A bioinformatics analysis for diagnostic roles of the E2F family in esophageal cancer

The predictive value of E2F7 in immunotherapy efficacy for lung adenocarcinoma: An observational study

Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. In recent years, immunotherapy has greatly changed the treatment pattern of advanced LUAD. However, only a small proportion of LUAD patients benefitted from immune checkpoint inhibitor therapy. There is an urgent need to develop a biomarker to predict immune therapy response. E2F7 has been shown to be closely related to immune cell infiltration and immune checkpoint expression in tumors. However, it is unclear whether the E2F7 expression is related to the immunotherapy efficacy in LUAD. Therefore, we conducted this study to investigate the clinical characteristics, function, and immunotherapy responsiveness of E2F7 expression, and to explore the potential of E2F7 as an immunotherapy response biomarker in LUAD. We analyzed the clinical characteristics and biological function of E2F7 expression based on data from the Cancer Genome Atlas and Gene Expression Omnibus database. In addition, we used single-cell sequencing data to analyze the immune regulatory effects of E2F7 in LUAD. Furthermore, we analyzed the immunotherapy response prediction ability of E2F7 expression based on the immunotherapy database. Compared to normal lung tissue, E2F7 was specifically overexpressed in LUAD, and its expression was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. E2F7 was enriched in cell division and cell cycle functions. In addition, the expressions of immune checkpoints were correlated with the E2F7 expression. E2F7 was highly expressed in myeloid cells, and E2F7 highly expressed myeloid cells were associated with immune and inflammatory responses. Moreover, the expression level of E2F7 can effectively distinguish different immune therapy responses in LUAD patients. E2F7 was upregulated in LUAD, and high expression of E2F7 was associated with higher malignancy and poor efficacy. E2F7 high expression was an independent risk factor affecting the prognosis of LUAD. Moreover, E2F7 may exert its immunosuppressive effect by affecting the function of myeloid cells. These results indicated the potential role of E2F7 as a biomarker for predicting LUAD immunotherapy responses.

The predictive efficacy of programmed cell death in immunotherapy of melanoma: A comprehensive analysis of gene expression data for programmed cell death biomarker and therapeutic target discovery

In this study, genes linked to prognosis in skin cutaneous melanoma (SKCM) involved in programmed cell death (PCD) were identified and confirmed and prognostic models based on these genes were constructed. Acquisition and analysis of clinical data and RNA sequencing information from The Cancer Genome Atlas-SKCM (TCGA-SKCM) and Sangerbox databases, gene expression data for 477 tumor samples and 2 normal samples were successfully gathered. The patients were separated into two clusters based on consensus clustering of PCD-related genes, with Cluster A having greater tumor purity, ESTIMATE score, immune score, and matrix score, and Cluster B having a significantly distinct pattern of immune cell infiltration. The use of gene set enrichment analysis and weighted correlation network analysis showed significant associations between certain genes and factors such as tumor mutation burden, age, stage, grade, and tumor subtype. Finally, based on the 12 genes selected by Least Absolute Shrinkage and Selection Operator regression analysis (STAT3, IRF2, SLC7A11, ZEB1, LIPT1, PML, GCH1, GYS1, ABCC1, XBP1, TFAP2C, NOX4), a prognostic model of PGD-related genes was constructed. The effectiveness of the model's prognostic value was confirmed through survival analysis, time-dependent receiver operating characteristic curve, single-factor Cox regression analysis, and nomogram. We also verified the relationship between the GCH1 and MKI67 expression by wet experiment. This model has high prediction accuracy in SKCM patients and can provide a reference for clinical treatment.