Evidence map›Paper›PMID 33718090›Full record

ArticleTranslational andrology and urology2021

Identification of a novel immune-related microRNA prognostic model in clear cell renal cell carcinoma.

Yuhe Guo, Xianbin Li, Junbin Zheng, Jiali Fang, Guanghui Pan, Zheng Chen

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Article in Translational andrology and urology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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9citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yuhe GuoDepartment of Organ Transplantation, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Xianbin LiSchool of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Junbin ZhengDepartment of Clinical Laboratory, Xiamen Hospital of Traditional Chinese Medicine, Xiamen, China.
Jiali FangDepartment of Organ Transplantation, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Guanghui PanDepartment of Organ Transplantation, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Zheng ChenDepartment of Organ Transplantation, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundClear cell renal cell carcinoma (ccRCC) is a type of kidney cancer, and one of the most common malignant tumors. Many studies have shown that certain microRNAs (miRNAs) play an important role in the occurrence and development of ccRCC. Nevertheless, the prognosis of ccRCC patients is very rarely based on these "immuno-miRs". Our aim was thus to determine the relationship between immune-related miRNA signatures and ccRCC.

methodsWe downloaded the miRNA expression data from 521 KIRC and 71 normal tissues in The Cancer Genome Atlas (TCGA). We used "limma" package and univariate Cox regression analysis to identify differentially expressed miRNAs (DEMs) that related to overall survival (OS). We applied lasso and multivariate Cox regression analyses to construct a prognostic model based on immuno-miRs. We evaluated the performance of model by using the Kaplan-Meier method. Furthermore, Cox regression analysis was used to determine independent prognostic signatures in ccRCC.

resultsA total of 59 significant immuno-miRs were identified. We use univariate Cox regression analysis to acquire 18 immune-related miRNAs which were markedly related to OS of ccRCC patients in the training set. We then constructed the 9-immune-related-miRNA prognostic model (miR-21, miR-342, miR-149, miR-130b, miR-223, miR-365a, miR-9-1, and miR-146b) by using lasso and multivariate Cox regression. Further analysis suggested that the immune-related prognostic model could be an independent prognostic indicator for patients with ccRCC. The prognostic performance of the 9-immune-related-miRNA prognostic model was further validated successfully in the testing set.

conclusionsWe established a novel immune-based prognostic model of ccRCC based on potential prognostic immune-related miRNAs. Our results indicated that the 9-miRNA signature could be a practical and reliable prognostic tool for ccRCC.

Indexed as

area under curveClear cell renal cell carcinoma (ccRCC)Cox regression analysisimmune-related microRNAs (immune-related miRNAs)signature

Identifiers

PMID33718090
PMCPMC7947456

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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.