Evidence map›Paper›PMID 34949938›Full record

ArticleInternational journal of general medicine2021

Prognostic Model Construction and Immune Microenvironment Analysis of Breast Cancer Based on Ferroptosis-Related lncRNAs.

Cong Li Jia, Fu Yang, Ruining Li

Open access · goldAbstract read
In one paragraph

Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 33% 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

8 citing papers in PubMed, 13 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

3 authors at 3 institutions in 1 country.

Cong Li JiaInstitute of Plastic Surgery, Weifang Medical College, Weifang, Shandong, People's Republic of China.
Fu YangDepartment of Hepatobiliary Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, People's Republic of China.ORCID 0000-0002-7090-6364
Ruining LiSecond Clinical Medical College, Lanzhou University, Lanzhou, Gansu, People's Republic of China.
Kunming Medical University · CNLanzhou University Second Hospital · CNWeifang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo construct a prognostic model of breast cancer using ferroptosis-related lncRNAs and explore novel therapeutic targets. MATERIALS AND

methodsA prognostic characteristic model based on differential expression of ferroptosis-related lncRNAs in breast cancer was established based on TCGA data.

resultsEleven ferroptosis-related lncRNAs associated with breast cancer prognosis were identified. Kaplan-Meier analysis suggested that high-risk lncRNA signatures correspond to a poor prognosis. The AUC of the signature lncRNAs was 0.682, demonstrating that it is accurate in predicting BC prognosis. GSEA showed that ferroptosis-related lncRNAs in high-risk individuals are mainly enriched in cell cycle, cell adhesion and tumor pathways. Immunity and gene expression analysis revealed that APC co-inhibition, check-point, HLA, inflammation-promoting and T cell co-stimulation among others were significantly different between the high-and low-risk group. Three immune checkpoints were highly expressed in the high-risk group.

conclusionFerroptosis-related lncRNAs can be used as a prognostic feature to construct a prognostic model of breast cancer, based on which early detection markers, therapeutic targets and anti-tumor immune microenvironment can be studied, and clinical treatment can also be instructive.

Indexed as

breast cancerferroptosisimmune microenvironmentlncRNA

Identifiers

PMID34949938
PMCPMC8691199
OpenAlexW4200320205

What OpenQuestion holds

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LicenceCC BY-NC
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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.