Expressions of HLA Class II Genes in Cutaneous Melanoma Were Associated with Clinical Outcome: Bioinformatics Approaches and Systematic Analysis of Public Microarray and RNA-Seq Datasets.
Yang-Yi ChenWei-An ChangEn-Shyh LinYi-Jen ChenPo-Lin KuoPublished in: Diagnostics (Basel, Switzerland) (2019)
Major histocompatibility complex (MHC) class II molecules, encoded by human leukocyte antigen (HLA) class II genes, play important roles in antigen presentation and initiation of immune responses. However, the correlation between HLA class II gene expression level and patient survival and disease progression in cutaneous melanoma is still under investigation. In the present study, we analyzed microarray and RNA-Seq data of cutaneous melanoma from The Cancer Genome Atlas (TCGA) using different bioinformatics tools. Survival analysis revealed higher expression level of HLA class II genes in cutaneous melanoma, especially HLA-DP and -DR, was significantly associated with better overall survival. Furthermore, the expressions of HLA class II genes were most closely associated with survival in cutaneous melanoma as compared with other cancer types. The expression of HLA class II co-expressed genes, which were found to associate with antigen processing, immune response, and inflammatory response, was also positively associated with overall survival in cutaneous melanoma. Therefore, the results indicated that increased HLA class II expression may contribute to enhanced anti-tumor immunity and related inflammatory response via presenting tumor antigens to the immune system. The expression pattern of HLA class II genes may serve as a prognostic biomarker and therapeutic targets in cutaneous melanoma.
Keyphrases
- rna seq
- single cell
- immune response
- genome wide
- inflammatory response
- poor prognosis
- bioinformatics analysis
- gene expression
- skin cancer
- genome wide identification
- endothelial cells
- healthcare
- free survival
- case report
- toll like receptor
- lipopolysaccharide induced
- papillary thyroid
- dna methylation
- squamous cell carcinoma
- mental health
- genome wide analysis
- emergency department
- machine learning
- lps induced
- squamous cell
- big data
- artificial intelligence
- transcription factor
- electronic health record
- lymph node metastasis