Evidence map›Paper›PMID 34050359›Full record

ArticleBriefings in bioinformatics2021

A multiple network-based bioinformatics pipeline for the study of molecular mechanisms in oncological diseases for personalized medicine.

Serena Dotolo, Anna Marabotti, Anna Maria Rachiglio, Riziero Esposito Abate, Marco Benedetto, Fortunato Ciardiello, Antonella De Luca, Nicola Normanno, Angelo Facchiano, Roberto Tagliaferri

Open access · hybridAbstract read
In one paragraph

Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 38% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 9 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 4 institutions in 1 country.

Serena DotoloDipartimento di Scienze Aziendali, Management & Innovation Systems, Università degli Studi di Salerno, Fisciano (SA), Italy.
Anna MarabottiDipartimento di Chimica e Biologia "A. Zambelli", Università degli Studi di Salerno, Fisciano (SA), Italy.
Anna Maria RachiglioCell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Naples, Italy.
Riziero Esposito AbateCell Biology and Biotherapy Unit, Istituto Nazionale Tumori -IRCCS - Fondazione G. Pascale, Naples, Italy.
Marco BenedettoR&D Department, Kelyon S.r.l., Naples, Italy.
Fortunato CiardielloDipartimento di Medicina di Precisione, Università degli Studi della Campania "Luigi Vanvitelli", Naples, Italy.
Antonella De LucaCell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Naples, Italy.
Nicola NormannoCell Biology and Biotherapy Unit, Istituto Nazionale Tumori - IRCCS - Fondazione G. Pascale, Naples, Italy.
Angelo FacchianoInstitute of Food Sciences, Italian National Research Council (CNR), Avellino, Italy.
Roberto TagliaferriDipartimento di Scienze Aziendali, Management & Innovation Systems, Università degli Studi di Salerno, Fisciano (SA), Italy.
Istituto Nazionale Tumori IRCCS "Fondazione G. Pascale" · ITUniversity of Salerno · ITNational Research Council · ITUniversity of Campania "Luigi Vanvitelli" · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationAssessment of genetic mutations is an essential element in the modern era of personalized cancer treatment. Our strategy is focused on 'multiple network analysis' in which we try to improve cancer diagnostics by using biological networks. Genetic alterations in some important hubs or in driver genes such as BRAF and TP53 play a critical role in regulating many important molecular processes. Most of the studies are focused on the analysis of the effects of single mutations, while tumors often carry mutations of multiple driver genes. The aim of this work is to define an innovative bioinformatics pipeline focused on the design and analysis of networks (such as biomedical and molecular networks), in order to: (1) improve the disease diagnosis; (2) identify the patients that could better respond to a given drug treatment; and (3) predict what are the primary and secondary effects of gene mutations involved in human diseases.

resultsBy using our pipeline based on a multiple network approach, it has been possible to demonstrate and validate what are the joint effects and changes of the molecular profile that occur in patients with metastatic colorectal carcinoma (mCRC) carrying mutations in multiple genes. In this way, we can identify the most suitable drugs for the therapy for the individual patient. This information is useful to improve precision medicine in cancer patients. As an application of our pipeline, the clinically significant case studies of a cohort of mCRC patients with the BRAF V600E-TP53 I195N missense combined mutation were considered. AVAILABILITY: The procedures used in this paper are part of the Cytoscape Core, available at (www.cytoscape.org). Data used here on mCRC patients have been published in [55]. SUPPLEMENTARY INFORMATION: A supplementary file containing a more detailed discussion of this case study and other cases is available at the journal site as Supplementary Data.

Indexed as

Biomarkers, TumorDisease SusceptibilityComputational BiologyGene Regulatory NetworksHumansMetabolic Networks and PathwaysNeoplasmsPrecision MedicineProtein Interaction MapsSignal TransductionBiomarkers, Tumorbiological-biomedical networkscolorectal cancerpersonalized medicinepipeline workflow

Identifiers

PMID34050359
PMCPMC8574709
OpenAlexW3165207183

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.