Integrative multi-omics analysis unveils stemness-associated molecular subtypes in prostate cancer and pan-cancer: prognostic and therapeutic significance.

BACKGROUND: Prostate cancer (PCA) is the fifth leading cause of cancer-related deaths worldwide, with limited treatment options in the advanced stages. The immunosuppressive tumor microenvironment (TME) of PCA results in lower sensitivity to immunotherapy. Although molecular subtyping is expected to offer important clues for precision treatment of PCA, there is currently a shortage of dependable and effective molecular typing methods available for clinical practice. Therefore, we aim to propose a novel stemness-based classification approach to guide personalized clinical treatments, including immunotherapy. METHODS: An integrative multi-omics analysis of PCA was performed to evaluate stemness-level heterogeneities. Unsupervised hierarchical clustering was used to classify PCAs based on stemness signature genes. To make stemness-based patient classification more clinically applicable, a stemness subtype predictor was jointly developed by using four PCA datasets and 76 machine learning algorithms. RESULTS: We identified stemness signatures of PCA comprising 18 signaling pathways, by which we classified PCA samples into three stemness subtypes via unsupervised hierarchical clustering: low stemness (LS), medium stemness (MS), and high stemness (HS) subtypes. HS patients are sensitive to androgen deprivation therapy, taxanes, and immunotherapy and have the highest stemness, malignancy, tumor mutation load (TMB) levels, worst prognosis, and immunosuppression. LS patients are sensitive to platinum-based chemotherapy but resistant to immunotherapy and have the lowest stemness, malignancy, and TMB levels, best prognosis, and the highest immune infiltration. MS patients represent an intermediate status of stemness, malignancy, and TMB levels with a moderate prognosis. We further demonstrated that these three stemness subtypes are conserved across pan-tumor. Additionally, the 9-gene stemness subtype predictor we developed has a comparable capability to 18 signaling pathways to make tumor diagnosis and to predict tumor recurrence, metastasis, progression, prognosis, and efficacy of different treatments. CONCLUSIONS: The three stemness subtypes we identified have the potential to be a powerful tool for clinical tumor molecular classification in PCA and pan-cancer, and to guide the selection of immunotherapy or other sensitive treatments for tumor patients.

Cancer cells co-opt nociceptive nerves to thrive in nutrient-poor environments and upon nutrient-starvation therapies.

Although nutrient-starvation therapies can elicit strong anti-tumor effects in multiple carcinomas, it has been convincingly demonstrated that cancer cells exploit the tumor microenvironment to thrive in nutrient-poor environments. Here, we reveal that cancer cells can co-opt nociceptive nerves to thrive in nutrient-poor environments. Initially examining the low-glucose environment of oral mucosa carcinomas, we discovered that cancer cells employ ROS-triggered activation of c-Jun to secrete nerve growth factor (NGF), which conditions nociceptive nerves for calcitonin gene-related peptide (CGRP) production. The neurogenic CGRP subsequently induces cytoprotective autophagy in cancer cells through Rap1-mediated disruption of the mTOR-Raptor interaction. Both anti-glycolysis and anti-angiogenesis-based nutrient-starvation therapies aggravate the vicious cycle of cancer cells and nociceptive nerves and therapeutically benefit from blocking neurogenic CGRP with an FDA-approved antimigraine drug. Our study sheds light on the role of the nociceptive nerve as a microenvironmental accomplice of cancer progression in nutrient-poor environments and upon nutrient-starvation therapies.