Identification of Molecular Subtype and Prognostic Signature for Prostate Adenocarcinoma based on Neutrophil Extracellular Traps

Abstract

Background : Prostate adenocarcinoma (PRAD) is one of the most common cancers in male. Increasing evidences pointed out that Neutrophil Extracellular Traps (NETs) play an important role in tumor angiogenesis, tumor metastasis and drug resistance. However, limited systematic studies regarding the role of NETs in PRAD have been performed. Identification of biomarkers based on NETs might facilitate risk stratification which help optimizing the clinical strategies. Methods : NETs-related genes with differential expressions were identified between PRAD and adjacent normal tissues in TCGA-PRAD dataset. Consensus cluster analysis was performed to determine the PRAD subtypes based on the different-expressed NETs-related genes. The difference of pathway enrichment, infiltrating immune cell and genomic mutation were also evaluated between subtypes. LASSO cox regression analysis was conducted to construct a NETs-related prognostic signature. Result : We identified 19 NETs related genes with differential expressions between PRAD and adjacent normal tissue in TCGA-PRAD dataset. Two significant subtypes were identified based on these 19 genes by consensus cluster analysis, namely subtype 1 and subtype 2. Significant differences in prognosis, immune infiltration and tumor mutation burden were observed in subtypes. LASSO Cox regression analysis identified a NETs-associated prognostic signature including 13 genes, and this signature had a good performance in predicting the progression-free survival of PRAD patients. Further integrated analysis indicated that MMP9 mostly expressed in Mono/Macrophage cells might play a role in regulating NETs formation via neutrophil activation in PRAD. Conclusion : To sum up, the current study identified two NETs-related molecular subtypes and based on which constructed a prognostic signature for PRAD.

Neutrophil extracellular traps in cancer

“…NET‐related genes in TCGA have been identified as predictors of overall survival, immunotherapy efficacy and are also associated with an immunosuppression microenvironment. 174 , 175 Additionally, NETs signature also could identify the population of tumor patients most likely to respond favorably to treatments such as neoadjuvant therapy 176 or Wnt signaling pathway inhibitors. 177 Others attempt to establish a robust NETs‐related long noncoding RNA (NETsLnc) signature for predicting the prognosis of soft tissue sarcoma by integrating machine learning algorithms.…”