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Kilicarslan S, Hiz-Cicekliyurt MM. Identification of potential biomarkers of papillary thyroid carcinoma. Endocrine 2024:10.1007/s12020-024-04068-9. [PMID: 39400774 DOI: 10.1007/s12020-024-04068-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Accepted: 10/03/2024] [Indexed: 10/15/2024]
Abstract
Papillary thyroid cancer (PTC) is the predominant form of malignant tumor affecting the thyroid gland. AIM This study aimed to identify candidate biomarkers for papillary thyroid carcinoma using an integrative analysis of bioinformatics and machine learning (ML). MATERIAL AND METHOD The PTC datasets GSE6004, GSE3467, and GSE33630 (species: Homo sapiens) were downloaded from NCBI and analyzed using the limma package to obtain DEGs. Once DEGs were identified, GO and KEGG enrichment analyses were performed as the first step in the bioinformatics process. Subsequently, a protein-protein interaction (PPI) network was constructed according to the common genes in bioinformatics and machine learning using STRING to elucidate the important genes involved in PTC pathogenesis. In machine learning, finding genes entails feature selection to identify the key genes that distinguish biological states. Hybrid feature selection will be used for this. In the second step, the original data sets were preprocessed to detect and correct missing and noisy data; after that, all data were merged. Following performing Linear and Discriminative Hybrid Feature Selection (LDHFS) on the processed dataset, machine learning algorithms such as Random Forest (RF), Naive Bayes (NB), and Support Vector Machines (SVM) are utilized. RESULTS Bioinformatics and machine learning analyses indicate that the genes RXRG, CDH2, ETV5, QPCT, LRP4, FN1, and LPAR5 are integral to the progression of thyroid cancer. This study attained the highest accuracy utilizing the RF algorithm, achieving an accuracy rate of 94.62%, a Kappa value of 91.36%, and an AUC value of 96.13%. These results offer additional evidence and confirmation for the genetic alterations of these genes. These findings may accelerate the development of prospective therapeutic and diagnostic methods in future research. CONCLUSIONS Bioinformatics and machine learning techniques identified the common genes "RXRG, CDH2, ETV5, QPCT, LRP4, FN1, and LPAR5" as PTC biomarkers, providing novel reference markers for the diagnosis and treatment of PTC patients. The model is anticipated to possess significant predictive value and assist in the early diagnosis and screening of clinical PTC. These insights enhance the field of PTC management and offer guidance for future research.
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Affiliation(s)
- Sabire Kilicarslan
- Çanakkale Onsekiz Mart University, Graduate School of Sciences, Department of Medical System Biology, Çanakkale, Turkey
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Claude E, Leclercq M, Thébault P, Droit A, Uricaru R. Optimizing hybrid ensemble feature selection strategies for transcriptomic biomarker discovery in complex diseases. NAR Genom Bioinform 2024; 6:lqae079. [PMID: 38993634 PMCID: PMC11237901 DOI: 10.1093/nargab/lqae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Revised: 06/03/2024] [Accepted: 06/21/2024] [Indexed: 07/13/2024] Open
Abstract
Biomedical research takes advantage of omic data, such as transcriptomics, to unravel the complexity of diseases. A conventional strategy identifies transcriptomic biomarkers characterized by expression patterns associated with a phenotype by relying on feature selection approaches. Hybrid ensemble feature selection (HEFS) has become increasingly popular as it ensures robustness of the selected features by performing data and functional perturbations. However, it remains difficult to make the best suited choices at each step when designing such approaches. We conducted an extensive analysis of four possible HEFS scenarios for the identification of Stage IV colorectal, Stage I kidney and lung and Stage III endometrial cancer biomarkers from transcriptomic data. These scenarios investigate the use of two types of feature reduction by filters (differentially expressed genes and variance) conjointly with two types of resampling strategies (repeated holdout by distribution-balanced stratified and random stratified) for downstream feature selection through an aggregation of thousands of wrapped machine learning models. Based on our results, we emphasize the advantages of using HEFS approaches to identify complex disease biomarkers, given their ability to produce generalizable and stable results to both data and functional perturbations. Finally, we highlight critical issues that need to be considered in the design of such strategies.
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Affiliation(s)
- Elsa Claude
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400 Talence, France
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Patricia Thébault
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400 Talence, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Raluca Uricaru
- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800, F-33400 Talence, France
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Xiu C, Deng X, Deng D, Zhou T, Jiang C, Wu D, Qian Y. miR-144-3p Targets GABRB2 to Suppress Thyroid Cancer Progression In Vitro. Cell Biochem Biophys 2024:10.1007/s12013-024-01446-y. [PMID: 39093515 DOI: 10.1007/s12013-024-01446-y] [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] [Accepted: 07/17/2024] [Indexed: 08/04/2024]
Abstract
Thyroid cancer, as one of the most common cancers in many countries, has attracted increasing attention, but its pathogenesis is still unclear. This research explored the effects of miR-144-3p and GABRB2 on thyroid cancer cells and the underlying mechanism. Gene expression data was obtained from the GEO database to analyze differential expression of mRNAs and miRNAs in patients with thyroid cancer. CCK-8, transwell, scratch, and flow cytometry assays were performed to detect cell proliferation, invasion, migration, and apoptosis, respectively. Dual-luciferase reporters were used to detect the binding of miR-144-3p to GABRB2. GABRB2 was highly expressed and miR-144-3p was underexpressed in thyroid cancer. In thyroid cancer cells, inhibiting GABRB2 or upregulating miR-144-3p reduced proliferation, invasion, and migration and increased apoptotic rates; GABRB2 overexpression or miR-144-3p inhibition brought about the opposite results. miR-144-3p targeted GABRB2 and negatively regulated its expression. PI3K/AKT activation was reduced in thyroid cancer cells overexpressing miR-144-3p. GABRB2 overexpression partially mitigated the tumor-suppressive effect of miR-144-3p overexpression. In conclusion, miR-144-3p targets GABRB2 to inhibit PI3K/AKT activation, thereby inhibiting the progression of thyroid cancer in vitro.
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Affiliation(s)
- Cheng Xiu
- Department of Head and Neck Surgery, Hainan Cancer Hospital, Haikou, Hainan, 570000, P. R. China
| | - Xiaocong Deng
- Department of Head and Neck Surgery, Hainan Cancer Hospital, Haikou, Hainan, 570000, P. R. China
| | - Da Deng
- Department of Head and Neck Surgery, Hainan Cancer Hospital, Haikou, Hainan, 570000, P. R. China
| | - Tao Zhou
- Department of Head and Neck Surgery, Hainan Cancer Hospital, Haikou, Hainan, 570000, P. R. China
| | - Chuiguang Jiang
- Department of Head and Neck Surgery, Hainan Cancer Hospital, Haikou, Hainan, 570000, P. R. China
| | - Di Wu
- Department of Head and Neck Surgery, Hainan Cancer Hospital, Haikou, Hainan, 570000, P. R. China
| | - Yong Qian
- Department of Head and Neck Surgery, Hainan Cancer Hospital, Haikou, Hainan, 570000, P. R. China.
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Haghzad T, Khorsand B, Razavi SA, Hedayati M. A computational approach to assessing the prognostic implications of BRAF and RAS mutations in patients with papillary thyroid carcinoma. Endocrine 2024:10.1007/s12020-024-03911-3. [PMID: 38886331 DOI: 10.1007/s12020-024-03911-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 06/01/2024] [Indexed: 06/20/2024]
Abstract
Papillary thyroid carcinoma (PTC) is the most common thyroid cancer, posing a growing clinical challenge. PTC exhibits two age-related peaks, with established risk factors including family history and radiation exposure. Managing even low-risk, localized PTC cases remain complex, with growing interest in active surveillance as an alternative to immediate surgery. This study employed single-cell RNA sequencing (scRNA-Seq) to explore the predictive value of BRAF and RAS mutations in PTC, shedding light on their impact on disease progression and outcomes. The analyses emphasized the significance of BRAF and RAS mutations in tumor advancement, particularly the unique BRAF V600E mutation associated with aggressive features. The methodology involved scRNA-Seq analysis of PTC and normal samples, unveiling distinct cell clusters and indicating upregulated BRAF and RAS genes. Pathway enrichment analysis highlighted altered biological processes and immune-related pathways in PTC. The study consolidated previous research showing the prevalence of BRAF and RAS mutations in PTC, subtypes with distinct molecular profiles, and the impact of TERT promoter mutations on disease severity. In summary, this study unveils the complex interplay of genetic mutations and the cellular microenvironment in PTC through scRNA-Seq. The upregulated BRAF and RAS genes suggest their roles as PTC drivers, and pathway enrichment reveals alterations in immune-related processes. This synthesis of prior research enhances our understanding of PTC's molecular foundations, informing better prognosis and personalized treatment approaches. These insights advance the landscape of PTC management and provide directions for further research.
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Affiliation(s)
- Tahereh Haghzad
- Department of Biology, Faculty of Sciences, University of Guilan, Rasht, Iran
| | - Babak Khorsand
- Department of Neurology, University of California, Irvine, CA, USA
- Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - S Adeleh Razavi
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mehdi Hedayati
- Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Caputo WL, de Souza MC, Basso CR, Pedrosa VDA, Seiva FRF. Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma. Cancers (Basel) 2023; 15:5653. [PMID: 38067357 PMCID: PMC10705715 DOI: 10.3390/cancers15235653] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 02/16/2024] Open
Abstract
Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.
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Affiliation(s)
- Wesley Ladeira Caputo
- Post Graduation Program in Experimental Pathology, State University of Londrina (UEL), Londrina 86057-970, PR, Brazil; (W.L.C.); (M.C.d.S.)
| | - Milena Cremer de Souza
- Post Graduation Program in Experimental Pathology, State University of Londrina (UEL), Londrina 86057-970, PR, Brazil; (W.L.C.); (M.C.d.S.)
| | - Caroline Rodrigues Basso
- Department of Chemical and Biological Sciences, Institute of Bioscience, São Paulo State University (UNESP), Botucatu 18610-034, SP, Brazil; (C.R.B.); (V.d.A.P.)
| | - Valber de Albuquerque Pedrosa
- Department of Chemical and Biological Sciences, Institute of Bioscience, São Paulo State University (UNESP), Botucatu 18610-034, SP, Brazil; (C.R.B.); (V.d.A.P.)
| | - Fábio Rodrigues Ferreira Seiva
- Post Graduation Program in Experimental Pathology, State University of Londrina (UEL), Londrina 86057-970, PR, Brazil; (W.L.C.); (M.C.d.S.)
- Department of Chemical and Biological Sciences, Institute of Bioscience, São Paulo State University (UNESP), Botucatu 18610-034, SP, Brazil; (C.R.B.); (V.d.A.P.)
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Valenzuela O, Ortuño F, Benso A, Schwartz JM, de Brevern AG, Rojas I. Special Issue: New Advances in Bioinformatics and Biomedical Engineering Using Machine Learning Techniques, IWBBIO-2022. Genes (Basel) 2023; 14:1574. [PMID: 37628626 PMCID: PMC10454610 DOI: 10.3390/genes14081574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 07/04/2023] [Indexed: 08/27/2023] Open
Abstract
Bioinformatics is revolutionizing Biomedicine in the way we treat and diagnose pathologies related to biological manifestations resulting from variations or mutations of our DNA [...].
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Affiliation(s)
- Olga Valenzuela
- Department of Applied Mathematics, University of Granada, 18071 Granada, Spain;
| | - Francisco Ortuño
- Department of Computer Architecture and Computer Technology, Information and Communications Technology Centre (CITIC-UGR), University of Granada, 18010 Granada, Spain;
| | - Alfredo Benso
- Systems Biology Group, Dip. Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129 Torino, Italy
| | - Jean-Marc Schwartz
- School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester M13 9PT, UK
| | | | - Ignacio Rojas
- Department of Computer Architecture and Computer Technology, Information and Communications Technology Centre (CITIC-UGR), University of Granada, 18010 Granada, Spain;
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