Evidence map›Paper›PMID 37600346›Full record

ReviewOncology letters2023

Advances in transcriptomics and proteomics in differentiated thyroid cancer: An updated perspective (Review).

Shici Yang, Gaohong Zhu, Rui He, Dong Fang, Jiaojiao Feng

Open access · diamondAbstract readReview
In one paragraph

Review in Oncology letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.8field-weighted citation impact, top 25% 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, 4 citations in OpenAlex.

  1. Article
  2. 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

5 authors at 2 institutions in 1 country.

Shici YangDepartment of Nuclear Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Gaohong ZhuDepartment of Nuclear Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Rui HeDepartment of Nuclear Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Dong FangDepartment of Nuclear Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Jiaojiao FengDepartment of Nuclear Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan 650032, P.R. China.
Kunming Medical University · CNFirst Affiliated Hospital of Kunming Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thyroid cancer (TC) is a broad classification of neoplasms that includes differentiated thyroid cancer (DTC) as a common histological subtype. DTC is characterized by an increased mortality rate in advanced stages, which contributes to the overall high mortality rate of DTC. This progression is mainly attributed to alterations in molecular driver genes, resulting in changes in phenotypes such as invasion, metastasis and dedifferentiation. Clinical management of DTC is challenging due to insufficient diagnostic and therapeutic options. The advent of-omics technology has presented a promising avenue for the diagnosis and treatment of DTC. Identifying molecular markers that can predict the early progression of DTC to a late adverse outcome is essential for precise diagnosis and treatment. The present review aimed to enhance our understanding of DTC by integrating big data with biological systems through-omics technology, specifically transcriptomics and proteomics, which can shed light on the molecular mechanisms underlying carcinogenesis.

Indexed as

biomarkersdifferentiating thyroid cancerproteomicstranscriptome

Identifiers

PMID37600346
PMCPMC10433702
OpenAlexW4385368147

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.