Identification and validation of a glycolysis-related taxonomy for improving outcomes in glioma.

BACKGROUND: Reprogramming of glucose metabolism is a prominent abnormal energy metabolism in glioma. However, the efficacy of treatments targeting glycolysis varies among patients. The present study aimed to classify distinct glycolysis subtypes (GS) of glioma, which may help to improve the therapy response. METHODS: The expression profiles of glioma were downloaded from public datasets to perform an enhanced clustering analysis to determine the GS. A total of 101 combinations based on 10 machine learning algorithms were performed to screen out the most valuable glycolysis-related glioma signature (GGS). Through RSF and plsRcox algorithms, adrenomedullin (ADM) was eventually obtained as the most significant glycolysis-related gene for prognostic prediction in glioma. Furthermore, drug sensitivity analysis, molecular docking, and in vitro experiments were utilized to verify the efficacy of ADM and ingenol mebutate (IM). RESULTS: Glioma patients were classified into five distinct GS (GS1-GS5), characterized by varying glycolytic metabolism levels, molecular expression, immune cell infiltration, immunogenic modulators, and clinical features. Anti-CTLA4 and anti-PD-L1 antibodies significantly improved the prognosis for GS2 and GS5, respectively. ADM has been identified as a potential biomarker for targeted glycolytic therapy in glioma patients. In vitro experiments demonstrated that IM inhibited glioma cell progression by inhibiting ADM. CONCLUSION: This study elucidates that evaluating GS is essential for comprehending the heterogeneity of glioma, which is pivotal for predicting immune cell infiltration (ICI) characterization, prognosis, and personalized immunotherapy regimens. We also explored the glycolysis-related genes ADM and IM to develop a theoretical framework for anti-tumor strategies targeting glycolysis.

[Risk factors for prostate cancer in male patients with MRI-negative and PSA-abnormal findings].

Objective: To investigate the risk factors for clinically significant PCa diagnosed by transrectal ultrasound-guided systematic prostate biopsy in patients with MRI-negative and PSA-abnormal findings. METHODS: From January 2014 to December 2017, 335 male patients with MRI-negative (PI-RADS 2.0 score ≤ 2) and PSA-abnormal (4－30 ng/ml ) findings underwent systematic prostate biopsy guided by transrectal ultrasound under local anesthesia in our department. We collected and analyzed the demographic data, clinical symptoms, complications, past history and PSA density (PSAD) of the patients. RESULTS: Clinically significant PCa was diagnosed in 21 (6.3%) of the 335 patients. Multivariate logistic regression analysis showed that the independent risk factors were higher age (AUC: 0.704, P < 0.01) and PSAD (AUC: 0.743, P < 0.01). The cutoff values of age and PSAD were 71 years and 0.18 ng/ml/ml, respectively. CONCLUSIONS: Higher age and PSAD are risk factors for clinically significant PCa. Prostate biopsy, even repeated or saturated puncture, is recommended for those aged >71 years old or with PSAD >0.18 ng/ml/ml so as to avoid missed diagnosis and unnecessary invasive biopsy as well. /.