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Deng YT, Wu BS, Yang L, He XY, Kang JJ, Liu WS, Li ZY, Wu XR, Zhang YR, Chen SD, Ge YJ, Huang YY, Feng JF, Zhu Y, Dong Q, Mao Y, Cheng W, Yu JT. Large-scale whole-exome sequencing of neuropsychiatric diseases and traits in 350,770 adults. Nat Hum Behav 2024; 8:1194-1208. [PMID: 38589703 DOI: 10.1038/s41562-024-01861-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 03/11/2024] [Indexed: 04/10/2024]
Abstract
While numerous genomic loci have been identified for neuropsychiatric conditions, the contribution of protein-coding variants has yet to be determined. Here we conducted a large-scale whole-exome-sequencing study to interrogate the impact of protein-coding variants on 46 neuropsychiatric diseases and 23 traits in 350,770 adults from the UK Biobank. Twenty new genes were associated with neuropsychiatric diseases through coding variants, among which 16 genes had impacts on the longitudinal risks of diseases. Thirty new genes were associated with neuropsychiatric traits, with SYNGAP1 showing pleiotropic effects across cognitive function domains. Pairwise estimation of genetic correlations at the coding-variant level highlighted shared genetic associations among pairs of neurodegenerative diseases and mental disorders. Lastly, a comprehensive multi-omics analysis suggested that alterations in brain structures, blood proteins and inflammation potentially contribute to the gene-phenotype linkages. Overall, our findings characterized a compendium of protein-coding variants for future research on the biology and therapeutics of neuropsychiatric phenotypes.
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Affiliation(s)
- Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Bang-Sheng Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Liu Yang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei-Shi Liu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ze-Yu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Xin-Rui Wu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Jun Ge
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Ying Zhu
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ying Mao
- Department of Neurosurgery, Huashan Hospital Fudan University, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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Morello M, Mastrogiovanni S, Falcione F, Rossi V, Bernardini S, Casciani S, Viola A, Reali M, Pieri M. Laboratory Diagnosis of Intrathecal Synthesis of Immunoglobulins: A Review about the Contribution of OCBs and K-index. Int J Mol Sci 2024; 25:5170. [PMID: 38791208 PMCID: PMC11121313 DOI: 10.3390/ijms25105170] [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: 03/09/2024] [Revised: 04/23/2024] [Accepted: 05/03/2024] [Indexed: 05/26/2024] Open
Abstract
The diagnosis of MS relies on a combination of imaging, clinical examinations, and biological analyses, including blood and cerebrospinal fluid (CSF) assessments. G-Oligoclonal bands (OCBs) are considered a "gold standard" for MS diagnosis due to their high sensitivity and specificity. Recent advancements have involved the introduced of kappa free light chain (k-FLC) assay into cerebrospinal fluid (CSF) and serum (S), along with the albumin quotient, leading to the development of a novel biomarker known as the "K-index" or "k-FLC index". The use of the K-index has been recommended to decrease costs, increase laboratory efficiency, and to skip potential subjective operator-dependent risk that could happen during the identification of OCBs profiles. This review aims to provide a comprehensive overview and analysis of recent scientific articles, focusing on updated methods for MS diagnosis with an emphasis on the utility of the K-index. Numerous studies indicate that the K-index demonstrates high sensitivity and specificity, often comparable to or surpassing the diagnostic accuracy of OCBs evaluation. The integration of the measure of the K-index with OCBs assessment emerges as a more precise method for MS diagnosis. This combined approach not only enhances diagnostic accuracy, but also offers a more efficient and cost-effective alternative.
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Affiliation(s)
- Maria Morello
- Clinical Biochemistry Department of Laboratory Medicine, Division of Proteins, University Hospital (PTV), 00133 Rome, Italy; (S.M.); (F.F.); (V.R.); (S.B.); (S.C.); (A.V.); (M.R.); (M.P.)
- Clinical Pathology and Clinical Biochemistry, Graduate School, Faculty of Medicine, University of Tor Vergata, 00133 Rome, Italy
- Department of Experimental Medicine, Faculty of Medicine, University of Tor Vergata, 00133 Rome, Italy
| | - Simone Mastrogiovanni
- Clinical Biochemistry Department of Laboratory Medicine, Division of Proteins, University Hospital (PTV), 00133 Rome, Italy; (S.M.); (F.F.); (V.R.); (S.B.); (S.C.); (A.V.); (M.R.); (M.P.)
- Clinical Pathology and Clinical Biochemistry, Graduate School, Faculty of Medicine, University of Tor Vergata, 00133 Rome, Italy
| | - Fabio Falcione
- Clinical Biochemistry Department of Laboratory Medicine, Division of Proteins, University Hospital (PTV), 00133 Rome, Italy; (S.M.); (F.F.); (V.R.); (S.B.); (S.C.); (A.V.); (M.R.); (M.P.)
- Clinical Pathology and Clinical Biochemistry, Graduate School, Faculty of Medicine, University of Tor Vergata, 00133 Rome, Italy
| | - Vanessa Rossi
- Clinical Biochemistry Department of Laboratory Medicine, Division of Proteins, University Hospital (PTV), 00133 Rome, Italy; (S.M.); (F.F.); (V.R.); (S.B.); (S.C.); (A.V.); (M.R.); (M.P.)
- Clinical Pathology and Clinical Biochemistry, Graduate School, Faculty of Medicine, University of Tor Vergata, 00133 Rome, Italy
| | - Sergio Bernardini
- Clinical Biochemistry Department of Laboratory Medicine, Division of Proteins, University Hospital (PTV), 00133 Rome, Italy; (S.M.); (F.F.); (V.R.); (S.B.); (S.C.); (A.V.); (M.R.); (M.P.)
- Clinical Pathology and Clinical Biochemistry, Graduate School, Faculty of Medicine, University of Tor Vergata, 00133 Rome, Italy
- Department of Experimental Medicine, Faculty of Medicine, University of Tor Vergata, 00133 Rome, Italy
| | - Stefania Casciani
- Clinical Biochemistry Department of Laboratory Medicine, Division of Proteins, University Hospital (PTV), 00133 Rome, Italy; (S.M.); (F.F.); (V.R.); (S.B.); (S.C.); (A.V.); (M.R.); (M.P.)
| | - Antonietta Viola
- Clinical Biochemistry Department of Laboratory Medicine, Division of Proteins, University Hospital (PTV), 00133 Rome, Italy; (S.M.); (F.F.); (V.R.); (S.B.); (S.C.); (A.V.); (M.R.); (M.P.)
| | - Marilina Reali
- Clinical Biochemistry Department of Laboratory Medicine, Division of Proteins, University Hospital (PTV), 00133 Rome, Italy; (S.M.); (F.F.); (V.R.); (S.B.); (S.C.); (A.V.); (M.R.); (M.P.)
| | - Massimo Pieri
- Clinical Biochemistry Department of Laboratory Medicine, Division of Proteins, University Hospital (PTV), 00133 Rome, Italy; (S.M.); (F.F.); (V.R.); (S.B.); (S.C.); (A.V.); (M.R.); (M.P.)
- Clinical Pathology and Clinical Biochemistry, Graduate School, Faculty of Medicine, University of Tor Vergata, 00133 Rome, Italy
- Department of Experimental Medicine, Faculty of Medicine, University of Tor Vergata, 00133 Rome, Italy
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Behr M, Kumbier K, Cordova-Palomera A, Aguirre M, Ronen O, Ye C, Ashley E, Butte AJ, Arnaout R, Brown B, Priest J, Yu B. Learning epistatic polygenic phenotypes with Boolean interactions. PLoS One 2024; 19:e0298906. [PMID: 38625909 PMCID: PMC11020961 DOI: 10.1371/journal.pone.0298906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 01/31/2024] [Indexed: 04/18/2024] Open
Abstract
Detecting epistatic drivers of human phenotypes is a considerable challenge. Traditional approaches use regression to sequentially test multiplicative interaction terms involving pairs of genetic variants. For higher-order interactions and genome-wide large-scale data, this strategy is computationally intractable. Moreover, multiplicative terms used in regression modeling may not capture the form of biological interactions. Building on the Predictability, Computability, Stability (PCS) framework, we introduce the epiTree pipeline to extract higher-order interactions from genomic data using tree-based models. The epiTree pipeline first selects a set of variants derived from tissue-specific estimates of gene expression. Next, it uses iterative random forests (iRF) to search training data for candidate Boolean interactions (pairwise and higher-order). We derive significance tests for interactions, based on a stabilized likelihood ratio test, by simulating Boolean tree-structured null (no epistasis) and alternative (epistasis) distributions on hold-out test data. Finally, our pipeline computes PCS epistasis p-values that probabilisticly quantify improvement in prediction accuracy via bootstrap sampling on the test set. We validate the epiTree pipeline in two case studies using data from the UK Biobank: predicting red hair and multiple sclerosis (MS). In the case of predicting red hair, epiTree recovers known epistatic interactions surrounding MC1R and novel interactions, representing non-linearities not captured by logistic regression models. In the case of predicting MS, a more complex phenotype than red hair, epiTree rankings prioritize novel interactions surrounding HLA-DRB1, a variant previously associated with MS in several populations. Taken together, these results highlight the potential for epiTree rankings to help reduce the design space for follow up experiments.
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Affiliation(s)
- Merle Behr
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany
| | - Karl Kumbier
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, United States of America
| | | | - Matthew Aguirre
- Department of Pediatrics, Stanford Medicine, Stanford, CA, United States of America
- Department of Biomedical Data Science, Stanford Medicine, Stanford, CA, United States of America
| | - Omer Ronen
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
| | - Chengzhong Ye
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
| | - Euan Ashley
- Division of Cardiovascular Medicine, Stanford Medicine, Stanford, CA, United States of America
| | - Atul J. Butte
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States of America
| | - Rima Arnaout
- Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States of America
- Division of Cardiology, Department of Medicine, University of California, San Francisco, San Francisco, CA, United States of America
| | - Ben Brown
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
- Biosciences Area, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America
| | - James Priest
- Department of Pediatrics, Stanford Medicine, Stanford, CA, United States of America
| | - Bin Yu
- Department of Statistics, University of California at Berkeley, Berkeley, CA, United States of America
- Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California at Berkeley, Berkeley, CA, United States of America
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Llera-Oyola J, Carceller H, Andreu Z, Hidalgo MR, Soler-Sáez I, Gordillo F, Gómez-Cabañes B, Roson B, de la Iglesia-Vayá M, Mancuso R, Guerini FR, Mizokami A, García-García F. The role of microRNAs in understanding sex-based differences in Alzheimer's disease. Biol Sex Differ 2024; 15:13. [PMID: 38297404 PMCID: PMC10832236 DOI: 10.1186/s13293-024-00588-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 01/23/2024] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND The incidence of Alzheimer's disease (AD)-the most frequent cause of dementia-is expected to increase as life expectancies rise across the globe. While sex-based differences in AD have previously been described, there remain uncertainties regarding any association between sex and disease-associated molecular mechanisms. Studying sex-specific expression profiles of regulatory factors such as microRNAs (miRNAs) could contribute to more accurate disease diagnosis and treatment. METHODS A systematic review identified six studies of microRNA expression in AD patients that incorporated information regarding the biological sex of samples in the Gene Expression Omnibus repository. A differential microRNA expression analysis was performed, considering disease status and patient sex. Subsequently, results were integrated within a meta-analysis methodology, with a functional enrichment of meta-analysis results establishing an association between altered miRNA expression and relevant Gene Ontology terms. RESULTS Meta-analyses of miRNA expression profiles in blood samples revealed the alteration of sixteen miRNAs in female and 22 miRNAs in male AD patients. We discovered nine miRNAs commonly overexpressed in both sexes, suggesting a shared miRNA dysregulation profile. Functional enrichment results based on miRNA profiles revealed sex-based differences in biological processes; most affected processes related to ubiquitination, regulation of different kinase activities, and apoptotic processes in males, but RNA splicing and translation in females. Meta-analyses of miRNA expression profiles in brain samples revealed the alteration of six miRNAs in female and four miRNAs in male AD patients. We observed a single underexpressed miRNA in female and male AD patients (hsa-miR-767-5p); however, the functional enrichment analysis for brain samples did not reveal any specifically affected biological process. CONCLUSIONS Sex-specific meta-analyses supported the detection of differentially expressed miRNAs in female and male AD patients, highlighting the relevance of sex-based information in biomedical data. Further studies on miRNA regulation in AD patients should meet the criteria for comparability and standardization of information.
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Affiliation(s)
- Jaime Llera-Oyola
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
- Carlos Simon Foundation-INCLIVA Instituto de Investigación Sanitaria, Valencia, Spain
| | - Héctor Carceller
- Neurobiology Unit, Program in Neurosciences and Institute of Biotechnology and Biomedicine (BIOTECMED), Universitat de València, Burjassot, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spanish National Network for Research in Mental Health, Madrid, Spain
- Joint Unit in Biomedical Imaging FISABIO-CIPF, Foundation for the Promotion of Health and Biomedical Research of Valencia Region, València, Spain
| | - Zoraida Andreu
- Foundation Valencian Institute of Oncology (FIVO), 46009, Valencia, Spain
| | - Marta R Hidalgo
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
| | - Irene Soler-Sáez
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
| | - Fernando Gordillo
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
| | - Borja Gómez-Cabañes
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain
| | - Beatriz Roson
- Carlos Simon Foundation-INCLIVA Instituto de Investigación Sanitaria, Valencia, Spain
| | - Maria de la Iglesia-Vayá
- Joint Unit in Biomedical Imaging FISABIO-CIPF, Foundation for the Promotion of Health and Biomedical Research of Valencia Region, València, Spain
| | - Roberta Mancuso
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | | | - Akiko Mizokami
- Oral Health/Brain Health/Total Health (OBT) Research Center, Faculty of Dental Science, Kyushu University, Fukuoka, Japan
| | - Francisco García-García
- Computational Biomedicine Laboratory, Príncipe Felipe Research Center (CIPF), C/ Eduardo Primo Yúfera, 3, 46012, Valencia, Spain.
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Li Z, Zheng C, Liu H, Lv J, Wang Y, Zhang K, Kong S, Chen F, Kong Y, Yang X, Cheng Y, Yang Z, Zhang C, Tian Y. A novel oxidative stress-related gene signature as an indicator of prognosis and immunotherapy responses in HNSCC. Aging (Albany NY) 2023; 15:14957-14984. [PMID: 38157249 PMCID: PMC10781479 DOI: 10.18632/aging.205323] [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: 06/05/2023] [Accepted: 11/02/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE To identify molecular subtypes of oxidative stress-related genes in head and neck squamous cell carcinoma (HNSCC) and to construct a scoring model of oxidative stress-related genes. METHODS R language based scRNA-seq and bulk RNA-seq analyses were used to identify molecular isoforms of oxidative stress-related genes in HNSCC. An oxidative stress-related gene scoring (OSRS) model was constructed, which were verified through online data and immunohistochemical staining of clinical samples. RESULTS Using TCGA-HNSCC datasets, nine predictive genes for overall patient survival, rarely reported in previous similar studies, were screened. AREG and CES1 were identified as prognostic risk factors. CSTA, FDCSP, JCHAIN, IFFO2, PGLYRP4, SPOCK2 and SPINK6 were identified as prognostic factors. Collectively, all genes formed a prognostic risk signature model for oxidative stress in HNSCC, which were validated in GSE41613, GSE103322 and PRJEB23709 datasets. Immunohistochemical staining of SPINK6 in nasopharyngeal cancer samples validated the gene panel. Subsequent analysis indicated that subgroups of the oxidative stress prognostic signature played important roles during cellular communication, the immune microenvironment, the differential activation of transcription factors, oxidative stress and immunotherapeutic responses. CONCLUSIONS The risk model might predict HNSCC prognosis and immunotherapeutic responses.
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Affiliation(s)
- Zhuoqi Li
- Department of Otolaryngology-Head and Neck Surgery, Shandong Provincial ENT Hospital, Shandong University, Jinan, Shandong 250299, P.R. China
- Radiotherapy Department, Shandong Second Provincial General Hospital, Shandong University, Jinan, Shandong 250299, P.R. China
| | - Chunning Zheng
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, P.R. China
| | - Hongtao Liu
- Department of Pathology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Clinical Pathology, Shandong Lung Cancer Institute, Shandong Institute of Nephrology, Jinan, Shandong 250014, P.R. China
| | - Jiling Lv
- Department of Respiratory and Critical Care Medicine, Shandong Second Provincial General Hospital, Jinan, Shandong 250299, P.R. China
| | - Yuanyuan Wang
- Department of Oncology, The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong 250299, P.R. China
| | - Kai Zhang
- Generalsurgery Department, Wenshang County People’s Hospital, Wenshang, Shandong 272500, P.R. China
| | - Shuai Kong
- Department of Gastrointestinal Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250021, P.R. China
| | - Feng Chen
- Department of Thoracic Surgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, P.R. China
| | - Yongmei Kong
- Department of Otolaryngology-Head and Neck Surgery, Shandong Provincial ENT Hospital, Shandong University, Jinan, Shandong 250299, P.R. China
- Radiotherapy Department, Shandong Second Provincial General Hospital, Shandong University, Jinan, Shandong 250299, P.R. China
| | - Xiaowei Yang
- Department of Hepatobiliary Intervention, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, P.R. China
| | - Yuxia Cheng
- Department of Pathology, The First Affiliated Hospital of Shandong First Medical University and Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Clinical Pathology, Shandong Lung Cancer Institute, Shandong Institute of Nephrology, Jinan, Shandong 250014, P.R. China
| | - Zhensong Yang
- Department of Gastrointestinal Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, P.R. China
| | - Chi Zhang
- Department of Cardiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250033, P.R. China
| | - Yuan Tian
- Department of Otolaryngology-Head and Neck Surgery, Shandong Provincial ENT Hospital, Shandong University, Jinan, Shandong 250299, P.R. China
- Radiotherapy Department, Shandong Second Provincial General Hospital, Shandong University, Jinan, Shandong 250299, P.R. China
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