# Mitochondrial non-coding RNAs as novel biomarkers and therapeutic targets in lung cancer integration of traditional bioinformatics and machine learning approaches

## TL;DR

This study explores mitochondrial non-coding RNAs as potential biomarkers and therapeutic targets in lung cancer using bioinformatics and machine learning.

## Contribution

The novel contribution is identifying specific mitochondrial non-coding RNAs as effective diagnostic biomarkers and potential therapeutic targets in lung cancer.

## Key findings

- Ten mitochondrial non-coding RNAs effectively distinguish cancer from normal lung tissues.
- Random Forest and Logistic Regression models achieved high classification accuracy (AUC > 0.92).
- t00043332 promotes cancer cell proliferation and resistance to apoptosis.

## Abstract

Lung cancer diagnosis requires cost-effective biomarkers. Mitochondrial non-coding RNAs (mtRNAs) represent unexplored diagnostic targets.

We analyzed TCGA-LUAD/LUSC miRNA-seq data to identify mtRNAs via mitochondrial genome alignment. Machine learning algorithms (SVM, Random Forest, Logistic Regression) classified samples using differentially expressed mtRNAs (P < 0.01, |log2FC| > 1). Top-ranked t00043332 was functionally validated in A549/PC9 cells.

Ten mtRNAs distinguished cancer from normal tissues. Random Forest and Logistic Regression achieved superior classification (AUC > 0.92) versus SVM. Nine mtRNAs were upregulated, one downregulated in cancer. No survival associations were observed. t00043332 overexpression promoted proliferation, migration, invasion, and apoptosis resistance.

mtRNAs serve as effective lung cancer diagnostic biomarkers through integrated traditional and AI approaches. t00043332 functions as an oncogene, providing therapeutic targets and advancing biomarker discovery.