Predicting Panel of Metabolism and Immune-Related Genes for the Prognosis of Human Ovarian Cancer

Zhang, Lingyun; Sun, Wenwen; Ren, Weimin; Zhang, Jinguo; Xu, Guoxiong

doi:10.3389/fcell.2021.690542

Cited by 9 publications

(9 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…BVES loss was also increase beat-catenin protein levels, leads to Wnt pathway activation and coordinates with Wnt ligand to further increase Wnt signaling expression 44 . Additionally, over-expression of SLFN13 and DGKD confers poor prognosis to the lung cancer patients and the ovarian cancer 45,46 .…”

Section: Discussionmentioning

confidence: 99%

Prognostic factors and Doxorubicin involved in malignant progression of meningioma

Huo

Song

Wang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Meningioma was the most primary intracranial tumor, but the molecular characteristics and the treatment of malignant meningioma were still unclear. Nine malignant progression-related genes based prognostic signatures were identified by transcriptome analysis between benign meningioma and malignant meningioma. The external dataset GEO136661 and quantitative Real-time Polymerase Chain Reaction were used to verify the prognostic factors. has-miR-3605-5p, hsa-miR-664b-5p, PNRC2, BTBD8, EXTL2, SLFN13, DGKD, NSD2, and BVES were closed with malignant progression. Moreover, Doxorubicin was identified by Connectivity Map website with the differential malignant progression-related genes. CCK-8 assay, Edu assay, wound healing assay, and trans-well experiment were used to reveal that Doxorubicin could inhibit proliferation, migration and invasion of IOMM-Lee Cells.

show abstract

Section: Discussionmentioning

confidence: 99%

Prognostic factors and Doxorubicin involved in malignant progression of meningioma

Huo

Song

Wang

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Some prognostic gene signatures were also explored in ovarian cancer. There were models consisting of autophagy-related genes, ferroptosis-related genes, hypoxia-related genes, immune-related genes, metabolism-and immune-related genes, and RNA binding proteins [4][5][6][33][34][35][36]. These researches focused on specific aspects of organic physical activities.…”

Section: Discussionmentioning

confidence: 99%

Protein expression profiling identifies a prognostic model for ovarian cancer

et al. 2022

View full text Add to dashboard Cite

Background Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress. Methods Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis. Results 394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication. Conclusions The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.

show abstract

“…Diacylglycerol kinase delta (DGKD) was thought to be crucial in cellular signal transduction such as calcium-sensing receptor signaling (Howles et al, 2019). It was also reported to have an impact on metabolism (Chee et al, 2021) and have a strong connection with the immunoreaction (Zhang et al, 2021). So far, the association between this gene and the TNF pathway enriched by GSEA had not been reported, which was still worth a further study.…”

Section: Discussionmentioning

confidence: 99%

Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis

Zhou

Yang

et al. 2021

Front. Genet.

View full text Add to dashboard Cite

Sepsis-induced cardiomyopathy (SIC), with a possibly reversible cardiac dysfunction, is a potential complication of septic shock. Despite quite a few mechanisms including the inflammatory mediator, exosomes, and mitochondrial dysfunction, having been confirmed in the existing research studies we still find it obscure about the overall situation of gene co-expression that how they can affect the pathological process of SIC. Thus, we intended to find out the crucial hub genes, biological signaling pathways, and infiltration of immunocytes underlying SIC. It was weighted gene co-expression network analysis that worked as our major method on the ground of the gene expression profiles: hearts of those who died from sepsis were compared to hearts donated by non-failing humans which could not be transplanted for technical reasons (GSE79962). The top 25 percent of variant genes were abstracted to identify 10 co-expression modules. In these modules, brown and green modules showed the strongest negative and positive correlation with SIC, which were primarily enriched in the bioenergy metabolism, immunoreaction, and cell death. Next, nine genes (LRRC39, COQ10A, FSD2, PPP1R3A, TNFRSF11B, IL1RAP, DGKD, POR, and THBS1) including two downregulated and seven upregulated genes which were chosen as hub genes that meant the expressive level of which was higher than the counterparts in control groups. Then, the gene set enrichment analysis (GSEA) demonstrated a close relationship of hub genes to the cardiac metabolism and the necroptosis and apoptosis of cells in SIC. Concerning immune cells infiltration, a higher level of neutrophils and B cells native and a lower level of mast cells resting and plasma cells had been observed in patients with SIC. In general, nine candidate biomarkers were authenticated as a reliable signature for deeper exploration of basic and clinical research studies on SIC.

show abstract

Predicting Panel of Metabolism and Immune-Related Genes for the Prognosis of Human Ovarian Cancer

Cited by 9 publications

References 61 publications

Prognostic factors and Doxorubicin involved in malignant progression of meningioma

Prognostic factors and Doxorubicin involved in malignant progression of meningioma

Protein expression profiling identifies a prognostic model for ovarian cancer

Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis

Contact Info

Product

Resources

About