Evidence map›Paper›PMID 34221185›Full record

ArticleDisease markers2021

A Five-Gene Prognostic Nomogram Predicting Disease-Free Survival of Differentiated Thyroid Cancer.

Pan Ruchong, Tang Haiping, Wang Xiang

Abstract read
In one paragraph

Article in Disease markers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

11 citing papers in PubMed.

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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

3 authors.

Pan RuchongFaculty of Medical Technology, Chongqing Three Gorges Medical College, Chongqing, China.ORCID https://orcid.org/0000-0001-5175-1814
Tang HaipingTransfusion Section, Chongqing University Three Gorges Hospital, Chongqing, China.ORCID https://orcid.org/0000-0002-0131-8278
Wang XiangTransfusion Section, Chongqing University Three Gorges Hospital, Chongqing, China.ORCID https://orcid.org/0000-0001-6969-2971

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDifferentiated thyroid cancer (DTC) is the most common type of thyroid tumor with a high recurrence rate. Here, we developed a nomogram to effectively predict postoperative disease-free survival (DFS) in DTC patients.

methodsThe mRNA expressions and clinical data of DTC patients were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Seventy percent of patients were randomly selected as the training dataset, and thirty percent of patients were classified into the testing dataset. Multivariate Cox regression analysis was adopted to establish a nomogram to predict 1-year, 3-year, and 5-year DFS rate of DTC patients.

resultsA five-gene signature comprised of TENM1, FN1, APOD, F12, and BTNL8 genes was established to predict the DFS rate of DTC patients. Results from the concordance index (C-index), area under curve (AUC), and calibration curve showed that both the training dataset and the testing dataset exhibited good prediction ability, and they were superior to other traditional models. The risk score and distant metastasis (M) of the five-gene signature were independent risk factors that affected DTC recurrence. A nomogram that could predict 1-year, 3-year, and 5-year DFS rate of DTC patients was established with a C-index of 0.801 (95% CI: 0.736, 0.866).

conclusionOur study developed a prediction model based on the gene expression and clinical characteristics to predict the DFS rate of DTC patients, which may be applied to more accurately assess patient prognosis and individualized treatment.

Indexed as

Gene Regulatory NetworksNomogramsApolipoproteins DButyrophilinsDatabases, GeneticFactor XIIFemaleFibronectinsGene Expression Regulation, NeoplasticHumansMaleNerve Tissue ProteinsPrognosisSurvival AnalysisTenascinThyroid NeoplasmsAPOD protein, humanApolipoproteins DBTNL8 protein, humanButyrophilinsFactor XIIFibronectinsFN1 protein, humanNerve Tissue ProteinsTenascinteneurin-1

Identifiers

PMID34221185
PMCPMC8221860

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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.