Evidence map›Paper›PMID 30057548›Full record

ArticleFrontiers in pharmacology2018

Targeted Next-Generation Sequencing Identifies Actionable Targets in Estrogen Receptor Positive and Estrogen Receptor Negative Endometriod Endometrial Cancer.

Siti Syazani Suhaimi, Nurul-Syakima Ab Mutalib, Sheau S Khor, Reena Rahayu Md Zain, Saiful Effendi Syafruddin, Nadiah Abu, Ahmad Zailani Hatta Mohd Dali, Rahman Jamal

Open access · goldAbstract read
In one paragraph

Article 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
0.4field-weighted citation impact, top 32% 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, 3 citations in OpenAlex.

  1. Review
  2. Article
  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

8 authors at 2 institutions in 1 country.

Siti Syazani SuhaimiUKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Center, Kuala Lumpur, Malaysia.
Nurul-Syakima Ab MutalibUKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Center, Kuala Lumpur, Malaysia.
Sheau S KhorThermo Fisher Scientific, Shah Alam, Malaysia.
Reena Rahayu Md ZainDepartment of Pathology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
Saiful Effendi SyafruddinUKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Center, Kuala Lumpur, Malaysia.
Nadiah AbuUKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Center, Kuala Lumpur, Malaysia.
Ahmad Zailani Hatta Mohd DaliDepartment of Pathology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia.
Rahman JamalUKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Center, Kuala Lumpur, Malaysia.
University Kebangsaan Malaysia Medical Centre · MYNational University of Malaysia · MY

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Endometrioid endometrial cancer (EEC) is the commonest form of endometrial cancer and can be divided into estrogen receptor (ER) positive and negative subtypes. The mutational profiles of EEC have been shown to aid in tailoring treatment; however, little is known about the differences between the gene mutation profiles between these two subtypes. This study aims to investigate the gene mutation profile in ER positive and negative EEC, and to further elucidate the role of

Indexed as

endometrial cancerendometrioid subtypeestrogen receptorFulvestrantnext-generation sequencingWHSC1

Identifiers

PMID30057548
PMCPMC6053487
OpenAlexW2809277406

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.