The signature composed of immune-related long noncoding ribonucleic acids (irlncRNAs) with no requirement of specific expression level seems to be valuable in predicting the survival of patients with hepatocellular carcinoma (HCC). Here, we retrieved raw transcriptome data from The Cancer Genome Atlas (TCGA), identified irlncRNAs by co-expression analysis, and recognized differently expressed irlncRNA (DEirlncRNA) pairs using univariate analysis. In addition, we modified Lasso penalized regression. Then, we compared the areas under curve, counted the Akaike information criterion (AIC) values of 5-year receiver operating characteristic curve, and identified the cut-off point to set up an optimal model for distinguishing the high-or low-disease-risk groups among patients with HCC. We then reevaluated them from the viewpoints of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. 36 DEirlncRNA pairs were identified, 12 of which were included in a Cox regression model. After regrouping the patients by the cut-off point, we could more effectively differentiate between them based on unfavorable survival outcome, aggressive clinic-pathological characteristics, specific tumor immune infiltration status, low chemotherapeutics sensitivity, and highly expressed immunosuppressed biomarkers. The signature established by paring irlncRNA regardless of expression levels showed a promising clinical prediction value.

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Background Osteosarcoma is a highly aggressive bone tumor that most commonly affects children and adolescents. Treatment and outcomes for osteosarcoma have remained unchanged over the past 30 years. The relationship between osteosarcoma and the immune microenvironment may represent a key to its undoing. Methods We calculated the immune and stromal scores of osteosarcoma cases from the Target database using the ESTIMATE algorithm. Then we used the CIBERSORT algorithm to explore the tumor microenvironment and analyze immune infiltration of osteosarcoma. Differentially expressed genes (DEGs) were identified based on immune scores and stromal scores. Search Tool for the Retrieval of Interacting Genes Database (STRING) was utilized to assess protein–protein interaction (PPI) information, and Molecular Complex Detection (MCODE) plugin was used to screen hub modules of PPI network in Cytoscape. The prognostic value of the gene signature was validated in an independent GSE39058 cohort. Gene set enrichment analysis (GSEA) was performed to study the hub genes in signaling pathways. Results From 83 samples of osteosarcoma obtained from the Target dataset, 137 DEGs were identified, including 134 upregulated genes and three downregulated genes. Functional enrichment analysis and PPI networks demonstrated that these genes were mainly involved in neutrophil degranulation and neutrophil activation involved in immune response, and participated in neuroactive ligand–receptor interaction and staphylococcus aureus infection. Conclusions Our study established an immune-related gene signature to predict outcomes of osteosarcoma, which may be important targets for individual treatment.

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Aim: To assess the reproducibility of a semiautomatic quantification tool for cervical stiffness and evaluate the normal changes in cervical elasticity during the three trimesters of pregnancy. Methods: This longitudinal prospective pilot study evaluated cervical elasticity during the three trimesters of pregnancy (11-14, 20-24 and 28-32 weeks) in women with singleton pregnancies. Women with a history of conization, cerclage, cervical Naboth cysts (diameter > 10 mm), cervical tumors, or uterine malformation were excluded. A semiautomatic tool was used to evaluate the stiffness of the whole cervix and the internal and external cervical os with multiple quantitative elasticity parameters and the cervical length (CL) on the sagittal view via transvaginal elastography. Intraclass correlation coefficients (ICC) and Bland-Altman analysis were used to assess intra-and interobserver variability. E-Cervix parameters during the three trimesters were compared using the Friedman test. Results: In total, 217 women with 651 strain examinations during the three trimesters were included. The intra-and interobserver ICC for the E-Cervix parameters ranged from 0.947 to 0.991 and 0.855 to 0.989, respectively. There were significant differences in all parameters among the three trimesters. Cervical elasticity showed significant softening and became heterogeneous during the three trimesters. The median CL was significantly shorter in the first trimester than in the second and third trimesters (P = 0.004, P < 0.001).Conclusion: E-Cervix provides a graphical tool for operators to easily define regions of interest and obtain multiple repeatable measures of elasticity. The normal references for E-Cervix parameters during the three trimesters reflect the physiological cervical changes during pregnancy.