Depiction of tumor stemlike features and underlying relationships with hazard immune infiltrations based on large prostate cancer cohorts

Abstract

Prostate cancer stemness (PCS) cells have been reported to drive tumor progression, recurrence and drug resistance. However, there is lacking systematical assessment of stemlike indices and associations with immunological properties in prostate adenocarcinoma (PRAD). We thus collected 7 PRAD cohorts with 1465 men and calculated the stemlike indices for each sample using one-class logistic regression machine learning algorithm. We selected the mRNAsi to quantify the stemlike indices that correlated significantly with prognosis and accordingly identified 21 PCS-related CpG loci and 13 pivotal signature. The 13-gene based PCS model possessed high predictive significance for progression-free survival (PFS) that was trained and validated in 7 independent cohorts. Meanwhile, we conducted consensus clustering and classified the total cohorts into 5 PCS clusters with distinct outcomes. Samples in PCScluster5 possessed the highest stemness fractions and suffered from the worst prognosis. Additionally, we implemented the CIBERSORT algorithm to infer the differential abundance across 5 PCS clusters. The activated immune cells (CD8+ T cell and dendritic cells) infiltrated significantly less in PCScluster5 than other clusters, supporting the negative regulations between stemlike indices and anticancer immunity. High mRNAsi was also found to be associated with up-regulation of immunosuppressive checkpoints, like PDL1. Lastly, we used the Connectivity Map (CMap) resource to screen potential compounds for targeting PRAD stemness, including the top hits of cell cycle inhibitor and FOXM1 inhibitor. Taken together, our study comprehensively evaluated the PRAD stemlike indices based on large cohorts and established a 13-gene based classifier for predicting prognosis or potential strategies for stemness treatment.

Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients

“…The GBM patients were stratified into high and low mRNAsi groups based on the median value of the stemness index [ 31 ]. Kaplan–Meier (K-M) survival analysis was performed to evaluate the OS and PFS of patients with high and low mRNAsi, and the survival differences were evaluated by the two-sided log-rank test.…”

Integrated Analysis of Stemness-Related LncRNAs Helps Predict the Immunotherapy Responsiveness of Gastric Cancer Patients

“…In recent years, studies committing to constructing stemness-related models for clinical decision guidance have emerged. Zhang C. et al (2020) built a 13-gene-based prostate cancer stemness model that had high predictive significance for progression-free survival (PFS). The model was also revealed to be closely linked to immune microenvironment changes ( Zhang C. et al, 2020 ).…”

“… Zhang C. et al (2020) built a 13-gene-based prostate cancer stemness model that had high predictive significance for progression-free survival (PFS). The model was also revealed to be closely linked to immune microenvironment changes ( Zhang C. et al, 2020 ). Wang et al (2021) established a novel stemness-based classification with appealing implications in discriminating the prognosis and immunotherapy and temozolomide responses of 906 glioblastoma patients.…”

Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma

“…Lastly, by integrating mRNAsi and other clinical characteristics, we proposed a prognostic nomogram that allows for individualized estimations of the 3-and 5-year OS probabilities among MM patients. Stemness signatures have been identified in many malignancies and have different prognostic values in gastric cancer, acute myeloid leukemia, prostate cancer, and breast cancer (Ng et al, 2016;Malta et al, 2018;Wang et al, 2018;Miranda et al, 2019;Chang et al, 2020;Zhang et al, 2020). However, there are few studies on the stemness of MM and its prognostic value in MM patients.…”