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Huang AL, He YZ, Yang Y, Pang M, Zheng GP, Wang HL. Exploring the potential of the TCR repertoire as a tumor biomarker (Review). Oncol Lett 2024; 28:413. [PMID: 38988449 PMCID: PMC11234811 DOI: 10.3892/ol.2024.14546] [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: 02/29/2024] [Accepted: 06/14/2024] [Indexed: 07/12/2024] Open
Abstract
T cells play an important role in adaptive immunity. Mature T cells specifically recognize antigens on major histocompatibility complex molecules through T-cell receptors (TCRs). As the TCR repertoire is highly diverse, its analysis is vital in the assessment of T cells. Advances in sequencing technology have provided convenient methods for further investigation of the TCR repertoire. In the present review, the TCR structure and the mechanisms by which TCRs function in tumor recognition are described. In addition, the potential value of the TCR repertoire in tumor diagnosis is reviewed. Furthermore, the role of the TCR repertoire in tumor immunotherapy is introduced, and the relationships between the TCR repertoire and the effects of different tumor immunotherapies are discussed. Based on the reviewed literature, it may be concluded that the TCR repertoire has the potential to serve as a biomarker for tumor prognosis. However, a wider range of cancer types and more diverse subjects require evaluation in future research to establish the TCR repertoire as a biomarker of tumor immunity.
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Affiliation(s)
- An-Li Huang
- Institute of Cancer Biology, Basic Medical Sciences Center, School of Basic Medicine, Shanxi Medical University, Jinzhong, Shanxi 030600, P.R. China
- The First Clinical Medical College, Shanxi Medical University, Jinzhong, Shanxi 030600, P.R. China
| | - Yan-Zhao He
- Institute of Cancer Biology, Basic Medical Sciences Center, School of Basic Medicine, Shanxi Medical University, Jinzhong, Shanxi 030600, P.R. China
| | - Yong Yang
- Institute of Cancer Biology, Basic Medical Sciences Center, School of Basic Medicine, Shanxi Medical University, Jinzhong, Shanxi 030600, P.R. China
| | - Min Pang
- NHC Key Laboratory of Pneumoconiosis, Shanxi Province Key Laboratory of Respiratory Disease, Department of Pulmonary and Critical Care Medicine, The First Hospital, Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China
| | - Guo-Ping Zheng
- Centre for Transplantation and Renal Research, Westmead Millennium Institute, University of Sydney, Sydney, New South Wales 2145, Australia
| | - Hai-Long Wang
- Institute of Cancer Biology, Basic Medical Sciences Center, School of Basic Medicine, Shanxi Medical University, Jinzhong, Shanxi 030600, P.R. China
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2
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Wang J, Dong L, Zheng Z, Zhu Z, Xie B, Xie Y, Li X, Chen B, Li P. Effects of different KRAS mutants and Ki67 expression on diagnosis and prognosis in lung adenocarcinoma. Sci Rep 2024; 14:4085. [PMID: 38374309 PMCID: PMC10876986 DOI: 10.1038/s41598-023-48307-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Accepted: 11/24/2023] [Indexed: 02/21/2024] Open
Abstract
Lung adenocarcinoma (LUAD) is a prevalent form of non-small cell lung cancer with a rising incidence in recent years. Understanding the mutation characteristics of LUAD is crucial for effective treatment and prediction of this disease. Among the various mutations observed in LUAD, KRAS mutations are particularly common. Different subtypes of KRAS mutations can activate the Ras signaling pathway to varying degrees, potentially influencing the pathogenesis and prognosis of LUAD. This study aims to investigate the relationship between different KRAS mutation subtypes and the pathogenesis and prognosis of LUAD. A total of 63 clinical samples of LUAD were collected for this study. The samples were analyzed using targeted gene sequencing panels to obtain sequencing data. To complement the dataset, additional clinical and sequencing data were obtained from TCGA and MSK. The analysis revealed significantly higher Ki67 immunohistochemical scores in patients with missense mutations compared to controls. Moreover, the expression level of KRAS was found to be significantly correlated with Ki67 expression. Enrichment analysis indicated that KRAS missense mutations activated the SWEET_LUNG_CANCER_KRAS_DN and CREIGHTON_ENDOCRINE_THERAPY_RESISTANCE_2 pathways. Additionally, patients with KRAS missense mutations and high Ki67 IHC scores exhibited significantly higher tumor mutational burden levels compared to other groups, which suggests they are more likely to be responsive to ICIs. Based on the data from MSK and TCGA, it was observed that patients with KRAS missense mutations had shorter survival compared to controls, and Ki67 expression level could more accurately predict patient prognosis. In conclusion, when utilizing KRAS mutations as biomarkers for the treatment and prediction of LUAD, it is important to consider the specific KRAS mutant subtypes and Ki67 expression levels. These findings contribute to a better understanding of LUAD and have implications for personalized therapeutic approaches in the management of this disease.
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Affiliation(s)
- Jun Wang
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Liwen Dong
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Zhaowei Zheng
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Zhen Zhu
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Baisheng Xie
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Yue Xie
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Xiongwei Li
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China
| | - Bing Chen
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
| | - Pan Li
- Department of Thoracic Surgery, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
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Zhang Y, Zhang C, He M, Xing W, Hou R, Zhang H. Co-expression of IL-21-Enhanced NKG2D CAR-NK cell therapy for lung cancer. BMC Cancer 2024; 24:119. [PMID: 38263004 PMCID: PMC10807083 DOI: 10.1186/s12885-023-11806-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 12/28/2023] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Adoptive cell therapy has achieved great success in treating hematological malignancies. However, the production of chimeric antigen receptor T (CAR-T) cell therapy still faces various difficulties. Natural killer (NK)-92 is a continuously expandable cell line and provides a promising alternative for patient's own immune cells. METHODS We established CAR-NK cells by co-expressing natural killer group 2 member D (NKG2D) and IL-21, and evaluated the efficacy of NKG2D-IL-21 CAR-NK cells in treating lung cancer in vitro and in vivo. RESULTS Our data suggested that the expression of IL-21 effectively increased the cytotoxicity of NKG2D CAR-NK cells against lung cancer cells in a dose-dependent manner and suppressed tumor growth in vitro and in vivo. In addition, the proliferation of NKG2D-IL-21 CAR-NK cells were enhanced while the apoptosis and exhaustion of these cells were suppressed. Mechanistically, IL-21-mediated NKG2D CAR-NK cells function by activating AKT signaling pathway. CONCLUSION Our findings provide a novel option for treating lung cancer using NKG2D-IL-21 CAR-NK cell therapy.
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Affiliation(s)
- Yan Zhang
- Department of Oncology, Shenyang 242 Hospital, 110034, Shenyang, China
| | - Cong Zhang
- Department of Oncology, Hospital of Chengdu University of Traditional Chinese Medicine, 610072, Chengdu, China
| | - Minghong He
- Department of Respiratory and Critical Care Medicine, Yidu Central Hospital of Weifang, 262500, Weifang, China
| | - Weipeng Xing
- Geneis Beijing Co., Ltd., 100102, Beijing, China
| | - Rui Hou
- Geneis Beijing Co., Ltd., 100102, Beijing, China.
| | - Haijin Zhang
- Department of Respiratory and Critical Care Medicine, Yidu Central Hospital of Weifang, 262500, Weifang, China.
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Su J, Tan S, Gong H, Luo Y, Cheng T, Yang H, Wen X, Jiang Z, Li Y, Zhang L. The Evaluation of Prognostic Value and Immune Characteristics of Ferroptosis-Related Genes in Lung Squamous Cell Carcinoma. Glob Med Genet 2023; 10:285-300. [PMID: 37915460 PMCID: PMC10615648 DOI: 10.1055/s-0043-1776386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2023] Open
Abstract
Background The purpose of our study was to construct a prognostic model based on ferroptosis-related gene signature to improve the prognosis prediction of lung squamous carcinoma (LUSC). Methods The mRNA expression profiles and clinical data of LUSC patients were downloaded. LUSC-related essential differentially expressed genes were integrated for further analysis. Prognostic gene signatures were identified through random forest regression and univariate Cox regression analyses for constructing a prognostic model. Finally, in a preliminary experiment, we used the reverse transcription-quantitative polymerase chain reaction assay to verify the relationship between the expression of three prognostic gene features and ferroptosis. Results Fifty-six ferroptosis-related essential genes were identified by using integrated analysis. Among these, three prognostic gene signatures (HELLS, POLR2H, and POLE2) were identified, which were positively affected by LUSC prognosis but negatively affected by immune cell infiltration. Significant overexpression of immune checkpoint genes occurred in the high-risk group. In preliminary experiments, we confirmed that the occurrence of ferroptosis can reduce three prognostic gene signature expression. Conclusions The three ferroptosis-related genes could predict the LUSC prognostic risk of antitumor immunity.
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Affiliation(s)
- Jialin Su
- Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
- School of Life and Health Sciences, Hunan University of Science and Technology, Xiangtan, People's Republic of China
| | - Shuhua Tan
- School of Life and Health Sciences, Hunan University of Science and Technology, Xiangtan, People's Republic of China
| | - Houwu Gong
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, People's Republic of China
| | - Yongzhong Luo
- Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
| | - Tianli Cheng
- Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
| | - Hua Yang
- Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
| | - Xiaoping Wen
- Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
| | - Zhou Jiang
- Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
| | - Yuning Li
- School of Life and Health Sciences, Hunan University of Science and Technology, Xiangtan, People's Republic of China
| | - Lemeng Zhang
- Thoracic Medicine Department 1, Hunan Cancer Hospital, Changsha, Hunan Province, People's Republic of China
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5
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Li X, Yuan X, Zhu X, Li C, Ji L, Lv K, Tian G, Ning K, Yang J. A meta-analysis of tissue microbial biomarkers for recurrence and metastasis in multiple cancer types. J Med Microbiol 2023; 72. [PMID: 37624368 DOI: 10.1099/jmm.0.001744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023] Open
Abstract
Background. Local recurrence and distant metastasis are the main causes of death in patients with cancer. Only considering species abundance changes when identifying markers of recurrence and metastasis in patients hinders finding solutions.Hypothesis. Consideration of microbial abundance changes and microbial interactions facilitates the identification of microbial markers of tumour recurrence and metastasis.Aim. This study aims to simultaneously consider microbial abundance changes and microbial interactions to identify microbial markers of recurrence and metastasis in multiple cancer types.Method. One thousand one hundred and six non-RM (patients without recurrence and metastasis within 3 years after initial surgery) tissue samples and 912 RM (patients with recurrence or metastasis within 3 years after initial surgery) tissue samples representing 11 cancer types were collected from The Cancer Genome Atlas (TCGA).Results. Tumour tissue bacterial composition differed significantly among 11 cancers. Among them, the tissue microbiome of four cancers, head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), stomach adenocarcinoma (STAD) and uterine corpus endometrial carcinoma (UCEC), showed relatively good performance in predicting recurrence and metastasis in patients, with areas under the receiver operating characteristic curve (AUCs) of 0.78, 0.74, 0.91 and 0.93, respectively. Considering both species abundance changes and microbial interactions for the four cancers, a combination of nine genera (Niastella, Schlesneria, Thioalkalivibrio, Phaeobacter, Sphaerotilus, Thiomonas, Lawsonia, Actinobacillus and Spiroplasma) performed best in predicting patient survival.Conclusion. Taken together, our results imply that comprehensive consideration of microbial abundance changes and microbial interactions is helpful for mining bacterial markers that carry prognostic information.
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Affiliation(s)
- Xuebo Li
- Department of Radiotherapy, Weifang Yidu Central Hospital, Weifang, 262500, PR China
| | - Xuelian Yuan
- School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, PR China
| | - Xiumin Zhu
- Department of Pathology, Daqing Oilfield General Hospital, Daqing, 163001, PR China
| | - Changjun Li
- School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, PR China
| | - Lei Ji
- Geneis Beijing Co. Ltd, Beijing, 100102, PR China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, PR China
| | - Kebo Lv
- School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, PR China
| | - Geng Tian
- Geneis Beijing Co. Ltd, Beijing, 100102, PR China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, PR China
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, PR China
| | - Jialiang Yang
- Geneis Beijing Co. Ltd, Beijing, 100102, PR China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, 266000, PR China
- Chifeng Municipal Hospital, Chifeng, 024000, PR China
- Academician Workstation, Changsha Medical University, Changsha, 410219, PR China
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Pan P, Li J, Wang B, Tan X, Yin H, Han Y, Wang H, Shi X, Li X, Xie C, Chen L, Chen L, Bai Y, Li Z, Tian G. Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis. Front Oncol 2023; 13:1067849. [PMID: 37546388 PMCID: PMC10401844 DOI: 10.3389/fonc.2023.1067849] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 06/21/2023] [Indexed: 08/08/2023] Open
Abstract
Introduction Colorectal adenoma can develop into colorectal cancer. Determining the risk of tumorigenesis in colorectal adenoma would be critical for avoiding the development of colorectal cancer; however, genomic features that could help predict the risk of tumorigenesis remain uncertain. Methods In this work, DNA and RNA parallel capture sequencing data covering 519 genes from colorectal adenoma and colorectal cancer samples were collected. The somatic mutation profiles were obtained from DNA sequencing data, and the expression profiles were obtained from RNA sequencing data. Results Despite some similarities between the adenoma samples and the cancer samples, different mutation frequencies, co-occurrences, and mutually exclusive patterns were detected in the mutation profiles of patients with colorectal adenoma and colorectal cancer. Differentially expressed genes were also detected between the two patient groups using RNA sequencing. Finally, two random forest classification models were built, one based on mutation profiles and one based on expression profiles. The models distinguished adenoma and cancer samples with accuracy levels of 81.48% and 100.00%, respectively, showing the potential of the 519-gene panel for monitoring adenoma patients in clinical practice. Conclusion This study revealed molecular characteristics and correlations between colorectal adenoma and colorectal cancer, and it demonstrated that the 519-gene panel may be used for early monitoring of the progression of colorectal adenoma to cancer.
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Affiliation(s)
- Peng Pan
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Jingnan Li
- Department of Gastroenterology, Peking Union Medical College Hospital, Beijing, China
| | - Bo Wang
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoyan Tan
- Department of Gastroenterology, Maoming People's Hospital, Maoming, China
| | - Hekun Yin
- Department of Gastroenterology, Jiangmen Central Hospital, Jiangmen, China
| | - Yingmin Han
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
| | - Haobin Wang
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
| | - Xiaoli Shi
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaoshuang Li
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Cuinan Xie
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Longfei Chen
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Lanyou Chen
- Department of Science, Geneis Beijing Co., Ltd., Beijing, China
| | - Yu Bai
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Zhaoshen Li
- Department of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China
| | - Geng Tian
- Department of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China
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7
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Yang P, Lang J, Li H, Lu J, Lin H, Tian G, Bai H, Yang J, Ning K. TCM-Suite: A comprehensive and holistic platform for Traditional Chinese Medicine component identification and network pharmacology analysis. IMETA 2022; 1:e47. [PMID: 38867910 PMCID: PMC10989960 DOI: 10.1002/imt2.47] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/05/2022] [Accepted: 07/20/2022] [Indexed: 06/14/2024]
Abstract
DNA-based biological ingredient identification and downstream pharmacology network analysis are commonly used in research for Traditional Chinese Medicine preparations (TCM formulas). Advancements in bioinformatics tools and the accumulation of related data have become driving forces for progress in this field. However, a lack of a platform integrating biological ingredient identification and downstream pharmacology network analysis hinders the deep understanding of TCM. In this study, we developed the TCM-Suite platform composed of two sub-databases, Holmes-Suite and Watson-Suite, for TCM biological ingredient identification and network pharmacology investigation, respectively, both are among the most complete: In the Holmes-Suite, we collected and processed six types of marker gene sequences, accounting for 1,251,548 marker gene sequences. In the Watson-Suite, we curated and integrated a massive number of entries from more than 10 public databases. Importantly, we developed a comprehensive pipeline to integrate TCM biological ingredient identification and downstream network pharmacology research, allowing users to simultaneously identify components of a TCM formula and analyze its potential pharmacology mechanism. Furthermore, we designed search engines and a user-friendly platform to better search and visualize these rich resources. TCM-Suite is a comprehensive and holistic platform for TCM-based drug discovery and repurposing. TCM-Suite website: http://TCM-Suite.AImicrobiome.cn.
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Affiliation(s)
- Pengshuo Yang
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems BiologyCollege of Life Science and Technology, Huazhong University of Science and TechnologyWuhanChina
| | - Jidong Lang
- Geneis Beijing Co., Ltd.BeijingChina
- Department of sciencesQingdao Genesis Institute of Big Data Mining and PrecisionQingdaoShandongChina
- Academician WorkstationChangsha Medical UniversityChangshaChina
| | - Hongjun Li
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems BiologyCollege of Life Science and Technology, Huazhong University of Science and TechnologyWuhanChina
| | - Jinxiang Lu
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems BiologyCollege of Life Science and Technology, Huazhong University of Science and TechnologyWuhanChina
| | - Hanyang Lin
- Sequenxe Biological Technology Co., Ltd.XiamenChina
| | - Geng Tian
- Geneis Beijing Co., Ltd.BeijingChina
- Department of sciencesQingdao Genesis Institute of Big Data Mining and PrecisionQingdaoShandongChina
| | - Hong Bai
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems BiologyCollege of Life Science and Technology, Huazhong University of Science and TechnologyWuhanChina
| | - Jialiang Yang
- Geneis Beijing Co., Ltd.BeijingChina
- Department of sciencesQingdao Genesis Institute of Big Data Mining and PrecisionQingdaoShandongChina
- Academician WorkstationChangsha Medical UniversityChangshaChina
| | - Kang Ning
- Key Laboratory of Molecular Biophysics of the Ministry of Education, Hubei Key Laboratory of Bioinformatics and Molecular‐imaging, Center for Artificial Intelligence Biology, Department of Bioinformatics and Systems BiologyCollege of Life Science and Technology, Huazhong University of Science and TechnologyWuhanChina
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Xu F, Cui W, Liu C, Feng F, Liu R, Zhang J, Sun C. Prognostic biomarkers correlated with immune infiltration in non-small cell lung cancer. FEBS Open Bio 2022; 13:72-88. [PMID: 36282125 PMCID: PMC9811604 DOI: 10.1002/2211-5463.13501] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 10/17/2022] [Accepted: 10/24/2022] [Indexed: 01/07/2023] Open
Abstract
Lung cancer is the leading cause of cancer-related mortality in men and women globally. Non-small cell lung cancer (NSCLC) is the most prevalent subtype, accounting for 85-90% of all cancers. Although there have been dramatic advances in therapeutic approaches in recent decades, the recurrence and metastasis rates of NSCLC are as high as 30-40% with the 5-year overall survival rate being less than 15%. Therefore, it is necessary to explore the pathogenesis of NSCLC at the genetic level and identify prognostic biomarkers and novel therapeutic targets. Here, we aimed to identify mutated genes with high frequencies in Chinese NSCLC patients using next-generation sequencing and to investigate their relationships with the tumor mutation burden (TMB) and tumor immune microenvironment. A total of 110 NSCLC patients were enrolled to profile the genetic variations. Mutations in EGFR (62.37%), TP53 (61.29%), LRP1B (13.98%), FAT1 (12.90%), KMT2D (11.83%), CREBBP (10.75%), and RB1 (9.68%) were most prevalent. TP53, LRP1B, KMT2D, and CREBBP mutations were all significantly associated with high TMB (P < 0.05 or P < 0.01). The infiltrating levels of immune cells and immune molecules were enriched significantly in the LRP1B mutation group. LRP1B mutations significantly correlated with stimulating and inhibitory immunoregulators. Gene set enrichment analysis revealed that cell cycle, the Notch signaling pathway, the insulin signaling pathway, and the mTOR signaling pathway are related to LRP1B mutations in the immune system. LRP1B mutations may be of clinical importance in enhancing the anti-tumor immune response and may be a promising biomarker for predicting immunotherapy responsiveness.
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Affiliation(s)
- Fei Xu
- Department of Geriatric MedicineAffiliated Hospital of Shandong University of Traditional Chinese MedicineJinanChina,First Clinical Medical CollegeShandong University of Traditional Chinese MedicineJinanChina
| | - Wen‐qiang Cui
- Department of NeurologyAffiliated Hospital of Shandong University of Traditional Chinese MedicineJinanChina
| | - Cun Liu
- College of Traditional Chinese MedicineShandong University of Traditional Chinese MedicineJinanChina
| | - Fubin Feng
- Department of OncologyWeifang Traditional Chinese HospitalChina
| | - Ruijuan Liu
- Department of OncologyWeifang Traditional Chinese HospitalChina
| | - Jingtao Zhang
- College of Traditional Chinese MedicineShandong University of Traditional Chinese MedicineJinanChina
| | - Chang‐gang Sun
- Department of OncologyWeifang Traditional Chinese HospitalChina,Qingdao Academy of Chinese Medical SciencesShandong University of Traditional Chinese MedicineQingdaoChina
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9
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Yao Y, Lv Y, Tong L, Liang Y, Xi S, Ji B, Zhang G, Li L, Tian G, Tang M, Hu X, Li S, Yang J. ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data. Brief Bioinform 2022; 23:6761046. [PMID: 36242564 DOI: 10.1093/bib/bbac448] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 07/18/2022] [Accepted: 07/18/2022] [Indexed: 12/14/2022] Open
Abstract
Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 × 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.
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Affiliation(s)
- Yuhua Yao
- School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China.,Key Laboratory of Data Science and Intelligence Education, Ministry of Education, Hainan Normal University, Haikou, China.,Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou, China
| | - Yaping Lv
- School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China.,Genies Beijing Co., Ltd., Beijing 100102, China
| | - Ling Tong
- Chifeng Municipal Hospital, Chifeng, Inner Mongolia 024000, China
| | - Yuebin Liang
- Genies Beijing Co., Ltd., Beijing 100102, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
| | - Shuxue Xi
- Genies Beijing Co., Ltd., Beijing 100102, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
| | - Binbin Ji
- Genies Beijing Co., Ltd., Beijing 100102, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
| | - Guanglu Zhang
- School of Mathematics and Statistics, Hainan Normal University, Haikou 570100, China
| | - Ling Li
- Basic Courses Department, Zhejiang Shuren University, Hangzhou 310000, China
| | - Geng Tian
- Genies Beijing Co., Ltd., Beijing 100102, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, 212013, China
| | - Xiyue Hu
- Dept. of Colorectal Surgery, National Cancer Center/ Cancer Hospital, Chinese Academy of Medical Science, 17 Panjiayuan Nanli, Chaoyang District, Beijing, China, 100021
| | - Shijun Li
- Chifeng Municipal Hospital, Chifeng, Inner Mongolia 024000, China
| | - Jialiang Yang
- Genies Beijing Co., Ltd., Beijing 100102, China.,Chifeng Municipal Hospital, Chifeng, Inner Mongolia 024000, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
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10
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Yuan X, Wang Z, Li C, Lv K, Tian G, Tang M, Ji L, Yang J. Bacterial biomarkers capable of identifying recurrence or metastasis carry disease severity information for lung cancer. Front Microbiol 2022; 13:1007831. [PMID: 36187983 PMCID: PMC9523266 DOI: 10.3389/fmicb.2022.1007831] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/01/2022] [Indexed: 11/13/2022] Open
Abstract
Background Local recurrence and distant metastasis are the main causes of death in patients with lung cancer. Multiple studies have described the recurrence or metastasis of lung cancer at the genetic level. However, association between the microbiome of lung cancer tissue and recurrence or metastasis remains to be discovered. Here, we aimed to identify the bacterial biomarkers capable of distinguishing patients with lung cancer from recurrence or metastasis, and how it related to the severity of patients with lung cancer. Methods We applied microbiome pipeline to bacterial communities of 134 non-recurrence and non-metastasis (non-RM) and 174 recurrence or metastasis (RM) samples downloaded from The Cancer Genome Atlas (TCGA). Co-occurrence network was built to explore the bacterial interactions in lung cancer tissue of RM and non-RM. Finally, the Kaplan–Meier survival analysis was used to evaluate the association between bacterial biomarkers and patient survival. Results Compared with non-RM, the bacterial community of RM had lower richness and higher Bray–Curtis dissimilarity index. Interestingly, the co-occurrence network of non-RM was more complex than RM. The top 500 genera in relative abundance obtained an area under the curve (AUC) of 0.72 when discriminating between RM and non-RM. There were significant differences in the relative abundances of Acidovorax, Clostridioides, Succinimonas, and Shewanella, and so on between RM and non-RM. These biomarkers played a role in predicting the survival of lung cancer patients and were significantly associated with lung cancer stage. Conclusion This study provides the first evidence for the prediction of lung cancer recurrence or metastasis by bacteria in lung cancer tissue. Our results highlights that bacterial biomarkers that distinguish RM and non-RM are also associated with patient survival and disease severity.
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Affiliation(s)
- Xuelian Yuan
- School of Mathematical Sciences, Ocean University of China, Qingdao, China
| | - Zhina Wang
- Department of Respiratory and Critical Care, Emergency General Hospital, Beijing, China
| | - Changjun Li
- School of Mathematical Sciences, Ocean University of China, Qingdao, China
- *Correspondence: Changjun Li,
| | - Kebo Lv
- School of Mathematical Sciences, Ocean University of China, Qingdao, China
| | - Geng Tian
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu, China
| | - Lei Ji
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
- Lei Ji,
| | - Jialiang Yang
- Geneis Beijing Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
- Chifeng Municipal Hospital, Chifeng, China
- Jialiang Yang,
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11
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Wu M, Liang L, Dai X. Discussion of tumor mutation burden as an indicator to predict efficacy of immune checkpoint inhibitors: A case report. Front Oncol 2022; 12:939022. [PMID: 35992799 PMCID: PMC9381827 DOI: 10.3389/fonc.2022.939022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 07/08/2022] [Indexed: 12/29/2022] Open
Abstract
There are many treatment options for advanced lung cancer, among which immunotherapy has developed rapidly and benefited a lot of patients. However, immunotherapy can only benefit a subgroup of patients, and how to select patients suitable for this therapy is critical. Tumor mutation burden (TMB) is one of the important reference indicators for immune checkpoint inhibitors (ICIs). However, there are many factors influencing the usage of this indicator, which will lead to considerable consequences if not treated well. In this study, we performed a case study on a male advanced lung squamous cell carcinoma patient of age 83. The patient suffered from “cough and sputum”, and did chest CT scans on 24 October 2018, which showed “a mass-like mass in the anterior segment of the right lung upper lobe, about 38mm×28mm”. He was treated with systemic chemotherapy; however, the tumor was still under progression. Although PD-L1 was not tested in gene testing, he had a TMB value of 10.26 mutations/Mb with a quantile value 88.63%. Thus, “toripalimab injection” was added as immunotherapy and the size of the lesion decreased. In summary, we adopted a clinical case as the basis to explore the value and significance of TMB in immunotherapy in this study. We hope that more predictive molecular markers will be discovered, which will bring more treatment methods for advanced lung cancer.
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Affiliation(s)
- Mingrui Wu
- Department of Respiratory and Critical Care Medicine, Affiliated People‘s Hospital of Chongqing Three Gorges Medical College, Chongqing, China
| | - Lan Liang
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Army Medical University, Chongqing, China
- *Correspondence: Lan Liang,
| | - Xiaotian Dai
- Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Army Medical University, Chongqing, China
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12
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Zhang SW, Zhang NN, Zhu WW, Liu T, Lv JY, Jiang WT, Zhang YM, Song TQ, Zhang L, Xie Y, Zhou YH, Lu W. A Novel Nomogram Model to Predict the Recurrence-Free Survival and Overall Survival of Hepatocellular Carcinoma. Front Oncol 2022; 12:946531. [PMID: 35936698 PMCID: PMC9352894 DOI: 10.3389/fonc.2022.946531] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 06/20/2022] [Indexed: 01/27/2023] Open
Abstract
BackgroundTreatments for patients with early‐stage hepatocellular carcinoma (HCC) include liver transplantation (LT), liver resection (LR), radiofrequency ablation (RFA), and microwave ablation (MWA), are critical for their long-term survival. However, a computational model predicting treatment-independent prognosis of patients with HCC, such as overall survival (OS) and recurrence-free survival (RFS), is yet to be developed, to our best knowledge. The goal of this study is to identify prognostic factors associated with OS and RFS in patients with HCC and develop nomograms to predict them, respectively.MethodsWe retrospectively retrieved 730 patients with HCC from three hospitals in China and followed them up for 3 and 5 years after invasive treatment. All enrolled patients were randomly divided into the training cohort and the validation cohort with a 7:3 ratio, respectively. Independent prognostic factors associated with OS and RFS were determined by the multivariate Cox regression analysis. Two nomogram prognostic models were built and evaluated by concordance index (C-index), calibration curves, area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), the Kaplan–Meier survival curve, and decision curve analyses (DCAs), respectively.ResultsPrognostic factors for OS and RFS were identified, and nomograms were successfully built. Calibration discrimination was good for both the OS and RFS nomogram prediction models (C-index: 0.750 and 0.746, respectively). For both nomograms, the AUC demonstrated outstanding predictive performance; the DCA shows that the model has good decision ability; and the calibration curve demonstrated strong predictive power. The nomograms successfully discriminated high-risk and low-risk patients with HCC associated with OS and RFS.ConclusionsWe developed nomogram survival prediction models to predict the prognosis of HCC after invasive treatment with acceptable accuracies in both training and independent testing cohorts. The models may have clinical values in guiding the selection of clinical treatment strategies.
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Affiliation(s)
- Shu-Wen Zhang
- Department of Hepatobiliary Oncology, Liver Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Ning-Ning Zhang
- Department of Hepatobiliary Oncology, Liver Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Wen-Wen Zhu
- Department of Hepatobiliary Oncology, Liver Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Tian Liu
- Department of Hepatobiliary Oncology, Liver Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Jia-Yu Lv
- Department of Hepatology, Tianjin Third Central Hospital, Tianjin, China
| | - Wen-Tao Jiang
- Department of Liver Transplantation, Tianjin First Center Hospital, NHC Key Laboratory for Critical Care Medicine, Key Laboratory of Transplantation, Chinese Academy of Medical Sciences, Tianjin, China
| | - Ya-Min Zhang
- Department of Hepatobiliary Surgery, Tianjin First Central Hospital, Tianjin Key Laboratory for Organ Transplantation, Tianjin, China
| | - Tian-Qiang Song
- Liver Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
| | - Li Zhang
- Department of Liver Transplantation, Tianjin First Center Hospital, NHC Key Laboratory for Critical Care Medicine, Key Laboratory of Transplantation, Chinese Academy of Medical Sciences, Tianjin, China
| | - Yan Xie
- Department of Liver Transplantation, Tianjin First Center Hospital, NHC Key Laboratory for Critical Care Medicine, Key Laboratory of Transplantation, Chinese Academy of Medical Sciences, Tianjin, China
| | - Yong-He Zhou
- Tianjin Second People's Hospital, Tianjin Medical Research Institute of Liver Disease, Tianjin, China
| | - Wei Lu
- Department of Hepatobiliary Oncology, Liver Cancer Center, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China
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13
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Lung Cancer Stage Prediction Using Multi-Omics Data. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:2279044. [PMID: 35880092 PMCID: PMC9308511 DOI: 10.1155/2022/2279044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 06/27/2022] [Indexed: 12/24/2022]
Abstract
Lung cancer is one of the leading causes of cancer death. Patients with early-stage lung cancer can be treated by surgery, while patients in the middle and late stages need chemotherapy or radiotherapy. Therefore, accurate staging of lung cancer is crucial for doctors to formulate accurate treatment plans for patients. In this paper, the random forest algorithm is used as the lung cancer stage prediction model, and the accuracy of lung cancer stage prediction is discussed in the microbiome, transcriptome, microbe, and transcriptome fusion groups, and the accuracy of the model is measured by indicators such as ACC, recall, and precision. The results showed that the prediction accuracy of microbial combinatorial transcriptome fusion analysis was the highest, reaching 0.809. The study reveals the role of multimodal data and fusion algorithm in accurately diagnosing lung cancer stage, which could aid doctors in clinics.
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14
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Niu Y, Wang L, Zhang X, Han Y, Yang C, Bai H, Huang K, Ren C, Tian G, Yin S, Zhao Y, Wang Y, Shi X, Zhang M. Predicting Tumor Mutational Burden From Lung Adenocarcinoma Histopathological Images Using Deep Learning. Front Oncol 2022; 12:927426. [PMID: 35756617 PMCID: PMC9213738 DOI: 10.3389/fonc.2022.927426] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 05/16/2022] [Indexed: 11/25/2022] Open
Abstract
Tumor mutation burden (TMB) is an important biomarker for tumor immunotherapy. It plays an important role in the clinical treatment process, but the gold standard measurement of TMB is based on whole exome sequencing (WES). WES cannot be done in most hospitals due to its high cost, long turnaround times and operational complexity. To seek out a better method to evaluate TMB, we divided the patients with lung adenocarcinoma (LUAD) in TCGA into two groups according to the TMB value, then analyzed the differences of clinical characteristics and gene expression between the two groups. We further explored the possibility of using histopathological images to predict TMB status, and developed a deep learning model to predict TMB based on histopathological images of LUAD. In the 5-fold cross-validation, the area under the receiver operating characteristic (ROC) curve (AUC) of the model was 0.64. This study showed that it is possible to use deep learning to predict genomic features from histopathological images, though the prediction accuracy was relatively low. The study opens up a new way to explore the relationship between genes and phenotypes.
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Affiliation(s)
- Yi Niu
- Department of Oncology, Municipal Hospital of Chifeng, Chifeng, China
| | | | - Xiaojie Zhang
- Department of Oncology, Municipal Hospital of Chifeng, Chifeng, China
| | - Yu Han
- Department of Oncology, Municipal Hospital of Chifeng, Chifeng, China
| | - Chunjie Yang
- Department of Oncology, Municipal Hospital of Chifeng, Chifeng, China
| | - Henan Bai
- Department of Oncology, Municipal Hospital of Chifeng, Chifeng, China
| | | | | | - Geng Tian
- Geneis Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Shengjie Yin
- Department of Oncology, Municipal Hospital of Chifeng, Chifeng, China
| | - Yan Zhao
- Department of Oncology, Municipal Hospital of Chifeng, Chifeng, China
| | - Ying Wang
- Department of Oncology, Inner Mongolia Medical University, Hohhot, China
| | - Xiaoli Shi
- Geneis Co., Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Minghui Zhang
- Department of Oncology, Municipal Hospital of Chifeng, Chifeng, China
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15
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A Framework to Predict the Molecular Classification and Prognosis of Breast Cancer Patients and Characterize the Landscape of Immune Cell Infiltration. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:4635806. [PMID: 35720039 PMCID: PMC9201713 DOI: 10.1155/2022/4635806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 03/25/2022] [Accepted: 05/16/2022] [Indexed: 11/27/2022]
Abstract
It is known that all current cancer therapies can only benefit a limited proportion of patients; thus, molecular classification and prognosis evaluation are critical for correctly classifying breast cancer patients and selecting the best treatment strategy. These processes usually involve the disclosure of molecular information like mutation, expression, and immune microenvironment of a breast cancer patient, which are not been fully studied until now. Therefore, there is an urgent clinical need to identify potential markers to enhance molecular classification, precision prognosis, and therapy stratification for breast cancer patients. In this study, we explored the gene expression profiles of 1,721 breast cancer patients through CIBERSORT and ESTIMATE algorithms; then, we obtained a comprehensive intratumoral immune landscape. The immune cell infiltration (ICI) patterns of breast cancer were classified into 3 separate subtypes according to the infiltration levels of 22 immune cells. The differentially expressed genes between these subtypes were further identified, and ICI scores were calculated to assess the immune landscape of BRCA patients. Importantly, we demonstrated that ICI scores correlate with patients' survival, tumor mutation burden, neoantigens, and sensitivity to specific drugs. Based on these ICI scores, we were able to predict the prognosis of patients and their response to immunotherapy. Together, these findings provide a realistic scenario to stratify breast cancer patients for precision medicine.
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16
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Liu Y, Huang K, Yang Y, Wu Y, Gao W. Prediction of Tumor Mutation Load in Colorectal Cancer Histopathological Images Based on Deep Learning. Front Oncol 2022; 12:906888. [PMID: 35686098 PMCID: PMC9171017 DOI: 10.3389/fonc.2022.906888] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Accepted: 04/18/2022] [Indexed: 02/05/2023] Open
Abstract
Colorectal cancer (CRC) is one of the most prevalent malignancies, and immunotherapy can be applied to CRC patients of all ages, while its efficacy is uncertain. Tumor mutational burden (TMB) is important for predicting the effect of immunotherapy. Currently, whole-exome sequencing (WES) is a standard method to measure TMB, but it is costly and inefficient. Therefore, it is urgent to explore a method to assess TMB without WES to improve immunotherapy outcomes. In this study, we propose a deep learning method, DeepHE, based on the Residual Network (ResNet) model. On images of tissue, DeepHE can efficiently identify and analyze characteristics of tumor cells in CRC to predict the TMB. In our study, we used ×40 magnification images and grouped them by patients followed by thresholding at the 10th and 20th quantiles, which significantly improves the performance. Also, our model is superior compared with multiple models. In summary, deep learning methods can explore the association between histopathological images and genetic mutations, which will contribute to the precise treatment of CRC patients.
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Affiliation(s)
- Yongguang Liu
- Department of Anorectal Surgery, Weifang People’s Hospital, Weifang, China
| | - Kaimei Huang
- Genies (Beijing) Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Yachao Yang
- School of Mathematics and Statistics, Hainan Normal University, Haikou, China
| | - Yan Wu
- Genies (Beijing) Co., Ltd., Beijing, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Wei Gao
- Department of Internal Medicine-Oncology, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou, China
- *Correspondence: Wei Gao,
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17
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Liu J, Lan Y, Tian G, Yang J. A Systematic Framework for Identifying Prognostic Genes in the Tumor Microenvironment of Colon Cancer. Front Oncol 2022; 12:899156. [PMID: 35664768 PMCID: PMC9161737 DOI: 10.3389/fonc.2022.899156] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 04/19/2022] [Indexed: 12/23/2022] Open
Abstract
As one of the most common cancers of the digestive system, colon cancer is a predominant cause of cancer-related deaths worldwide. To investigate prognostic genes in the tumor microenvironment of colon cancer, we collected 461 colon adenocarcinoma (COAD) and 172 rectal adenocarcinoma (READ) samples from The Cancer Genome Atlas (TCGA) database, and calculated the stromal and immune scores of each sample. We demonstrated that stromal and immune scores were significantly associated with colon cancer stages. By analyzing differentially expressed genes (DEGs) between two stromal and immune score groups, we identified 952 common DEGs. The significantly enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms for these DEGs were associated with T-cell activation, immune receptor activity, and cytokine–cytokine receptor interaction. Through univariate Cox regression analysis, we identified 22 prognostic genes. Furthermore, nine key prognostic genes, namely, HOXC8, SRPX, CCL22, CD72, IGLON5, SERPING1, PCOLCE2, FABP4, and ARL4C, were identified using the LASSO Cox regression analysis. The risk score of each sample was calculated using the gene expression of the nine genes. Patients with high-risk scores had a poorer prognosis than those with low-risk scores. The prognostic model established with the nine-gene signature was able to effectively predict the outcome of colon cancer patients. Our findings may help in the clinical decisions and improve the prognosis for colon cancer.
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Affiliation(s)
- Jinyang Liu
- Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China
- Department of Data Mining,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Yu Lan
- Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China
- Department of Data Mining,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Geng Tian
- Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China
- Department of Data Mining,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jialiang Yang
- Department of Sciences, Geneis Beijing Co., Ltd., Beijing, China
- Department of Data Mining,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
- PhD Workstation, Chifeng Municipal Hospital, Chifeng, China
- *Correspondence: Jialiang Yang,
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18
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Pang H, Zhang G, Yan N, Lang J, Liang Y, Xu X, Cui Y, Wu X, Li X, Shan M, Wang X, Meng X, Liu J, Tian G, Cai L, Yuan D, Wang X. Evaluating the Risk of Breast Cancer Recurrence and Metastasis After Adjuvant Tamoxifen Therapy by Integrating Polymorphisms in Cytochrome P450 Genes and Clinicopathological Characteristics. Front Oncol 2021; 11:738222. [PMID: 34868931 PMCID: PMC8639703 DOI: 10.3389/fonc.2021.738222] [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: 07/08/2021] [Accepted: 10/25/2021] [Indexed: 11/13/2022] Open
Abstract
Tamoxifen (TAM) is the most commonly used adjuvant endocrine drug for hormone receptor-positive (HR+) breast cancer patients. However, how to accurately evaluate the risk of breast cancer recurrence and metastasis after adjuvant TAM therapy is still a major concern. In recent years, many studies have shown that the clinical outcomes of TAM-treated breast cancer patients are influenced by the activity of some cytochrome P450 (CYP) enzymes that catalyze the formation of active TAM metabolites like endoxifen and 4-hydroxytamoxifen. In this study, we aimed to first develop and validate an algorithm combining polymorphisms in CYP genes and clinicopathological signatures to identify a subpopulation of breast cancer patients who might benefit most from TAM adjuvant therapy and meanwhile evaluate major risk factors related to TAM resistance. Specifically, a total of 256 patients with invasive breast cancer who received adjuvant endocrine therapy were selected. The genotypes at 10 loci from three TAM metabolism-related CYP genes were detected by time-of-flight mass spectrometry and multiplex long PCR. Combining the 10 loci with nine clinicopathological characteristics, we obtained 19 important features whose association with cancer recurrence was assessed by importance score via random forests. After that, a logistic regression model was trained to calculate TAM risk-of-recurrence score (TAM RORs), which is adopted to assess a patient's risk of recurrence after TAM treatment. The sensitivity and specificity of the model in an independent test cohort were 86.67% and 64.56%, respectively. This study showed that breast cancer patients with high TAM RORs were less sensitive to TAM treatment and manifested more invasive characteristics, whereas those with low TAM RORs were highly sensitive to TAM treatment, and their conditions were stable during the follow-up period. There were some risk factors that had a significant effect on the efficacy of TAM. They were tissue classification (tumor Grade < 2 vs. Grade ≥ 2, p = 2.2e-16), the number of lymph node metastases (Node-Negative vs. Node < 4, p = 5.3e-07; Node < 4 vs. Node ≥ 4, p = 0.003; Node-Negative vs. Node ≥ 4, p = 7.2e-15), and the expression levels of estrogen receptor (ER) and progesterone receptor (PR) (ER < 50% vs. ER ≥ 50%, p = 1.3e-12; PR < 50% vs. PR ≥ 50%, p = 2.6e-08). The really remarkable thing is that different genotypes of CYP2D6*10(C188T) show significant differences in prediction function (CYP2D6*10 CC vs. TT, p < 0.019; CYP2D6*10 CT vs. TT, p < 0.037). There are more than 50% Chinese who have CYP2D6*10 mutation. So the genotype of CYP2D6*10(C188T) should be tested before TAM therapy.
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Affiliation(s)
- Hui Pang
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Guoqiang Zhang
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Na Yan
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
- Department of Science, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Jidong Lang
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
- Department of Science, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Yuebin Liang
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
- Department of Science, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xinyuan Xu
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Yaowen Cui
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xueya Wu
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xianjun Li
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Ming Shan
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Xiaoqin Wang
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
| | - Xiangzhi Meng
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiaxiang Liu
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Geng Tian
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
- Department of Science, Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Li Cai
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, China
| | - Dawei Yuan
- Department of Science, Geneis (Beijing) Co., Ltd., Beijing, China
| | - Xin Wang
- Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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19
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Lang J, Zhu R, Sun X, Zhu S, Li T, Shi X, Sun Y, Yang Z, Wang W, Bing P, He B, Tian G. Evaluation of the MGISEQ-2000 Sequencing Platform for Illumina Target Capture Sequencing Libraries. Front Genet 2021; 12:730519. [PMID: 34777467 PMCID: PMC8578046 DOI: 10.3389/fgene.2021.730519] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 09/24/2021] [Indexed: 01/19/2023] Open
Abstract
Illumina is the leading sequencing platform in the next-generation sequencing (NGS) market globally. In recent years, MGI Tech has presented a series of new sequencers, including DNBSEQ-T7, MGISEQ-2000 and MGISEQ-200. As a complex application of NGS, cancer-detecting panels pose increasing demands for the high accuracy and sensitivity of sequencing and data analysis. In this study, we used the same capture DNA libraries constructed based on the Illumina protocol to evaluate the performance of the Illumina Nextseq500 and MGISEQ-2000 sequencing platforms. We found that the two platforms had high consistency in the results of hotspot mutation analysis; more importantly, we found that there was a significant loss of fragments in the 101-133 bp size range on the MGISEQ-2000 sequencing platform for Illumina libraries, but not for the capture DNA libraries prepared based on the MGISEQ protocol. This phenomenon may indicate fragment selection or low fragment ligation efficiency during the DNA circularization step, which is a unique step of the MGISEQ-2000 sequence platform. In conclusion, these different sequencing libraries and corresponding sequencing platforms are compatible with each other, but protocol and platform selection need to be carefully evaluated in combination with research purpose.
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Affiliation(s)
- Jidong Lang
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Academician Workstation, Changsha Medical University, Changsha, China
| | - Rongrong Zhu
- Vascular Surgery Department, Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital, Beijing, China
| | - Xue Sun
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Siyu Zhu
- Department of Medicine, School of Medicine, University of California at San Diego, La Jolla, CA, United States
| | - Tianbao Li
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Xiaoli Shi
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Yanqi Sun
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Zhou Yang
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China
| | - Weiwei Wang
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
| | - Pingping Bing
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Binsheng He
- Academician Workstation, Changsha Medical University, Changsha, China
| | - Geng Tian
- Bioinformatics and R and D Department, Geneis (Beijing) Co. Ltd., Beijing, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China
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20
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Yang J, Hui Y, Zhang Y, Zhang M, Ji B, Tian G, Guo Y, Tang M, Li L, Guo B, Ma T. Application of Circulating Tumor DNA as a Biomarker for Non-Small Cell Lung Cancer. Front Oncol 2021; 11:725938. [PMID: 34422670 PMCID: PMC8375502 DOI: 10.3389/fonc.2021.725938] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 07/19/2021] [Indexed: 12/21/2022] Open
Abstract
Background Non-small cell lung cancer (NSCLC) is one of the most prevalent causes of cancer-related death worldwide. Recently, there are many important medical advancements on NSCLC, such as therapies based on tyrosine kinase inhibitors and immune checkpoint inhibitors. Most of these therapies require tumor molecular testing for selecting patients who would benefit most from them. As invasive biopsy is highly risky, NSCLC molecular testing based on liquid biopsy has received more and more attention recently. Objective We aimed to introduce liquid biopsy and its potential clinical applications in NSCLC patients, including cancer diagnosis, treatment plan prioritization, minimal residual disease detection, and dynamic monitoring on the response to cancer treatment. Method We reviewed recent studies on circulating tumor DNA (ctDNA) testing, which is a minimally invasive approach to identify the presence of tumor-related mutations. In addition, we evaluated potential clinical applications of ctDNA as blood biomarkers for advanced NSCLC patients. Results Most studies have indicated that ctDNA testing is critical in diagnosing NSCLC, predicting clinical outcomes, monitoring response to targeted therapies and immunotherapies, and detecting cancer recurrence. Moreover, the changes of ctDNA levels are associated with tumor mutation burden and cancer progression. Conclusion The ctDNA testing is promising in guiding the therapies on NSCLC patients.
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Affiliation(s)
- Jialiang Yang
- Chifeng Municipal Hospital, Chifeng, China.,Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Geneis Beijing Co., Ltd., Beijing, China
| | - Yan Hui
- Chifeng Municipal Hospital, Chifeng, China
| | | | | | - Binbin Ji
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Geneis Beijing Co., Ltd., Beijing, China
| | - Geng Tian
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Geneis Beijing Co., Ltd., Beijing, China
| | - Yangqiang Guo
- China National Intellectual Property Administration, Beijing, China
| | - Min Tang
- School of Life Sciences, Jiangsu University, Zhenjiang, China
| | | | - Bella Guo
- Genetron Health (Beijing) Co. Ltd., Beijing, China
| | - Tonghui Ma
- Genetron Health (Beijing) Co. Ltd., Beijing, China
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21
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Liu Z, Hong ZP, Xi SX. RUNX3 Expression Level Is Correlated with the Clinical and Pathological Characteristics in Endometrial Cancer: A Systematic Review and Meta-analysis. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9995384. [PMID: 34337071 PMCID: PMC8298141 DOI: 10.1155/2021/9995384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 05/07/2021] [Accepted: 06/30/2021] [Indexed: 11/20/2022]
Abstract
Human Runt-associated transcription factor 3 (RUNX3) plays an important role in the development and progression of endometrial cancer (EC). However, the clinical and pathological significance of RUNX3 in EC needs to be further studied. In order to clarify the clinical and pathological significance of RUNX3, a systematic review and meta-analysis was conducted in EC patients. Keywords RUNX3, endometrial cancer, and uterine cancer were searched in Cochrane Library, Web of Knowledge, PubMed, CBM, MEDLINE, and Chinese CNKI database for data up to Dec 31, 2018. References, abstracts, and meeting proceedings were manually searched in supplementary. Outcomes were various clinical and pathological features. The two reviewers performed the literature searching, data extracting, and method assessing independently. Meta-analysis was performed by RevMan5.3.0. A total of 563 EC patients were enrolled from eight studies. Meta-analysis results showed that the expression of RUNX3 has significant differences in these comparisons: lymph node (LN) metastasis vs. non-LN metastasis (P = 0.26), EC tissues vs. normal tissues (P < 0.00001), clinical stages I/II vs. II/IV (P < 0.00001), muscular infiltration < 1/2 vs. muscular infiltration ≥ 1/2 (P < 0.00001), and G1 vs. G2/G3 (P < 0.00001). The decreasing expression of RUNX3 is associated with poor TNM stage and muscular infiltration. It is indicated that RUNX3 was involved in the suppression effect of EC. However, further multicenter randomized controlled trials are needed considering the small sample size of the included trials.
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Affiliation(s)
- Zhen Liu
- Department of Gynecology, Chifeng Municipal Hospital, Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng, China
| | - Zhi-pan Hong
- Department of Tumor Surgery, Chifeng Municipal Hospital, Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng, China
| | - Shu-xue Xi
- Geneis (Beijing) Co. Ltd., Beijing 100102, China
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao 266000, China
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22
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Yan D, Yang J, Ji Z, Wang J, Lu X, Huang Y, Zhong C, Li L. Profiling T cell receptor β-chain in responders after immunization with recombinant hepatitis B vaccine. J Gene Med 2021; 23:e3367. [PMID: 34048625 DOI: 10.1002/jgm.3367] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 05/22/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND T cells with edited T cell receptor β-chain variable (TRBV) are involved in the immune response to recombinant hepatitis B surface antigen (rHBsAg) vaccine and the production of hepatitis B surface antibody (HBsAb). The immune repertoire (IR) profile and mechanism of vaccination positive responders (VPR) with rHBsAg are not fully understood. METHODS The IR of six VPRs (HBsAb+, HbsAg-) with rHBsAg vaccination was established by the high throughput sequencing technique and bioinformatics analysis and compared with those in five vaccination negative responders (VNRs) (HbsAb-, HbsAg-) who were also inoculated with rHBsAg. The repertoire features of the BV, BJ and V (CDR3) J genes and immune diversity in peripheral blood mononuclear cells, respectively, were analyzed for each subject. RESULTS There was no significant difference in sequencing amplification indices of each sample. However, TRBV15/BJ2-3 demonstrated significantly high expression levels in VPR compared to those in the VNR group (both p < 0.05). Further results showed that the BV15/BJ2-5 level was significantly increased for VPR compared to that of VNR group. Interestingly, the motif of CDR3 in TRBV15/BJ2-5 was mostly expressed as "GGETQ" or "GETQ". Additionally, there was no remarkable difference between the two groups of distribution with respect to the different clone expression levels of V (CDR3) J. CONCLUSIONS The features of IR in the VPR and VNR will contribute to the exploration of the mechanism of the positive response to rHBsAg, and also contribute to development of optimized hepatitis B vaccine, in addition to providing a partial interpretation of the VNR who has a relatively low infection with HBV.
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Affiliation(s)
- Dong Yan
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jiezuan Yang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhongkang Ji
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Ju Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaoqing Lu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yandi Huang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chengli Zhong
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Lanjuan Li
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases; National Clinical Research Center for Infectious Diseases; the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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23
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Li P, Li Y, Ma L. Potential role of chimeric genes in pathway-related gene co-expression modules. World J Surg Oncol 2021; 19:149. [PMID: 33980272 PMCID: PMC8117532 DOI: 10.1186/s12957-021-02248-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2020] [Accepted: 04/19/2021] [Indexed: 12/11/2022] Open
Abstract
Background Gene fusion has epigenetic modification functions. The novel proteins encoded by gene fusion products play a role in cancer development. Therefore, a better understanding of the novel protein products may provide insights into the pathogenesis of tumors. However, the characteristics of chimeric genes are rarely studied. Here, we used weighted co-expression network analysis to investigate the biological roles and underlying mechanisms of chimeric genes. Methods Download the pig transcriptome data, we screened chimeric genes and parental genes from 688 sequences and 153 samples, predict their domains, and analyze their associations. We constructed a co-expression network of chimeric genes in pigs and conducted Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis on the generated modules using DAVID to identify key networks and modules related to chimeric genes. Results Our findings showed that most of the protein domains of chimeric genes were derived from fused pre-genes. Chimeric genes were enriched in modules involved in the negative regulation of cell proliferation and protein localization to centrosomes. In addition, the chimeric genes were related to the growth factor-β superfamily, which regulates cell growth and differentiation. Furthermore, in helper T cells, chimeric genes regulate the specific recognition of T cell receptors, implying that chimeric genes play a key role in the regulation pathway of T cells. Chimeric genes can produce new domains, and some chimeric genes are a key role involved in pathway-related function. Conclusions Most chimeric genes show binding activity. Domains of chimeric genes are derived from several combinations of parent genes. Chimeric genes play a key role in the regulation of several cellular pathways. Our findings may provide new directions to explore the roles of chimeric genes in tumors. Supplementary Information The online version contains supplementary material available at 10.1186/s12957-021-02248-9.
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Affiliation(s)
- Piaopiao Li
- College of Life Science, Shihezi University, Shihezi, Xinjiang, 832000, China
| | - Yingxia Li
- College of Life Science, Shihezi University, Shihezi, Xinjiang, 832000, China
| | - Lei Ma
- College of Life Science, Shihezi University, Shihezi, Xinjiang, 832000, China.
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24
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Cancer neoantigens as potential targets for immunotherapy. Clin Exp Metastasis 2021; 39:51-60. [PMID: 33950415 PMCID: PMC8097110 DOI: 10.1007/s10585-021-10091-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Accepted: 03/22/2021] [Indexed: 12/13/2022]
Abstract
Immune checkpoint inhibitors (ICIs) targeting the cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) and programed cell death protein 1 (PD-1) or its ligand PD-L1 have increased the survival and cure rates for patients with many cancer types in various disease settings. However, only 10–40% of cancer patients benefited from these ICIs, of whom ~ 20% have treatment interruption or discontinuation due to immune-related adverse events that can be severe and even fatal. Current efforts in precision immunotherapy are focused on improving biomarker-based patient selection for currently available ICIs and exploring rationale combination and novel strategies to expand the benefit of immunotherapy to more cancer patients. Neoantigens arise from ~ 10% of the non-synonymous somatic mutations in cancer cells, are important targets of T cell-mediated anti-tumor immunity for individual patients. Advances in next generation sequencing technology and computational bioinformatics have enable the identification of genomic alterations, putative neoantigens, and gene expression profiling in individual tumors for personal oncology in a rapid and cost-effective way. Among the genomic biomarkers, defective mismatch DNA repair (dMMR), microsatellite instability high (MSI-H) and high tumor mutational burden (H-TMB) have received FDA approvals for selecting patients for ICI treatment. All these biomarkers measure high neoantigen load and tumor antigenicity, supporting the current development of neoantigen-based personalized cancer vaccines for patients with high TMB tumor. Several studies have shown neoantigen vaccines are feasible, safe and have promising clinical activity in patients with high TMB tumors in both metastatic and adjuvant settings. This review summarizes the emerging data and technologies for neoantigen-based personalized immunotherapy.
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