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Yang S, Luo Y, Zhou D, Xiang J, Xi X. RNA 5-Methylcytosine regulators are associated with cell adhesion and predict prognosis of endometrial cancer. Transl Cancer Res 2023; 12:2556-2571. [PMID: 37969377 PMCID: PMC10643971 DOI: 10.21037/tcr-23-742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 09/21/2023] [Indexed: 11/17/2023]
Abstract
Background RNA methylation is a significant form of post-transcriptional modification that has been implicated in various diseases, including cancers. One prominent type of RNA methylation is 5-Methylcytosine (m5C), which primarily regulates RNA stability, transcription, and translation. However, the role of m5C-related gene regulation in cell adhesion within uterine corpus endometrial carcinoma (UCEC) remains unexplored. Therefore, the objective of this study was to investigate the association between RNA m5C methylation and UCEC and develop a prognostic predictive model to forecast survival outcomes in UCEC patients. Methods The RNA datasets were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The dataset was used to explore the interaction relationships of m5C regulators in UCEC. Unsupervised clustering analysis identified clusters with distinct m5C modification patterns. Different clusters underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment level analysis to investigate the effects of pathways related to m5C methylation, which were further validated through in vitro cellular experiments. A prognostic predictive model was developed using the least absolute shrinkage and selection operator (LASSO) and multivariate regression analysis. Results Two clusters with distinct m5C modification patterns were identified using unsupervised cluster analysis. Furthermore, the prognosis of cluster 2 was found to be worse. Enrichment analysis showed alterations in cell adhesion-related pathways in both clusters, as well as differences between the clusters. Through this analysis, we identified 25 genes with significant prognostic value. Finally, a prognostic predictive model comprising NSUN2 and YBX1 was constructed. Conclusions In conclusion, diverse m5C modification patterns display distinct cell adhesion properties in UCEC, which are correlated with prognosis and offer significant potential as prognostic markers for UCEC assessment. We developed a prognostic predictive model to accurately predict the prognosis of UCEC.
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Affiliation(s)
- Shimin Yang
- Department of Gynecology and Obstetrics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yifan Luo
- Department of Gynecology and Obstetrics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dongmei Zhou
- Department of Gynecology and Obstetrics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiangdong Xiang
- Department of Gynecology and Obstetrics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaowei Xi
- Department of Gynecology and Obstetrics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Gong D, Zhao Q, Liu J, Zhao S, Yi C, Lv J, Yu H, Bian E, Tian D. Identification of a novel MYC target gene set signature for predicting the prognosis of osteosarcoma patients. Front Oncol 2023; 13:1169430. [PMID: 37342196 PMCID: PMC10277635 DOI: 10.3389/fonc.2023.1169430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 05/04/2023] [Indexed: 06/22/2023] Open
Abstract
Osteosarcoma is a primary malignant tumor found mainly in teenagers and young adults. Patients have very little long-term survival. MYC controls tumor initiation and progression by regulating the expression of its target genes; thus, constructing a risk signature of osteosarcoma MYC target gene set will benefit the evaluation of both treatment and prognosis. In this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. Then, a risk signature consisting of 10 MYC target genes was developed using Cox regression analysis. The signature indicates that patients in the high-risk group performed poorly. After that, we verified it in the GSE21257 dataset. In addition, the difference in tumor immune function among the low- and high-risk populations was compared by single sample gene enrichment analysis. Immunotherapy and prediction of response to the anticancer drug have shown that the risk signature of the MYC target gene set was positively correlated with immune checkpoint response and drug sensitivity. Functional analysis has demonstrated that these genes are enriched in malignant tumors. Finally, STX10 was selected for functional experimentation. STX10 silence has limited osteosarcoma cell migration, invasion, and proliferation. Therefore, these findings indicated that the MYC target gene set risk signature could be used as a potential therapeutic target and prognostic indicator in patients with osteosarcoma.
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Affiliation(s)
- Deliang Gong
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Qingzhong Zhao
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jun Liu
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Shibing Zhao
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Chengfeng Yi
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jianwei Lv
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hang Yu
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Erbao Bian
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Dasheng Tian
- Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
- Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China
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Zhang P, Zhang T, Chen D, Gong L, Sun M. Prognosis and Novel Drug Targets for Key lncRNAs of Epigenetic Modification in Colorectal Cancer. Mediators Inflamm 2023; 2023:6632205. [PMID: 37091904 PMCID: PMC10116225 DOI: 10.1155/2023/6632205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 12/20/2022] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
Background Colorectal cancer (CRC) has been the 3rd most commonly malignant tumor of the gastrointestinal tract in the world. 5-Methylcytosine (m5C) and long noncoding RNAs (lncRNAs) have an essential role in predicting the prognosis and immune response for CRC patients. Therefore, we built a m5C-related lncRNA (m5CRlncRNA) model to investigate the prognosis and treatment methods for CRC patients. Methods Firstly, we secured the transcriptome and clinical data for CRC from The Cancer Genome Atlas (TCGA). Then, m5CRlncRNAs were recognized by coexpression analysis. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were utilized to build m5C-related prognostic characteristics. Besides, Kaplan-Meier analysis, ROC, PCA, C-index, enrichment analysis, and nomogram were performed to investigate the model. Additionally, immunotherapy responses and antitumor medicines were explored for CRC patients. Results A total of 8 m5C-related lncRNAs (AC093157.1, LINC00513, AC025171.4, AC090948.2, ZEB1-AS1, AC109449.1, AC009041.3, and LINC02516) were adopted to construct a risk model to investigate survival and prognosis for CRC patients. CRC samples were separated into low- and high-risk groups, with the latter having a worse prognosis. The m5C-related lncRNA model helps us to better distinguish immunotherapy responses and IC50 of antitumor medicines in different groups of CRC patients. Conclusion The research may give new perspectives on tailored therapy approaches as well as novel theories for forecasting the prognosis of CRC patients.
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Affiliation(s)
- Peng Zhang
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Tingting Zhang
- Department of Clinical Oncology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Denggang Chen
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Li Gong
- Department of Endocrinology, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Min Sun
- Department of General Surgery, Taihe Hospital, Hubei University of Medicine, Shiyan, China
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Song Y, Jiang Y, Shi L, He C, Zhang W, Xu Z, Yang M, Xu Y. Comprehensive analysis of key m5C modification-related genes in type 2 diabetes. Front Genet 2022; 13:1015879. [PMID: 36276976 PMCID: PMC9582283 DOI: 10.3389/fgene.2022.1015879] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background: 5-methylcytosine (m5C) RNA methylation plays a significant role in several human diseases. However, the functional role of m5C in type 2 diabetes (T2D) remains unclear.Methods: The merged gene expression profiles from two Gene Expression Omnibus (GEO) datasets were used to identify m5C-related genes and T2D-related differentially expressed genes (DEGs). Least-absolute shrinkage and selection operator (LASSO) regression analysis was performed to identify optimal predictors of T2D. After LASSO regression, we constructed a diagnostic model and validated its accuracy. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to confirm the biological functions of DEGs. Gene Set Enrichment Analysis (GSEA) was used to determine the functional enrichment of molecular subtypes. Weighted gene co-expression network analysis (WGCNA) was used to select the module that correlated with the most pyroptosis-related genes. Protein-protein interaction (PPI) network was established using the STRING database, and hub genes were identified using Cytoscape software. The competitive endogenous RNA (ceRNA) interaction network of the hub genes was obtained. The CIBERSORT algorithm was applied to analyze the interactions between hub gene expression and immune infiltration.Results: m5C-related genes were significantly differentially expressed in T2D and correlated with most T2D-related DEGs. LASSO regression showed that ZBTB4 could be a predictive gene for T2D. GO, KEGG, and GSEA indicated that the enriched modules and pathways were closely related to metabolism-related biological processes and cell death. The top five genes were identified as hub genes in the PPI network. In addition, a ceRNA interaction network of hub genes was obtained. Moreover, the expression levels of the hub genes were significantly correlated with the abundance of various immune cells.Conclusion: Our findings may provide insights into the molecular mechanisms underlying T2D based on its pathophysiology and suggest potential biomarkers and therapeutic targets for T2D.
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Affiliation(s)
- Yaxian Song
- Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yan Jiang
- Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Li Shi
- Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Chen He
- Department of Geriatric Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Wenhua Zhang
- Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Zhao Xu
- Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Mengshi Yang
- Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yushan Xu
- Department of Endocrinology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
- *Correspondence: Yushan Xu,
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