Evidence map›Paper›PMID 37901864›Full record

ArticleCentral-European journal of immunology2023

A biomarker and molecular mechanism investigation for thyroid cancer.

Keju Xie

Open access · goldAbstract read
In one paragraph

Article in Central-European journal of immunology, 2023. 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
1.2field-weighted citation impact, top 19% 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

5 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. 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

1 author at 1 institution in 1 country.

Keju XieDepartment of Plastic Surgery, Affiliated Hospital of Shaoxing University (The Shaoxing Municipal Hospital), China.
Shaoxing People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study aimed to reveal the potential molecular mechanism associated with thyroid cancer (THCA) prognosis, and investigate promising biomarkers for THCA. Material and methods: Differentially expressed genes (DEGs) were compared between THCA samples (THCA group) and normal samples (N group). Then, enrichment analysis and protein-protein interaction (PPI) network analysis were performed, followed by prognostic hub gene exploration from the PPI network. Furthermore, the prognostic and mutation analysis was performed on these hub genes. Finally, the associations of the hub gene with immune cells were investigated. Results: A total of 802 DEGs were obtained between the THCA group and the N group. These DEGs were mainly enriched in pathways such as lysine degradation. From the PPI network, 20 hub genes, including CD44, CCND1, SNAI1, and KIT, were investigated. The survival analysis showed that the up-regulation of CD44 and down-regulation of SNAI1 contributed to the favorable and unfavorable outcomes of patients with THCA, respectively. Meanwhile, the diagnostic analysis showed that the AUC of KIT in THCA was larger than 0.9. Furthermore, the gene mutation analysis showed that the alternated CCND1 participated in the cell cycle pathway. Finally, the correlation analysis showed that prognostic genes such as CD44 were positively correlated with immune cells such as M1 macrophages. Conclusions: A total of 20 hub genes including CCND1, CD44, SNAI1, and KIT were revealed as potential biomarkers for the differential diagnosis, prognosis, and development of drug targets of THCA. The lysine degradation pathway and cell cycle pathway might take part in the progression of THCA.

Indexed as

biomarkersdifferentially expressed genesfunction and pathway analysisimmune cell infiltrationthyroid cancer

Identifiers

PMID37901864
PMCPMC10604643
OpenAlexW4387830511

What Socratic holds

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LicenceCC BY-NC-SA
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Registered trials

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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 Socratic graph.