1
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Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024; 23:2700-2722. [PMID: 38451675 PMCID: PMC11296931 DOI: 10.1021/acs.jproteome.3c00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
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Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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2
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Ozaki Y, Broughton P, Abdollahi H, Valafar H, Blenda AV. Integrating Omics Data and AI for Cancer Diagnosis and Prognosis. Cancers (Basel) 2024; 16:2448. [PMID: 39001510 PMCID: PMC11240413 DOI: 10.3390/cancers16132448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 06/27/2024] [Accepted: 07/01/2024] [Indexed: 07/16/2024] Open
Abstract
Cancer is one of the leading causes of death, making timely diagnosis and prognosis very important. Utilization of AI (artificial intelligence) enables providers to organize and process patient data in a way that can lead to better overall outcomes. This review paper aims to look at the varying uses of AI for diagnosis and prognosis and clinical utility. PubMed and EBSCO databases were utilized for finding publications from 1 January 2020 to 22 December 2023. Articles were collected using key search terms such as "artificial intelligence" and "machine learning." Included in the collection were studies of the application of AI in determining cancer diagnosis and prognosis using multi-omics data, radiomics, pathomics, and clinical and laboratory data. The resulting 89 studies were categorized into eight sections based on the type of data utilized and then further subdivided into two subsections focusing on cancer diagnosis and prognosis, respectively. Eight studies integrated more than one form of omics, namely genomics, transcriptomics, epigenomics, and proteomics. Incorporating AI into cancer diagnosis and prognosis alongside omics and clinical data represents a significant advancement. Given the considerable potential of AI in this domain, ongoing prospective studies are essential to enhance algorithm interpretability and to ensure safe clinical integration.
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Affiliation(s)
- Yousaku Ozaki
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
| | - Phil Broughton
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
| | - Hamed Abdollahi
- Department of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA;
| | - Homayoun Valafar
- Department of Computer Science and Engineering, Molinaroli College of Engineering and Computing, Columbia, SC 29208, USA;
| | - Anna V. Blenda
- Department of Biomedical Sciences, University of South Carolina School of Medicine Greenville, Greenville, SC 29605, USA; (Y.O.); (P.B.)
- Prisma Health Cancer Institute, Prisma Health, Greenville, SC 29605, USA
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3
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Gim JA. Survival Rate and Chronic Diseases of TCGA Cancer and KoGES Normal Samples by Clustering for DNA Methylation. Life (Basel) 2024; 14:768. [PMID: 38929750 PMCID: PMC11204879 DOI: 10.3390/life14060768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/03/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Insights from public DNA methylation data derived from cancer or normal tissues from cancer patients or healthy people can be obtained by machine learning. The goal is to determine methylation patterns that could be useful for predicting the prognosis for cancer patients and correcting lifestyles for healthy people. DNA methylation data were obtained from the DNA of 446 healthy participants from the Korean Genome Epidemiology Study (KoGES) and from the DNA of normal tissues or from cancer tissues of 11 types of carcinomas from The Cancer Genome Atlas (TCGA) database. To correct for the batch effect, R's ComBat function was used. Using the K-mean clustering (k = 3), the survival rates of the cancer patients and the incidence of chronic diseases were compared between the three clusters for TCGA and KoGES, respectively. Based on the public DNA methylation and clinical data of healthy participants and cancer patients, I present an analysis pipeline that integrates and clusters the methylation data from the two groups. As a result of clustering, CpG sites from gene or genomic regions, such as AFAP1, NINJ2, and HOOK2 genes, that correlated with survival rate and chronic disease are presented.
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Affiliation(s)
- Jeong-An Gim
- Department of Medical Science, Soonchunhyang University, Asan 31538, Republic of Korea
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4
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Ogunleye A, Piyawajanusorn C, Ghislat G, Ballester PJ. Large-Scale Machine Learning Analysis Reveals DNA Methylation and Gene Expression Response Signatures for Gemcitabine-Treated Pancreatic Cancer. HEALTH DATA SCIENCE 2024; 4:0108. [PMID: 38486621 PMCID: PMC10904073 DOI: 10.34133/hds.0108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Accepted: 12/08/2023] [Indexed: 03/17/2024]
Abstract
Background: Gemcitabine is a first-line chemotherapy for pancreatic adenocarcinoma (PAAD), but many PAAD patients do not respond to gemcitabine-containing treatments. Being able to predict such nonresponders would hence permit the undelayed administration of more promising treatments while sparing gemcitabine life-threatening side effects for those patients. Unfortunately, the few predictors of PAAD patient response to this drug are weak, none of them exploiting yet the power of machine learning (ML). Methods: Here, we applied ML to predict the response of PAAD patients to gemcitabine from the molecular profiles of their tumors. More concretely, we collected diverse molecular profiles of PAAD patient tumors along with the corresponding clinical data (gemcitabine responses and clinical features) from the Genomic Data Commons resource. From systematically combining 8 tumor profiles with 16 classification algorithms, each of the resulting 128 ML models was evaluated by multiple 10-fold cross-validations. Results: Only 7 of these 128 models were predictive, which underlines the importance of carrying out such a large-scale analysis to avoid missing the most predictive models. These were here random forest using 4 selected mRNAs [0.44 Matthews correlation coefficient (MCC), 0.785 receiver operating characteristic-area under the curve (ROC-AUC)] and XGBoost combining 12 DNA methylation probes (0.32 MCC, 0.697 ROC-AUC). By contrast, the hENT1 marker obtained much worse random-level performance (practically 0 MCC, 0.5 ROC-AUC). Despite not being trained to predict prognosis (overall and progression-free survival), these ML models were also able to anticipate this patient outcome. Conclusions: We release these promising ML models so that they can be evaluated prospectively on other gemcitabine-treated PAAD patients.
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Affiliation(s)
- Adeolu Ogunleye
- Department of Organismal Biology,
Uppsala University, Uppsala, Sweden
| | | | - Ghita Ghislat
- Department of Life Sciences,
Imperial College London, London, UK
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5
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Olender RT, Roy S, Nishtala PS. Application of machine learning approaches in predicting clinical outcomes in older adults - a systematic review and meta-analysis. BMC Geriatr 2023; 23:561. [PMID: 37710210 PMCID: PMC10503191 DOI: 10.1186/s12877-023-04246-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 08/19/2023] [Indexed: 09/16/2023] Open
Abstract
BACKGROUND Machine learning-based prediction models have the potential to have a considerable positive impact on geriatric care. DESIGN Systematic review and meta-analyses. PARTICIPANTS Older adults (≥ 65 years) in any setting. INTERVENTION Machine learning models for predicting clinical outcomes in older adults were evaluated. A random-effects meta-analysis was conducted in two grouped cohorts, where the predictive models were compared based on their performance in predicting mortality i) under and including 6 months ii) over 6 months. OUTCOME MEASURES Studies were grouped into two groups by the clinical outcome, and the models were compared based on the area under the receiver operating characteristic curve metric. RESULTS Thirty-seven studies that satisfied the systematic review criteria were appraised, and eight studies predicting a mortality outcome were included in the meta-analyses. We could only pool studies by mortality as there were inconsistent definitions and sparse data to pool studies for other clinical outcomes. The area under the receiver operating characteristic curve from the meta-analysis yielded a summary estimate of 0.80 (95% CI: 0.76 - 0.84) for mortality within 6 months and 0.81 (95% CI: 0.76 - 0.86) for mortality over 6 months, signifying good discriminatory power. CONCLUSION The meta-analysis indicates that machine learning models display good discriminatory power in predicting mortality. However, more large-scale validation studies are necessary. As electronic healthcare databases grow larger and more comprehensive, the available computational power increases and machine learning models become more sophisticated; there should be an effort to integrate these models into a larger research setting to predict various clinical outcomes.
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Affiliation(s)
- Robert T Olender
- Department of Life Sciences, University of Bath, Bath, BA2 7AY, UK.
| | - Sandipan Roy
- Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, UK
| | - Prasad S Nishtala
- Department of Life Sciences & Centre for Therapeutic Innovation, University of Bath, Bath, BA2 7AY, UK
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6
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Wang S, Wang S, Wang Z. A survey on multi-omics-based cancer diagnosis using machine learning with the potential application in gastrointestinal cancer. Front Med (Lausanne) 2023; 9:1109365. [PMID: 36703893 PMCID: PMC9871466 DOI: 10.3389/fmed.2022.1109365] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 12/28/2022] [Indexed: 01/12/2023] Open
Abstract
Gastrointestinal cancer is becoming increasingly common, which leads to over 3 million deaths every year. No typical symptoms appear in the early stage of gastrointestinal cancer, posing a significant challenge in the diagnosis and treatment of patients with gastrointestinal cancer. Many patients are in the middle and late stages of gastrointestinal cancer when they feel uncomfortable, unfortunately, most of them will die of gastrointestinal cancer. Recently, various artificial intelligence techniques like machine learning based on multi-omics have been presented for cancer diagnosis and treatment in the era of precision medicine. This paper provides a survey on multi-omics-based cancer diagnosis using machine learning with potential application in gastrointestinal cancer. Particularly, we make a comprehensive summary and analysis from the perspective of multi-omics datasets, task types, and multi-omics-based integration methods. Furthermore, this paper points out the remaining challenges of multi-omics-based cancer diagnosis using machine learning and discusses future topics.
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Affiliation(s)
- Suixue Wang
- School of Information and Communication Engineering, Hainan University, Haikou, China
| | - Shuling Wang
- Department of Neurology, Affiliated Haikou Hospital of Xiangya School of Medicine, Central South University, Haikou, China
| | - Zhengxia Wang
- School of Computer Science and Technology, Hainan University, Haikou, China
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7
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Cheng X, Dai C, Wen Y, Wang X, Bo X, He S, Peng S. NeRD: a multichannel neural network to predict cellular response of drugs by integrating multidimensional data. BMC Med 2022; 20:368. [PMID: 36244991 PMCID: PMC9575288 DOI: 10.1186/s12916-022-02549-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 09/01/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Considering the heterogeneity of tumors, it is a key issue in precision medicine to predict the drug response of each individual. The accumulation of various types of drug informatics and multi-omics data facilitates the development of efficient models for drug response prediction. However, the selection of high-quality data sources and the design of suitable methods remain a challenge. METHODS In this paper, we design NeRD, a multidimensional data integration model based on the PRISM drug response database, to predict the cellular response of drugs. Four feature extractors, including drug structure extractor (DSE), molecular fingerprint extractor (MFE), miRNA expression extractor (mEE), and copy number extractor (CNE), are designed for different types and dimensions of data. A fully connected network is used to fuse all features and make predictions. RESULTS Experimental results demonstrate the effective integration of the global and local structural features of drugs, as well as the features of cell lines from different omics data. For all metrics tested on the PRISM database, NeRD surpassed previous approaches. We also verified that NeRD has strong reliability in the prediction results of new samples. Moreover, unlike other algorithms, when the amount of training data was reduced, NeRD maintained stable performance. CONCLUSIONS NeRD's feature fusion provides a new idea for drug response prediction, which is of great significance for precise cancer treatment.
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Affiliation(s)
- Xiaoxiao Cheng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Chong Dai
- College of Life Science and Technology, Beijing University of Chemical Technology, Beijing, China.,Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Yuqi Wen
- Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China
| | - Xiaoqi Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Xiaochen Bo
- Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China.
| | - Song He
- Department of Biotechnology, Beijing Institute of Health Service and Transfusion Medicine, Beijing, China.
| | - Shaoliang Peng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China. .,The State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University, Changsha, China.
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8
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A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data. JOURNAL OF ONCOLOGY 2022; 2022:9886044. [PMID: 36245971 PMCID: PMC9553508 DOI: 10.1155/2022/9886044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/18/2022] [Accepted: 08/14/2022] [Indexed: 11/18/2022]
Abstract
In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.
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9
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Fan X, Nie X, Huang J, Zhang L, Wang X, Lu M. A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data. JOURNAL OF ONCOLOGY 2022. [DOI: https:/doi.org/10.1155/2022/9886044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.
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Affiliation(s)
- Xin Fan
- Department of Emergency, Shangrao Hospital Affiliated to Nanchang University, Shangrao People’s Hospital, Shangrao 334000, China
- Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Nanchang University, Nanchang 330000, China
| | - Xiyi Nie
- Department of Neurosurgery, Yichun Hospital Affiliated to Nanchang University, Yichun People’s Hospital, Yichun 334000, China
| | - Junwen Huang
- The First Clinical Medical College of Nanchang University, Nanchang 330000, China
| | - Lingling Zhang
- School of Stomatology, Nanchang University, Nanchang 330000, China
| | - Xifu Wang
- Department of Emergency, Shangrao Hospital Affiliated to Nanchang University, Shangrao People’s Hospital, Shangrao 334000, China
| | - Min Lu
- Department of Emergency, Shangrao Hospital Affiliated to Nanchang University, Shangrao People’s Hospital, Shangrao 334000, China
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10
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Fan X, Nie X, Huang J, Zhang L, Wang X, Lu M. A Composite Bioinformatic Analysis to Explore Endoplasmic Reticulum Stress-Related Prognostic Marker and Potential Pathogenic Mechanisms in Glioma by Integrating Multiomics Data. JOURNAL OF ONCOLOGY 2022. [DOI: doi.org/10.1155/2022/9886044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
In recent years, abnormal endoplasmic reticulum stress (ERS) response, as an important regulator of immunity, may play a vital role in the occurrence, development, and treatment of glioma. Weighted correlation network analysis (WGCNA) based on six glioma datasets was used to screen eight prognostic-related differentially expressed ERS-related genes (PR-DE-ERSGs) and to construct a prognostic model. BMP2 and HEY2 were identified as protective factors (HR < 1), and NUP107, DRAM1, F2R, PXDN, RNF19A, and SCG5 were identified as risk factors for glioma (HR > 1). QRT-PCR further supported significantly higher DRAM1 and lower SCG5 relative mRNA expression in gliomas. Our model has demonstrated excellent performance in predicting the prognosis of glioma patients from numerous datasets. In addition, the model shows good stability in multiple tests. Our model also shows broad clinical promise in predicting drug treatment effects. More immune cells/processes in the high-risk population with poor prognosis illustrate the importance of the tumor immunosuppressive environment in glioma. The potential role of the HEY2-based competitive endogenous RNA (ceRNA) regulatory network in glioma was validated and revealed the possible important role of glycolysis in glioma ERS. IDH1 and TP53 mutations with better prognosis were strongly associated with the risk score and PR-DE-ERSGs expression in the model. mDNAsi was also closely related to the risk score and clinical characteristics.
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Affiliation(s)
- Xin Fan
- Department of Emergency, Shangrao Hospital Affiliated to Nanchang University, Shangrao People’s Hospital, Shangrao 334000, China
- Department of Otolaryngology-Head and Neck Surgery, The First Affiliated Hospital of Nanchang University, Nanchang 330000, China
| | - Xiyi Nie
- Department of Neurosurgery, Yichun Hospital Affiliated to Nanchang University, Yichun People’s Hospital, Yichun 334000, China
| | - Junwen Huang
- The First Clinical Medical College of Nanchang University, Nanchang 330000, China
| | - Lingling Zhang
- School of Stomatology, Nanchang University, Nanchang 330000, China
| | - Xifu Wang
- Department of Emergency, Shangrao Hospital Affiliated to Nanchang University, Shangrao People’s Hospital, Shangrao 334000, China
| | - Min Lu
- Department of Emergency, Shangrao Hospital Affiliated to Nanchang University, Shangrao People’s Hospital, Shangrao 334000, China
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11
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He D, Liu Q, Wu Y, Xie L. A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening. NAT MACH INTELL 2022; 4:879-892. [PMID: 38895093 PMCID: PMC11185412 DOI: 10.1038/s42256-022-00541-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Accepted: 09/08/2022] [Indexed: 11/09/2022]
Abstract
Accurate and robust prediction of patient-specific responses to a new compound is critical to personalized drug discovery and development. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell-line screens for predicting clinical responses, their performances are unreliable owing to data heterogeneity and distribution shift. Here we have developed a novel context-aware deconfounding autoencoder (CODE-AE) that can extract intrinsic biological signals masked by context-specific patterns and confounding factors. Extensive comparative studies demonstrated that CODE-AE effectively alleviated the out-of-distribution problem for the model generalization and significantly improved accuracy and robustness over state-of-the-art methods in predicting patient-specific clinical drug responses purely from cell-line compound screens. Using CODE-AE, we screened 59 drugs for 9,808 patients with cancer. Our results are consistent with existing clinical observations, suggesting the potential of CODE-AE in developing personalized therapies and drug response biomarkers.
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Affiliation(s)
- Di He
- PhD program in Computer Science, Graduate Center, City University of New York, New York, NY, USA
| | - Qiao Liu
- Department of Computer Science, Hunter College, City University of New York, New York, NY, USA
| | - You Wu
- PhD program in Computer Science, Graduate Center, City University of New York, New York, NY, USA
| | - Lei Xie
- PhD program in Computer Science, Graduate Center, City University of New York, New York, NY, USA
- Department of Computer Science, Hunter College, City University of New York, New York, NY, USA
- Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, Cornell University, New York, NY, USA
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12
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Cook CJ, Miller AE, Barker TH, Di Y, Fogg KC. Characterizing the extracellular matrix transcriptome of cervical, endometrial, and uterine cancers. Matrix Biol Plus 2022; 15:100117. [PMID: 35898192 PMCID: PMC9309672 DOI: 10.1016/j.mbplus.2022.100117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 07/09/2022] [Accepted: 07/14/2022] [Indexed: 11/17/2022] Open
Abstract
The matrisome plays a critical role in the progression of cancer, but the matrisomes of gynecological cancers have not been well characterized. We built an in silico analysis pipeline to analyze publicly available bulk RNA-seq datasets of cervical, endometrial, and uterine cancers. Using a machine learning approach, we identified genes and gene networks that held inferential significance for cancer stage and patient survival. Cervical, endometrial, and uterine cancers are highly distinct from one another and should be analyzed separately.
Increasingly, the matrisome, a set of proteins that form the core of the extracellular matrix (ECM) or are closely associated with it, has been demonstrated to play a key role in tumor progression. However, in the context of gynecological cancers, the matrisome has not been well characterized. A holistic, yet targeted, exploration of the tumor microenvironment is critical for better understanding the progression of gynecological cancers, identifying key biomarkers for cancer progression, establishing the role of gene expression in patient survival, and for assisting in the development of new targeted therapies. In this work, we explored the matrisome gene expression profiles of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS) using publicly available RNA-seq data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) portal. We hypothesized that the matrisomal expression patterns of CESC, UCEC, and UCS would be highly distinct with respect to genes which are differentially expressed and hold inferential significance with respect to tumor progression, patient survival, or both. Through a combination of statistical and machine learning analysis techniques, we identified sets of genes and gene networks which characterized each of the gynecological cancer cohorts. Our findings demonstrate that the matrisome is critical for characterizing gynecological cancers and transcriptomic mechanisms of cancer progression and outcome. Furthermore, while the goal of pan-cancer transcriptional analyses is often to highlight the shared attributes of these cancer types, we demonstrate that they are highly distinct diseases which require separate analysis, modeling, and treatment approaches. In future studies, matrisome genes and gene ontology terms that were identified as holding inferential significance for cancer stage and patient survival can be evaluated as potential drug targets and incorporated into in vitro models of disease.
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Affiliation(s)
- Carson J Cook
- Department of Bioengineering, Oregon State University, Corvallis, OR 97331, USA
| | - Andrew E Miller
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
| | - Thomas H Barker
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
| | - Yanming Di
- Department of Statistics, Oregon State University, Corvallis, OR 97331, USA
| | - Kaitlin C Fogg
- Department of Bioengineering, Oregon State University, Corvallis, OR 97331, USA.,Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, OR 97201, USA
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13
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Jian H, Zhang Y, Wang J, Chen Z, Wen T. Zeolitic imidazolate framework-based nanoparticles for the cascade enhancement of cancer chemodynamic therapy by targeting glutamine metabolism. NANOSCALE 2022; 14:8727-8743. [PMID: 35674088 DOI: 10.1039/d2nr01736a] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The reprogrammed amino acid metabolism maintains the powerful antioxidant defense and DNA damage repair capacity of cancer cells, which could promote their escape from reactive oxygen species (ROS)-induced damage and inevitably diminish the efficacy of ROS-based therapies. Herein, we propose a strategy to enhance the effect of chemodynamic therapy (CDT) via glutaminolysis-targeted inhibition for cancer cells dependent on abnormal glutamine metabolism. To screen optimum drugs targeting glutamine metabolism, transcriptomic analysis is performed to identify predictive biomarkers. Eventually, telaglenastat (CB-839) is used to block mitochondrial glutaminase 1 (GLS 1) in basal-like breast cancer and loaded into the developed iron-doped zeolitic imidazolate frameworks (ZIF(Fe) NPs) to form ZIF(Fe)&CB nanoparticles, which are able to co-deliver Fe2+ and CB-839 into the tumor. CB-839 induced-glutaminolysis inhibition not only reduces intracellular antioxidants (glutathione, taurine) to amplify Fe2+-induced oxidative stress, but also decreases nucleotide pools (e.g., adenosine, dihydroorotate) to incur the deficiency of building blocks for DNA damage repair, thereby promoting the cell-killing effect of CDT. In vivo assessments further confirm the enhanced anticancer performance and good biocompatibility of ZIF(Fe)&CB nanoparticles. This study provides a promising strategy for the development and improvement of ROS-based anticancer nanosystems.
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Affiliation(s)
- Hui Jian
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yun Zhang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
- Innovation Academy for Green Manufacture, Chinese Academy of Sciences, Beijing 100190, China
| | - Junyue Wang
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhenxiang Chen
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
| | - Tingyi Wen
- CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
- Innovation Academy for Green Manufacture, Chinese Academy of Sciences, Beijing 100190, China
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing 100049, China
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14
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Dong H, Wang X. Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1562511. [PMID: 35432828 PMCID: PMC9010146 DOI: 10.1155/2022/1562511] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/21/2022] [Accepted: 03/23/2022] [Indexed: 11/22/2022]
Abstract
This study aimed to establish an artificial neural network (ANN) model based on prostate cancer signature genes (PCaSGs) to predict the patients with prostate cancer (PCa). In the present study, 270 differentially expressed genes (DEGs) were identified between PCa and normal prostate (NP) groups by differential gene expression analysis. Next, we performed Metascape gene annotation, pathway and process enrichment analysis, and PPI enrichment analysis on all 270 DEGs. Then, we identified and screened out 30 PCaSGs based on the random forest analysis and constructed an ANN model based on the gene score matrix consisting of 30 PCaSGs. Lastly, analysis of microarray dataset GSE46602 showed that the accuracy of this model for predicating PCa and NP samples was 88.9 and 78.6%, respectively. Our results suggested that the ANN model based on PCaSGs can be used for effectively predicting the patients with PCa and will be helpful for early PCa diagnosis and treatment.
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Affiliation(s)
- Hongye Dong
- Department of Kidney Disease and Blood Purifification Center, The Second Hospital of Tianjin Medical University, Tianjin 300211, China
| | - Xu Wang
- Department of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China
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15
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Liang L, Mai S, Mai G, Chen Y, Liu L. DNA damage repair-related gene signature predicts prognosis and indicates immune cell infiltration landscape in skin cutaneous melanoma. Front Endocrinol (Lausanne) 2022; 13:882431. [PMID: 35957812 PMCID: PMC9361349 DOI: 10.3389/fendo.2022.882431] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/27/2022] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND DNA damage repair plays an important role in the onset and progression of cancers and its resistance to treatment therapy. This study aims to assess the prognostic potential of DNA damage repair markers in skin cutaneous melanoma (SKCM). METHOD In this study, we have analyzed the gene expression profiles being downloaded from TCGA, GTEx, and GEO databases. We sequentially used univariate and LASSO Cox regression analyses to screen DNA repair genes associated with prognosis. Then, we have conducted a multivariate regression analysis to construct the prognostic profile of DNA repair-related genes (DRRGs). The risk coefficient is used to calculate the risk scores and divide the patients into two cohorts. Additionally, we validated our prognosis model on an external cohort as well as evaluated the link between immune response and the DRRGs prognostic profiles. The risk signature is compared to immune cell infiltration, chemotherapy, and immune checkpoint inhibitors (ICIs) treatment. RESULTS An analysis using LASSO-Cox stepwise regression established a prognostic signature consisting of twelve DRRGs with strong predictive ability. Disease-specific survival (DSS) is found to be lower among high-risk patients group as compared to low-risk patients. The signature may be employed as an independent prognostic predictor after controlling for clinicopathological factors, as demonstrated by validation on one external GSE65904 cohort. A strong correlation is also found between the risk score and the immune microenvironment, along with the infiltrating immune cells, and ICIs key molecules. The gene enrichment analysis results indicate a wide range of biological activities and pathways to be exhibited by high-risk groups. Furthermore, Cisplatin exhibited a considerable response sensitivity in low-risk groups as opposed to the high-risk incidents, while docetaxel exhibited a considerable response sensitivity in high-risk groups. CONCLUSIONS Our findings provide a thorough investigation of DRRGs to develop an DSS-related prognostic indicator which may be useful in forecasting SKCM progression and enabling more enhanced clinical benefits from immunotherapy.
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Affiliation(s)
- Liping Liang
- Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Shijie Mai
- Department of Thoracic Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Genghui Mai
- Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Ye Chen
- Department of Gastroenterology, State Key Laboratory of Organ Failure Research, Guangdong Provincial Key Laboratory of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, China
- Department of Gastroenterology, Integrated Clinical Microecology Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- *Correspondence: Le Liu, ; Ye Chen,
| | - Le Liu
- Department of Gastroenterology, Integrated Clinical Microecology Center, Shenzhen Hospital, Southern Medical University, Shenzhen, China
- *Correspondence: Le Liu, ; Ye Chen,
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16
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Pidò S, Crovari P, Garzotto F. Modelling the bioinformatics tertiary analysis research process. BMC Bioinformatics 2021; 22:452. [PMID: 34592928 PMCID: PMC8482564 DOI: 10.1186/s12859-021-04310-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2021] [Accepted: 07/29/2021] [Indexed: 11/13/2022] Open
Abstract
Background With the advancements of Next Generation Techniques, a tremendous amount of genomic information has been made available to be analyzed by means of computational methods. Bioinformatics Tertiary Analysis is a complex multidisciplinary process that represents the final step of the whole bioinformatics analysis pipeline. Despite the popularity of the subject, the Bioinformatics Tertiary Analysis process has not yet been specified in a systematic way. The lack of a reference model results into a plethora of technological tools that are designed mostly on the data and not on the human process involved in Tertiary Analysis, making such systems difficult to use and to integrate. Methods To address this problem, we propose a conceptual model that captures the salient characteristics of the research methods and human tasks involved in Bioinformatics Tertiary Analysis. The model is grounded on a user study that involved bioinformatics specialists for the elicitation of a hierarchical task tree representing the Tertiary Analysis process. The outcome was refined and validated using the results of a vast survey of the literature reporting examples of Bioinformatics Tertiary Analysis activities. Results The final hierarchical task tree was then converted into an ontological representation using an ontology standard formalism. The results of our research provides a reference process model for Tertiary Analysis that can be used both to analyze and to compare existing tools, or to design new tools. Conclusions To highlight the potential of our approach and to exemplify its concrete applications, we describe a new bioinformatics tool and how the proposed process model informed its design. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-021-04310-5.
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Affiliation(s)
- Sara Pidò
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
| | - Pietro Crovari
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Franca Garzotto
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
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17
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An X, Chen X, Yi D, Li H, Guan Y. Representation of molecules for drug response prediction. Brief Bioinform 2021; 23:6375515. [PMID: 34571534 DOI: 10.1093/bib/bbab393] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 08/28/2021] [Accepted: 08/30/2021] [Indexed: 12/18/2022] Open
Abstract
The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure-activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.
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Affiliation(s)
- Xin An
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Xi Chen
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Daiyao Yi
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Hongyang Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Yuanfang Guan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
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18
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Feng Z, Li K, Lou J, Wu Y, Peng C. An EMT-Related Gene Signature for Predicting Response to Adjuvant Chemotherapy in Pancreatic Ductal Adenocarcinoma. Front Cell Dev Biol 2021; 9:665161. [PMID: 33996821 PMCID: PMC8119901 DOI: 10.3389/fcell.2021.665161] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 04/13/2021] [Indexed: 12/11/2022] Open
Abstract
Background For pancreatic ductal adenocarcinoma (PDAC) patients, chemotherapy failure is the major reason for postoperative recurrence and poor outcomes. Establishment of novel biomarkers and models for predicting chemotherapeutic efficacy may provide survival benefits by tailoring treatments. Methods Univariate cox regression analysis was employed to identify EMT-related genes with prognostic potential for DFS. These genes were subsequently submitted to LASSO regression analysis and multivariate cox regression analysis to identify an optimal gene signature in TCGA training cohort. The predictive accuracy was assessed by Kaplan–Meier (K-M), receiver operating characteristic (ROC) and calibration curves and was validated in PACA-CA cohort and our local cohort. Pathway enrichment and function annotation analyses were conducted to illuminate the biological implication of this risk signature. Results LASSO and multivariate Cox regression analyses selected an 8-gene signature comprised DLX2, FGF9, IL6R, ITGB6, MYC, LGR5, S100A2, and TNFSF12. The signature had the capability to classify PDAC patients with different DFS, both in the training and validation cohorts. It provided improved DFS prediction compared with clinical indicators. This signature was associated with several cancer-related pathways. In addition, the signature could also predict the response to immune-checkpoint inhibitors (ICIs)-based immunotherapy. Conclusion We established a novel EMT-related gene signature that was capable of predicting therapeutic response to adjuvant chemotherapy and immunotherapy. This signature might facilitate individualized treatment and appropriate management of PDAC patients.
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Affiliation(s)
- Zengyu Feng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Department of General Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Kexian Li
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianyao Lou
- Department of General Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yulian Wu
- Department of General Surgery, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Chenghong Peng
- Department of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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19
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A primer on applying AI synergistically with domain expertise to oncology. Biochim Biophys Acta Rev Cancer 2021; 1876:188548. [PMID: 33901609 DOI: 10.1016/j.bbcan.2021.188548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 04/13/2021] [Accepted: 04/15/2021] [Indexed: 12/24/2022]
Abstract
BACKGROUND The concurrent growth of large-scale oncology data alongside the computational methods with which to analyze and model it has created a promising environment for revolutionizing cancer diagnosis, treatment, prevention, and drug discovery. Computational methods applied to large datasets have accelerated the drug discovery process by reducing bottlenecks and widening the search space beyond what is experimentally tractable. As the research community gains understanding of the myriad genetic underpinnings of cancer via sequencing, imaging, screens, and more that are ingested, transformed, and modeled by top open-source machine learning and artificial intelligence tools readily available, the next big drug candidate might seem merely an "Enter" key away. Of course, the reality is more convoluted, but still promising. SCOPE OF REVIEW We present methods to approach the process of building an AI model, with strong emphasis on the aspects of model development we believe to be crucial to success but that are not commonly discussed: diligence in posing questions, identifying suitable datasets and curating them, and collaborating closely with biology and oncology experts while designing and evaluating the model. Digital pathology, Electronic Health Records, and other data types outside of high-throughput molecular data are reviewed well by others and outside of the scope of this review. This review emphasizes the importance of considering the limitations of the datasets, computational methods, and our minds when designing AI models. For example, datasets can be biased towards areas of research interest, funding, and particular patient populations. Neural networks may learn representations and correlations within the data that are grounded not in biological phenomena, but statistical anomalies erroneously extracted from the training data. Researchers may mis-interpret or over-interpret the output, or design and evaluate the training process such that the resultant model generalizes poorly. Fortunately, awareness of the strengths and limitations of applying data analytics and AI to drug discovery enables us to leverage them carefully and insightfully while maximizing their utility. These applications when performed in close collaboration with domain experts, together with continuous critical evaluation, generation of new data to minimize known blind spots as they are found, and rigorous experimental validation, increases the success rate of the study. We will discuss applications including AI-assisted target identification, drug repurposing, patient stratification, and gene prioritization. MAJOR CONCLUSIONS Data analytics and AI have demonstrated capabilities to revolutionize cancer research, prevention, and treatment by maximizing our understanding and use of the expanding panoply of experimental data. However, to separate promise from true utility, computational tools must be carefully designed, critically evaluated, and constantly improved. Once that is achieved, a human-computer hybrid discovery process will outperform one driven by each alone. GENERAL SIGNIFICANCE This review highlights the challenges and promise of synergizing predictive AI models with human expertise towards greater understanding of cancer.
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20
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An introduction to machine learning for clinicians: How can machine learning augment knowledge in geriatric oncology? J Geriatr Oncol 2021; 12:1159-1163. [PMID: 33795205 DOI: 10.1016/j.jgo.2021.03.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 02/24/2021] [Accepted: 03/18/2021] [Indexed: 12/30/2022]
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21
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Guo E, Wu C, Ming J, Zhang W, Zhang L, Hu G. The Clinical Significance of DNA Damage Repair Signatures in Clear Cell Renal Cell Carcinoma. Front Genet 2021; 11:593039. [PMID: 33488669 PMCID: PMC7820869 DOI: 10.3389/fgene.2020.593039] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 12/04/2020] [Indexed: 12/16/2022] Open
Abstract
DNA damage repair plays an important role in cancer’s initiation and progression, and in therapeutic resistance. The prognostic potential of damage repair indicators was studied in the case of clear cell renal cell carcinoma (ccRCC). Gene expression profiles of the disease were downloaded from cancer genome databases and gene ontology was applied to the DNA repair-related genes. Twenty-six differentially expressed DNA repair genes were identified, and regression analysis was used to identify those with prognostic potential and to construct a risk model. The model accurately predicted patient outcomes and distinguished among patients with different expression levels of immune evasion genes. The data indicate that DNA repair genes can be valuable for predicting the progression of clear cell renal cell carcinoma and the clinical benefits of immunotherapy.
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Affiliation(s)
- Ergang Guo
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan
| | - Cheng Wu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan
| | - Jun Ming
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan
| | - Wei Zhang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan
| | - Linli Zhang
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan
| | - Guoqing Hu
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, Wuhan
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22
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Kumar R, Dhanda SK. Bird Eye View of Protein Subcellular Localization Prediction. Life (Basel) 2020; 10:E347. [PMID: 33327400 PMCID: PMC7764902 DOI: 10.3390/life10120347] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 12/11/2020] [Accepted: 12/11/2020] [Indexed: 12/12/2022] Open
Abstract
Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.
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Affiliation(s)
- Ravindra Kumar
- Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, NIH, 9609 Medical Center Drive, Rockville, MD 20850, USA
| | - Sandeep Kumar Dhanda
- Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
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23
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Hamamoto R, Suvarna K, Yamada M, Kobayashi K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai A, Takasawa K, Bolatkan A, Shozu K, Dozen A, Machino H, Takahashi S, Asada K, Komatsu M, Sese J, Kaneko S. Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine. Cancers (Basel) 2020; 12:E3532. [PMID: 33256107 PMCID: PMC7760590 DOI: 10.3390/cancers12123532] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 11/21/2020] [Accepted: 11/24/2020] [Indexed: 02/07/2023] Open
Abstract
In recent years, advances in artificial intelligence (AI) technology have led to the rapid clinical implementation of devices with AI technology in the medical field. More than 60 AI-equipped medical devices have already been approved by the Food and Drug Administration (FDA) in the United States, and the active introduction of AI technology is considered to be an inevitable trend in the future of medicine. In the field of oncology, clinical applications of medical devices using AI technology are already underway, mainly in radiology, and AI technology is expected to be positioned as an important core technology. In particular, "precision medicine," a medical treatment that selects the most appropriate treatment for each patient based on a vast amount of medical data such as genome information, has become a worldwide trend; AI technology is expected to be utilized in the process of extracting truly useful information from a large amount of medical data and applying it to diagnosis and treatment. In this review, we would like to introduce the history of AI technology and the current state of medical AI, especially in the oncology field, as well as discuss the possibilities and challenges of AI technology in the medical field.
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Affiliation(s)
- Ryuji Hamamoto
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Kruthi Suvarna
- Indian Institute of Technology Bombay, Powai, Mumbai 400 076, India;
| | - Masayoshi Yamada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Department of Endoscopy, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku Tokyo 104-0045, Japan
| | - Kazuma Kobayashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Norio Shinkai
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Mototaka Miyake
- Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Masamichi Takahashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Shunichi Jinnai
- Department of Dermatologic Oncology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan;
| | - Ryo Shimoyama
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
| | - Akira Sakai
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Department of NCC Cancer Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8510, Japan
| | - Ken Takasawa
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Amina Bolatkan
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Kanto Shozu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
| | - Ai Dozen
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
| | - Hidenori Machino
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Satoshi Takahashi
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Ken Asada
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Masaaki Komatsu
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
| | - Jun Sese
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Humanome Lab, 2-4-10 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan
| | - Syuzo Kaneko
- Division of Molecular Modification and Cancer Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo 104-0045, Japan; (M.Y.); (K.K.); (N.S.); (M.T.); (R.S.); (A.S.); (K.T.); (A.B.); (K.S.); (A.D.); (H.M.); (S.T.); (K.A.); (M.K.); (J.S.); (S.K.)
- Cancer Translational Research Team, RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan
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Yoon BJ, Qian X, Kahveci T, Pal R. Selected Research Articles from the 2019 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC). BMC Genomics 2020; 21:584. [PMID: 32900374 PMCID: PMC7487676 DOI: 10.1186/s12864-020-06934-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Byung-Jun Yoon
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA.
- TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering (CBGSE), College Station, TX, 77845, USA.
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, 11973, USA.
| | - Xiaoning Qian
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, 77843, USA
- TEES-AgriLife Center for Bioinformatics and Genomic Systems Engineering (CBGSE), College Station, TX, 77845, USA
| | - Tamer Kahveci
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611-6125, USA
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409-3102, USA
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