Selymatra: A web application for protein‐profiling analysis of mass spectra

Nardone, Davide; Ciaramella, Angelo; Cerreta, Mariangela; Pulcrano, Salvatore; Bellenchi, Gian Carlo; Leone, Linda; Manco, Giuseppe; Febbraio, Ferdinando

doi:10.1002/bab.2249

Cited by 2 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the next future, the authors will perform this methodology on new patient-derived samples obtained by clinical practices and they will explore and compare new feature selection methodologies (e.g., sparse coding [ 17 ], learning with Bayesian framework [ 18 ]), and temporal behaviour of T-UCRs for robust prognosis [ 19 ]. Furthermore, the panel can be used for developing an eXplainable Artificial Intelligent (XAI) based decision support system that helps clinicians in diagnosis of bladder cancer from tissue or fluid samples also by user-friendly applications [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

A new biomarker panel of ultraconserved long non-coding RNAs for bladder cancer prognosis by a machine learning based methodology

et al. 2023

Self Cite

View full text Add to dashboard Cite

Background Recent studies have indicated that a special class of long non-coding RNAs (lncRNAs), namely Transcribed-Ultraconservative Regions are transcribed from specific DNA regions (T-UCRs), 100$$\%$$ % conserved in human, mouse, and rat genomes. This is noticeable, as lncRNAs are usually poorly conserved. Despite their peculiarities, T-UCRs remain very understudied in many diseases, including cancer and, yet, it is known that dysregulation of T-UCRs is associated with cancer as well as with human neurological, cardiovascular, and developmental pathologies. We have recently reported the T-UCR uc.8+ as a potential prognostic biomarker in bladder cancer. Results The aim of this work is to develop a methodology, based on machine learning techniques, for the selection of a predictive signature panel for bladder cancer onset. To this end, we analyzed the expression profiles of T-UCRs from surgically removed normal and bladder cancer tissues, by using custom expression microarray. Bladder tissue samples from 24 bladder cancer patients (12 Low Grade and 12 High Grade), with complete clinical data, and 17 control samples from normal bladder epithelium were analysed. After the selection of preferentially expressed and statistically significant T-UCRs, we adopted an ensemble of statistical and machine learning based approaches (i.e., logistic regression, Random Forest, XGBoost and LASSO) for ranking the most important diagnostic molecules. We identified a signature panel of 13 selected T-UCRs with altered expression profiles in cancer, able to efficiently discriminate between normal and bladder cancer patient samples. Also, using this signature panel, we classified bladder cancer patients in four groups, each characterized by a different survival extent. As expected, the group including only Low Grade bladder cancer patients had greater overall survival than patients with the majority of High Grade bladder cancer. However, a specific signature of deregulated T-UCRs identifies sub-types of bladder cancer patients with different prognosis regardless of the bladder cancer Grade. Conclusions Here we present the results for the classification of bladder cancer (Low and High Grade) patient samples and normal bladder epithelium controls by using a machine learning application. The T-UCR’s panel can be used for learning an eXplainable Artificial Intelligent model and develop a robust decision support system for bladder cancer early diagnosis providing urinary T-UCRs data of new patients. The use of this system instead of the current methodology will result in a non-invasive approach, reducing uncomfortable procedures (such as cystoscopy) for the patients. Overall, these results raise the possibility of new automatic systems, which could help the RNA-based prognosis and/or the cancer therapy in bladder cancer patients, and demonstrate the successful application of Artificial Intelligence to the definition of an independent prognostic biomarker panel.

show abstract

Section: Discussionmentioning

confidence: 99%

A new biomarker panel of ultraconserved long non-coding RNAs for bladder cancer prognosis by a machine learning based methodology

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

Selymatra: A web application for protein‐profiling analysis of mass spectra

Nardone

Ciaramella

Cerreta

et al. 2021

Biotech and App Biochem

View full text Add to dashboard Cite

Surface Enhanced Laser Desorption/Ionization-Time Of Flight (SELDI-TOF) Mass Spectrometry is a variant of the MALDI. It is used in many cases especially for the analysis of protein profiling and for preliminary screening of biomarkers in complex samples.Unfortunately, these analyses are time-consuming and protein identification is generally strictly limited. SELDI-TOF analysis of mass spectra (SELYMATRA) is a Web Application (WA) developed to reduce these limitations by (i) automating the identification processes and (ii) introducing the possibility to predict proteins in complex mixtures from cells and tissues.The WA architectural pattern is the Model-View-Controller, commonly used in software development. The WA compares the mass value between two mass spectra (sample vs This article is protected by copyright. All rights reserved. control) to extract differences, and, according to the set parameters, it queries a local database to predict most likely proteins based on their masses and different expression amplification.The WA was validated in a cellular model overexpressing a tagged NURR1 receptor, being able to recognize the tagged protein in the profiling of transformed cells. A help page, including description of parameters for WA use, is available on the website.

show abstract

Selymatra: A web application for protein‐profiling analysis of mass spectra

Cited by 2 publications

References 36 publications

A new biomarker panel of ultraconserved long non-coding RNAs for bladder cancer prognosis by a machine learning based methodology

A new biomarker panel of ultraconserved long non-coding RNAs for bladder cancer prognosis by a machine learning based methodology

Selymatra: A web application for protein‐profiling analysis of mass spectra

Contact Info

Product

Resources

About