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Background <p>Prostate cancer is a prevalent disease with diverse tumor characteristics that complicate treatment. The integration of spatial patterns from prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT), pathology and expanding genomic data represents a groundbreaking advancement in histo-imaging genomics. The aim of this study was to elucidate the internetwork mapping between genetic biomarkers and PSMA PET/CT imaging in prostate cancer patients.</p> Results <p>mRNA sequencing and clinical data from 433 prostate cancer patients were retrieved from The Cancer Genome Atlas (TCGA) database. Differential gene expression between the Gleason score (GS) &gt; 7 and GS ≤ 7 groups was analyzed. Feature selection was performed following the univariate and multivariate logistic regression analyses. A GS predictive model was developed using multivariate logistic regression. Additionally, local samples and images from 27 patients were collected. PSMA PET/CT imaging was performed before radical prostatectomy, and mRNA sequencing of prostate cancer lesions was conducted using next-generation sequencing. Differentially expressed genes identified from the TCGA dataset were subsequently analyzed for correlations with PET-related metrics in the local dataset by utilizing Pearson correlation analysis.Out of the TCGA dataset, 174 genes exhibited differential expression. After feature selection, 53 genes remained. In the local dataset, ten genes (EFNA2, CACNA1I, CA1, MYBPC3, CYP1A1, TLCD3B, LRTM2, GBX2, SPSB4, and GDF3) demonstrated significant associations with PET-related metrics. When comparing the differential expression of genes between the GS&gt;7 and GS≤7 groups, six genes (STMN2, CYP1A1, THRSP, LIPC, GBX2, and SPSB4) in the GS&gt;7 group and eight genes (FBXL16, KLK14, DIRAS2, TERB2, PRAME, UTS2B, UGT2B15, and LINC02798) in the GS≤7 group were significantly correlated with PET-related parameters.</p> Conclusions <p>This study identified genetic markers significantly correlated with PSMA PET/CT imaging features in prostate cancer patients. These findings may provide a valuable foundation for optimizing prostate cancer diagnostic procedures and tailoring therapeutic approaches based on genetic and imaging biomarkers.</p>

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Identifying genetics biomarkers in correlation with pathological and PSMA PET/CT characteristics in prostate cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月4日

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  • Lili Qu,
  • Kaiyue Li,
  • Muwen Wang,
  • Yadi Xiao,
  • Xin Jin,
  • Hang Zhou,
  • Lujie Yuan,
  • Yuekai Li,
  • Shiwei Wang,
  • Rui Li,
  • Marcus Hacker,
  • Xin Li,
  • Xiang Li

摘要

Background

Prostate cancer is a prevalent disease with diverse tumor characteristics that complicate treatment. The integration of spatial patterns from prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT), pathology and expanding genomic data represents a groundbreaking advancement in histo-imaging genomics. The aim of this study was to elucidate the internetwork mapping between genetic biomarkers and PSMA PET/CT imaging in prostate cancer patients.

Results

mRNA sequencing and clinical data from 433 prostate cancer patients were retrieved from The Cancer Genome Atlas (TCGA) database. Differential gene expression between the Gleason score (GS) > 7 and GS ≤ 7 groups was analyzed. Feature selection was performed following the univariate and multivariate logistic regression analyses. A GS predictive model was developed using multivariate logistic regression. Additionally, local samples and images from 27 patients were collected. PSMA PET/CT imaging was performed before radical prostatectomy, and mRNA sequencing of prostate cancer lesions was conducted using next-generation sequencing. Differentially expressed genes identified from the TCGA dataset were subsequently analyzed for correlations with PET-related metrics in the local dataset by utilizing Pearson correlation analysis.Out of the TCGA dataset, 174 genes exhibited differential expression. After feature selection, 53 genes remained. In the local dataset, ten genes (EFNA2, CACNA1I, CA1, MYBPC3, CYP1A1, TLCD3B, LRTM2, GBX2, SPSB4, and GDF3) demonstrated significant associations with PET-related metrics. When comparing the differential expression of genes between the GS>7 and GS≤7 groups, six genes (STMN2, CYP1A1, THRSP, LIPC, GBX2, and SPSB4) in the GS>7 group and eight genes (FBXL16, KLK14, DIRAS2, TERB2, PRAME, UTS2B, UGT2B15, and LINC02798) in the GS≤7 group were significantly correlated with PET-related parameters.

Conclusions

This study identified genetic markers significantly correlated with PSMA PET/CT imaging features in prostate cancer patients. These findings may provide a valuable foundation for optimizing prostate cancer diagnostic procedures and tailoring therapeutic approaches based on genetic and imaging biomarkers.