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Snijesh VP, Nimbalkar VP, Patil S, Rajarajan S, Anupama CE, Mahalakshmi S, Alexander A, Soundharya R, Ramesh R, Srinath BS, Jolly MK, Prabhu JS. Differential role of glucocorticoid receptor based on its cell type specific expression on tumor cells and infiltrating lymphocytes. Transl Oncol 2024; 45:101957. [PMID: 38643748 PMCID: PMC11039344 DOI: 10.1016/j.tranon.2024.101957] [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: 12/20/2023] [Revised: 03/12/2024] [Accepted: 04/03/2024] [Indexed: 04/23/2024] Open
Abstract
BACKGROUND The glucocorticoid receptor (GR) is frequently expressed in breast cancer (BC), and its prognostic implications are contingent on estrogen receptor (ER) status. To address conflicting reports and explore therapeutic potential, a GR signature (GRsig) independent of ER status was developed. We also investigated cell type-specific GR protein expression in BC tumor epithelial cells and infiltrating lymphocytes. METHODS GRsig was derived from Dexamethasone treated cell lines through a bioinformatic pipeline. Immunohistochemistry assessed GR protein expression. Associations between GRsig and tumor phenotypes (proliferation, cytolytic activity (CYT), immune cell distribution, and epithelial-to-mesenchymal transition (EMT) were explored in public datasets. Single-cell RNA sequencing data evaluated context-dependent GR roles, and a cell type-specific prognostic role was assessed in an independent BC cohort. RESULTS High GRsig levels were associated with a favorable prognosis across BC subtypes. Tumor-specific high GRsig correlated with lower proliferation, increased CYT, and anti-tumorigenic immune cells. Single-cell data analysis revealed higher GRsig expression in immune cells, negatively correlating with EMT while a positive correlation was observed with EMT primarily in tumor and stromal cells. Univariate and multivariate analyses demonstrated the robust and independent predictive capability of GRsig for favorable prognosis. GR protein expression on immune cells in triple-negative tumors indicated a favorable prognosis. CONCLUSION This study underscores the cell type-specific role of GR, where its expression on tumor cells is associated with aggressive features like EMT, while in infiltrating lymphocytes, it predicts a better prognosis, particularly within TNBC tumors. The GRsig emerges as a promising independent prognostic indicator across diverse BC subtypes.
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Affiliation(s)
- V P Snijesh
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India; Centre for Doctoral Studies, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - Vidya P Nimbalkar
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - Sharada Patil
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - Savitha Rajarajan
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India; Centre for Doctoral Studies, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - C E Anupama
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - S Mahalakshmi
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - Annie Alexander
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India
| | - Ramu Soundharya
- IISc Mathematics Initiative, Indian Institute of Science, Bangalore, Karnataka-560012, India
| | - Rakesh Ramesh
- Department of Surgical Oncology, St. John's Medical College and Hospital, Bangalore, Karnataka, India
| | - B S Srinath
- Department of Surgery, Sri Shankara Cancer Hospital and Research Centre, Bangalore, Karnataka, India
| | - Mohit Kumar Jolly
- Department of Bioengineering, Indian Institute of Science, Bangalore, Karnataka-560012, India
| | - Jyothi S Prabhu
- Division of Molecular Medicine, St. John's Research Institute, St. John's Medical College, Bangalore, Karnataka, India.
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Sarhadi S, Armani A, Jafari-Gharabaghlou D, Sadeghi S, Zarghami N. Cross-platform gene expression profiling of breast cancer: Exploring the relationship between breast cancer grades and gene expression pattern. Heliyon 2024; 10:e29736. [PMID: 38681607 PMCID: PMC11053269 DOI: 10.1016/j.heliyon.2024.e29736] [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: 05/12/2023] [Revised: 04/12/2024] [Accepted: 04/15/2024] [Indexed: 05/01/2024] Open
Abstract
Gene expression profiling is a powerful tool that has been extensively used to investigate the underlying biology and etiology of diseases, including cancer. Microarray gene expression analysis enables simultaneous measurement of thousands of mRNA levels. Sophisticated computational approaches have evolved in parallel with the rapid progress in bioassay technologies, enabling more effective analysis of the large and complex datasets that these technologies produce. In this study, we utilized systems biology approaches to examine gene expression profiles across different grades of breast cancer progression. We conducted a meta-analysis of publicly available microarray data to elucidate the molecular mechanisms underlying breast cancer grade classification. Our results suggest that while grade index is commonly used for evaluating cancer progression status in the clinic, the complexity of molecular mechanisms, histological characteristics, and other factors related to patient outcomes raises doubts about the utility of breast cancer grades as a foundation for formulating treatment protocols. Our study underscores the importance of advancing personalized strategies for breast cancer classification and management. More research is crucial to refine diagnostic tools and treatment modalities, aiming for greater precision and tailored care in patient outcomes.
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Affiliation(s)
- Shamim Sarhadi
- Institute of Clinical Chemistry and Pathobiochemistry, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Germany
| | - Arta Armani
- Department of Medical Biology and Genetic, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
| | - Davoud Jafari-Gharabaghlou
- Department of Clinical Biochemistry and Laboratory Medicine, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Somayeh Sadeghi
- Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Nosratollah Zarghami
- Department of Clinical Biochemistry and Laboratory Medicine, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
- Department of Medical Biochemistry, Faculty of Medicine, Istanbul Aydin University, Istanbul, Turkey
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Li Y, Li P, Liu Y, Geng W. A novel gene-based model for prognosis prediction of head and neck squamous cell carcinoma. Heliyon 2024; 10:e29449. [PMID: 38660262 PMCID: PMC11040035 DOI: 10.1016/j.heliyon.2024.e29449] [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: 04/05/2024] [Accepted: 04/08/2024] [Indexed: 04/26/2024] Open
Abstract
Background Head and neck squamous cell carcinoma (HNSCC) is a significant global health challenge. The identification of reliable prognostic biomarkers and construction of an accurate prognostic model are crucial. Methods In this study, mRNA expression data and clinical data of HNSCC patients from The Cancer Genome Atlas were used. Overlapping candidate genes (OCGs) were identified by intersecting differentially expressed genes and prognosis-related genes. Best prognostic genes were selected using the least absolute shrinkage and selection operator Cox regression based on OCGs, and a risk score was developed using the Cox coefficient of each gene. The prognostic power of the risk score was assessed using Kaplan-Meier survival analysis and time-dependent receiver operating characteristic analysis. Univariate and multivariate Cox regression were performed to identify independent prognostic parameters, which were used to construct a nomogram. The predictive accuracy of the nomogram was evaluated using calibration plots. Functional enrichment analysis of risk score related genes was performed to explore the potential biological functions and pathways. External validation was conducted using data from the Gene Expression Omnibus and ArrayExpress databases. Results FADS3, TNFRSF12A, TJP3, and FUT6 were screened to be significantly related to prognosis in HNSCC patients. The risk score effectively stratified patients into high-risk group with poor overall survival (OS) and low-risk group with better OS. Risk score, age, clinical M stage and clinical N stage were regarded as independent prognostic parameters by Cox regression analysis and used to construct a nomogram. The nomogram performed well in 1-, 2-, 3-, 5- and 10-year survival predictions. Functional enrichment analysis suggested that tight junction was closely related to the cancer. In addition, the prognostic power of the risk score was validated by external datasets. Conclusions This study constructed a gene-based model integrating clinical prognostic parameters to accurately predict prognosis in HNSCC patients.
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Affiliation(s)
- Yanxi Li
- Department of Dental Implant Center, Beijing Stomatological Hospital, School of Stomatology, Capital Medical University, Beijing, 100050, China
| | - Peiran Li
- Department of Maxillofacial Surgery, Beijing Stomatological Hospital, School of Stomatology, Capital Medical University, Beijing, 100050, China
| | - Yuqi Liu
- Department of Dental Implant Center, Beijing Stomatological Hospital, School of Stomatology, Capital Medical University, Beijing, 100050, China
| | - Wei Geng
- Department of Dental Implant Center, Beijing Stomatological Hospital, School of Stomatology, Capital Medical University, Beijing, 100050, China
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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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Yiong CS, Lin TP, Lim VY, Toh TB, Yang VS. Biomarkers for immune checkpoint inhibition in sarcomas - are we close to clinical implementation? Biomark Res 2023; 11:75. [PMID: 37612756 PMCID: PMC10463641 DOI: 10.1186/s40364-023-00513-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 07/26/2023] [Indexed: 08/25/2023] Open
Abstract
Sarcomas are a group of diverse and complex cancers of mesenchymal origin that remains poorly understood. Recent developments in cancer immunotherapy have demonstrated a potential for better outcomes with immune checkpoint inhibition in some sarcomas compared to conventional chemotherapy. Immune checkpoint inhibitors (ICIs) are key agents in cancer immunotherapy, demonstrating improved outcomes in many tumor types. However, most patients with sarcoma do not benefit from treatment, highlighting the need for identification and development of predictive biomarkers for response to ICIs. In this review, we first discuss United States (US) Food and Drug Administration (FDA)-approved and European Medicines Agency (EMA)-approved biomarkers, as well as the limitations of their use in sarcomas. We then review eight potential predictive biomarkers and rationalize their utility in sarcomas. These include gene expression signatures (GES), circulating neutrophil-to-lymphocyte ratio (NLR), indoleamine 2,3-dioxygenase (IDO), lymphocyte activation gene 3 (LAG-3), T cell immunoglobin and mucin domain-containing protein 3 (TIM-3), TP53 mutation status, B cells, and tertiary lymphoid structures (TLS). Finally, we discuss the potential for TLS as both a predictive and prognostic biomarker for ICI response in sarcomas to be implemented in the clinic.
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Affiliation(s)
- Chin Sern Yiong
- Translational Precision Oncology Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore
- Department of Pharmacy, National University of Singapore, Singapore, 117544, Singapore
| | - Tzu Ping Lin
- Translational Precision Oncology Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore
- Department of Pharmacy, National University of Singapore, Singapore, 117544, Singapore
| | - Vivian Yujing Lim
- Translational Precision Oncology Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore
| | - Tan Boon Toh
- The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
- The Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
| | - Valerie Shiwen Yang
- Translational Precision Oncology Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, 138673, Singapore.
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, 169610, Singapore.
- Duke-NUS Medical School, Oncology Academic Clinical Program, Singapore, 169857, Singapore.
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Mirza Z, Ansari MS, Iqbal MS, Ahmad N, Alganmi N, Banjar H, Al-Qahtani MH, Karim S. Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis. Cancers (Basel) 2023; 15:3237. [PMID: 37370847 DOI: 10.3390/cancers15123237] [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: 05/15/2023] [Revised: 06/10/2023] [Accepted: 06/13/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. METHODS A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan-Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. RESULTS We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. CONCLUSION The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.
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Affiliation(s)
- Zeenat Mirza
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Department of Medical Laboratory Science, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Md Shahid Ansari
- Department of Clinical Data Analytics, Max Super Speciality Hospital, Saket, New Delhi 110017, India
| | - Md Shahid Iqbal
- Department of Statistics and Computer Applications, Tilka Manjhi Bhagalpur University, Bhagalpur 812007, India
| | - Nesar Ahmad
- Department of Statistics and Computer Applications, Tilka Manjhi Bhagalpur University, Bhagalpur 812007, India
| | - Nofe Alganmi
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Haneen Banjar
- Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mohammed H Al-Qahtani
- Department of Medical Laboratory Science, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Sajjad Karim
- Department of Medical Laboratory Science, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Xiao J, Wang G, Zhu C, Liu K, Wang Y, Shen K, Fan H, Ma X, Xu Z, Yang L. A thirty-three gene-based signature predicts lymph node metastasis and prognosis in patients with gastric cancer. Heliyon 2023; 9:e17017. [PMID: 37484383 PMCID: PMC10361117 DOI: 10.1016/j.heliyon.2023.e17017] [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: 07/11/2022] [Revised: 06/01/2023] [Accepted: 06/04/2023] [Indexed: 07/25/2023] Open
Abstract
Recently, several studies have indicated the great potential of gene expression signature of the primary tumor in predicting lymph node metastasis; however, few current gene biomarkers can predict lymph node status and prognosis in gastric cancer (GC). Thus, we used the RNA-seq data from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes between pathological lymph node-negative (pN0) and positive (pN+) patients and to establish a gene signature that could predict lymph node metastasis. Meanwhile, the robustness of identified gene signatures was validated in an independent dataset Asian Cancer Research Group (n = 300). In this study, our thirty-three gene-based signature was highly correlated with lymph node metastasis and could successfully discriminate pN + patients in the training set (Area under the receiver operating characteristic curve = 0.951). Moreover, Disease-free survival (P = 0.0029) and overall survival (P = 0.026) were significantly worse in high-risk compared with low-risk patients overall and when confined to pN0 patients only (P < 0.0001). Of note, this gene signature also proved useful in predicting lymph node status and survival in the validation cohort. The present study suggests a thirty-three gene-based signature that could effectively predict lymph node metastasis and prognosis in GC.
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Affiliation(s)
- Jian Xiao
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Gang Wang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Chuming Zhu
- Department of General Surgery, Liyang People's Hospital, Liyang Branch Hospital of Jiangsu Province Hospital, Liyang, Jiangsu Province, China
| | - Kanghui Liu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Yuanhang Wang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Kuan Shen
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Hao Fan
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Xiang Ma
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Zekuan Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
| | - Li Yang
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China
- Department of General Surgery, Liyang People's Hospital, Liyang Branch Hospital of Jiangsu Province Hospital, Liyang, Jiangsu Province, China
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de Olazarra AS, Wang SX. Advances in point-of-care genetic testing for personalized medicine applications. BIOMICROFLUIDICS 2023; 17:031501. [PMID: 37159750 PMCID: PMC10163839 DOI: 10.1063/5.0143311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 04/12/2023] [Indexed: 05/11/2023]
Abstract
Breakthroughs within the fields of genomics and bioinformatics have enabled the identification of numerous genetic biomarkers that reflect an individual's disease susceptibility, disease progression, and therapy responsiveness. The personalized medicine paradigm capitalizes on these breakthroughs by utilizing an individual's genetic profile to guide treatment selection, dosing, and preventative care. However, integration of personalized medicine into routine clinical practice has been limited-in part-by a dearth of widely deployable, timely, and cost-effective genetic analysis tools. Fortunately, the last several decades have been characterized by tremendous progress with respect to the development of molecular point-of-care tests (POCTs). Advances in microfluidic technologies, accompanied by improvements and innovations in amplification methods, have opened new doors to health monitoring at the point-of-care. While many of these technologies were developed with rapid infectious disease diagnostics in mind, they are well-suited for deployment as genetic testing platforms for personalized medicine applications. In the coming years, we expect that these innovations in molecular POCT technology will play a critical role in enabling widespread adoption of personalized medicine methods. In this work, we review the current and emerging generations of point-of-care molecular testing platforms and assess their applicability toward accelerating the personalized medicine paradigm.
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Affiliation(s)
- A. S. de Olazarra
- Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA
| | - S. X. Wang
- Author to whom correspondence should be addressed:
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Fajarda O, Almeida JR, Duarte-Pereira S, Silva RM, Oliveira JL. Methodology to identify a gene expression signature by merging microarray datasets. Comput Biol Med 2023; 159:106867. [PMID: 37060770 DOI: 10.1016/j.compbiomed.2023.106867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/01/2023] [Accepted: 03/30/2023] [Indexed: 04/17/2023]
Abstract
A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.
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Affiliation(s)
- Olga Fajarda
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal.
| | - João Rafael Almeida
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Computation, University of A Coruña, A Coruña, Spain.
| | - Sara Duarte-Pereira
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Medical Sciences and iBiMED-Institute of Biomedicine, University of Aveiro, Aveiro, Portugal.
| | - Raquel M Silva
- Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal.
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Weaver C, Bin Satter K, Richardson KP, Tran LKH, Tran PMH, Purohit S. Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma. Biomedicines 2022; 10:biomedicines10112953. [PMID: 36428521 PMCID: PMC9687861 DOI: 10.3390/biomedicines10112953] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022] Open
Abstract
Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.
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Affiliation(s)
- Chaston Weaver
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th St., Augusta, GA 30912, USA
| | - Khaled Bin Satter
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th St., Augusta, GA 30912, USA
| | - Katherine P. Richardson
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th St., Augusta, GA 30912, USA
- Department of Interdisciplinary Health Science, College of Allied Health Sciences, Augusta University, 1120 15th St., Augusta, GA 30912, USA
| | - Lynn K. H. Tran
- Department of Urology, Baylor College of Medicine, Houston, TX 76798, USA
| | - Paul M. H. Tran
- Department of Internal Medicine, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Sharad Purohit
- Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, 1120 15th St., Augusta, GA 30912, USA
- Department of Interdisciplinary Health Science, College of Allied Health Sciences, Augusta University, 1120 15th St., Augusta, GA 30912, USA
- Department of Undergraduate Health Professionals, College of Allied Health Sciences, Augusta University, 1120 15th St., Augusta, GA 30912, USA
- Department of Obstetrics and Gynecology, Medical College of Georgia, Augusta University, 1120 15th St., Augusta, GA 30912, USA
- Correspondence:
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Olivier T, Prasad V. Molecular testing to deliver personalized chemotherapy recommendations: risking over and undertreatment. BMC Med 2022; 20:392. [PMID: 36348413 PMCID: PMC9644653 DOI: 10.1186/s12916-022-02589-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 09/28/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In the adjuvant setting of cancer treatment, de-escalation strategies have the goal of omitting or minimizing treatment in patients, without compromising outcomes. Historically, eligibility for adjuvant treatment solely relied on the patient's clinical and tumor's pathological characteristics. At the turn of the century, based on new biological understanding, molecular-based strategies were tested and sometimes implemented. MAIN BODY However, we illustrate how molecularly based de-escalation strategies may paradoxically lead to overtreatment. This may happen when the novel approach is tested in lieu of standard management and may not yield the same results when being implemented in addition to usual practice. In the DYNAMIC trial, adjuvant chemotherapy decision in stage II colon cancer was compared between a circulating tumor DNA (ctDNA)-based approach and the standard care. We show this may result in more patients receiving oxaliplatin-based chemotherapy and may expose a similar proportion of patients to chemotherapy if the novel strategy is implemented in addition to usual practice. The other potential risk is undertreatment. We provide an illustration of early breast cancer, where the decision of adjuvant chemotherapy based on the gene expression signature MammaPrint may lead to inferior outcomes as compared with the clinico-pathologic strategy. This may also happen when non-inferiority designs have large margins. Among solutions, it should be acknowledged that clinico-pathological features, like T4 in colon cancer, may not be abandoned and replaced by novel strategies in real-life practice. Therefore, novel strategies should be tested in addition to standard of care, and not in lieu of. Second, de-escalation trials should focus on the settings where the standard of care has a widespread agreement. This would avoid the risk of testing non-inferiority against an ineffective therapy, which guarantees successes without providing informative data. CONCLUSION Simply because a molecular test is rational does not mean it can improve patient outcomes. Here, we highlight how molecular test-based strategies may result in either overtreatment or undertreatment. In the rapidly evolving field of medicine, where technological advances may be transformative, our piece highlights scientific pitfalls to be aware of when considering running such trials or before implementing novel strategies in daily practice.
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Affiliation(s)
- Timothée Olivier
- Department of Oncology, Geneva University Hospital, 4 Gabrielle-Perret-Gentil Street, 1205, Geneva, Switzerland.
- Department of Epidemiology and Biostatistics, University of California San Francisco, 550 16th St., 2nd Fl, San Francisco, CA, 94158, USA.
| | - Vinay Prasad
- Department of Epidemiology and Biostatistics, University of California San Francisco, 550 16th St., 2nd Fl, San Francisco, CA, 94158, USA
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Tian XM, Xiang B, Jin LM, Mi T, Wang JK, Zhanghuang C, Zhang ZX, Chen ML, Shi QL, Liu F, Lin T, Wei GH. Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with Wilms tumour. Front Immunol 2022; 13:920666. [PMID: 36172369 PMCID: PMC9510599 DOI: 10.3389/fimmu.2022.920666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 08/22/2022] [Indexed: 11/13/2022] Open
Abstract
Wilms tumour (WT) is the most common kidney malignancy in children. Chemoresistance is the leading cause of tumour recurrence and poses a substantial therapeutic challenge. Increasing evidence has underscored the role of the tumour immune microenvironment (TIM) in cancers and the potential for immunotherapy to improve prognosis. There remain no reliable molecular markers for reflecting the immune landscape and predicting patient survival in WT. Here, we examine differences in gene expression by high-throughput RNA sequencing, focused on differentially expressed immune-related genes (IRGs) based on the ImmPort database. Via univariate Cox regression analysis and Lasso-penalized Cox regression analysis, IRGs were screened out to establish an immune signature. Kaplan-Meier curves, time-related ROC analysis, univariate and multivariate Cox regression studies, and nomograms were used to evaluate the accuracy and prognostic significance of this signature. Furthermore, we found that the immune signature could reflect the immune status and the immune cell infiltration character played in the tumour microenvironment (TME) and showed significant association with immune checkpoint molecules, suggesting that the poor outcome may be partially explained by its immunosuppressive TME. Remarkably, TIDE, a computational method to model tumour immune evasion mechanisms, showed that this signature holds great potential for predicting immunotherapy responses in the TARGET-wt cohort. To decipher the underlying mechanism, GSEA was applied to explore enriched pathways and biological processes associated with immunophenotyping and Connectivity map (CMap) along with DeSigN analysis for drug exploration. Finally, four candidate immune genes were selected, and their expression levels in WT cell lines were monitored via qRT-PCR. Meanwhile, we validated the function of a critical gene, NRP2. Taken together, we established a novel immune signature that may serve as an effective prognostic signature and predictive biomarker for immunotherapy response in WT patients. This study may give light on therapeutic strategies for WT patients from an immunological viewpoint.
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Affiliation(s)
- Xiao-Mao Tian
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
| | - Bin Xiang
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
| | - Li-Ming Jin
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
| | - Tao Mi
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
| | - Jin-Kui Wang
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
| | - Chenghao Zhanghuang
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
| | - Zhao-Xia Zhang
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
| | - Mei-Ling Chen
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
| | - Qin-Lin Shi
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
| | - Feng Liu
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
- *Correspondence: Feng Liu,
| | - Tao Lin
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Guang-Hui Wei
- Department of Urology, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Chongqing Key Laboratory of Children Urogenital Development and Tissue Engineering, Chongqing, China
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Prostate Cancer Secretome and Membrane Proteome from Pten Conditional Knockout Mice Identify Potential Biomarkers for Disease Progression. Int J Mol Sci 2022; 23:ijms23169224. [PMID: 36012492 PMCID: PMC9409251 DOI: 10.3390/ijms23169224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/10/2022] [Accepted: 08/12/2022] [Indexed: 12/24/2022] Open
Abstract
Prostate cancer (PCa) is the second most common cause of mortality among men. Tumor secretome is a promising strategy for understanding the biology of tumor cells and providing markers for disease progression and patient outcomes. Here, transcriptomic-based secretome analysis was performed on the PCa tumor transcriptome of Genetically Engineered Mouse Model (GEMM) Pb-Cre4/Ptenf/f mice to identify potentially secreted and membrane proteins—PSPs and PMPs. We combined a selection of transcripts from the GSE 94574 dataset and a list of protein-coding genes of the secretome and membrane proteome datasets using the Human Protein Atlas Secretome. Notably, nine deregulated PMPs and PSPs were identified in PCa (DMPK, PLN, KCNQ5, KCNQ4, MYOC, WIF1, BMP7, F3, and MUC1). We verified the gene expression patterns of Differentially Expressed Genes (DEGs) in normal and tumoral human samples using the GEPIA tool. DMPK, KCNQ4, and WIF1 targets were downregulated in PCa samples and in the GSE dataset. A significant association between shorter survival and KCNQ4, PLN, WIF1, and F3 expression was detected in the MSKCC dataset. We further identified six validated miRNAs (mmu-miR-6962-3p, mmu-miR- 6989-3p, mmu-miR-6998-3p, mmu-miR-5627-5p, mmu-miR-15a-3p, and mmu-miR-6922-3p) interactions that target MYOC, KCNQ5, MUC1, and F3. We have characterized the PCa secretome and membrane proteome and have spotted new dysregulated target candidates in PCa.
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Bioinformatics Analysis Revealing the Correlation between NF-κB Signaling Pathway and Immune Infiltration in Gastric Cancer. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5385456. [PMID: 35936362 PMCID: PMC9352505 DOI: 10.1155/2022/5385456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 07/05/2022] [Accepted: 07/13/2022] [Indexed: 12/24/2022]
Abstract
Although the emerging of immunotherapy conferred a new landscape of gastric cancer (GC) treatment, its response rate was of significant individual differences. Insight into GC immune microenviroment may contribute to breaking the dilemma. To this end, the enrichment score of NF-κB signaling pathway was calculated in each GC sample from The Cancer Genome Atlas (TCGA) via ssGSEA algorithm, and its association with immune infiltration was estimated. Based on NF-κB-related genes, a risk score was established and its involvement in immune infiltration, tumor mutational burden (TMB), and N6-methyladenosine (M6A) modification was analyzed in GC. The results showed that NF-κB signaling pathway promoted the infiltration of immune cells in GC. In addition, GC samples were divided into low- and high-risk groups according to a seven-gene (CARD11, CCL21, GADD45B, LBP, RELB, TRAF1, and VCAM1) risk score. Although the high-risk group displayed high immune infiltration and high expression of M6A regulatory genes, it remains in an immunosuppressive microenviroment and whereby suffers a poorer outcome. Of note, most of hub genes were related to immune infiltration and could serve as an independent prognostic biomarker. Conclusively, our study emphasized the crucial role of NF-κB signaling pathway in GC immune microenviroment and provided several candidate genes that may participate in immune infiltration.
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15
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Bakr MN, Takahashi H, Kikuchi Y. Analysis of Melanoma Gene Expression Signatures at the Single-Cell Level Uncovers 45-Gene Signature Related to Prognosis. Biomedicines 2022; 10:biomedicines10071478. [PMID: 35884783 PMCID: PMC9313451 DOI: 10.3390/biomedicines10071478] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 06/12/2022] [Accepted: 06/19/2022] [Indexed: 11/16/2022] Open
Abstract
Since the current melanoma clinicopathological staging system remains restricted to predicting survival outcomes, establishing precise prognostic targets is needed. Here, we used gene expression signature (GES) classification and Cox regression analyses to biologically characterize melanoma cells at the single-cell level and construct a prognosis-related gene signature for melanoma. By analyzing publicly available scRNA-seq data, we identified six distinct GESs (named: “Anti-apoptosis”, “Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, “Extracellular structure organization”, and “Epithelial-Mesenchymal Transition (EMT)”). We verified these GESs in the bulk RNA-seq data of patients with skin cutaneous melanoma (SKCM) from The Cancer Genome Atlas (TCGA). Four GESs (“Immune cell interactions”, “Melanogenesis”, “Ribosomal biogenesis”, and “Extracellular structure organization”) were significantly correlated with prognosis (p = 1.08 × 10−5, p = 0.042, p = 0.001, and p = 0.031, respectively). We identified a prognostic signature of melanoma composed of 45 genes (MPS_45). MPS_45 was validated in TCGA-SKCM (HR = 1.82, p = 9.08 × 10−6) and three other melanoma datasets (GSE65904: HR = 1.73, p = 0.006; GSE19234: HR = 3.83, p = 0.002; and GSE53118: HR = 1.85, p = 0.037). MPS_45 was independently associated with survival (p = 0.002) and was proved to have a high potential for predicting prognosis in melanoma patients.
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Affiliation(s)
- Mohamed Nabil Bakr
- Department of Biological Science, Graduate School of Science, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan;
- National Institute of Oceanography and Fisheries (NIOF), Cairo 11516, Egypt
| | - Haruko Takahashi
- Department of Biological Science, Graduate School of Science, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan;
- Graduate School of Integrated Sciences for Life, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan
- Correspondence: (H.T.); (Y.K.); Tel.: +81-82-424-7440 (Y.K.)
| | - Yutaka Kikuchi
- Department of Biological Science, Graduate School of Science, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan;
- Graduate School of Integrated Sciences for Life, Hiroshima University, Kagamiyama 1-3-1, Higashi-Hiroshima, Hiroshima 739-8526, Japan
- Correspondence: (H.T.); (Y.K.); Tel.: +81-82-424-7440 (Y.K.)
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Vignet P, Coquet J, Auber S, Boudet M, Siegel A, Théret N. Discrete modeling for integration and analysis of large-scale signaling networks. PLoS Comput Biol 2022; 18:e1010175. [PMID: 35696426 PMCID: PMC9232147 DOI: 10.1371/journal.pcbi.1010175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 06/24/2022] [Accepted: 05/06/2022] [Indexed: 11/18/2022] Open
Abstract
Most biological processes are orchestrated by large-scale molecular networks which are described in large-scale model repositories and whose dynamics are extremely complex. An observed phenotype is a state of this system that results from control mechanisms whose identification is key to its understanding. The Biological Pathway Exchange (BioPAX) format is widely used to standardize the biological information relative to regulatory processes. However, few modeling approaches developed so far enable for computing the events that control a phenotype in large-scale networks. Here we developed an integrated approach to build large-scale dynamic networks from BioPAX knowledge databases in order to analyse trajectories and to identify sets of biological entities that control a phenotype. The Cadbiom approach relies on the guarded transitions formalism, a discrete modeling approach which models a system dynamics by taking into account competition and cooperation events in chains of reactions. The method can be applied to every BioPAX (large-scale) model thanks to a specific package which automatically generates Cadbiom models from BioPAX files. The Cadbiom framework was applied to the BioPAX version of two resources (PID, KEGG) of the Pathway Commons database and to the Atlas of Cancer Signalling Network (ACSN). As a case-study, it was used to characterize sets of biological entities implicated in the epithelial-mesenchymal transition. Our results highlight the similarities between the PID and ACSN resources in terms of biological content, and underline the heterogeneity of usage of the BioPAX semantics limiting the fusion of models that require curation. Causality analyses demonstrate the smart complementarity of the databases in terms of combinatorics of controllers that explain a phenotype. From a biological perspective, our results show the specificity of controllers for epithelial and mesenchymal phenotypes that are consistent with the literature and identify a novel signature for intermediate states. The computation of sets of biological entities implicated in phenotypes is hampered by the complex nature of controllers acting in competitive or cooperative combinations. These biological mechanisms are underlied by chains of reactions involving interactions between biomolecules (DNA, RNA, proteins, lipids, complexes, etc.), all of which form complex networks. Hence, the identification of controllers relies on computational methods for dynamical systems, which require the biological information about the interactions to be translated into a formal language. The BioPAX standard is a reference ontology associated with a description language to describe biological mechanisms, which satisfies the Linked Open Data initiative recommendations for data interoperability. Although it has been widely adopted by the community to describe biological pathways, no computational method is able of studying the dynamics of the networks described in the BioPAX large-scale resources. To solve this issue, our Cadbiom framework was designed to automatically transcribe the biological systems knowledge of large-scale BioPAX networks into discrete models. The framework then identifies the trajectories that explain a biological phenotype (e.g., all the biomolecules that are activated to induce the expression of a gene). Here, we created Cadbiom models from three biological pathway databases (KEGG, PID and ACSN). The comparative analysis of these models highlighted the diversity of molecules in sets of biological entities that can explain a same phenotype. The application of our framework to the search of biomolecules regulating the epithelial-mesenchymal transition not only confirmed known pathways in the control of epithelial or mesenchymal cell markers but also highlighted new pathways for transient states.
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Affiliation(s)
- Pierre Vignet
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Jean Coquet
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Sébastien Auber
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
| | - Matéo Boudet
- IGEPP, Agrocampus Ouest, INRAE, Université de Rennes 1, Le Rheu, France
| | - Anne Siegel
- Univ Rennes, Inria, CNRS, IRISA, UMR 6074, Rennes, France
- * E-mail: (AS); (NT)
| | - Nathalie Théret
- Univ Rennes, Inserm, EHESP, Irset, UMR S1085, Rennes, France
- * E-mail: (AS); (NT)
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Li L, Yang Z, Zheng Y, Chen Z, Yue X, Bian E, Zhao B. Identification of an endoplasmic reticulum stress-related signature associated with clinical prognosis and immune therapy in glioma. BMC Neurol 2022; 22:192. [PMID: 35614390 PMCID: PMC9131635 DOI: 10.1186/s12883-022-02709-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 05/05/2022] [Indexed: 11/25/2022] Open
Abstract
Background Glioma is the most common brain tumor in adults and is characterized by a short survival time and high resistance to chemotherapy. It is imperative to determine the prognosis and therapy-related targets for glioma. Endoplasmic reticulum stress (ERS), as an adaptive protective mechanism, indicates the unfolded protein response (UPR) to determine cell survival and affects chemotherapy sensitivity, which is related to the prognosis of glioma. Methods Our research used the TCGA database as the training group and the CGGA database as the testing group. Lasso regression and Cox analysis were performed to construct an ERS signature-based risk score model in glioma. Three methods (time-dependent receiver operating characteristic analysis and multivariate and univariate Cox regression analysis) were applied to assess the independent prognostic effect of texture parameters. Consensus clustering was used to classify the two clusters. In addition, functional and immune analyses were performed to assess the malignant process and immune microenvironment. Immunotherapy and anticancer drug response prediction were adopted to evaluate immune checkpoint and chemotherapy sensitivity. Results The results revealed that the 7-gene signature strongly predicts glioma prognosis. The two clusters have markedly distinct molecular and prognostic features. The validation group result revealed that the signature has exceptional repeatability and certainty. Functional analysis showed that the ERS-related gene signature was closely associated with the malignant process and prognosis of tumors. Immune analysis indicated that the ERS-related gene signature is strongly related to immune infiltration. Immunotherapy and anticancer drug response prediction indicated that the ERS-related gene signature is positively correlated with immune checkpoint and chemotherapy sensitivity. Conclusions Collectively, the ERS-related risk model can provide a novel signature to predict glioma prognosis and treatment. Supplementary Information The online version contains supplementary material available at 10.1186/s12883-022-02709-y.
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Affiliation(s)
- Lianxin Li
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China
| | - Zhihao Yang
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China
| | - Yinfei Zheng
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China.,Cerebral Vascular Disease Research Center, Anhui Medical University, 678 Fu Rong Road, Hefei, 230601, Anhui Province, China
| | - Zhigang Chen
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China.,Cerebral Vascular Disease Research Center, Anhui Medical University, 678 Fu Rong Road, Hefei, 230601, Anhui Province, China
| | - Xiaoyu Yue
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China.,Cerebral Vascular Disease Research Center, Anhui Medical University, 678 Fu Rong Road, Hefei, 230601, Anhui Province, China
| | - Erbao Bian
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China. .,Cerebral Vascular Disease Research Center, Anhui Medical University, 678 Fu Rong Road, Hefei, 230601, Anhui Province, China.
| | - Bing Zhao
- Department of Neurosurgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, China. .,Cerebral Vascular Disease Research Center, Anhui Medical University, 678 Fu Rong Road, Hefei, 230601, Anhui Province, China.
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A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients. Cancers (Basel) 2022; 14:cancers14092111. [PMID: 35565241 PMCID: PMC9103317 DOI: 10.3390/cancers14092111] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 02/05/2023] Open
Abstract
Simple Summary Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice. Abstract Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.
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Palkina NV, Ruksha TG, Khorzhevskii VA, Sergeeva EY, Fefelova YA. [Gene expression profiling in melanoma diagnostics: problems and future application in clinical practice]. Arkh Patol 2022; 84:64-71. [PMID: 35417951 DOI: 10.17116/patol20228402164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Difficulties in the diagnosis and differential diagnosis of melanoma in the work of a pathologist include not only conflicting structural and morphological features, but also the insufficient effectiveness of biochemical and some molecular markers in immunohistochemical studies. The review presents modern alternative methods for diagnosing malignant tumors based on the assessment of gene expression, the performance, objectivity and reliability of the determination of which may in the future have clinical application as an addition to histopathological methods in the diagnosis and differential diagnosis of various malignant neoplasms, including melanocytic neoplasms, which is changing the paradigm of routine medical practice, introducing diagnostic tests that carry molecular information into it.
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Affiliation(s)
- N V Palkina
- Prof. V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, Krasnoyarsk, Russia
| | - T G Ruksha
- Prof. V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, Krasnoyarsk, Russia
| | - V A Khorzhevskii
- Prof. V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, Krasnoyarsk, Russia
| | - E Yu Sergeeva
- Prof. V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, Krasnoyarsk, Russia
| | - Yu A Fefelova
- Prof. V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, Krasnoyarsk, Russia
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20
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Wang R, Zheng X, Wang J, Wan S, Song F, Wong MH, Leung KS, Cheng L. Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia. Brief Bioinform 2022; 23:6523149. [PMID: 35136933 DOI: 10.1093/bib/bbac002] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 12/22/2021] [Accepted: 01/04/2022] [Indexed: 12/13/2022] Open
Abstract
The advances in single-cell RNA sequencing (scRNA-seq) technologies enable the characterization of transcriptomic profiles at the cellular level and demonstrate great promise in bulk sample analysis thereby offering opportunities to transfer gene signature from scRNA-seq to bulk data. However, the gene expression signatures identified from single cells are typically inapplicable to bulk RNA-seq data due to the profiling differences of distinct sequencing technologies. Here, we propose single-cell pair-wise gene expression (scPAGE), a novel method to develop single-cell gene pair signatures (scGPSs) that were beneficial to bulk RNA-seq classification to transfer knowledge across platforms. PAGE was adopted to tackle the challenge of profiling differences. We applied the method to acute myeloid leukemia (AML) and identified the scGPS from mouse scRNA-seq that allowed discriminating between AML and control cells. The scGPS was validated in bulk RNA-seq datasets and demonstrated better performance (average area under the curve [AUC] = 0.96) than the conventional gene expression strategies (average AUC$\le$ 0.88) suggesting its potential in disclosing the molecular mechanism of AML. The scGPS also outperformed its bulk counterpart, which highlighted the benefit of gene signature transfer. Furthermore, we confirmed the utility of scPAGE in sepsis as an example of other disease scenarios. scPAGE leveraged the advantages of single-cell profiles to enhance the analysis of bulk samples revealing great potential of transferring knowledge from single-cell to bulk transcriptome studies.
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Affiliation(s)
- Ran Wang
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Xubin Zheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China.,Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Jun Wang
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
| | - Shibiao Wan
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Fangda Song
- School of Data Science, The Chinese University of Hong Kong, Shenzhen 518000, China
| | - Man Hon Wong
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Kwong Sak Leung
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
| | - Lixin Cheng
- Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen 518020, China
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21
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Pawar A, Chowdhury OR, Chauhan R, Talole S, Bhattacharjee A. Identification of key gene signatures for the overall survival of ovarian cancer. J Ovarian Res 2022; 15:12. [PMID: 35057823 PMCID: PMC8780391 DOI: 10.1186/s13048-022-00942-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Accepted: 12/31/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The five-year overall survival (OS) of advanced-stage ovarian cancer remains nearly 25-35%, although several treatment strategies have evolved to get better outcomes. A considerable amount of heterogeneity and complexity has been seen in ovarian cancer. This study aimed to establish gene signatures that can be used in better prognosis through risk prediction outcome for the survival of ovarian cancer patients. Different studies' heterogeneity into a single platform is presented to explore the penetrating genes for poor or better survival. The integrative analysis of multiple data sets was done to determine the genes that influence poor or better survival. A total of 6 independent data sets was considered. The Cox Proportional Hazard model was used to obtain significant genes that had an impact on ovarian cancer patients. The gene signatures were prepared by splitting the over-expressed and under-expressed genes parallelly by the variable selection technique. The data visualisation techniques were prepared to predict the overall survival, and it could support the therapeutic regime. RESULTS We preferred to select 20 genes in each data set as upregulated and downregulated. Irrespective of the selection of multiple genes, not even a single gene was found common among data sets for the survival of ovarian cancer patients. However, the same analytical approach adopted. The chord plot was presented to make a comprehensive understanding of the outcome. CONCLUSIONS This study helps us to understand the results obtained from different studies. It shows the impact of the heterogeneity from one study to another. It shows the requirement of integrated studies to make a holistic view of the gene signature for ovarian cancer survival.
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Affiliation(s)
- Akash Pawar
- Section of Biostatistics, Center for Cancer Epidemiology, Tata Memorial Centre, Mumbai, India
| | - Oindrila Roy Chowdhury
- Section of Biostatistics, Center for Cancer Epidemiology, Tata Memorial Centre, Mumbai, India
| | - Ruby Chauhan
- Section of Biostatistics, Center for Cancer Epidemiology, Tata Memorial Centre, Mumbai, India
| | - Sanjay Talole
- Section of Biostatistics, Center for Cancer Epidemiology, Tata Memorial Centre, Mumbai, India
- Homi Bhabha National Institute, Mumbai, India
| | - Atanu Bhattacharjee
- Section of Biostatistics, Center for Cancer Epidemiology, Tata Memorial Centre, Mumbai, India.
- Homi Bhabha National Institute, Mumbai, India.
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22
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Kotlyar M, Wong SWH, Pastrello C, Jurisica I. Improving Analysis and Annotation of Microarray Data with Protein Interactions. Methods Mol Biol 2022; 2401:51-68. [PMID: 34902122 DOI: 10.1007/978-1-0716-1839-4_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Gene expression microarrays are one of the most widely used high-throughput technologies in molecular biology, with applications such as identification of disease mechanisms and development of diagnostic and prognostic gene signatures. However, the success of these tasks is often limited because microarray analysis does not account for the complex relationships among genes, their products, and overall signaling and regulatory cascades. Incorporating protein-protein interaction data into microarray analysis can help address these challenges. This chapter reviews how protein-protein interactions can help with microarray analysis, leading to benefits such as better explanations of disease mechanisms, more complete gene annotations, improved prioritization of genes for future experiments, and gene signatures that generalize better to new data.
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Affiliation(s)
- Max Kotlyar
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Serene W H Wong
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Chiara Pastrello
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Igor Jurisica
- Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, ON, Canada.
- Data Science Discovery Centre for Chronic Diseases, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada.
- Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON, Canada.
- Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia.
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23
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The Stroma Liquid Biopsy Panel Contains a Stromal-Epithelial Gene Signature Ratio That Is Associated with the Histologic Tumor-Stroma Ratio and Predicts Survival in Colon Cancer. Cancers (Basel) 2021; 14:cancers14010163. [PMID: 35008327 PMCID: PMC8750571 DOI: 10.3390/cancers14010163] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 12/18/2021] [Accepted: 12/27/2021] [Indexed: 12/22/2022] Open
Abstract
Liquid biopsy has emerged as a novel approach to tumor characterization, offering advantages in sample accessibility and tissue heterogeneity. However, as mutational analysis predominates, the tumor microenvironment has largely remained unacknowledged in liquid biopsy research. The current work provides an explorative transcriptomic characterization of the Stroma Liquid BiopsyTM (SLB) proteomics panel in colon carcinoma by integrating single-cell and bulk transcriptomics data from publicly available repositories. Expression of SLB genes was significantly enriched in tumors with high histologic stromal content in comparison to tumors with low stromal content (median enrichment score 0.308 vs. 0.222, p = 0.036). In addition, we identified stromal-specific and epithelial-specific expression of the SLB genes, that was subsequently integrated into a gene signature ratio. The stromal-epithelial signature ratio was found to have prognostic significance in a discovery cohort of 359 colon adenocarcinoma patients (OS HR 2.581, 95%CI 1.567-4.251, p < 0.001) and a validation cohort of 229 patients (OS HR 2.590, 95%CI 1.659-4.043, p < 0.001). The framework described here provides transcriptomic evidence for the prognostic significance of the SLB panel constituents in colon carcinoma. Plasma protein levels of the SLB panel may reflect histologic intratumoral stromal content, a poor prognostic tumor characteristic, and hence provide valuable prognostic information in liquid biopsy.
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24
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Ye H, Sun M, Huang S, Xu F, Wang J, Liu H, Zhang L, Luo W, Guo W, Wu Z, Zhu J, Li H. Gene Network Analysis of Hepatocellular Carcinoma Identifies Modules Associated with Disease Progression, Survival, and Chemo Drug Resistance. Int J Gen Med 2021; 14:9333-9347. [PMID: 34898998 PMCID: PMC8654693 DOI: 10.2147/ijgm.s336729] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/08/2021] [Indexed: 12/11/2022] Open
Abstract
Background Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related mortality worldwide. HCC transcriptome has been extensively studied; however, the progress in disease mechanisms, prognosis, and treatment is still slow. Methods A rank-based module-centric workflow was introduced to analyze important modules associated with HCC development, prognosis, and drug resistance. The currently largest HCC cell line RNA-Seq dataset from the LIMORE database was used to construct the reference modules by weighted gene co-expression network analysis. Results Thirteen reference modules were identified with validated reproducibility. These modules were all associated with specific biological functions. Differentially expressed module analysis revealed the crucial modules during HCC development. Modules and hub genes are indicative of patient survival. Modules can differentiate patients in different HCC stages. Furthermore, drug resistance was revealed by drug-module association analysis. Based on differentially expressed modules and hub genes, six candidate drugs were screened. The hub genes of those modules merit further investigation. Conclusion We proposed a reference module-based analysis of the HCC transcriptome. The identified modules are associated with HCC development, survival, and drug resistance. M3 and M6 may play important roles during HCV to HCC development. M1, M3, M5, and M7 are associated with HCC survival. High M4, high M9, low M1, and low M3 may be associated with dasatinib, doxorubicin, CD532, and simvastatin resistance. Our analysis provides useful information for HCC diagnosis and treatment.
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Affiliation(s)
- Hua Ye
- Department of Gastroenterology, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Mengxia Sun
- Department of Clinical Medicine, Medical School of Ningbo University, Ningbo, Zhejiang, 315211, People's Republic of China
| | - Shiliang Huang
- Department of Gastroenterology, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Feng Xu
- Department of Gastroenterology, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Jian Wang
- Department of Dermatology, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Huiwei Liu
- Department of Gastroenterology, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Liangshun Zhang
- Department of Gastroenterology, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Wenjing Luo
- Department of Gastroenterology, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Wenying Guo
- Department of Gastroenterology, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Zhe Wu
- Department of Gastroenterology, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Jie Zhu
- Department of Hepatobiliary Surgery, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
| | - Hong Li
- Department of Hepatobiliary Surgery, Ningbo Medical Treatment Center Lihuili Hospital, Medical School of Ningbo University, Ningbo, Zhejiang, 315040, People's Republic of China
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25
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Zhang P, Ma K, Ke X, Liu L, Li Y, Liu Y, Wang Y. Development and Validation of a Five-RNA-Based Signature and Identification of Candidate Drugs for Neuroblastoma. Front Genet 2021; 12:685646. [PMID: 34745201 PMCID: PMC8564070 DOI: 10.3389/fgene.2021.685646] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 09/24/2021] [Indexed: 12/12/2022] Open
Abstract
Neuroblastoma (NBL) originating from the sympathetic nervous system is the most prevalent solid tumor in infancy. Although there is sufficient variability in prognosis among different age pyramids, age-related gene expression profiles and biomarkers remain poorly explored. The present study aimed to construct a signature based on differentially expressed genes (DEGs) between two age groups in NBL. Univariate Cox regression, multivariate Cox regression, and LASSO analyses were used to identify the optimal prognostic factors. The prediction ability of the model was assessed using the receiver operating characteristic (ROC) curve and C-index. Functional enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes and gene ontology databases. A total of 1,160 DEGs were identified between the two groups, and 204 DEGs impacted the survival of NBL. Functional enrichment analysis revealed that the DEGs were involved in retinol metabolism, cholesterol metabolism, and glycolysis/gluconeogenesis pathways. Five RNAs, namely F8A3, PDF, ANKRD24, FAXDC2, and TMEM160 were recruited into the signature. They were correlated with COG risk classification, INSS stage, and histology. MYCN amplification was linked to FAXDC2, TMEM160, PDF, and F8A3. The expression levels of ANKRD24, PDF, and TMEM160 were lower in the hyperdiploid groups. Only FAXDC2 levels were different in the different MKI grades. The ROC curve showed that the five-RNA–based signatures effectively predicted the OS of NBL (3-years AUC = 0.791, 5-years AUC = 0.816) in the TARGET cohort. The predictive capability was also validated by the GSE49711 cohort (3-years AUC = 0.851, 5-years AUC = 0.848). The C-index in the TARGET and GSE49711 cohorts was 0.749 and 0.809, respectively. The potential mechanisms of the five RNAs were also explored via gene set enrichment analysis, and candidate drugs targeting the five genes, including dabrafenib, vemurafenib, and bafetinib, were screened. In conclusion, we constructed a five-RNA–based signature to predict the survival of NBL and screened candidate agents against NBL.
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Affiliation(s)
- PeiPei Zhang
- Department of Pediatrics, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - KeXin Ma
- Department of Pediatrics, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - XiaoFei Ke
- Department of Pediatrics, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Liu Liu
- Department of Pediatrics, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - Ying Li
- Department of Pediatrics, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - YaJuan Liu
- Department of Pediatrics, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
| | - YouJun Wang
- Department of Pediatrics, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China
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26
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Lai E, Tai Y, Jiang J, Zhao C, Xiao Y, Quan X, Wu H, Gao J. Prognostic evaluation and immune infiltration analysis of five bioinformatic selected genes in hepatocellular carcinoma. J Cell Mol Med 2021; 25:11128-11141. [PMID: 34726341 PMCID: PMC8650024 DOI: 10.1111/jcmm.17035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/24/2021] [Accepted: 09/30/2021] [Indexed: 02/05/2023] Open
Abstract
Despite the development in hepatocellular carcinoma (HCC) treatment in recent years, the therapeutic outcome of HCC remains unfavourable. This study examines the prognosis of HCC from a genetic level using clinical databases and single‐cell data to identify genes with a high prognostic value. Three up‐regulated genes (UBE2S, PTTG1, and CDC20) and two down‐regulated genes (SOCS2 and DNASE1L3) in HCC tissues were identified. Various analyses confirmed its correlation with tumour stage (p < 0.01) and patient survival time (log‐rank p < 0.001). Immune analysis, single‐cell analysis, and gene set enrichment analysis (GSEA) were employed to provide insight on how they affect cancer progression, and we observed a close relation between these genes and tumour immune infiltration. Eventually, we constructed a risk score system that risk score = (0.0465) × UBE2S + (0.1851) × CDC20 + (−0.0461) × DNASE1L3 + (−0.2279) × SOCS2 (5‐year area under curve = 0.706). The risk score system may serve as an effective novel prognostic system for HCC patients. This study might provide novel ideas for prognostic or therapeutic biomarkers for HCC.
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Affiliation(s)
- Enjiang Lai
- Lab of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China.,Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Tai
- Lab of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China.,Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Jingsun Jiang
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Chong Zhao
- Lab of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China.,Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Xiao
- Lab of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China.,Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Xin Quan
- Lab of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China.,Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Hao Wu
- Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
| | - Jinhang Gao
- Lab of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China.,Department of Gastroenterology, West China Hospital, Sichuan University, Chengdu, China
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27
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Sathasivam HP, Kist R, Sloan P, Thomson P, Nugent M, Alexander J, Haider S, Robinson M. Predicting the clinical outcome of oral potentially malignant disorders using transcriptomic-based molecular pathology. Br J Cancer 2021; 125:413-421. [PMID: 33972745 PMCID: PMC8329212 DOI: 10.1038/s41416-021-01411-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 04/06/2021] [Accepted: 04/16/2021] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND This study was undertaken to develop and validate a gene expression signature that characterises oral potentially malignant disorders (OPMD) with a high risk of undergoing malignant transformation. METHODS Patients with oral epithelial dysplasia at one hospital were selected as the 'training set' (n = 56) whilst those at another hospital were selected for the 'test set' (n = 66). RNA was extracted from formalin-fixed paraffin-embedded (FFPE) diagnostic biopsies and analysed using the NanoString nCounter platform. A targeted panel of 42 genes selected on their association with oral carcinogenesis was used to develop a prognostic gene signature. Following data normalisation, uni- and multivariable analysis, as well as prognostic modelling, were employed to develop and validate the gene signature. RESULTS A prognostic classifier composed of 11 genes was developed using the training set. The multivariable prognostic model was used to predict patient risk scores in the test set. The prognostic gene signature was an independent predictor of malignant transformation when assessed in the test set, with the high-risk group showing worse prognosis [Hazard ratio = 12.65, p = 0.0003]. CONCLUSIONS This study demonstrates proof of principle that RNA extracted from FFPE diagnostic biopsies of OPMD, when analysed on the NanoString nCounter platform, can be used to generate a molecular classifier that stratifies the risk of malignant transformation with promising clinical utility.
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Affiliation(s)
- Hans Prakash Sathasivam
- grid.1006.70000 0001 0462 7212School of Dental Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK ,Cancer Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Setia Alam, Malaysia
| | - Ralf Kist
- grid.1006.70000 0001 0462 7212School of Dental Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK ,grid.1006.70000 0001 0462 7212Newcastle University Biosciences Institute, Newcastle University Centre for Cancer, Newcastle upon Tyne, UK
| | - Philip Sloan
- grid.1006.70000 0001 0462 7212School of Dental Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK ,grid.420004.20000 0004 0444 2244Department of Cellular Pathology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Peter Thomson
- grid.194645.b0000000121742757Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong, Hong Kong SAR
| | - Michael Nugent
- grid.416726.00000 0004 0399 9059Oral and Maxillofacial Surgery, Sunderland Royal Hospital, Sunderland, UK
| | - John Alexander
- grid.18886.3f0000 0001 1271 4623The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Syed Haider
- grid.18886.3f0000 0001 1271 4623The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | - Max Robinson
- grid.1006.70000 0001 0462 7212School of Dental Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK ,grid.420004.20000 0004 0444 2244Department of Cellular Pathology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
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28
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Gillyard T, Davis J. DNA double-strand break repair in cancer: A path to achieving precision medicine. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2021; 364:111-137. [PMID: 34507781 DOI: 10.1016/bs.ircmb.2021.06.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The assessment of DNA damage can be a significant diagnostic for precision medicine. DNA double strand break (DSBs) pathways in cancer are the primary targets in a majority of anticancer therapies, yet the molecular vulnerabilities that underlie each tumor can vary widely making the application of precision medicine challenging. Identifying and understanding these interindividual vulnerabilities enables the design of targeted DSB inhibitors along with evolving precision medicine approaches to selectively kill cancer cells with minimal side effects. A major challenge however, is defining exactly how to target unique differences in DSB repair pathway mechanisms. This review comprises a brief overview of the DSB repair mechanisms in cancer and includes results obtained with revolutionary advances such as CRISPR/Cas9 and machine learning/artificial intelligence, which are rapidly advancing not only our understanding of determinants of DSB repair choice, but also how it can be used to advance precision medicine. Scientific innovation in the methods used to diagnose and treat cancer is converging with advances in basic science and translational research. This revolution will continue to be a critical driver of precision medicine that will enable precise targeting of unique individual mechanisms. This review aims to lay the foundation for achieving this goal.
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Affiliation(s)
- Taneisha Gillyard
- Department of Biochemistry, Cancer Biology, Neuroscience and Pharmacology, Meharry Medical College, Nashville, TN, United States
| | - Jamaine Davis
- Department of Biochemistry, Cancer Biology, Neuroscience and Pharmacology, Meharry Medical College, Nashville, TN, United States.
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29
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Zhang C, Zhao Z, Liu H, Yao S, Zhao D. Weighted Gene Co-expression Network Analysis Identified a Novel Thirteen-Gene Signature Associated With Progression, Prognosis, and Immune Microenvironment of Colon Adenocarcinoma Patients. Front Genet 2021; 12:657658. [PMID: 34322151 PMCID: PMC8312261 DOI: 10.3389/fgene.2021.657658] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 06/22/2021] [Indexed: 02/05/2023] Open
Abstract
Colon adenocarcinoma (COAD) is one of the most common malignant tumors and has high migration and invasion capacity. In this study, we attempted to establish a multigene signature for predicting the prognosis of COAD patients. Weighted gene co-expression network analysis and differential gene expression analysis methods were first applied to identify differentially co-expressed genes between COAD tissues and normal tissues from the Cancer Genome Atlas (TCGA)-COAD dataset and GSE39582 dataset, and a total of 309 overlapping genes were screened out. Then, our study employed TCGA-COAD cohort as the training dataset and an independent cohort by merging the GES39582 and GSE17536 datasets as the testing dataset. After univariate and multivariate Cox regression analyses were performed for these overlapping genes and overall survival (OS) of COAD patients in the training dataset, a 13-gene signature was constructed to divide COAD patients into high- and low-risk subgroups with significantly different OS. The testing dataset exhibited the same results utilizing the same predictive signature. The area under the curve of receiver operating characteristic analysis for predicting OS in the training and testing datasets were 0.789 and 0.868, respectively, which revealed the enhanced predictive power of the signature. Multivariate Cox regression analysis further suggested that the 13-gene signature could independently predict OS. Among the 13 prognostic genes, NAT1 and NAT2 were downregulated with deep deletions in tumor tissues in multiple COAD cohorts and exhibited significant correlations with poorer OS based on the GEPIA database. Notably, NAT1 and NAT2 expression levels were positively correlated with infiltrating levels of CD8+ T cells and dendritic cells, exhibiting a foundation for further research investigating the antitumor immune roles played by NAT1 and NAT2 in COAD. Taken together, the results of our study showed that the 13-gene signature could efficiently predict OS and that NAT1 and NAT2 could function as biomarkers for prognosis and the immune response in COAD.
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Affiliation(s)
- Cangang Zhang
- Department of Pathogenic Microbiology and Immunology, School of Basic Medical Sciences, Xi'an Jiaotong University, Xi'an, China
| | - Zhe Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China (Ministry of Education), College of Life Science, Northwest University, Xi'an, China
| | - Haibo Liu
- Department of Hematology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Shukun Yao
- Graduate School, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Gastroenterology, China-Japan Friendship Hospital, Beijing, China
| | - Dongyan Zhao
- Graduate School, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Department of Gastroenterology, China-Japan Friendship Hospital, Beijing, China
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Emerging Biomarkers for the Selection of Advanced NSCLC-Affected Immunotherapy Patients. JOURNAL OF MOLECULAR PATHOLOGY 2021. [DOI: 10.3390/jmp2020017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Immunotherapy in the form of ICIs has revolutionized advanced NSCLC treatment algorithms, with ICI-containing combination treatments being the latest addition to approved regimens. However, PD-L1 still represents the only routinely assessed and validated biomarker apart from genetic drivers testing, impairing our capacity to personalize and guide treatment. Therefore, this paper aims to analyze the most promising emerging predictive biomarkers that could help us in the near future to select patients more effectively.
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Prognostic Cancer Gene Expression Signatures: Current Status and Challenges. Cells 2021; 10:cells10030648. [PMID: 33804045 PMCID: PMC8000474 DOI: 10.3390/cells10030648] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 12/27/2022] Open
Abstract
Current staging systems of cancer are mainly based on the anatomical extent of disease. They need refinement by biological parameters to improve stratification of patients for tumor therapy or surveillance strategies. Thanks to developments in genomic, transcriptomic, and big-data technologies, we are now able to explore molecular characteristics of tumors in detail and determine their clinical relevance. This has led to numerous prognostic and predictive gene expression signatures that have the potential to establish a classification of tumor subgroups by biological determinants. However, only a few gene signatures have reached the stage of clinical implementation so far. In this review article, we summarize the current status, and present and future challenges of prognostic gene signatures in three relevant cancer entities: breast cancer, colorectal cancer, and hepatocellular carcinoma.
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Vandel J, Dubois-Chevalier J, Gheeraert C, Derudas B, Raverdy V, Thuillier D, Gaal L, Francque S, Pattou F, Staels B, Eeckhoute J, Lefebvre P. Hepatic Molecular Signatures Highlight the Sexual Dimorphism of Nonalcoholic Steatohepatitis (NASH). Hepatology 2021; 73:920-936. [PMID: 32394476 PMCID: PMC8048532 DOI: 10.1002/hep.31312] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 04/14/2020] [Accepted: 04/16/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIMS Nonalcoholic steatohepatitis (NASH) is considered as a pivotal stage in nonalcoholic fatty liver disease (NAFLD) progression, given that it paves the way for severe liver injuries such as fibrosis and cirrhosis. The etiology of human NASH is multifactorial, and identifying reliable molecular players and/or biomarkers has proven difficult. Together with the inappropriate consideration of risk factors revealed by epidemiological studies (altered glucose homeostasis, obesity, ethnicity, sex, etc.), the limited availability of representative NASH cohorts with associated liver biopsies, the gold standard for NASH diagnosis, probably explains the poor overlap between published "omics"-defined NASH signatures. APPROACH AND RESULTS Here, we have explored transcriptomic profiles of livers starting from a 910-obese-patient cohort, which was further stratified based on stringent histological characterization, to define "NoNASH" and "NASH" patients. Sex was identified as the main factor for data heterogeneity in this cohort. Using powerful bootstrapping and random forest (RF) approaches, we identified reliably differentially expressed genes participating in distinct biological processes in NASH as a function of sex. RF-calculated gene signatures identified NASH patients in independent cohorts with high accuracy. CONCLUSIONS This large-scale analysis of transcriptomic profiles from human livers emphasized the sexually dimorphic nature of NASH and its link with fibrosis, calling for the integration of sex as a major determinant of liver responses to NASH progression and responses to drugs.
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Affiliation(s)
- Jimmy Vandel
- Univ. LilleInserm, CHU LilleInstitut Pasteur de LilleU1011-EGIDLilleFrance
| | | | - Céline Gheeraert
- Univ. LilleInserm, CHU LilleInstitut Pasteur de LilleU1011-EGIDLilleFrance
| | - Bruno Derudas
- Univ. LilleInserm, CHU LilleInstitut Pasteur de LilleU1011-EGIDLilleFrance
| | | | | | - Luc Gaal
- Department of Endocrinology, Diabetology and MetabolismAntwerp University HospitalEdegem (Antwerp)Belgium.,Laboratory of Experimental Medicine and Pediatrics (LEMP)University of AntwerpWilrijk (Antwerp)Belgium
| | - Sven Francque
- Laboratory of Experimental Medicine and Pediatrics (LEMP)University of AntwerpWilrijk (Antwerp)Belgium.,Department of Gastroenterology and HepatologyAntwerp University HospitalEdegem (Antwerp)Belgium
| | | | - Bart Staels
- Univ. LilleInserm, CHU LilleInstitut Pasteur de LilleU1011-EGIDLilleFrance
| | - Jérôme Eeckhoute
- Univ. LilleInserm, CHU LilleInstitut Pasteur de LilleU1011-EGIDLilleFrance
| | - Philippe Lefebvre
- Univ. LilleInserm, CHU LilleInstitut Pasteur de LilleU1011-EGIDLilleFrance
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Emad A, Sinha S. Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study. NPJ Syst Biol Appl 2021; 7:9. [PMID: 33558504 PMCID: PMC7870953 DOI: 10.1038/s41540-021-00169-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Accepted: 01/05/2021] [Indexed: 01/30/2023] Open
Abstract
Reconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify "phenotype-relevant" TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes' expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.
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Affiliation(s)
- Amin Emad
- Department of Electrical and Computer Engineering, McGill University, Montreal, QC, Canada.
| | - Saurabh Sinha
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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Amirmahani F, Ebrahimi N, Molaei F, Faghihkhorasani F, Jamshidi Goharrizi K, Mirtaghi SM, Borjian‐Boroujeni M, Hamblin MR. Approaches for the integration of big data in translational medicine: single‐cell and computational methods. Ann N Y Acad Sci 2021; 1493:3-28. [DOI: 10.1111/nyas.14544] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/31/2020] [Accepted: 11/12/2020] [Indexed: 12/11/2022]
Affiliation(s)
- Farzane Amirmahani
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Nasim Ebrahimi
- Genetics Division, Department of Cell and Molecular Biology and Microbiology, Faculty of Science and Technology University of Isfahan Isfahan Iran
| | - Fatemeh Molaei
- Department of Anesthesiology, Faculty of Paramedical Jahrom University of Medical Sciences Jahrom Iran
| | | | | | | | | | - Michael R. Hamblin
- Laser Research Centre, Faculty of Health Science University of Johannesburg South Africa
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Wang Y, Liang N, Xue Z, Xue X. Identifying an Eight-Gene Signature to Optimize Overall Survival Prediction of Esophageal Adenocarcinoma Using Bioinformatics Analysis of ceRNA Network. Onco Targets Ther 2020; 13:13041-13054. [PMID: 33376353 PMCID: PMC7764560 DOI: 10.2147/ott.s287084] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 11/29/2020] [Indexed: 12/31/2022] Open
Abstract
Background and Aims Esophageal adenocarcinoma (EAC) patients usually have a poor prognosis without early diagnosis. In this study, we aimed to identify a novel signature to improve the prediction of overall survival (OS) in EAC. Methods Eighty-one and 68 samples from The Cancer Genome Atlas (TCGA) and GSE19417 dataset were included for discovery and survival validation, respectively. In the TCGA cohort, a total of 1,811 DEmRNAs, 1,096 DElncRNAs, and 31 DEmiRNAs were identified between EAC and normal esophagus tissues. A mRNA–miRNA–lncRNA ceRNA network of EAC was established, which consisted of 94 DEmRNAs, 13 DEmiRNAs, and 46 DElncRNAs. Results In this study, we identified eight genes (UBE2B, LAMP2, B3GNT2, TAF9B, EFNA1, PHF8, PIGA, and NEURL1) which were related to survival in EAC. The independent external microarray data from the Gene Expression Omnibus (GEO) was used to validate these candidate genes. The prognostic ability of the signature was also validated in EAC patients in our hospital. Patients assigned to the high-risk group had a poor overall survival rate compared with the low-risk. Conclusion The current study provides novel insights into the mRNA-related ceRNA network in EAC and the eight mRNA biomarkers may be independent prognostic signatures in predicting the survival of EAC patients.
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Affiliation(s)
- Yuanyong Wang
- Department of Thoracic Surgery, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Naixin Liang
- Department of Thoracic Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, People's Republic of China
| | - Zhiqiang Xue
- Department of Thoracic Surgery, Chinese PLA General Hospital, Beijing, People's Republic of China
| | - Xinying Xue
- Department of Respiratory Disease, Beijing Shijitan Hospital, Capital Medical University, Beijing, People's Republic of China.,Department of Respiratory Disease, School of Clinical Medicine, Weifang Medical University, Weifang, Shandong, People's Republic of China
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Radiosensitivity-Related Genes and Clinical Characteristics of Nasopharyngeal Carcinoma. BIOMED RESEARCH INTERNATIONAL 2020; 2020:1705867. [PMID: 33299859 PMCID: PMC7704138 DOI: 10.1155/2020/1705867] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 10/05/2020] [Accepted: 10/16/2020] [Indexed: 12/30/2022]
Abstract
Materials and Methods Clinicopathological data of 185 patients with NPC treated at Nanfang Hospital of Southern Medical University between January 2013 and December 2014 were retrospectively analyzed. SPSS statistical software was used to analyze the clinicopathological data related to radiotherapy efficacy. Three patients who achieved complete remission and three with disease progression after CRT were selected. Differentially expressed genes (DEGs) were screened via mRNA microarray analysis of primary diagnostic endoscopy specimens. Results The peripheral blood leukocyte count, platelet count, and EBV-DNA copy number in NPC patients who were resistant to radiotherapy were higher than those in NPC patients who were sensitive to radiotherapy. The RobustRankAggreg (RRA) analysis method identified 392 DEGs, and the 66 most closely related genes among the DEGs were identified from the PPI network. Conclusion The results of this study indicate that screening for DEGs and pathways in NPC using integrated in silico analyses can help identify a series of genetic and clinical signatures for NPC patients treated with neoadjuvant chemotherapy followed by concurrent chemoradiotherapy.
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Prognostic and Predictive Factors in Metastatic Renal Cell Carcinoma: Current Perspective and a Look Into the Future. ACTA ACUST UNITED AC 2020; 26:365-375. [PMID: 32947304 DOI: 10.1097/ppo.0000000000000468] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Metastatic renal cell carcinoma (mRCC) comprises a highly heterogeneous group of diseases with varied clinical outcomes. As a result, models to estimate prognosis were developed in an attempt to aid patient counseling, treatment selection, and clinical trial design. Contemporary prognostic models have been mostly generated based on clinical factors because of their ease of use. Recent advances in molecular techniques have allowed unprecedented molecular profiling of RCC and the discovery of genomic and proteotranscriptomic factors that may contribute to disease trajectory. With the advent of multiple systemic therapies in mRCC in recent years, predictive biomarkers have become increasingly relevant in treatment selection. In this review, we discuss the existing staging systems and prognostic models in mRCC. We also highlight various promising molecular biomarkers according to the subtypes of RCC and explore their integration into the traditional prognostic models. In addition, we discuss emerging predictive biomarkers in the era of immuno-oncology. Lastly, we explore future directions with a focus on liquid biopsies and composite biomarkers.
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NRF2 metagene signature is a novel prognostic biomarker in colorectal cancer. Cancer Genet 2020; 248-249:1-10. [PMID: 32871287 DOI: 10.1016/j.cancergen.2020.08.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 08/14/2020] [Accepted: 08/17/2020] [Indexed: 12/12/2022]
Abstract
We hypothesise that the NRF2 transcription factor would act a biomarker of poor prognosis in colorectal cancer. We derived and validated an mRNA based metagene signature of NRF2 signalling and validated it in 1360 patients from 4 different datasets as an independent biomarker of poor prognosis. This is a novel insight into the molecular signalling of colorectal cancer. BACKGROUND NRF2 over activity confers poor prognosis in some cancers but its prognostic role in colorectal cancer (CRC) is unknown. As a transcription factor, we hypothesise a signature of NRF2 regulated genes could act as a prognostic biomarker in CRC and reveal novel biological insights. METHODS Using known NRF2 regulated genes, differentially expressed in CRC, we defined a signature of NRF2 pathway activity using principal component analysis and Cox proportional hazard models and tested it in four independent mRNA datasets, profiled on three different mRNA platforms. RESULTS 36 genes comprised the final NRF2 signature. 1360 patients were included in the validation. High NRF2 was associated with worse disease free survival (DFS) and/or overall survival (OS) in all datasets: (GSE14333 HR=1.55, 95% C.I 1.2-2.004, p = 0.0008; GSE39582 HR=1.24, 95% C.I 1.086-1.416, p = 0.001; GSE87211 HR=1.431, 95% C.I 1.06-1.93, p = 0.056; MRC FOCUS trial HR=1.14, 95% C.I 1.04-1.26, p = 0.008). In multivariate analyses, NRF2 remained significant when adjusted for stage and adjuvant chemotherapy in stage I-III disease, and BRAF V600E mutation and sidedness in stage IV disease. NRF2 activity was particularly enriched in Consensus Molecular Subtype (CMS) 4. CONCLUSION For the first time, NRF2 is shown to be a consistent, robust prognostic biomarker across all stages of colorectal cancer with additional clinical value to current known prognostic biomarkers. High NRF2 signalling in CMS 4 further refines the molecular taxonomy of CRC, a new biological insight, suggesting avenues of further study.
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Fajarda O, Duarte-Pereira S, Silva RM, Oliveira JL. Merging microarray studies to identify a common gene expression signature to several structural heart diseases. BioData Min 2020; 13:8. [PMID: 32670412 PMCID: PMC7346458 DOI: 10.1186/s13040-020-00217-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 06/05/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Heart disease is the leading cause of death worldwide. Knowing a gene expression signature in heart disease can lead to the development of more efficient diagnosis and treatments that may prevent premature deaths. A large amount of microarray data is available in public repositories and can be used to identify differentially expressed genes. However, most of the microarray datasets are composed of a reduced number of samples and to obtain more reliable results, several datasets have to be merged, which is a challenging task. The identification of differentially expressed genes is commonly done using statistical methods. Nonetheless, these methods are based on the definition of an arbitrary threshold to select the differentially expressed genes and there is no consensus on the values that should be used. RESULTS Nine publicly available microarray datasets from studies of different heart diseases were merged to form a dataset composed of 689 samples and 8354 features. Subsequently, the adjusted p-value and fold change were determined and by combining a set of adjusted p-values cutoffs with a list of different fold change thresholds, 12 sets of differentially expressed genes were obtained. To select the set of differentially expressed genes that has the best accuracy in classifying samples from patients with heart diseases and samples from patients with no heart condition, the random forest algorithm was used. A set of 62 differentially expressed genes having a classification accuracy of approximately 95% was identified. CONCLUSIONS We identified a gene expression signature common to different cardiac diseases and supported our findings by showing their involvement in the pathophysiology of the heart. The approach used in this study is suitable for the identification of gene expression signatures, and can be extended to different diseases.
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Affiliation(s)
- Olga Fajarda
- IEETA/DETI, University of Aveiro, Aveiro, 3810-193 Portugal
| | - Sara Duarte-Pereira
- IEETA/DETI, University of Aveiro, Aveiro, 3810-193 Portugal
- Department of Medical Sciences and iBiMED-Institute of Biomedicine, University of Aveiro, Aveiro, 3810-193 Portugal
| | - Raquel M. Silva
- IEETA/DETI, University of Aveiro, Aveiro, 3810-193 Portugal
- Department of Medical Sciences and iBiMED-Institute of Biomedicine, University of Aveiro, Aveiro, 3810-193 Portugal
- Current Address: Universidade Católica Portuguesa, Faculdade de Medicina Dentária, CIIS-Centro de Investigação Interdisciplinar em Saúde, Campus de Viseu, Viseu, 3504-505 Portugal
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Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020; 2020:6795392. [PMID: 32670394 PMCID: PMC7350178 DOI: 10.1155/2020/6795392] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 05/06/2020] [Accepted: 05/20/2020] [Indexed: 12/01/2022]
Abstract
Over the last decades, molecular signatures have become increasingly important in oncology and are opening up a new area of personalized medicine. Nevertheless, biological relevance and statistical tools necessary for the development of these signatures have been called into question in the literature. Here, we investigate six typical selection methods for high-dimensional settings and survival endpoints, including LASSO and some of its extensions, component-wise boosting, and random survival forests (RSF). A resampling algorithm based on data splitting was used on nine high-dimensional simulated datasets to assess selection stability on training sets and the intersection between selection methods. Prognostic performances were evaluated on respective validation sets. Finally, one application on a real breast cancer dataset has been proposed. The false discovery rate (FDR) was high for each selection method, and the intersection between lists of predictors was very poor. RSF selects many more variables than the other methods and thus becomes less efficient on validation sets. Due to the complex correlation structure in genomic data, stability in the selection procedure is generally poor for selected predictors, but can be improved with a higher training sample size. In a very high-dimensional setting, we recommend the LASSO-pcvl method since it outperforms other methods by reducing the number of selected genes and minimizing FDR in most scenarios. Nevertheless, this method still gives a high rate of false positives. Further work is thus necessary to propose new methods to overcome this issue where numerous predictors are present. Pluridisciplinary discussion between clinicians and statisticians is necessary to ensure both statistical and biological relevance of the predictors included in molecular signatures.
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Giunta EF, De Falco V, Napolitano S, Argenziano G, Brancaccio G, Moscarella E, Ciardiello D, Ciardiello F, Troiani T. Optimal treatment strategy for metastatic melanoma patients harboring BRAF-V600 mutations. Ther Adv Med Oncol 2020; 12:1758835920925219. [PMID: 32612709 PMCID: PMC7307282 DOI: 10.1177/1758835920925219] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 03/13/2020] [Indexed: 12/15/2022] Open
Abstract
BRAF-V600 mutations occur in approximately 50% of patients with
metastatic melanoma. Immune-checkpoint inhibitors and targeted therapies are
both active as first-line treatments in these patients regardless of their
mechanisms of action and toxicities. However, an upfront therapeutic strategy is
still controversial. In fact, waiting for results of ongoing clinical trials and
for new biomarkers, clinicians should base their decision on the clinical
characteristics of the patient and on the biological aspects of the tumor. This
review provides an overview on BRAF-V600 mutations in melanoma
and will discuss their prognostic and clinical significance. Moreover, it will
suggest a therapeutic algorithm that can drive therapeutic choice in a
first-line setting for BRAF-V600 mutant melanoma patients.
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Affiliation(s)
- Emilio Francesco Giunta
- Medical Oncology, Department of Precision Medicine, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Vincenzo De Falco
- Medical Oncology, Department of Precision Medicine, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Stefania Napolitano
- Medical Oncology, Department of Precision Medicine, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Giuseppe Argenziano
- Dermatology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Gabriella Brancaccio
- Dermatology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Elvira Moscarella
- Dermatology Unit, Department of Mental and Physical Health and Preventive Medicine, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Davide Ciardiello
- Medical Oncology, Department of Precision Medicine, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Fortunato Ciardiello
- Medical Oncology, Department of Precision Medicine, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy
| | - Teresa Troiani
- Dipartimento di Medicina di Precisione, Università degli Studi della Campania "Luigi Vanvitelli", Via S Pansini 5, Naples 80131, Italy
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Qi X, Shen M, Fan P, Guo X, Wang T, Feng N, Zhang M, Sweet RA, Kirisci L, Wang L. The Performance of Gene Expression Signature-Guided Drug-Disease Association in Different Categories of Drugs and Diseases. Molecules 2020; 25:molecules25122776. [PMID: 32560162 PMCID: PMC7357095 DOI: 10.3390/molecules25122776] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 06/05/2020] [Accepted: 06/05/2020] [Indexed: 12/27/2022] Open
Abstract
A gene expression signature (GES) is a group of genes that shows a unique expression profile as a result of perturbations by drugs, genetic modification or diseases on the transcriptional machinery. The comparisons between GES profiles have been used to investigate the relationships between drugs, their targets and diseases with quite a few successful cases reported. Especially in the study of GES-guided drugs–disease associations, researchers believe that if a GES induced by a drug is opposite to a GES induced by a disease, the drug may have potential as a treatment of that disease. In this study, we data-mined the crowd extracted expression of differential signatures (CREEDS) database to evaluate the similarity between GES profiles from drugs and their indicated diseases. Our study aims to explore the application domains of GES-guided drug–disease associations through the analysis of the similarity of GES profiles on known pairs of drug–disease associations, thereby identifying subgroups of drugs/diseases that are suitable for GES-guided drug repositioning approaches. Our results supported our hypothesis that the GES-guided drug–disease association method is better suited for some subgroups or pathways such as drugs and diseases associated with the immune system, diseases of the nervous system, non-chemotherapy drugs or the mTOR signaling pathway.
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Affiliation(s)
- Xiguang Qi
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, 3501 Terrace St Pittsburgh, PA 15261, USA; (X.Q.); (M.S.); (P.F.); (X.G.)
| | - Mingzhe Shen
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, 3501 Terrace St Pittsburgh, PA 15261, USA; (X.Q.); (M.S.); (P.F.); (X.G.)
| | - Peihao Fan
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, 3501 Terrace St Pittsburgh, PA 15261, USA; (X.Q.); (M.S.); (P.F.); (X.G.)
| | - Xiaojiang Guo
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, 3501 Terrace St Pittsburgh, PA 15261, USA; (X.Q.); (M.S.); (P.F.); (X.G.)
| | - Tianqi Wang
- Department of Biological Sciences, University of Pittsburgh School of Arts & Sciences, Pittsburgh, PA 15260, USA;
| | - Ning Feng
- Division of Cardiology, Vascular Medicine Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; (N.F.); (M.Z.)
| | - Manling Zhang
- Division of Cardiology, Vascular Medicine Institute, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; (N.F.); (M.Z.)
| | - Robert A. Sweet
- Department of Neurology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
- Correspondence: (R.A.S.); (L.K.); (L.W.); Tel.: +1 412-624-8118 (L.K.); +1 412-383-6089 (R.A.S.)
| | - Levent Kirisci
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, 3501 Terrace St Pittsburgh, PA 15261, USA; (X.Q.); (M.S.); (P.F.); (X.G.)
- Correspondence: (R.A.S.); (L.K.); (L.W.); Tel.: +1 412-624-8118 (L.K.); +1 412-383-6089 (R.A.S.)
| | - Lirong Wang
- Department of Pharmaceutical Sciences, Computational Chemical Genomics Screening Center, University of Pittsburgh School of Pharmacy, 3501 Terrace St Pittsburgh, PA 15261, USA; (X.Q.); (M.S.); (P.F.); (X.G.)
- Correspondence: (R.A.S.); (L.K.); (L.W.); Tel.: +1 412-624-8118 (L.K.); +1 412-383-6089 (R.A.S.)
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Martínez-Paz P, Aragón-Camino M, Gómez-Sánchez E, Lorenzo-López M, Gómez-Pesquera E, López-Herrero R, Sánchez-Quirós B, de la Varga O, Tamayo-Velasco Á, Ortega-Loubon C, García-Morán E, Gonzalo-Benito H, Heredia-Rodríguez M, Tamayo E. Gene Expression Patterns Distinguish Mortality Risk in Patients with Postsurgical Shock. J Clin Med 2020; 9:jcm9051276. [PMID: 32354167 PMCID: PMC7287660 DOI: 10.3390/jcm9051276] [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: 03/06/2020] [Revised: 04/21/2020] [Accepted: 04/22/2020] [Indexed: 12/29/2022] Open
Abstract
Nowadays, mortality rates in intensive care units are the highest of all hospital units. However, there is not a reliable prognostic system to predict the likelihood of death in patients with postsurgical shock. Thus, the aim of the present work is to obtain a gene expression signature to distinguish the low and high risk of death in postsurgical shock patients. In this sense, mRNA levels were evaluated by microarray on a discovery cohort to select the most differentially expressed genes between surviving and non-surviving groups 30 days after the operation. Selected genes were evaluated by quantitative real-time polymerase chain reaction (qPCR) in a validation cohort to validate the reliability of data. A receiver-operating characteristic analysis with the area under the curve was performed to quantify the sensitivity and specificity for gene expression levels, which were compared with predictions by established risk scales, such as acute physiology and chronic health evaluation (APACHE) and sequential organ failure assessment (SOFA). IL1R2, CD177, RETN, and OLFM4 genes were upregulated in the non-surviving group of the discovery cohort, and their predictive power was confirmed in the validation cohort. This work offers new biomarkers based on transcriptional patterns to classify the postsurgical shock patients according to low and high risk of death. The results present more accuracy than other mortality risk scores.
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Affiliation(s)
- Pedro Martínez-Paz
- Department of Surgery, Faculty of Medicine, University of Valladolid, 47005 Valladolid, Spain; (P.M.-P.); (E.G.-S.); (M.L.-L.); (E.G.-P.); (M.H.-R.); (E.T.)
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
| | - Marta Aragón-Camino
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Anesthesiology and Resuscitation Service, University Clinical Hospital, 47003 Valladolid, Spain
| | - Esther Gómez-Sánchez
- Department of Surgery, Faculty of Medicine, University of Valladolid, 47005 Valladolid, Spain; (P.M.-P.); (E.G.-S.); (M.L.-L.); (E.G.-P.); (M.H.-R.); (E.T.)
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Anesthesiology and Resuscitation Service, University Clinical Hospital, 47003 Valladolid, Spain
| | - Mario Lorenzo-López
- Department of Surgery, Faculty of Medicine, University of Valladolid, 47005 Valladolid, Spain; (P.M.-P.); (E.G.-S.); (M.L.-L.); (E.G.-P.); (M.H.-R.); (E.T.)
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Anesthesiology and Resuscitation Service, University Clinical Hospital, 47003 Valladolid, Spain
| | - Estefanía Gómez-Pesquera
- Department of Surgery, Faculty of Medicine, University of Valladolid, 47005 Valladolid, Spain; (P.M.-P.); (E.G.-S.); (M.L.-L.); (E.G.-P.); (M.H.-R.); (E.T.)
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Anesthesiology and Resuscitation Service, University Clinical Hospital, 47003 Valladolid, Spain
| | - Rocío López-Herrero
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Anesthesiology and Resuscitation Service, University Clinical Hospital, 47003 Valladolid, Spain
| | - Belén Sánchez-Quirós
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Anesthesiology and Resuscitation Service, University Clinical Hospital, 47003 Valladolid, Spain
| | - Olga de la Varga
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Anesthesiology and Resuscitation Service, University Clinical Hospital, 47003 Valladolid, Spain
| | - Álvaro Tamayo-Velasco
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Haematology and Hemotherapy Service, University Clinical Hospital, 47003 Valladolid, Spain
| | - Christian Ortega-Loubon
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Cardiac Surgery Service, University Clinical Hospital, 37007 Salamanca, Spain
| | - Emilio García-Morán
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Cardiology Service, University Clinical Hospital, 47003 Valladolid, Spain
- Correspondence:
| | - Hugo Gonzalo-Benito
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Institute of Health Sciences of Castile and Leon (IECSCYL), 47003 Valladolid, Spain
| | - María Heredia-Rodríguez
- Department of Surgery, Faculty of Medicine, University of Valladolid, 47005 Valladolid, Spain; (P.M.-P.); (E.G.-S.); (M.L.-L.); (E.G.-P.); (M.H.-R.); (E.T.)
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Anesthesiology and Resuscitation Service, University Hospital, 37007 Salamanca, Spain
| | - Eduardo Tamayo
- Department of Surgery, Faculty of Medicine, University of Valladolid, 47005 Valladolid, Spain; (P.M.-P.); (E.G.-S.); (M.L.-L.); (E.G.-P.); (M.H.-R.); (E.T.)
- BioCritic. Group for Biomedical Research in Critical Care Medicine, 47005 Valladolid, Spain; (M.A.-C.); (R.L.-H.); (B.S.-Q.); (O.d.l.V.); (A.T.-V.); (C.O.-L.); (H.G.-B.)
- Anesthesiology and Resuscitation Service, University Clinical Hospital, 47003 Valladolid, Spain
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Barneh F, Mirzaie M, Nickchi P, Tan TZ, Thiery JP, Piran M, Salimi M, Goshadrou F, Aref AR, Jafari M. Integrated use of bioinformatic resources reveals that co-targeting of histone deacetylases, IKBK and SRC inhibits epithelial-mesenchymal transition in cancer. Brief Bioinform 2020; 20:717-731. [PMID: 29726962 DOI: 10.1093/bib/bby030] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2018] [Revised: 03/04/2018] [Indexed: 02/07/2023] Open
Abstract
With the advent of high-throughput technologies leading to big data generation, increasing number of gene signatures are being published to predict various features of diseases such as prognosis and patient survival. However, to use these signatures for identifying therapeutic targets, use of additional bioinformatic tools is indispensible part of research. Here, we have generated a pipeline comprised of nearly 15 bioinformatic tools and enrichment statistical methods to propose and validate a drug combination strategy from already approved drugs and present our approach using published pan-cancer epithelial-mesenchymal transition (EMT) signatures as a case study. We observed that histone deacetylases were critical targets to tune expression of multiple epithelial versus mesenchymal genes. Moreover, SRC and IKBK were the principal intracellular kinases regulating multiple signaling pathways. To confirm the anti-EMT efficacy of the proposed target combination in silico, we validated expression of targets in mesenchymal versus epithelial subtypes of ovarian cancer. Additionally, we inhibited the pinpointed proteins in vitro using an invasive lung cancer cell line. We found that whereas low-dose mono-therapy failed to limit cell dispersion from collagen spheroids in a microfluidic device as a metric of EMT, the combination fully inhibited dissociation and invasion of cancer cells toward cocultured endothelial cells. Given the approval status and safety profiles of the suggested drugs, the proposed combination set can be considered in clinical trials.
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Affiliation(s)
- Farnaz Barneh
- Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.,Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Mehdi Mirzaie
- Department of Applied Mathematics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Payman Nickchi
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.,Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada
| | - Tuan Zea Tan
- Cancer Science Institute of Singapore, National University of Singapore, 14 Medical Drive, Singapore 117599, Singapore, Translational Centre for Development and Research, National University Health System, MD11, #03-10, 10 Medical Drive, Singapore 117597, Singapore
| | - Jean Paul Thiery
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore.,Institut Gustave Roussy, Inserm Unit 1186 Comprehensive Cancer Center, Villejuif, France.,CNRS UMR 7057 Matter and Complex Systems, University Paris Denis Diderot, Paris, France
| | - Mehran Piran
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Mona Salimi
- Department of Physiology and Pharmacology, Pasteur Institute of Iran, Tehran, Iran
| | - Fatemeh Goshadrou
- Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amir R Aref
- Department of Medical Oncology, Belfer Center for Applied Cancer Science, Dana-Farber Cancer Institute, Harvard Medical School, Boston 02215, USA
| | - Mohieddin Jafari
- Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
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45
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Shi G, Tufaro AP. Gene Signatures in Cutaneous Malignancies. CURRENT SURGERY REPORTS 2019. [DOI: 10.1007/s40137-019-0245-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Zhou YY, Chen LP, Zhang Y, Hu SK, Dong ZJ, Wu M, Chen QX, Zhuang ZZ, Du XJ. Integrated transcriptomic analysis reveals hub genes involved in diagnosis and prognosis of pancreatic cancer. Mol Med 2019; 25:47. [PMID: 31706267 PMCID: PMC6842480 DOI: 10.1186/s10020-019-0113-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 09/20/2019] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND The hunt for the molecular markers with specificity and sensitivity has been a hot area for the tumor treatment. Due to the poor diagnosis and prognosis of pancreatic cancer (PC), the excision rate is often low, which makes it more urgent to find the ideal tumor markers. METHODS Robust Rank Aggreg (RRA) methods was firstly applied to identify the differentially expressed genes (DEGs) between PC tissues and normal tissues from GSE28735, GSE15471, GSE16515, and GSE101448. Among these DEGs, the highly correlated genes were clustered using WGCNA analysis. The co-expression networks and molecular complex detection (MCODE) Cytoscape app were then performed to find the sub-clusters and confirm 35 candidate genes. For these genes, least absolute shrinkage and selection operator (lasso) regression model was applied and validated to build a diagnostic risk score model. Cox proportional hazard regression analysis was used and validated to build a prognostic model. RESULTS Based on integrated transcriptomic analysis, we identified a 19 gene module (SYCN, PNLIPRP1, CAP2, GNMT, MAT1A, ABAT, GPT2, ADHFE1, PHGDH, PSAT1, ERP27, PDIA2, MT1H, COMP, COL5A2, FN1, COL1A2, FAP and POSTN) as a specific predictive signature for the diagnosis of PC. Based on the two consideration, accuracy and feasibility, we simplified the diagnostic risk model as a four-gene model: 0.3034*log2(MAT1A)-0.1526*log2(MT1H) + 0.4645*log2(FN1) -0.2244*log2(FAP), log2(gene count). Besides, a four-hub gene module was also identified as prognostic model = - 1.400*log2(CEL) + 1.321*log2(CPA1) + 0.454*log2(POSTN) + 1.011*log2(PM20D1), log2(gene count). CONCLUSION Integrated transcriptomic analysis identifies two four-hub gene modules as specific predictive signatures for the diagnosis and prognosis of PC, which may bring new sight for the clinical practice of PC.
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Affiliation(s)
- Yang-Yang Zhou
- Department of Rheumatology and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province China
| | - Li-Ping Chen
- Department of Rheumatology and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province China
- Chemical Biology Research Center, College of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325000 Zhejiang China
| | - Yi Zhang
- Chemical Biology Research Center, College of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325000 Zhejiang China
| | - Sun-Kuan Hu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province China
| | - Zhao-Jun Dong
- Chemical Biology Research Center, College of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325000 Zhejiang China
| | - Ming Wu
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province China
| | - Qiu-Xiang Chen
- Department of Ultrasound, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province China
| | - Zhi-Zhi Zhuang
- Department of Rheumatology and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province China
| | - Xiao-Jing Du
- Department of Gastroenterology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000 Zhejiang Province China
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47
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Rashid NU, Li Q, Yeh JJ, Ibrahim JG. Modeling Between-Study Heterogeneity for Improved Replicability in Gene Signature Selection and Clinical Prediction. J Am Stat Assoc 2019; 115:1125-1138. [PMID: 33012902 DOI: 10.1080/01621459.2019.1671197] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
In the genomic era, the identification of gene signatures associated with disease is of significant interest. Such signatures are often used to predict clinical outcomes in new patients and aid clinical decision-making. However, recent studies have shown that gene signatures are often not replicable. This occurrence has practical implications regarding the generalizability and clinical applicability of such signatures. To improve replicability, we introduce a novel approach to select gene signatures from multiple datasets whose effects are consistently non-zero and account for between-study heterogeneity. We build our model upon some rank-based quantities, facilitating integration over different genomic datasets. A high dimensional penalized Generalized Linear Mixed Model (pGLMM) is used to select gene signatures and address data heterogeneity. We compare our method to some commonly used strategies that select gene signatures ignoring between-study heterogeneity. We provide asymptotic results justifying the performance of our method and demonstrate its advantage in the presence of heterogeneity through thorough simulation studies. Lastly, we motivate our method through a case study subtyping pancreatic cancer patients from four gene expression studies.
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Affiliation(s)
- Naim U Rashid
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.,Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A
| | - Quefeng Li
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A
| | - Jen Jen Yeh
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.,Department of Surgery, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A.,Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A
| | - Joseph G Ibrahim
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, U.S.A
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A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning. Cancers (Basel) 2019; 11:cancers11101606. [PMID: 31640282 PMCID: PMC6827106 DOI: 10.3390/cancers11101606] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 10/14/2019] [Accepted: 10/15/2019] [Indexed: 02/06/2023] Open
Abstract
The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.
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Liu B, Liu J, Liu K, Huang H, Li Y, Hu X, Wang K, Cao H, Cheng Q. A prognostic signature of five pseudogenes for predicting lower-grade gliomas. Biomed Pharmacother 2019; 117:109116. [DOI: 10.1016/j.biopha.2019.109116] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2019] [Revised: 06/07/2019] [Accepted: 06/10/2019] [Indexed: 10/26/2022] Open
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Issac MSM, Yousef E, Tahir MR, Gaboury LA. MCM2, MCM4, and MCM6 in Breast Cancer: Clinical Utility in Diagnosis and Prognosis. Neoplasia 2019; 21:1015-1035. [PMID: 31476594 PMCID: PMC6726925 DOI: 10.1016/j.neo.2019.07.011] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 07/24/2019] [Accepted: 07/29/2019] [Indexed: 12/25/2022] Open
Abstract
Breast cancer is a heterogeneous disease comprising the estrogen receptor (ER)-positive luminal subtype which is subdivided into luminal A and luminal B and ER-negative breast cancer which includes the triple-negative subtype. This study has four aims: 1) to examine whether Minichromosome Maintenance (MCM)2, MCM4, and MCM6 can be used as markers to differentiate between luminal A and luminal B subtypes; 2) to study whether MCM2, MCM4, and MCM6 are highly expressed in triple-negative breast cancer, as there is an urgent need to search for surrogate markers in this aggressive subtype, for drug development purposes; 3) to compare the prognostic values of these markers in predicting relapse-free survival; and 4) to compare the three approaches used for scoring the protein expression of these markers by immunohistochemistry (IHC). MCM2, MCM4, MCM6, and MKI67 mRNA expression was first studied using in silico analysis of available breast cancer datasets. We next used IHC to evaluate their protein expression on tissue microarrays using three scoring methods. MCM2, MCM4, and MCM6 can help in distinction between luminal A and luminal B whose therapeutic management and clinical outcomes are different. MCM2, MCM4, MCM6, and Ki-67 are highly expressed in breast cancer of high histological grades that comprise clinically aggressive tumors such as luminal B, HER2-positive, and triple-negative subtypes. Low transcript expression of these markers is associated with increased probability of relapse-free survival. A positive relationship exists among the three scoring methods of each of the four markers. An independent validation cohort is needed to confirm their clinical utility.
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Affiliation(s)
- Marianne Samir Makboul Issac
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montréal, Quebec, Canada H3T 1J4
| | - Einas Yousef
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montréal, Quebec, Canada H3T 1J4
| | - Muhammad Ramzan Tahir
- The University of Montreal Hospital Research Centre, Montréal, Quebec, Canada H2X 0A9
| | - Louis A Gaboury
- Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montréal, Quebec, Canada H3T 1J4; Department of Pathology and Cell Biology, Faculty of Medicine, Université de Montréal, Montréal, Quebec, Canada H3T 1J4.
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