Non-negative matrix factorization model-based construction for molecular clustering and prognostic assessment of head and neck squamous carcinoma.
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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 prognostic predictor.Conclusion
The NMF-based prognostic model has the potential for prognostic assessment of HNSCC.
SUBMITTER: Li XY
PROVIDER: S-EPMC9389204 | biostudies-literature | 2022 Aug
REPOSITORIES: biostudies-literature
Publications
Non-negative matrix factorization model-based construction for molecular clustering and prognostic assessment of head and neck squamous carcinoma.
Li Xin-Yu XY An Hong-Bang HB Zhang Lu-Yu LY Liu Hui H Shen Yu-Chen YC Yang Xi-Tao XT
Heliyon 20220807 8
<h4>Purpose</h4>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).<h4>Methods</h4>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.<h4>Results</h4>Based on NMF, samples we ...[more]
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