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Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

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Human miRNAs in Cancer: Statistical Trends and Cross Kingdom Approach.

Maksym Zoziuk¹, Vittorio Colizzi², Maurizio Mattei^1,3

¹Interdepartmental Center for Comparative Medicine, Alternative Techniques, and Aquaculture, University of Rome Tor Vergata, Via Montpellier 1, 00133 Rome, Italy.

International Journal of Molecular Sciences

|December 11, 2025

Summary

This study identifies stable human microRNAs (miRNAs) and their target genes in cancer, revealing cross-kingdom sequence similarities with plant miRNAs. This computational framework aids future research in miRNA regulation and comparative genomics.

Keywords:

cancer bioinformatics cross-kingdom comparison microRNA (miRNA)plant miRNA sequence similarity

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Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

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Published on: April 18, 2025

Area of Science:

Molecular Biology
Genomics
Bioinformatics

Background:

MicroRNAs (miRNAs) are key post-transcriptional regulators of gene expression.
Dysregulation of miRNAs is common in various cancers.
Global miRNA patterns and cross-kingdom similarities are underexplored.

Purpose of the Study:

Identify statistically stable human miRNAs in cancer.
Determine key target genes regulated by these miRNAs.
Analyze sequence complementarity between human and plant miRNAs.

Main Methods:

Integrated miRTarBase interactions with TCGA cancer expression data.
Applied a nonlinear threshold to identify dysregulated miRNAs and target genes.
Performed computational sequence complementarity analysis between human and plant miRNAs.

Main Results:

Identified 115 underexpressed and 93 overexpressed human miRNAs regulating 200 key cancer genes.
Observed consistent miRNA expression and target gene patterns across cancers.
Found systematic, albeit modest, sequence complementarity between human and plant miRNAs.

Conclusions:

Established a computational framework for miRNA research.
Identified statistically stable cancer-associated miRNAs and their targets.
Highlighted potential cross-kingdom sequence similarities for future investigation.