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Pham DT, Tran TD. Drivergene.net: A Cytoscape app for the identification of driver nodes of large-scale complex networks and case studies in discovery of drug target genes. Comput Biol Med 2024; 179:108888. [PMID: 39047507 DOI: 10.1016/j.compbiomed.2024.108888] [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] [Received: 02/23/2024] [Revised: 06/15/2024] [Accepted: 07/11/2024] [Indexed: 07/27/2024]
Abstract
There are no tools to identify driver nodes of large-scale networks in approach of competition-based controllability. This study proposed a novel method for this computation of large-scale networks. It implemented the method in a new Cytoscape plug-in app called Drivergene.net. Experiments of the software on large-scale biomolecular networks have shown outstanding speed and computing power. Interestingly, 86.67% of the top 10 driver nodes found on these networks are anticancer drug target genes that reside mostly at the innermost K-cores of the networks. Finally, compared method with those of five other researchers and confirmed that the proposed method outperforms the other methods on identification of anticancer drug target genes. Taken together, Drivergene.net is a reliable tool that efficiently detects not only drug target genes from biomolecular networks but also driver nodes of large-scale complex networks. Drivergene.net with a user manual and example datasets are available https://github.com/tinhpd/Drivergene.git.
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Affiliation(s)
- Duc-Tinh Pham
- Complex Systems and Bioinformatics Lab, Hanoi University of Industry, 298 Cau Dien Street, Bac Tu Liem District, Hanoi, Viet Nam; Graduate University of Science and Technology, Academy of Science and Technology Viet Nam, 18 Hoang Quoc Viet Street, Cau Giay District, Hanoi, Viet Nam
| | - Tien-Dzung Tran
- Complex Systems and Bioinformatics Lab, Hanoi University of Industry, 298 Cau Dien Street, Bac Tu Liem District, Hanoi, Viet Nam; Faculty of Information and Communication Technology, Hanoi University of Industry, 298 Cau Dien Street, Bac Tu Liem District, Hanoi, Viet Nam.
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2
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Pukhta IR, Rout RK. Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features. Comput Methods Biomech Biomed Engin 2024:1-16. [PMID: 38424698 DOI: 10.1080/10255842.2024.2311322] [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: 07/04/2023] [Accepted: 01/20/2024] [Indexed: 03/02/2024]
Abstract
Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still problematic since other genomic factors cannot be fully integrated for combined analyses. This research paper presents a novel gene identification and segregation model with five key processes (a) pre-processing, (b) treatment of class imbalances, (c) feature extraction, (d) feature selection, and (e) gene classification. To increase the data quality, the gathered initial information is first pre-processed utilizing data cleaning and data normalization. This turns the raw data into something that is both useful and effective. In actuality, the sample is skewed against drivers because passenger mutation markers appear in proportionally less instances than drivers do. To address the Class Imbalance Problem, improved K-Means + SMOTE are applied to the preprocessed data. The most crucial characteristics, including those at the gene and mutation levels, are then extracted from the balanced dataset. To lessen the computational load in terms of time, the best features from the retrieved features are selected using Forensic interpretation tailored hunger food search optimization (FIHFSO). The ideal features are used to train the deep learning classifier that conducts the separation procedure. In this research, an Improved Recurrent Neural Network (I-RNN) is used to make a final decision about genes. At 90% of learning percentage, the accuracy of the proposed method achieves 0.98% of 0.83, 0.81, 0.65, 0.80, 0.92 and 0.63% which is compared to the other methods like HGS, FBIO, AOA, AO, GOA and PRO respectively.
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Affiliation(s)
- Irfan Rashid Pukhta
- Assistant Professor, Department of Computer Science and Engineering National Institute of Technology, Srinagar, Jammu and Kashmir 190006, India
| | - Ranjeet Kumar Rout
- Assistant Professor, Department of Computer Science and Engineering National Institute of Technology, Srinagar, Jammu and Kashmir 190006, India
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Rakhshaninejad M, Fathian M, Shirkoohi R, Barzinpour F, Gandomi AH. Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach. BMC Bioinformatics 2024; 25:33. [PMID: 38253993 PMCID: PMC10810249 DOI: 10.1186/s12859-024-05657-1] [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] [Received: 12/03/2023] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.
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Affiliation(s)
- Morteza Rakhshaninejad
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran
| | - Mohammad Fathian
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran.
| | - Reza Shirkoohi
- Cancer Biology Research Center, Cancer Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Keshavarz Boulevard, Tehran, 1419733141, Tehran, Iran
| | - Farnaz Barzinpour
- Industrial Engineering Department, Iran University of Science and Technology, Hengam Street, Tehran, 1684613114, Tehran, Iran
| | - Amir H Gandomi
- Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, 2007, NSW, Australia
- University Research and Innovation Center (EKIK), Óbuda University, Budapest, 1034, Hungary
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4
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Abstract
Calcium ions (Ca2+) are the basis of a unique and potent array of cellular responses. Calmodulin (CaM) is a small but vital protein that is able to rapidly transmit information about changes in Ca2+ concentrations to its regulatory targets. CaM plays a critical role in cellular Ca2+ signaling, and interacts with a myriad of target proteins. Ca2+-dependent modulation by CaM is a major component of a diverse array of processes, ranging from gene expression in neurons to the shaping of the cardiac action potential in heart cells. Furthermore, the protein sequence of CaM is highly evolutionarily conserved, and identical CaM proteins are encoded by three independent genes (CALM1-3) in humans. Mutations within any of these three genes may lead to severe cardiac deficits including severe long QT syndrome (LQTS) and/or catecholaminergic polymorphic ventricular tachycardia (CPVT). Research into disease-associated CaM variants has identified several proteins modulated by CaM that are likely to underlie the pathogenesis of these calmodulinopathies, including the cardiac L-type Ca2+ channel (LTCC) CaV1.2, and the sarcoplasmic reticulum Ca2+ release channel, ryanodine receptor 2 (RyR2). Here, we review the research that has been done to identify calmodulinopathic CaM mutations and evaluate the mechanisms underlying their role in disease.
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Affiliation(s)
- John W. Hussey
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Worawan B. Limpitikul
- Department of Medicine, Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
| | - Ivy E. Dick
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, USA
- CONTACT Ivy E. Dick School of Medicine, University of Maryland, Baltimore, MD21210
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Petinrin OO, Saeed F, Toseef M, Liu Z, Basurra S, Muyide IO, Li X, Lin Q, Wong KC. Machine Learning in Metastatic Cancer Research: Potentials, Possibilities, and Prospects. Comput Struct Biotechnol J 2023; 21:2454-2470. [PMID: 37077177 PMCID: PMC10106342 DOI: 10.1016/j.csbj.2023.03.046] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 03/26/2023] [Accepted: 03/27/2023] [Indexed: 03/31/2023] Open
Abstract
Cancer has received extensive recognition for its high mortality rate, with metastatic cancer being the top cause of cancer-related deaths. Metastatic cancer involves the spread of the primary tumor to other body organs. As much as the early detection of cancer is essential, the timely detection of metastasis, the identification of biomarkers, and treatment choice are valuable for improving the quality of life for metastatic cancer patients. This study reviews the existing studies on classical machine learning (ML) and deep learning (DL) in metastatic cancer research. Since the majority of metastatic cancer research data are collected in the formats of PET/CT and MRI image data, deep learning techniques are heavily involved. However, its black-box nature and expensive computational cost are notable concerns. Furthermore, existing models could be overestimated for their generality due to the non-diverse population in clinical trial datasets. Therefore, research gaps are itemized; follow-up studies should be carried out on metastatic cancer using machine learning and deep learning tools with data in a symmetric manner.
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Subramanian S, George TP, George J, Thomas T. Ensemble learning based assessment of the role of transcription factors in gene expression. Comput Biol Med 2023; 152:106455. [PMID: 36566628 DOI: 10.1016/j.compbiomed.2022.106455] [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: 06/12/2022] [Revised: 11/29/2022] [Accepted: 12/19/2022] [Indexed: 12/24/2022]
Abstract
Cancer cells are formed when the associated, active genes fail to function the way they are meant to function. Multiple genes collectively control cell growth by activating a proper set of genes. Regulation of gene expression is controlled through the combined effort of multiple regulatory elements. Transcription of each gene is affected differently according to the combinatorial patterns of regulatory elements bound in the nearby regions. Identifying and analysing such patterns will give a better insight into the cell function. The main focus of this study is on developing a computational model to predict the functional role of transcriptional factors residing between divergent gene pairs. Acute Myeloid Leukaemia (AML) gene expression data from GEO and the two TFs EP300 and CTCF binding data calibrated in k562 cell line from ENCODE consortium are taken as a case study.
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Affiliation(s)
| | | | - Jeslin George
- Department of Statistical Sciences, Kannur University, India.
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He Z, Lin Y, Wei R, Liu C, Jiang D. Repulsion and attraction in searching: A hybrid algorithm based on gravitational kernel and vital few for cancer driver gene prediction. Comput Biol Med 2022; 151:106236. [PMID: 36370584 DOI: 10.1016/j.compbiomed.2022.106236] [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: 07/26/2022] [Revised: 10/15/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
By taking a new perspective to combine a machine learning method with an evolutionary algorithm, a new hybrid algorithm is developed to predict cancer driver genes. Firstly, inspired by the search strategy with the capability of global search in evolutionary algorithms, a gravitational kernel is proposed to act on the full range of gene features. Constructed by fusing PPI and mutation features, the gravitational kernel is capable to produce repulsion effects. The candidate genes with greater mutation effects and PPI have higher similarity scores. According to repulsion, the similarity score of these promising genes is larger than ordinary genes, which is beneficial to search for these promising genes. Secondly, inspired by the idea of elite populations related to evolutionary algorithms, the concept of vital few is proposed. Targeted at a local scale, it acts on the candidate genes associated with vital few genes. Under attraction effect, these vital few driver genes attract those with similar mutational effects to them, which leads to greater similarity scores. Lastly, the model and parameters are optimized by using an evolutionary algorithm, so as to obtain the optimal model and parameters for cancer driver gene prediction. Herein, a comparison is performed with six other advanced methods of cancer driver gene prediction. According to the experimental results, the method proposed in this study outperforms these six state-of-the-art algorithms on the pan-oncogene dataset.
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Affiliation(s)
- Zhihui He
- Department of Computer Science, Shantou University, 515063, China
| | - Yingqing Lin
- Department of Computer Science, Shantou University, 515063, China
| | - Runguo Wei
- Department of Computer Science, Shantou University, 515063, China
| | - Cheng Liu
- Department of Computer Science, Shantou University, 515063, China
| | - Dazhi Jiang
- Department of Computer Science, Shantou University, 515063, China; Guangdong Provincial Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou 510399, China.
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Rahbari R, Rasmi Y, Khadem-Ansari MH, Abdi M. The role of histone deacetylase 3 in breast cancer. Med Oncol 2022; 39:84. [PMID: 35578147 DOI: 10.1007/s12032-022-01681-4] [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: 12/11/2021] [Accepted: 02/05/2022] [Indexed: 11/25/2022]
Abstract
It has been recently revealed that Histone Deacetylase (HDAC) 3, a unique member of the HDACs family, can trigger and progress cancers by alternation in genes expression and proteins activity. Epigenetic modifications by HDACs have been studied well in various cancer cells. Recent studies have focused on the HDAC enzymes as a possible target in cancer therapy. There are significant documents on upregulation of HDAC3 in breast cancer (BC) cells which suggest an oncogenic role for this enzyme. Interestingly, some studies showed that HDAC3 inhibition could be considered as a promising target in breast cancer therapy, and thus far, several inhibitors from different nature have been introduced. In this review, we discussed the function and highlight the existing inhibitors of HDAC3 in BC pathogenesis and therapy.
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Affiliation(s)
- Rezgar Rahbari
- Department of Biochemistry, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran
| | - Yousef Rasmi
- Department of Biochemistry, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran
| | | | - Mohammad Abdi
- Cellular and Molecular Research Center, Research Institute for Health Development, Kurdistan University of Medical Sciences, Sanandaj, Iran. .,Department of Clinical Biochemistry, Faculty of Medicine, Kurdistan University of Medical Sciences, Sanandaj, Iran.
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Pospiech K, Orzechowska M, Nowakowska M, Anusewicz D, Płuciennik E, Kośla K, Bednarek AK. TGFα-EGFR pathway in breast carcinogenesis, association with WWOX expression and estrogen activation. J Appl Genet 2022; 63:339-359. [PMID: 35290621 PMCID: PMC8979909 DOI: 10.1007/s13353-022-00690-3] [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: 11/29/2021] [Revised: 02/22/2022] [Accepted: 03/02/2022] [Indexed: 11/29/2022]
Abstract
WWOX is a tumor-suppressive steroid dehydrogenase, which relationship with hormone receptors was shown both in animal models and breast cancer patients. Herein, through nAnT-iCAGE high-throughput gene expression profiling, we studied the interplay of estrogen receptors and the WWOX in breast cancer cell lines (MCF7, T47D, MDA-MB-231, BT20) under estrogen stimulation and either introduction of the WWOX gene by retroviral transfection (MDA-MB-231, T47D) or silenced with shRNA (MCF7, BT20). Additionally, we evaluated the consequent biological characteristics by proliferation, apoptosis, invasion, and adhesion assays. TGFα-EGFR signaling was found to be significantly affected in all examined breast cancer cell lines in response to estrogen and strongly associated with the level of WWOX expression, especially in ER-positive MCF7 cells. Under the influence of 17β-estradiol presence, biological characteristics of the cell lines were also delineated. The study revealed modulation of adhesion, invasion, and apoptosis. The obtained results point at a complex role of the WWOX gene in the carcinogenesis of the breast tissue, which seems to be closely related to the presence of estrogen α and/or β receptors.
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Affiliation(s)
- Karolina Pospiech
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
| | | | - Magdalena Nowakowska
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
| | - Dorota Anusewicz
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
| | - Elżbieta Płuciennik
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
| | - Katarzyna Kośla
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland
| | - Andrzej K Bednarek
- Department of Molecular Carcinogenesis, Medical University of Lodz, Lodz, Poland.
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Kivrak M. Breast Cancer Risk Prediction with Stochastic Gradient Boosting. CLINICAL CANCER INVESTIGATION JOURNAL 2022. [DOI: 10.51847/21qrrklo4y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Rajput A, Kumar M. Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning. Mol Divers 2021; 26:1635-1644. [PMID: 34357513 PMCID: PMC8343361 DOI: 10.1007/s11030-021-10291-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Accepted: 07/28/2021] [Indexed: 01/17/2023]
Abstract
Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure-activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC50 values were extracted from the 'DrugRepV' database. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T274) dataset. The robustness of the developed models was cross-evaluated using William's plot. The highly robust computational models are integrated into the web server. The 'anti-Ebola' web server is freely available at https://bioinfo.imtech.res.in/manojk/antiebola . We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus.
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Affiliation(s)
- Akanksha Rajput
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh, 160036, India
| | - Manoj Kumar
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific and Industrial Research (CSIR), Sector 39A, Chandigarh, 160036, India. .,Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
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