# Machine learning-based integration develops an immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy response in lung adenocarcinoma

## TL;DR

This study uses machine learning to create a lncRNA signature that predicts prognosis and immunotherapy response in lung adenocarcinoma patients.

## Contribution

The study introduces a novel immunogenic cell death-derived lncRNA signature for predicting prognosis and immunotherapy benefit in lung adenocarcinoma.

## Key findings

- The lncRNA signature showed stable performance in predicting prognosis compared to existing signatures.
- The signature demonstrated predictive value for immunotherapy benefit across multiple cancer cohorts.
- The signature also guided the potential use of chemotherapy drugs.

## Abstract

Accumulating evidence demonstrates that lncRNAs are involved in the regulation of the immune microenvironment and early tumor development. Immunogenic cell death occurs mainly through the release or increase of tumor-associated antigen and tumor-specific antigen, exposing “danger signals” to stimulate the body’s immune response. Given the recent development of immunotherapy in lung adenocarcinoma, we explored the role of tumor immunogenic cell death-related lncRNAs in lung adenocarcinoma for prognosis and immunotherapy benefit, which has never been uncovered yet. Based on the lung adenocarcinoma cohorts from the TCGA database and GEO database, the study developed the immunogenic cell death index signature by several machine learning algorithms and then validated the signature for prognosis and immunotherapy benefit of lung adenocarcinoma patients, which had a more stable performance compared with published signatures in predicting the prognosis, and demonstrated predictive value for benefiting from immunotherapy in multiple cohorts of multiple cancers, and also guided the utilization of chemotherapy drugs.