Comprehensive Analysis of E2F Family Members in Human Gastric Cancer

PYGO2 as a novel prognostic biomarker and its correlation with immune infiltrates in liver cancer

OBJECTIVE

The PYGO2 gene plays a significant role in various cancers. However, its prognostic significance and involvement in immune infiltration in liver cancer remain unclear. This study aimed to comprehensively evaluate PYGO2 expression and its associations with prognosis and clinicopathological features in liver cancer.

METHODS

Data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were analyzed. Functional enrichment analysis and immune cell infiltration assessments were performed to explore potential pathogenic mechanisms.

RESULTS

PYGO2 was highly expressed in multiple cancer types, including bladder urothelial carcinoma, breast invasive carcinoma, cholangiocarcinoma, diffuse large B-cell lymphoma, and liver cancer. Analysis of 50 paired liver cancer tissues from TCGA revealed significant upregulation of PYGO2 expression. Moreover, high PYGO2 expression was significantly associated with pathological T stage, overall pathological stage, tumor status, and race. Kaplan-Meier survival analysis showed that low PYGO2 expression correlated with improved overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in liver cancer patients. Functional enrichment analysis identified several enriched pathways, including the reactive oxygen species signaling pathway, MYC targets, interferon-alpha response, immune response regulation signaling pathway, and leukocyte migration. Additionally, PYGO2 overexpression was associated with lower proportions of cytotoxic cells, dendritic cells, immature dendritic cells, mast cells, neutrophils, plasmacytoid dendritic cell-like cells, Th17 cells, and regulatory T cells, but a higher proportion of Th2 cells. Furthermore, the high PYGO2 expression group exhibited increased immune checkpoint gene expression, particularly PDCD1.

CONCLUSION

PYGO2 is a promising prognostic biomarker for liver cancer, given its strong associations with clinicopathological features, survival outcomes, and immune-related characteristics.

DeepCG: A cell graph model for predicting prognosis in lung adenocarcinoma

Lung cancer is the first leading cause of cancer-related death in the United States, with lung adenocarcinoma as the major subtype accounting for 40% of all cases. To improve patient survival, image-based prognostic models were developed due to the ready availability of pathological images at diagnosis. However, the application of these models is hampered by two main challenges: the lack of publicly available image datasets with high-quality survival information and the poor interpretability of conventional convolutional neural network models. Here, we integrated matched transcriptomic and H&E staining data from TCGA (The Cancer Genome Atlas) to develop an image-based prognostic model, termed Deep-learning based Cell Graph (DeepCG) model. Instead of survival data, we used a gene signature to predict patient prognostic risks, which was then used as labels for training DeepCG. Importantly, by employing graph structures to capture cell patterns, DeepCG can provide cell-level interpretation, which was more biologically relevant than previous region-level insights. We validated the prognostic values of DeepCG in independent datasets and demonstrated its ability to identify prognostically informative cells in images.

Expression patterns of E2Fs identify tumor microenvironment features in human gastric cancer

Objective

E2F transcription factors are associated with tumor development, but their underlying mechanisms in gastric cancer (GC) remain unclear. This study explored whether E2Fs determine the prognosis or immune and therapy responses of GC patients.

Methods

E2F regulation patterns from The Cancer Genome Atlas (TCGA) were systematically investigated and E2F patterns were correlated with the characteristics of cellular infiltration in the tumor microenvironment (TME). A principal component analysis was used to construct an E2F scoring model based on prognosis-related differential genes to quantify the E2F regulation of a single tumor. This scoring model was then tested in patient cohorts to predict effects of immunotherapy.

Results

Based on the expression profiles of E2F transcription factors in GC, two different regulatory patterns of E2F were identified. TME and survival differences emerged between the two clusters. Lower survival rates in the Cluster2 group were attributed to limited immune function due to stromal activation. The E2F scoring model was then constructed based on the E2F-related prognostic genes. Evidence supported the E2F score as an independent and effective prognostic factor and predictor of immunotherapy response. A gene-set analysis correlated E2F score with the characteristics of immune cell infiltration within the TME. The immunotherapy cohort database showed that patients with a higher E2F score demonstrated better survival and immune responses.

Conclusions

This study found that differences in GC prognosis might be related to the E2F patterns in the TME. The E2F scoring system developed in this study has practical value as a predictor of survival and treatment response in GC patients.

Long Non-Coding LEF1-AS1 Sponge miR-5100 Regulates Apoptosis and Autophagy in Gastric Cancer Cells via the miR-5100/DEK/AMPK-mTOR Axis

DEK and miR-5100 play critical roles in many steps of cancer initiation and progression and are directly or indirectly regulated by most promoters and repressors. LEF1-AS1 as a long non-coding RNA can regulate tumor development through sponge miRNA. The effect and regulatory mechanism of DEK on autophagy and apoptosis in gastric cancer (GC), and the role between miR-5100 and DEK or miR-5100 and LEF1-AS1 are still unclear. Our study found that DEK was highly expressed in gastric cancer tissues and cell lines, and knockdown of DEK inhibited the autophagy of cells, promoted apoptosis, and suppressed the malignant phenotype of gastric cancer. DEK regulates autophagy and apoptosis through the AMPK/mTOR signaling pathway. In addition, miR-5100 inhibits autophagy and promotes apoptosis in GC cells while LEF1-AS1 had the opposite effect. Studies have shown that miR-5100 acts by targeting the 3'UTR of DEK, and LEF1-AS1 regulates the expression of miR-5100 by sponging with mIR-5100. In conclusion, our results found that LEF1-AS1 and miR-5100 sponge function, and the miR-5100/DEK/AMPK/mTOR axis regulates autophagy and apoptosis in gastric cancer cells.

Inhibition of HDACs Suppresses Cell Proliferation and Cell Migration of Gastric Cancer by Regulating E2F5 Targeting BCL2

(1) Background: Gastric cancer (GC) is the most common high death-rate cancer type worldwide, with an enhanced prevalence and increased rate of mortality. Although significant evidence on surgery strategy has been generated for the treatment of GC, conclusions are still uncertain regarding profound metastatic or persevering gastric cancer. Therefore, it is essential to develop novel and effective biomarkers or therapeutic targets for the diagnosis of GC. Histone deacetylations (HDACs) are important epigenetic regulators that control the aberrant transcription of critical genes that are mainly involved in cell proliferation, cell migration, regulation of the cell cycle, and different signal pathways. (2) Methods: Expression analysis of HDACs family members and E2F5 in gastric cancer cell lines was determined by RT-PCR and Western blotting. The cell proliferation was determined through an MTT assay. Cell migration was determined using a wound-healing assay. Flow cytometry experiments were used to determine cell-cycle analysis. The statistical software OriginPro 2015 (OriginLab, Northampton, MA, USA) was used to analyze data. A p value of < 0.05 was regarded as significant. (3) Results: The present study shows that E2F5 expression is upregulated in GC cancer cell lines compared to normal cell lines, and is positively associated with the level of HDACs and BCL2. HDACi and knocking down of E2F5 as tumor suppressors inhibited cell proliferation, migration invasion, and blocked the cell cycle in gastric cancer cells by suppressing BCL2. The results conclude that the anticancer mechanism of HDACi was determined by regulating E2F5 via targeting BCL2. (4) Conclusions: Our results suggest that the HDAC–E2F5–BCL2 signaling axis might be a novel potential biomarker in gastric cancer.