Non-negative matrix factorization model-based construction for molecular clustering and prognostic assessment of head and neck squamous carcinoma.

ABSTRACT:

Purpose

We aimed at exploring the efficacy of non-negative matrix factorization (NMF) model-based clustering for prognostic assessment of head and neck squamous carcinoma (HNSCC).

Methods

The transcriptome microarray data of HNSCC samples were downloaded from The Cancer Genome Atlas (TCGA) and the Shanghai Ninth People's Hospital. R software packages were used to establish NMF clustering, from which relevant prognostic models were developed.

Results

Based on NMF, samples were allocated into 2 subgroups. Predictive models were constructed using differentially expressed genes between the two subgroups. The high-risk group was associated with poor prognostic outcomes. Moreover, multi-factor Cox regression analysis revealed that the predictive model was an independent pro

SUBMITTER: Li XY 

PROVIDER: S-EPMC9389204 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature