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Related Concept Videos

RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
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Related Experiment Video

Updated: May 10, 2026

miRNA Expression Analyses in Prostate Cancer Clinical Tissues
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Transcriptomic signatures of prostate cancer progression: a comprehensive RNA-seq study.

Shristi Modanwal1, Viswajit Mulpuru2, Ashutosh Mishra1

  • 1Department of Applied Sciences, Indian Institute of Information of Technology Allahabad, Prayagraj, Uttar Pradesh 211012 India.

3 Biotech
|April 22, 2025
PubMed
Summary

This study used RNA-sequencing to find new biomarkers for prostate cancer (PCa) detection and prognosis. Key genes like BIRC5 and MKI67 show promise for improving early diagnosis and patient outcomes.

Keywords:
DEGsDiagnostic biomarkerGene ontologyNomogramPPI networkPrognostic biomarkerProstate cancer

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Prostate cancer (PCa) remains a significant health concern for aging men.
  • Current biomarkers for PCa detection and prognosis are insufficient.
  • RNA-sequencing (RNA-seq) offers high sensitivity and accuracy for transcriptomic analysis.

Purpose of the Study:

  • To identify novel RNA-seq-based biomarkers for prostate cancer (PCa) detection and prognosis.
  • To analyze differentially expressed genes (DEGs) in medium-risk (MR) and high-risk (HR) PCa.
  • To explore gene interactions and construct predictive models for PCa.

Main Methods:

  • RNA-sequencing (RNA-seq) data analysis of PCa patient samples.
  • Identification and functional enrichment analysis of differentially expressed genes (DEGs).
  • Construction of a Protein-Protein Interaction (PPI) network and a nomogram model.

Main Results:

  • 174 DEGs were shared between MR and HR PCa samples, involved in p53 signaling, nuclear division, and inflammation.
  • Key genes (KIF20A, TPX2, BUB1, BIRC5, BUB1B, MKI67) showed increased expression with PCa progression.
  • BIRC5, MKI67, and KIF20A identified as potential prognostic biomarkers; NIFK and PPP1CC as therapeutic targets.

Conclusions:

  • Identified DEGs and key genes offer potential for improved PCa early detection and prognosis.
  • The constructed nomogram model demonstrates prognostic value for identified biomarkers.
  • New therapeutic targets (NIFK, PPP1CC) were proposed for PCa treatment.