PTEN and ERG expression in MRI-ultrasound guided fusion biopsy correlated with radical prostatectomy findings in men with prostate cancer

Molecular Mechanism of the Regulatory Effect of Schisandrol A on the Immune Function of Mice Based on a Transcription Factor Regulatory Network

The molecular mechanism of the regulatory effed of schizandrol A (SA) on the immune function of cyclophosphamide-induced immunosuppressive mice was explored in this study. On the basis of 1619 differentially expressed genes related to the regulatory effect of SA on the immune function of mice screened in our previous study, transcription factors and their corresponding target genes were screened in the Transcriptional Regulatory Element Database (TRED), and a transcription factor target gene regulatory network was constructed. The key nodes of the network were statistically analyzed to clarify the role of transcription factors in the regulatory network. The correlation of network genes with diseases was analyzed with an online annotation tool through the Database for Annotation, Visualization and Integrated Discovery (DAVID). Finally, the key factors related to the regulatory effect of SA on the immune function of mice were screened and verified by animal experiments and the detection of related protein expression by western blot analysis. The results showed that SA could alleviate the immunosuppression induced by cyclophosphamide in mice and regulate the protein expression of Jun, Trp53, and Creb1 in the spleen tissue of mice, together with the transcription factors Atf4 and E2f2. SA may thus play a role in the alleviation of some immunity-related diseases (such as cancer) by regulating the immune function of mice through multiple genes and their transcription factors.

Transcript analysis of commercial prostate cancer risk stratification panels in hard-to-predict grade group 2-4 prostate cancers

BACKGROUND

Improved prognostication is needed to minimize overtreatment in grade group (GG) 2-4 prostate cancer. Our aim was to determine, at messenger RNA (mRNA) level, the performance of the genes in the commercial panels Decipher, Oncotype DX, Prolaris, and mutational panel MSK-IMPACT to predict metastasis-free and prostate cancer-specific death (PCSD) in patients with GG 2-4 prostate cancer at radical prostatectomy.

METHODS

The retrospective cohort consisted of GG 2-4 patients treated with radical prostatectomy (median follow-up 10.4 years). Seventy-six cases with postoperative metastasis or PCSD and 84 controls with similar clinical baseline risk, but without progression, were analyzed. Index lesion mRNA transcripts were analyzed using NanoString technology. Random forest models were trained using panel gene sets to predict clinical endpoints and area under the curve (AUC), sensitivity, specificity, Youden index, and number needed to diagnose (NND) was measured. Survival probability was assessed with Kaplan-Meier estimator.

RESULTS

All gene sets outperformed clinical parameters and predicted metastasis-free and prostate cancer-specific survival. However, there were significant differences between the panels. In metastasis prediction, the genes in Oncotype DX had inferior performance (area under the curve [AUC] = 0.65) compared to other panels (AUC = 0.73-0.74). Decipher, MSK-IMPACT and Prolaris showed similar NND (2.83-3.12) with Oncotype DX having highest NND (4.79). In PCSD prediction, the Prolaris gene set performed worse (AUC = 0.66) than MSK-IMPACT or Decipher (AUC = 0.72). Oncotype DX performed similarly to other panels (AUC = 0.69, p > .05). Oncotype DX demonstrated lowest NND (2.79) compared to other panels (4.22-5.66).

CONCLUSION

Transcript analysis of genes included in commercial panels is feasible in survival prediction of GG 2-4 patients after radical prostatectomy and may aid in clinical decision making. There were significant differences between the panels, and overall stronger predictive gene sets are needed. Prospective investigation is warranted in biopsy materials.