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Article ; Online: Pan-Cancer Classification of Gene Expression Data Based on Artificial Neural Network Model

Claudia Cava / Christian Salvatore / Isabella Castiglioni

Applied Sciences, Vol 13, Iss 7355, p

2023 Volume 7355

Abstract: Although precision classification is a vital issue for therapy, cancer diagnosis has been shown to have serious constraints. In this paper, we proposed a deep learning model based on gene expression data to perform a pan-cancer classification on 16 ... ...

Abstract	Although precision classification is a vital issue for therapy, cancer diagnosis has been shown to have serious constraints. In this paper, we proposed a deep learning model based on gene expression data to perform a pan-cancer classification on 16 cancer types. We used principal component analysis (PCA) to decrease data dimensionality before building a neural network model for pan-cancer prediction. The performance of accuracy was monitored and optimized using the Adam algorithm. We compared the results of the model with a random forest classifier and XGBoost. The results show that the neural network model and random forest achieve high and similar classification performance (neural network mean accuracy: 0.84; random forest mean accuracy: 0.86; XGBoost mean accuracy: 0.90). Thus, we suggest future studies of neural network, random forest and XGBoost models for the detection of cancer in order to identify early treatment approaches to enhance cancer survival.
Keywords	pan-cancer ; gene expression ; neural network ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2023-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
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Article ; Online: Roles of RNA-binding proteins in neurological disorders, COVID-19, and cancer.

Sanya, Daniel Ruben Akiola / Cava, Claudia / Onésime, Djamila

Human cell

2022 Volume 36, Issue 2, Page(s) 493–514

Abstract: RNA-binding proteins (RBPs) have emerged as important players in multiple biological processes including transcription regulation, splicing, R-loop homeostasis, DNA rearrangement, miRNA function, biogenesis, and ribosome biogenesis. A large number of ... ...

Abstract	RNA-binding proteins (RBPs) have emerged as important players in multiple biological processes including transcription regulation, splicing, R-loop homeostasis, DNA rearrangement, miRNA function, biogenesis, and ribosome biogenesis. A large number of RBPs had already been identified by different approaches in various organisms and exhibited regulatory functions on RNAs' fate. RBPs can either directly or indirectly interact with their target RNAs or mRNAs to assume a key biological function whose outcome may trigger disease or normal biological events. They also exert distinct functions related to their canonical and non-canonical forms. This review summarizes the current understanding of a wide range of RBPs' functions and highlights their emerging roles in the regulation of diverse pathways, different physiological processes, and their molecular links with diseases. Various types of diseases, encompassing colorectal carcinoma, non-small cell lung carcinoma, amyotrophic lateral sclerosis, and Severe acute respiratory syndrome coronavirus 2, aberrantly express RBPs. We also highlight some recent advances in the field that could prompt the development of RBPs-based therapeutic interventions.
MeSH term(s)	Humans ; COVID-19 ; MicroRNAs/genetics ; RNA-Binding Proteins/genetics ; Nervous System Diseases ; Neoplasms
Chemical Substances	MicroRNAs ; RNA-Binding Proteins
Language	English
Publishing date	2022-12-18
Publishing country	Japan
Document type	Journal Article ; Review
ZDB-ID	1149134-6
ISSN	1749-0774 ; 0914-7470
ISSN (online)	1749-0774
ISSN	0914-7470
DOI	10.1007/s13577-022-00843-w
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Article: Potential drugs against COVID-19 revealed by gene expression profile, molecular docking and molecular dynamic simulation.

Cava, Claudia / Bertoli, Gloria / Castiglioni, Isabella

Future virology

2021

Abstract: Aim: ...

Abstract	Aim:
Language	English
Publishing date	2021-07-20
Publishing country	England
Document type	Journal Article
ISSN	1746-0794
ISSN	1746-0794
DOI	10.2217/fvl-2020-0392
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Article ; Online: Phenome-wide genetic-correlation analysis and genetically informed causal inference of amyotrophic lateral sclerosis.

D'Antona, Salvatore / Pathak, Gita A / Koller, Dora / Porro, Danilo / Cava, Claudia / Polimanti, Renato

Human genetics

2023 Volume 142, Issue 8, Page(s) 1173–1183

Abstract: Leveraging genome-wide association statistics generated from a large study of amyotrophic lateral sclerosis (ALS; 29,612 cases and 122,656 controls) and UK Biobank (UKB; 4,024 phenotypes, up to 361,194 participants), we conducted a phenome-wide analysis ... ...

Abstract	Leveraging genome-wide association statistics generated from a large study of amyotrophic lateral sclerosis (ALS; 29,612 cases and 122,656 controls) and UK Biobank (UKB; 4,024 phenotypes, up to 361,194 participants), we conducted a phenome-wide analysis of ALS genetic liability and identified 46 genetically correlated traits, such as fluid intelligence score (r
MeSH term(s)	Humans ; Amyotrophic Lateral Sclerosis/genetics ; Genome-Wide Association Study ; Duodenitis ; Phenotype ; Gastritis ; Mendelian Randomization Analysis
Language	English
Publishing date	2023-02-11
Publishing country	Germany
Document type	Journal Article
ZDB-ID	223009-4
ISSN	1432-1203 ; 0340-6717
ISSN (online)	1432-1203
ISSN	0340-6717
DOI	10.1007/s00439-023-02525-5
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Article ; Online: Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles.

Cava, Claudia / Pisati, Mirko / Frasca, Marco / Castiglioni, Isabella

Medicina (Kaunas, Lithuania)

2021 Volume 57, Issue 3

Abstract: Background and ... ...

Abstract	Background and Objectives
MeSH term(s)	Biomarkers, Tumor/genetics ; Breast Neoplasms/genetics ; DNA Copy Number Variations/genetics ; Gene Expression Regulation, Neoplastic ; Humans ; Membrane Proteins ; Protein Phosphatase 2 ; RNA-Binding Proteins ; Transcriptome/genetics
Chemical Substances	Biomarkers, Tumor ; MTDH protein, human ; Membrane Proteins ; PPP2R5E protein, human ; RNA-Binding Proteins ; Protein Phosphatase 2 (EC 3.1.3.16)
Language	English
Publishing date	2021-03-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2188113-3
ISSN	1648-9144 ; 1010-660X
ISSN (online)	1648-9144
ISSN	1010-660X
DOI	10.3390/medicina57030261
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Article ; Online: From genetic correlations of Alzheimer's disease to classification with artificial neural network models.

Cava, Claudia / D'Antona, Salvatore / Maselli, Francesca / Castiglioni, Isabella / Porro, Danilo

Functional & integrative genomics

2023 Volume 23, Issue 4, Page(s) 293

Abstract: Sporadic Alzheimer's disease (AD) is a complex neurological disorder characterized by many risk loci with potential associations with different traits and diseases. AD, characterized by a progressive loss of neuronal functions, manifests with different ... ...

Abstract	Sporadic Alzheimer's disease (AD) is a complex neurological disorder characterized by many risk loci with potential associations with different traits and diseases. AD, characterized by a progressive loss of neuronal functions, manifests with different symptoms such as decline in memory, movement, coordination, and speech. The mechanisms underlying the onset of AD are not always fully understood, but involve a multiplicity of factors. Early diagnosis of AD plays a central role as it can offer the possibility of early treatment, which can slow disease progression. Currently, the methods of diagnosis are cognitive testing, neuroimaging, or cerebrospinal fluid analysis that can be time-consuming, expensive, invasive, and not always accurate. In the present study, we performed a genetic correlation analysis using genome-wide association statistics from a large study of AD and UK Biobank, to examine the association of AD with other human traits and disorders. In addition, since hippocampus, a part of cerebral cortex could play a central role in several traits that are associated with AD; we analyzed the gene expression profiles of hippocampus of AD patients applying 4 different artificial neural network models. We found 65 traits correlated with AD grouped into 9 clusters: medical conditions, fluid intelligence, education, anthropometric measures, employment status, activity, diet, lifestyle, and sexuality. The comparison of different 4 neural network models along with feature selection methods on 5 Alzheimer's gene expression datasets showed that the simple basic neural network model obtains a better performance (66% of accuracy) than other more complex methods with dropout and weight regularization of the network.
MeSH term(s)	Humans ; Alzheimer Disease/diagnosis ; Alzheimer Disease/genetics ; Genome-Wide Association Study ; Chromosome Mapping ; Hippocampus ; Neural Networks, Computer
Language	English
Publishing date	2023-09-08
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2014670-X
ISSN	1438-7948 ; 1438-793X
ISSN (online)	1438-7948
ISSN	1438-793X
DOI	10.1007/s10142-023-01228-4
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Zs.A 5230: Show issues

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Article ; Online: Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data.

Cava, Claudia / Sabetian, Soudabeh / Castiglioni, Isabella

Entropy (Basel, Switzerland)

2021 Volume 23, Issue 2

Abstract: The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or ... ...

Abstract	The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein-protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug-protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.
Language	English
Publishing date	2021-02-11
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e23020225
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Article ; Online: In silico perturbation of drug targets in pan-cancer analysis combining multiple networks and pathways.

Cava, Claudia / Castiglioni, Isabella

Gene

2019 Volume 698, Page(s) 100–106

Abstract: The knowledge of cancer cell response to conventional therapies is crucial in order to choose the correct therapy of patients affected by cancer. The major problem is generally attributed to the lack of specific biological processes able to predict the ... ...

Abstract	The knowledge of cancer cell response to conventional therapies is crucial in order to choose the correct therapy of patients affected by cancer. The major problem is generally attributed to the lack of specific biological processes able to predict the therapy efficacy. Here, we optimized a computational method for the analysis of gene networks able to detect and quantify the effects of a drug in a pan-cancer study. Overall, our method, using several network topological measures has identified a cancer gene network with a key role in biological processes. The gene network, able to classify with a good performance cancer vs normal samples, was modulated in silico to evaluate the effects of new or approved drugs. This computational model could offer an interesting hint to decipher molecular mechanisms contributing to resistance or inefficacy of drugs.
MeSH term(s)	Biomarkers, Pharmacological ; Computational Biology/methods ; Computer Simulation ; Gene Expression Profiling/methods ; Gene Expression Regulation, Neoplastic/genetics ; Gene Regulatory Networks/genetics ; Gene Regulatory Networks/physiology ; Humans ; Machine Learning ; Neoplasms/genetics ; Neoplasms/metabolism ; Protein Interaction Maps/genetics ; Signal Transduction/genetics ; Treatment Outcome
Chemical Substances	Biomarkers, Pharmacological
Language	English
Publishing date	2019-03-03
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	391792-7
ISSN	1879-0038 ; 0378-1119
ISSN (online)	1879-0038
ISSN	0378-1119
DOI	10.1016/j.gene.2019.02.064
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Article ; Online: Consequences of exposure to pollutants on respiratory health: From genetic correlations to causal relationships.

D'Antona, Salvatore / Castiglioni, Isabella / Porro, Danilo / Cava, Claudia

PloS one

2022 Volume 17, Issue 11, Page(s) e0277235

Abstract: Modern society grew rapidly over the last few decades and this led to an alarming increase in air pollutants and a worsening of the human health, especially in relation to the respiratory system. Indeed, chronic respiratory diseases were the third main ... ...

Abstract	Modern society grew rapidly over the last few decades and this led to an alarming increase in air pollutants and a worsening of the human health, especially in relation to the respiratory system. Indeed, chronic respiratory diseases were the third main cause of death in 2017, with over 3 million of deaths. Furthermore, the pollution has considerable consequences both for burden medical expenses and environmental. However, the mechanisms linking pollutants to the onset of these diseases remain unclear. Thus, in this study we addressed this problem through the United Kingdom BioBank database, analyzing 170 genome-wide association studies (103 related to respiratory diseases and 67 related to pollutants). We analyzed the genetic correlations and causal relationships of these traits, leveraging the summary statistics and bioinformatics packages such as Linkage Disequilibrium Score Regression and Latent Causal Variable. We obtained 158 significant genetic correlations and subsequently we analyzed them through the Latent Causal Variable analysis, obtaining 20 significant causal relationships. The most significant were between "Workplace full of chemicals or other fumes: Sometimes" and "Condition that has ever been diagnosed by a doctor: Asthma" and between "Workplace very dusty: Sometimes" and "Condition that has ever been diagnosed by a doctor: Emphysema or chronic bronchitis". Finally, we identified single nucleotide polymorphisms independently associated with sveral pollutants to analyze the genes and pathways that could be involved in the onset of the aforementioned respiratory system disorders and that could be useful clinical target. This study highlighted how crucial are the air condition of the working environments and the type of transport used in the onset of respiratory-related morbidity. Based on that, we also suggested some interventions, in order to improve quality life and develop new and eco-friendly society and life style, such as improving indoor air circulation, the use of public transport and urban reforestation.
MeSH term(s)	Humans ; Environmental Pollutants ; Genome-Wide Association Study ; Air Pollutants/adverse effects ; Respiratory Tract Diseases/etiology ; Respiratory Tract Diseases/genetics ; Respiratory System
Chemical Substances	Environmental Pollutants ; Air Pollutants
Language	English
Publishing date	2022-11-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0277235
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Article ; Online: Integration of Molecular Docking and In Vitro Studies

Claudia Cava / Isabella Castiglioni

Applied Sciences, Vol 10, Iss 6981, p

A Powerful Approach for Drug Discovery in Breast Cancer

2020 Volume 6981

Abstract: Molecular docking in the pharmaceutical industry is a powerful in silico approach for discovering novel therapies for unmet medical needs predicting drug–target interactions. It not only provides binding affinity between drugs and targets at the atomic ... ...

Abstract	Molecular docking in the pharmaceutical industry is a powerful in silico approach for discovering novel therapies for unmet medical needs predicting drug–target interactions. It not only provides binding affinity between drugs and targets at the atomic level, but also elucidates the fundamental pharmacological properties of specific drugs. The purpose of this review was to illustrate newer and emergent uses of docking when combined with in vitro techniques for drug discovery in metastatic breast cancer. We grouped the selected articles into five main categories; namely, systematic repositioning of drugs, natural drugs, new synthesized molecules, combinations of drugs, and drug latentiation. We focused on new promising drugs that have a good affinity with their targets, thus inducing a favorable biological response. This review suggests that the integration of molecular docking and in vitro studies can accelerate cancer drug discovery showing a good consistency of the results between the two approaches.
Keywords	molecular docking ; in vitro ; metastatic breast cancer ; drug discovery ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	004
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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