No abstract

Metabolic gene variants, smoking, and alcohol consumption are important upper digestive tract cancer (UDTC) risk factors. However, the gene-gene and gene-environment interactions remain unclear. A case-control study in a high incidence area for upper digestive tract cancer was conducted in China. DNA was extracted from buffy coat samples for PCR or PCR-restriction fragment length polymorphism. Smoking and alcohol drinking status was determined by questionnaires. Odds ratios (ORs) and 95% confidence intervals (CIs) were used to assess the associations. After adjusting for confounding factors, smoking increased esophageal cancer (EC), gastric cardia cancer (GCC) and gastric antral carcinoma (GAC) risk by 3.594, 4.658, and 3.999-fold, respectively. Alcohol consumption increased EC, GCC and GAC risk by 1.953, 2.442 and 1.765-fold, respectively. The cytochrome P4501A1 ( CYP1A1) rs4646903 T>C polymorphism increased GCC risk, the cytochrome P4502E1 ( CYP2E1) rs2031920 C>T polymorphism increased EC risk, while the GSTM1 null genotype decreased EC risk. An association existed between the following: CYP1A1 rs4646903 and smoking in EC, GCC and GAC; CYP1A1 rs4646903 and alcohol consumption in EC and GCC; CYP2E1 rs2031920 and smoking in EC, GCC and GAC and CYP2E1 rs2031920 and alcohol consumption in EC and GCC. No association was observed between CYP1A1 and CYP2E1 . The glutathione S-transferase mu 1 ( GSTM1 ) null genotype decreased EC risk (OR=0.510). Smoking/drinking are upper digestive tract cancer risk factors. The CYP1A1 rs4646903 and CYP2E1 rs2031920 polymorphisms were risk factors of GCC or EC, and the GSTM1 null genotype may serve a protective role against EC. The results of the present study indicated that gene-environment interactions increase the risk of UDTC.

Background: Our previous studies demonstrate that the major histocompatibility complex (MHC) is associated with the progression of esophageal squamous cell carcinoma (ESCC). HLA-DQA1, which belongs to the MHC Class II family, may be a potential biomarker in ESCC progression. However, the association between HLA-DQA1 and ESCC in high-incidence area of northern China has not been well characterized. The purpose of this study is to investigate the relationship of HLA-DQA1 expression with the progression and prognosis of ESCC. Methods: We analyzed the expression profiles of HLA-DQA1 in esophageal cancer (EC) samples in the TCGA database and validated HLA-DQA1 expression by immunohistochemistry, western blotting, and quantitative reverse-transcription polymerase chain reaction in matched EC and normal tissues, respectively. The correlation between HLA-DQA1 expression and clinicopathologic characteristics of ESCC was further analyzed. Result: Immunohistochemical analysis indicated that the expression level of HLA-DQA1 in ESCC tissues was significantly higher than the matched normal tissues ( P < .001). HLA-DQA1 mRNA and protein expression were significantly higher in ESCC tissues compared to the matched normal tissues. Patients with family history negative or with tumor sizes >4 cm were associated with higher HLA-DQA1 expression levels. A prognostic significance of HLA-DQA1 was also found by the Log-rank method, in which high expression of HLA-DQA1 was correlated with a shorter overall survival time. The receiver operating characteristic (ROC) curve analysis yielded the area under the ROC curve value of 0.693. Univariate and multivariate analyses also suggest that high expression of HLA-DQA1 is a potential indicator for poor prognosis of ESCC. Conclusions: Our results demonstrate that HLA-DQA1 plays an important role in ESCC progression and may be a biomarker for ESCC diagnosis and prognosis, as well as a potential target for the treatment of patients with ESCC.

Objective: The study aims to investigate the factors causing the difference of stroke patients' in-hospital cost and study these factors on health outcome in terms of mortality. Methods: Eight hundred and sixty-two in-patients with stroke in a tertiary hospital in China from 2017 to 2019 were included in the database. Descriptive statistics indexes were used to describe patients' in-hospital cost and mortality. Based on Elixhauser coding algorithms, multiple linear regression and logistic regressions (LRs) were used to evaluate the impact of factors identified from univariate analysis on in-hospital cost and mortality, respectively. In addition to LRs, a comparison study was then carried out with random forest, gradient boosting decision tree and artificial neural network. Results: Factors affecting both cost and mortality are age, discharged day-of-week, length of stay, stroke subtype, other neurological disorders, renal failure, fluid and electrolyte disorders and total number of comorbidities. Conclusion: With the increase of age, the mortality rate of in-patients (except for the juvenile) with stroke increases and the cost of hospitalization decreases. Intracerebral haemorrhage is the most devastating stroke for its highest mortality in short length of stay. Medical services should focus on these specific comorbidities.