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Syed AH, Abujabal HAS, Ahmad S, Malebary SJ, Alromema N. Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection. Diagnostics (Basel) 2024; 14:1182. [PMID: 38893707 PMCID: PMC11172026 DOI: 10.3390/diagnostics14111182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 05/25/2024] [Accepted: 06/01/2024] [Indexed: 06/21/2024] Open
Abstract
This study, utilizing high-throughput technologies and Machine Learning (ML), has identified gene biomarkers and molecular signatures in Inflammatory Bowel Disease (IBD). We could identify significant upregulated or downregulated genes in IBD patients by comparing gene expression levels in colonic specimens from 172 IBD patients and 22 healthy individuals using the GSE75214 microarray dataset. Our ML techniques and feature selection methods revealed six Differentially Expressed Gene (DEG) biomarkers (VWF, IL1RL1, DENND2B, MMP14, NAAA, and PANK1) with strong diagnostic potential for IBD. The Random Forest (RF) model demonstrated exceptional performance, with accuracy, F1-score, and AUC values exceeding 0.98. Our findings were rigorously validated with independent datasets (GSE36807 and GSE10616), further bolstering their credibility and showing favorable performance metrics (accuracy: 0.841, F1-score: 0.734, AUC: 0.887). Our functional annotation and pathway enrichment analysis provided insights into crucial pathways associated with these dysregulated genes. DENND2B and PANK1 were identified as novel IBD biomarkers, advancing our understanding of the disease. The validation in independent cohorts enhances the reliability of these findings and underscores their potential for early detection and personalized treatment of IBD. Further exploration of these genes is necessary to fully comprehend their roles in IBD pathogenesis and develop improved diagnostic tools and therapies. This study significantly contributes to IBD research with valuable insights, potentially greatly enhancing patient care.
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Affiliation(s)
- Asif Hassan Syed
- Department of Computer Science, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 22254, Saudi Arabia;
| | - Hamza Ali S. Abujabal
- Department of Mathematics, Faculty of Science, King Abdulaziz University, P.O. Box 80203, Jeddah 21589, Saudi Arabia;
| | - Shakeel Ahmad
- Department of Computer Science, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, Jeddah 22254, Saudi Arabia;
| | - Sharaf J. Malebary
- Department of Information Technology, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia;
| | - Nashwan Alromema
- Department of Computer Science, Faculty of Computing and Information Technology-Rabigh, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia;
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Liu P, Xue X, Zhang C, Zhou H, Ding Z, Wang L, Jiang Y, Shen WD, Yang S, Wang F. Transcriptional-profile changes in the medial geniculate body after noise-induced tinnitus. Exp Biol Med (Maywood) 2024; 249:10057. [PMID: 38562529 PMCID: PMC10984379 DOI: 10.3389/ebm.2024.10057] [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: 11/22/2023] [Accepted: 02/20/2024] [Indexed: 04/04/2024] Open
Abstract
Tinnitus is a disturbing condition defined as the occurrence of acoustic hallucinations with no actual sound. Although the mechanisms underlying tinnitus have been explored extensively, the pathophysiology of the disease is not completely understood. Moreover, genes and potential treatment targets related to auditory hallucinations remain unknown. In this study, we examined transcriptional-profile changes in the medial geniculate body after noise-induced tinnitus in rats by performing RNA sequencing and validated differentially expressed genes via quantitative polymerase chain reaction analysis. The rat model of tinnitus was established by analyzing startle behavior based on gap-pre-pulse inhibition of acoustic startles. We identified 87 differently expressed genes, of which 40 were upregulated and 47 were downregulated. Pathway-enrichment analysis revealed that the differentially enriched genes in the tinnitus group were associated with pathway terms, such as coronavirus disease COVID-19, neuroactive ligand-receptor interaction. Protein-protein-interaction networks were established, and two hub genes (Rpl7a and AC136661.1) were identified among the selected genes. Further studies focusing on targeting and modulating these genes are required for developing potential treatments for noise-induced tinnitus in patients.
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Affiliation(s)
- Peng Liu
- Medical School of Chinese PLA, Beijing, China
- Department of Otolaryngology, Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center for Otolaryngologic Diseases, Beijing, China
| | - Xinmiao Xue
- Medical School of Chinese PLA, Beijing, China
- Department of Otolaryngology, Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center for Otolaryngologic Diseases, Beijing, China
| | - Chi Zhang
- Department of Otolaryngology, Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center for Otolaryngologic Diseases, Beijing, China
| | - Hanwen Zhou
- Medical School of Chinese PLA, Beijing, China
- Department of Otolaryngology, Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center for Otolaryngologic Diseases, Beijing, China
| | - Zhiwei Ding
- Medical School of Chinese PLA, Beijing, China
- Department of Otolaryngology, Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center for Otolaryngologic Diseases, Beijing, China
| | - Li Wang
- Medical School of Chinese PLA, Beijing, China
- Department of Otolaryngology, Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center for Otolaryngologic Diseases, Beijing, China
| | - Yuke Jiang
- Medical School of Chinese PLA, Beijing, China
- Department of Otolaryngology, Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center for Otolaryngologic Diseases, Beijing, China
| | - Wei-Dong Shen
- Department of Otolaryngology, Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
- National Clinical Research Center for Otolaryngologic Diseases, Beijing, China
| | - Shiming Yang
- Medical School of Chinese PLA, Beijing, China
- Department of Otolaryngology, Head and Neck Surgery, Institute of Otolaryngology, Chinese PLA General Hospital, Beijing, China
| | - Fangyuan Wang
- National Clinical Research Center for Otolaryngologic Diseases, Beijing, China
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Yang Y, Hua Y, Zheng H, Jia R, Ye Z, Su G, Gu Y, Zhan K, Tang K, Qi S, Wu H, Qin S, Huang S. Biomarkers prediction and immune landscape in ulcerative colitis: Findings based on bioinformatics and machine learning. Comput Biol Med 2024; 168:107778. [PMID: 38070204 DOI: 10.1016/j.compbiomed.2023.107778] [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: 08/03/2023] [Revised: 11/02/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Ulcerative colitis (UC) presents diagnostic and therapeutic difficulties. The primary objective of this study is to identify efficacious biomarkers for diagnosis and treatment, as well as acquire a deeper understanding of the immuneological characteristics associated with the disease. METHODS Datasets relating to UC were obtained from GEO database. Among these, three datasets were merged to create a metadata for bioinformatics analysis and machine learning. Additionally, one dataset specifically utilized for external validation. Least absolute shrinkage and selection operator (LASSO) and random forest (RF) were employed to screen signature genes. The artificial neural network (ANN) model and receiver operating characteristic (ROC) curve were used to assess the diagnostic performance of signature genes. The single sample gene set enrichment analysis (ssGSEA) was applied to reveal the immune landscape. Finally, the relationship between the signature genes, immune infiltration, and clinical characteristics was investigated through correlation analysis. RESULT By intersecting the result of LASSO, RF and WGCNA, 8 signature genes were identified, including S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14. The biological progress of this gene mostly encompasses acute inflammatory response, aggregation and chemotaxis of leukocyte, and response to lipopolysaccharide by mediating IL-17 signaling pathway, NF-kappa B signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway. Immune infiltration analysis shows 25 immune cells are significantly elevated in UC samples. Moreover, these signature genes exhibit a strong correlation with various immune cells and a mild to moderate correlation with the Mayo score. CONCLUSION S100A8, IL-1B, CXCL1, TCN1, MMP10, GREM1, DUOX2 and SLC6A14 have been identified as credible potential biomarkers for the diagnosis and therapy of UC. The immune response mediated by these signature biomarkers plays a crucial role in the occurrence and advancement of UC by means of the reciprocal interaction between the signature biomarkers and immune-infiltrated cells.
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Affiliation(s)
- Yuanming Yang
- Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan, 523000, China
| | - Yiwei Hua
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Huan Zheng
- Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan, 523000, China
| | - Rui Jia
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China
| | - Zhining Ye
- Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan, 523000, China
| | - Guifang Su
- Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan, 523000, China
| | - Yueming Gu
- Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan, 523000, China
| | - Kai Zhan
- Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan, 523000, China
| | - Kairui Tang
- Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan, 523000, China
| | - Shuhao Qi
- Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan, 523000, China
| | - Haomeng Wu
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China; State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China; Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou, 510120, China; Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou 510120, China
| | - Shumin Qin
- The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China; State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China; Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou, 510120, China; Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou 510120, China.
| | - Shaogang Huang
- Dongguan Hospital of Guangzhou University of Chinese Medicine, Dongguan, 523000, China; The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China; State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China; Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou, 510120, China; Guangdong Provincial Key Laboratory of Clinical Research on Traditional Chinese Medicine Syndrome, Guangzhou 510120, China; Yang Chunbo academic experience inheritance studio of Guangdong provincial hospital of Chinese Medicine, Guangzhou, 510006, China.
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