ALDOB represents a potential prognostic biomarker for patients with clear cell renal cell carcinoma

Shao, Yuan; Wu, Bo; Zhang, Yang; Liu, Zihao; Ma, Yanhe; Hua, Huang; Liu, Yang; Wang, Zeyuan; Hu, Weijing; Wang, Yong; Niu, Yuanjie

doi:10.21037/tau-22-743

Cited by 6 publications

(3 citation statements)

References 65 publications

(82 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among the 11 GRGs examined, GPI is a predictor of outcomes in UCEC patients [36][37][38] as well as other cancers [39][40][41][42] . Similarly, other genes such as HK1 43,44 , PDHA1 [45][46][47] , PGK2 48 , NUP155 49 , PC [50][51][52] , ALDOB [53][54][55][56] , and PFKM 57,58 have been implicated in carcinogenesis and have demonstrated effects on patient prognoses in various cancer types. We observed that PDHA1 and GPI exhibit the highest positive correlation.…”

Section: Discussionmentioning

confidence: 99%

An 11-GRG Predicting Model for UCEC Prognosis

Su,

Qiao

2023

Preprint

View full text Add to dashboard Cite

Background Endometrial cancer (EC) is indeed one of the most prevalent gynecological malignancies. Further research is required to gain insights into the molecular pathways involved in EC tumorigenesis and to develop more accurate prognostic prediction methods.Method Data from the Cancer Genome Atlas( TCGA) database was used and validated using two GEO datasets, specifically GSE6008 and GSE17025. Various bioinformatics analyses were performed, including the Least Absolute Shrinkage and Selection Operator regression (lasso) regression, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis Gene Set Variation Analysis (GSVA), gene set enrichment analysis (GSEA), protein-protein interaction (PPI) network analysis, COX regression, calibration curves, and decision curve analysis (DCA). These analyses aimed to explore the associations and functions of GRGs in EC development, as well as develop a predictive model for prognosis assessment.Results There were 11 GRGs identified as significantly associated with EC by the Lasso regression, GSEA, and PPI. The further analysis identifies 61 miRNA molecules, 24 RBPs, 139 TFs, and 21 potential drugs or molecular compounds that might have links to these 11 key genes. These 11 GRGs were made into a Cox regression prediction model, among which the PGK2 shows significantly higher utility than other variables. Calibration analysis and DCA indicate that the clinical predictive performance of this 11-GRGs multivariate Cox regression model is highest at 5 years, followed by 3 years and 1 year. There were 6 genes (GPI, HK1, NUP188, PDHA1, PDHA2, PGK2) that exhibited higher predictive accuracy in time-dependent ROC curves.Conclusion The highly enriched GRGs that have been identified might provide a new understanding of the development of EC and its treatment. Moreover, the 11-GRGs model that has been constructed holds significant clinical implications for evaluating prognosis and providing specific therapy guidance to individuals with EC. Among the variables in the 11-GRG model, PGK2 demonstrates notably higher usefulness, highlighting its potential clinical value in EC.

show abstract

Section: Discussionmentioning

confidence: 99%

An 11-GRG Predicting Model for UCEC Prognosis

Su,

Qiao

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Chen et al ( 11 ) found SPOCK1 participates in the epithelial-mesenchymal transition process and may be a potential prognostic biomarker in ccRCC. Shao et al ( 12 ) found that the down-regulation of ALDOB was closely related to the clinicopathological features, poor prognosis, immune infiltration and m6A modification of ccRCC patients by integrating multiple public databases. Liao et al ( 13 ) established a survival and prognosis model system by exploring immune-related genes, and provided an effective prediction tool for the future.…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of a chromatin regulator-related gene signature for prognostic and therapeutic significance of clear cell renal cell carcinoma

Zhang,

Zeng,

et al. 2024

Transl Cancer Res

View full text Add to dashboard Cite

Background Epigenetic alterations driven by chromatin regulators (CRs) are well-recognized cancer hallmarks. Growing evidence suggests that the imbalance of CRs may lead to the occurrence of various diseases including tumors. However, the role and prognostic value of CRs in clear cell renal cell carcinoma (ccRCC) remain undefined. Methods Consensus clustering analysis was used to identify different subtypes. Univariate and multivariate Cox regression analysis were performed to identify prognosis-related CRs and constructed a risk model. Transcriptome sequencing was used to verify gene expression levels. Kaplan-Meier survival analysis was used to compare overall survival (OS) between high- and low-risk groups. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve was used to evaluate the performance of the model. The ESTIMATE algorithm and single-sample gene set enrichment analysis (ssGSEA) were executed to evaluate the immune characteristics of samples. Correlation analysis was used to assess the relationship between risk score and immune checkpoint genes, the relationship between expression levels of CRs and immune cell infiltration and drug therapeutic response. Finally, we also compared differences in drug sensitivity between low- and high-risk groups. Results We identified three CRs-related subtypes with different characteristics. A prognostic model was built with four CRs and can precisely predict the OS of patients in different risk groups. The model has good stability and applicability and was further verified in the internal and external dataset. The transcriptomic levels of the four CRs were also validated, and the risk score was an independent prognostic factor for ccRCC. Obvious differences in the immune microenvironment and the expression levels of immune checkpoints were observed in low- and high-risk group. Higher immune activity and immune cell infiltration were found in the high-risk group. Besides, the expression levels of CRs were associated with drug therapeutic response. Patients with high-risk score may be more sensitive to gemcitabine, vinblastine, paclitaxel, axitinib, sunitinib, and temsirolimus. Conclusions CRs were strongly associated with the occurrence and development of ccRCC. Targeting CRs may become a new therapeutic strategy for ccRCC. Besides, CRs-related gene signature can predict the prognosis and therapeutic significance of ccRCC, which provides an important reference for clinical decision-making.

show abstract

“…Presently, a plethora of studies has been dedicated to exploring potential prognostic biomarkers for ccRCC. Notably, some investigations have revealed that ccRCC patients exhibiting low expression of ALDOB ( 11 ), high expression of p-CREB1 ( 12 ), or elevated expression of SPOCK1 ( 13 ) are associated with a less favorable prognosis. However, the association between deoxyribonuclease 1 like 3 (DNASE1L3) and ccRCC remains unexplored.…”

Section: Introductionmentioning

confidence: 99%

Integrative analysis of deoxyribonuclease 1-like 3 as a potential biomarker in renal cell carcinoma

Ge,

Zhu,

Song

et al. 2023

Transl Androl Urol

View full text Add to dashboard Cite

Background Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma (RCC), is insensitive to radiotherapy and chemotherapy after surgery. Deoxyribonuclease 1-like 3 (DNASE1L3), an endonuclease that cleaves both membrane-encapsulated single- and double-stranded DNA, suppresses cell cycle progression, proliferation and metabolism in hepatocellular carcinoma cells. There is currently no established link between DNASE1L3 and RCC inhibition. We are gonging to explored the mechanism underlying the relationship between DNASEL1L3 and RCC. Methods RNA sequencing data for RCC tissue and peritumoral tissue were downloaded from The Cancer Genome Atlas database and analyzed. The expression levels of DNASE1L3 in RCC and normal samples were verified using the Gene Expression Omnibus (GEO) database, Human Protein Atlas database and western blotting. The role and potential mechanism of DNASE1L3 were investigated by analysis of immune-related databases and wound healing, invasion, cell counting kit 8 and immunofluorescence assays. Results We revealed that DNASE1L3 expression was downregulated in RCC group compared with control group [The Cancer Genome Atlas (TCGA): 7.98 vs. 10.87, P<0.001]. Meanwhile, DNASE1L3 expression correlated with the clinical characteristics of patients. Patients with low DNASE1L3 expression had worse survival (P<0.001) and larger (r=−0.32, P<0.001) and heavier tumors (r=−0.17, P<0.001). DNASE1L3 overexpression inhibited the proliferation (786-O: 0.135±0.014 vs. 0.322±0.027, P<0.001) and invasion (786-O: 1,479±134 vs. 832±67, P<0.05) of RCC cells. The expression of DNASE1L3 was significantly correlated with the tumor immune microenvironment and drug sensitivity in ccRCC. Moreover, the level of the key phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathway protein P-AKT was decreased in the group of cells transfected with DNASE1L3. Conclusions This study strongly suggest that DNASE1L3 may be a promising potential biomarker for the diagnosis and treatment of ccRCC patients.

show abstract

ALDOB represents a potential prognostic biomarker for patients with clear cell renal cell carcinoma

Cited by 6 publications

References 65 publications

An 11-GRG Predicting Model for UCEC Prognosis

An 11-GRG Predicting Model for UCEC Prognosis

Construction and validation of a chromatin regulator-related gene signature for prognostic and therapeutic significance of clear cell renal cell carcinoma

Integrative analysis of deoxyribonuclease 1-like 3 as a potential biomarker in renal cell carcinoma

Contact Info

Product

Resources

About