Evidence map›Paper›PMID 33522650›Full record

ArticleJournal of clinical laboratory analysis2021

PUM1 and RNase P genes as potential cell-free DNA markers in breast cancer.

Alexis Murillo Carrasco, Oscar Acosta, Jaime Ponce, José Cotrina, Alfredo Aguilar, Jhajaira Araujo, Pamela Rebaza, Joseph A Pinto, Ricardo Fujita, José Buleje

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical laboratory analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. 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

10 authors at 3 institutions in 1 country.

Alexis Murillo CarrascoFacultad de Medicina Humana, Centro de Investigación de Genética y Biología Molecular, Instituto de Investigación, Universidad de San Martín de Porres, Lima, Perú.ORCID https://orcid.org/0000-0002-7372-2608
Oscar AcostaFacultad de Medicina Humana, Centro de Investigación de Genética y Biología Molecular, Instituto de Investigación, Universidad de San Martín de Porres, Lima, Perú.ORCID https://orcid.org/0000-0002-1912-0251
Jaime PonceOncosalud-AUNA, Unidad de la Mama, Lima, Perú.
José CotrinaDepartamento de Cirugía de Mamas, Instituto Nacional de Enfermedades Neoplásicas-INEN, Lima, Perú.ORCID https://orcid.org/0000-0002-8330-803X
Alfredo AguilarOncosalud-AUNA, Unidad de Investigación Básica y Traslacional, Lima, Perú.
Jhajaira AraujoOncosalud-AUNA, Unidad de Investigación Básica y Traslacional, Lima, Perú.ORCID https://orcid.org/0000-0002-9639-8070
Pamela RebazaOncosalud-AUNA, Unidad de la Mama, Lima, Perú.
Joseph A PintoOncosalud-AUNA, Unidad de Investigación Básica y Traslacional, Lima, Perú.ORCID https://orcid.org/0000-0002-7744-1635
Ricardo FujitaFacultad de Medicina Humana, Centro de Investigación de Genética y Biología Molecular, Instituto de Investigación, Universidad de San Martín de Porres, Lima, Perú.ORCID https://orcid.org/0000-0002-9617-5109
José BulejeFacultad de Medicina Humana, Centro de Investigación de Genética y Biología Molecular, Instituto de Investigación, Universidad de San Martín de Porres, Lima, Perú.ORCID https://orcid.org/0000-0002-2834-8290
EsSALUD · PEUniversidad de San Martín de Porres · PENational University of San Marcos · PE

Funding

Oncosalud - AUNAPrograma Nacional de Innovación para la Competitividad y Productividad - Innóvate Perú 138-PNICP-PIAP-2015Universidad de San Martin de Porres E1001-2016038
6 · The paper itself

Abstract

backgroundCell-free DNA (cfDNA) is used in clinical research to identify biomarkers for diagnosis of and follow-up on cancer. Here, we propose a fast and innovative approach using traditional housekeeping genes as cfDNA targets in a copy number analysis. We focus on the application of highly sensitive technology such as digital PCR (dPCR) to differentiate breast cancer (BC) patients and controls by quantifying regions of PUM1 and RPPH1 (RNase P) in plasma samples.

methodsWe conducted a case-control study with 82 BC patients and 82 healthy women. cfDNA was isolated from plasma using magnetic beads and quantified by spectrophotometry to estimate total cfDNA. Then, both PUM1 and RPPH1 genes were specifically quantified by dPCR. Data analysis was calibrated using a reference genomic DNA in different concentrations.

resultsWe found RNase P and PUM1 values were correlated in the patient group (intraclass correlation coefficient [ICC] = 0.842), but they did not have any correlation in healthy women (ICC = 0.519). In dPCR quantification, PUM1 showed the capacity to distinguish early-stage patients and controls with good specificity (98.67%) and sensitivity (100%). Conversely, RNase P had lower cfDNA levels in triple-negative BC patients than luminal subtypes (p < 0.025 for both), confirming their utility for patient classification.

conclusionWe propose the PUM1 gene as a cfDNA marker for early diagnosis of BC and RNase P as a cfDNA marker related to hormonal status and subtype classification in BC. Further studies with larger sample sizes are warranted.

Indexed as

Biomarkers, TumorBreast NeoplasmsCase-Control StudiesCell-Free Nucleic AcidsEstrogensFemaleFluorescenceHumansMiddle AgedNeoplasm StagingRibonuclease PRNA-Binding ProteinsROC CurveSensitivity and SpecificityTriple Negative Breast NeoplasmsBiomarkers, TumorCell-Free Nucleic AcidsEstrogensPUM1 protein, humanRibonuclease PRNA-Binding Proteinsbreast cancercell-free DNAdigital PCRliquid biopsyultrasensitive detection method

Identifiers

PMID33522650
PMCPMC8059717
OpenAlexW3127508949

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.