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Li Y, Yu H, Han X, Pan Y. Analyses of hypoxia-related risk factors and clinical relevance in breast cancer. Front Oncol 2024; 14:1350426. [PMID: 38500661 PMCID: PMC10946248 DOI: 10.3389/fonc.2024.1350426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/12/2024] [Indexed: 03/20/2024] Open
Abstract
Introduction Hypoxia plays an important role in the heterogeneity, relapse, metastasis, and drug resistance of breast cancer. In this study, we explored the hypoxia-related biological signatures in different subtypes of breast cancer and identified the key prognostic factors by bioinformatics methods. Methods Based on The Cancer Genome Atlas (TCGA) Breast Cancer datasets, we divided the samples into immune-activated/suppressed populations by single-sample gene set enrichment analysis (ssGSEA) and then used hierarchical clustering to further identify hypoxic/non-hypoxic populations from the immune-suppressed samples. A hypoxia related risk model of breast cancer was constructed. Results Nuclear factor interleukin-3 regulated (NFIL3), serpin family E member 1 (SERPINE1), FOS, biglycan (BGN), epidermal growth factor receptor (EGFR), and sushi-repeat-containing protein, X-linked (SRPX) were identified as key hypoxia-related genes. Margin status, American Joint Committee on Cancer (AJCC) stage, hypoxia status, estrogen receptor/progesterone receptor (ER/PR) status, NFIL3, SERPINE1, EGFR, and risk score were identified as independent prognostic indicators for breast cancer patients. The 3- and 5-year survival curves of the model and immunohistochemical staining on the breast cancer microarray verified the statistical significance and feasibility of our model. Among the different molecular types of breast cancer, ER/PR+ and HER2+ patients might have higher hypoxia-related risk scores. ER/PR-negative samples demonstrated more activated immune-related pathways and better response to most anticancer agents. Discussion Our study revealed a novel risk model and potential feasible prognostic factors for breast cancer and might provide new perspectives for individual breast cancer treatment.
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Affiliation(s)
- Yan Li
- Department of Clinical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Haiyang Yu
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Xinghua Han
- Department of Clinical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
| | - Yueyin Pan
- Department of Clinical Oncology, The First Affiliated Hospital of University of Science and Technology of China (USTC), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, China
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2
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A new border for circadian rhythm gene NFIL3 in diverse fields of cancer. Clin Transl Oncol 2023:10.1007/s12094-023-03098-5. [PMID: 36788184 DOI: 10.1007/s12094-023-03098-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/19/2023] [Indexed: 02/16/2023]
Abstract
The circadian rhythm disorder and abnormal expression of rhythm genes are related to many diseases, especially cancer. Rhythm gene NFIL3 is involved in energy metabolism and immune cell differentiation, and its aberrant expression is associated with metabolic diseases and inflammation. Previously, numerous studies have shown that aberrant NFIL3 expression is associated with tumorigenesis, progression, and chemotherapy resistance. For instance, NFIL3 performs as a nuclear transcription factor, impacts cell proliferation, represses apoptosis, and promotes cancer cell invasion and metastasis by regulating the transcription of target genes. In addition, NFIL3 expressed in cancer cells influences the type and proportion of infiltrated immune cells in the tumor microenvironment. Increased expression of NFIL3 induces the chemotherapy and immunotherapy resistance in cancer. In this review, we summarized the pathological functions of NFIL3 in tumorigenesis, cancer development, and treatment. The rhythm gene NFIL3 can be used as a promising target in cancer therapy in the future.
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Wu Y, Guo Y, Ma J, Sa Y, Li Q, Zhang N. Research Progress of Gliomas in Machine Learning. Cells 2021; 10:cells10113169. [PMID: 34831392 PMCID: PMC8622230 DOI: 10.3390/cells10113169] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 11/04/2021] [Accepted: 11/05/2021] [Indexed: 12/29/2022] Open
Abstract
In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.
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Li Q, Dai W, Liu J, Sang Q, Li YX, Li YY. Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer. J Mol Cell Biol 2020; 12:881-893. [PMID: 32717065 PMCID: PMC7883816 DOI: 10.1093/jmcb/mjaa041] [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: 03/12/2020] [Revised: 06/21/2020] [Accepted: 07/01/2020] [Indexed: 12/09/2022] Open
Abstract
The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from The Cancer Genome Atlas. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation (4-DysReg) signature, which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.
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Affiliation(s)
- Quanxue Li
- School of Biotechnology, East China University of Science and Technology, Shanghai 200237, China.,Shanghai Center for Bioinformation Technology, Shanghai 201203, China
| | - Wentao Dai
- Shanghai Center for Bioinformation Technology, Shanghai 201203, China.,Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.,Shanghai Engineering Research Center of Pharmaceutical Translation and Shanghai Industrial Technology Institute, Shanghai 201203, China
| | - Jixiang Liu
- Shanghai Center for Bioinformation Technology, Shanghai 201203, China.,Shanghai Engineering Research Center of Pharmaceutical Translation and Shanghai Industrial Technology Institute, Shanghai 201203, China
| | - Qingqing Sang
- Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yi-Xue Li
- School of Biotechnology, East China University of Science and Technology, Shanghai 200237, China.,Shanghai Center for Bioinformation Technology, Shanghai 201203, China.,CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,Shanghai Engineering Research Center of Pharmaceutical Translation and Shanghai Industrial Technology Institute, Shanghai 201203, China
| | - Yuan-Yuan Li
- Shanghai Center for Bioinformation Technology, Shanghai 201203, China.,Shanghai Engineering Research Center of Pharmaceutical Translation and Shanghai Industrial Technology Institute, Shanghai 201203, China
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5
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Li J, Ping Y, Li H, Li H, Liu Y, Liu B, Wang Y. Prognostic prediction of carcinoma by a differential-regulatory-network-embedded deep neural network. Comput Biol Chem 2020; 88:107317. [PMID: 32622180 DOI: 10.1016/j.compbiolchem.2020.107317] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 06/21/2020] [Indexed: 02/04/2023]
Abstract
The accurate prognostic prediction is essential for precise diagnosis and treatment of carcinoma. In addition to clinical survival prediction method, many computational methods based on transcriptomic data have been proposed to build the prediction models and study the prognosis of cancer patients. We propose a differential-regulatory-network-embedded deep neural network (DRE-DNN) method by integrating differential regulatory analysis based on gene co-expression network and deep neural network (DNN) method. From three public hepatocellular carcinoma (HCC) datasets, we derive differential regulatory network and embed regulatory information into DNN. By employing 1869 differential regulatory genes and survival data, we apply DRE-DNN to build a prediction model. We compare our method with the one which has all gene features in normal DNN, and results show that our method has better generalization ability and accuracy. We modify the normal DNN and develop an efficient method to predict prognosis of HCC from gene expression data. Our method decreases the inconsistence caused by the overfitting problem when the training sample size is small. DRE-DNN is also extendable for prognostic prediction of other cancers.
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Affiliation(s)
- Junyi Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
| | - Yuan Ping
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Hong Li
- CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Huinian Li
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Ying Liu
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Bo Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China; School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China.
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Singh AJ, Ramsey SA, Filtz TM, Kioussi C. Differential gene regulatory networks in development and disease. Cell Mol Life Sci 2018; 75:1013-1025. [PMID: 29018868 PMCID: PMC11105524 DOI: 10.1007/s00018-017-2679-6] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 09/19/2017] [Accepted: 10/04/2017] [Indexed: 02/02/2023]
Abstract
Gene regulatory networks, in which differential expression of regulator genes induce differential expression of their target genes, underlie diverse biological processes such as embryonic development, organ formation and disease pathogenesis. An archetypical systems biology approach to mapping these networks involves the combined application of (1) high-throughput sequencing-based transcriptome profiling (RNA-seq) of biopsies under diverse network perturbations and (2) network inference based on gene-gene expression correlation analysis. The comparative analysis of such correlation networks across cell types or states, differential correlation network analysis, can identify specific molecular signatures and functional modules that underlie the state transition or have context-specific function. Here, we review the basic concepts of network biology and correlation network inference, and the prevailing methods for differential analysis of correlation networks. We discuss applications of gene expression network analysis in the context of embryonic development, cancer, and congenital diseases.
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Affiliation(s)
- Arun J Singh
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR, 97331, USA
| | - Stephen A Ramsey
- Department of Biomedical Sciences, College of Veterinary Medicine, Oregon State University, Corvallis, OR, 97331, USA
- School of Electrical Engineering and Computer Science, College of Engineering, Oregon State University, Corvallis, OR, 97331, USA
| | - Theresa M Filtz
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR, 97331, USA
| | - Chrissa Kioussi
- Department of Pharmaceutical Sciences, College of Pharmacy, Oregon State University, Corvallis, OR, 97331, USA.
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Sim W, Lee J, Choi C. Robust method for identification of prognostic gene signatures from gene expression profiles. Sci Rep 2017; 7:16926. [PMID: 29208919 PMCID: PMC5717170 DOI: 10.1038/s41598-017-17213-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 11/22/2017] [Indexed: 12/11/2022] Open
Abstract
In the last decade, many attempts have been made to use gene expression profiles to identify prognostic genes for various types of cancer. Previous studies evaluating the prognostic value of genes suffered by failing to solve the critical problem of classifying patients into different risk groups based on specific gene expression threshold levels. Here, we present a novel method, called iterative patient partitioning (IPP), which was inspired by the receiver operating characteristic (ROC) curve, is based on the log-rank test and overcomes the threshold decision problem. We applied IPP to analyze datasets pertaining to various subtypes of breast cancer. Using IPP, we discovered both novel and well-studied prognostic genes related to cell cycle/proliferation or the immune response. The novel genes were further analyzed using copy-number alteration and mutation data, and these results supported their relationship with prognosis.
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Affiliation(s)
- Woogwang Sim
- Department of Bio and Brain Engineering, KAIST, Daejeon, 34141, Republic of Korea
| | - Jungsul Lee
- Cellex Life Sciences Incorporated, Daejeon, 34051, Republic of Korea.
| | - Chulhee Choi
- Department of Bio and Brain Engineering, KAIST, Daejeon, 34141, Republic of Korea. .,Cellex Life Sciences Incorporated, Daejeon, 34051, Republic of Korea.
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Zhao Z, Liu Y, Huang Y, Huang K, Ruan J. Advancing Systems Biology in the International Conference on Intelligent Biology and Medicine (ICIBM) 2015. BMC SYSTEMS BIOLOGY 2016; 10 Suppl 3:61. [PMID: 27587087 PMCID: PMC5009564 DOI: 10.1186/s12918-016-0305-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) was held on November 13–15, 2015 in Indianapolis, Indiana, USA. ICIBM 2015 included eight scientific sessions, three tutorial sessions, one poster session, and four keynote presentations that covered the frontier research in broad areas related to bioinformatics, systems biology, big data science, biomedical informatics, pharmacogenomics, and intelligent computing. Here, we present a summary of the 10 research articles that were selected from ICIBM 2015 and included in the supplement to BMC Systems Biology.
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Affiliation(s)
- Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.,Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Yufei Huang
- Department of Electrical and Computer Engineering, The University of Texas at San Antonio, San Antonio, TX, 78249, USA
| | - Kun Huang
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
| | - Jianhua Ruan
- Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX, 78249, USA.
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Differential Regulatory Analysis Based on Coexpression Network in Cancer Research. BIOMED RESEARCH INTERNATIONAL 2016; 2016:4241293. [PMID: 27597964 PMCID: PMC4997028 DOI: 10.1155/2016/4241293] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Revised: 06/09/2016] [Accepted: 06/12/2016] [Indexed: 12/15/2022]
Abstract
With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene coexpression network (GCN) increasingly plays a robust complement to regular differential expression analysis in revealing regulatory functions of cancer related genes such as evading growth suppressors and resisting cell death. Differential regulatory analysis based on GCN is prospective and shows its essential role in discovering the system properties of carcinogenesis features. Here we briefly review the paradigm of differential regulatory analysis based on GCN. We also focus on the applications of differential regulatory analysis based on GCN in cancer research and point out that DRA is necessary and extraordinary to reveal underlying molecular mechanism in large-scale carcinogenesis studies.
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