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Zaravinos A. Unveiling the Future of Oncology and Precision Medicine through Data Science. Int J Mol Sci 2024; 25:5797. [PMID: 38891982 PMCID: PMC11171842 DOI: 10.3390/ijms25115797] [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: 04/06/2024] [Accepted: 05/22/2024] [Indexed: 06/21/2024] Open
Abstract
Information generated via next-generation sequencing (NGS) technologies is often termed multi-omics data [...].
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Affiliation(s)
- Apostolos Zaravinos
- Department of Life Sciences, School of Sciences, European University Cyprus, 2404 Nicosia, Cyprus;
- Cancer Genetics, Genomics and Systems Biology Laboratory, Basic and Translational Cancer Research Center (BTCRC), 1516 Nicosia, Cyprus
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Thirumani L, Helan M, S V, Jamal Mohamed U, Vimal S, Madar IH. The Molecular Landscape of Lung Metastasis in Primary Head and Neck Squamous Cell Carcinomas. Cureus 2024; 16:e57497. [PMID: 38707175 PMCID: PMC11066729 DOI: 10.7759/cureus.57497] [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: 02/23/2024] [Accepted: 04/03/2024] [Indexed: 05/07/2024] Open
Abstract
Background Lung metastasis in head and neck cancer (HNC) patients is a critical concern, often indicating an advanced disease stage and a poor prognosis. This study explores the molecular complexities of such metastases, identifying specific genes and pathways that may serve as valuable targets for diagnosis and treatment. The findings underscore the potential for significantly improved patient outcomes through targeted therapeutic strategies. Methodology In this research, we systematically collected raw gene expression data from head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LSCC). By comparing tumorous and normal gene expression profiles from paired patient samples, we identified differentially expressed genes (DEGs). Network analysis helped visualize protein interactions and pinpoint crucial hub genes. Through validation and comparison across several datasets, we identified common DEGs. Additionally, we employed Kaplan-Meier analysis and log-rank testing to examine the relationship between gene expression patterns and patient survival. Result The study identified 145 overlapping DEGs in both HNSCC and LSCC, which are crucial for cancer progression and linked to lung metastasis, offering vital targets for personalized therapy by identifying key genes affecting disease development and patient survival. Pathway analyses linked these to lung metastasis, while protein-protein interaction network construction and hub gene identification highlighted genes crucial for development and patient survival, offering targets for personalized therapy. Conclusion Identifying key genes and pathways in lung metastasis from HNC, this study highlights potential targets for enhanced diagnosis and therapy. It underscores the crucial role of molecular insights in driving forward personalized treatment approaches and improving patient outcomes.
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Affiliation(s)
- Logalakshmi Thirumani
- Multiomics and Precision Medicine Laboratory, Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, IND
| | - Mizpha Helan
- Multiomics and Precision Medicine Laboratory, Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, IND
| | - Vijayaraghavan S
- Multiomics and Precision Medicine Laboratory, Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, IND
| | - Umargani Jamal Mohamed
- Multiomics and Precision Medicine Laboratory, Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, IND
| | - Sugumar Vimal
- Biochemistry, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, IND
| | - Inamul Hasan Madar
- Multiomics and Precision Medicine Laboratory, Center for Global Health Research, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, IND
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Mu L, Hu S, Li G, Wu P, Zheng K, Zhang S. Comprehensive analysis of DNA methylation gene expression profiles in GEO dataset reveals biomarkers related to malignant transformation of sinonasal inverted papilloma. Discov Oncol 2024; 15:53. [PMID: 38427106 PMCID: PMC10907326 DOI: 10.1007/s12672-024-00903-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 02/21/2024] [Indexed: 03/02/2024] Open
Abstract
BACKGROUND DNA methylation may be involved in the regulation of malignant transformation from sinonasal inverted papilloma (SNIP) to squamous cell carcinoma (SCC). The study of gene methylation changes and screening of differentially methylated loci (DMLs) are helpful to predict the possible key genes in the malignant transformation of SNIP-SCC. MATERIALS AND METHODS Microarray dataset GSE125399 was downloaded from the Gene Expression Omnibus (GEO) database and differentially methylated loci (DMLs) were analyzed using R language (Limma package). ClusterProfiler R package was used to perform Gene Ontology (GO) analysis on up-methylated genes and draw bubble maps. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and its visualization analysis were analyzed to speculate the possible key Genes in SNIP-SCC malignant transformation. Subsequently, SNIP cases archived in our department were collected, tissue microarray was made, and immunohistochemical staining was performed to analyze the expression levels of UCKL1, GSTT1, HLA-G, MAML2 and NRGN in different grades of sinonasal papilloma tissues. RESULTS Analysis of dataset GSE125399 identified 56 DMLs, including 49 upregulated DMLs and 7 downregulated DMLs. Thirty-one genes containing upregulated DNA methylation loci and three genes containing downregulated DNA methylation loci were obtained by methylation microarray annotation analysis. In addition, KEGG pathway visualization analysis of 31 up-methylated genes showed that there were four significantly up-methylated genes including UCKL1, GSTT1, HLA-G and MAML2, and one significantly down-methylated gene NRGN. Subsequently, compared with non-neoplasia nasal epithelial tissues, the expression of HLA-G and NRGN was upregulated in grade I, II, III and IV tissues, while the expression of MAML2 was lost. The protein expression changes of MAML2 and NRGN were significantly negatively correlated with their gene methylation levels. CONCLUSIONS By analyzing the methylation dataset, we obtained four up-regulated methylation genes UCKL1, GSTT1, HLA-G and MAML2 and one down-regulated gene NRGN. MAML2, a tumor suppressor gene with high methylation modification but loss of protein expression, and NRGN, a tumor gene with low methylation modification but upregulated protein expression, can be used as biological indicators to judge the malignant transformation of SNIP-SCC.
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Affiliation(s)
- Li Mu
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, Fujian, China
- Department of Pathology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Fuzhou, 350212, China
| | - Shun Hu
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, Fujian, China
- Department of Pathology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Fuzhou, 350212, China
| | - Guoping Li
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, Fujian, China
- Department of Pathology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Fuzhou, 350212, China
| | - Ping Wu
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, Fujian, China
- Department of Pathology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Fuzhou, 350212, China
| | - Ke Zheng
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, Fujian, China.
- Department of Pathology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Fuzhou, 350212, China.
| | - Sheng Zhang
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, Fujian, China.
- Department of Pathology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 999 Huashan Road, Fuzhou, 350212, China.
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Xiao Y, Jiang C, Li H, Xu D, Liu J, Huili Y, Nie S, Guan X, Cao F. Genes associated with inflammation for prognosis prediction for clear cell renal cell carcinoma: a multi-database analysis. Transl Cancer Res 2023; 12:2629-2645. [PMID: 37969384 PMCID: PMC10643973 DOI: 10.21037/tcr-23-1183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/19/2023] [Indexed: 11/17/2023]
Abstract
Background Clear cell renal cell carcinoma (ccRCC) is the largest subtype of kidney tumour, with inflammatory responses characterising all stages of the tumour. Establishing the relationship between the genes related to inflammatory responses and ccRCC may help the diagnosis and treatment of patients with ccRCC. Methods First, we obtained the data for this study from a public database. After differential analysis and Cox regression analysis, we obtained the genes for the establishment of a prognostic model for ccRCC. As we used data from multiple databases, we standardized all the data using the surrogate variable analysis (SVA) package to make the data from different sources comparable. Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation. The data used for modelling and internal validation came from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) series (GSE29609) databases. ccRCC data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Tumour data from the E-MTAB-1980 cohort were used for external validation. The GSE40453 and GSE53757 datasets were used to verify the differential expression of inflammation-related gene model signatures (IRGMS). The immunohistochemistry of IRGMS was queried through the Human Protein Atlas (HPA) database. After the adequate validation of the IRGM, we further explored its application by constructing nomograms, pathway enrichment analysis, immunocorrelation analysis, drug susceptibility analysis, and subtype identification. Results The IRGM can robustly predict the prognosis of samples from patients with ccRCC from different databases. The verification results show that nomogram can accurately predict the survival rate of patients. Pathway enrichment analysis showed that patients in the high-risk (HR) group were associated with a variety of tumorigenesis biological processes. Immune-related analysis and drug susceptibility analysis suggested that patients with higher IRGM scores had more treatment options. Conclusions The IRGMS can effectively predict the prognosis of ccRCC. Patients with higher IRGM scores may be better candidates for treatment with immune checkpoint inhibitors and have more chemotherapy options.
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Affiliation(s)
- Yonggui Xiao
- School of Clinical Medicine, Affiliated Hospital, North China University of Science and Technology, Tangshan, China
| | - Chonghao Jiang
- Department of Urology, Affiliated Hospital of North China University of Science and Technology, Tangshan, China
| | - Hubo Li
- School of Clinical Medicine, Affiliated Hospital, North China University of Science and Technology, Tangshan, China
| | - Danping Xu
- School of Medicine, University of Electronic Science and Technology of China, Chengdu, China
| | - Jinzheng Liu
- School of Clinical Medicine, Affiliated Hospital, North China University of Science and Technology, Tangshan, China
| | - Youlong Huili
- School of Clinical Medicine, Affiliated Hospital, North China University of Science and Technology, Tangshan, China
| | - Shiwen Nie
- School of Clinical Medicine, Affiliated Hospital, North China University of Science and Technology, Tangshan, China
| | - Xiaohai Guan
- Department of Urology, Affiliated Hospital of North China University of Science and Technology, Tangshan, China
| | - Fenghong Cao
- Department of Urology, Affiliated Hospital of North China University of Science and Technology, Tangshan, China
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Wang Z, Yan S, Yang Y, Luo X, Wang X, Tang K, Zhao J, He Y, Bian L. Identifying M1-like macrophage related genes for prognosis prediction in lung adenocarcinoma based on a gene co-expression network. Heliyon 2023; 9:e12798. [PMID: 36711278 PMCID: PMC9876840 DOI: 10.1016/j.heliyon.2023.e12798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 12/25/2022] [Accepted: 01/02/2023] [Indexed: 01/06/2023] Open
Abstract
Macrophages are one of the most important players in the tumor microenvironment. But the contribution of macrophages to lung adenocarcinoma (LUAD) is still controversial. The current study aimed to display an immune landscape to clarify the function of macrophages and detect prognostic hub genes in LUAD. The transcriptome data were adopted to screen differently expressed genes (DEGs) in The Cancer Genome Atlas database (TCGA). The cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to reveal the immune landscape. Weighted gene co-expression network analysis (WGCNA) analysis was performed to identify the hub module associated with macrophages. Function Enrichment analysis was conducted on hub module genes. Moreover, univariate and multivariate Cox regression analyses were performed to identify prognostic hub genes. Kaplan-Meier (KM) and Time-dependent receiver operating characteristic (ROC) curves were plotted to assess the prognostic capacity of the four prognostic hub genes. The GES1196959 dataset from the Gene Expression Omnibus (GEO) database was downloaded to verify the differential expression of the 4 prognostic hub genes.
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Affiliation(s)
- Zhiyuan Wang
- School of Basic Medicine, Kunming Medical University, Kunming, 650500, China,Department of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650031, China
| | - Shan Yan
- Institute of Biomedical Engineering, Kunming Medical University, Kunming, 650031, China
| | - Ying Yang
- Department of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650031, China
| | - Xuan Luo
- School of Basic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Xiaofang Wang
- Department of Pathology, The Second Affiliated Hospital of Kunming Medical University, Kunming, 650031, China
| | - Kun Tang
- Intensive Care Unit, The First Affiliated Hospital of Kunming Medical University, Kunming, 650031, China
| | - Juan Zhao
- School of Basic Medicine, Kunming Medical University, Kunming, 650500, China
| | - Yongwen He
- School of Stomatology, Kunming Medical University, Kunming, 650021, China,Qujing Medical College, Qujing, 655099, China,Corresponding author.School of Stomatology, Kunming Medical University, Kunming, 650021, China.
| | - Li Bian
- Department of Pathology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650031, China,Corresponding author.
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Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Lung Cancer. Int J Mol Sci 2022; 23:ijms231810933. [PMID: 36142846 PMCID: PMC9504879 DOI: 10.3390/ijms231810933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 09/13/2022] [Indexed: 11/24/2022] Open
Abstract
Despite the significant progress made towards comprehending the deregulated signatures in lung cancer, these vary from study to study. We reanalyzed 25 studies from the Gene Expression Omnibus (GEO) to detect and annotate co-deregulated signatures in lung cancer and in single-gene or single-drug perturbation experiments. We aimed to decipher the networks that these co-deregulated genes (co-DEGs) form along with their upstream regulators. Differential expression and upstream regulators were computed using Characteristic Direction and Systems Biology tools, including GEO2Enrichr and X2K. Co-deregulated gene expression profiles were further validated across different molecular and immune subtypes in lung adenocarcinoma (TCGA-LUAD) and lung adenocarcinoma (TCGA-LUSC) datasets, as well as using immunohistochemistry data from the Human Protein Atlas, before being subjected to subsequent GO and KEGG enrichment analysis. The functional alterations of the co-upregulated genes in lung cancer were mostly related to immune response regulating the cell surface signaling pathway, in contrast to the co-downregulated genes, which were related to S-nitrosylation. Networks of hub proteins across the co-DEGs consisted of overlapping TFs (SOX2, MYC, KAT2A) and kinases (MAPK14, CSNK2A1 and CDKs). Furthermore, using Connectivity Map we highlighted putative repurposing drugs, including valproic acid, betonicine and astemizole. Similarly, we analyzed the co-DEG signatures in single-gene and single-drug perturbation experiments in lung cancer cell lines. In summary, we identified critical co-DEGs in lung cancer providing an innovative framework for their potential use in developing personalized therapeutic strategies.
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