Prostate cancer is a common malignancy in men, with substantial prognostic heterogeneity that calls for improved risk stratification at the tissue microenvironment level. Perineural invasion (PNI) is a frequent pathological feature in prostate cancer, associated with local aggressiveness and postoperative recurrence; however, its microenvironmental characteristics and prognostic value remain incompletely characterized. This study plans to enroll approximately 120 prostate cancer patients, collecting tissue specimens and clinicopathological data. We will integrate HE/WSI pathological images, Xenium spatial transcriptomics, PhenoCycler-Fusion spatial proteomics, whole-exome sequencing, and single-cell transcriptomics to systematically compare PNI-positive regions, PNI-negative tumor regions, and adjacent-normal regions in terms of cellular composition, spatial proximity relationships, molecular pathway activities, and genomic alterations. The objectives are to screen candidate molecular markers associated with PNI burden, local invasion, and poor postoperative outcomes, and to explore the establishment of a PNI classification and prognostic risk assessment model based on spatial multi-omics features, thereby providing a foundation for individualized risk stratification and further mechanistic studies in prostate cancer.
This study will utilize tissue specimens and clinical follow-up data from prostate cancer patients, integrating HE/WSI pathological images, spatial transcriptomics, spatial proteomics, whole-exome sequencing (WES), and single-cell transcriptomics to systematically characterize the cellular composition, spatial proximity relationships, molecular expression profiles, and genomic alterations within the perineural invasion (PNI) microenvironment of prostate cancer. Specifically, this study aims to delineate the spatial multi-omics differences among PNI-positive regions, PNI-negative tumor regions, and adjacent-normal regions; to screen for candidate molecular markers associated with PNI burden, local tumor aggressiveness, and poor postoperative outcomes; and to explore the establishment of a PNI classification and prognostic risk assessment model based on spatial multi-omics features, thereby providing a foundation for individualized risk stratification and future mechanistic investigations in prostate cancer.
Study Type
OBSERVATIONAL
Enrollment
120
HE/WSI pathological image analysis Xenium spatial transcriptomics PhenoCycler-Fusion spatial proteomics Whole-exome sequencing (WES) Single-cell transcriptome sequencing
Universitythe First Affiliatedhospital of Anhuimedical
Hefei, Anhui, China
Persistent PSA elevation
Record PSA levels at 6-8 weeks and 3 months post-surgery; predefined thresholds in the study protocol may be used for exploratory analyses.
Time frame: post-operative PSA levels at 6-8 weeks and 3 months
Biochemical Progression-Free Survival (bPFS)
Time from initiation of ADT plus ARPl therapy to biochemical progression or deathfrom any cause, whichever occurs first. Biochemical progression is defined as a PSA rise to a0.2ng/mL after having reached an undetectable level, confirmed by a second measurement at least 2weeks apart. Participants without an event will be censored at the date of last follow-up.
Time frame: From treatment initiation until biochemical progression or last follow-up, assessed every 3 months (+1 month) for up to 24 months.
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