Single-cell RNA combined with bulk RNA analysis to explore oxidative stress and energy metabolism factors and found a new prostate cancer oncogene MXRA8.

BACKGROUND: Prostate cancer is the most common malignancy among men worldwide, and its diagnosis and treatment are challenging due to its heterogeneity. METHODS: Integrating single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data, we identified two molecular subtypes of prostate cancer based on dysregulated genes involved in oxidative stress and energy metabolism. We constructed a risk score model (OMR) using common differentially expressed genes, which effectively evaluated prostate cancer prognosis. RESULTS: Our analysis demonstrated a significant correlation between the risk score model and various factors, including tumor immune microenvironment, genomic variations, chemotherapy resistance, and immune response. Notably, patients with low-risk scores exhibited increased sensitivity to chemotherapy and immunotherapy compared to those with high-risk scores, indicating the model's potential to predict patient response to treatment. Additionally, our investigation of MXRA8 in prostate cancer showed significant upregulation of this gene in the disease as confirmed by PCR and immunohistochemistry. Functional assays including CCK-8, transwell, plate cloning, and ROS generation assay demonstrated that depletion of MXRA8 reduced the proliferative, invasive, migratory capabilities of PC-3 cells, as well as their ROS generation capacity. CONCLUSIONS: Our study highlights the potential of oxidative stress and energy metabolism-related genes as prognostic markers and therapeutic targets in prostate cancer. The integration of scRNA-seq and bulk RNA-seq data enables a better understanding of prostate cancer heterogeneity and promotes personalized treatment development. Additionally, we identified a novel oncogene MXRA8 in prostate cancer.

Leveraging diverse cell-death related signature predicts the prognosis and immunotherapy response in renal clear cell carcinoma.

Background: Modulation of programmed cell death in tumor cells alters the tumor microenvironment and the influx of tumor-infiltrating lymphocytes, and the combination of its inducers and immune checkpoint inhibitors plays a synergistic role in enhancing antitumor effects. Methods: We downloaded the data of clear cell renal cell carcinoma samples from The Cancer Genome Atlas and used a machine learning approach to build a new programmed cell death index (PCDI) through 13 programmed cell death-related genes. Based on PCDI, clinical features, tumor immune microenvironment, chemotherapy response and immunotherapy response were systematically analyzed. Results: PCDI consists of eight programmed cell death-related genes (TBX3, BID, TCIRG1, IDUA, KDR, PYCARD, IFNG and LRRK2). PCDI is a reliable predictor of survival in clear cell renal cell carcinoma patients and has been validated in multiple external datasets. We found that the high PCDI group showed higher levels of immune cell infiltration and better response to immunotherapy compared to the low PCDI group, and PCDI can also be used for prognostic prediction in a variety of cancers other than clear cell renal cell carcinoma. In vitro experiments demonstrated that knockdown of IDUA inhibited the proliferation and migration of clear cell renal cell carcinoma. Conclusions: The PCDI identified in this study provides valuable insights into the clinical management of clear cell renal cell carcinoma by accurately evaluating the prognosis of patients with clear cell renal carcinoma and identifying the patient population that would benefit from immunotherapy.

Soil water content and nitrogen differentially correlate with multidimensional leaf traits of two temperate broadleaf species.

The variation and correlation of leaf economics and vein traits are crucial for predicting plant ecological strategies under different environmental changes. However, correlations between these two suites of traits and abiotic factors such as soil water and nitrogen content remain ambiguous. We measured leaf economics and vein traits as well as soil water and nitrogen content for two different shade-tolerant species (Betula platyphylla and Acer mono) in four mixed broadleaved-Korean pine (Pinus koraiensis) forests along a latitudinal gradient in Northeast China. We found that leaf economics traits and vein traits were decoupled in shade-intolerant species, Betula platphylla, but significantly coupled in a shade-tolerant species, A. mono. We found stronger correlations among leaf traits in the shade tolerant species than in the shade intolerant species. Furthermore, leaf economic traits were positively correlated with the soil water gradient for both species, whereas vein traits were positively correlated with soil water gradient for the shade intolerant species but negatively correlated in the shade tolerant species. Although economic traits were positively correlated with soil nitrogen gradient in shade intolerant species but not correlated in shade tolerant species, vein traits were negatively correlated with soil nitrogen gradient in shade tolerant species but not correlated in shade intolerant species. Our study provides evidence for distinct correlations between leaf economics and vein traits and local abiotic factors of species differing in light demands. We recommend that the ecological significance of shade tolerance be considered for species when evaluating ecosystem functions and predicting plant responses to environmental changes.