Evidence map›Paper›PMID 38484738›Full record

ArticleAging2024

A novel immune-related lncRNA signature predicts the prognosis and immune landscape in ccRCC.

Longlong Dai, Daen Pan, Jiafei Jin, Wenhui Lv

Abstract read
In one paragraph

Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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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

1 citing paper in PubMed.

  1. Article
4 · The record

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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

4 authors.

Longlong DaiDepartment of Urology, Yongjia People’s Hospital, Wenzhou 325100, China.
Daen PanDepartment of Urology, Yongjia People’s Hospital, Wenzhou 325100, China.
Jiafei JinDepartment of Urology, Yongjia People’s Hospital, Wenzhou 325100, China.
Wenhui LvDepartment of Urology, Yongjia People’s Hospital, Wenzhou 325100, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs one of the most common tumors, the pathogenesis and progression of clear cell renal cell carcinoma (ccRCC) in the immune microenvironment are still unknown.

methodsThe differentially expressed immune-related lncRNA (DEirlncRNA) was screened through co-expression analysis and the limma package of R, which based on the ccRCC project of the TCGA database. Then, we designed the risk model by irlncRNA pairs. In RCC patients, we have compared the area under the curve, calculated the Akaike Information Criterion (AIC) value of the 5-year receiver operating characteristic curve, determined the cut-off point, and established the optimal model for distinguishing the high-risk group from the low-risk group. We used the model for immune system assessment, immune point detection and drug sensitivity analysis after verifying the feasibility of the above model through clinical features.

resultsIn our study, 1541 irlncRNAs were included. 739 irlncRNAs were identified as DEirlncRNAs to construct irlncRNA pairs. Then, 38 candidate DEirlncRNA pairs were included in the best risk assessment model through improved LASSO regression analysis. As a result, we found that in addition to age and gender, T stage, M stage, N stage, grade and clinical stage are significantly related to risk. Moreover, univariate and multivariate Cox regression analysis results reveals that in addition to gender, age, grade, clinical stage and risk score are independent prognostic factors. The results show that patients in the high-risk group are positively correlated with tumor infiltrating immune cells when the above model is applied to the immune system. But they are negatively correlated with endothelial cells, macrophages M2, mast cell activation, and neutrophils. In addition, the risk model was positively correlated with overexpressed genes (CTLA, LAG3 and SETD2, P<0.05). Finally, risk models can also play as an important role in predicting the sensitivity of targeted drugs.

conclusionsThe new risk model may be a new method to predict the prognosis and immune status of ccRCC.

Indexed as

CarcinomaCarcinoma, Renal CellKidney NeoplasmsRNA, Long NoncodingEndothelial CellsHumansPrognosisTumor MicroenvironmentRNA, Long NoncodingccRCCdrugsimmunelncRNA

Identifiers

PMID38484738
PMCPMC11006461

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.