Mining featured micro ribonucleic acids associated with lung cancer based on bioinformatics

Area of Science:

• Genomics
• Molecular Biology
• Cancer Research

Background:

• Limited genetic markers exist for lung cancer risk screening.
• Micro ribonucleic acid (miRNA) expression profiles differ between lung cancer and normal lung tissues.
• The precise molecular mechanisms driving lung cancer remain unclear.

Purpose of the Study:

• To analyze differentially expressed genes (DEGs) in lung cancer.
• To identify specific miRNAs involved in lung cancer pathogenesis.
• To understand the molecular mechanisms underlying lung cancer.

Main Methods:

• Utilized gene expression profile GSE10072 (49 non-tumor, 58 tumor specimens).
• Identified DEGs using the Limma package in R.
• Constructed miRNA regulatory networks using TarBase and screened miRNAs with the miR2Disease database.

Main Results:

• Identified 5572 DEGs between lung cancer and control specimens.
• Established a miRNA regulatory network with 398 regulations (57 miRNAs, 321 target genes).
• Identified six featured miRNAs (miR-520h, miR-133a, miR-34, miR-103, miR-370, miR-148) potentially regulating key genes in lung cancer progression.

Conclusions:

• The top 10 significant miRNAs, including miR-520h, miR-133a, miR-34, and miR-103, are potential therapeutic targets for lung cancer.
• These findings contribute to understanding lung cancer pathogenetic mechanisms.
• Further research into these miRNAs could lead to novel diagnostic and therapeutic strategies.