File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Molecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach

TitleMolecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach
Authors
KeywordsImmunosuppression
Oral squamous cell carcinoma
Survival
Deep learning
Bioinformatics
Issue Date2021
PublisherFrontiers Research Foundation. The Journal's web site is located at https://www.frontiersin.org/journals/cell-and-developmental-biology
Citation
Frontiers in Cell and Developmental Biology, 2021, v. 9, article no. 687245 How to Cite?
AbstractBackground: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCC-related genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumor-infiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area.
Persistent Identifierhttp://hdl.handle.net/10722/309426
ISSN
2023 Impact Factor: 4.6
2023 SCImago Journal Rankings: 1.576
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, S-
dc.contributor.authorMai, Z-
dc.contributor.authorGu, W-
dc.contributor.authorOgbuehi, AC-
dc.contributor.authorAcharya, A-
dc.contributor.authorPelekos, G-
dc.contributor.authorNing, W-
dc.contributor.authorLiu, X-
dc.contributor.authorDeng, Y-
dc.contributor.authorLi, H-
dc.contributor.authorLethaus, B-
dc.contributor.authorSavkovic, V-
dc.contributor.authorZimmerer, R-
dc.contributor.authorZiebolz, D-
dc.contributor.authorSchmalz, G-
dc.contributor.authorWang, H-
dc.contributor.authorXiao, H-
dc.contributor.authorZhao, J-
dc.date.accessioned2021-12-29T02:14:57Z-
dc.date.available2021-12-29T02:14:57Z-
dc.date.issued2021-
dc.identifier.citationFrontiers in Cell and Developmental Biology, 2021, v. 9, article no. 687245-
dc.identifier.issn2296-634X-
dc.identifier.urihttp://hdl.handle.net/10722/309426-
dc.description.abstractBackground: The mechanisms through which immunosuppressed patients bear increased risk and worse survival in oral squamous cell carcinoma (OSCC) are unclear. Here, we used deep learning to investigate the genetic mechanisms underlying immunosuppression in the survival of OSCC patients, especially from the aspect of various survival-related subtypes. Materials and methods: OSCC samples data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and OSCC-related genetic datasets with survival data in the National Center for Biotechnology Information (NCBI). Immunosuppression genes (ISGs) were obtained from the HisgAtlas and DisGeNET databases. Survival analyses were performed to identify the ISGs with significant prognostic values in OSCC. A deep learning (DL)-based model was established for robustly differentiating the survival subpopulations of OSCC samples. In order to understand the characteristics of the different survival-risk subtypes of OSCC samples, differential expression analysis and functional enrichment analysis were performed. Results: A total of 317 OSCC samples were divided into one inferring cohort (TCGA) and four confirmation cohorts (ICGC set, GSE41613, GSE42743, and GSE75538). Eleven ISGs (i.e., BGLAP, CALCA, CTLA4, CXCL8, FGFR3, HPRT1, IL22, ORMDL3, TLR3, SPHK1, and INHBB) showed prognostic value in OSCC. The DL-based model provided two optimal subgroups of TCGA-OSCC samples with significant differences (p = 4.91E-22) and good model fitness [concordance index (C-index) = 0.77]. The DL model was validated by using four external confirmation cohorts: ICGC cohort (n = 40, C-index = 0.39), GSE41613 dataset (n = 97, C-index = 0.86), GSE42743 dataset (n = 71, C-index = 0.87), and GSE75538 dataset (n = 14, C-index = 0.48). Importantly, subtype Sub1 demonstrated a lower probability of survival and thus a more aggressive nature compared with subtype Sub2. ISGs in subtype Sub1 were enriched in the tumor-infiltrating immune cells-related pathways and cancer progression-related pathways, while those in subtype Sub2 were enriched in the metabolism-related pathways. Conclusion: The two survival subtypes of OSCC identified by deep learning can benefit clinical practitioners to divide immunocompromised patients with oral cancer into two subpopulations and give them target drugs and thus might be helpful for improving the survival of these patients and providing novel therapeutic strategies in the precision medicine area.-
dc.languageeng-
dc.publisherFrontiers Research Foundation. The Journal's web site is located at https://www.frontiersin.org/journals/cell-and-developmental-biology-
dc.relation.ispartofFrontiers in Cell and Developmental Biology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectImmunosuppression-
dc.subjectOral squamous cell carcinoma-
dc.subjectSurvival-
dc.subjectDeep learning-
dc.subjectBioinformatics-
dc.titleMolecular Subtypes of Oral Squamous Cell Carcinoma Based on Immunosuppression Genes Using a Deep Learning Approach-
dc.typeArticle-
dc.identifier.emailPelekos, G: george74@hku.hk-
dc.identifier.authorityPelekos, G=rp01894-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.3389/fcell.2021.687245-
dc.identifier.pmid34422810-
dc.identifier.pmcidPMC8375681-
dc.identifier.scopuseid_2-s2.0-85113165435-
dc.identifier.hkuros331349-
dc.identifier.volume9-
dc.identifier.spagearticle no. 687245-
dc.identifier.epagearticle no. 687245-
dc.identifier.isiWOS:000684974000005-
dc.publisher.placeSwitzerland-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats