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Niu J, Chen Y, Chai HC, Sasidharan S. Exploring MiR-484 Regulation by Polyalthia longifolia: A Promising Biomarker and Therapeutic Target in Cervical Cancer through Integrated Bioinformatics and an In Vitro Analysis. Biomedicines 2024; 12:909. [PMID: 38672263 PMCID: PMC11047986 DOI: 10.3390/biomedicines12040909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 04/05/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND MiR-484, implicated in various carcinomas, holds promise as a prognostic marker, yet its relevance to cervical cancer (CC) remains unclear. Our prior study demonstrated the Polyalthia longifolia downregulation of miR-484, inhibiting HeLa cells. This study investigates miR-484's potential as a biomarker and therapeutic target in CC through integrated bioinformatics and an in vitro analysis. METHODS MiR-484 levels were analyzed across cancers, including CC, from The Cancer Genome Atlas. The limma R package identified differentially expressed genes (DEGs) between high- and low-miR-484 CC cohorts. We assessed biological functions, tumor microenvironment (TME), immunotherapy, stemness, hypoxia, RNA methylation, and chemosensitivity differences. Prognostic genes relevant to miR-484 were identified through Cox regression and Kaplan-Meier analyses, and a prognostic model was captured via multivariate Cox regression. Single-cell RNA sequencing determined cell populations related to prognostic genes. qRT-PCR validated key genes, and the miR-484 effect on CC proliferation was assessed via an MTT assay. RESULTS MiR-484 was upregulated in most tumors, including CC, with DEGs enriched in skin development, PI3K signaling, and immune processes. High miR-484 expression correlated with specific immune cell infiltration, hypoxia, and drug sensitivity. Prognostic genes identified were predominantly epidermal and stratified patients with CC into risk groups, with the low-risk group showing enhanced survival and immunotherapeutic responses. qRT-PCR confirmed FGFR3 upregulation in CC cells, and an miR-484 mimic reversed the P. longifolia inhibitory effect on HeLa proliferation. CONCLUSION MiR-484 plays a crucial role in the CC progression and prognosis, suggesting its potential as a biomarker for targeted therapy.
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Affiliation(s)
- Jiaojiao Niu
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia;
- School of Biological Engineering, Xinxiang University, Xinxiang 453003, China
| | - Yeng Chen
- Department of Oral & Craniofacial Sciences, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Hwa Chia Chai
- Department of Biomedical Science, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
| | - Sreenivasan Sasidharan
- Institute for Research in Molecular Medicine (INFORMM), Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia;
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Guo C, Qu X, Tang X, Song Y, Wang J, Hua K, Qiu J. Spatiotemporally deciphering the mysterious mechanism of persistent HPV-induced malignant transition and immune remodelling from HPV-infected normal cervix, precancer to cervical cancer: Integrating single-cell RNA-sequencing and spatial transcriptome. Clin Transl Med 2023; 13:e1219. [PMID: 36967539 PMCID: PMC10040725 DOI: 10.1002/ctm2.1219] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND The mechanism underlying cervical carcinogenesis that is mediated by persistent human papillomavirus (HPV) infection remains elusive. AIMS Here, for the first time, we deciphered both the temporal transition and spatial distribution of cellular subsets during disease progression from normal cervix tissues to precursor lesions to cervical cancer. MATERIALS & METHODS We generated scRNA-seq profiles and spatial transcriptomics data from nine patient samples, including two HPV-negative normal, two HPV-positive normal, two HPV-positive HSIL and three HPV-positive cancer samples. RESULTS We not only identified three 'HPV-related epithelial clusters' that are unique to normal, high-grade squamous intraepithelial lesions (HSIL) and cervical cancer tissues but also discovered node genes that potentially regulate disease progression. Moreover, we observed the gradual transition of multiple immune cells that exhibited positive immune responses, followed by dysregulation and exhaustion, and ultimately established an immune-suppressive microenvironment during the malignant program. In addition, analysis of cellular interactions further verified that a 'homeostasis-balance-malignancy' change occurred within the cervical microenvironment during disease progression. DISCUSSION We for the first time presented a spatiotemporal atlas that systematically described the cellular heterogeneity and spatial map along the four developmental steps of HPV-related cervical oncogenesis, including normal, HPV-positive normal, HSIL and cancer. We identified three unique HPV-related clusters, discovered critical node genes that determined the cell fate and uncovered the immune remodeling during disease escalation. CONCLUSION Together, these findings provided novel possibilities for accurate diagnosis, precise treatment and prognosis evaluation of patients with precancer and cervical cancer.
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Affiliation(s)
- Chenyan Guo
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Xinyu Qu
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Xiaoyan Tang
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Yu Song
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Jue Wang
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Keqin Hua
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Junjun Qiu
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
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Tang J, Wu X, Cheng B, Lu Y. Identification of a polyamine-related signature and six novel prognostic biomarkers in oral squamous cell carcinoma. Front Mol Biosci 2023; 10:1073770. [PMID: 36733434 PMCID: PMC9887031 DOI: 10.3389/fmolb.2023.1073770] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 01/03/2023] [Indexed: 01/18/2023] Open
Abstract
Elevated polyamine levels are required for tumor transformation and development; however, expression patterns of polyamines and their diagnostic potential have not been investigated in oral squamous cell carcinoma (OSCC), and its impact on prognosis has yet to be determined. A total of 440 OSCC samples and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Consensus clustering was conducted to classify OSCC patients into two subgroups based on the expression of the 17 polyamine regulators. Polyamine-related differentially expressed genes (PARDEGs) among distinct polyamine clusters were determined. To create a prognostic model, PARDEGs were examined in the training cohorts using univariate-Lasso-multivariate Cox regression analyses. Six prognostic genes, namely, "CKS2," "RIMS3," "TRAC," "FMOD," CALML5," and "SPINK7," were identified and applied to develop a predictive model for OSCC. According to the median risk score, the patients were split into high-risk and low-risk groups. The predictive performance of the six gene models was proven by the ROC curve analysis of the training and validation cohorts. Kaplan-Meier curves revealed that the high-risk group had poorer prognosis. Furthermore, the low-risk group was more susceptible to four chemotherapy drugs according to the IC50 of the samples computed by the "pRRophetic" package. The correlation between the risk scores and the proportion of immune cells was calculated. Meanwhile, the tumor mutational burden (TMB) value of the high-risk group was higher. Real-time quantitative polymerase chain reaction was applied to verify the genes constructing the model. The possible connections of the six genes with various immune cell infiltration and therapeutic markers were anticipated. In conclusion, we identified a polyamine-related prognostic signature, and six novel biomarkers in OSCC, which may provide insights to identify new treatment targets for OSCC.
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Affiliation(s)
- Jiezhang Tang
- Department of Plastic and Reconstructive Surgery, Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Xuechen Wu
- Department of Stomatology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Bo Cheng
- Department of Stomatology, Zhongnan Hospital of Wuhan University, Wuhan, China,*Correspondence: Bo Cheng, ; Yajie Lu,
| | - Yajie Lu
- Department of Clinical Oncology, Xijing Hospital, Fourth Military Medical University, Xi’an, China,*Correspondence: Bo Cheng, ; Yajie Lu,
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Guo N, Minas G, Synowsky SA, Dunne MR, Ahmed H, McShane R, Bhardwaj A, Donlon NE, Lorton C, O'Sullivan J, Reynolds JV, Caie PD, Shirran SL, Lynch AG, Stewart AJ, Arya S. Identification of plasma proteins associated with oesophageal cancer chemotherapeutic treatment outcomes using SWATH-MS. J Proteomics 2022; 266:104684. [PMID: 35842220 DOI: 10.1016/j.jprot.2022.104684] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 10/17/2022]
Abstract
Oesophageal adenocarcinoma (OAC) is an aggressive cancer with a five-year survival of <15%. Current chemotherapeutic strategies only benefit a minority (20-30%) of patients and there are no methods available to differentiate between responders and non-responders. We performed quantitative proteomics using Sequential Window Acquisition of all THeoretical fragment-ion spectra-Mass Spectrometry (SWATH-MS) on albumin/IgG-depleted and non-depleted plasma samples from 23 patients with locally advanced OAC prior to treatment. Individuals were grouped based on tumour regression (TRG) score (TRG1/2/3 vs TRG4/5) after chemotherapy, and differentially abundant proteins were compared. Protein depletion of highly abundant proteins led to the identification of around twice as many proteins. SWATH-MS revealed significant quantitative differences in the abundance of several proteins between the two groups. These included complement c1q subunit proteins, C1QA, C1QB and C1QC, which were of higher abundance in the low TRG group. Of those that were found to be of higher abundance in the high TRG group, glutathione S-transferase pi (GSTP1) exhibited the lowest p-value and highest classification accuracy and Cohen's kappa value. Concentrations of these proteins were further examined using ELISA-based assays. This study provides quantitative information relating to differences in the plasma proteome that underpin response to chemotherapeutic treatment in oesophageal cancers. SIGNIFICANCE: Oesophageal cancers, including oesophageal adenocarcinoma (OAC) and oesophageal gastric junction cancer (OGJ), are one of the leading causes of cancer mortality worldwide. Curative therapy consists of surgery, either alone or in combination with adjuvant or neoadjuvant chemotherapy or radiation, or combination chemoradiotherapy regimens. There are currently no clinico-pathological means of predicting which patients will benefit from chemotherapeutic treatments. There is therefore an urgent need to improve oesophageal cancer disease management and treatment strategies. This work compared proteomic differences in OAC patients who responded well to chemotherapy as compared to those who did not, using quantitative proteomics prior to treatment commencement. SWATH-MS analysis of plasma (with and without albumin/IgG-depletion) from OAC patients prior to chemotherapy was performed. This approach was adopted to determine whether depletion offered a significant improvement in peptide coverage. Resultant datasets demonstrated that depletion increased peptide coverage significantly. Additionally, there was good quantitative agreement between commonly observed peptides. Data analysis was performed by adopting both univariate as well as multivariate analysis strategies. Differentially abundant proteins were identified between treatment response groups based on tumour regression grade. Such proteins included complement C1q sub-components and GSTP1. This study provides a platform for further work, utilising larger sample sets across different treatment regimens for oesophageal cancer, that will aid the development of 'treatment response prediction assays' for stratification of OAC patients prior to chemotherapy.
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Affiliation(s)
- Naici Guo
- School of Mathematics and Statistics, University of St Andrews, St Andrews KY16 9SS, United Kingdom
| | - Giorgos Minas
- School of Mathematics and Statistics, University of St Andrews, St Andrews KY16 9SS, United Kingdom
| | - Silvia A Synowsky
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews KY16 9ST, United Kingdom
| | - Margaret R Dunne
- Department of Surgery, Trinity Translational Medicine Institute, Trinity College Dublin, St James's Hospital, Dublin D08 W9RT, Ireland; Trinity St James's Cancer Institute, St James's Hospital, Dublin D08 W9RT, Ireland; Department of Applied Science, Technological University Dublin, Tallaght, Dublin 24 D24 FKT9, Ireland
| | - Hasnain Ahmed
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews KY16 9ST, United Kingdom; School of Medicine, University of St Andrews, St Andrews KY16 9TF, United Kingdom
| | - Rhiannon McShane
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews KY16 9ST, United Kingdom; School of Medicine, University of St Andrews, St Andrews KY16 9TF, United Kingdom
| | - Anshul Bhardwaj
- Department of Surgery, Trinity Translational Medicine Institute, Trinity College Dublin, St James's Hospital, Dublin D08 W9RT, Ireland
| | - Noel E Donlon
- Department of Surgery, Trinity Translational Medicine Institute, Trinity College Dublin, St James's Hospital, Dublin D08 W9RT, Ireland; Trinity St James's Cancer Institute, St James's Hospital, Dublin D08 W9RT, Ireland
| | - Cliona Lorton
- Department of Surgery, Trinity Translational Medicine Institute, Trinity College Dublin, St James's Hospital, Dublin D08 W9RT, Ireland; Trinity St James's Cancer Institute, St James's Hospital, Dublin D08 W9RT, Ireland; Our Lady's Hospice & Care Services, Harold's Cross, Dublin 6w, Ireland
| | - Jacintha O'Sullivan
- Department of Surgery, Trinity Translational Medicine Institute, Trinity College Dublin, St James's Hospital, Dublin D08 W9RT, Ireland; Trinity St James's Cancer Institute, St James's Hospital, Dublin D08 W9RT, Ireland
| | - John V Reynolds
- Department of Surgery, Trinity Translational Medicine Institute, Trinity College Dublin, St James's Hospital, Dublin D08 W9RT, Ireland; Trinity St James's Cancer Institute, St James's Hospital, Dublin D08 W9RT, Ireland
| | - Peter D Caie
- School of Medicine, University of St Andrews, St Andrews KY16 9TF, United Kingdom
| | - Sally L Shirran
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews KY16 9ST, United Kingdom
| | - Andy G Lynch
- School of Mathematics and Statistics, University of St Andrews, St Andrews KY16 9SS, United Kingdom; School of Medicine, University of St Andrews, St Andrews KY16 9TF, United Kingdom
| | - Alan J Stewart
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews KY16 9ST, United Kingdom; School of Medicine, University of St Andrews, St Andrews KY16 9TF, United Kingdom.
| | - Swati Arya
- Biomedical Sciences Research Complex, University of St Andrews, St Andrews KY16 9ST, United Kingdom; School of Medicine, University of St Andrews, St Andrews KY16 9TF, United Kingdom.
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Ramírez-Torres A, Gil J, Contreras S, Ramírez G, Valencia-González HA, Salazar-Bustamante E, Gómez-Caudillo L, García-Carranca A, Encarnación-Guevara S. Quantitative Proteomic Analysis of Cervical Cancer Tissues Identifies Proteins Associated With Cancer Progression. Cancer Genomics Proteomics 2022; 19:241-258. [PMID: 35181591 DOI: 10.21873/cgp.20317] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 12/09/2021] [Accepted: 01/07/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND/AIM To date, several proteomics studies in cervical cancer (CC) have focused mainly on squamous cervical cancer (SCC). Our study aimed to discover and clarify differences in SCC and CAD that may provide valuable information for the identification of proteins involved in tumor progression, in CC as a whole, or specific for SCC or CAD. MATERIALS AND METHODS Total protein extracts from 15 individual samples corresponding to 5 different CC tissue types were compared with a non-cancerous control group using bidimensional liquid chromatography-mass spectrometry (2D LC-MS/MS), isobaric tags for relative and absolute quantitation (ITRAQ), principal component analysis (PCA) and gene set enrichment analysis (GSEA). RESULTS A total of 622 statistically significant different proteins were detected. Exocytosis-related proteins were the most over-represented, accounting for 25% of the identified and quantified proteins. Based on the experimental results, reticulocalbin 3 (RCN3) and Ras-related protein Rab-14 (RAB14) were chosen for further downstream in vitro and vivo analyses. RCN3 was overexpressed in all CC tissues compared to the control and RAB14 was overexpressed in squamous cervical cancer (SCC) compared to invasive cervical adenocarcinoma (CAD). In the tumor xenograft experiment, RAB14 protein expression was positively correlated with increased tumor size. In addition, RCN3-expressing HeLa cells induced a discrete size increment compared to control, at day 47 after inoculation. CONCLUSION RAB14 and RCN3 are suggested as potential biomarkers and therapeutic targets in the treatment of CC.
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Affiliation(s)
- Alberto Ramírez-Torres
- Proteomics, Center for Genomic Sciences, The National Autonomous University of Mexico (UNAM), Cuernavaca, Mexico
| | - Jeovanis Gil
- Proteomics, Center for Genomic Sciences, The National Autonomous University of Mexico (UNAM), Cuernavaca, Mexico.,Division of Oncology, Section for Clinical Chemistry, Department of Translational Medicine, Lund University, Lund, Sweden
| | - Sandra Contreras
- Proteomics, Center for Genomic Sciences, The National Autonomous University of Mexico (UNAM), Cuernavaca, Mexico
| | - Graciela Ramírez
- The National Institute of Cancerology (INCan), Mexico City, Mexico
| | | | - Emmanuel Salazar-Bustamante
- Proteomics, Center for Genomic Sciences, The National Autonomous University of Mexico (UNAM), Cuernavaca, Mexico
| | - Leopoldo Gómez-Caudillo
- Proteomics, Center for Genomic Sciences, The National Autonomous University of Mexico (UNAM), Cuernavaca, Mexico
| | | | - Sergio Encarnación-Guevara
- Proteomics, Center for Genomic Sciences, The National Autonomous University of Mexico (UNAM), Cuernavaca, Mexico;
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Zhu X, Li S, Luo J, Ying X, Li Z, Wang Y, Zhang M, Zhang T, Jiang P, Wang X. Subtyping of Human Papillomavirus-Positive Cervical Cancers Based on the Expression Profiles of 50 Genes. Front Immunol 2022; 13:801639. [PMID: 35126391 PMCID: PMC8814347 DOI: 10.3389/fimmu.2022.801639] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 01/05/2022] [Indexed: 12/29/2022] Open
Abstract
Background Human papillomavirus-positive (HPV+) cervical cancers are highly heterogeneous in molecular and clinical features. However, the molecular classification of HPV+ cervical cancers remains insufficiently unexplored. Methods Based on the expression profiles of 50 genes having the largest expression variations across the HPV+ cervical cancers in the TCGA-CESC dataset, we hierarchically clustered HPV+ cervical cancers to identify new subtypes. We further characterized molecular, phenotypic, and clinical features of these subtypes. Results We identified two subtypes of HPV+ cervical cancers, namely HPV+G1 and HPV+G2. We demonstrated that this classification method was reproducible in two validation sets. Compared to HPV+G2, HPV+G1 displayed significantly higher immune infiltration level and stromal content, lower tumor purity, lower stemness scores and intratumor heterogeneity (ITH) scores, higher level of genomic instability, lower DNA methylation level, as well as better disease-free survival prognosis. The multivariate survival analysis suggests that the disease-free survival difference between both subtypes is independent of confounding variables, such as immune signature, stemness, and ITH. Pathway and gene ontology analysis confirmed the more active tumor immune microenvironment in HPV+G1 versus HPV+G2. Conclusions HPV+ cervical cancers can be classified into two subtypes based on the expression profiles of the 50 genes with the largest expression variations across the HPV+ cervical cancers. Both subtypes have significantly different molecular, phenotypic, and clinical features. This new subtyping method captures the comprehensive heterogeneity in molecular and clinical characteristics of HPV+ cervical cancers and provides potential clinical implications for the diagnosis and treatment of this disease.
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Affiliation(s)
- Xiaojun Zhu
- Department of Obstetrics, Women’s Hospital, Medical School of Zhejiang University, Hangzhou, China
| | - Shengwei Li
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Big Data Research Institute, China Pharmaceutical University, Nanjing, China
| | - Jiangti Luo
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Big Data Research Institute, China Pharmaceutical University, Nanjing, China
| | - Xia Ying
- Department of Obstetrics, Women’s Hospital, Medical School of Zhejiang University, Hangzhou, China
| | - Zhi Li
- Department of Obstetrics, Women’s Hospital, Medical School of Zhejiang University, Hangzhou, China
| | - Yuanhe Wang
- Department of Obstetrics, Women’s Hospital, Medical School of Zhejiang University, Hangzhou, China
| | - Mengmeng Zhang
- Department of Obstetrics, Women’s Hospital, Medical School of Zhejiang University, Hangzhou, China
| | - Tianfang Zhang
- Department of Rehabilitation Medicine, First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, China
| | - Peiyue Jiang
- Department of Obstetrics, Women’s Hospital, Medical School of Zhejiang University, Hangzhou, China
- *Correspondence: Peiyue Jiang, ; Xiaosheng Wang,
| | - Xiaosheng Wang
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China
- Big Data Research Institute, China Pharmaceutical University, Nanjing, China
- *Correspondence: Peiyue Jiang, ; Xiaosheng Wang,
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