Objective Lung cancer (LC) is one of the top ten malignant tumors and the first leading cause of cancer-related death among both men and women worldwide. It is imperative to identify immune-related biomarkers for early LC diagnosis and treatment. Methods Three Gene Expression Omnibus (GEO) datasets were selected to acquire the differentially expressed genes(DEGs) between LC and normal lung samples through GEO2R tools of NCBI. To identify hub genes, the DEGs were performed functional enrichment analysis, the protein–protein interaction (PPI) network construction, and Lasso regression. Then, a nomogram was constructed to predict the prognosis of patients with carcinoma based on hub genes. We further evaluated the influence of COL1A1 on clinical prognosis using GSE3141, GSE31210, and TCGA database. Also, the correlations between COL1A1 and cancer immune infiltrates and the B7-CD28 family was investigated via TIMER and GEPIA. Further analysis of immunohistochemistry shown that the COL1A1 expression level is positively correlated with CD276 expression level. Results By difference analysis, there were 340 DEGs between LC and normal lung samples. Then, we picked out seven hub genes, which were identified as components of the risk signature to divide LC into low and high-risk groups. Among them, the expression of COL1A1 is highly correlated with overall survival(OS) and progression-free survival (PFS) (p < 0.05). Importantly, there is a moderate to strong positive relationships between COL1A1 expression level and infiltration level of CD4+ T cells, Macrophage, Neutrophil, and Dendritic cell, as well as CD276 expression level. Conclusion These findings suggest that COL1A1 is correlated with prognosis and immune infiltrating levels, including CD4+ T cells, Macrophage, Neutrophil, and Dendritic cell, as well as CD276 expression level, indicating COL1A1 can be a potential immunity-related biomarker and therapeutic target in LC.
BackgroundThyroid cancer (THCA) is a malignancy affecting the endocrine system, which currently has no effective treatment due to a limited number of suitable drugs and prognostic markers.MethodsThree Gene Expression Omnibus (GEO) datasets were selected to identify differentially expressed genes (DEGs) between THCA and normal thyroid samples using GEO2R tools of National Center for Biotechnology Information. We identified hub gene FN1 using functional enrichment and protein-protein interaction network analyses. Subsequently, we evaluated the importance of gene expression on clinical prognosis using The Cancer Genome Atlas (TCGA) database and GEO datasets. MEXPRESS was used to investigate the correlation between gene expression and DNA methylation; the correlations between FN1 and cancer immune infiltrates were investigated using CIBERSORT. In addition, we assessed the effect of silencing FN1 expression, using an in vitro cellular model of THCA. Immunohistochemical(IHC) was used to elevate the correlation between CD276 and FN1.ResultsFN1 expression was highly correlated with progression-free survival and moderately to strongly correlated with the infiltration levels of M2 macrophages and resting memory CD4+ T cells, as well as with CD276 expression. We suggest promoter hypermethylation as the mechanism underlying the observed changes in FN1 expression, as 20 CpG sites in 507 THCA cases in TCGA database showed a negative correlation with FN1 expression. In addition, silencing FN1 expression suppressed clonogenicity, motility, invasiveness, and the expression of CD276 in vitro. The correlation between FN1 and CD276 was further confirmed by immunohistochemical.ConclusionOur findings show that FN1 expression levels correlate with prognosis and immune infiltration levels in THCA, suggesting that FN1 expression be used as an immunity-related biomarker and therapeutic target in THCA.
N6-methyladenosine (m6A) methylation is the most universal internal modification in eukaryotic mRNA. With elaborate functions executed by m6A writers, erasers, and readers, m6A modulation is involved in myriad physiological and pathological processes. Extensive studies have demonstrated m6A modulation in diverse tumours, with effects on tumorigenesis, metastasis, and resistance. Recent evidence has revealed an emerging role of m6A modulation in tumour immunoregulation, and divergent m6A methylation patterns have been revealed in the tumour microenvironment. To depict the regulatory role of m6A methylation in the tumour immune microenvironment (TIME) and its effect on immune evasion, this review focuses on the TIME, which is characterized by hypoxia, metabolic reprogramming, acidity, and immunosuppression, and outlines the m6A-regulated TIME and immune evasion under divergent stimuli. Furthermore, m6A modulation patterns in anti-tumour immune cells are summarized.
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