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Zhang L, Hao C, Han B, Zeng G, Han L, Cao C, Liu H, Zhong Z, Zhao X, Wang J, Zhang Q. RMI2
is a novel prognostic and predictive biomarker for breast cancer. Cancer Med 2022; 12:8331-8350. [PMID: 36533385 PMCID: PMC10134310 DOI: 10.1002/cam4.5533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 10/14/2022] [Accepted: 12/02/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND RecQ-mediated genome instability 2 (RMI2) maintains genome stability by promoting DNA damage repair. It has been reported to accelerate the progression of several tumors. However, the functional mechanism of RMI2 in breast cancer remains unclear. METHODS Gene expression profiles were obtained from TCGA, GTEx, and GEO databases. The expression of RMI2 and its prognostic value in breast cancer was explored. In addition, we calculated pooled standardized mean deviation (SMD) and performed a summary receiver operating characteristic (sROC) curve analysis to further determine RMI2 expression status and diagnostic significance. The functions and related signaling pathways were investigated based on GO and KEGG analyses. The PPI network was constructed by combining the STRING database and Cytoscape software. Subsequently, in vitro assays were conducted to detect the effect of RMI2 on the proliferation and migration of breast cancer cells. RESULTS The expression of RMI2 was markedly upregulated in breast cancer tissues relative to that in normal tissues. Moreover, pooled SMD further confirmed the overexpression of RMI2 in breast cancer (SMD = 1.29, 95% confidence interval (CI): 1.18-1.41, p = 0.000). The sROC curve analysis result suggested that RMI2 had a relatively high diagnostic ability in breast cancer (AUC = 0.87, 95% CI: 0.84-0.90). High RMI2 expression was associated with poor prognosis. GO and KEGG analyses revealed that RMI2 was closely related to cell adhesion, various enzyme activities, and PI3K/AKT signaling pathway. PPI analysis showed that RMI2 had interactions with proteins involved in DNA damage repair. knockdown of RMI2 remarkably inhibited the proliferation and migration of breast cancer cells, while overexpression of RMI2 exerted the opposite effects. Furthermore, we identified that RMI2 accelerates the proliferation and migration of breast cancer cells via activation of the PI3K/AKT pathway. CONCLUSION The results suggest that RMI2 is a potential diagnostic and prognostic biomarker associated with cell proliferation and migration, and may be used as a novel therapeutic target for breast cancer in the future.
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Affiliation(s)
- Lijie Zhang
- Department of Medical Oncology Harbin Medical University Cancer Hospital Harbin China
| | - Chuncheng Hao
- Department of Head and Neck Radiation Oncology Harbin Medical University Cancer Hospital Harbin China
| | - Baojuan Han
- Department of Medical Oncology Harbin Medical University Cancer Hospital Harbin China
| | - Guangchun Zeng
- Department of Pathology Harbin Medical University Cancer Hospital Harbin China
| | - Lili Han
- Department of Orthopedic Surgery, The First Hospital of Suihua Suihua China
| | - Cong Cao
- Department of Medical Oncology Harbin Medical University Cancer Hospital Harbin China
| | - Hui Liu
- Department of Head and Neck Radiation Oncology Harbin Medical University Cancer Hospital Harbin China
| | - Zhenbin Zhong
- Department of Head and Neck Radiation Oncology Harbin Medical University Cancer Hospital Harbin China
| | - Xue Zhao
- Department of Head and Neck Radiation Oncology Harbin Medical University Cancer Hospital Harbin China
| | - Jingxuan Wang
- Department of Medical Oncology Harbin Medical University Cancer Hospital Harbin China
| | - Qingyuan Zhang
- Department of Medical Oncology Harbin Medical University Cancer Hospital Harbin China
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Habibi M, Taheri G. A new machine learning method for cancer mutation analysis. PLoS Comput Biol 2022; 18:e1010332. [PMID: 36251702 PMCID: PMC9612828 DOI: 10.1371/journal.pcbi.1010332] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 10/27/2022] [Accepted: 10/05/2022] [Indexed: 11/23/2022] Open
Abstract
It is complicated to identify cancer-causing mutations. The recurrence of a mutation in patients remains one of the most reliable features of mutation driver status. However, some mutations are more likely to happen than others for various reasons. Different sequencing analysis has revealed that cancer driver genes operate across complex pathways and networks, with mutations often arising in a mutually exclusive pattern. Genes with low-frequency mutations are understudied as cancer-related genes, especially in the context of networks. Here we propose a machine learning method to study the functionality of mutually exclusive genes in the networks derived from mutation associations, gene-gene interactions, and graph clustering. These networks have indicated critical biological components in the essential pathways, especially those mutated at low frequency. Studying the network and not just the impact of a single gene significantly increases the statistical power of clinical analysis. The proposed method identified important driver genes with different frequencies. We studied the function and the associated pathways in which the candidate driver genes participate. By introducing lower-frequency genes, we recognized less studied cancer-related pathways. We also proposed a novel clustering method to specify driver modules. We evaluated each driver module with different criteria, including the terms of biological processes and the number of simultaneous mutations in each cancer. Materials and implementations are available at: https://github.com/MahnazHabibi/MutationAnalysis.
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Affiliation(s)
- Mahnaz Habibi
- Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran
| | - Golnaz Taheri
- Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
- * E-mail:
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Wei W, Ying X, Chen L, Sun Q, Lu X, Xia Y, Xu R, Zhu Z, Zhang D, Tang Q, Li L, Xie J, Yu H. RecQ mediated genome instability 2 ( RMI2): a potential prognostic and immunological biomarker for pan-cancers. Aging (Albany NY) 2022; 14:4107-4136. [PMID: 35552266 PMCID: PMC9134953 DOI: 10.18632/aging.204076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 05/02/2022] [Indexed: 11/25/2022]
Abstract
Background: RecQ mediated genome instability 2 (RMI2) is an essential component of the BLM-TopoIIIa-RMI1-RMI2 (BTR) complex. However, the mysterious veil of the potential immunological relationship of RMI2 in tumorigenesis and development has not been revealed. Methods: We conducted the differential expression (DE) analysis of the RMI2 in pan-cancer using data onto Oncomine, TIMER, and GEPIA databases. Afterward, survival analysis and clinical-stage correlation analysis were performed via the TCGA database. Subsequently, we used R software to further explore the relationship between the expression level of RMI2 and tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment (TME), tumor immune-infiltrated cells (TILs), immune checkpoints (ICP), mismatch repairs (MMRs) -related genes, m6A-related genes, DNA methylation-related genes. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional networks were also performed for annotation via gene set enrichment analysis (GSEA). Results: The RMI2 expressed remarkably high in most cancer types compared to cancer adjacent normal tissues (P < 0.05). High expression of RMI2 was linked to unfavorable prognosis and advanced stage of disease, especially in LIHC and PAAD. RMI2 expression was related to TMB in 16 cancer types and MSI in 8 cancer types. Furthermore, it is significant positive correlations between RMI2 and stromal and immune cells, ICP-related genes, MMRs-related genes, m6A-related genes, and DNA methylation-related genes. Finally, GSEA analysis revealed that RMI2 was engaged in a variety of signaling pathways in pan-cancers. Conclusions: RMI2 may serve as a potential biological target and probably assume a crucial part in tumorigenesis and progression.
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Affiliation(s)
- Wei Wei
- Department of General Surgery, Fuyang Hospital of Anhui Medical University, Fuyang 236000, Anhui, China
| | - Xiaomei Ying
- Department of General Surgery, Suzhou Hospital of Anhui Medical University, Suzhou 234000, China
| | - Liang Chen
- Department of General Surgery, Fuyang Hospital of Anhui Medical University, Fuyang 236000, Anhui, China
| | - Qingmei Sun
- Pancreas Center, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Xiaohuan Lu
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, Hubei, China
| | - Yang Xia
- Department of Immunology, School of Basic Medical Sciences, Anhui Medical University, Hefei 230032, Anhui, China
| | - Rubin Xu
- Department of General Surgery, Fuyang Hospital of Anhui Medical University, Fuyang 236000, Anhui, China
| | - Zhechen Zhu
- Department of Burn and Plastic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Dong Zhang
- The State Key Lab of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Qikai Tang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Li Li
- Department of General Surgery, Fuyang Hospital of Anhui Medical University, Fuyang 236000, Anhui, China
| | - Jiaheng Xie
- Department of Burn and Plastic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Hongzhu Yu
- Department of General Surgery, Fuyang Hospital of Anhui Medical University, Fuyang 236000, Anhui, China
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DNA Damage Response Genes in Osteosarcoma. JOURNAL OF ONCOLOGY 2021; 2021:9365953. [PMID: 35251167 PMCID: PMC8894034 DOI: 10.1155/2021/9365953] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 12/27/2022]
Abstract
BACKGROUND Improving the osteosarcoma (OS) patients' survival has long been a challenge, even though the disease's treatment is on the verge of progress. DNA damage response (DDR) has traditionally been associated with carcinogenesis, tumor growth, and genomic instability. No study has used DDR genes as a signature to identify the prognosis of OS. The goal of this work was to find an effective possible DDR gene biomarker for predicting OS prognosis, which may be useful in clinical diagnosis and therapy. METHODS To assess gene methylation, univariate and multivariate cox regression analyses were performed on data from OS patients. The data were retrieved from public databases, including the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and the Gene Expression Omnibus (GEO). RESULTS The DDR gene signature was chosen, which included seven genes (NHEJ1, RMI2, SWI5, ERCC2, CLK2, POLG, and MLH1). In the TARGET dataset, patients were categorized into two groups: high-risk and low-risk. Patients with a high-risk score revealed a shorter OS rate (hazard ratio (HR): 3.15, 95% confidence interval (CI): 1.38-4.34, P < 0.001) in comparison with the patients with a low-risk score in the TARGET as a training group. The validation of the prognostic signature accuracy was carried out in relapse and validation cohorts (TARGET, n = 75; GSE21257, n = 53). The signature was found to be an independent predictive factor for OS in multivariate cox regression analysis, and a nomogram model was developed to predict an individual's risk of OS. DDR gene signature involved in Fanconi anemia pathway, nonhomologous end-joining pathway, mismatch repair, and nucleotide excision repair pathway. CONCLUSIONS Our study suggests that the identified novel DDR genes could be a powerful prognostic tool for prognosis evaluation and a valuable tool in predicting the risk factors in OS patients.
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