Background: 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 OSCCrelated
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 tumorinfiltrating
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.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:84509 |
| Date | 03 April 2023 |
| Creators | Li, Simin, Mai, Zhaoyi, Gu, Wenli, Chukwunonso Ogbuehi, Anthony, Acharya, Aneesha, Pelekos, George, Ning, Wanchen, Liu, Xiangqiong, Deng, Yupei, Li, Hanluo, Lethaus, Bernd, Savkovic, Vuk, Zimmerer, Rüdiger, Ziebolz, Dirk, Schmalz, Gerhard, Wang, Hao, Xiao, Hui, Zhao, Jianjiang |
| Publisher | Frontiers Research Foundation |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 2296-634X, 687245 |
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