Evidence map›Paper›PMID 34552353›Full record

ArticleCancer management and research2021

Identification of Novel Pyroptosis-Related lncRNAs Associated with the Prognosis of Breast Cancer Through Interactive Analysis.

Lili Gao, Qing Li

Open access · goldAbstract read
In one paragraph

Article in Cancer management and research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

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3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 14 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

2 authors at 1 institution in 1 country.

Lili Gao *Department of Pathology, Pudong New Area People's Hospital, Shanghai, People's Republic of China.
Qing Li *Department of Pathology, Pudong New Area People's Hospital, Shanghai, People's Republic of China.ORCID 0000-0001-5805-5256
Pudong New Area People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe role of pyroptosis and lncRNAs in breast cancer remains controversial. This study aimed to explore the pyroptosis-related lncRNAs in breast cancer.

methodsAll the data used for bioinformatics analysis were downloaded from The Cancer Genome Atlas database. Limma package was used to perform difference analysis, and distinguish mRNA and lncRNA. Survival package was used to conduct prognosis analysis. LASSO algorithm, univariate cox analysis and multivariate cox analysis were used to construct the prognosis model. P value <0.05 was regarded as statistically significant.

resultsBased on the seven pyroptosis-related lncRNAs tightly associated with patients' prognosis, a prognostic prediction model was finally developed, which showed powerful effectiveness (Training cohort, one-year AUC = 0.82, 95% Cl = 0.69-0.95, three-year AUC = 0.77, 95% Cl = 0.68-0.85, five-year AUC = 0.74, 95% Cl = 0.66-0.82; Validation cohort, one-year AUC = 0.68, 95% Cl = 0.53-0.84, three-year AUC = 0.72, 95% Cl = 0.64-0.81, five-year AUC = 0.67, 95% Cl = 0.57-0.77). GSEA analysis demonstrated that the protein secretion, angiogenesis, TGF-β signaling and MTORC1 signaling might be involved in the high-risk patients. Moreover, immune infiltration analysis showed that the risk score was positively correlated with Tgd and Th2 cells, yet negatively correlated with CD8+ T cells, cytotoxic cells and T helper cells, which might partly explain the poor prognosis of high-risk patients. Finally, the expression level of seven model lncRNAs in the real world was validated by qRT-PCR using four cancer cell lines (MCF-7, T47D, MDA-MB-231, MDA-MB-469).

conclusionIn conclusion, our study identified lncRNAs that are remarkably correlated with patients' survival and might participate in the pyroptosis process, which might be underlying tumor biomarker and therapeutic targets. This study may provide direction for future research.

Indexed as

breast cancerlncRNAspyroptosis

Identifiers

PMID34552353
PMCPMC8450763
OpenAlexW3201627731

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.