Immune infiltration phenotypes of prostate adenocarcinoma and their clinical implications.

BACKGROUND

Tumor-infiltrating immune cells participate in the initiation and progression of prostate adenocarcinoma (PRAD). However, it is not fully known how immune infiltration affects the development of PRAD and its clinical presentation.

METHODS

Herein, we investigated the immune infiltration phenotypes in PRAD based on transcriptome profiles, methylation profiles, somatic mutation, and copy number variations. We also developed an immune prognostic model (IPM) to identify unfavorable prognosis. To verify this model, immunohistochemistry staining was performed on a cohort of PRAD samples. Moreover, we constructed a nomogram to assess the survival of PRAD incorporating immune infiltration and other clinical features.

RESULTS

We categorized PRAD patients into high and low-level clusters based on immune infiltration phenotypes. The patients in the high-level clusters had worse survival than their low-level counterparts. Gene set enrichment analysis indicated that both anti- and pro-tumor terms were enriched in high-level cluster. Moreover, we identified a positive correlation between anti- and pro-tumor immune cells in PRAD microenvironment. Notably, Somatic mutation analysis showed patients in high-level cluster had a higher somatic mutation burden of KMT2D, HSPA8, CHD7, and MAP1A. In addition, we developed an IPM with robust predictive ability. The model can distinguish high-risk PRAD patients with poor prognosis from low-risk PRAD patients in both training and another three independent validation datasets. Besides, we constructed a nomogram incorporating Gleason score, pathological T stage, and IPM for the prognosis prediction of PRAD patients, which displayed robust predictive ability and might contribute to clinical practice.

CONCLUSION

Our work illustrated the immune infiltration phenotypes strongly related to the poor prognosis of PRAD patients, and highlighted the potential of the IPM to identify unfavorable tumor features.