BackgroundAlthough an elevated hemoglobin A1c (HbAc1) level is an independent predictor of worse survival in patients with both digestive cancer and diabetes mellitus, its relationship to short-term prognosis in these patients has not been addressed. This study assessed this relationship in gastrointestinal cancer (GIC) patients with type 2 diabetes mellitus (T2DM).MethodsA retrospective review of patients with GIC with or without T2DM from 2004 to 2014 was performed. Patients with T2DM were grouped according to HbA1c level, either normal (mean < 7.0%) or elevated (mean ≥ 7.0%). Age- and sex-matched GIC patients without T2DM served as controls.ResultsOne hundred and eighteen patients aged 33 - 81 years with T2DM met the study eligibility criteria; 51 were in the normal HbA1c group, and 67 were in the elevated HbA1c group. The 91 patients in the non-T2DM group were randomly selected and matched to the T2DM group in terms of admittance date, age, and sex. There was a trend toward a higher 180-day mortality rate in the T2DM group compared with the non-T2DM group (15.3% vs. 7.7%, P = 0.095) and in the elevated HbA1c group compared with the normal HbA1c group (19.4% vs. 9.8%, P = 0.151); however, the differences were not significant. The duration of the hospital stay was longer in patients with T2DM than in those without T2DM (13.2 vs. 8.9 days, P < 0.05) and in patients with elevated versus normal HbA1c levels (14.5 vs. 11.4 days, P < 0.05). Diabetic GIC patients with elevated HbA1c levels had significantly more total postoperative complications than those with normal HbA1c levels (25.4% vs. 9.8%, P < 0.05). In multivariate regression analyses, short-term adverse outcomes were strongly associated with elevated HbA1c levels (odds ratio (OR): 5.276; 95% confidence level (CI): 1.73 - 16.095; P < 0.05) and no strict antidiabetic treatment (OR: 7.65; 95% CI: 2.49 - 23.54; P < 0.001).ConclusionAn elevated level of HbA1c significantly correlated with and was an independent predictor of short-term adverse outcomes in GIC patients with T2DM.

The use of vaccines for cancer therapy is a promising immunotherapeutic strategy that has been shown to be effective against various cancers. Vaccines directly target tumors but their efficacy against glioblastoma multiforme (GBM) remains unclear. Immunotyping that classifies tumor samples is considered to be a biomarker for immunotherapy. This study aimed to identify potential GBM antigens suitable for vaccine development and develop a tool to predict the response of GBM patients to vaccination based on the immunotype. Gene Expression Profiling Interactive Analysis (GEPIA) was applied to evaluate the expression profile of GBM antigens and their influence on clinical prognosis, while the cBioPortal program was utilized to integrate and analyze genetic alterations. The correlation between antigens and antigen processing cells was assessed using TIMER. RNA-seq data of GBM samples and their corresponding clinical data were downloaded from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) for further clustering analysis. Six overexpressed and mutated tumor antigens (ARHGAP9, ARHGAP30, CLEC7A, MAN2B1, ARPC1B and PLB1) were highly correlated with the survival rate of GBM patients and the infiltration of antigen presenting cells in GBMs. With distinct cellular and molecular characteristics, three immune subtypes (IS1-IS3) of GBMs were identified and GBMs from IS3 subtype were more likely to benefit from vaccination. Through graph learning-based dimensional reduction, immune landscape was depicted and revealed the existence of heterogeneity among individual GBM patients. Finally, WGCNA can identify potential vaccination biomarkers by clustering immune related genes. In summary, the six tumor antigens are potential targets for developing anti-GBMs mRNA vaccine, and the immunotypes can be used for evaluating vaccination response.

Synapse and synapse associated proteins (SAPs) play critical roles in various neurodegeneration diseases and brain tumors. However, in lower-grade gliomas (LGG), SAPs have not been explored systematically. Herein, we are going to explore SAPs expression profile and its clinicopathological significance in LGG which can offer new insights to glioma therapy. In this study, we integrate a list of SAPs that covered 231 proteins with synaptogenesis activity and post synapse formation. The LGG RNA-seq data were downloaded from GEO, TCGA and CGGA database. The prognosis associated SAPs in key modules of PPI (protein-protein interaction networks) was regarded as hub SAPs. Western blot, quantitative reverse transcription PCR (qRT-PCR) and immunochemistry results from HPA database were used to verify the expression of hub SAPs. There were 68 upregulated SAPs and 44 downregulated SAPs in LGG tissue compared with normal brain tissue. Data from function enrichment analysis revealed functions of differentially expressed SAPs in synapse organization and glutamatergic receptor pathway in LGGs. Survival analysis revealed that four SAPs, GRIK2, GABRD, GRID2 and ARC were correlate with the prognosis of LGG patients. Interestingly, we found that GABRD were upregulated in LGG patients with seizures, indicating that SAPs may link to the pathogenesis of seizures in glioma patients. The four-SAPs signature was revealed as an independent prognostic factor in gliomas. Our study presented a novel strategy to assess the prognostic risks of LGGs, based on the expression of SAPs.