[Retracted] A New Survival Model Based on Cholesterol Biosynthesis‐Related Genes for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

Qi, Xiaochen; Lv, Xiaoyi; Wang, Xiaoxi; Ruan, Zihao; Zhang, Peizhi; Wang, Qifei; Xu, Yingkun; Wu, Guangzhen

doi:10.1155/2021/9972968

Cited by 8 publications

(9 citation statements)

References 34 publications

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“…In particular, HIF expression drives lipid deposition in ccRCC via the repression of fatty acid metabolism (Du et al, 2017). Importantly, it was previously shown that low expression of FDPS, FDFT1, HMGCS1, HMGCR and IDI1 genes and high expression of SQLE were associated with patients with high-risk of ccRCC (Qi et al, 2021). Thus, by up-regulating 5 out of 6 genes in this prognostic signature, AB668 could represent a therapeutic option to counteract ccRCC tumor progression.…”

Section: Resultsmentioning

confidence: 99%

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AB668, a novel highly selective protein kinase CK2 inhibitor with a distinct anti-tumor mechanism as compared to CX-4945 and SGC-CK2-1

Bancet

Frem

Jeanneret

et al. 2022

Preprint

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Although the involvement of protein kinase CK2 in cancer is well-documented, there is a need for selective CK2 inhibitors suitable for investigating CK2 specific roles in cancer-related biological pathways and further explore its therapeutic potential. Here we have discovered AB668, a new bivalent inhibitor that binds both at the ATP site and an allosteric αD pocket unique to CK2. The molecule inhibits CK2 activity with an outstanding selectivity over other kinases. Using caspase activation assay, live-cell imaging and transcriptomic analysis, we have compared the effects of this bivalent inhibitor to the non-selective ATP-competitive inhibitor CX-4945 that reached clinic and to the selective ATP-competitive SGC-CK2-1 molecule. Our results show that in contrast to CX-4945 or SGC-CK2-1, AB668 has a distinct mechanism of action regarding its anti-cancer activity, inducing apoptotic cell death and stimulating distinct biological pathways in several cancer cell lines while sparing healthy cells. Our data suggest that targeting a cryptic CK2 αD pocket validates an allosteric approach to targeting CK2 and provides a starting point for creating drug-like CK2 inhibitors for aggressive cancers.

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Section: Resultsmentioning

confidence: 99%

“…AB668 activated p38MAPK, a tumor suppressor well known for its role in transducing stress signals from the environment (Figure 3a). Interestingly, mouse modeling studies showed that mTOR activation in combination with inactivation of the p38MAPK initiates renal cell carcinoma (Wu et al, 2021).…”

Section: Target Engagement Of Ab668 In Cancer and Healthy Cellsmentioning

confidence: 99%

AB668, a novel highly selective protein kinase CK2 inhibitor with a distinct anti-tumor mechanism as compared to CX-4945 and SGC-CK2-1

Bancet

Frem

Jeanneret

et al. 2022

Preprint

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“…In addition, in the past few decades, the construction of risk models around cancer-related biological processes or signaling pathway-related genes has succeeded [ 41 – 43 ]. Therefore, inspired by previous research, we used angiogenesis-related genes to construct a risk model for BRCA.…”

Section: Discussionmentioning

confidence: 99%

Construction and Validation of Angiogenesis-Related Prognostic Risk Signature to Facilitate Survival Prediction and Biomarker Excavation of Breast Cancer Patients

Peng

Shen

et al. 2022

Journal of Oncology

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This study is aimed at exploring the potential mechanism of angiogenesis, a biological process-related gene in breast cancer (BRCA), and constructing a risk model related to the prognosis of BRCA patients. We used multiple bioinformatics databases and multiple bioinformatics analysis methods to complete our exploration in this research. First, we use the RNA-seq transcriptome data in the TCGA database to conduct a preliminary screening of angiogenesis-related genes through univariate Cox curve analysis and then use LASSO regression curve analysis for secondary screening. We successfully established a risk model consisting of seven angiogenesis-related genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide BRCA patients into the high-risk and low-risk groups with significant prognostic differences based on this risk model. In addition, we used angiogenesis-related genes to perform cluster analysis in BRCA patients and successfully divided BRCA patients into three clusters with significant prognostic differences, namely, cluster 1, cluster 2, and cluster 3. Subsequently, we combined the clinical-pathological data for correlation analysis, and there is a significant correlation between the risk model and the patient’s T and stage. Multivariate Cox regression curve analysis showed that the age of BRCA patients and the risk score of the risk model could be used as independent risk factors in the progression of BRCA. In particular, based on this angiogenesis-related risk model, we have drawn a matching nomogram that can predict the 5-, 7-, and 10-year overall survival rates of BRCA patients. Subsequently, we performed a series of pan-cancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to explore drug sensitivity. Subsequently, to gain insight into the protein expression of these risk model genes in BRCA, we used the immunohistochemical data in the THPA database for verification. The results showed that the protein expressions of IL18, RUNX1, SCG2, and THY1 molecules in BRCA tissues were significantly higher than those in normal breast tissues, while the protein expressions of PF4 and TNFSF12 molecules in BRCA tissues were significantly lower than those in normal breast tissues. Finally, we conducted multiple GSEA analyses to explore the biological pathways these risk model genes can cross in cancer progression. In summary, we believe that this study can provide valuable data and clues for future studies on angiogenesis in BRCA.

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“…Its development often involves biological pathways and multiple gene clusters with specific functions. Therefore, current scientific research is increasingly observing cancer progress at the level of biological pathways [ 49 – 51 ].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel PPAR Signaling Pathway-Related Predictive Model to Predict Prognosis in Breast Cancer

Shu

Shen

et al. 2022

Journal of Immunology Research

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This study is aimed at exploring the potential mechanism of the PPAR signaling pathway in breast cancer (BRCA) and constructing a novel prognostic-related risk model. We used various bioinformatics methods and databases to complete our exploration in this research. Based on TCGA database, we use multiple extension packages based on the R language for data conversion, processing, and statistics. We use LASSO regression analysis to establish a prognostic-related risk model in BRCA. And we combined the data of multiple online websites, including GEPIA, ImmuCellAI, TIMER, GDSC, and the Human Protein Atlas database to conduct a more in-depth exploration of the risk model. Based on the mRNA data in TCGA database, we conducted a preliminary screening of genes related to the PPAR signaling pathway through univariate Cox analysis, then used LASSO regression analysis to conduct a second screening, and successfully established a risk model consisting of ten genes in BRCA. The results of ROC curve analysis show that the risk model has good prediction accuracy. We can successfully divide breast cancer patients into high- and low-risk groups with significant prognostic differences ( P = 1.92 e − 05 ) based on this risk model. Combined with the clinical data in TCGA database, there is a correlation between the risk model and the patient’s N, T, gender, and fustat. The results of multivariate Cox regression show that the risk score of this risk model can be used as an independent risk factor for BRCA patients. In particular, we draw a nomogram that can predict the 5-, 7-, and 10-year survival rates of BRCA patients. Subsequently, we conducted a series of pancancer analyses of CNV, SNV, OS, methylation, and immune infiltration for this risk model gene and used GDSC data to investigate drug sensitivity. Finally, to gain insight into the predictive value and protein expression of these risk model genes in breast cancer, we used GEO and HPA databases for validation. This study provides valuable clues for future research on the PPAR signaling pathway in BRCA.

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[Retracted] A New Survival Model Based on Cholesterol Biosynthesis‐Related Genes for Prognostic Prediction in Clear Cell Renal Cell Carcinoma

Cited by 8 publications

References 34 publications

AB668, a novel highly selective protein kinase CK2 inhibitor with a distinct anti-tumor mechanism as compared to CX-4945 and SGC-CK2-1

AB668, a novel highly selective protein kinase CK2 inhibitor with a distinct anti-tumor mechanism as compared to CX-4945 and SGC-CK2-1

Construction and Validation of Angiogenesis-Related Prognostic Risk Signature to Facilitate Survival Prediction and Biomarker Excavation of Breast Cancer Patients

Development and Validation of a Novel PPAR Signaling Pathway-Related Predictive Model to Predict Prognosis in Breast Cancer

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