Machine learning-based signature of necrosis-associated lncRNAs for prognostic and immunotherapy response prediction in cutaneous melanoma and tumor immune landscape characterization.

ABSTRACT:

Background

Cutaneous melanoma (CM) is one of the malignant tumors with a relative high lethality. Necroptosis is a novel programmed cell death that participates in anti-tumor immunity and tumor prognosis. Necroptosis has been found to play an important role in tumors like CM. However, the necroptosis-associated lncRNAs' potential prognostic value in CM has not been identified.

Methods

The RNA sequencing data collected from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression Project (GTEx) was utilized to identify differentially expressed genes in CM. By using the univariate Cox regression analysis and machine learning LASSO algorithm, a prognostic risk model had been built depending on 5 necroptosis-associated lncRNAs and was verified by internal validation. The pe

SUBMITTER: Cui Z 

PROVIDER: S-EPMC10203625 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature