# Identification of lung adenocarcinoma subtypes and a prognostic signature based on activity changes of the hallmark and immunologic gene sets

## TL;DR

This study identifies two subtypes of lung adenocarcinoma and develops a prognostic model to predict patient survival based on gene activity changes.

## Contribution

The novel contribution is the identification of two LUAD subtypes and a prognostic risk score model based on differential gene sets.

## Key findings

- Two LUAD subtypes were identified based on hallmark and immunologic gene activity changes.
- A prognostic risk score model using two DEGs effectively stratifies patients by survival risk.
- High-risk patients showed shorter survival and higher tumor mutation burden.

## Abstract

Lung adenocarcinoma (LUAD) has a complex tumor heterogeneity. Our research attempts to clearness LUAD subtypes and build a reliable prognostic signature according to the activity changes of the hallmark and immunologic gene sets.

According to The Cancer Genome Atlas (TCGA) - LUAD dataset, changes in marker and immune gene activity were analyzed, followed by identification of prognosis-related differential gene sets (DGSs) and their related LUAD subtypes. Survival analysis, correlation with clinical characteristics, and immune microenvironment assessment for subtypes were performed. Moreover, the differentially expressed genes (DEGs) between different subtypes were identified, followed by the construction of a prognostic risk score (RS) model and nomogram model. The tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) of different risk groups were compared.

Two LUAD subtypes were determined according to the activity changes of the hallmark and immunologic gene sets. Cluster 2 had worse prognosis, more advanced tumor and clinical stages than cluster 1. Moreover, a prognostic RS signature was established using two LUAD subtype-related DEGs, which could stratify patients at different risk levels. Nomogram model incorporated RS and clinical stage exerted good prognostic performance in LUAD patients. A shorter survival time and higher TMB were observed in the high-risk patients.

Our findings revealed that our constructed prognostic signature could exactly predict the survival status of LUAD cases, which was helpful in predicting the prognosis and guiding personalized therapeutic strategies for LUAD.

•Two molecular subtypes of LUAD were identified.•Cluster 2 showed worse prognosis and higher immune infiltration than cluster 1.•Use two subtype related DGS to set up a prognostic risk scoring model.•Compared to the low-risk group, the high-risk group has poorer survival rates and higher TMB.•Nomogram structured according to the risk score and clinicopathological data can predict survival.

Two molecular subtypes of LUAD were identified.

Cluster 2 showed worse prognosis and higher immune infiltration than cluster 1.

Use two subtype related DGS to set up a prognostic risk scoring model.

Compared to the low-risk group, the high-risk group has poorer survival rates and higher TMB.

Nomogram structured according to the risk score and clinicopathological data can predict survival.