Evidence map›Paper›PMID 35659616›Full record

ArticleBMC cancer2022

Simulating the restoration of normal gene expression from different thyroid cancer stages using deep learning.

Nicole M Nelligan, M Reed Bender, F Alex Feltus

Open access · goldAbstract read
In one paragraph

Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.2field-weighted citation impact, top 53% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.

  1. Pooled it
  2. 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

3 authors at 1 institution in 1 country.

Nicole M NelliganDepartment of Genetics & Biochemistry, Clemson University, Biosystems Research Complex, 302C, 19 105 Collings St,., SC, 29634, Clemson, USA.
M Reed BenderBiomedical Data Science and Informatics Program, Clemson, SC, 29634, USA.
F Alex FeltusDepartment of Genetics & Biochemistry, Clemson University, Biosystems Research Complex, 302C, 19 105 Collings St,., SC, 29634, Clemson, USA. ffeltus@clemson.edu.
Clemson University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThyroid cancer (THCA) is the most common endocrine malignancy and incidence is increasing. There is an urgent need to better understand the molecular differences between THCA tumors at different pathologic stages so appropriate diagnostic, prognostic, and treatment strategies can be applied. Transcriptome State Perturbation Generator (TSPG) is a tool created to identify the changes in gene expression necessary to transform the transcriptional state of a source sample to mimic that of a target.

methodsWe used TSPG to perturb the bulk RNA expression data from various THCA tumor samples at progressive stages towards the transcriptional pattern of normal thyroid tissue. The perturbations produced were analyzed to determine if there are consistently up- or down-regulated genes or functions in certain stages of tumors.

resultsSome genes of particular interest were investigated further in previous research. SLC6A15 was found to be down-regulated in all stage 1-3 samples. This gene has previously been identified as a tumor suppressor. The up-regulation of PLA2G12B in all samples was notable because the protein encoded by this gene belongs to the PLA2 superfamily, which is involved in metabolism, a major function of the thyroid gland. REN was up-regulated in all stage 3 and 4 samples. The enzyme renin encoded by this gene, has a role in the renin-angiotensin system; this system regulates angiogenesis and may have a role in cancer development and progression. This is supported by the consistent up-regulation of REN only in later stage tumor samples. Functional enrichment analysis showed that olfactory receptor activities and similar terms were enriched for the up-regulated genes which supports previous research concluding that abundance and stimulation of olfactory receptors is linked to cancer.

conclusionsTSPG can be a useful tool in exploring large gene expression datasets and extracting the meaningful differences between distinct classes of data. We identified genes that were characteristically perturbed in certain sample types, including only late-stage THCA tumors. Additionally, we provided evidence for potential transcriptional signatures of each stage of thyroid cancer. These are potentially relevant targets for future investigation into THCA tumorigenesis.

Indexed as

Amino Acid Transport Systems, NeutralDeep LearningThyroid NeoplasmsGene Expression Regulation, NeoplasticHumansNerve Tissue ProteinsPrognosisTranscriptomeAmino Acid Transport Systems, NeutralNerve Tissue ProteinsSLC6A15 protein, humanDeep learningThyroid cancerTranscriptomeTSPG

Identifiers

PMID35659616
PMCPMC9166476
OpenAlexW4281650462

What Socratic 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 Socratic graph.