Evidence map›Paper›PMID 36033501›Full record

ArticleFrontiers in oncology2022

Design and experimental validation of OPERA_MET-A panel for deep methylation analysis by next generation sequencing.

Federico Pio Fabrizio, Stefano Castellana, Flavia Centra, Angelo Sparaneo, Mario Mastroianno, Tommaso Mazza, Michelina Coco, Domenico Trombetta, Nicola Cingolani, Antonella Centonza and 4 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

5 citing papers in PubMed.

  1. Article
  2. Decoding the NRF2-NOTCH Crosstalk in Lung Cancer-An Update.Antioxidants (Basel, Switzerland) · 2025
    Review
  3. Article
  4. Article
  5. MonitoringFrontiers in oncology · 2023
    Review
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

14 authors.

Federico Pio FabrizioLaboratory of Oncology, Fondazione IRCCS, Scientific Institute for Research and Health Care Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Stefano CastellanaUnit of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Flavia CentraLaboratory of Oncology, Fondazione IRCCS, Scientific Institute for Research and Health Care Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Angelo SparaneoLaboratory of Oncology, Fondazione IRCCS, Scientific Institute for Research and Health Care Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Mario MastroiannoScientific Direction, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Tommaso MazzaUnit of Bioinformatics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Michelina CocoLaboratory of Oncology, Fondazione IRCCS, Scientific Institute for Research and Health Care Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Domenico TrombettaLaboratory of Oncology, Fondazione IRCCS, Scientific Institute for Research and Health Care Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Nicola CingolaniUnit of Pathology, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Antonella CentonzaUnit of Oncology, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Paolo GrazianoUnit of Pathology, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Evaristo MaielloUnit of Oncology, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Vito Michele FazioLaboratory of Oncology, Fondazione IRCCS, Scientific Institute for Research and Health Care Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.
Lucia Anna MuscarellaLaboratory of Oncology, Fondazione IRCCS, Scientific Institute for Research and Health Care Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

DNA methylation is the most recognized epigenetic mark that leads to a massive distortion in cancer cells. It has been observed that a large number of DNA aberrant methylation events occur simultaneously in a group of genes, thus providing a growth advantage to the cell in promoting cell differentiation and neoplastic transformation. Due to this reason, methylation profiles have been suggested as promising cancer biomarkers. Here, we designed and performed a first step of validation of a novel targeted next generation sequencing (NGS) panel for methylation analysis, which can simultaneously evaluate the methylation levels at CpG sites of multiple cancer-related genes. The OPERA_MET-A methylation panel was designed using the Ion AmpliSeq™ technology to amplify 155 regions with 125-175 bp mean length and covers a total of 1107 CpGs of 18 cancer-related genes. The performance of the panel was assessed by running commercially available fully methylated and unmethylated control human genomic DNA (gDNA) samples and a variable mixture of them. The libraries were run on Ion Torrent platform and the sequencing output was analyzed using the "methylation_analysis" plugin. DNA methylation calls on both Watson (W) and Crick (C) strands and methylated:unmethylated ratio for each CpG site were obtained. Cell lines, fresh frozen and formalin-fixed paraffin-embedded (FFPE) lung cancer tissues were tested. The OPERA_MET-A panel allows to run a minimum of 6 samples/530 chip to reach an observed mean target depth ≥2,500X (W and C strands) and an average number of mapped reads >750,000/sample. The conversion efficiency, determined by spiking-in unmethylated Lambda DNA into each sample before the bisulfite conversion process, was >97% for all samples. The observed percentage of global methylation for all CpGs was >95% and <5% for fully methylated and unmethylated gDNA samples, respectively, and the observed results for the variable mixtures were in agreement with what was expected. Methylation-specific NGS analysis represents a feasible method for a fast and multiplexed screening of cancer patients by a high-throughput approach. Moreover, it offers the opportunity to construct a more robust algorithm for disease prediction in cancer patients having a low quantity of biological material available.

Indexed as

biomarkercancerdriver genemethylationnext generation sequencing

Identifiers

PMID36033501
PMCPMC9404304

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.