Noncanonical Wnt as a prognostic marker in prostate cancer: “you can’t always get what you Wnt”

Integrative exploration of the mutual gene signatures and immune microenvironment between benign prostate hyperplasia and castration-resistant prostate cancer

BACKGROUND

Benign prostate hyperplasia (BPH) and prostate cancer (CaP) are among the most frequently occurring prostatic diseases. When CaP progressed to castration-resistant CaP (CRPC), the prognosis is poor. Although CaP/CRPC and BPH frequently coexist in prostate, the inter-relational mechanism between them is largely unknown.

METHODS

Single-cell RNA sequencing, bulk-RNA sequencing, and microarray data of BPH, CaP in the Gene Expression Omnibus database were obtained and comprehensively analyzed. Weighted Gene Co-Expression Network Analysis (WGCNA) and lasso regression analysis were performed to explore the potential biomarkers.

RESULTS

With WGCNA, five modules in BPH, two in CaP, and three in CRPC were identified as significant modules. Pathway enrichment analysis found that the epigenetics and chromosomal-related signaling were dominantly clustered in the CaP group but not in BPH and CRPC. Lasso regression analysis was used to analyze further the mutual genes between the BPH module and the CRPC module. As a result, DDA1, ERG28, OGFOD1, and OXA1L were significantly correlated with the transcriptomic features in both BPH and CRPC. More importantly, the role of the four gene signatures was validated in two independent anti-PD-1 immunotherapy cohort.

CONCLUSION

This study revealed the shared gene signatures and immune microenvironment between BPH and CRPC. The identified hub genes, including DDA1, ERG28, OGFOD1, and OXA1L, might be potential therapeutic targets for facilitating immunotherapy in prostate cancer.

Identification of 9-Gene Epithelial-Mesenchymal Transition Related Signature of Osteosarcoma by Integrating Multi Cohorts

BACKGROUND

The prognosis of patients with osteosarcoma is still poor due to the lack of effective prognostic markers. The EMT (epithelial-mesenchymal transition) serves as a promoter in the progression of osteosarcoma. This study systematically analyzed EMT-related genes to explore new markers for predicting the prognosis of osteosarcoma.

METHODS

RNA-Seq data and clinical information were obtained from the GEO database; GSVA and GSEA analysis were used to enrich pathways related to osteosarcoma progression; LASSO method analysis was used to construct the prognosis risk signature. The "Nomogram" package generated the risk prediction nomogram, and its clinical applicability was evaluated by decision curve analysis (DCA).

RESULTS

GSVA and GSEA analysis showed that the EMT signaling pathway was closely related to osteosarcoma progression. A 9-genes signature (LAMA3, LGALS1, SGCG, VEGFA, WNT5A, MATN3, ANPEP, FUCA1, and FLNA) was constructed. The overall survival (OS) of the high-risk scores group was significantly lower than the low-risk scores group. The 9-gene signature demonstrated good predictive accuracy. Cox regression analysis showed that the 9-gene signature provided independent prognostic factors for osteosarcoma patients. In addition, the predictive nomogram model could effectively predict the prognosis of osteosarcoma patients.

CONCLUSION

This study constructed a 9-gene signature as a new prognostic marker to predict osteosarcoma patients' survival.