No abstract

This study was aimed to reveal the changes in survival rates and prognostic factors to survival of chondroblastic osteosarcoma (COS).Patients from the Surveillance, Epidemiology, and End Results (SEER) database were retrieved. Kaplan–Meier survival analysis and Cox proportional hazard model were used during analysis.There were significant differences on overall survival between subtypes of osteosarcoma (P < .001∗). Overall survival of COS did not change significantly during last forty years (P = .610), and cancer-specific survival increased to a plateau in 1980s and then remained stable (P = .058). Younger onset age, patients of white race, well and moderately differentiated tumors, and surgery independently predicted better overall (Hazard ratio [HR]: 1.034, P < .001∗; HR: 0.538, P = .004∗; HR: 0.240, P = .020∗ and HR: 0.350, P < .001∗, respectively) and cancer-specific (HR: 1.031, P = .002∗; HR: 0.592, P = .036∗; HR: 0.098, P = .027∗ and HR: 0.253, P < .001∗, respectively) survival. Metastasis at diagnosis independently predicted worse overall (HR: 3.108, P < .001∗) and cancer-specific (HR: 4.26, P < .001∗) survival compared to no metastasis.Younger onset age, white race, well and moderately differentiated tumors, no metastasis at diagnosis and surgical resection can independently predict better overall and cancer-specific survival of COS.

Coronavirus disease 2019 (COVID‐19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), has become a global pandemic worldwide. Long non‐coding RNAs (lncRNAs) are a subclass of endogenous, non‐protein‐coding RNA, which lacks an open reading frame and is more than 200 nucleotides in length. However, the functions for lncRNAs in COVID‐19 have not been unravelled. The present study aimed at identifying the related lncRNAs based on RNA sequencing of peripheral blood mononuclear cells from patients with SARS‐CoV‐2 infection as well as health individuals. Overall, 17 severe, 12 non‐severe patients and 10 healthy controls were enrolled in this study. Firstly, we reported some altered lncRNAs between severe, non‐severe COVID‐19 patients and healthy controls. Next, we developed a 7‐lncRNA panel with a good differential ability between severe and non‐severe COVID‐19 patients using least absolute shrinkage and selection operator regression. Finally, we observed that COVID‐19 is a heterogeneous disease among which severe COVID‐19 patients have two subtypes with similar risk score and immune score based on lncRNA panel using iCluster algorithm. As the roles of lncRNAs in COVID‐19 have not yet been fully identified and understood, our analysis should provide valuable resource and information for the future studies.

Since autophagy and the immune microenvironment are deeply involved in the tumor development and progression of Lower-grade gliomas (LGG), our study aimed to construct an autophagy-related risk model for prognosis prediction and investigate the relationship between the immune microenvironment and risk signature in LGG. Therefore, we identified six autophagy-related genes (BAG1, PTK6, EEF2, PEA15, ITGA6, and MAP1LC3C) to build in the training cohort (n = 305 patients) and verify the prognostic model in the validation cohort (n = 128) and the whole cohort (n = 433), based on the data from The Cancer Genome Atlas (TCGA). The six-gene risk signature could divide LGG patients into high- and low-risk groups with distinct overall survival in multiple cohorts (all p &lt; 0.001). The prognostic effect was assessed by area under the time-dependent ROC (t-ROC) analysis in the training, validation, and whole cohorts, in which the AUC value at the survival time of 5 years was 0.837, 0.755, and 0.803, respectively. Cox regression analysis demonstrated that the risk model was an independent risk predictor of OS (HR &gt; 1, p &lt; 0.05). A nomogram including the traditional clinical parameters and risk signature was constructed, and t-ROC, C-index, and calibration curves confirmed its robust predictive capacity. KM analysis revealed a significant difference in the subgroup analyses’ survival. Functional enrichment analysis revealed that these autophagy-related signatures were mainly involved in the phagosome and immune-related pathways. Besides, we also found significant differences in immune cell infiltration and immunotherapy targets between risk groups. In conclusion, we built a powerful predictive signature and explored immune components (including immune cells and emerging immunotherapy targets) in LGG.