# Development and Validation of a 7-eRNA Prognostic Signature for Lung Adenocarcinoma

## TL;DR

This study develops a 7-eRNA model to predict survival in lung adenocarcinoma patients and identifies potential drug targets for high-risk individuals.

## Contribution

A novel 7-eRNA prognostic signature for lung adenocarcinoma with robust validation and drug sensitivity insights.

## Key findings

- The 7-eRNA model effectively stratifies patients into high-risk and low-risk groups.
- High-risk patients show distinct pathways in amino acid biosynthesis and proteasome activity.
- AZD4547 and Nutlin-3a are identified as potential therapies for high-risk individuals.

## Abstract

Lung cancer, with the highest incidence and mortality rates among malignant tumors in recent years, saw approximately 2.48 million new cases globally in 2022, accounting for 12.4% of all new cancer cases. Lung adenocarcinoma is the most prevalent subtype of lung cancer, representing about 40% of all lung cancer cases. Identifying biomarkers for prognostic assessment of lung adenocarcinoma to improve patients’ 5-year survival rate and prognosis is of utmost urgency. With the advancement of bioinformatics technologies, measuring the expression levels of enhancer RNAs has become increasingly convenient and stable. Enhancer RNAs play crucial roles in disease progression. However, research on the functions of enhancer RNAs in lung adenocarcinoma is still insufficient. In this study, data from the databases were used to construct a prognostic assessment model for lung adenocarcinoma based on enhancer RNAs. A 7-enhancer RNA model was developed and validated for its effectiveness and robustness in both the train and test sets. Using this 7-enhancer RNA model, samples were divided into high-risk and low-risk groups. Enrichment pathways, tumor-immune microenvironments, drug sensitivity analysis and somatic mutations were analyzed for these two groups. These results are expected to promote in-depth explorations of the mechanisms underlying lung adenocarcinoma development and open new perspectives for enhancer RNA regulation mechanism research.

Enhancer RNAs (eRNAs) are abundant in most human cells and tissues, and quantifying eRNAs has become a robust approach for biomarker discovery. While eRNAs play crucial roles in regulating biological processes and cancer progression, their functions in lung adenocarcinoma (LUAD) remain poorly understood. Here, we developed a LUAD prognostic model based on eRNA expression data from The Cancer Genome Atlas (TCGA). Through rigorous validation, a 7-eRNA signature was identified, which robustly stratified LUAD patients into high-risk and low-risk groups in both training and testing sets. Functional analyses revealed distinct enrichment of pathways related to amino acid biosynthesis, ribosome biogenesis, and proteasome activity in high-risk patients. Somatic mutation profiling highlighted TP53 and TTN as frequently mutated genes, while drug sensitivity prediction identified four potential therapeutic agents (including AZD4547 and Nutlin-3a) for high-risk individuals. Collectively, this study constructed a 7-eRNA prognostic model for LUAD, providing a powerful tool for clinical risk assessment and uncovering eRNA-mediated regulatory mechanisms.