Pan-cancer analysis reveals PDK family as potential indicators related to prognosis and immune infiltration.

Pyruvate dehydrogenase kinases (PDKs) play a key role in glucose metabolism by exerting negative regulation over pyruvate dehyrogenase complex (PDC) activity through phosphorylation. Inhibition of PDKs holds the potential to enhance PDC activity, prompting cells to adopt a more aerobic metabolic profile. Consequently, PDKs emerge as promising targets for condition rooted in metabolic dysregulation, including malignance and diabetes. However, a comprehensive exploration of the distinct contribution of various PDK family members, particularly PDK3, across diverse tumor types remain incomplete. This study undertakes a systematic investigation of PDK family expression patterns, forging association with clinical parameters, using data from the TCGA and GTEx datasets. Survival analysis of PDKs is executed through both Kaplan-Meier analysis and COX regression analysis. Furthermore, the extent of immune infiltration is assessed by leveraging the CIBERSORT algorithm. Our study uncovers pronounced genetic heterogeneity among PDK family members, coupled with discernible clinical characteristic. Significantly, the study establishes the potential utility of PDK family genes as prognostic indicators and as predictors of therapeutic response. Additionally, our study sheds light on the immune infiltration profile of PDK family. The results showed the intimate involvement of these genes in immune-related metrics, including immune scoring, immune subtypes, tumor-infiltrating lymphocytes, and immune checkpoints expression. In sum, the findings of this study offer insightful strategies to guide the therapeutic direction, aiming at leveraging the impact of PDK family genes in cancer treatment.

Red cell distribution width to serum albumin ratio as an early prognostic marker for severe acute pancreatitis: A retrospective study.

BACKGROUND AND STUDY AIMS: The ability to predict severe acute pancreatitis (SAP) at an early stage is crucial for reducing the associated complications and mortality. In this study, we compared the ratio of red cell distribution width to albumin (RDW-to-ALB) using predictive scoring systems, such as the Ranson score, BISAP, and MCTSI, to develop a simple and accurate method of predicting SAP. PATIENTS AND METHODS: We included 212 patients with mild acute pancreatitis (MAP) and 89 with SAP between January 2013 and December 2018. The differences in the general characteristics and biochemical analysis as well as the various predictive scores were compared between the two groups. We evaluated the sensitivity and specificity between the RDW-to-ALB ratio, RDW, ALB, and multiple predictive scores in patients with early acute pancreatitis (AP) by using the receiver operating characteristic (ROC) curve. RESULTS: The RDW-to-ALB ratio (%) of patients with SAP was higher than that of patients with MAP (0.43 ± 0.08 vs. 0.32 ± 0.04, p < 0.001). Patients with SAP had higher Ranson, BISAP, and MCTSI scores than those with MAP. The ROC curve revealed that, when the RDW-to-ALB ratio (%) was >0.36, the sensitivity and specificity of the predicted SAP were 80.0% and 80.7%, respectively. Further statistical analysis found that the RDW-to-ALB ratio and Ranson, BISAP, and MCTSI scores were consistent in predicting SAP effectiveness (P > 0.05). CONCLUSIONS: The RDW-to-ALB ratio has a promising predictive power for SAP, and its effectiveness is comparable with those of Ranson, BISAP, and MCTSI scores.

Necroptosis-Associated lncRNA Prognostic Model and Clustering Analysis: Prognosis Prediction and Tumor-Infiltrating Lymphocytes in Breast Cancer.

Necroptosis plays an important role in tumor genesis and progression. This study aims to identify necroptosis-related lncRNAs (NR-lncRNAs) in breast cancer (BC), and their prognostic value and relationship with the tumor immune environment (TIE) through bioinformatics. Methods. A total of 67 necroptosis-related genes (NRGs) are retrieved, and 13 prognostically relevant NR-lncRNAs are identified by co-expression and Univariate Cox regression analyses. After unsupervised clustering analysis, the patients are classified into three clusters, and their survival and immune infiltration are compared. Lasso regression analysis is conducted to construct a prognostic model using eight lncRNAs (USP30-AS1, AC097662.1, AC007686.3, AL133467.1, AP006284.1, NDUFA6-DT, LINC01871, AL135818.1). The model is validated by Kaplan-Meier survival analysis, Multivariate Cox regression analysis, and receiver-operating characteristic (ROC) curves. Correlation analysis is useful to identify associations between risk scores and clinicopathological features. GSEA, drug prediction, and immune checkpoints analysis are further used to differentiate between the risk groups. Results. The C3 cluster has longer overall survival (OS) and the highest immune score, indicative of an immunologically hot tumor that may be sensitive to immunotherapy. Furthermore, the OS is significantly higher in the low-risk group, even after dividing the patients into subgroups with different clinical characteristics. The area under the ROC curve (AUC) for 1-, 3-, and 5-year survival in the training set are 0.761, 0.734, and 0.664, respectively, which indicate the moderate predictive performance of the model. Conclusion. NR-lncRNAs can predict the prognosis of BC, distinguish between hot and cold tumors, and are potential predictive markers of the immunotherapy response.

Identification and external validation of the optimal FIB-4 and APRI thresholds for ruling in chronic hepatitis B related liver fibrosis in tertiary care settings.

BACKGROUND: With the initially defined thresholds, the most widely used serum biomarkers for staging liver fibrosis (ie, APRI and FIB-4 scores) proved to be ineffective among patients with chronic hepatitis B virus infection (CHB). Whether optimizing the FIB-4 and APRI thresholds could improve their diagnostic accuracy requires further research. METHODS: Using data of treat-naïve CHB patients from three tertiary hospitals, we explored the optimal FIB-4 and APRI thresholds to rule in liver fibrosis accurately. Subsequently, we validated the applicability of the newly defined thresholds to the CHB patients from another two tertiary hospitals. RESULTS: The fibrosis stages between discovery cohort (n = 433) and the external validation cohort (n = 568) were statistically different (P < .001). When ruling in significant fibrosis and advanced fibrosis by the newly defined FIB-4 thresholds (2.25 and 3.00, respectively), 24.0% and 14.3% of patients, respectively, could be classified with excellent accuracy (PPVs of 91.3% and 80.6%, respectively; misdiagnosis rates of 6.0% and 5.4%, respectively), supported by the internal and external validation tests. Regrettably, the more accurate and robust thresholds of APRI score for ruling in significant fibrosis and advanced fibrosis could not be found. Besides, the FIB-4 and APRI scores should not be recommended for ruling in cirrhosis because of poor clinical diagnostic performance. CONCLUSION: The newly defined FIB-4 thresholds for ruling in significant fibrosis and advanced fibrosis showed superior and reproducible clinical diagnostic accuracy. The well-validated threshold (≥2.25) of FIB-4 score could aid in antiviral treatment decisions for treat-naïve adult CHB patients by accurately ruling in significant fibrosis in tertiary care settings.