Evidence map›Paper›PMID 34717685›Full record

ArticleJournal of translational medicine2021

Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression.

Hongyu Zhao, Yu Teng, Wende Hao, Jie Li, Zhefeng Li, Qi Chen, Chenghong Yin, Wentao Yue

Open access · goldAbstract read
In one paragraph

Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

26 citing papers in PubMed, 35 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

8 authors at 2 institutions in 1 country.

Hongyu ZhaoCentral Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China.
Yu TengCentral Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China.
Wende HaoCentral Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China.
Jie LiCentral Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China.
Zhefeng LiCentral Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China.
Qi ChenCentral Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China.
Chenghong YinCentral Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China. yinchh@ccmu.edu.cn.
Wentao YueCentral Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, China. yuewt@ccmu.edu.cn.
Capital Medical University · CNBeijing Obstetrics and Gynecology Hospital · CN

Funding

Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support No. XMLX201705National Natural Science Foundation of China No. 81672838the Beijing Municipal Science and Technology Commission No. Z181100001718193
6 · The paper itself

Abstract

backgroundOvarian cancer was one of the leading causes of female deaths. Patients with OC were essentially incurable and portends a poor prognosis, presumably because of profound genetic heterogeneity limiting reproducible prognostic classifications.

methodsWe comprehensively analyzed an ovarian cancer single-cell RNA sequencing dataset, GSE118828, and identified nine major cell types. Relationship between the clusters was explored with CellPhoneDB. A malignant epithelial cluster was confirmed using pseudotime analysis, CNV and GSVA. Furthermore, we constructed the prediction model (i.e., RiskScore) consisted of 10 prognosis-specific genes from 2397 malignant epithelial genes using the LASSO Cox regression algorithm based on public datasets. Then, the prognostic value of Riskscore was assessed with Kaplan-Meier survival analysis and time-dependent ROC curves. At last, a series of in-vitro assays were conducted to explore the roles of IL4I1, an important gene in Riskscore, in OC progression.

resultsWe found that macrophages possessed the most interaction pairs with other clusters, and M2-like TAMs were the dominant type of macrophages. C0 was identified as the malignant epithelial cluster. Patients with a lower RiskScore had a greater OS (log-rank P < 0.01). In training set, the AUC of RiskScore was 0.666, 0.743 and 0.809 in 1-year, 3-year and 5-year survival, respectively. This was also validated in another two cohorts. Moreover, downregulation of IL4I1 inhibited OC cells proliferation, migration and invasion.

conclusionsOur work provide novel insights into our understanding of the heterogeneity among OCs, and would help elucidate the biology of OC and provide clinical guidance in prognosis for OC patients.

Indexed as

Ovarian NeoplasmsSingle-Cell AnalysisCarcinoma, Ovarian EpithelialFemaleGene Expression Regulation, NeoplasticHumansL-Amino Acid OxidasePrognosisIL4I1 protein, humanL-Amino Acid OxidaseHeterogeneityIL4I1M2-like TAMsMalignant epithelial clusterOvarian cancerPrognosisSingle-cell RNA-sequencing

Identifiers

PMID34717685
PMCPMC8557560
OpenAlexW3210276561

What Socratic 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 Socratic graph.