Evidence map›Paper›PMID 37215759›Full record

ArticleHeliyon2023

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance.

Dongqing Su, Yuqiang Xiong, Haodong Wei, Shiyuan Wang, Jiawei Ke, Pengfei Liang, Haoxin Zhang, Yao Yu, Yongchun Zuo, Lei Yang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2023. 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
1.2field-weighted citation impact, top 20% 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, 5 citations in OpenAlex.

  1. Article
  2. An Overview of Advances in Rare Cancer Diagnosis and Treatment.International journal of molecular sciences · 2024
    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

10 authors at 2 institutions in 1 country.

Dongqing SuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yuqiang XiongCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Haodong WeiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Shiyuan WangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Jiawei KeCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Pengfei LiangThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
Haoxin ZhangDepartment of Gastrointestinal Oncology, Harbin Medical University Cancer Hospital, Harbin 150081, China.
Yao YuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Yongchun ZuoThe State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China.
Lei YangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
Harbin Medical University · CNInner Mongolia University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

Indexed as

Clustering subtypeImmune-related transcription factorsOvarian cancerPrognosis

Identifiers

PMID37215759
PMCPMC10199194
OpenAlexW4376126670

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.