Promoting cell proliferation, cell cycle progression, and glycolysis: Glycometabolism-related genes act as prognostic signatures for prostate cancer

Systematic review and integrated analysis of prognostic gene signatures for prostate cancer patients

Prostate cancer (PC) is one of the most common cancers in men and becoming the second leading cause of cancer fatalities. At present, the lack of effective strategies for prognosis of PC patients is still a problem to be solved. Therefore, it is significant to identify potential gene signatures for PC patients' prognosis. Here, we summarized 71 different prognostic gene signatures for PC and concluded 3 strategies for signature construction after extensive investigation. In addition, 14 genes frequently appeared in 71 different gene signatures, which enriched in mitotic and cell cycle. This review provides extensive understanding and integrated analysis of current prognostic signatures of PC, which may help researchers to construct gene signatures of PC and guide future clinical treatment.

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Glycometabolism-related gene signature of hepatocellular carcinoma predicts prognosis and guides immunotherapy

Hepatocellular carcinoma (HCC) is a severe cancer endangering human health. We constructed a novel glycometabolism-related risk score to predict prognosis and immunotherapy strategies in HCC patients. The HCC data sets were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, and the glycometabolism-related gene sets were obtained from the Molecular Signature Database. The least absolute contraction and selection operator (LASSO) regression model was used to construct a risk score based on glycometabolism-related genes. A simple visual nomogram model with clinical indicators was constructed and its effectiveness in calibration, accuracy, and clinical value was evaluated. We also explored the correlation between glycometabolism-related risk scores and molecular pathways, immune cells, and functions. Patients in the low-risk group responded better to anti-CTLA-4 immune checkpoint treatment and benefited from immune checkpoint inhibitor (ICI) therapy. The study found that glycometabolism-related risk score can effectively distinguish the prognosis, molecular and immune-related characteristics of HCC patients, and may provide a new strategy for individualized treatment.

B-MYB-p53-related relevant regulator for the progression of clear cell renal cell carcinoma

PURPOSE

To investigate the mRNA expression of B-MYB and MDM2 together with their p53 relatedness in clear cell renal cell carcinoma (ccRCC).

METHODS

Genes were screened for their mRNA expression from 529 patients in a publicly available ccRCC cohort (TCGA). A cohort of 101 patients with ccRCC served as validation by qRT-PCR mRNA tissue expression analysis.

RESULTS

Expression: B-MYB expression was significantly higher in high-grade tumours (p < 0.0001 and p = 0.048) and in advanced stages (p = 0.005 and p = 0.037) in both cohorts. Correlation: p53-B-MYB as well as MDM2-B-MYB showed significant correlations in local and low-grade ccRCCs, but not in high grade tumours or advanced stages (r < 0.3 and/or p > 0.05). Survival: Multivariable Cox regression of the TCGA cohort revealed B-MYB upregulation and low MDM2 expression as predictors for an impaired overall survival (OS) (HR 1.97; p = 0.0003; HR 2.94, p < 0.0001) and progression-free survival (PFS) (HR 2.86; p = 0.0005; HR 1.58, p = 0.046). In the validation cohort, the results were confirmed for OS by univariable, but not multivariable regression: high B-MYB expression (HR = 3.05, p = 0.035) and low MDM2 expression (HR 3.81, p value 0.036).

CONCLUSION

In ccRCC patients with high-grade tumours and advanced stages, high B-MYB expression is common and is associated with poorer OS and PFS. These patients show a loss of their physiological B-MYB-p53 network correlation, suggesting an additional, alternative regulatory, oncogenic mechanism. Assuming further characterization of its signalling pathways, B-MYB could be a potential therapy target for ccRCC.