, m6A/m5C/m1A regulators were validated by immunohistochemical staining and RT-qPCR.ResultsIn our study, three m6A/m5C/m1A clusters and gene clusters were identified. Most importantly, we constructed a m6A/m5C/m1A scoring system to assess the clinical risk of the individuals. Besides, the prognostic value of the score was validated with three independent cohorts. Moreover, the level of the immunophenoscore of the low m6A/m5C/m1A score group increased significantly with CTLA-4/PD-1 immunotherapy. Finally, we validated that the mRNA and protein expression of VIRMA and DNMT3B increased in colon cancer tissues.ConclusionsWe constructed and validated a stable and powerful m6A/m5C/m1A score signature to assess the survival outcomes and immune infiltration characteristics of colon cancer patients, which further guides optimization of personalized treatment, making it valuable for clinical translation and implementation.
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Project description:5-methylcytosine (m5C) modification is involved in tumor progression. However, the lncRNAs associated with m5C in lung squamous cell carcinoma (LUSC) have not been elucidated. The Cancer Genome Atlas database was used to get the open-accessed transcriptional profiling and clinical information of LUSC patients. All the statistical analyses were performed based on R software v 4.0.0 and SPSS13.0. First, there were 614 m5C-related lncRNAs identified under the criterion of |R|>0.4 and p < 0.001 with m5C genes. Next, a prognosis model based on ERICD, AL021068.1, LINC01341, AC254562.3, and AP002360.1 was established, which showed good prediction efficiency in both the training and validation cohorts. Next, a nomogram plot was established by combining the risk score and clinical features for a better application in clinical settings. Pathway enrichment analysis showed that the pathways of angiogenesis, TGF-β signaling, IL6-JAK-STAT3 signaling, protein secretion, androgen response, interferon-α response, and unfolded protein response were significantly enriched in the high-risk patients. Immune infiltration analysis showed that the risk score was positively correlated with neutrophils, resting CD4+ memory T cells, and M2 macrophages, yet negatively correlated with follicular helper T cells, CD8+ T cells, and activated NK cells. Moreover, we found that high-risk patients might be more sensitive to immunotherapy, imatinib, yet resistant to erlotinib, gefitinib, and vinorelbine. In summary, our prognosis model is an effective tool that could robustly predict LUSC patient prognosis, which had the potential for clinical guidance.
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Project description:BackgroundN6-methyladenosine (m6A) and 5-methylcytosine (m5C) are the main RNA methylation modifications involved in the oncogenesis of cancer. However, it remains obscure whether m6A/m5C-related long non-coding RNAs (lncRNAs) affect the development and progression of low grade gliomas (LGG).MethodsWe summarized 926 LGG tumor samples with RNA-seq data and clinical information from The Cancer Genome Atlas and Chinese Glioma Genome Atlas. 105 normal brain samples with RNA-seq data from the Genotype Tissue Expression project were collected for control. We obtained a molecular classification cluster from the expression pattern of sreened lncRNAs. The least absolute shrinkage and selection operator Cox regression was employed to construct a m6A/m5C-related lncRNAs prognostic signature of LGG. In vitro experiments were employed to validate the biological functions of lncRNAs in our risk model.ResultsThe expression pattern of 14 sreened highly correlated lncRNAs could cluster samples into two groups, in which various clinicopathological features and the tumor immune microenvironment were significantly distinct. The survival time of cluster 1 was significantly reduced compared with cluster 2. This prognostic signature is based on 8 m6A/m5C-related lncRNAs (GDNF-AS1, HOXA-AS3, LINC00346, LINC00664, LINC00665, MIR155HG, NEAT1, RHPN1-AS1). Patients in the high-risk group harbored shorter survival times. Immunity microenvironment analysis showed B cells, CD4 + T cells, macrophages, and myeloid-derived DC cells were significantly increased in the high-risk group. Patients in high-risk group had the worse overall survival time regardless of followed TMZ therapy or radiotherapy. All observed results from the TCGA-LGG cohort could be validated in CGGA cohort. Afterwards, LINC00664 was found to promote cell viability, invasion and migration ability of glioma cells in vitro.ConclusionOur study elucidated a prognostic prediction model of LGG by 8 m6A/m5C methylated lncRNAs and a critical lncRNA regulation function involved in LGG progression. High-risk patients have shorter survival times and a pro-tumor immune microenvironment.
| S-EPMC10320943 | biostudies-literature
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.
| S-EPMC9272990 | biostudies-literature
Project description:Head and neck cancer (HNC) is the fifth most common cancer worldwide. In this study, we performed an integrative analysis of the discovery set and established an eight-gene signature for the prediction of prognosis in patients with head and neck squamous cell carcinoma (HNSCC). Univariate Cox analysis was used to identify prognosis-related genes (with P < 0.05) in the GSE41613, GSE65858, and TCGA-HNSC RNA-Seq datasets after data collection. We performed LASSO Cox regression analysis and identified eight genes (CBX3, GNA12, P4HA1, PLAU, PPL, RAB25, EPHX3, and HLF) with non-zero regression coefficients in TCGA-HNSC datasets. Survival analysis revealed that the overall survival (OS) of GSE41613 and GSE65858 datasets and the progression-free survival(DFS)of GSE27020 and GSE42743 datasets in the low-risk group exhibited better survival outcomes compared with the high-risk group. To verify that the eight-mRNA prognostic model was independent of other clinical features, KM survival analysis of the specific subtypes with different clinical characteristics was performed. Univariate and multivariate Cox regression analyses were used to identify three independent prognostic factors to construct a prognostic nomogram. Finally, the GSVA algorithm identified six pathways that were activated in the intersection of the TCGA-HNSC, GSE65858, and GSE41613 datasets, including early estrogen response, cholesterol homeostasis, oxidative phosphorylation, fatty acid metabolism, bile acid metabolism, and Kras signaling. However, the epithelial-mesenchymal transition pathway was inhibited at the intersection of the three datasets. In conclusion, the eight-gene prognostic signature proved to be a useful tool in the prognostic evaluation and facilitate personalized treatment of HNSCC patients.
| S-EPMC8252784 | biostudies-literature
Project description:IntroductionRNA 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.MethodsThe 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.ResultsA 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.DiscussionThis 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.
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Project description:RNA modifications implicate pathological and prognosis significance in cancer development and progression, of which, m6A and m5C are representative regulators. These RNA modifications could produce effects on the function of other RNA by regulating gene expression. Thus, in this study, we aimed to explore the correlation between m6A/m5C regulators and early-stage lung adenocarcinoma (LUAD). Only the early-stage LUAD samples were included in this investigation, and the RNA-seq dataset of The Cancer Genome Atlas (TCGA) cohort was utilized to evaluate the expression of 37 m6A/m5C regulated genes. Based on the expression level of these 37 genes, early-stage LUAD patients were divided into 2 clusters, which were performed by consensus clustering, and the m6A/m5C subtypes had significantly different prognostic outcomes (p < 0.001). Cluster1, which has a better prognosis, was characterized by the C3 (inflammatory) immune subtype, low immune infiltration, chemokine expression, major histocompatibility complex (MHC) expression, and immune checkpoint molecule expression. Furthermore, compared with cluster1, cluster2 showed a T cell exhaustion state, characterized by a high expression of immune checkpoint genes, and immune cells, such as T cells, CD8+ T cells, cytotoxic lymphocytes, NK cells, and so on. In addition, patients in cluster2 were with high tumor mutational burden (TMB) and numerous significant mutated oncogene and tumor suppressor genes, such as WNT10B, ERBB4, SMARCA4, TP53, and CDKN2A (p < 0.001). A total of 19 genes were mostly related to the prognosis of LUAD and were upregulated in cluster2 (p < 0.05), showing a positive correlation with the mRNA expression of 37 m6A/m5C regulated genes. The predictive risk model was constructed using Cox and LASSO (least absolute shrinkage and selection operator) regression analysis. Finally, a seven-gene m6A/m5C risk model, comprising of METTL3, NPLOC4, RBM15, YTHDF1, IGF2BP1, NSUN3, and NSUN7, was constructed to stratify the prognosis of early-stage LUAD (p = 0.0049, AUC = 0.791). The high-risk score was associated with a poorer prognosis. This model was also validated using two additional GEO datasets: GSE72094 (p = 0.011, AUC = 0.736) and GSE50081 (p = 0.012, AUC = 0.628). In summary, it was established that the m6A/m5C-regulated genes performed a crosstalk function in the mRNA expression of early-stage LUAD. By interacting with other mRNA genes, m6A/m5C modification disturbs DNA replication and the tumor immune microenvironment (TIME). The seven-gene risk model may be a critical tool for the prognostic assessment of early-stage LUAD.
| S-EPMC10095361 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is characterized by a high incidence and mortality rate. Despite advancements in therapeutic interventions, the prognosis for renal cancer patients remains suboptimal. Of late, methylation modifications have emerged as promising molecular targets for tumor assessment and treatment, yet their potential has not been fully investigated in the context of ccRCC. Transcriptomic and clinical data were extracted from The Cancer Genome Atlas, Gene Expression Omnibus, and ArrayExpress databases, leading to the identification of 57 methylation-related genes (MRGs). Utilizing DESeq2 analysis, Cox regression analysis, and the LASSO regression algorithm, a Methylation-Related Risk Score (MARS) was constructed. Cluster analysis, Gene Ontology (GO) analysis, clinical feature analysis, immune infiltration analysis, and mutation analysis were further employed to evaluate the model. Our investigation identified six pivotal prognostic MRGs and established a risk score predicated on m6A/m5C/m1A/m7G regulatory factors. This score was validated across two external cohorts and can be utilized to assess individual immune infiltration statuses and predict responses to immunotherapy. Moreover, cluster analysis delineated two distinct m6A/m5C/m1A/m7G gene clusters. We have developed and validated a robust prognostic signature based on genes associated with m6A, m5C, m1A, and m7G modifications. This gene signature demonstrates significant prognostic value in assessing survival outcomes, clinical characteristics, immune infiltration, and responses to immunotherapy in ccRCC patients. This finding provides valuable insights for refining precision treatment strategies.
| S-EPMC11319670 | biostudies-literature
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.
| S-EPMC10101678 | biostudies-literature
Project description:PurposeTo construct a prognostic signature composed of DNA repair genes to effectively predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).MethodsAfter downloading the transcriptome and clinical data of HNSCC from the Cancer Genome Atlas (TCGA), 499 patients with HNSCC were equally divided into training and testing sets. In the training set, 13 DNA repair genes were screened using univariate proportional hazard (Cox) regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a risk model, which was validated in the testing set.ResultsIn the training and testing sets, there were significant differences in the clinical outcomes of patients in the high- and low-risk groups showed by Kaplan-Meier survival curves (P < 0.001). Univariate and multivariate Cox regression analyses showed that the risk score had independent prognostic predictive ability (P < 0.001). At the same time, the immune cell infiltration, immune score, immune-related gene expression, and tumor mutation burden (TMB) of patients with HNSCC were also different between the high- and low-risk groups (P < 0.05). Finally, we screened several chemotherapeutics for HNSCC, which showed significant differences in drug sensitivity between the high- and low-risk groups (P < 0.05).ConclusionThis study constructed a 13-DNA-repair-gene signature for the prognosis of HNSCC, which could accurately and independently predict the clinical outcome of the patient. We then revealed the immune landscape, TMB, and sensitivity to chemotherapy drugs in different risk groups, which might be used to guide clinical treatment decisions.
| S-EPMC8362833 | biostudies-literature