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Yan W, Hu W, Song Y, Liu X, Zhou Z, Li W, Cao Z, Pei W, Zhou G, Hu G. Differential network analysis reveals the key role of the ECM-receptor pathway in α-particle-induced malignant transformation. MOLECULAR THERAPY. NUCLEIC ACIDS 2024; 35:102260. [PMID: 39049874 PMCID: PMC11268105 DOI: 10.1016/j.omtn.2024.102260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 06/14/2024] [Indexed: 07/27/2024]
Abstract
Space particle radiation is a major environmental factor in spaceflight, and it is known to cause body damage and even trigger cancer, but with unknown molecular etiologies. To examine these causes, we developed a systems biology approach by focusing on the co-expression network analysis of transcriptomics profiles obtained from single high-dose (SE) and multiple low-dose (ME) α-particle radiation exposures of BEAS-2B human bronchial epithelial cells. First, the differential network and pathway analysis based on the global network and the core modules showed that genes in the ME group had higher enrichment for the extracellular matrix (ECM)-receptor interaction pathway. Then, collagen gene COL1A1 was screened as an important gene in the ME group assessed by network parameters and an expression study of lung adenocarcinoma samples. COL1A1 was found to promote the emergence of the neoplastic characteristics of BEAS-2B cells by both in vitro experimental analyses and in vivo immunohistochemical staining. These findings suggested that the degree of malignant transformation of cells in the ME group was greater than that of the SE, which may be caused by the dysregulation of the ECM-receptor pathway.
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Affiliation(s)
- Wenying Yan
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China
| | - Wentao Hu
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Yidan Song
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
| | - Xingyi Liu
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
| | - Ziyun Zhou
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China
| | - Wanshi Li
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Zhifei Cao
- Department of Pathology, the Second Affiliated Hospital of Soochow University, Suzhou 215004, China
| | - Weiwei Pei
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Guangming Zhou
- State Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, Suzhou 215123, China
| | - Guang Hu
- MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Department of Bioinformatics, Center for Systems Biology, School of Biology and Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215213, China
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Soochow University, Suzhou 215123, China
- Key Laboratory of Alkene-carbon Fibres-based Technology & Application for Detection of Major Infectious Diseases, Soochow University, Suzhou 215123, China
- Jiangsu Key Laboratory of Infection and Immunity, Soochow University, Suzhou 215123, China
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2
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Xiang L, Zhao JH, Tang Y, Tan JW, Li LB, Gong C. Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model. Medicine (Baltimore) 2024; 103:e38472. [PMID: 38847736 PMCID: PMC11155612 DOI: 10.1097/md.0000000000038472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 05/15/2024] [Indexed: 06/10/2024] Open
Abstract
The dysregulation of protein-coding genes involved in various biological functions is closely associated with the progression of thyroid cancer. This study aimed to investigate the effects of dysregulated gene expressions on the prognosis of classical papillary thyroid carcinoma (cPTC). Using expression profiling datasets from the Cancer Genome Atlas (TCGA) database, we performed differential expression analysis to identify differentially expressed genes (DEGs). Cox regression and Kaplan-Meier analysis were used to identify DEGs, which were used to construct a risk model to predict the prognosis of cPTC patients. Functional enrichment analysis unveiled the potential significance of co-expressed protein-encoding genes in tumors. We identified 4 DEGs (SALL3, PPBP, MYH1, and SYNDIG1), which were used to construct a risk model to predict the prognosis of cPTC patients. These 4 genes were independent of clinical parameters and could be functional in cPTC carcinogenesis. Furthermore, PPBP exhibited a strong correlation with poorer overall survival (OS) in the advanced stage of the disease. This study suggests that the 4-gene signature could be an independent prognostic biomarker to improve prognosis prediction in cPTC patients older than 46.
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Affiliation(s)
- Lin Xiang
- Department of Otolaryngology-Head and Neck Surgery, Minda Hospital of Hubei Minzu University, Enshi, Hubei, China
| | - Jun-Hui Zhao
- Department of Otolaryngology-Head and Neck Surgery, Minda Hospital of Hubei Minzu University, Enshi, Hubei, China
| | - Yao Tang
- Department of Otolaryngology-Head and Neck Surgery, Minda Hospital of Hubei Minzu University, Enshi, Hubei, China
| | - Jun-Wu Tan
- Department of Otolaryngology-Head and Neck Surgery, Minda Hospital of Hubei Minzu University, Enshi, Hubei, China
| | - Liang-Bo Li
- Department of Otolaryngology-Head and Neck Surgery, Minda Hospital of Hubei Minzu University, Enshi, Hubei, China
| | - Cheng Gong
- Department of Otolaryngology-Head and Neck Surgery, Minda Hospital of Hubei Minzu University, Enshi, Hubei, China
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Liu X, Yang B, Huang X, Yan W, Zhang Y, Hu G. Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis. Interdiscip Sci 2023; 15:525-541. [PMID: 37115388 DOI: 10.1007/s12539-023-00568-w] [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: 01/25/2023] [Revised: 04/13/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023]
Abstract
Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein-protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the "core network module" that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein-protein interactions, pathways, and driver mutations, are predicted by the topological-functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.
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Affiliation(s)
- Xingyi Liu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Bin Yang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Xinpeng Huang
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, Jiangsu, China
| | - Wenying Yan
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, Jiangsu, China.
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou, 215123, Jiangsu, China.
| | - Yujuan Zhang
- Experimental Center of Suzhou Medical College, Soochow University, Suzhou, 215123, Jiangsu, China.
| | - Guang Hu
- Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, Jiangsu, China.
- Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou, 215123, Jiangsu, China.
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Shang J, Zhu X, Sun Y, Li F, Kong X, Liu JX. DM-MOGA: a multi-objective optimization genetic algorithm for identifying disease modules of non-small cell lung cancer. BMC Bioinformatics 2023; 24:13. [PMID: 36624376 PMCID: PMC9830734 DOI: 10.1186/s12859-023-05136-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
BACKGROUND Constructing molecular interaction networks from microarray data and then identifying disease module biomarkers can provide insight into the underlying pathogenic mechanisms of non-small cell lung cancer. A promising approach for identifying disease modules in the network is community detection. RESULTS In order to identify disease modules from gene co-expression networks, a community detection method is proposed based on multi-objective optimization genetic algorithm with decomposition. The method is named DM-MOGA and possesses two highlights. First, the boundary correction strategy is designed for the modules obtained in the process of local module detection and pre-simplification. Second, during the evolution, we introduce Davies-Bouldin index and clustering coefficient as fitness functions which are improved and migrated to weighted networks. In order to identify modules that are more relevant to diseases, the above strategies are designed to consider the network topology of genes and the strength of connections with other genes at the same time. Experimental results of different gene expression datasets of non-small cell lung cancer demonstrate that the core modules obtained by DM-MOGA are more effective than those obtained by several other advanced module identification methods. CONCLUSIONS The proposed method identifies disease-relevant modules by optimizing two novel fitness functions to simultaneously consider the local topology of each gene and its connection strength with other genes. The association of the identified core modules with lung cancer has been confirmed by pathway and gene ontology enrichment analysis.
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Affiliation(s)
- Junliang Shang
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Xuhui Zhu
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Yan Sun
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Feng Li
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Xiangzhen Kong
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
| | - Jin-Xing Liu
- grid.412638.a0000 0001 0227 8151School of Computer Science, Qufu Normal University, Rizhao, 276826 China
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5
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Lu H, Shang C, Zou S, Cheng L, Yang S, Wang L. A Novel Method for Predicting Essential Proteins by Integrating Multidimensional Biological Attribute Information and Topological Properties. Curr Bioinform 2022. [DOI: 10.2174/1574893617666220304201507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Essential proteins are indispensable to the maintenance of life activities and play essential roles in the areas of synthetic biology. Identification of essential proteins by computational methods has become a hot topic in recent years because of its efficiency.
Objective:
Identification of essential proteins is of important significance and practical use in the areas of synthetic biology, drug targets, and human disease genes.
Method:
In this paper, a method called EOP(Edge clustering coefficient -Orthologous-Protein) is proposed to infer potential essential proteins by combining Multidimensional Biological Attribute Information of proteins with Topological Properties of the protein-protein interaction network.
Results:
The simulation results on the yeast protein interaction network show that the number of essential proteins identified by this method is more than the number identified by the other 12 methods(DC, IC, EC, SC, BC, CC, NC, LAC, PEC, CoEWC, POEM, DWE). Especially compared with DC(Degree Centrality), the SN(sensitivity) is 9% higher, when the candidate protein is 1%, the recognition rate is 34% higher, when the candidate protein is 5%, 10%, 15%, 20%, 25% the recognition rate is 36%, 22%, 15%, 11%, 8% higher respectively.
Conclusion:
Experimental results show that our method can achieve satisfactory prediction results, which may provide references for future research.
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Affiliation(s)
- Hanyu Lu
- College of Big Data and Information Engineering, Guizhou University, Guizhou, China
| | - Chen Shang
- College of Big Data and Information Engineering, Guizhou University, Guizhou, China
| | - Sai Zou
- College of Big Data and Information Engineering, Guizhou University, Guizhou, China
| | - Lihong Cheng
- College of Foreign Languages, Dalian Jiaotong University, China
| | - Shikong Yang
- College of Big Data and Information Engineering, Guizhou University, Guizhou, China
| | - Lei Wang
- College of Computer Engineering and Applied Mathematics, Changsha University, China
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Deligiorgi MV, Trafalis DT. The Intriguing Thyroid Hormones-Lung Cancer Association as Exemplification of the Thyroid Hormones-Cancer Association: Three Decades of Evolving Research. Int J Mol Sci 2021; 23:436. [PMID: 35008863 PMCID: PMC8745569 DOI: 10.3390/ijms23010436] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 12/22/2021] [Accepted: 12/28/2021] [Indexed: 12/21/2022] Open
Abstract
Exemplifying the long-pursued thyroid hormones (TH)-cancer association, the TH-lung cancer association is a compelling, yet elusive, issue. The present narrative review provides background knowledge on the molecular aspects of TH actions, with focus on the contribution of TH to hallmarks of cancer. Then, it provides a comprehensive overview of data pertinent to the TH-lung cancer association garnered over the last three decades and identifies obstacles that need to be overcome to enable harnessing this association in the clinical setting. TH contribute to all hallmarks of cancer through integration of diverse actions, currently classified according to molecular background. Despite the increasingly recognized implication of TH in lung cancer, three pending queries need to be resolved to empower a tailored approach: (1) How to stratify patients with TH-sensitive lung tumors? (2) How is determined whether TH promote or inhibit lung cancer progression? (3) How to mimic the antitumor and/or abrogate the tumor-promoting TH actions in lung cancer? To address these queries, research should prioritize the elucidation of the crosstalk between TH signaling and oncogenic signaling implicated in lung cancer initiation and progression, and the development of efficient, safe, and feasible strategies leveraging this crosstalk in therapeutics.
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Affiliation(s)
- Maria V. Deligiorgi
- Department of Pharmacology—Clinical Pharmacology Unit, Faculty of Medicine, National and Kapodistrian University of Athens, Building 16, 1st Floor, 75 Mikras Asias Str, 11527 Athens, Greece;
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7
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Shuai M, He D, Chen X. Optimizing weighted gene co-expression network analysis with a multi-threaded calculation of the topological overlap matrix. Stat Appl Genet Mol Biol 2021; 20:145-153. [PMID: 34757703 DOI: 10.1515/sagmb-2021-0025] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 10/08/2021] [Indexed: 02/06/2023]
Abstract
Biomolecular networks are often assumed to be scale-free hierarchical networks. The weighted gene co-expression network analysis (WGCNA) treats gene co-expression networks as undirected scale-free hierarchical weighted networks. The WGCNA R software package uses an Adjacency Matrix to store a network, next calculates the topological overlap matrix (TOM), and then identifies the modules (sub-networks), where each module is assumed to be associated with a certain biological function. The most time-consuming step of WGCNA is to calculate TOM from the Adjacency Matrix in a single thread. In this paper, the single-threaded algorithm of the TOM has been changed into a multi-threaded algorithm (the parameters are the default values of WGCNA). In the multi-threaded algorithm, Rcpp was used to make R call a C++ function, and then C++ used OpenMP to start multiple threads to calculate TOM from the Adjacency Matrix. On shared-memory MultiProcessor systems, the calculation time decreases as the number of CPU cores increases. The algorithm of this paper can promote the application of WGCNA on large data sets, and help other research fields to identify sub-networks in undirected scale-free hierarchical weighted networks. The source codes and usage are available at https://github.com/do-somethings-haha/multi-threaded_calculate_unsigned_TOM_from_unsigned_or_signed_Adjacency_Matrix_of_WGCNA.
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Affiliation(s)
- Min Shuai
- State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, People's Republic of China.,Pharmacy College, Chengdu University of Traditional Chinese Medicine, Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province - Key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chengdu 611137, China
| | - Dongmei He
- State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, People's Republic of China.,Pharmacy College, Chengdu University of Traditional Chinese Medicine, Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province - Key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chengdu 611137, China.,Center for Post-doctoral research, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, State Key Laboratory Breeding Base of Dao-di Herbs, Beijing 100700, China
| | - Xin Chen
- State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, People's Republic of China.,Pharmacy College, Chengdu University of Traditional Chinese Medicine, Ministry of Education Key Laboratory of Standardization of Chinese Herbal Medicine, Key Laboratory of Systematic Research, Development and Utilization of Chinese Medicine Resources in Sichuan Province - Key Laboratory Breeding Base of Co-founded by Sichuan Province and MOST, Chengdu 611137, China
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