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Zhang B, Zhu B, Yu J, Liu H, Zhou Y, Sun G, Ma Y, Luan Y, Chen M. A combined model of six serum microRNAs as diagnostic markers for hepatocellular carcinoma. Clin Chim Acta 2024; 565:119977. [PMID: 39332657 DOI: 10.1016/j.cca.2024.119977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Revised: 09/23/2024] [Accepted: 09/23/2024] [Indexed: 09/29/2024]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is associated with high morbidity and mortality, and its poor prognosis is mainly due to the lack of an effective means of early diagnosis. This study aimed to identify a group of serum microRNAs (miRNAs) as potential biomarkers for the diagnosis of HCC. METHODS We collected 190 HCC cases, 109 benign lesions of the liver, 40 cases of non-HCC tumors, and 130 healthy controls. The 469 participants were divided into training and validation sets. A literature search revealed 12 miRNAs closely associated with HCC. In the training set, significantly differentially expressed miRNAs (DEmiRNAs) were screened using real-time quantitative PCR, and a diagnostic model of HCC was constructed using logistic regression analysis. An independent validation was performed using a validation set. The identified DE miRNAs were subjected to target gene prediction and functional analyses. RESULTS Compared to the controls, the levels of miR-21, miR-221, miR-801, and miR-1246 significantly decreased in HCC (P < 0.05), while the levels of miR-26a and miR-122 significantly increased (P < 0.05). A diagnostic model based on the six DE miRNAs was successfully constructed, with AUC values of 0.953 for the training set and 0.952 for the verification set. Finally, 100 target genes of the DE miRNAs were predicted and were significantly enriched in the B cell receptor, neurotrophin, ferroptosis, and EGFR tyrosine kinase inhibitor resistance signaling pathways. CONCLUSIONS The constructed diagnostic model based on six DE miRNA combinations has important clinical value for the early diagnosis of HCC.
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Affiliation(s)
- Bingqiang Zhang
- Qingdao Ruiside Medical Laboratory Co., LTD, Qingdao, Shandong 266111, PR China
| | - Boyang Zhu
- School of Clinical and Basic Medical Sciences, Shandong First Medical University& Shandong Academy of Medical Sciences, Jinan, Shandong, 250117, PR China
| | - Junmei Yu
- Qingdao Ruiside Medical Laboratory Co., LTD, Qingdao, Shandong 266111, PR China
| | - He Liu
- Qingdao Ruiside Medical Laboratory Co., LTD, Qingdao, Shandong 266111, PR China
| | - Yang Zhou
- Qingdao Ruiside Medical Laboratory Co., LTD, Qingdao, Shandong 266111, PR China
| | - Guolong Sun
- Qingdao Ruiside Medical Laboratory Co., LTD, Qingdao, Shandong 266111, PR China
| | - Yongchao Ma
- School of Chemistry and Pharmacy, Qingdao Agricultural University, Qingdao, Shandong 266111, PR China
| | - Yansong Luan
- Qingdao Ruiside Medical Laboratory Co., LTD, Qingdao, Shandong 266111, PR China.
| | - Mengmeng Chen
- Qingdao Ruiside Medical Laboratory Co., LTD, Qingdao, Shandong 266111, PR China.
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Wang K, Shi J, Tong X, Qu N, Kong X, Ni S, Xing J, Li X, Zheng M. TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy. Brief Bioinform 2024; 25:bbae017. [PMID: 38390990 PMCID: PMC10886443 DOI: 10.1093/bib/bbae017] [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: 11/10/2023] [Revised: 12/27/2023] [Accepted: 01/15/2024] [Indexed: 02/24/2024] Open
Abstract
Enhancing cancer treatment efficacy remains a significant challenge in human health. Immunotherapy has witnessed considerable success in recent years as a treatment for tumors. However, due to the heterogeneity of diseases, only a fraction of patients exhibit a positive response to immune checkpoint inhibitor (ICI) therapy. Various single-gene-based biomarkers and tumor mutational burden (TMB) have been proposed for predicting clinical responses to ICI; however, their predictive ability is limited. We propose the utilization of the Text Graph Convolutional Network (GCN) method to comprehensively assess the impact of multiple genes, aiming to improve the predictive capability for ICI response. We developed TG468, a Text GCN model framing drug response prediction as a text classification task. By combining natural language processing (NLP) and graph neural network techniques, TG468 effectively handles sparse and high-dimensional exome sequencing data. As a result, TG468 can distinguish survival time for patients who received ICI therapy and outperforms single gene biomarkers, TMB and some classical machine learning models. Additionally, TG468's prediction results facilitate the identification of immune status differences among specific patient types in the Cancer Genome Atlas dataset, providing a rationale for the model's predictions. Our approach represents a pioneering use of a GCN model to analyze exome data in patients undergoing ICI therapy and offers inspiration for future research using NLP technology to analyze exome sequencing data.
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Affiliation(s)
- Kun Wang
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
| | - Jiangshan Shi
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences; 555 Zuchongzhi Road, Shanghai 201203, China
| | - Xiaochu Tong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences; 555 Zuchongzhi Road, Shanghai 201203, China
| | - Ning Qu
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences; 555 Zuchongzhi Road, Shanghai 201203, China
| | - Xiangtai Kong
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences; 555 Zuchongzhi Road, Shanghai 201203, China
| | - Shengkun Ni
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences; 555 Zuchongzhi Road, Shanghai 201203, China
| | - Jing Xing
- Lingang Laboratory, Shanghai 200031, China
| | - Xutong Li
- Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
| | - Mingyue Zheng
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
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Peng Q, Ren B, Xin K, Liu W, Alam MS, Yang Y, Gu X, Zhu Y, Tian Y. CYFIP2 serves as a prognostic biomarker and correlates with tumor immune microenvironment in human cancers. Eur J Med Res 2023; 28:364. [PMID: 37735711 PMCID: PMC10515071 DOI: 10.1186/s40001-023-01366-2] [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: 07/11/2023] [Accepted: 09/12/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND The mechanisms whereby CYFIP2 acts in tumor development and drives immune infiltration have been poorly explored. Thus, this study aimed to identifying the role of CYFIP2 in tumors and immune response. METHODS In this study, we first explored expression patterns, diagnostic role and prognostic value of CYFIP2 in cancers, particularly in lung adenocarcinoma (LUAD). Then, we performed functional enrichment, genetic alterations, DNA methylation analysis, and immune cell infiltration analysis of CYFIP2 to uncover its potential mechanisms involved in immune microenvironment. RESULTS We found that CYFIP2 significantly differentially expressed in different tumors including LUAD compared with normal tissues. Furthermore, CYFIP2 was found to be significantly correlated with clinical parameters in LUAD. According to the diagnostic and survival analysis, CYFIP2 may be employed as a potential diagnostic and prognostic biomarker. Moreover, genetic alterations revealed that mutation of CYFIP2 was the main types of alterations in different cancers. DNA methylation analysis indicated that CYFIP2 mRNA expression correlated with hypomethylation. Afterwards, functional enrichment analysis uncovered that CYFIP2 was involved in tumor-associated and immune-related pathways. Immune infiltration analysis indicated that CYFIP2 was significantly correlated with immune cells infiltration. In particular, CYFIP2 was strongly linked with immune microenvironment scores. Additionally, CYFIP2 exhibited a significant relationship with immune regulators and immune-related genes including chemokines, chemokines receptors, and MHC genes. CONCLUSION Our results suggested that CYFIP2 may serve as a prognostic cancer biomarker for determining prognosis and might be a promising therapeutic strategy for tumor immunotherapy.
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Affiliation(s)
- Qiliang Peng
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China
| | - Bixin Ren
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Kedao Xin
- Department of Radiation Oncology, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, China
| | - Weihui Liu
- Department of Oncology, Dazhou Central Hospital, Dazhou, China
| | - Md Shahin Alam
- Laboratory of Molecular Neuropathology, Department of Pharmacology, Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Soochow University, Suzhou, China
| | - Yinyin Yang
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Xuhao Gu
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China
| | - Yaqun Zhu
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China.
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China.
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China.
| | - Ye Tian
- Department of Radiotherapy & Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No. 1055, Suzhou, 215004, Jiangsu, China.
- Institute of Radiotherapy & Oncology, Soochow University, Suzhou, China.
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