Evidence map›Paper›PMID 33203236›Full record

ArticleExperimental biology and medicine (Maywood, N.J.)2021

Identification of two molecular subtypes of dysregulated immune lncRNAs in ovarian cancer.

Xiaojun Liu, Jinghai Gao, Jing Wang, Jing Chu, Jiahao You, Zhijun Jin

Open access · greenAbstract read
In one paragraph

Article in Experimental biology and medicine (Maywood, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.7field-weighted citation impact, top 35% 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

4 citing papers in PubMed, 7 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Xiaojun LiuDepartment of Gynaecology and Obstetrics, Changzheng Hospital, Naval Medical University, Shanghai 200003, China.
Jinghai GaoDepartment of Gynaecology and Obstetrics, Changzheng Hospital, Naval Medical University, Shanghai 200003, China.ORCID 0000-0002-7986-8333
Jing WangDepartment of Gynaecology and Obstetrics, Changzheng Hospital, Naval Medical University, Shanghai 200003, China.
Jing ChuDepartment of Gynaecology and Obstetrics, Changzheng Hospital, Naval Medical University, Shanghai 200003, China.
Jiahao YouDepartment of Gynaecology and Obstetrics, Changzheng Hospital, Naval Medical University, Shanghai 200003, China.
Zhijun JinDepartment of Gynaecology and Obstetrics, Changzheng Hospital, Naval Medical University, Shanghai 200003, China.
Shanghai Changzheng Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Long non-coding RNA (lncRNA) has increasingly been identified as a key regulator in pathologies such as cancer. Multiple platforms were used for comprehensive analysis of ovarian cancer to identify molecular subgroups. However, lncRNA and its role in mapping the ovarian cancer subpopulation are still largely unknown. RNA-sequencing and clinical characteristics of ovarian cancer were acquired from The Cancer Genome Atlas database (TCGA). A total of 52 lncRNAs were identified as aberrant immune lncRNAs specific to ovarian cancer. We redefined two different molecular subtypes, C1(188) and C2(184 samples), in "iClusterPlus" R package, among which C2 grouped ovarian cancer samples have higher survival probability and longer median survival time (

Indexed as

Gene Expression Regulation, NeoplasticEndometrial NeoplasmsFemaleGene Expression ProfilingHumansOvarian NeoplasmsRNA, Long NoncodingUterine Cervical NeoplasmsRNA, Long NoncodingbioinformaticslncRNAmolecular subtypesovarian cancerThe Cancer Genome Atlas database

Identifiers

PMID33203236
PMCPMC7934149
OpenAlexW3141440371

What OpenQuestion holds

Textmetadata
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.