A Liquid–Liquid Phase Separation-Related Index Associate with Biochemical Recurrence and Tumor Immune Environment of Prostate Cancer Patients

Abstract

To identify liquid–liquid phase separation (LLPS)-related molecular clusters, and to develop and validate a novel index based on LLPS for predicting the prognosis of prostate cancer (PCa) patients. We download the clinical and transcriptome data of PCa from TCGA and GEO database. The LLPS-related genes (LRGs) were extracted from PhaSepDB. Consensus clustering analysis was used to develop LLPS-related molecular subtypes for PCa. The LASSO cox regression analysis was performed to establish a novel LLPS-related index for predicting biochemical recurrence (BCR)-free survival (BCRFS). Preliminary experimental verification was performed. We initially identified a total of 102 differentially expressed LRGs for PCa. Three LLPS related molecular subtypes were identified. Moreover, we established a novel LLPS related signature for predicting BCRFS of PCa patients. Compared to low-risk patients in the training cohort, testing cohort and validating cohort, high-risk populations meant a higher risk of BCR and significantly poorer BCRFS. The area under receiver operating characteristic curve were 0.728, 0.762, and 0.741 at 1 year in the training cohort, testing cohort and validating cohort. Additionally, the subgroup analysis indicated that this index was especially suitable for PCa patients with age ≤ 65, T stage III-IV, N0 stage or in cluster 1. The FUS, which was the potential biomarker related to PCa liquid–liquid phase separation, was preliminarily identified and verified. This study successfully developed three LLPS-related molecular subtypes and identified a novel LLPS related molecular signature, which performed well in predicting BCRFS of PCa.

Integrated machine learning algorithms reveal a bone metastasis-related signature of circulating tumor cells in prostate cancer

“…Moreover, the combination of algorithms can reduce the dimensionality of the variables to simplify the model and improve its generalizability. ROC and C-index analyses showed that the BMGPI exhibited higher accuracy and stability compared to previous studies, indicating its great potential for clinical applications 27 , 28 .…”

“…You et al . constructed a new liquid-liquid phase separation-related index to predict biochemical recurrence and tumor microenvironment of PCa with the help of univariate Cox and Lasso analysis 27 . Similarly, Zhang et al .…”

Identification of cell differentiation trajectory-related gene signature to reveal the prognostic significance and immune landscape in prostate cancer based on multiomics analysis

“…The prognostic landscape is significantly influenced by biochemical recurrence (BCR) in PCa patients, amplifying the risk of clinical metastasis and death [ 6 , 7 ]. Patients diagnosed with prostate cancer may experience BCR months to years after their initial treatment [ 8 , 9 ]. The timing of this recurrence is contingent upon the cancer's aggressiveness and the specific treatment modality employed.…”

A novel risk signature based on liquid-liquid phase separation-related genes reveals prognostic and tumour microenvironmental features in clear cell renal cell carcinoma

“…Previous studies have shown that LLPS-related gene markers can be used as prognostic markers in patients with some tumors, such as prostate cancer, endometrial cancer, breast cancer, hepatocellular carcinoma, and epithelial ovarian cancer [21][22][23][24][25]. Consequently, the primary objective of this investigation was to develop and substantiate a predictive model for prognosticating LLPS-related genes within ccRCC.…”