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Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal human cancers. It thrives in a nutrient-poor environment; however, the mechanisms by which PDAC cells undergo metabolic reprogramming to adapt to metabolic stress are still poorly understood. Here, we show that microRNA-135 is significantly increased in PDAC patient samples compared to adjacent normal tissue. Mechanistically, miR-135 accumulates specifically in response to glutamine deprivation and requires ROS-dependent activation of mutant p53, which directly promotes miR-135 expression. Functionally, we found miR-135 targets phosphofructokinase-1 (PFK1) and inhibits aerobic glycolysis, thereby promoting the utilization of glucose to support the tricarboxylic acid (TCA) cycle. Consistently, miR-135 silencing sensitizes PDAC cells to glutamine deprivation and represses tumor growth in vivo. Together, these results identify a mechanism used by PDAC cells to survive the nutrient-poor tumor microenvironment, and also provide insight regarding the role of mutant p53 and miRNA in pancreatic cancer cell adaptation to metabolic stresses.

The objective of this study is to determine if the incorporation of perineural invasion (PNI) into the T‐classification would improve the prognostic performance of TNM‐8. An international, multicenter study of 1049 patients with oral cavity squamous cell carcinoma that were treated from 1994 to 2018 is performed. Various classification models are developed within each T‐category and evaluated using the Harrel‐concordance index (C‐index), Akaike‐information criterion (AIC), and visual inspection. Stratification into distinct prognostic categories, with internal validation, is performed using bootstrapping analysis (SPSS and R‐software). Through multivariate analysis, PNI is significantly associated with disease‐specific survival (p < 0.001). PNI integration into the staging system results in a significantly improved model compared with the current T category alone (lower AIC, p < 0.001). The PNI‐integrated model is superior in predicting differential outcomes between T3 and T4 patients. A new model for T‐classification of oral cavity squamous cell carcinoma is proposed, which is based on incorporating PNI into the staging system. These data can be used for future evaluations of the TNM staging system. [ABSTRACT FROM AUTHOR]