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Kong R, Wang N, Zhou CL, Lu J. Prognostic Value of an Immune Long Non-Coding RNA Signature in Liver Hepatocellular Carcinoma. J Microbiol Biotechnol 2024; 34:958-968. [PMID: 38494878 DOI: 10.4014/jmb.2308.08022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 11/28/2023] [Accepted: 01/18/2024] [Indexed: 03/19/2024]
Abstract
In recent years, there has been a growing recognition of the important role that long non-coding RNAs (lncRNAs) play in the immunological process of hepatocellular carcinoma (LIHC). An increasing number of studies have shown that certain lncRNAs hold great potential as viable options for diagnosis and treatment in clinical practice. The primary objective of our investigation was to devise an immune lncRNA profile to explore the significance of immune-associated lncRNAs in the accurate diagnosis and prognosis of LIHC. Gene expression profiles of LIHC samples obtained from TCGA database were screened for immune-related genes. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate Cox analysis. Then, the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were performed to evaluate the capability of the immune lncRNA signature as a prognostic indicator. Six long non-coding RNAs were identified via correlation analysis and Cox regression analysis considering their interactions with immune genes. Subsequently, tumor samples were categorized into two distinct risk groups based on different clinical outcomes. Stratification analysis indicated that the prognostic ability of this signature acted as an independent factor. The Kaplan-Meier method was employed to conduct survival analysis, results showed a significant difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Additionally, data obtained from gene set enrichment analysis (GSEA) revealed several potential biological processes in which these biomarkers may be involved. To summarize, this study demonstrated that this six-lncRNA signature could be identified as a potential factor that can independently predict the prognosis of LIHC patients.
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Affiliation(s)
- Rui Kong
- Department of Gastroenterology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, P.R. China
| | - Nan Wang
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, P.R. China
| | - Chun Li Zhou
- Department of Gastroenterology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, P.R. China
| | - Jie Lu
- Department of Gastroenterology, Pu Dong Area Gongli Hospital, School of Medicine, Shanghai University, Shanghai 200135, P.R. China
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, P.R. China
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Draškovič T, Hauptman N. Discovery of novel DNA methylation biomarker panels for the diagnosis and differentiation between common adenocarcinomas and their liver metastases. Sci Rep 2024; 14:3095. [PMID: 38326602 PMCID: PMC10850119 DOI: 10.1038/s41598-024-53754-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 02/05/2024] [Indexed: 02/09/2024] Open
Abstract
Differentiation between adenocarcinomas is sometimes challenging. The promising avenue for discovering new biomarkers lies in bioinformatics using DNA methylation analysis. Utilizing a 2853-sample identification dataset and a 782-sample independent verification dataset, we have identified diagnostic DNA methylation biomarkers that are hypermethylated in cancer and differentiate between breast invasive carcinoma, cholangiocarcinoma, colorectal cancer, hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma and stomach adenocarcinoma. The best panels for cancer type exhibit sensitivity of 77.8-95.9%, a specificity of 92.7-97.5% for tumors, a specificity of 91.5-97.7% for tumors and normal tissues and a diagnostic accuracy of 85.3-96.4%. We have shown that the results can be extended from the primary cancers to their liver metastases, as the best panels diagnose and differentiate between pancreatic adenocarcinoma liver metastases and breast invasive carcinoma liver metastases with a sensitivity and specificity of 83.3-100% and a diagnostic accuracy of 86.8-91.9%. Moreover, the panels could detect hypermethylation of selected regions in the cell-free DNA of patients with liver metastases. At the same time, these were unmethylated in the cell-free DNA of healthy donors, confirming their applicability for liquid biopsies.
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Affiliation(s)
- Tina Draškovič
- Faculty of Medicine, Institute of Pathology, University of Ljubljana, Ljubljana, Slovenia
| | - Nina Hauptman
- Faculty of Medicine, Institute of Pathology, University of Ljubljana, Ljubljana, Slovenia.
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Salido-Guadarrama I, Romero-Cordoba SL, Rueda-Zarazua B. Multi-Omics Mining of lncRNAs with Biological and Clinical Relevance in Cancer. Int J Mol Sci 2023; 24:16600. [PMID: 38068923 PMCID: PMC10706612 DOI: 10.3390/ijms242316600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 12/18/2023] Open
Abstract
In this review, we provide a general overview of the current panorama of mining strategies for multi-omics data to investigate lncRNAs with an actual or potential role as biological markers in cancer. Several multi-omics studies focusing on lncRNAs have been performed in the past with varying scopes. Nevertheless, many questions remain regarding the pragmatic application of different molecular technologies and bioinformatics algorithms for mining multi-omics data. Here, we attempt to address some of the less discussed aspects of the practical applications using different study designs for incorporating bioinformatics and statistical analyses of multi-omics data. Finally, we discuss the potential improvements and new paradigms aimed at unraveling the role and utility of lncRNAs in cancer and their potential use as molecular markers for cancer diagnosis and outcome prediction.
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Affiliation(s)
- Ivan Salido-Guadarrama
- Departamento de Bioinformatìca y Análisis Estadísticos, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico
| | - Sandra L. Romero-Cordoba
- Departamento de Medicina Genómica y Toxicología Ambiental, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
- Biochemistry Department, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City 14080, Mexico
| | - Bertha Rueda-Zarazua
- Posgrado en Ciencias Biológicas, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico;
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Guo T, Zhao S, Zhu W, Zhou H, Cheng H. Research progress on the biological basis of Traditional Chinese Medicine syndromes of gastrointestinal cancers. Heliyon 2023; 9:e20653. [PMID: 38027682 PMCID: PMC10643116 DOI: 10.1016/j.heliyon.2023.e20653] [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: 04/30/2023] [Revised: 09/23/2023] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
Gastrointestinal cancers account for 11.6 % of all cancers, and are the second most frequently diagnosed type of cancer worldwide. Traditional Chinese medicine (TCM), together with Western medicine or alone, has unique advantages for the prevention and treatment of cancers, including gastrointestinal cancers. Syndrome differentiation and treatment are basic characteristics of the theoretical system of TCM. TCM syndromes are the result of the differentiation of the syndrome and the basis of treatment. Genomics, transcriptomics, proteomics, metabolomics, intestinal microbiota, and serology, generated around the central law, are used to study the biological basis of TCM syndromes in gastrointestinal cancers. This review summarizes current research on the biological basis of TCM syndrome in gastrointestinal cancers and provides useful references for future research on TCM syndrome in gastrointestinal cancers.
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Affiliation(s)
- Tianhao Guo
- Institute of Health and Regimen, Jiangsu Open University, Nanjing, Jiangsu 210036, China
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
| | - Shuoqi Zhao
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
| | - Wenjian Zhu
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
| | - Hongguang Zhou
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
- Departments of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210029, China
| | - Haibo Cheng
- Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
- Departments of Oncology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210029, China
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