1
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Yu X, Pan X, Zhang S, Zhang YH, Chen L, Wan S, Huang T, Cai YD. Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas. Front Genet 2021; 11:605012. [PMID: 33584803 PMCID: PMC7876317 DOI: 10.3389/fgene.2020.605012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Accepted: 12/21/2020] [Indexed: 11/13/2022] Open
Abstract
Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients’ deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT.
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Affiliation(s)
- Xiangtian Yu
- Clinical Research Center, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - XiaoYong Pan
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - ShiQi Zhang
- Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Yu-Hang Zhang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China.,Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai, China
| | - Sibao Wan
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Tao Huang
- CAS Key Laboratory of Computational Biology, Bio-Med Big Data Center, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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2
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Hozhabri H, Lashkari A, Razavi SM, Mohammadian A. Integration of gene expression data identifies key genes and pathways in colorectal cancer. Med Oncol 2021; 38:7. [PMID: 33411100 DOI: 10.1007/s12032-020-01448-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 11/21/2020] [Indexed: 12/16/2022]
Abstract
Colorectal cancer (CRC) is one of the most common malignant tumor and prevalent cause of cancer-related death worldwide. In this study, we analyzed the gene expression profiles of patients with CRC with the aim of better understanding the molecular mechanism and key genes in CRC. Four gene expression profiles including, GSE9348, GSE41328, GSE41657, and GSE113513 were downloaded from GEO database. The data were processed using R programming language, in which 319 common differentially expressed genes including 94 up-regulated and 225 down-regulated were identified. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were conducted to find the most significant enriched pathways in CRC. Based on the GO and KEGG pathway analysis, the most important dysregulated pathways were regulation of cell proliferation, biocarbonate transport, Wnt, and IL-17 signaling pathways, and nitrogen metabolism. The protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software and hub genes including MYC, CXCL1, CD44, MMP1, and CXCL12 were identified as the most critical hub genes. The present study enhances our understanding of the molecular mechanisms of the CRC, which might potentially be applied in the treatment strategies of CRC as molecular targets and diagnostic biomarkers.
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Affiliation(s)
- Hossein Hozhabri
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Ali Lashkari
- Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Seyed-Morteza Razavi
- Department of Cell and Molecular Biology, Faculty of Biological Sciences, Kharazmi University, Tehran, Iran.,Salari Institute of Cognitive and Behavioral Disorders (SICBD), Karaj, Alborz, Iran.,Systems Biology Research Lab, Bioinformatics Group, Systems Biology of Next Generation Company (SBNGC), Qom, Iran
| | - Ali Mohammadian
- Department of Medical Biotechnology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
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3
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Das P, Taube JH. Regulating Methylation at H3K27: A Trick or Treat for Cancer Cell Plasticity. Cancers (Basel) 2020; 12:E2792. [PMID: 33003334 PMCID: PMC7600873 DOI: 10.3390/cancers12102792] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 09/21/2020] [Accepted: 09/23/2020] [Indexed: 12/13/2022] Open
Abstract
Properly timed addition and removal of histone 3 lysine 27 tri-methylation (H3K27me3) is critical for enabling proper differentiation throughout all stages of development and, likewise, can guide carcinoma cells into altered differentiation states which correspond to poor prognoses and treatment evasion. In early embryonic stages, H3K27me3 is invoked to silence genes and restrict cell fate. Not surprisingly, mutation or altered functionality in the enzymes that regulate this pathway results in aberrant methylation or demethylation that can lead to malignancy. Likewise, changes in expression or activity of these enzymes impact cellular plasticity, metastasis, and treatment evasion. This review focuses on current knowledge regarding methylation and de-methylation of H3K27 in cancer initiation and cancer cell plasticity.
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Affiliation(s)
| | - Joseph H. Taube
- Department of Biology, Baylor University, Waco, TX 76706, USA;
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4
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Pan X, Zeng T, Zhang YH, Chen L, Feng K, Huang T, Cai YD. Investigation and Prediction of Human Interactome Based on Quantitative Features. Front Bioeng Biotechnol 2020; 8:730. [PMID: 32766217 PMCID: PMC7379396 DOI: 10.3389/fbioe.2020.00730] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Accepted: 06/09/2020] [Indexed: 01/27/2023] Open
Abstract
Protein is one of the most significant components of all living creatures. All significant and essential biological structures and functions relies on proteins and their respective biological functions. However, proteins cannot perform their unique biological significance independently. They have to interact with each other to realize the complicated biological processes in all living creatures including human beings. In other words, proteins depend on interactions (protein-protein interactions) to realize their significant effects. Thus, the significance comparison and quantitative contribution of candidate PPI features must be determined urgently. According to previous studies, 258 physical and chemical characteristics of proteins have been reported and confirmed to definitively affect the interaction efficiency of the related proteins. Among such features, essential physiochemical features of proteins like stoichiometric balance, protein abundance, molecular weight and charge distribution have been validated to be quite significant and irreplaceable for protein-protein interactions (PPIs). Therefore, in this study, we, on one hand, presented a novel computational framework to identify the key factors affecting PPIs with Boruta feature selection (BFS), Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and on the other hand, built a quantitative decision-rule system to evaluate the potential PPIs under real conditions with random forest (RF) and RIPPER algorithms, thereby supplying several new insights into the detailed biological mechanisms of complicated PPIs. The main datasets and codes can be downloaded at https://github.com/xypan1232/Mass-PPI.
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Affiliation(s)
- Xiaoyong Pan
- School of Life Sciences, Shanghai University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Hang Zhang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Kaiyan Feng
- Department of Computer Science, Guangdong AIB Polytechnic, Guangzhou, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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5
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Li M, Chen F, Zhang Y, Xiong Y, Li Q, Huang H. Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies. Front Physiol 2020; 11:483. [PMID: 32581823 PMCID: PMC7287215 DOI: 10.3389/fphys.2020.00483] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 04/20/2020] [Indexed: 12/24/2022] Open
Abstract
Myocardial infarction (MI) is a type of serious heart attack in which the blood flow to the heart is suddenly interrupted, resulting in injury to the heart muscles due to a lack of oxygen supply. Although clinical diagnosis methods can be used to identify the occurrence of MI, using the changes of molecular markers or characteristic molecules in blood to characterize the early phase and later trend of MI will help us choose a more reasonable treatment plan. Previously, comparative transcriptome studies focused on finding differentially expressed genes between MI patients and healthy people. However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. Subsequently, functional enrichment analyses followed by protein-protein interaction analysis on 134 genes identified several hub genes (IL1R1, TLR2, and TLR4) associated with progression of MI, which can be used as new diagnostic molecules for MI.
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Affiliation(s)
- Ming Li
- Department of Cardiology, Eastern Hospital, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China
| | - Fuli Chen
- Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China
| | - Yaling Zhang
- Department of Nephrology, Eastern Hospital, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China
| | - Yan Xiong
- Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China
| | - Qiyong Li
- Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China
| | - Hui Huang
- Department of Cardiology, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China
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6
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Chen L, Li D, Shao Y, Wang H, Liu Y, Zhang Y. Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine. Front Genet 2019; 10:1146. [PMID: 31803234 PMCID: PMC6872643 DOI: 10.3389/fgene.2019.01146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 10/21/2019] [Indexed: 12/12/2022] Open
Abstract
Gut microbiomes are integral microflora located in the human intestine with particular symbiosis. Among all microorganisms in the human intestine, bacteria are the most significant subgroup that contains many unique and functional species. The distribution patterns of bacteria in the human intestine not only reflect the different microenvironments in different sections of the intestine but also indicate that bacteria may have unique biological functions corresponding to their proper regions of the intestine. However, describing the functional differences between the bacterial subgroups and their distributions in different individuals is difficult using traditional computational approaches. Here, we first attempted to introduce four effective sets of bacterial features from independent databases. We then presented a novel computational approach to identify potential distinctive features among bacterial subgroups based on a systematic dataset on the gut microbiome from approximately 1,500 human gut bacterial strains. We also established a group of quantitative rules for explaining such distinctions. Results may reveal the microstructural characteristics of the intestinal flora and deepen our understanding on the regulatory role of bacterial subgroups in the human intestine.
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Affiliation(s)
- Lijuan Chen
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Daojie Li
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Ye Shao
- School of Medicine, Huaqiao University, Quanzhou, China
| | - Hui Wang
- College of Animal Science and Technology, Anhui Agricultural University, Hefei, China
| | - Yuqing Liu
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, China
| | - Yunhua Zhang
- Anhui Province Key Laboratory of Farmland Ecological Conservation and Pollution Prevention, School of Resources and Environment, Anhui Agricultural University, Hefei, China
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7
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Pan X, Zeng T, Yuan F, Zhang YH, Chen L, Zhu L, Wan S, Huang T, Cai YD. Screening of Methylation Signature and Gene Functions Associated With the Subtypes of Isocitrate Dehydrogenase-Mutation Gliomas. Front Bioeng Biotechnol 2019; 7:339. [PMID: 31803734 PMCID: PMC6871504 DOI: 10.3389/fbioe.2019.00339] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2019] [Accepted: 10/30/2019] [Indexed: 02/05/2023] Open
Abstract
Isocitrate dehydrogenase (IDH) is an oncogene, and the expression of a mutated IDH promotes cell proliferation and inhibits cell differentiation. IDH exists in three different isoforms, whose mutation can cause many solid tumors, especially gliomas in adults. No effective method for classifying gliomas on genetic signatures is currently available. DNA methylation may be applied to distinguish cancer cells from normal tissues. In this study, we focused on three subtypes of IDH-mutation gliomas by examining methylation data. Several advanced computational methods were used, such as Monte Carlo feature selection (MCFS), incremental feature selection (IFS), support machine vector (SVM), etc. The MCFS method was adopted to analyze methylation features, resulting in a feature list. Then, the IFS method incorporating SVM was applied to the list to extract important methylation features and construct an optimal SVM classifier. As a result, several methylation features (sites) were found to relate to glioma subclasses, which are annotated onto multiple genes, such as FLJ37543, LCE3D, FAM89A, ADCY5, ESR1, C2orf67, REST, EPHA7, etc. These genes are enriched in biological functions, including cellular developmental process, neuron differentiation, cellular component morphogenesis, and G-protein-coupled receptor signaling pathway. Our results, which are supported by literature reports and independent dataset validation, showed that our identified genes and functions contributed to the detailed glioma subtypes. This study provided a basic research on IDH-mutation gliomas.
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Affiliation(s)
- XiaoYong Pan
- School of Life Sciences, Shanghai University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Ministry of Education of China, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China.,IDLab, Department for Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Tao Zeng
- Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai, China
| | - Fei Yuan
- Department of Science and Technology, Binzhou Medical University Hospital, Binzhou, China
| | - Yu-Hang Zhang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China.,Shanghai Key Laboratory of PMMP, East China Normal University, Shanghai, China
| | - LiuCun Zhu
- School of Life Sciences, Shanghai University, Shanghai, China
| | - SiBao Wan
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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8
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Zhang GL, Pan LL, Huang T, Wang JH. The transcriptome difference between colorectal tumor and normal tissues revealed by single-cell sequencing. J Cancer 2019; 10:5883-5890. [PMID: 31737124 PMCID: PMC6843882 DOI: 10.7150/jca.32267] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Accepted: 06/17/2019] [Indexed: 12/29/2022] Open
Abstract
The previous cancer studies were difficult to reproduce since the tumor tissues were analyzed directly. But the tumor tissues were actually a mixture of different cancer cells. The transcriptome of single-cell was much robust than the transcriptome of a mixed tissue. The single-cell transcriptome had much smaller variance. In this study, we analyzed the single-cell transcriptome of 272 colorectal cancer (CRC) epithelial cells and 160 normal epithelial cells and identified 342 discriminative transcripts using advanced machine learning methods. The most discriminative transcripts were LGALS4, PHGR1, C15orf48, HEPACAM2, PERP, FABP1, FCGBP, MT1G, TSPAN1 and CKB. We further clustered the 342 transcripts into two categories. The upregulated transcripts in CRC epithelial cells were significantly enriched in Ribosome, Protein processing in endoplasmic reticulum, Antigen processing and presentation and p53 signaling pathway. The downregulated transcripts in CRC epithelial cells were significantly enriched in Mineral absorption, Aldosterone-regulated sodium reabsorption and Oxidative phosphorylation pathways. The biological analysis of the discriminative transcripts revealed the possible mechanism of colorectal cancer.
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Affiliation(s)
- Guo-Liang Zhang
- Department of Colorectal Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang, China
| | - Le-Lin Pan
- Department of Colorectal Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Jin-Hai Wang
- Department of Colorectal Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, Zhejiang, China
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9
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Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes. Int J Mol Sci 2019; 20:ijms20174269. [PMID: 31480430 PMCID: PMC6747348 DOI: 10.3390/ijms20174269] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 08/19/2019] [Accepted: 08/29/2019] [Indexed: 12/18/2022] Open
Abstract
Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.
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10
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Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer. BIOMED RESEARCH INTERNATIONAL 2019; 2019:6763596. [PMID: 31008109 PMCID: PMC6441508 DOI: 10.1155/2019/6763596] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 02/01/2019] [Accepted: 02/14/2019] [Indexed: 01/02/2023]
Abstract
The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 – 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 – 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 – 0.94) and AUC = 0.83, 95% CI = (0.69 – 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 – 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.
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Lin H, Qiu X, Zhang B, Zhang J. Identification of the predictive genes for the response of colorectal cancer patients to FOLFOX therapy. Onco Targets Ther 2018; 11:5943-5955. [PMID: 30271178 PMCID: PMC6149834 DOI: 10.2147/ott.s167656] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Background Colorectal cancer is a malignant tumor with high death rate. Chemotherapy, radiotherapy and surgery are the three common treatments of colorectal cancer. For early colorectal cancer patients, postoperative adjuvant chemotherapy can reduce the risk of recurrence. For advanced colorectal cancer patients, palliative chemotherapy can significantly improve the life quality of patients and prolong survival. FOLFOX is one of the mainstream chemotherapies in colorectal cancer, however, its response rate is only about 50%. Methods To systematically investigate why some of the colorectal cancer patients have response to FOLFOX therapy while others do not, we searched all publicly available database and combined three gene expression datasets of colorectal cancer patients with FOLFOX therapy. With advanced minimal redundancy maximal relevance and incremental feature selection method, we identified the biomarker genes. Results A Support Vector Machine-based classifier was constructed to predict the response of colorectal cancer patients to FOLFOX therapy. Its accuracy, sensitivity and specificity were 0.854, 0.845 and 0.863, respectively. Conclusion The biological analysis of representative biomarker genes suggested that apoptosis and inflammation signaling pathways were essential for the response of colorectal cancer patients to FOLFOX chemotherapy.
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Affiliation(s)
- Hengjun Lin
- Department of Tumor, Anus and Intestine, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China,
| | - Xueke Qiu
- Department of Tumor, Anus and Intestine, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China,
| | - Bo Zhang
- Department of Tumor, Anus and Intestine, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China,
| | - Jichao Zhang
- Department of Tumor, Anus and Intestine, Jinhua People's Hospital, Jinhua, Zhejiang 321000, China,
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12
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Li J, Lan CN, Kong Y, Feng SS, Huang T. Identification and Analysis of Blood Gene Expression Signature for Osteoarthritis With Advanced Feature Selection Methods. Front Genet 2018; 9:246. [PMID: 30214455 PMCID: PMC6125376 DOI: 10.3389/fgene.2018.00246] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 06/22/2018] [Indexed: 12/15/2022] Open
Abstract
Osteoarthritis (OA) is a complex disease that affects articular joints and may cause disability. The incidence of OA is extremely high. Most elderly people have the symptoms of osteoarthritis. The physiotherapy of OA is time consuming, and the chances of full recovery from OA are very minimal. The most effective way of fighting OA is early diagnosis and early intervention. Liquid biopsy has become a popular noninvasive test. To find the blood gene expression signature for OA, we reanalyzed the publicly available blood gene expression profiles of 106 patients with OA and 33 control samples using an automatic computational pipeline based on advanced feature selection methods. Finally, a compact 23-gene set was identified. On the basis of these 23 genes, we constructed a Support Vector Machine (SVM) classifier and evaluated it with leave-one-out cross-validation. Its sensitivity (Sn), specificity (Sp), accuracy (ACC), and Mathew's correlation coefficient (MCC) were 0.991, 0.909, 0.971, and 0.920, respectively. Obviously, the performance needed to be validated in an independent large dataset, but the in-depth biological analysis of the 23 biomarkers showed great promise and suggested that mRNA surveillance pathway and multicellular organism growth played important roles in OA. Our results shed light on OA diagnosis through liquid biopsy.
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Affiliation(s)
- Jing Li
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Chun-Na Lan
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ying Kong
- Department of Rehabilitation, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Song-Shan Feng
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Tao Huang
- Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
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