Background: Triglyceride-glucose (TyG) index has been determined to play a role in the onset of metabolic syndrome (MetS). Whether the TyG index and TyG with the combination of obesity indicators are associated with the clinical outcomes of the MetS population remains unknown. Method: Participants were extracted from multiple cycles of the National Health and Nutrition Examination Survey (NHANES) between 1999 and 2018 years. Three indicators were constructed including TyG index, TyG combining with waist circumference (TyG-WC), and TyG combining with waist-to-height ratio (TyG-WHtR). The MetS was defined according to the National Cholesterol Education Program (NCPE) Adult Treatment Panel III. Kaplan-Meier (KM) curves, restricted cubic splines (RCS), and the Cox proportional hazard model were used to evaluate the associations between TyG-related indices and mortality of the MetS population. The sensitive analyses were performed to check the robustness of the main findings. Results: There were 10,734 participants with MetS included in this study, with 5,570 females and 5,164 males. The median age of the study population was 59 years old. The multivariate Cox regression analyses showed high levels of TyG-related indices were significantly associated with the all-cause mortality of MetS population [TyG index: _adjustedhazard ratio (aHR): 1.36, 95%confidence interval (CI): 1.18–1.56, p < 0.001; TyG-WHtR index: aHR = 1.29, 95%CI: 1.13–1.47, p < 0.001]. Meanwhile, the TyG-WC and TyG-WHtR index were associated with cardiovascular mortality of the MetS population (TyG-WC: aHR = 1.45, 95%CI: 1.13–1.85, p = 0.004; TyG-WHtR: aHR = 1.50 95%CI: 1.17–1.92, p = 0.002). Three TyG-related indices showed consistent significant correlations with diabetes mortality (TyG: aHR = 4.06, 95%CI: 2.81–5.87, p < 0.001; TyG-WC: aHR = 2.55, 95%CI: 1.82–3.58, p < 0.001; TyG-WHtR: aHR = 2.53 95%CI: 1.81–3.54, p < 0.001). The RCS curves showed a non-linear trend between TyG and TyG-WC indices with all-cause mortality (p for nonlinearity = 0.004 and 0.001, respectively). The sensitive analyses supported the positive correlations between TyG-related indices with mortality of the MetS population. Conclusion: Our study highlights the clinical value of TyG-related indices in predicting the survival of the MetS population. TyG-related indices would be the surrogate biomarkers for the follow-up of the MetS population.

An increasing number of studies have examined the ability of programmed death-ligand 1 (PD-L1) to function as a marker for tumor prognosis. However, whether PD-L1 expression is a prognostic factor for the poor outcomes in many human cancers remains controversial. This study aims to investigate the prognostic role of PD-L1 expression through a meta-analysis update of 60 studies. The studies were identified by searching PubMed, Embase, Google Scholar, and Cochrane Library, and were assessed by further quality evaluation. The pooled hazard ratios (HRs) with 95% confidence intervals (CIs) for total and stratified analyses were calculated to investigate the association between the PD-L1 expression and the overall (OS) and disease-free (DFS) or progression-free survivals (PFS) of cancer patients. Heterogeneity and publication bias were also investigated. The results indicated that PD-L1 overexpression can predict a poor OS (HR = 1.58, 95% CI = 1.38–1.81, P <.000) and DFS/PFS (HR = 1.72, 95% CI = 1.26–2.33, P = .001). Subgroup analyses showed that PD-L1 overexpression was significantly related to the poor OS in patients with breast (HR = 1.98, 95% CI = 1.15–3.41, P = .014), urothelial (HR = 2.24, 95% CI = 1.61–3.12, P <.000), renal (HR = 3.30, 95% CI = 2.23–4.86, P <.000), and gastric cancers (HR = 1.56, 95% CI = 1.02–2.37, P = .040). Furthermore, PD-L1 overexpresion was significantly associated with poor DFS/PFS in patients with hepatocellular carcinoma (HCC) (HR = 1.72, 95% CI = 1.21–2.46, P = .003), melanoma (HR = 3.39, 95% CI = 2.02–5.69, P <.000), and renal carcinoma, (HR = 5.04, 95% CI = 2.87–8.86, P <.000). The adverse prognostic impact of PD-L1 was observed in patients of different ethnicities. The findings of this meta-analysis suggest the correlation of PD-L1 overexpression with worse OS in patients with solid tumors. However, the correlations differed according to tumor types.

Though single tumor immunotherapy and radiotherapy have significantly improved the survival rate of tumor patients, there are certain limitations in overcoming tumor metastasis, recurrence, and reducing side effects. Therefore, it is urgent to explore new tumor treatment methods. The new combination of radiotherapy and immunotherapy shows promise in improving therapeutic efficacy and reducing recurrence by enhancing the ability of the immune system to recognize and eradicate tumor cells, to overcome tumor immune tolerance mechanisms. Nanomaterials, as new drug-delivery-system materials of the 21st century, can maintain the activity of drugs, improve drug targeting, and reduce side effects in tumor immunotherapy. Additionally, nanomaterials, as radiosensitizers, have shown great potential in tumor radiotherapy due to their unique properties, such as light, heat, electromagnetic effects. Here, we review the mechanisms of tumor immunotherapy and radiotherapy and the synergy of radiotherapy with multiple types of immunotherapies, including immune checkpoint inhibitors (ICIs), tumor vaccines, adoptive cell therapy, and cytokine therapy. Finally, we propose the potential for nanomaterials in tumor radiotherapy and immunotherapy.

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