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Aptamer-Based Cell-Surface Profiling with Single-Cell Resolution Enables Precise cancer Characterization

CCS Chemistry 2024年第1期6卷 196-207页

作者： Liujun Xu Yawei Feng Tong Wang Shenhuan Li Kangli Xu Yue Sun Yi Luo Yishan Ye Yan Miao Yun Dong Zhenzhen Guo Qing Zhang Benshang Li He Huang Xue-Qiang Wang Liping Qiu Weihong Tan Molecular Science and Biomedicine Laboratory State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringCollege of BiologyAptamer Engineering Center of Hunan ProvinceHunan UniversityChangshaHunan 410082 The Key Laboratory of Zhejiang Province for Aptamers and Theranostics Zhejiang Cancer HospitalHangzhou Institute of MedicineChinese Academy of SciencesHangzhouZhejiang 310022 Institute of Molecular Medicine Renji HospitalShanghai Jiao Tong University School of MedicineCollege of Chemistry and Chemical EngineeringShanghai Jiao Tong UniversityShanghai 200240 Bone Marrow Transplantation Center The First Affiliated HospitalSchool of MedicineZhejiang UniversityHangzhou 310003 Institute of Hematology Zhejiang UniversityHangzhou 310003 Department of Hematology and Oncology Shanghai Children’s Medical CenterShanghai Jiao Tong University School of MedicineShanghai Jiao Tong UniversityShanghai 200127

Molecular profiling of cell-surface proteins is a powerful strategy for precise cancer diagnosis.While mass cytometry(MC)enables synchronous detection of over 40 cellular parameters,its full potential in disease classification is challenged by the limited types of recognition probes currently available.In this work,we synthesize a panel of heavy isotopeconjugated aptamers to profile cancer-associated signatures on the surface of hematological malignancy(HM)cells.Based on 15 molecular signatures,we performed cell-surface profiling that allowed the precise classification of 8 HM cell lines.Combined with machine-learning technology,this aptamer-based MC platform also achieved multiclass identification of HM subtypes in clinical sampleswith 100%accuracy in the training cohort and 80%accuracy in the test cohort.Therefore,we report an effective and practical strategy for precise cancer classification at the singlecell level,paving the way for its clinical use in the near future.

关键词： molecular profiling cancer diagnosis mass cytometry aptamers machine learning

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Biosensor-based assay of exosome biomarker for early diagnosis of cancer

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Frontiers of Medicine 2022年第2期16卷 157-175页

作者： Ying Deng Zhaowei Sun Lei Wang Minghui Wang Jie Yang Genxi Li State Key Laboratory of Pharmaceutical Biotechnology School of Life SciencesNanjing UniversityNanjing 210023China

cancer imposes a severe threat to people’s health and lives,thus pressing a huge medical and economic burden on individuals and communities.Therefore,early diagnosis of cancer is indispensable in the timely prevention and effective treatment for patients.Exosome has recently become an attractive cancer biomarker in noninvasive early diagnosis because of the unique physiology and pathology functions,which reflects remarkable information regarding the cancer microenvironment,and plays an important role in the occurrence and evolution of cancer.Meanwhile,biosensors have gained great attention for the detection of exosomes due to their superior properties,such as convenient operation,real-time readout,high sensitivity,and remarkable specificity,suggesting promising biomedical applications in the early diagnosis of cancer.In this review,the latest advances of biosensors regarding the assay of exosomes were summarized,and the superiorities of exosomes as markers for the early diagnosis of cancer were evaluated.Moreover,the recent challenges and further opportunities of developing effective biosensors for the early diagnosis of cancer were discussed.

关键词： biosensor exosome cancer diagnosis

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Oral Microbiota:A New Insight into cancer Progression,diagnosis and Treatment

Phenomics

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Phenomics 2023年第5期3卷 535-547页

作者： Xiu-Li Wang Hua-Wen Xu Ning-Ning Liu State Key Laboratory of Systems Medicine for cancer Center for Single-Cell OmicsSchool of Public HealthSchool of MedicineShanghai Jiao Tong UniversityShanghai 200025China

The polymorphic microbiome has been defined as one of the“Hallmarks of cancer”.Extensive studies have now uncovered the role of oral microbiota in cancer development and progression.Bacteria,fungi,archaea,and viruses in the oral cavity interact dynamically with the oral microenvironment to maintain the oral micro-ecological homeostasis.This complex interaction is influenced by many factors,such as maternal transmission,personal factors and environmental factors.Dysbiosis of oral microbiota can disturbed this host-microbiota interaction,leading to systemic diseases.Numerous studies have shown the potential associations between oral microbiota and a variety of cancers.However,the underlying mechanisms and therapeutic insights are still poorly understood.In this review,we mainly focus on the following aspects:(1)the factors affect oral microbiota composition and function;(2)the interaction between microenvironment and oral microbiota;(3)the role of multi-kingdom oral microbiota in human health;(4)the potential underlying mechanisms and therapeutic benefits of oral microbiota against cancer.Finally,we aim to describe the impact of oral microbiota on cancer progression and provide novel therapeutic insights into cancer prevention and treatment by targeting oral microbiota.

关键词： Oral microbiota cancer cancer diagnosis Tumor therapy

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DNA Processor Modules Enabled Pattern Recognition of Extracellular Vesicles for Facile cancer diagnosis

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Chinese Journal of Chemistry 2023年第18期41卷 2269-2274页

作者： Qianyun Yang Ruiyan Wang Kun Wu Di Li School of Chemistry and Molecular Engineering East China Normal Un iversity500 Dongchuan RoadShanghai 200241China Institute of Biomedical Engineering College of Life ScienceQingdao University308 Ningxia RoodQingdooShandong 266071China China Notional Tobacco Quality Supervision&Test Center 6 Cuizhu StreetZhengzhouHenan 450001China

Comprehensive Summary,Extracellular vesicles(EVs)carry rich protein and nucleic acid information of host cells,thus,they are considered to be reliable biomarkers for cancer diagnosis.However,current EVs detection relies on technical expertise that requires special equipment to readout signals that prevent its point-of-care testing.In this study,we propose a Pattern Recognition of Molecular in Interest on Single EVs(PROMISE)strategy for clinical EVs detection.This strategy combines an aptamer-based DNA processor on single EVs,and a color-rendering enzyme to provide a visual output for naked-eyes enabled profiling.We demonstrate 100%accuracy in breast cancer discrimination.Furthermore,by utilizing thin-layer chromatography(TLC),we achieve a simultaneous screening of two types of cancers(breast and prostate cancer)in one sample.This PROMISE strategy could serve as a versatile platform for point-of-care EVs diagnosis.

关键词： Extracellular vesicles cancer diagnosis Single-molecule imaging Point-of-care DNA Protein

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动态全场光学相干断层扫描结合深度学习在肿瘤患者术中诊断的应用:一项乳腺癌患者的前瞻性队列研究

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Science Bulletin 2024年第11期69卷 1748-1756页

作者：张舒玮杨滨杨后圃赵进张原媛高元绪 Olivia Monteiro 张康刘博王殊 Breast Center Peking University People’s HospitalBeijing 100044China China ESG Institute Capital University of Economics and BusinessBeijing 100070China Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Department of Pathology Peking University People’s HospitalBeijing 100044China Center for Biomedicine and Innovations Faculty of MedicineMacao University of Science and TechnologyMacao 999078China College of Future Technology Peking UniversityBeijing 100091China School of Mathematical and Computational Sciences Massey UniversityAuckland 0745New Zealand

An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin(H&E) histology, such as frozen section, are time-,resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography(D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group(n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma(IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ(DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests(n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%;only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow(approximately 3 min)was significantly shorter than that required for traditional procedures(approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.

关键词： cancer diagnosis Breast neoplasms Dynamic full-field optical coherence tomography Deep learning Image classification

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Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

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cancer Communications 2021年第11期41卷 1100-1115页

作者： Zi-Hang Chen Li Lin Chen-Fei Wu Chao-Feng Li Rui-Hua Xu Ying Sun Department of Radiation Oncology State Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat-sen University Cancer CenterGuangzhouGuangdong 510060P.R.China Zhongshan School of Medicine Sun Yat-sen UniversityGuangzhouGuangdong 510080P.R.China Artificial Intelligence Laboratory State Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat-sen University Cancer CenterGuangzhouGuangdong 510060P.R.China Department of Medical Oncology State Key Laboratory of Oncology in South ChinaCollaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapySun Yat-sen University Cancer CenterGuangzhouGuangdong 510060P.R.China

Over the past decade,artificial intelligence(AI)has contributed substantially to the resolution of various medical problems,including cancer.Deep learning(DL),a subfield of AI,is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data.On the basis of a large quantity of medical data and novel computational technologies,AI,especially DL,has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment.These applications range from early cancer detection,diagnosis,classification and grading,molecular characterization of tumors,prediction of patient outcomes and treatment responses,personalized treatment,automatic radiotherapy workflows,novel anti-cancer drug discovery,and clinical trials.In this review,we introduced the general principle of AI,summarized major areas of its application for cancer diagnosis and treatment,and discussed its future directions and remaining challenges.As the adoption of AI in clinical use is increasing,we anticipate the arrival of AI-powered cancer care.

关键词： artificial intelligence cancer diagnosis cancer research cancer treatment convolutional neural network deep learning deep neural network oncology

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Diagnostic accuracy of the multi-target stool DNA test in detecting colorectal cancer:A hospital-based study

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World Journal of Gastrointestinal Oncology 2023年第1期15卷 102-111页

作者： Han-Lu Gao Le-Bin Lv Wang-Fang Zhao Qi-Wen Lu Jin-Qing Fan Department of Preventive Health The Affiliated Hospital of Medical School of Ningbo UniversityNingboZhejiang ProvinceChina Department of Gastroenterology The Affiliated Hospital of Medical School of Ningbo UniversityNingbo 315000Zhejiang ProvinceChina Department of Traditional Chinese Medicine The Affiliated Hospital of Medical School of Ningbo UniversityNingbo 315000Zhejiang ProvinceChina

BACKGROUND The multi-target stool DNA test(MT-sDNA)has potential utility in the detection of colorectal cancer(CRC),but validation of its clinical accuracy has been limited in China.AIM To evaluate the diagnostic performance of MT-sDNA and investigate the combined diagnostic value of alpha-fetoprotein(AFP),carcinoembryonic antigen(CEA),and carbohydrate antigen 199(CA199)with MT-sDNA in CRC and adenomas.METHODS We evaluated the performance of the MT-sDNA kit based on a hospital clinical trial.In this case-control study,135 participants from the Affiliated Hospital of Medical School of Ningbo University,including 51 CRC patients,23 patients with adenomas,and 61 healthy controls were enrolled.We used a risk scoring system to determine the positivity of tests with histological diagnosis or colonoscopy as the reference standard.RESULTS The main indices of sensitivity,specificity and accuracy were evaluated.The sensitivity and specificity for CRC detection were 90.2%and 83.3%,respectively,with an accuracy of 89.8%.For adenoma,the sensitivity and specificity were 56.5%and 68.9%,respectively,with an accuracy of 73.1%.The sensitivity and specificity of MT-sDNA combined with CEA in the diagnosis of adenoma were 78.3%and 60.7%,respectively.CONCLUSION The MT-sDNA test showed better performance in the detection of CRC,which was superior to AFP,CEA,and CA199 separately,but not for predicting adenomas.The combination of MT-sDNA with CEA further improved the sensitivity for adenoma diagnosis.

关键词： Colorectal cancer MT-sDNA cancer diagnosis Adenoma Sensitivity Specificity

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Dual microenvironmental parameter-responsive lysosome-targeting carbon dots for the high contrast discrimination of a broad spectrum of cancer cells

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Chinese Chemical Letters 2022年第12期33卷 5051-5055页

作者： Yue Xiao Xiaohui Yin Pengjuan Sun Yuanqiang Sun Lingbo Qu Zhaohui Li College of Chemistry Institute of Analytical Chemistry for Life ScienceHenan Joint International Research Laboratory of Green Construction of Functional Molecules and Their Bioanalytical ApplicationsZhengzhou UniversityZhengzhou 450001China

The development of new carbon dots(CDs)for fluorescence-based cancer diagnosis has recently attracted extensive attention.diagnosis methods based on ligand-receptor fluorescence suffer from the heterogeneity of receptor expression.Changes in the microenvironments of cancer cells provide opportunities for accurate and broad-spectrum cancer diagnosis.The lysosomes in cancer cells have lower polarity and higher viscosity than normal cells.Based on these two key microenvironmental parameters,dual-responsive CDs with inherent lysosome-targeting ability were synthesized via one-step hydrothermal treatment.The CDs exhibit many advantageous properties including facile synthesis,good water solubility,pH-independent emission,excellent photostability,good biocompatibility,and wash-free imaging ability.The CDs were successfully employed in the fluorescence-based discrimination of a broad spectrum of cancer cells from normal cells with high contrast.The CDs are promising candidates for use in the field of cancer diagnosis.

关键词： Carbon dots Multiple microenvironmental parameters Lysosome-targeting High contrast imaging cancer diagnosis

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Improved Model for Genetic Algorithm-Based Accurate Lung cancer Segmentation and Classification

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Computer Systems Science & Engineering 2023年第5期45卷 2017-2032页

作者： K.Jagadeesh A.Rajendran Department of Electronics and Communication Engineering SNS College of EngineeringCoimbatoreTamilnaduIndia Department of Electronics and Communication Engineering Karpagam College of EngineeringCoimbatoreTamilnaduIndia

Lung cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients.For lung cancer diagnosis,the computed tomography(CT)scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis.In present scenario of medical data processing,the cancer detection process is very time consuming and exactitude.For that,this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm.In the model,the input CT images are pre-processed with the filters called adaptive median filter and average filter.The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest)cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique.For classification of images,Probabilistic Neural Networks(PNN)based classification is used.The experimentation is carried out by simulating the model in MATLAB,with the input CT lung images LIDC-IDRI(Lung Image Database Consortium-Image Database Resource Initiative)benchmark Dataset.The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.

关键词： cancer diagnosis segmentation enhancement histogram equalization probabilistic rate neural networks(PNN) classification

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Octahedral silver oxide nanoparticles enabling remarkable SERS activity for detecting circulating tumor cells

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Science China(Life Sciences) 2022年第3期65卷 561-571页

作者： Meng He Jie Lin Ozioma Udochukwu Akakuru Xiawei Xu Yanying Li Yi Cao Yanping Xu Aiguo Wu Cixi Institute of Biomedical Engineering International Cooperation Base of Biomedical Materials Technology and ApplicationCAS Key Laboratory of Magnetic Materials and Devices&Zhejiang Engineering Research Center for Biomedical MaterialsNingbo Institute of Materials Technology and EngineeringChinese Academy of SciencesNingbo 315201China Nano Science and Technology Institute University of Science and Technology of ChinaSuzhou 215123China

The detection of circulating tumor cells(CTCs)is a crucial tool for early cancer diagnosis,prognosis,and postoperative evaluation.However,detection sensitivity remains a major challenge because CTCs are extremely rare in peripheral blood.To effectively detect CTCs,octahedral Ag_(2)O nanoparticles(NPs)with high dispersibility,good biocompatibility,remarkable surface-enhanced Raman scattering(SERS)enhancement,and obvious enhancement selectivity are designed as an SERS platform.Ag_(2)O NPs with many oxygen vacancy defects are successfully synthesized,which exhibit an ultra-high SERS enhancement factor(1.98×10^(6))for 4-mercaptopyridine molecules.The remarkable SERS activity of octahedral Ag_(2)O NPs is derived from the synergistic effect of the surface defect-promoted photo-induced charge transfer(PICT)process and strong vibration coupling resonance in the Ag_(2)O-molecule SERS complex,greatly amplifying the molecular Raman scattering crosssection.The promoted PICT process is confirmed using ultraviolet-visible(UV-Vis)absorption spectroscopy,demonstrating that obvious PICT resonance occurs in Ag_(2)O SERS system under visible light.An additional growth step of SERS bioprobe is proposed by modifying the Raman signal molecules and functional biological molecules on Ag_(2)O NPs for CTC detection.The Ag_(2)O-based SERS bioprobe exhibits excellent detection specificity for different cancer cells in rabbit blood.Importantly,the high-sensitivity Ag_(2)O-based SERS bioprobe satisfies the requirement for rare CTC detection in the peripheral blood of cancer patients,and the detection limit can reach 1 cell per m L.To our knowledge,this study is the first time that a semiconductor SERS substrate has been successfully utilized in CTC detection.This work provides new insights into CTC detection and the development of novel semiconductor-based SERS platforms for cancer diagnosis.

关键词： SERS CTCs octahedral Ag_(2)O NPs PICT cancer diagnosis

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