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Liu S, Bian YC, Wang WL, Liu TJ, Zhang T, Chang Y, Xiao R, Zhang CL. Identification of hub genes associated with spermatogenesis by bioinformatics analysis. Sci Rep 2023; 13:18435. [PMID: 37891374 PMCID: PMC10611713 DOI: 10.1038/s41598-023-45620-3] [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: 08/23/2023] [Accepted: 10/21/2023] [Indexed: 10/29/2023] Open
Abstract
Spermatogenesis is a complex process related to male infertility. Till now, the critical genes and specific mechanisms have not been elucidated clearly. Our objective was to determine the hub genes that play a crucial role in spermatogenesis by analyzing the differentially expressed genes (DEGs) present in non-obstructive azoospermia (NOA) compared to OA and normal samples using bioinformatics analysis. Four datasets, namely GSE45885, GSE45887, GSE9210 and GSE145467 were used. Functional enrichment analyses were performed on the DEGs. Hub genes were identified based on protein-protein interactions between DEGs. The expression of the hub genes was further examined in the testicular germ cell tumors from the TCGA by the GEPIA and validated by qRT-PCR in the testes of lipopolysaccharide-induced acute orchitis mice with impaired spermatogenesis. A total of 203 DEGs including 34 up-regulated and 169 down-regulated were identified. Functional enrichment analysis showed DEGs were mainly involved in microtubule motility, the process of cell growth and protein transport. PRM2, TEKT2, FSCN3, UBQLN3, SPATS1 and GTSF1L were identified and validated as hub genes for spermatogenesis. Three of them (PRM2, FSCN3 and TEKT2) were significantly down-regulated in the testicular germ cell tumors and their methylation levels were associated with the pathogenesis. In summary, the hub genes identified may be related to spermatogenesis and may act as potential therapeutic targets for NOA and testicular germ cell tumors.
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Affiliation(s)
- Shuang Liu
- Inner Mongolia Key Laboratory of Molecular Pathology, Inner Mongolia Medical University, Huhhot, 010059, Inner Mongolia Autonomous Region, China
| | - Yan-Chao Bian
- Inner Mongolia Key Laboratory of Molecular Pathology, Inner Mongolia Medical University, Huhhot, 010059, Inner Mongolia Autonomous Region, China
| | - Wan-Lun Wang
- Inner Mongolia Key Laboratory of Molecular Pathology, Inner Mongolia Medical University, Huhhot, 010059, Inner Mongolia Autonomous Region, China
| | - Tong-Jia Liu
- Inner Mongolia Key Laboratory of Molecular Pathology, Inner Mongolia Medical University, Huhhot, 010059, Inner Mongolia Autonomous Region, China
| | - Ting Zhang
- Inner Mongolia Key Laboratory of Molecular Pathology, Inner Mongolia Medical University, Huhhot, 010059, Inner Mongolia Autonomous Region, China
| | - Yue Chang
- Inner Mongolia Key Laboratory of Molecular Pathology, Inner Mongolia Medical University, Huhhot, 010059, Inner Mongolia Autonomous Region, China
| | - Rui Xiao
- Inner Mongolia Key Laboratory of Molecular Pathology, Inner Mongolia Medical University, Huhhot, 010059, Inner Mongolia Autonomous Region, China.
| | - Chuan-Ling Zhang
- Department of Pharmacy, Inner Mongolia Medical University, Huhhot, 010110, Inner Mongolia Autonomous Region, China.
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Hamidi F, Gilani N, Arabi Belaghi R, Yaghoobi H, Babaei E, Sarbakhsh P, Malakouti J. Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta. Front Digit Health 2023; 5:1187578. [PMID: 37621964 PMCID: PMC10445490 DOI: 10.3389/fdgth.2023.1187578] [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: 03/16/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023] Open
Abstract
Introduction In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult. Methods By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost. Results Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.
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Affiliation(s)
- Farzaneh Hamidi
- Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Neda Gilani
- Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
- Road Traffic Injury Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Reza Arabi Belaghi
- Department of Mathematics, Applied Mathematics and Statistics, Uppsala University, Uppsala, Sweden
- Department of Statistics, Faculty of Mathematical Science, University of Tabriz, Tabriz, Iran
- Department of Energy and Technology, Swedish Agricultural University, Uppsala, Sweden
| | - Hanif Yaghoobi
- Department of Biological Sciences, School of Natural Sciences, University of Tabriz, Tabriz, Iran
| | - Esmaeil Babaei
- Department of Biological Sciences, School of Natural Sciences, University of Tabriz, Tabriz, Iran
- Interfaculty Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, Tübingen, Germany
| | - Parvin Sarbakhsh
- Department of Statistics and Epidemiology, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Jamileh Malakouti
- Department of Midwifery, Faculty of Nursing and Midwifery, Tabriz University of Medical Science, Tabriz, Iran
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Nøst TH, Skogholt AH, Urbarova I, Mjelle R, Paulsen E, Dønnem T, Andersen S, Markaki M, Røe OD, Johansson M, Johansson M, Grønberg BH, Sandanger TM, Sætrom P. Increased levels of microRNA-320 in blood serum and plasma is associated with imminent and advanced lung cancer. Mol Oncol 2023; 17:312-327. [PMID: 36337027 PMCID: PMC9892825 DOI: 10.1002/1878-0261.13336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 10/04/2022] [Accepted: 11/04/2022] [Indexed: 11/09/2022] Open
Abstract
Lung cancer (LC) incidence is increasing globally and altered levels of microRNAs (miRNAs) in blood may contribute to identification of individuals with LC. We identified miRNAs differentially expressed in peripheral blood at LC diagnosis and evaluated, in pre-diagnostic blood specimens, how long before diagnosis expression changes in such candidate miRNAs could be detected. We identified upregulated candidate miRNAs in plasma specimens from a hospital-based study sample of 128 patients with confirmed LC and 62 individuals with suspected but confirmed negative LC (FalsePos). We then evaluated the expression of candidate miRNAs in pre-diagnostic plasma or serum specimens of 360 future LC cases and 375 matched controls. There were 1663 miRNAs detected in diagnostic specimens, nine of which met our criteria for candidate miRNAs. Higher expression of three candidates, miR-320b, 320c, and 320d, was associated with poor survival, independent of LC stage and subtype. Moreover, miR-320c and miR-320d expression was higher in pre-diagnostic specimens collected within 2 years of LC diagnosis. Our results indicated that elevated levels of miR-320c and miR-320d may be early indications of imminent and advanced LC.
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Affiliation(s)
- Therese Haugdahl Nøst
- Department of Community Medicine, Faculty of Health SciencesUiT The Arctic University of NorwayTromsøNorway
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic EpidemiologyNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| | - Anne Heidi Skogholt
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic EpidemiologyNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| | - Ilona Urbarova
- Department of Community Medicine, Faculty of Health SciencesUiT The Arctic University of NorwayTromsøNorway
| | - Robin Mjelle
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic EpidemiologyNTNU – Norwegian University of Science and TechnologyTrondheimNorway
- Department of Clinical and Molecular MedicineNTNU – Norwegian University of Science and TechnologyTrondheimNorway
- Bioinformatics Core FacilityNTNU – Norwegian University of Science and TechnologyTrondheimNorway
| | - Erna‐Elise Paulsen
- Department of Clinical Medicine, Faculty of Health SciencesUiT The Arctic University of NorwayTromsøNorway
- Department of PulmonologyUniversity Hospital of North NorwayTromsøNorway
| | - Tom Dønnem
- Department of Clinical Medicine, Faculty of Health SciencesUiT The Arctic University of NorwayTromsøNorway
- Department of OncologyUniversity Hospital of North NorwayTromsøNorway
| | - Sigve Andersen
- Department of Clinical Medicine, Faculty of Health SciencesUiT The Arctic University of NorwayTromsøNorway
- Department of OncologyUniversity Hospital of North NorwayTromsøNorway
| | | | - Oluf Dimitri Røe
- Department of Clinical and Molecular MedicineNTNU – Norwegian University of Science and TechnologyTrondheimNorway
- Cancer Clinic, Levanger HospitalNord‐Trøndelag Health TrustLevangerNorway
| | | | | | - Bjørn Henning Grønberg
- Department of Clinical and Molecular MedicineNTNU – Norwegian University of Science and TechnologyTrondheimNorway
- Department of Oncology, St. Olavs HospitalTrondheim University HospitalNorway
| | - Torkjel Manning Sandanger
- Department of Community Medicine, Faculty of Health SciencesUiT The Arctic University of NorwayTromsøNorway
| | - Pål Sætrom
- Department of Public Health and Nursing, K.G. Jebsen Center for Genetic EpidemiologyNTNU – Norwegian University of Science and TechnologyTrondheimNorway
- Department of Clinical and Molecular MedicineNTNU – Norwegian University of Science and TechnologyTrondheimNorway
- Bioinformatics Core FacilityNTNU – Norwegian University of Science and TechnologyTrondheimNorway
- Department of Computer ScienceNorwegian University of Science and TechnologyTrondheimNorway
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Erdem A, Eksin E. Zip Nucleic Acid-Based Genomagnetic Assay for Electrochemical Detection of microRNA-34a. BIOSENSORS 2023; 13:bios13010144. [PMID: 36671979 PMCID: PMC9856502 DOI: 10.3390/bios13010144] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Revised: 12/20/2022] [Accepted: 12/23/2022] [Indexed: 05/17/2023]
Abstract
Zip nucleic acid (ZNA)-based genomagnetic assay was developed herein for the electrochemical detection of microRNA-34a (miR-34a), which is related to neurological disorders and cancer. The hybridization between the ZNA probe and miR-34a target was performed in the solution phase; then, the resultant hybrids were immobilized onto the surface of magnetic beads (MBs). After magnetic separation, the hybrids were separated from the surface of MBs and then immobilized on the surface of pencil graphite electrodes (PGEs). In the case of a full-match hybridization, the guanine oxidation signal was measured via the differential pulse voltammetry (DPV) technique. All the experimental parameters that influenced the hybridization efficiency (i.e., hybridization strategy, probe concentration, hybridization temperature, etc.) were optimized. The cross-selectivity of the genomagnetic assay was tested against two different miRNAs, miR-155 and miR-181b, individually as well as in mixture samples. To show the applicability of the ZNA-based genomagnetic assay for miR-34a detection in real samples, a batch of experiments was carried out in this study by using the total RNA samples isolated from the human hepatocellular carcinoma cell line (HUH-7).
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Affiliation(s)
- Arzum Erdem
- Department of Analytical Chemistry, Faculty of Pharmacy, Ege University, Izmir 35100, Turkey
- Correspondence: ; Tel.: +90-232-311-5131
| | - Ece Eksin
- Department of Analytical Chemistry, Faculty of Pharmacy, Ege University, Izmir 35100, Turkey
- Biomedical Device Technology Program, Vocational School of Health Services, Izmir Democracy University, Izmir 35290, Turkey
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Hong G, Luo F, Chen Z, Ma L, Lin G, Wu T, Li N, Cai H, Hu T, Zhong H, Guo Y, Li H. Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers. Front Med (Lausanne) 2022; 9:923275. [PMID: 35983098 PMCID: PMC9378834 DOI: 10.3389/fmed.2022.923275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/15/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectiveThe accuracy of CA125 or clinical examination in ovarian cancer (OVC) screening is still facing challenges. Serum miRNAs have been considered as promising biomarkers for clinical applications. Here, we propose a single sample classifier (SSC) method based on within-sample relative expression orderings (REOs) of serum miRNAs for OVC diagnosis.MethodsBased on the stable REOs within 4,965 non-cancer serum samples, we developed the SSC for OVC in the training cohort (GSE106817: OVC = 200, non-cancer = 2,000) by focusing on highly reversed REOs within OVC. The best diagnosis is achieved using a combination of reversed miRNA pairs, considering the largest evaluation index and the lowest number of miRNA pairs possessed according to the voting rule. The SSC was then validated in internal data (GSE106817: OVC = 120, non-cancer = 759) and external data (GSE113486: OVC = 40, non-cancer = 100).ResultsThe obtained 13-miRPairs classifier showed high diagnostic accuracy on distinguishing OVC from non-cancer controls in the training set (sensitivity = 98.00%, specificity = 99.60%), which was reproducible in internal data (sensitivity = 98.33%, specificity = 99.21%) and external data (sensitivity = 97.50%, specificity = 100%). Compared with the published models, it stood out in terms of correct positive predictive value (PPV) and negative predictive value (NPV) (PPV = 96.08% and NPV=95.16% in training set, and both above 99% in validation set). In addition, 13-miRPairs demonstrated a classification accuracy of over 97.5% for stage I OVC samples. By integrating other non-OVC serum samples as a control, the obtained 17-miRPairs classifier could distinguish OVC from other cancers (AUC>92% in training and validation set).ConclusionThe REO-based SSCs performed well in predicting OVC (including early samples) and distinguishing OVC from other cancer types, proving that REOs of serum miRNAs represent a robust and non-invasive biomarker.
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Affiliation(s)
- Guini Hong
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Fengyuan Luo
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Zhihong Chen
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Liyuan Ma
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Guiyang Lin
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Tong Wu
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Na Li
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Hao Cai
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
| | - Tao Hu
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Haijian Zhong
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China
- You Guo
| | - Hongdong Li
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
- *Correspondence: Hongdong Li
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