Evidence map›Paper›PMID 40702098›Full record

ArticleScientific reports2025

Construction of a risk and prognostic model for migrasome-associated lncRNAs in renal cell carcinoma.

Liangwei Zhao, Fengjiao Geng, Xiaomin Ji, Chaoqun Geng, Tian Liu

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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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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

5 citing papers in PubMed.

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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

5 authors.

Liangwei ZhaoDepartment of Urology, Huzhou Traditional Chinese Medicine Hospital, Zhejiang Chinese Medical University, Huzhou, 313000, China.
Fengjiao GengThe Second Clinical Medical College, Jining Medical University, Jining, 272067, Shandong, China.
Xiaomin JiDepartment of Surgery, Puning People's Hospital, Puning, 515300, China.
Chaoqun GengDepartment of Pharmacy, Caoxian People's Hospital, Caoxian, 274400, Shandong, China.
Tian LiuDepartment of Surgery, Puning People's Hospital, Puning, 515300, China. 568523745@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The migrasome, a recently identified cellular organelle, is closely associated with cell migration and plays a critical role in various physiological and pathological processes. The relationship between migrasomes and the prognosis of kidney cancer patients remains unclear. We utilized least absolute shrinkage and selection operator (LASSO) Cox regression analysis on data from The Cancer Genome Atlas- kidney renal clear cell carcinoma (TCGA-KIRC) dataset to identify migrasome-related long non-coding RNAs (lncRNAs) with prognostic significance in KIRC. We then developed and validated a prognostic risk model based on these lncRNAs. In addition, to identify potential immunotherapeutic agents, we analyzed tumor immune dysfunction and exclusion scores; we also conducted immune cell infiltration profiling. Risk score analysis identified 12 migrasome-related lncRNAs significantly associated with the prognosis of TCGA-KIRC patients. Kaplan-Meier survival analysis demonstrated that the prognostic model effectively stratified patients into high- and low-risk groups; the high-risk group showed a significantly worse prognosis relative to the low-risk group. Importantly, higher risk scores were associated with increased immune cell infiltration. This prognostic model underscores the importance of migrasome-related lncRNAs in KIRC and provides a novel tool for predicting patient prognosis and immune response in KIRC.

Indexed as

Carcinoma, Renal CellKidney NeoplasmsRNA, Long NoncodingBiomarkers, TumorCell MovementFemaleGene Expression Regulation, NeoplasticHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisRisk FactorsBiomarkers, TumorRNA, Long NoncodingKidney renal clear cell carcinomaKIRClncRNAMigrasomePrognostic model

Identifiers

PMID40702098
PMCPMC12287361

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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.