Comparative analysis of prognosis and gene expression in prostate cancer patients with site-specific visceral metastases

Peng Zhang ¹, Tieding Chen ¹, Ming Yang ¹

⁰Department of Urology, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang, China.

Urologic Oncology +

|

March 3, 2024

View abstract on PubMed

Summary

Prostate cancer patients with liver or lung metastases have a poorer prognosis. Gene expression analysis revealed distinct profiles, with SCGB3A1 potentially improving survival in lung metastasis cases.

Area Of Science

Oncology
Genomics
Cancer Metastasis

Background

Prostate cancer with visceral metastases often has a poor prognosis.
Limited research compares prognostic impacts and gene expression across different visceral metastatic sites.

Purpose Of The Study

To comprehensively analyze the prognostic impact and gene expression profiles of distinct visceral metastatic sites in prostate cancer.
To compare prostate cancer-specific mortality (PCSM) risk and gene expression between liver and lung metastases.

Main Methods

Utilized SEER database (8,170 patients, 2000-2019) for PCSM risk analysis of metastatic prostate cancer (mPCa).
Employed competing risks regression to assess the effect of visceral metastatic sites on PCSM.
Analyzed differentially expressed genes (DEGs) in liver vs. lung metastases using GSE6752 data and constructed a protein-protein interaction network.

Main Results

Liver and lung metastases significantly impacted PCSM (HR 2.24 and 1.30, respectively).
Seven DEGs (e.g., SCGB3A1, CEACAM6) were identified, linked to respiratory gaseous exchange and fibronectin matrix formation.
Three DEGs upregulated in lung metastases were also higher in normal lung vs. liver tissues.

Conclusions

mPCa patients with liver and/or lung metastases have poorer prognoses.
SCGB3A1, a tumor suppressor, may contribute to better survival in lung metastasis compared to liver metastasis.
Gene expression profiles show both heterogeneity and homogeneity between liver and lung metastatic sites.

Keywords:

Mortality Protein interaction networks Regression analysis SEER program Transcriptome analyses