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Updated: Dec 26, 2025

MicroRNA In situ Hybridization for Formalin Fixed Kidney Tissues

MicroRNA In situ Hybridization for Formalin Fixed Kidney Tissues

Published on: November 30, 2013

A miRNA- and mRNA-seq-Based Feature Selection Approach for Kidney Cancer Biomakers.

¹Department of Civil Engineering, Sangmyung University, Cheonan, Republic of Korea.

Cancer Informatics

|March 14, 2020

Summary

This study identifies novel kidney cancer biomarkers using deep sequencing data and statistical methods. Researchers discovered 3 mRNA and 27 microRNA biomarkers, improving cancer prediction accuracy.

Keywords:

NMF clustering feature selection mRNA- and miRNA-seq survival analysis

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Last Updated: Dec 26, 2025

MicroRNA In situ Hybridization for Formalin Fixed Kidney Tissues

MicroRNA In situ Hybridization for Formalin Fixed Kidney Tissues

Published on: November 30, 2013

A Quantitative Detection Method for MicroRNAs in the Kidney of an Ischemic Kidney Injury Mouse Model

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Published on: September 11, 2020

Detection of MicroRNA Expression in the Kidneys of Immunoglobulin A Nephropathic Mice

Detection of MicroRNA Expression in the Kidneys of Immunoglobulin A Nephropathic Mice

Published on: July 8, 2020

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Microarray data has limitations in cancer biomarker prediction accuracy.
Deep sequencing offers a less noisy alternative for computational analysis.
Identifying reliable kidney cancer biomarkers remains a challenge.

Purpose of the Study:

To predict kidney cancer biomarkers using deep sequencing data.
To compare the efficacy of different statistical feature selection methods.
To identify potential therapeutic targets through survival analysis.

Main Methods:

Analysis of kidney miRNA and mRNA deep sequencing data.
Application of 5 statistical feature selection methods.
Clustering of kidney cancer subtypes using nonnegative matrix factorization.

Main Results:

Identification of 3 mRNA and 27 miRNA-based kidney cancer biomarkers.
Significant survival analysis results for miRNA-342 and its target EIF5A.
Successful clustering of kidney cancer subtypes.

Conclusions:

Deep sequencing data combined with statistical methods can effectively identify kidney cancer biomarkers.
miRNA-342 and EIF5A show potential as prognostic markers for kidney cancer.
This study provides a foundation for developing improved diagnostic and therapeutic strategies for kidney cancer.