RACGAP1 is transcriptionally regulated by E2F3, and its depletion leads to mitotic catastrophe in esophageal squamous cell carcinoma

Oncogenic and immunological roles of RACGAP1 in pan-cancer and its potential value in nasopharyngeal carcinoma

A particular GTPase-activating protein called RACGAP1 is involved in apoptosis, proliferation, invasion, metastasis, and drug resistance in a variety of malignancies. Nevertheless, the role of RACGAP1 in pan-cancer was less studied, and its value of the expression and prognostic of nasopharyngeal carcinoma (NPC) has not been explored. Hence, the goal of this study was to investigate the oncogenic and immunological roles of RACGAP1 in various cancers and its potential value in NPC. We comprehensively analyzed RACGAP1 expression, prognostic value, function, methylation levels, relationship with immune cells, immune infiltration, and immunotherapy response in pan-cancer utilizing multiple databases. The results discovered that RACGAP1 expression was elevated in most cancers and suggested poor prognosis, which could be related to the involvement of RACGAP1 in various cancer-related pathways such as the cell cycle and correlated with RACGAP1 methylation levels, immune cell infiltration and reaction to immunotherapy, and chemoresistance. RACGAP1 could inhibit anti-tumor immunity and immunotherapy responses by fostering immune cell infiltration and cytotoxic T lymphocyte dysfunction. Significantly, we validated that RACGAP1 mRNA and protein were highly expressed in NPC. The Gene Expression Omnibus database revealed that elevated RACGAP1 expression was associated with shorter PFS in patients with NPC, and RACGAP1 potentially influenced cell cycle progression, DNA replication, metabolism, and immune-related pathways, resulting in the recurrence and metastasis of NPC. This study indicated that RACGAP1 could be a potential biomarker in pan-cancer and NPC.

RACGAP1 promotes the progression and poor prognosis of lung adenocarcinoma through its effects on the cell cycle and tumor stemness

OBJECTION

Investigating the key genes and mechanisms that influence stemness in lung adenocarcinoma.

METHODS

First, consistent clustering analysis was performed on lung adenocarcinoma patients using stemness scoring to classify them. Subsequently, WGCNA was utilized to identify key modules and hub genes. Then, machine learning methods were employed to screen and identify the key genes within these modules. Lastly, functional analysis of the key genes was conducted through cell scratch assays, colony formation assays, transwell migration assays, flow cytometry cell cycle analysis, and xenograft tumor models.

RESULTS

First, two groups of patients with different stemness scores were obtained, where the high stemness score group exhibited poor prognosis and immunotherapy efficacy. Next, LASSO regression analysis and random forest regression were employed to identify genes (PBK, RACGAP1) associated with high stemness scores. RACGAP1 was significantly upregulated in the high stemness score group of lung adenocarcinoma and closely correlated with clinical pathological features, poor overall survival (OS), recurrence-free survival (RFS), and unfavorable prognosis in lung adenocarcinoma patients. Knockdown of RACGAP1 suppressed the migration, proliferation, and tumor growth of cancer cells.

CONCLUSION

RACGAP1 not only indicates poor prognosis and limited immunotherapy benefits but also serves as a potential targeted biomarker influencing tumor stemness.

Telomere-related prognostic biomarkers for survival assessments in pancreatic cancer

Human telomeres are linked to genetic instability and a higher risk of developing cancer. Therefore, to improve the dismal prognosis of pancreatic cancer patients, a thorough investigation of the association between telomere-related genes and pancreatic cancer is required. Combat from the R package "SVA" was performed to correct the batch effects between the TCGA-PAAD and GTEx datasets. After differentially expressed genes (DEGs) were assessed, we constructed a prognostic risk model through univariate Cox regression, LASSO-Cox regression, and multivariate Cox regression analysis. Data from the ICGC, GSE62452, GSE71729, and GSE78229 cohorts were used as test cohorts for validating the prognostic signature. The major impact of the signature on the tumor microenvironment and its response to immune checkpoint drugs was also evaluated. Finally, PAAD tissue microarrays were fabricated and immunohistochemistry was performed to explore the expression of this signature in clinical samples. After calculating 502 telomere-associated DEGs, we constructed a three-gene prognostic signature (DSG2, LDHA, and RACGAP1) that can be effectively applied to the prognostic classification of pancreatic cancer patients in multiple datasets, including TCGA, ICGC, GSE62452, GSE71729, and GSE78229 cohorts. In addition, we have screened a variety of tumor-sensitive drugs targeting this signature. Finally, we also found that protein levels of DSG2, LDHA, and RACGAP1 were upregulated in pancreatic cancer tissues compared to normal tissues by immunohistochemistry analysis. We established and validated a telomere gene-related prognostic signature for pancreatic cancer and confirmed the upregulation of DSG2, LDHA, and RACGAP1 expression in clinical samples, which may provide new ideas for individualized immunotherapy.

Exploring Key Biomarkers and Common Pathogenesis of Seven Digestive System Cancers and Their Correlation with COVID-19

Digestive system cancer and COVID-19 significantly affect the digestive system, but the mechanism of interaction between COVID-19 and the digestive system cancers has not been fully elucidated. We downloaded the gene expression of COVID-19 and seven digestive system cancers (oral, esophageal, gastric, colorectal, hepatocellular, bile duct, pancreatic) from GEO and identified hub differentially expressed genes. Multiple verifications, diagnostic efficacy, prognostic analysis, functional enrichment and related transcription factors of hub genes were explored. We identified 23 common DEGs for subsequent analysis. CytoHubba identified nine hub genes (CCNA2, CCNB1, CDKN3, ECT2, KIF14, KIF20A, KIF4A, NEK2, TTK). TCGA and GEO data validated the expression and excellent diagnostic and prognostic ability of hub genes. Functional analysis revealed that the processes of cell division and the cell cycle were essential in COVID-19 and digestive system cancers. Furthermore, six related transcription factors (E2F1, E2F3, E2F4, MYC, TP53, YBX1) were involved in hub gene regulation. Via in vitro experiments, CCNA2, CCNB1, and MYC expression was verified in 25 colorectal cancer tissue pairs. Our study revealed the key biomarks and common pathogenesis of digestive system cancers and COVID-19. These may provide new ideas for further mechanistic research.

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

BACKGROUND

Esophageal cancer (EC) is the eighth most commonly occurring cancer worldwide and the sixth leading cause of cancer-related deaths. The therapeutic effect of EC patients is not ideal, and new biomarkers are needed to guide diagnosis and prognosis of EC patients. E2F family transcription factors are among the most important links in the cell cycle regulatory network. E2Fs dysregulation not only promotes the early stages of tumor development but also the progression of benign tumors to malignant tumors. E2F is expected to be a new biomarker. The prognostic significance of the E2F family in EC requires further research.

METHODS

We analyzed The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA), and GeneMANIA databases to obtain RNA-sequencing data and clinical data. The clinical data included age, gender, race, stage, type, status, etc. The prognosis outcome included overall survival (OS) and progression-free interval (PFI). Subsequently, we conducted further research on gene expressions, enrichment analysis, interaction network, and prognostic values by R software, containing ggplot2, ComplexHeatmap, DESeq2, pROC R package, based on t-test, Wilcoxon rank sum test, Spearman rank correlation analysis, log-rank test and COX model.

RESULTS

We found that mRNA transcription levels of E2F1, E2F3-8 were more highly expressed in esophageal carcinoma (ESCA) tissues than in normal tissues. E2F8 expression was correlated with tumor stage [Pr(>F)=0.00856]. E2F-related genes played a role in development and differentiation, and were prevalent in the endoplasmic reticulum lumen, Golgi lumen, and lipoprotein particle, catalyzing translation activities and lipid metabolism. Each gene was found to be related to each other to some degree. The GeneMANIA network analysis revealed links between E2Fs and other genes. We compared the correlations between 24 kinds of tumor-infiltrating immune cells and E2Fs. E2F1 (AUC =0.945, CI: 0.890-1.000) and E2F7 (AUC =0.958, CI: 0.920-0.996) exhibited higher predictive power accuracy. However, only E2F7 was closely related to OS [HR =1.91 (1.16-3.16), P=0.011].

CONCLUSIONS

We discover that E2F7 is a prognostic biomarker. E2F family may take part in the development of EC through lipid metabolism pathways, which is helpful to predict the prognosis of EC patients and guide accurate diagnosis and treatment.

A novel circ_0000654/miR-375/E2F3 ceRNA network in esophageal squamous cell carcinoma

Background

The competing endogenous RNA (ceRNA) activity of circular RNAs (circRNAs) has been implicated in the pathogenesis of cancers, including esophageal squamous cell carcinoma (ESCC). Here, we identified the ceRNA mechanism of circ_0000654 regulation in ESCC.

Methods

The levels of circ_0000654, E2F transcription factor 3 (E2F3), and microRNA (miR)‐375 were gauged by quantitative real‐time PCR (qRT‐PCR) and western blot. Cell proliferation was assessed by 3‐(4,5‐dimethylthiazol‐2‐yl)‐5‐(3‐carboxymethoxyphenyl)‐2‐(4‐sulfophenyl)‐2H‐tetrazolium (MTS) and 5‐ethynyl‐2′‐deoxyuridine (EdU) assays. Cell apoptosis was detected by flow cytometry. Cell colony formation was tested by colony formation assay. Dual‐luciferase reporter, RNA pull‐down and RNA immunoprecipitation (RIP) assays were performed to confirm the direct relationship between miR‐375 and circ_0000654 or E2F3. Xenograft model assays were used to evaluate the effect of circ_0000654 in vivo.

Results

Circ_0000654 and E2F3 were upregulated in ESCC. Circ_0000654 depletion enhanced cell apoptosis and hindered cell proliferation and glycolysis in vitro, as well as weakened tumor growth in vivo. Increased expression of E2F3 counteracted the effects of circ_0000654 depletion. Mechanistically, E2F3 was a target of miR‐375, and circ_0000654 modulated E2F3 expression through sequestering miR‐375. Furthermore, miR‐375 upregulation phenocopied circ_0000654 knockdown in inhibiting ESCC progression.

Conclusion

Our findings identify a new circ_0000654/miR‐375/E2F3 ceRNA crosstalk for the oncogenic role of circ_0000654 in ESCC and establish a notion that targeting circ_0000654 and its pathways may have the potential to improve ESCC outcome.

A Novel Five-Gene Signature for Prognosis Prediction in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) has been a global health issue and attracted wide attention due to its high incidence and poor outcomes. In this study, our purpose was to explore an effective prognostic marker for HCC. Five cohort profile datasets from GEO (GSE25097, GSE36376, GSE62232, GSE76427 and GSE101685) were integrated with TCGA-LIHC and GTEx dataset to identify differentially expressed genes (DEGs) between normal and cancer tissues in HCC patients, then 5 upregulated differentially expressed genes and 32 downregulated DEGs were identified as common DEGs in total. Next, we systematically explored the relationship between the expression of 37 common DEGs in tumor tissues and overall survival (OS) rate of HCC patients in TCGA and constructed a novel prognostic model composed of five genes (AURKA, PZP, RACGAP1, ACOT12 and LCAT). Furthermore, the predicted performance of the five-gene signature was verified in ICGC and another independent clinical samples cohort, and the results demonstrated that the signature performed well in predicting the OS rate of patients with HCC. What is more, the signature was an independent hazard factor for HCC patients when considering other clinical factors in the three cohorts. Finally, we found the signature was significantly associated with HCC immune microenvironment. In conclusion, the prognostic five-gene signature identified in our present study could efficiently classify patients with HCC into subgroups with low and high risk of longer overall survival time and help clinicians make decisions for individualized treatment.