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Background and PurposeHypoxia is one of the basic characteristics of the physical microenvironment of solid tumors. The relationship between radiotherapy and hypoxia is complex. However, there is no radiosensitivity prediction model based on hypoxia genes. We attempted to construct a radiosensitivity prediction model developed based on hypoxia genes for lower-grade glioma (LGG) by using weighted correlation network analysis (WGCNA) and least absolute shrinkage and selection operator (Lasso).MethodsIn this research, radiotherapy-related module genes were selected after WGCNA. Then, Lasso was performed to select genes in patients who received radiotherapy. Finally, 12 genes (AGK, ETV4, PARD6A, PTP4A2, RIOK3, SIGMAR1, SLC34A2, SMURF1, STK33, TCEAL1, TFPI, and UROS) were included in the model. A radiosensitivity-related risk score model was established based on the overall rate of The Cancer Genome Atlas (TCGA) dataset in patients who received radiotherapy. The model was validated in TCGA dataset and two Chinese Glioma Genome Atlas (CGGA) datasets. A novel nomogram was developed to predict the overall survival of LGG patients.ResultsWe developed and verified a radiosensitivity-related risk score model based on hypoxia genes. The radiosensitivity-related risk score served as an independent prognostic indicator. This radiosensitivity-related risk score model has prognostic prediction ability. Moreover, a nomogram integrating risk score with age and tumor grade was established to perform better for predicting 1-, 3-, and 5-year survival rates.ConclusionsWe developed and validated a radiosensitivity prediction model that can be used by clinicians and researchers to predict patient survival rates and achieve personalized treatment of LGG.

Background. The impacts of health insurance status on survival outcomes in children, adolescents, and young adults (aged 0-39 years) with malignant tumors have not been addressed in depth. The present study aimed to identify significant relationships of health insurance condition with overall survival or all-cause mortality among children (age 0-14 years) and adolescents and young adults (AYAs, age 15-39 years) with malignant tumors.Methods. PubMed, Wiley Cochrane Central Register of Controlled Trials, Econlit, CINAHL, Web of Knowledge, PsychInfo, Business Source Premier, ProQuest Dissertation & Theses Database, and SCOPUS were systematically searched from inception to February 29, 2020 with no language restriction. All related articles comparing the effect of health insurance status on the risk of overall survival and the risk of all-cause mortality in malignant conditions affecting children and AYAs were identified. Pooled risk ratios (RRs) and 95% confidence intervals (CIs) were computed using a random-or fixed-effect model as per the heterogeneity evaluated using Cochran's Q and I 2 statistics.Results. Fourteen studies including 149,680 individuals were selected for this meta-analysis. The pooled RR for all-cause mortality with insurance versus without insurance was 0.78 (95%CI, 0.71-0.86; I 2 =33.7%). Among the insurance types, patients with private insurance presented with a lower all-cause mortality (RR 0.70, 95% CI 0.60-0.82), with considerable heterogeneity (I 2 =83.3%).Conclusions. The findings of this review suggest that a lack of or insufficient insurance is related to all-cause mortality of AYAs with malignant cancers. Strategies aimed at identifying causality and reducing disparities are warranted.

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<b><i>Background/Aim:</i></b> The impacts of health insurance status on survival outcomes in multiple myeloma (MM) have not been addressed in depth. The present study was conducted to identify definite relationships of cancer-specific survival (CSS) and overall survival (OS) with health insurance status in MM patients. <b><i>Methods:</i></b> MM patients aged 18–64 years and with complete insurance records between January 1, 2007, and December 31, 2016, were identified from 18 Surveillance, Epidemiology, and End Results (SEER) Database registries. Health insurance condition was categorized as uninsured, any Medicaid, insured, and insured (no specifics). Relationships of health insurance condition with OS/CSS were identified through Kaplan-Meier, and uni-/multivariate Cox regressions using the hazard ratio and 95% confidence interval. Potential baseline confounding was adjusted using multiple propensity score (mPS). <b><i>Results:</i></b> Totally 17,981 patients were included, including 68.3% with private insurance and only 4.9% with uninsurance. Log-rank test uncovered significant difference between health insurance status and OS/CSS among MM patients. Patients with non-insurance or Medicaid coverage in comparison with private insurance tended to present poorer OS/CSS both in multivariate Cox regression and in mPS-adjusted model (non-insurance vs. private insurance [OS/CSS]: 1.33 [1.20–1.48]/1.13 [1.00–1.28] and 1.45 [1.25–1.69]/1.18 [1.04–1.33], respectively; Medicaid coverage vs. private insurance [OS/CSS]: 1.67 [1.56–1.78]/1.25 [1.16–1.36] and 1.76 [1.62–1.90]/1.23 [1.13–1.35], respectively). <b><i>Conclusions:</i></b> Our observational study of exposure-outcome associations suggests that insufficient or no insurance is moderately linked with OS among MM patients aged 18–64 years. Wide insurance coverage and health-care availability may strengthen some disparate outcomes. In the future, prospective cohort research is needed to further clarify concrete risks with insurance type, owing to the lack of definite division of insurance data in SEER.