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Sun C, Cheng X, Xu J, Chen H, Tao J, Dong Y, Wei S, Chen R, Meng X, Ma Y, Tian H, Guo X, Bi S, Zhang C, Kang J, Zhang M, Lv H, Shang Z, Lv W, Zhang R, Jiang Y. A review of disease risk prediction methods and applications in the omics era. Proteomics 2024; 24:e2300359. [PMID: 38522029 DOI: 10.1002/pmic.202300359] [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: 09/15/2023] [Revised: 03/08/2024] [Accepted: 03/12/2024] [Indexed: 03/25/2024]
Abstract
Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.
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Affiliation(s)
- Chen Sun
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Xiangshu Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Haiyan Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Junxian Tao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Yu Dong
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Siyu Wei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Rui Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xin Meng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yingnan Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
| | - Hongsheng Tian
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Xuying Guo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Shuo Bi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Chen Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jingxuan Kang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhenwei Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Wenhua Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Ruijie Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- The EWAS Project, Harbin, China
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Chen Y, Liu S, Tan S, Zheng Y, Chen Y, Yang C, Lin S, Mi Y, Li W. KRAS mutations promote the intratumoral colonization of enterotoxigenic bacteroides fragilis in colorectal cancer through the regulation of the miRNA3655/SURF6/IRF7/IFNβ axis. Gut Microbes 2024; 16:2423043. [PMID: 39523457 PMCID: PMC11556274 DOI: 10.1080/19490976.2024.2423043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Revised: 10/16/2024] [Accepted: 10/24/2024] [Indexed: 11/16/2024] Open
Abstract
KRAS mutations are associated with poor prognosis in colorectal cancer (CRC). Although the association between the gut microbiota and CRC has been extensively documented, it is unclear whether KRAS mutations can regulate the gut microbiota. Metagenomics has identified changes in the diversity of the gut microbiota in CRC due to KRAS mutations. Specifically, KRAS mutations positively correlate with the abundance of the bacteroides. Understanding how to regulate the classic carcinogenic bacterium within the bacteroides, such as enterotoxigenic bacteroides fragilis (ETBF), to enhance treatment efficacy of tumors is a key focus of research. Mechanistically, we found that the reduction of miR3655 is indispensable for KRAS mutation-promoted proliferation of CRC and the abundance of ETBF. miR3655 targets SURF6 to inhibit its transcription. Further transcriptomic sequencing revealed that SURF6 promotes intratumoral colonization of ETBF in CRC by inhibiting the nuclear translocation and transcription levels of the IRF7, affecting the activation of the IFNβ promoter. Regulating miR3655 and SURF6 can promote IFNβ secretion in CRC, directly killing ETBF. These data indicate that KRAS mutations affect the intratumoral colonization of ETBF in CRC through the miR3655/SURF6/IRF7/IFNβ axis. This provides new potential strategies for treating CRC associated with KRAS mutations or high levels of ETBF.
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Affiliation(s)
- Yizhen Chen
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China
| | - Shaolin Liu
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China
| | - Song Tan
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China
| | - Yuanyuan Zheng
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Geriatric Medicine, Fujian Key Laboratory of Geriatrics Diseases, Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China
| | - Yifan Chen
- Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China
| | - Changshun Yang
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China
| | - Shengtao Lin
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China
| | - Yulong Mi
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China
| | - Weihua Li
- Department of Gastrointestinal Surgery, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, School of Medicine, Fuzhou University, Fuzhou, Fujian, China
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Zhou X, Chen B, Zhang Z, Huang Y, Li J, Wei Q, Cao D, Ai J. Crosstalk between Tumor-Associated Macrophages and MicroRNAs: A Key Role in Tumor Microenvironment. Int J Mol Sci 2022; 23:13258. [PMID: 36362044 PMCID: PMC9653885 DOI: 10.3390/ijms232113258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/25/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022] Open
Abstract
As an in-depth understanding of immunotherapy continues to grow, current anticancer therapy research is increasingly focused on the tumor microenvironment (TME). MicroRNAs (miRNAs) play crucial roles in the regulation of genetic information and expression and mediate interactions between tumor cells and components in the TME, such as tumor-associated macrophages (macrophages). Macrophages are abundant in the TME, and their different polarization directions can promote or inhibit tumor growth and progression. By regulating biological behaviors, such as macrophage recruitment, infiltration, and polarization, miRNAs can affect various molecular pathways to regulate tumor progression and treatment response. In this review, we discuss in detail the effects of macrophages on tumors and the multifaceted effects of miRNAs on macrophages. We also discuss the potential clinical applications and prospects of targeted therapy based on miRNAs, novel clinical biomarkers, and drug delivery systems.
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Affiliation(s)
| | | | | | | | | | | | - Dehong Cao
- Department of Urology, Institute of Urology, Sichuan University, Chengdu 610041, China
| | - Jianzhong Ai
- Department of Urology, Institute of Urology, Sichuan University, Chengdu 610041, China
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