A tumor-infiltrating B lymphocytes -related index based on machine-learning predicts prognosis and immunotherapy response in lung adenocarcinoma.

ABSTRACT:

Introduction

Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune microenvironment of tumors (TIME) and in the progression of lung adenocarcinoma (LUAD). However, there remains a scarcity of research that has thoroughly and systematically delineated the characteristics of TILBs in LUAD.

Method

The research employed single-cell RNA sequencing from the GSE117570 dataset to identify markers linked to TILBs. A comprehensive machine learning approach, utilizing ten distinct algorithms, facilitated the creation of a TILB-related index (BRI) across the TCGA, GSE31210, and GSE72094 datasets. We used multiple algorithms to evaluate the relationships between BRI and TIME, as well as immune therapy-related biomarkers. Additionally, we assessed the role of

SUBMITTER: Fang J 

PROVIDER: S-EPMC11973313 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature