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Article ; Online: The role of KNTC1 in the regulation of proliferation, migration and tumorigenesis in colorectal cancer.

Wang, Junshan / Ding, Zhixuan / Shu, Wei / Zhuge, Ying

2023 Volume 108, Page(s) 110728

Abstract: Background: Current findings have revealed that kinetochore-associated protein 1 (KNTC1) plays a pivotal role in the carcinogenesis of numerous types of cancer. This study was undertaken to inspect the role and probable underlying mechanisms of KNTC1 ... ...

Abstract	Background: Current findings have revealed that kinetochore-associated protein 1 (KNTC1) plays a pivotal role in the carcinogenesis of numerous types of cancer. This study was undertaken to inspect the role and probable underlying mechanisms of KNTC1 during the genesis and progression of colorectal cancer. Methods: Immunohistochemistry was implemented to determine KNTC1 expression levels in colorectal cancer tissues and para-carcinoma tissues. The association between KNTC1 expression profiles and several clinicopathological traits of colorectal cancer cases was examined employing Mann-Whitney U, Spearman, and Kaplan-Meier analyses. To track the proliferation, apoptosis, cell cycle, migration and in vivo carcinogenesis of colorectal cancer cells, KNTC1 was knocked down in colorectal cell line via RNA interference. To investigate the potential mechanism, the expression profile alterations of associated proteins were detected using human apoptosis antibody arrays, and verified by Western blot analysis. Results: In colorectal cancer tissues, KNTC1 was substantially expressed, and it was associated with the pathological grade as well as overall survival rate of the disease. The knockdown of KNTC1 was able to inhibit proliferation, cell cycle, migration and in vivo tumorigenesis of colorectal cancer cells, but promote apoptosis. Conclusions: KNTC1 is a key player in the emergence of colorectal cancer and may serve as an early diagnostic indicator of precancerous lesions.
MeSH term(s)	Humans ; Cell Line, Tumor ; Cell Proliferation/genetics ; Cell Movement/genetics ; Carcinogenesis/genetics ; Cell Transformation, Neoplastic/genetics ; Colorectal Neoplasms/pathology ; Gene Expression Regulation, Neoplastic ; Apoptosis/genetics ; Microtubule-Associated Proteins/metabolism ; Cell Cycle Proteins/metabolism
Chemical Substances	KNTC1 protein, human ; Microtubule-Associated Proteins ; Cell Cycle Proteins
Language	English
Publishing date	2023-05-23
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1002702-6
ISSN	1873-3913 ; 0898-6568
ISSN (online)	1873-3913
ISSN	0898-6568
DOI	10.1016/j.cellsig.2023.110728
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Article: pH-Sensitive Nanoparticles for Colonic Delivery Anti-miR-301a in Mouse Models of Inflammatory Bowel Diseases.

Wang, Junshan / Yao, Min / Zou, Jiafeng / Ding, Wenxing / Sun, Mingyue / Zhuge, Ying / Gao, Feng

Nanomaterials (Basel, Switzerland)

2023 Volume 13, Issue 20

Abstract: Though the anti-miR-301a (anti-miR) is a promising treatment strategy for inflammatory bowel disease (IBD), the degradability and the poor targeting of the intestine are a familiar issue. This study aimed to develop a multifunctional oral nanoparticle ... ...

Abstract	Though the anti-miR-301a (anti-miR) is a promising treatment strategy for inflammatory bowel disease (IBD), the degradability and the poor targeting of the intestine are a familiar issue. This study aimed to develop a multifunctional oral nanoparticle delivery system loaded with anti-miR for improving the targeting ability and the therapeutic efficacy. The HA-CS/ES100/PLGA nanoparticles (HCeP NPs) were prepared using poly (lactic-co-glycolic acid) copolymer (PLGA), enteric material Eudragit
Language	English
Publishing date	2023-10-20
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662255-5
ISSN	2079-4991
ISSN	2079-4991
DOI	10.3390/nano13202797
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Article: GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics.

Tasci, Erdal / Jagasia, Sarisha / Zhuge, Ying / Camphausen, Kevin / Krauze, Andra Valentina

Cancers

2023 Volume 15, Issue 18

Abstract: Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired ... ...

Abstract	Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes.
Language	English
Publishing date	2023-09-19
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers15184628
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Article: Identifying patients suitable for targeted adjuvant therapy: advances in the field of developing biomarkers for tumor recurrence following irradiation.

Jagasia, S / Tasci, E / Zhuge, Ying / Camphausen, K / Krauze, A V

Expert review of precision medicine and drug development

2023 Volume 8, Issue 1, Page(s) 33–42

Abstract: Introduction: Radiation therapy (RT) is commonly used to treat cancer in conjunction with chemotherapy, immunotherapy, and targeted therapies. Despite the effectiveness of RT, tumor recurrence due to treatment resistance still lead to treatment failure. ...

Abstract	Introduction: Radiation therapy (RT) is commonly used to treat cancer in conjunction with chemotherapy, immunotherapy, and targeted therapies. Despite the effectiveness of RT, tumor recurrence due to treatment resistance still lead to treatment failure. RT-specific biomarkers are currently lacking and remain challenging to investigate with existing data since, for many common malignancies, standard of care (SOC) paradigms involve the administration of RT in conjunction with other agents. Areas covered: Established clinically relevant biomarkers are used in surveillance, as prognostic indicators, and sometimes for treatment planning; however, the inability to intercept early recurrence or predict upfront resistance to treatment remains a significant challenge that limits the selection of patients for adjuvant therapy. We discuss attempts at intercepting early failure. We examine biomarkers that have made it into the clinic where they are used for treatment monitoring and management alteration, and novel biomarkers that lead the field with targeted adjuvant therapy seeking to harness these. Expert opinion: Given the growth of data correlating interventions with omic analysis toward identifying biomarkers of radiation resistance, more robust markers of recurrence that link to biology will increasingly be leveraged toward targeted adjuvant therapy to make a successful transition to the clinic in the coming years.
Language	English
Publishing date	2023-11-16
Publishing country	England
Document type	Journal Article
ISSN	2380-8993
ISSN	2380-8993
DOI	10.1080/23808993.2023.2276927
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Article ; Online: DNA Walkers for Biosensing Development.

Song, Lu / Zhuge, Ying / Zuo, Xiaolei / Li, Min / Wang, Fang

Advanced science (Weinheim, Baden-Wurttemberg, Germany)

2022 Volume 9, Issue 18, Page(s) e2200327

Abstract: The ability to design nanostructures with arbitrary shapes and controllable motions has made DNA nanomaterials used widely to construct diverse nanomachines with various structures and functions. The DNA nanostructures exhibit excellent properties, ... ...

Abstract	The ability to design nanostructures with arbitrary shapes and controllable motions has made DNA nanomaterials used widely to construct diverse nanomachines with various structures and functions. The DNA nanostructures exhibit excellent properties, including programmability, stability, biocompatibility, and can be modified with different functional groups. Among these nanoscale architectures, DNA walker is one of the most popular nanodevices with ingenious design and flexible function. In the past several years, DNA walkers have made amazing progress ranging from structural design to biological applications including constructing biosensors for the detection of cancer-associated biomarkers. In this review, the key driving forces of DNA walkers are first summarized. Then, the DNA walkers with different numbers of legs are introduced. Furthermore, the biosensing applications of DNA walkers including the detection- of nucleic acids, proteins, ions, and bacteria are summarized. Finally, the new frontiers and opportunities for developing DNA walker-based biosensors are discussed.
MeSH term(s)	Biosensing Techniques ; DNA ; Ions ; Nanostructures/chemistry ; Nucleic Acids/chemistry
Chemical Substances	Ions ; Nucleic Acids ; DNA (9007-49-2)
Language	English
Publishing date	2022-04-22
Publishing country	Germany
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2808093-2
ISSN	2198-3844 ; 2198-3844
ISSN (online)	2198-3844
ISSN	2198-3844
DOI	10.1002/advs.202200327
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Article ; Online: Machine learning based survival prediction in Glioma using large-scale registry data.

Zhao, Rachel / Zhuge, Ying / Camphausen, Kevin / Krauze, Andra V

Health informatics journal

2022 Volume 28, Issue 4, Page(s) 14604582221135427

Abstract: Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for ... ...

Abstract	Gliomas are the most common central nervous system tumors exhibiting poor clinical outcomes. The ability to estimate prognosis is crucial for both patients and providers in order to select the most appropriate treatment. Machine learning (ML) allows for sophisticated approaches to survival prediction using real world clinical parameters needed to achieve superior predictive accuracy. We employed Cox Proportional hazards (CPH) model, Support Vector Machine (SVM) model, Random Forest (RF) model in a large glioma dataset (3462 patients, diagnosed 2000-2018) to explore the most optimal approach to survival prediction. Features employed were age, sex, surgical resection status, tumor histology and tumor site, administration of radiation therapy (RT) and chemotherapy status. Concordance index (c-index) was employed to assess the accuracy of survival time prediction. All three models performed well with prediction accuracy (CI 0.767, 0.771, 0.57 for CPH, SVM, RF models respectively) with the best performance achieved when incorporating RT and chemotherapy administration status which emerged as key predictive features. Within the subset of glioblastoma patients, similar prediction accuracy was achieved. These findings should prompt stricter clinician oversight over registry data accuracy through quality assurance as we move towards meaningful predictive ability using ML approaches in glioma.
MeSH term(s)	Humans ; Glioma/diagnosis ; Glioma/therapy ; Machine Learning ; Support Vector Machine ; Prognosis ; Registries
Language	English
Publishing date	2022-10-20
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Intramural
ZDB-ID	2213115-2
ISSN	1741-2811 ; 1460-4582
ISSN (online)	1741-2811
ISSN	1460-4582
DOI	10.1177/14604582221135427
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Article: Bias and Class Imbalance in Oncologic Data-Towards Inclusive and Transferrable AI in Large Scale Oncology Data Sets.

Tasci, Erdal / Zhuge, Ying / Camphausen, Kevin / Krauze, Andra V

Cancers

2022 Volume 14, Issue 12

Abstract: Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to ... ...

Abstract	Recent technological developments have led to an increase in the size and types of data in the medical field derived from multiple platforms such as proteomic, genomic, imaging, and clinical data. Many machine learning models have been developed to support precision/personalized medicine initiatives such as computer-aided detection, diagnosis, prognosis, and treatment planning by using large-scale medical data. Bias and class imbalance represent two of the most pressing challenges for machine learning-based problems, particularly in medical (e.g., oncologic) data sets, due to the limitations in patient numbers, cost, privacy, and security of data sharing, and the complexity of generated data. Depending on the data set and the research question, the methods applied to address class imbalance problems can provide more effective, successful, and meaningful results. This review discusses the essential strategies for addressing and mitigating the class imbalance problems for different medical data types in the oncologic domain.
Language	English
Publishing date	2022-06-12
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14122897
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Article ; Online: Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics.

Tasci, Erdal / Zhuge, Ying / Kaur, Harpreet / Camphausen, Kevin / Krauze, Andra Valentina

International journal of molecular sciences

2022 Volume 23, Issue 22

Abstract: Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to ... ...

Abstract	Determining the aggressiveness of gliomas, termed grading, is a critical step toward treatment optimization to increase the survival rate and decrease treatment toxicity for patients. Streamlined grading using molecular information has the potential to facilitate decision making in the clinic and aid in treatment planning. In recent years, molecular markers have increasingly gained importance in the classification of tumors. In this study, we propose a novel hierarchical voting-based methodology for improving the performance results of the feature selection stage and machine learning models for glioma grading with clinical and molecular predictors. To identify the best scheme for the given soft-voting-based ensemble learning model selections, we utilized publicly available TCGA and CGGA datasets and employed four dimensionality reduction methods to carry out a voting-based ensemble feature selection and five supervised models, with a total of sixteen combination sets. We also compared our proposed feature selection method with the LASSO feature selection method in isolation. The computational results indicate that the proposed method achieves 87.606% and 79.668% accuracy rates on TCGA and CGGA datasets, respectively, outperforming the LASSO feature selection method.
MeSH term(s)	Humans ; Algorithms ; Glioma/genetics ; Machine Learning
Language	English
Publishing date	2022-11-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2019364-6
ISSN	1422-0067 ; 1422-0067 ; 1661-6596
ISSN (online)	1422-0067
ISSN	1422-0067 ; 1661-6596
DOI	10.3390/ijms232214155
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Article: Cost Matrix of Molecular Pathology in Glioma-Towards AI-Driven Rational Molecular Testing and Precision Care for the Future.

Jagasia, Sarisha / Tasci, Erdal / Zhuge, Ying / Camphausen, Kevin / Krauze, Andra Valentina

Biomedicines

2022 Volume 10, Issue 12

Abstract: Gliomas are the most common and aggressive primary brain tumors. Gliomas carry a poor prognosis because of the tumor's resistance to radiation and chemotherapy leading to nearly universal recurrence. Recent advances in large-scale genomic research have ... ...

Abstract	Gliomas are the most common and aggressive primary brain tumors. Gliomas carry a poor prognosis because of the tumor's resistance to radiation and chemotherapy leading to nearly universal recurrence. Recent advances in large-scale genomic research have allowed for the development of more targeted therapies to treat glioma. While precision medicine can target specific molecular features in glioma, targeted therapies are often not feasible due to the lack of actionable markers and the high cost of molecular testing. This review summarizes the clinically relevant molecular features in glioma and the current cost of care for glioma patients, focusing on the molecular markers and meaningful clinical features that are linked to clinical outcomes and have a realistic possibility of being measured, which is a promising direction for precision medicine using artificial intelligence approaches.
Language	English
Publishing date	2022-11-24
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2720867-9
ISSN	2227-9059
ISSN	2227-9059
DOI	10.3390/biomedicines10123029
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Article ; Online: Canine model of electrical conduction recurrence after radiofrequency catheter ablation constructed by CARTO3 and preliminary application evaluation of DOX-L.

Zhuge, Ying / Li, Gonghao / Ge, Yulong / Zhang, Jiajia / Liu, Xiaoqiang / Wang, Junshan / Wang, Fang

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing

2022 Volume 66, Issue 5, Page(s) 1269–1277

Abstract: Background: Radiofrequency catheter ablation (RFCA) is widely used to treat arrhythmias. However, for atrial fibrillation, the recurrence rate after RFCA is still high. The development of an animal model that mimics the recurrence of electrical ... ...

Abstract	Background: Radiofrequency catheter ablation (RFCA) is widely used to treat arrhythmias. However, for atrial fibrillation, the recurrence rate after RFCA is still high. The development of an animal model that mimics the recurrence of electrical conduction after ablation is essential before we can explore the mechanisms involved or develop new therapeutic strategies. Methods: Eighteen beagles aged 12 to 24 months were randomly assigned to this study. RFCA ablation of the right atrial free wall was performed. Then, electrical block and conduction recovery in the ablation area were evaluated using voltage mapping and pacing tests assisted by CARTO3 system. Finally, liposome doxorubicin (DOX-L) was intravenously injected after ablation to investigate the effect of DOX-L on this animal model. Results: The conduction block (CB) rates at 5 min after ablation were 16.7%, 83.3%, and 100%, corresponding to 30w, 35w, and 40w power, respectively. However, after 20 min, the rate of CB was 0%, 33.3%, and 75%; thus, the combined success rate of CB and conduction recurrence was 16.7%, 50%, and 25%, respectively. The optimal ablation parameter is 35 W for 20 s, based on the CB rate, REC rate. After 10 days of ablation, the residual conduction recurrence rate was as high as 83.3% in the RFCA alone group, whereas there was no recurrence with RFCA combined with DOX-L treatment. Conclusions: The novel model accurately simulated the electrical conduction recurrence after cardiac radiofrequency ablation. RFCA combined with DOX-L treatment dramatically reduces the recurrence rate of electrical conduction after ablation.
MeSH term(s)	Animals ; Dogs ; Atrial Fibrillation/surgery ; Catheter Ablation ; Doxorubicin ; Heart Atria/surgery ; Heart Rate ; Treatment Outcome
Chemical Substances	Doxorubicin (80168379AG)
Language	English
Publishing date	2022-12-16
Publishing country	Netherlands
Document type	Journal Article ; Randomized Controlled Trial, Veterinary
ZDB-ID	1329179-8
ISSN	1572-8595 ; 1383-875X
ISSN (online)	1572-8595
ISSN	1383-875X
DOI	10.1007/s10840-022-01433-4
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