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Li S, Chen Y, Guo Y, Xu J, Wang X, Ning W, Ma L, Qu Y, Zhang M, Zhang H. Mutation-derived, genomic instability-associated lncRNAs are prognostic markers in gliomas. PeerJ 2023; 11:e15810. [PMID: 37547724 PMCID: PMC10404032 DOI: 10.7717/peerj.15810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 07/07/2023] [Indexed: 08/08/2023] Open
Abstract
Background Gliomas are the most commonly-detected malignant tumors of the brain. They contain abundant long non-coding RNAs (lncRNAs), which are valuable cancer biomarkers. LncRNAs may be involved in genomic instability; however, their specific role and mechanism in gliomas remains unclear. LncRNAs that are related to genomic instability have not been reported in gliomas. Methods The transcriptome data from The Cancer Genome Atlas (TCGA) database were analyzed. The co-expression network of genomic instability-related lncRNAs and mRNA was established, and the model of genomic instability-related lncRNA was identified by univariate Cox regression and LASSO analyses. Based on the median risk score obtained in the training set, we divided the samples into high-risk and low-risk groups and proved the survival prediction ability of genomic instability-related lncRNA signatures. The results were verified in the external data set. Finally, a real-time quantitative polymerase chain reaction assay was performed to validate the signature. Results The signatures of 17 lncRNAs (LINC01579, AL022344.1, AC025171.5, LINC01116, MIR155HG, AC131097.3, LINC00906, CYTOR, AC015540.1, SLC25A21.AS1, H19, AL133415.1, SNHG18, FOXD3.AS1, LINC02593, AL354919.2 and CRNDE) related to genomic instability were identified. In the internal data set and Gene Expression Omnibus (GEO) external data set, the low-risk group showed better survival than the high-risk group (P < 0.001). In addition, this feature was identified as an independent risk factor, showing its independent prognostic value with different clinical stratifications. The majority of patients in the low-risk group had isocitrate dehydrogenase 1 (IDH1) mutations. The expression levels of these lncRNAs were significantly higher in glioblastoma cell lines than in normal cells. Conclusions Our study shows that the signature of 17 lncRNAs related to genomic instability has prognostic value for gliomas and could provide a potential therapeutic method for glioblastoma.
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Rupp B, Owen S, Ball H, Smith KJ, Gunchick V, Keller ET, Sahai V, Nagrath S. Integrated Workflow for the Label-Free Isolation and Genomic Analysis of Single Circulating Tumor Cells in Pancreatic Cancer. Int J Mol Sci 2022; 23:7852. [PMID: 35887203 PMCID: PMC9316651 DOI: 10.3390/ijms23147852] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/12/2022] [Accepted: 07/13/2022] [Indexed: 02/04/2023] Open
Abstract
As pancreatic cancer is the third deadliest cancer in the U.S., the ability to study genetic alterations is necessary to provide further insight into potentially targetable regions for cancer treatment. Circulating tumor cells (CTCs) represent an especially aggressive subset of cancer cells, capable of causing metastasis and progressing the disease. Here, we present the Labyrinth-DEPArray pipeline for the isolation and analysis of single CTCs. Established cell lines, patient-derived CTC cell lines and freshly isolated CTCs were recovered and sequenced to reveal single-cell copy number variations (CNVs). The resulting CNV profiles of established cell lines showed concordance with previously reported data and highlight several gains and losses of cancer-related genes such as FGFR3 and GNAS. The novel sequencing of patient-derived CTC cell lines showed gains in chromosome 8q, 10q and 17q across both CTC cell lines. The pipeline was used to process and isolate single cells from a metastatic pancreatic cancer patient revealing a gain of chromosome 1q and a loss of chromosome 5q. Overall, the Labyrinth-DEPArray pipeline offers a validated workflow combining the benefits of antigen-free CTC isolation with single cell genomic analysis.
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Affiliation(s)
- Brittany Rupp
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (B.R.); (S.O.); (H.B.); (K.J.S.)
- BioInterface Institute, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Sarah Owen
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (B.R.); (S.O.); (H.B.); (K.J.S.)
- BioInterface Institute, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Harrison Ball
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (B.R.); (S.O.); (H.B.); (K.J.S.)
- BioInterface Institute, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Kaylee Judith Smith
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (B.R.); (S.O.); (H.B.); (K.J.S.)
- BioInterface Institute, University of Michigan, Ann Arbor, MI 48109, USA;
| | - Valerie Gunchick
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA; (V.G.); (V.S.)
| | - Evan T. Keller
- BioInterface Institute, University of Michigan, Ann Arbor, MI 48109, USA;
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Vaibhav Sahai
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA; (V.G.); (V.S.)
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sunitha Nagrath
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI 48109, USA; (B.R.); (S.O.); (H.B.); (K.J.S.)
- BioInterface Institute, University of Michigan, Ann Arbor, MI 48109, USA;
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
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Wang X, Ye L, Li B. Development of a Genomic Instability-Derived lncRNAs-Based Risk Signature as a Predictor of Prognosis for Endometrial Cancer. J Cancer 2022; 13:2213-2225. [PMID: 35517417 PMCID: PMC9066205 DOI: 10.7150/jca.65581] [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: 08/01/2021] [Accepted: 03/06/2022] [Indexed: 11/30/2022] Open
Abstract
Endometrial cancer (EC) ranks fourth in the incidence rate among the most frequent gynaecological malignancies reported in the developed countries. Approximately 280,000 endometrial cancer cases are reported worldwide every year. Genomic instability and mutation are some of the favourable characteristics of human malignancies such as endometrial cancer. Studies have established that the majority of genomic mutations in human malignancies are found in the chromosomal regions that do not code for proteins. In addition, the majority of transcriptional products of these mutations are long non-coding RNAs (lncRNAs). In this study, 78 lncRNA genes were found on the basis of their mutation counts. Then, these lncRNAs were investigated to determine their relationship with genomic instability through hierarchical cluster analysis, mutation analysis, and differential analysis of driving genes responsible for genomic instability. The prognostic value of these lncRNAs was also assessed in patients with EC, and a risk factor score formula composed of 15 lncRNAs was constructed. We then identified this formula as genome instability-derived lncRNA-based gene signature (GILncSig), which stratified patients into high- and low-risk groups with significantly different outcome. And GILncSig was further validated in multiple independent patient cohorts as a prognostic factor of other clinicopathological features, such as stage, grade, overall survival rate. We observed that a high-risk score is often associated with an unfavourable prognosis in patients with EC.
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Affiliation(s)
- Xiaojun Wang
- Department of Gynaecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Lei Ye
- Department of Gynaecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
| | - Bilan Li
- Department of Gynaecology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China
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Alqahtani K, Taylor CC, Wood HM, Gusnanto A. Sparse modelling of cancer patients' survival based on genomic copy number alterations. J Biomed Inform 2022; 128:104025. [PMID: 35181494 DOI: 10.1016/j.jbi.2022.104025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/03/2022] [Accepted: 02/05/2022] [Indexed: 11/24/2022]
Abstract
Copy number alterations (CNA) are structural variation in the genome, in which some regions exhibit more or less than the normal two chromosomal copies. This genomic CNA profile provides critical information in tumour progression and is therefore informative for patients' survival. It is currently a statistical challenge to model patients' survival using their genomic CNA profiles while at the same time identify regions in the genome that are associated with patients' survival. Some methods have been proposed, including Cox proportional hazard (PH) model with ridge, lasso, or elastic net penalties. However, these methods do not take the general dependencies between genomic regions into account and produce results that are difficult to interpret. In this paper, we extend the elastic net penalty by introducing additional penalty that takes into account general dependencies between genomic regions. This new model produces smooth parameter estimates while simultaneously performs variable selection via sparse solution. The results indicate that the proposed method shows a better prediction performance than other models in our simulation study, while enabling us to investigate regions in the genome that are associated with the patients' survival with sensible interpretation. We illustrate the method using a real dataset from a lung cancer cohort and simulated data.
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Affiliation(s)
- Khaled Alqahtani
- Department of Mathematics, College of Science and Humanitarian Studies, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia; Department of Statistics, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Charles C Taylor
- Department of Statistics, University of Leeds, Leeds LS2 9JT, United Kingdom
| | - Henry M Wood
- Leeds Institute of Medical Research at St. James's, University of Leeds, Leeds LS9 7TF
| | - Arief Gusnanto
- Department of Statistics, University of Leeds, Leeds LS2 9JT, United Kingdom
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5
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Roy S, Gupta D. DriverFuse: An R package for analysis of next-generation sequencing datasets to identify cancer driver fusion genes. PLoS One 2022; 17:e0262686. [PMID: 35113898 PMCID: PMC8812906 DOI: 10.1371/journal.pone.0262686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 01/03/2022] [Indexed: 11/28/2022] Open
Abstract
We developed the DriverFuse package to integrate orthogonal data types such as Structural Variants (SV) and Copy Number Variations (CNV) to characterize fusion genes in cancer datasets. A fusion gene is reported as a driver or passenger fusion gene, based on mapping SV and CNV profiles. DriverFuse generates a fusion plot of fusion genes with their mapping SV, CNV profile, domain architecture and classification of its role in cancer. The analysis facilitates discrimination of driver fusions from passenger fusions. To demonstrate the utility of DriverFuse, we analyzed two datasets, one each for CCLE (Cancer Cell Line Encyclopedia) for lung cancer and HCC1395BL for breast cancer. The analysis validates the driver fusion genes that are already reported for the datasets. Thus, DriverFuse is a valuable tool for studying the driver fusion genes in cancers, enabling the identification of recurrent complex rearrangements that provide intuitive insights into disease driver events.
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Affiliation(s)
- Shikha Roy
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, New Delhi, India
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6
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Predicting RNA 5-Methylcytosine Sites by Using Essential Sequence Features and Distributions. BIOMED RESEARCH INTERNATIONAL 2022; 2022:4035462. [PMID: 35071593 PMCID: PMC8776474 DOI: 10.1155/2022/4035462] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/07/2021] [Accepted: 12/22/2021] [Indexed: 12/15/2022]
Abstract
Methylation is one of the most common and considerable modifications in biological systems mediated by multiple enzymes. Recent studies have shown that methylation has been widely identified in different RNA molecules. RNA methylation modifications have various kinds, such as 5-methylcytosine (m5C). However, for individual methylation sites, their functions still remain to be elucidated. Testing of all methylation sites relies heavily on high-throughput sequencing technology, which is expensive and labor consuming. Thus, computational prediction approaches could serve as a substitute. In this study, multiple machine learning models were used to predict possible RNA m5C sites on the basis of mRNA sequences in human and mouse. Each site was represented by several features derived from
-mers of an RNA subsequence containing such site as center. The powerful max-relevance and min-redundancy (mRMR) feature selection method was employed to analyse these features. The outcome feature list was fed into incremental feature selection method, incorporating four classification algorithms, to build efficient models. Furthermore, the sites related to features used in the models were also investigated.
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7
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Alterations of non-coding RNA expression and mitochondrial biogenesis in colorectal cancer tissue: Possible crosstalk with macrophage polarization. GENE REPORTS 2021. [DOI: 10.1016/j.genrep.2021.101344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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8
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Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu CW, Qiu J, Hua K, Su W, Wu J, Xu H, Han Y, Fu C, Yin Z, Liu M, Roepman R, Dietmann S, Virta M, Kengara F, Zhang Z, Zhang L, Zhao T, Dai J, Yang J, Lan L, Luo M, Liu Z, An T, Zhang B, He X, Cong S, Liu X, Zhang W, Lewis JP, Tiedje JM, Wang Q, An Z, Wang F, Zhang L, Huang T, Lu C, Cai Z, Wang F, Zhang J. Artificial intelligence: A powerful paradigm for scientific research. Innovation (N Y) 2021; 2:100179. [PMID: 34877560 PMCID: PMC8633405 DOI: 10.1016/j.xinn.2021.100179] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2021] [Accepted: 10/26/2021] [Indexed: 12/18/2022] Open
Abstract
Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.
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Affiliation(s)
- Yongjun Xu
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Liu
- Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Cao
- Zhongshan Hospital Institute of Clinical Science, Fudan University, Shanghai 200032, China
| | - Changping Huang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Enke Liu
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Sen Qian
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Xingchen Liu
- Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
| | - Yanjun Wu
- Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fengliang Dong
- National Center for Nanoscience and Technology, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Cheng-Wei Qiu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore
| | - Junjun Qiu
- Department of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai 200011, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai 200011, China
| | - Keqin Hua
- Department of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai 200011, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai 200011, China
| | - Wentao Su
- School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China
| | - Jian Wu
- Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou 310058, China
| | - Huiyu Xu
- Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China
| | - Yong Han
- Zhejiang Provincial People’s Hospital, Hangzhou 310014, China
| | - Chenguang Fu
- School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China
| | - Zhigang Yin
- Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China
| | - Miao Liu
- Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China
| | - Ronald Roepman
- Medical Center, Radboud University, 6500 Nijmegen, the Netherlands
| | - Sabine Dietmann
- Institute for Informatics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Marko Virta
- Department of Microbiology, University of Helsinki, 00014 Helsinki, Finland
| | - Fredrick Kengara
- School of Pure and Applied Sciences, Bomet University College, Bomet 20400, Kenya
| | - Ze Zhang
- Agriculture College of Shihezi University, Xinjiang 832000, China
| | - Lifu Zhang
- Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
- Agriculture College of Shihezi University, Xinjiang 832000, China
| | - Taolan Zhao
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Ji Dai
- The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
| | | | - Liang Lan
- Department of Communication Studies, Hong Kong Baptist University, Hong Kong, China
| | - Ming Luo
- South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
- Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Guangzhou 510650, China
| | - Zhaofeng Liu
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tao An
- Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China
| | - Bin Zhang
- Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
| | - Xiao He
- Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Shan Cong
- Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China
| | - Xiaohong Liu
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - Wei Zhang
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
| | - James P. Lewis
- Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China
| | - James M. Tiedje
- Center for Microbial Ecology, Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Qi Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Zhejiang Lab, Hangzhou 311121, China
| | - Zhulin An
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fei Wang
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Libo Zhang
- Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tao Huang
- Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chuan Lu
- Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3FL, UK
| | - Zhipeng Cai
- Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
| | - Fang Wang
- Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiabao Zhang
- Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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Zhang YH, Guo W, Zeng T, Zhang S, Chen L, Gamarra M, Mansour RF, Escorcia-Gutierrez J, Huang T, Cai YD. Identification of Microbiota Biomarkers With Orthologous Gene Annotation for Type 2 Diabetes. Front Microbiol 2021; 12:711244. [PMID: 34305880 PMCID: PMC8299781 DOI: 10.3389/fmicb.2021.711244] [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: 05/18/2021] [Accepted: 06/21/2021] [Indexed: 01/03/2023] Open
Abstract
Type 2 diabetes (T2D) is a systematic chronic metabolic condition with abnormal sugar metabolism dysfunction, and its complications are the most harmful to human beings and may be life-threatening after long-term durations. Considering the high incidence and severity at late stage, researchers have been focusing on the identification of specific biomarkers and potential drug targets for T2D at the genomic, epigenomic, and transcriptomic levels. Microbes participate in the pathogenesis of multiple metabolic diseases including diabetes. However, the related studies are still non-systematic and lack the functional exploration on identified microbes. To fill this gap between gut microbiome and diabetes study, we first introduced eggNOG database and KEGG ORTHOLOGY (KO) database for orthologous (protein/gene) annotation of microbiota. Two datasets with these annotations were employed, which were analyzed by multiple machine-learning models for identifying significant microbiota biomarkers of T2D. The powerful feature selection method, Max-Relevance and Min-Redundancy (mRMR), was first applied to the datasets, resulting in a feature list for each dataset. Then, the list was fed into the incremental feature selection (IFS), incorporating support vector machine (SVM) as the classification algorithm, to extract essential annotations and build efficient classifiers. This study not only revealed potential pathological factors for diabetes at the microbiome level but also provided us new candidates for drug development against diabetes.
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Affiliation(s)
- Yu-Hang Zhang
- School of Life Sciences, Shanghai University, Shanghai, China.,Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences (CAS) and Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - ShiQi Zhang
- Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Margarita Gamarra
- Department of Computational Science and Electronic, Universidad de la Costa, CUC, Barranquilla, Colombia
| | - Romany F Mansour
- Department of Mathematics, Faculty of Science, New Valley University, El-Kharga, Egypt
| | - José Escorcia-Gutierrez
- Electronic and Telecommunications Engineering Program, Universidad Autónoma del Caribe, Barranquilla, Colombia
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.,CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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10
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Chen L, Li Z, Zeng T, Zhang YH, Feng K, Huang T, Cai YD. Identifying COVID-19-Specific Transcriptomic Biomarkers with Machine Learning Methods. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9939134. [PMID: 34307679 PMCID: PMC8272456 DOI: 10.1155/2021/9939134] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 06/03/2021] [Accepted: 06/24/2021] [Indexed: 12/11/2022]
Abstract
COVID-19, a severe respiratory disease caused by a new type of coronavirus SARS-CoV-2, has been spreading all over the world. Patients infected with SARS-CoV-2 may have no pathogenic symptoms, i.e., presymptomatic patients and asymptomatic patients. Both patients could further spread the virus to other susceptible people, thereby making the control of COVID-19 difficult. The two major challenges for COVID-19 diagnosis at present are as follows: (1) patients could share similar symptoms with other respiratory infections, and (2) patients may not have any symptoms but could still spread the virus. Therefore, new biomarkers at different omics levels are required for the large-scale screening and diagnosis of COVID-19. Although some initial analyses could identify a group of candidate gene biomarkers for COVID-19, the previous work still could not identify biomarkers capable for clinical use in COVID-19, which requires disease-specific diagnosis compared with other multiple infectious diseases. As an extension of the previous study, optimized machine learning models were applied in the present study to identify some specific qualitative host biomarkers associated with COVID-19 infection on the basis of a publicly released transcriptomic dataset, which included healthy controls and patients with bacterial infection, influenza, COVID-19, and other kinds of coronavirus. This dataset was first analysed by Boruta, Max-Relevance and Min-Redundancy feature selection methods one by one, resulting in a feature list. This list was fed into the incremental feature selection method, incorporating one of the classification algorithms to extract essential biomarkers and build efficient classifiers and classification rules. The capacity of these findings to distinguish COVID-19 with other similar respiratory infectious diseases at the transcriptomic level was also validated, which may improve the efficacy and accuracy of COVID-19 diagnosis.
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Affiliation(s)
- Lei Chen
- School of Life Sciences, Shanghai University, shanghai 200444, China
- College of Information Engineering, Shanghai Maritime University, shanghai 201306, China
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun 130052, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, shanghai 200031, China
| | - Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - KaiYan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou 510507, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, shanghai 200031, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, shanghai 200444, China
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11
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Geng W, Lv Z, Fan J, Xu J, Mao K, Yin Z, Qing W, Jin Y. Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma. Front Cell Dev Biol 2021; 9:657667. [PMID: 33855028 PMCID: PMC8039462 DOI: 10.3389/fcell.2021.657667] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 03/11/2021] [Indexed: 12/24/2022] Open
Abstract
Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD. Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset. Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis. Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.
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Affiliation(s)
- Wei Geng
- NHC Key Laboratory of Pulmonary Diseases, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhilei Lv
- NHC Key Laboratory of Pulmonary Diseases, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jinshuo Fan
- NHC Key Laboratory of Pulmonary Diseases, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Juanjuan Xu
- NHC Key Laboratory of Pulmonary Diseases, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kaimin Mao
- NHC Key Laboratory of Pulmonary Diseases, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhengrong Yin
- NHC Key Laboratory of Pulmonary Diseases, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wanlu Qing
- Institute of Pathology and Southwest Cancer Center, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yang Jin
- NHC Key Laboratory of Pulmonary Diseases, Department of Respiratory and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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12
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Hopp AK, Hottiger MO. Uncovering the Invisible: Mono-ADP-ribosylation Moved into the Spotlight. Cells 2021; 10:680. [PMID: 33808662 PMCID: PMC8003356 DOI: 10.3390/cells10030680] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/12/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
Adenosine diphosphate (ADP)-ribosylation is a nicotinamide adenine dinucleotide (NAD+)-dependent post-translational modification that is found on proteins as well as on nucleic acids. While ARTD1/PARP1-mediated poly-ADP-ribosylation has extensively been studied in the past 60 years, comparably little is known about the physiological function of mono-ADP-ribosylation and the enzymes involved in its turnover. Promising technological advances have enabled the development of innovative tools to detect NAD+ and NAD+/NADH (H for hydrogen) ratios as well as ADP-ribosylation. These tools have significantly enhanced our current understanding of how intracellular NAD dynamics contribute to the regulation of ADP-ribosylation as well as to how mono-ADP-ribosylation integrates into various cellular processes. Here, we discuss the recent technological advances, as well as associated new biological findings and concepts.
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Affiliation(s)
| | - Michael O. Hottiger
- Department of Molecular Mechanisms of Disease (DMMD), University of Zurich, 8057 Zurich, Switzerland;
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13
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Pan X, Li H, Zeng T, Li Z, Chen L, Huang T, Cai YD. Identification of Protein Subcellular Localization With Network and Functional Embeddings. Front Genet 2021; 11:626500. [PMID: 33584818 PMCID: PMC7873866 DOI: 10.3389/fgene.2020.626500] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/21/2020] [Indexed: 01/15/2023] Open
Abstract
The functions of proteins are mainly determined by their subcellular localizations in cells. Currently, many computational methods for predicting the subcellular localization of proteins have been proposed. However, these methods require further improvement, especially when used in protein representations. In this study, we present an embedding-based method for predicting the subcellular localization of proteins. We first learn the functional embeddings of KEGG/GO terms, which are further used in representing proteins. Then, we characterize the network embeddings of proteins on a protein–protein network. The functional and network embeddings are combined as novel representations of protein locations for the construction of the final classification model. In our collected benchmark dataset with 4,861 proteins from 16 locations, the best model shows a Matthews correlation coefficient of 0.872 and is thus superior to multiple conventional methods.
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Affiliation(s)
- Xiaoyong Pan
- School of Life Sciences, Shanghai University, Shanghai, China.,Key Laboratory of System Control and Information Processing, Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai, China
| | - Hao Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, China
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Tao Huang
- Key Laboratory of Tissue Microenvironment and Tumor, 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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14
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Zhang YH, Li H, Zeng T, Chen L, Li Z, Huang T, Cai YD. Identifying Transcriptomic Signatures and Rules for SARS-CoV-2 Infection. Front Cell Dev Biol 2021; 8:627302. [PMID: 33505977 PMCID: PMC7829664 DOI: 10.3389/fcell.2020.627302] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 12/14/2020] [Indexed: 12/26/2022] Open
Abstract
The world-wide Coronavirus Disease 2019 (COVID-19) pandemic was triggered by the widespread of a new strain of coronavirus named as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Multiple studies on the pathogenesis of SARS-CoV-2 have been conducted immediately after the spread of the disease. However, the molecular pathogenesis of the virus and related diseases has still not been fully revealed. In this study, we attempted to identify new transcriptomic signatures as candidate diagnostic models for clinical testing or as therapeutic targets for vaccine design. Using the recently reported transcriptomics data of upper airway tissue with acute respiratory illnesses, we integrated multiple machine learning methods to identify effective qualitative biomarkers and quantitative rules for the distinction of SARS-CoV-2 infection from other infectious diseases. The transcriptomics data was first analyzed by Boruta so that important features were selected, which were further evaluated by the minimum redundancy maximum relevance method. A feature list was produced. This list was fed into the incremental feature selection, incorporating some classification algorithms, to extract qualitative biomarker genes and construct quantitative rules. Also, an efficient classifier was built to identify patients infected with SARS-COV-2. The findings reported in this study may help in revealing the potential pathogenic mechanisms of COVID-19 and finding new targets for vaccine design.
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Affiliation(s)
- Yu-Hang Zhang
- School of Life Sciences, Shanghai University, Shanghai, China
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Hao Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, China
| | - Tao Zeng
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, 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
| | - Zhandong Li
- College of Food Engineering, Jilin Engineering Normal University, Changchun, 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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15
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Ramone T, Mulè C, Ciampi R, Bottici V, Cappagli V, Prete A, Matrone A, Piaggi P, Torregrossa L, Basolo F, Elisei R, Romei C. RET Copy Number Alteration in Medullary Thyroid Cancer Is a Rare Event Correlated with RET Somatic Mutations and High Allelic Frequency. Genes (Basel) 2020; 12:35. [PMID: 33383911 PMCID: PMC7824333 DOI: 10.3390/genes12010035] [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] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/17/2020] [Accepted: 12/24/2020] [Indexed: 01/10/2023] Open
Abstract
Copy number variations (CNV) of the RET gene have been described in 30% of Medullary Thyroid Cancer (MTC), but no information is available about their role in this tumor. This study was designed to clarify RET gene CNV prevalence and their potential role in MTC development. RET gene CNV were analyzed in 158 sporadic MTC cases using the ION Reporter Software (i.e., in silico analysis) while the multiplex ligation-dependent probe amplification assay (i.e., in vitro analysis) technique was performed in 78 MTC cases. We identified three categories of RET ploidy: 137 in 158 (86.7%) cases were diploid and 21 in 158 (13.3%) were aneuploid. Among the aneuploid cases, five out of 21 (23.8%) showed an allelic deletion while 16 out of 21 (76.2%) had an allelic amplification. The prevalence of amplified or deleted RET gene cases (aneuploid) was higher in RET positive tumors. Aneuploid cases also showed a higher allelic frequency of the RET driver mutation. The prevalence of patients with metastatic disease was higher in the group of aneuploid cases while the higher prevalence of disease-free patients was observed in diploid tumors. A statistically significant difference was found when comparing the ploidy status and mortality. RET gene CNVs are rare events in sporadic MTC and are associated with RET somatic mutation, suggesting that they could not be a driver mechanism of tumoral transformation per se. Finally, we found a positive correlation between RET gene CNV and a worse clinical outcome.
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Affiliation(s)
- Teresa Ramone
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
| | - Chiara Mulè
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
| | - Raffaele Ciampi
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
| | - Valeria Bottici
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
| | - Virginia Cappagli
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
| | - Alessandro Prete
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
| | - Antonio Matrone
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
| | - Paolo Piaggi
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
| | - Liborio Torregrossa
- Department of Surgical, Medical, Molecular Pathology, University of Pisa, 56124 Pisa, Italy; (L.T.); (F.B.)
| | - Fulvio Basolo
- Department of Surgical, Medical, Molecular Pathology, University of Pisa, 56124 Pisa, Italy; (L.T.); (F.B.)
| | - Rossella Elisei
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
| | - Cristina Romei
- Endocrine Unit, Department of Clinical and Experimental Medicine, University of Pisa, 56124 Pisa, Italy; (T.R.); (C.M.); (R.C.); (V.B.); (V.C.); (A.P.); (A.M.); (P.P.); (C.R.)
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16
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Kou F, Wu L, Ren X, Yang L. Chromosome Abnormalities: New Insights into Their Clinical Significance in Cancer. MOLECULAR THERAPY-ONCOLYTICS 2020; 17:562-570. [PMID: 32637574 PMCID: PMC7321812 DOI: 10.1016/j.omto.2020.05.010] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Chromosomal abnormalities, consisting of numerical and structural chromosome abnormalities, are a common characteristic of cancer. Numerical chromosome abnormalities, mainly including aneuploidy and chromosome instability, are caused by chromosome segregation errors in mitosis, whereas structural chromosome abnormalities are a consequence of DNA damage and comprise focal/arm-level chromosome gain or loss. Recent advances have started to unveil the mechanisms by which chromosomal abnormalities can facilitate tumorigenesis and change the cellular fitness and the expression or function of RNAs and proteins. Accumulating evidence suggests that chromosome abnormalities represent a genomic signature that is linked to cancer prognosis and reaction to chemotherapy and immunotherapy. In this review, we discuss the most recent findings on the role of chromosome abnormalities in tumorigenesis and cancer progression, with a particular emphasis on how aneuploidy and chromosome instability influence cancer therapy and prognosis. We also highlight the distribution and clinical application of the structural chromosome abnormalities in various cancer types. A better understanding of the role of chromosome abnormalities will be beneficial to the development of precision oncology and suggest future directions for the field.
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Affiliation(s)
- Fan Kou
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Lei Wu
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
| | - Xiubao Ren
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Corresponding author: Xiubao Ren, Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Tiyuanbei, Hexi District, Tianjin 300060, China.
| | - Lili Yang
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Corresponding author: Lili Yang, Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Tiyuanbei, Hexi District, Tianjin 300060, China.
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