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Li J, Tian J, Liu Y, Liu Z, Tong M. Personalized analysis of human cancer multi-omics for precision oncology. Comput Struct Biotechnol J 2024; 23:2049-2056. [PMID: 38783900 PMCID: PMC11112262 DOI: 10.1016/j.csbj.2024.05.011] [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: 01/30/2024] [Revised: 04/29/2024] [Accepted: 05/07/2024] [Indexed: 05/25/2024] Open
Abstract
Multi-omics technologies, encompassing genomics, proteomics, and transcriptomics, provide profound insights into cancer biology. A fundamental computational approach for analyzing multi-omics data is differential analysis, which identifies molecular distinctions between cancerous and normal tissues. Traditional methods, however, often fail to address the distinct heterogeneity of individual tumors, thereby neglecting crucial patient-specific molecular traits. This shortcoming underscores the necessity for tailored differential analysis algorithms, which focus on particular patient variations. Such approaches offer a more nuanced understanding of cancer biology and are instrumental in pinpointing personalized therapeutic strategies. In this review, we summarize the principles of current individualized techniques. We also review their efficacy in analyzing cancer multi-omics data and discuss their potential applications in clinical practice.
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Affiliation(s)
- Jiaao Li
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
- School of Informatics, Xiamen University, Xiamen 316000, China
| | - Jingyi Tian
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
- School of Informatics, Xiamen University, Xiamen 316000, China
| | - Yachen Liu
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
- School of Informatics, Xiamen University, Xiamen 316000, China
| | - Zan Liu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
- School of Informatics, Xiamen University, Xiamen 316000, China
| | - Mengsha Tong
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
- School of Informatics, Xiamen University, Xiamen 316000, China
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2
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Cai H, Chen L, Yang S, Jiang R, Guo Y, He M, Luo Y, Hong G, Li H, Song K. Personalized differential expression analysis in triple-negative breast cancer. Brief Funct Genomics 2024:elad057. [PMID: 38197537 DOI: 10.1093/bfgp/elad057] [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: 01/30/2023] [Revised: 11/16/2023] [Accepted: 12/04/2023] [Indexed: 01/11/2024] Open
Abstract
Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.
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Affiliation(s)
- Hao Cai
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Liangbo Chen
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
| | - Shuxin Yang
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China
| | - Ronghong Jiang
- 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 341000, China
| | - Ming He
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Yun Luo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Guini Hong
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Hongdong Li
- School of Medical Information Engineering, Gannan Medical University, Ganzhou, China
| | - Kai Song
- Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong SAR 999077, China
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Identification of Common Oncogenic Genes and Pathways Both in Osteosarcoma and Ewing’s Sarcoma Using Bioinformatics Analysis. J Immunol Res 2022; 2022:3655908. [PMID: 35578666 PMCID: PMC9107040 DOI: 10.1155/2022/3655908] [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: 03/04/2022] [Revised: 04/01/2022] [Accepted: 04/09/2022] [Indexed: 11/17/2022] Open
Abstract
This study was aimed at exploring common oncogenic genes and pathways both in osteosarcoma and Ewing's sarcoma. Microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using the limma package. Then, protein-protein interaction (PPI) networks were constructed and hub genes were identified. Furthermore, functional enrichment analysis was analyzed. The expression of common oncogenic genes was validated in 38 osteosarcoma and 17 Ewing's sarcoma tissues by RT-qPCR and western blot compared to normal tissues. 201 genes were differentially expressed. There were 121 nodes and 232 edges of the PPI network. Among 12 hub genes, hub genes FN1, COL1A1, and COL1A2 may involve in the development of osteosarcoma and Ewing's sarcoma. And they were reduced to expression both in osteosarcoma and Ewing's sarcoma tissues at mRNA and protein levels compared to normal tissues. Knockdown of FN1, COL1A1, and COL1A2 enhanced the cell proliferation and migration of U2OS under the restriction of cisplatin. Our findings revealed the common oncogenic genes such as FN1, COL1A1, and COL1A2, which may act as antioncogene by enhancing cisplatin sensitivity in osteosarcoma cells, and pathways were both in osteosarcoma and Ewing's sarcoma.
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Liu Y, Lin Y, Yang W, Lin Y, Wu Y, Zhang Z, Lin N, Wang X, Tong M, Yu R. Application of individualized differential expression analysis in human cancer proteome. Brief Bioinform 2022; 23:6562685. [DOI: 10.1093/bib/bbac096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/06/2022] [Accepted: 02/23/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Liquid chromatography–mass spectrometry-based quantitative proteomics can measure the expression of thousands of proteins from biological samples and has been increasingly applied in cancer research. Identifying differentially expressed proteins (DEPs) between tumors and normal controls is commonly used to investigate carcinogenesis mechanisms. While differential expression analysis (DEA) at an individual level is desired to identify patient-specific molecular defects for better patient stratification, most statistical DEP analysis methods only identify deregulated proteins at the population level. To date, robust individualized DEA algorithms have been proposed for ribonucleic acid data, but their performance on proteomics data is underexplored. Herein, we performed a systematic evaluation on five individualized DEA algorithms for proteins on cancer proteomic datasets from seven cancer types. Results show that the within-sample relative expression orderings (REOs) of protein pairs in normal tissues were highly stable, providing the basis for individualized DEA for proteins using REOs. Moreover, individualized DEA algorithms achieve higher precision in detecting sample-specific deregulated proteins than population-level methods. To facilitate the utilization of individualized DEA algorithms in proteomics for prognostic biomarker discovery and personalized medicine, we provide Individualized DEP Analysis IDEPAXMBD (XMBD: Xiamen Big Data, a biomedical open software initiative in the National Institute for Data Science in Health and Medicine, Xiamen University, China.) (https://github.com/xmuyulab/IDEPA-XMBD), which is a user-friendly and open-source Python toolkit that integrates individualized DEA algorithms for DEP-associated deregulation pattern recognition.
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Affiliation(s)
- Yachen Liu
- School of Informatics, Xiamen University, Xiamen, Fujian 316000, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 316005, China
| | - Yalan Lin
- School of Informatics, Xiamen University, Xiamen, Fujian 316000, China
| | - Wenxian Yang
- Aginome Scientific, Xiamen, Fujian 316005, China
| | - Yuxiang Lin
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 316005, China
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Yujuan Wu
- School of Informatics, Xiamen University, Xiamen, Fujian 316000, China
| | - Zheyang Zhang
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 316005, China
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Nuoqi Lin
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Xianlong Wang
- Department of Bioinformatics, School of Medical Technology and Engineering, Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, Fujian 350122, China
| | - Mengsha Tong
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 316005, China
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cell Signaling Network, School of Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Rongshan Yu
- School of Informatics, Xiamen University, Xiamen, Fujian 316000, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 316005, China
- Aginome Scientific, Xiamen, Fujian 316005, China
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Zhou S, Meng Q, Li L, Hai L, Wang Z, Li Z, Sun Y. Identification of a Qualitative Signature for the Diagnosis of Dementia With Lewy Bodies. Front Genet 2021; 12:758103. [PMID: 34868234 PMCID: PMC8640079 DOI: 10.3389/fgene.2021.758103] [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: 08/17/2021] [Accepted: 10/28/2021] [Indexed: 11/24/2022] Open
Abstract
Background and purpose: Diagnosis of dementia with Lewy bodies (DLB) is highly challenging, primarily due to a lack of valid and reliable diagnostic tools. To date, there is no report of qualitative signature for the diagnosis of DLB. We aimed to develop a blood-based qualitative signature for differentiating DLB patients from healthy controls. Methods: The GSE120584 dataset was downloaded from the public database Gene Expression Omnibus (GEO). We combined multiple methods to select features based on the within-sample relative expression orderings (REOs) of microRNA (miRNA) pairs. Specifically, we first quickly selected miRNA pairs related to DLB by identifying reversal stable miRNA pairs. Then, an optimal miRNA pair subset was extracted by random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) methods. Furthermore, we applied logistic regression (LR) and SVM to build several prediction models. The model performance was assessed using the receiver operating characteristic curve (ROC) analysis. Lastly, we conducted bioinformatics analyses to explore the molecular mechanisms of the discovered miRNAs. Results: A qualitative signature consisted of 17 miRNA pairs and two clinical factors was identified for discriminating DLB patients from healthy controls. The signature is robust against experimental batch effects and applicable at the individual levels. The accuracies of the-signature-based models on the test set are 82.61 and 79.35%, respectively, indicating that the signature has acceptable discrimination performance. Moreover, bioinformatics analyses revealed that predicted target genes were enriched in 11 Go terms and 2 KEGG pathways. Moreover, five potential hub genes were found for DLB, including SRF, MAPK1, YWHAE, RPS6KA3, and KDM7A. Conclusion: This study provided a blood-based qualitative signature with the potential to be used as an effective tool to improve the accuracy of DLB diagnosis.
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Affiliation(s)
- Shu Zhou
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Qingchun Meng
- Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Lingyu Li
- Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Luo Hai
- Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zexuan Wang
- Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zhicheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yingli Sun
- Central Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
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The Effects of Age, Cigarette Smoking, Sex, and Race on the Qualitative Characteristics of Lung Transcriptome. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6418460. [PMID: 32802863 PMCID: PMC7424369 DOI: 10.1155/2020/6418460] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 06/29/2020] [Indexed: 11/18/2022]
Abstract
The within-sample relative expression orderings (REOs) of genes, which are stable qualitative transcriptional characteristics, can provide abundant information for a disease. Methods based on REO comparisons have been proposed for identifying differentially expressed genes (DEGs) at the individual level and for detecting disease-associated genes based on one-phenotype disease data by reusing data of normal samples from other sources. Here, we evaluated the effects of common potential confounding factors, including age, cigarette smoking, sex, and race, on the REOs of gene pairs within normal lung tissues transcriptome. Our results showed that age has little effect on REOs within lung tissues. We found that about 0.23% of the significantly stable REOs of gene pairs in nonsmokers' lung tissues are reversed in smokers' lung tissues, introduced by 344 DEGs between the two groups of samples (RankCompV2, FDR <0.05), which are enriched in metabolism of xenobiotics by cytochrome P450, glutathione metabolism, and other pathways (hypergeometric test, FDR <0.05). Comparison between the normal lung tissue samples of males and females revealed fewer reversal REOs introduced by 24 DEGs between the sex groups, among which 19 DEGs are located on sex chromosomes and 5 DEGs involving in spermatogenesis and regulation of oocyte are located on autosomes. Between the normal lung tissue samples of white and black people, we identified 22 DEGs (RankCompV2, FDR <0.05) which introduced a few reversal REOs between the two races. In summary, the REO-based study should take into account the confounding factors of cigarette smoking, sex, and race.
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Zhang S, Zhou Y, Wang Y, Wang Z, Xiao Q, Zhang Y, Lou Y, Qiu Y, Zhu F. The mechanistic, diagnostic and therapeutic novel nucleic acids for hepatocellular carcinoma emerging in past score years. Brief Bioinform 2020; 22:1860-1883. [PMID: 32249290 DOI: 10.1093/bib/bbaa023] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 02/09/2020] [Accepted: 02/12/2020] [Indexed: 02/07/2023] Open
Abstract
Despite The Central Dogma states the destiny of gene as 'DNA makes RNA and RNA makes protein', the nucleic acids not only store and transmit genetic information but also, surprisingly, join in intracellular vital movement as a regulator of gene expression. Bioinformatics has contributed to knowledge for a series of emerging novel nucleic acids molecules. For typical cases, microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) exert crucial role in regulating vital biological processes, especially in malignant diseases. Due to extraordinarily heterogeneity among all malignancies, hepatocellular carcinoma (HCC) has emerged enormous limitation in diagnosis and therapy. Mechanistic, diagnostic and therapeutic nucleic acids for HCC emerging in past score years have been systematically reviewed. Particularly, we have organized recent advances on nucleic acids of HCC into three facets: (i) summarizing diverse nucleic acids and their modification (miRNA, lncRNA, circRNA, circulating tumor DNA and DNA methylation) acting as potential biomarkers in HCC diagnosis; (ii) concluding different patterns of three key noncoding RNAs (miRNA, lncRNA and circRNA) in gene regulation and (iii) outlining the progress of these novel nucleic acids for HCC diagnosis and therapy in clinical trials, and discuss their possibility for clinical applications. All in all, this review takes a detailed look at the advances of novel nucleic acids from potential of biomarkers and elaboration of mechanism to early clinical application in past 20 years.
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Affiliation(s)
- Song Zhang
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China.,College of Pharmaceutical Sciences in Zhejiang University, China
| | - Ying Zhou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China
| | - Yanan Wang
- School of Life Sciences in Nanchang University, China
| | - Zhengwen Wang
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Qitao Xiao
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Ying Zhang
- College of Pharmaceutical Sciences in Zhejiang University, China
| | - Yan Lou
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China
| | - Yunqing Qiu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China
| | - Feng Zhu
- State Key Laboratory for Diagnosis and Treatment of Infectious Disease, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital in Zhejiang University, China.,College of Pharmaceutical Sciences in Zhejiang University, China
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8
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Qi L, Gao C, Feng F, Zhang T, Yao Y, Wang X, Liu C, Li J, Li J, Sun C. MicroRNAs associated with lung squamous cell carcinoma: New prognostic biomarkers and therapeutic targets. J Cell Biochem 2019; 120:18956-18966. [PMID: 31241205 DOI: 10.1002/jcb.29216] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 05/31/2019] [Indexed: 12/24/2022]
Affiliation(s)
- Lingyu Qi
- College of First Clinical MedicineShandong University of Traditional Chinese Medicine Jinan Shandong PR China
| | - Chundi Gao
- College of First Clinical MedicineShandong University of Traditional Chinese Medicine Jinan Shandong PR China
| | - Fubin Feng
- Department of OncologyWeifang Traditional Chinese Hospital Weifang Shandong PR China
| | - Tingting Zhang
- College of Traditional Chinese MedicineShandong University of Traditional Chinese Medicine Jinan Shandong PR China
| | - Yan Yao
- Clinical Medical CollegesWeifang Medical University Weifang Shandong PR China
| | - Xue Wang
- College of Basic MedicineQingdao University Qingdao Shandong PR China
| | - Cun Liu
- College of Traditional Chinese MedicineShandong University of Traditional Chinese Medicine Jinan Shandong PR China
| | - Jia Li
- Clinical Medical CollegesWeifang Medical University Weifang Shandong PR China
| | - Jie Li
- College of First Clinical MedicineShandong University of Traditional Chinese Medicine Jinan Shandong PR China
| | - Changgang Sun
- Department of OncologyAffiliated Hospital of Weifang Medical University Weifang Shandong PR China
- Department of OncologyAffiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan Shandong PR China
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9
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Hu G, Cheng Z, Wu Z, Wang H. Identification of potential key genes associated with osteosarcoma based on integrated bioinformatics analyses. J Cell Biochem 2019; 120:13554-13561. [PMID: 30920023 DOI: 10.1002/jcb.28630] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Revised: 01/09/2019] [Accepted: 01/14/2019] [Indexed: 12/14/2022]
Abstract
Due to high rates of metastasis and poor clinical outcomes for patients, it is important to study the pathomechanisms of osteosarcoma. However, due to the fact that osteosarcoma shows significant interindividual variation and high heterogeneity, the identification of differentially expressed genes (DEGs) at the population level cannot answer many important questions related to osteosarcoma tumorigenesis. Therefore, a new strategy to identify dysregulated genes in osteosarcoma samples is required. The aim of this study was to improve our understanding of osteosarcoma pathogenesis by identifying genes with universal aberrant expression in osteosarcoma samples. Because the relative expression ordering of genes is stable in normal bone tissues but is disrupted in osteosarcoma tissues, we used the RankComp algorithm to identify DEGs in normal and osteosarcoma tissue samples. We then calculated the dysregulation frequency for each gene. Genes with deregulation frequencies above 80% were deemed to be universal DEGs. Next, coexpression, pathway enrichment, and protein-protein interaction network analyses were performed to characterize the functions of these genes. From 188 samples of osteosarcoma obtained from four datasets measured on different platforms, 51 universal DEGs were identified, including 4 universally upregulated genes and 47 universally downregulated genes. Genes that were differentially coexpressed with these universal DEGs were found to be enriched in 46 cancer-related pathways. In addition, functional and network analyses showed that genes with high dysregulation frequencies were involved in cancer-related functions. Thus, the commonly aberrant genes identified in osteosarcoma tissues may be important targets for osteosarcoma diagnosis and therapy.
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Affiliation(s)
- Guangbing Hu
- Department of Orthopedics, Nanchang Hongdu Hospital of Traditional Chinese Medicine, Nanchang, Jiangxi, China
| | - Zhian Cheng
- Department of Orthopedics, Guangdong Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.,The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Zizhuo Wu
- Department of Orthopedics, Guangdong Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.,The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Hanyu Wang
- Department of Orthopedics, Guangdong Hospital of Chinese Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.,The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
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Cai H, Li X, He J, Zhou W, Song K, Guo Y, Liu H, Guan Q, Yan H, Wang X, Guo Z. Identification and characterization of genes with absolute mRNA abundances changes in tumor cells with varied transcriptome sizes. BMC Genomics 2019; 20:134. [PMID: 30760197 PMCID: PMC6374894 DOI: 10.1186/s12864-019-5502-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Accepted: 01/31/2019] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND The amount of RNA per cell, namely the transcriptome size, may vary under many biological conditions including tumor. If the transcriptome size of two cells is different, direct comparison of the expression measurements on the same amount of total RNA for two samples can only identify genes with changes in the relative mRNA abundances, i.e., cellular mRNA concentration, rather than genes with changes in the absolute mRNA abundances. RESULTS Our recently proposed RankCompV2 algorithm identify differentially expressed genes (DEGs) through comparing the relative expression orderings (REOs) of disease samples with that of normal samples. We reasoned that both the mRNA concentration and the absolute abundances of these DEGs must have changes in disease samples. In simulation experiments, this method showed excellent performance for identifying DEGs between normal and disease samples with different transcriptome sizes. Through analyzing data for ten cancer types, we found that a significantly higher proportion of the DEGs with absolute mRNA abundance changes overlapped or directly interacted with known cancer driver genes and anti-cancer drug targets than that of the DEGs only with mRNA concentration changes alone identified by the traditional methods. The DEGs with increased absolute mRNA abundances were enriched in DNA damage-related pathways, while DEGs with decreased absolute mRNA abundances were enriched in immune and metabolism associated pathways. CONCLUSIONS Both the mRNA concentration and the absolute abundances of the DEGs identified through REOs comparison change in disease samples in comparison with normal samples. In cancers these genes might play more important upstream roles in carcinogenesis.
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Affiliation(s)
- Hao Cai
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Xiangyu Li
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China
| | - Jun He
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China
| | - Wenbin Zhou
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, Fujian, China
| | - Kai Song
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, Fujian, China
| | - You Guo
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Huaping Liu
- Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Qingzhou Guan
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China
| | - Haidan Yan
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China
| | - Xianlong Wang
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China.
| | - Zheng Guo
- Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China. .,Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, Fujian, China.
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