Molecular classification of human papilloma virus-negative head and neck squamous cell carcinomas: Cell cycle-based classifier and prognostic signature.

The molecular classification of human papillomavirus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs) remains questionable. Differentially expressed genes were detected between tumor and normal tissues and GSEA showed they are associated with cell cycle pathways. This study aimed to classify HPV-negative HNSCCs based on cell cycle-related genes. The established gene pattern was correlated with tumor progression, clinical prognosis, and drug treatment efficacy. Biological analysis was performed using HNSCC patient sample data obtained from the Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Gene Expression Omnibus (GEO) databases. All samples included in this study contained survival information. RNA sequencing data from 740 samples were used for the analysis. Previously characterized cell cycle-related genes were included for unsupervised consensus clustering. Two subtypes of HPV-negative HNSCCs (C1, C2) were identified. Subtype C1 displayed low cell cycle activity, 'hot' tumor microenvironment (TME), earlier N stage, lower pathological grade, better prognosis, and higher response rate to the immunotherapy and targeted therapy. Subtype C2 was associated with higher cell cycle activity, 'cold' TME, later N stage, higher pathological grade, worse prognosis, and lower response rate to the treatment. According to the nearest template prediction method, classification rules were established and verified. Our work explored the molecular mechanism of HPV-negative HNSCCs in the view of cell cycle and might provide new sights for personalized anti-cancer treatment.

The limiting effect of genome size on xylem vessel diameter is shifted by environmental pressures in seed plants.

Current and previous studies have extensively studied the physiological and ecological consequences of genome size (GS) on plants because of the limiting effect of GS on cell size. However, it is still obscure whether such limiting effect could be shifted by environmental pressures, or not. Here, we compiled a global dataset comprised of GS, xylem vessel diameter (V dia), xylem hydraulic conductivity (K S), P 50 (xylem water potential at the loss of 50% maximum K S), and climate factors of 251 phylogeny and habitat divergent species from 59 families. The results showed that GS could limit the V dia of the species from the same family sampled in the similar climate conditions. But the expected positive relationship between GS and V dia became uncertain and even negative across different environmental conditions. V dia was strongly positively coordinated with mean annual temperature (MAT), mean annual precipitation (MAP), and potential evapotranspiration (PET). Furthermore, V dia as the anatomic foundation of plant hydraulic performance was strongly positively coordinated with K S and negatively coordinated with -P 50. The strong environmental selection on K S and P 50 explained the concerted regulation of V dia by environmental factors. The findings revealed the combined regulation of GS and environmental pressures on xylem cell size and thus affected plant eco-physiological performance. The shifted cell size limiting effect of GS by environmental factors manifests plants great plasticity under changed environmental conditions.

Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke.

Background: Stroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients. Purpose: We aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke. Materials and methods: A total of 476 patients were enrolled in the study and divided into the training set (n = 381) and testing set (n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)-binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients-the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation. Results: In the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781-0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model. Conclusion: The novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency. Summary: Combining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay. Key Results: Using a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800).