Evidence map›Paper›PMID 30250431›Full record

ReviewFrontiers in pharmacology2018

Current Standards and Recent Advances in Biomarkers of Major Endocrine Tumors.

Yanhong Luo, Hua Zhu, Tao Tan, Jianfeng He

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.2field-weighted citation impact, top 44% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Review
  2. Review
  3. 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

4 authors at 3 institutions in 2 countries.

Yanhong LuoChildren's Hospital of Chongqing Medical University, Chongqing, China.
Hua ZhuDepartment of Surgery, Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
Tao TanDepartment of Surgery, Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
Jianfeng HeChildren's Hospital of Chongqing Medical University, Chongqing, China.
The Ohio State University Wexner Medical Center · USChildren's Hospital of Chongqing Medical University · CNChongqing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The complexity of endocrine tumor diagnosis stems from its variable symptoms and presentation that may mimic many other disease states, or display asymptomatic properties for a prolonged amount of time. Early and accurate disease identification is needed for better patient prognosis. The key to this may be in using validated biomarkers with enhanced sensitivity and specificity. Several biomarkers are consistently used across various endocrine tumor types, possibly indicating a deeper pathophysiological mechanism behind endocrine cancer genesis and development. For example, carbohydrate antigen (CA) is measured in both pancreatic adenocarcinoma as well as ovarian cancer for diagnosis, surveillance, and risk stratification. The discovery of measuring miRNAs that are highly expressed in malignant tumors is also a novel strategy across multiple endocrine tumor types, and is propelling the future advancement of biomarker development. This review introduces currently utilized biomarkers in some of the commonly known endocrine tumors, including thyroid, adrenal, pituitary, pancreatic, and gonadal carcinoma, as well as future research directions.

Indexed as

adrenal carcinomabiomarkersneuroendocrine carcinomapituitary carcinomathyroid carcinoma

Identifiers

PMID30250431
PMCPMC6139354
OpenAlexW2891778431

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.