Background and objective Currently, lung cancer is one of the malignant tumors with a high morbidity and mortality all over the world. Howev
Background and objective Currently, lung cancer is one of the malignant tumors with a high morbidity and mortality all over the world. However, the exact mechanisms underlying lung cancer progression remain unclear. The tumor necrosis factor receptor associated factor (TRAF) family members are cytoplasmic adaptor proteins, which function as both adaptor proteins and ubiquitin ligases to regulate diverse receptor signalings, leading to the activation of nuclear factor kappa-B (NF-κB), mitogen-activated protein kinase (MAPK) and interferon regulatory factor (IRF) signaling. The aim of this study was to investigate the expression of TRAFs in different tissues and cancer types, as well as its mRNA expression, protein expression, prognostic significance and functional enrichment analysis in non-small cell lung cancer (NSCLC), in order to provide new strategies for the diagnosis and treatment of NSCLC. Methods RNA sequencing data from the The Genotype-Tissue Expression database was used to analyze the expression patterns of TRAF family members in different human tissues. RNA sequencing data from the Cancer Cell Line Encyclopedia database was used to analyze the expression patterns of TRAF family members in different types of cancer cell lines. RNA sequencing data from the The Cancer Genome Atlas (TCGA) database was used to analyze the mRNA levels of TRAF family members across different types of human cancers. Immunohistochemistry (IHC) analyses from HPA database were used to analyze the TRAF protein levels in NSCLC [lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC)]. Overall survival analysis was performed by Log-rank test using original data from Kaplan-Meier Plotter database to evaluate the correlation between TRAF expressions and prognosis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on the TRAF family-related genes using RNA sequencing data from the TCGA database for NSCLC. The correlation between the expression levels of TRAF family members and the tumor immune microenvironment was analyzed using the ESTIMATE algorithm based on RNA sequencing data from the TCGA database. Results The TRAF family members exhibited significant tissue-specific expression heterogeneity. TRAF2, TRAF3, TRAF6 and TRAF7 were widely expressed in most tissues, while the expressions of TRAF1, TRAF4 and TRAF5 were restricted to specific tissues. The expressions of TRAF family members were highly specific among different types of cancer cell lines. In mRNA database of LUAD and LUSC, the expressions of TRAF2, TRAF4, TRAF5 and TRAF7 were significantly upregulated; while TRAF6 did the opposite; moveover, TRAF1 and TRAF3 only displayed a significant upregulation in LUAD and LUSC, respectively. Except for TRAF3, TRAF4 and TRAF7, other TRAF proteins displayed an obviously deeper IHC staining in LUAD and LUSC tissues compared with normal tissues. Additionally, patients with higher expression levels of TRAF2, TRAF4 and TRAF7 had shorter overall survival; while patients with higher expression levels of TRAF3, TRAF5 and TRAF6 had significantly longer overall survival; however, no significant difference had been observed between TRAF1 expression and the overall survival. TRAF family members differentially regulated multiple pathways, including NF-κB, immune response, cell adhesion and RNA splicing. The expression levels of TRAF family members were closely associated with immune cell infiltration and stromal cell content in the tumor immune microenvironment, with varying positive and negative correlations among different members. Conclusion TRAF family members exhibit highly specific expression differences across different tissues and cancer types. Most TRAF proteins exhibit upregulation at both mRNA and protein levels in NSCLC, whereas, only upregulated expressions of TRAF2, TRAF4 and TRAF7 predict worse prognosis. The TRAF family members regulate processes such as inflammation, immunity, adhesion and splicing, and influence the tumor immune microenvironment.
