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Page 20 of 26 Li et al. Cancer Drug Resist. 2025;8:31

role in the development of castration-resistant PCa by generating lymphotoxins . Similarly, samples from
[42]
patients with recurrent or progressive PCa displayed higher and more abundant B cell infiltration .
[43]
Furthermore, the immune score, a critical parameter for studying immune features, exhibited significant
variations among the different subgroups, suggesting that patients with PCa and distinct immune
characteristics might have varying prognoses and treatment responses.

We investigated interactions among the immune cells within the PCa microenvironment. We analyzed the
correlations between 22 different immune cell types within the two subgroups. The observed enhanced
correlations between specific immune cell types in cluster 1, particularly plasma cells and naïve B cells, as
well as eosinophils and monocytes, suggest intensified intercellular collaboration within the immunologically
active TME. This pattern may reflect heightened B cell differentiation and innate immune cell coordination,
a process supported by prior studies to facilitate antigen presentation and antitumor immunity.
Understanding these correlation dynamics helps unravel PCa immune heterogeneity and provides a
scientific rationale for immunomodulatory therapeutic strategies. Furthermore, significant differences were
observed in the expression of PD-1 and PD-L1 among the different immune subgroups. Immune checkpoint
molecules such as PD-1 and PD-L1 may be involved in tumor immune evasion, and their high expression
levels indicate a positive response to immunotherapy . In addition, various biomarkers associated with
[44]
immune activity and immunotherapy responses displayed differential expression, further highlighting the
heterogeneous nature of PCa immune characteristics. Therefore, the identification of gene modules related
to immune characteristics is crucial for the improved classification of patients with PCa.

In our study, a set of immune-related genes was derived from the WGCNA modules using univariate Cox
analysis and was used for prognostication. These genes exhibit a strong correlation with patient prognosis,
indicating their potential as clinical tools for risk stratification, particularly CCDC3. Previously, Ke et al.
employed multivariate Cox analysis to identify immune markers closely associated with PCa prognosis and
discovered that high expression of CCDC3 in cancer cells is more likely to lead to radiation resistance . The
[45]
inhibition of CCDC3 expression in PCa cell lines significantly inhibited cancer cell migration and
invasion . However, in LASSO regression analyses within the TCGA cohort, CCDC3 did not demonstrate a
[45]
significant association with poor prognosis or drug resistance. We postulate that this may be attributed to
differences in cohort composition, analytical methodologies, and biological context. Furthermore, genes in
our model such as MUC5B and TREM1 have been documented in relevant literature for their roles in the
tumor immune microenvironment and prognosis [46,47] , underscoring the model’s multidimensional novelty
and clinical applicability. Considering the role of post-transcriptional regulation in mRNA dynamics, the
discordant results for RAB33A between qRT-PCR and WB analyses may be attributable to translational
suppression. Furthermore, activation of the ubiquitin-proteasome system or autophagy pathways could
contribute to the specific degradation of RAB family proteins. Notably, the aberrantly activated degradation
pathways that may be present in the PCa microenvironment warrant particular consideration. Collectively,
our results reveal the critical value of immune-related genes in PCa prognosis assessment and provide
direction for subsequent mechanistic investigations.

Conventional assessment systems, such as TNM staging, often have limitations in accurately predicting
patient prognoses. The integration of diverse biomarkers encompassing different types and functions into a
predictive model substantially improves the precision of prognostic prediction and offers valuable insights
into treatment decisions. Feng et al. leveraged data from the TCGA and GEO databases to identify lncRNAs
associated with aging. These age-related lncRNAs were subsequently used as potential biomarkers to build a
prognostic model for patients with PCa . The scoring system used within this model effectively evaluates
[48]
patient prognosis . Herein, we used the LASSO analysis to construct an immune-related predictive model
[48]
for the prognosis of patients with PCa. The results demonstrated that our model has robust predictive

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