Chronic inflammation is thought to be the leading cause of colorectal cancer, and interleukin-10 (IL10) has been identified as a potent immunomodulatory cytokine that regulates inflammatory responses in the gastrointestinal tract. Although several single nucleotide polymorphisms (SNPs) in IL10 have been associated with the risk of colorectal cancer, their prognostic significance has not been determined. Two hundred and eighty-two colorectal cancer patients were genotyped for two candidate cancer-associated SNPs in IL10. The associations of these SNPs with distant metastasis-free survival and overall survival were evaluated by Kaplan-Meier analysis and Cox regression model. The minor homozygote GG genotype of IL10 rs3021094 was significantly associated with a 3.30-fold higher risk of death compared with the TT+TG genotypes (P=0.011). The patients with IL10 rs3021094 GG genotype also had a poorer overall survival in Kaplan-Meier analysis (log-rank P=0.007) and in multivariate Cox regression model (P=0.044) adjusting for age, gender, carcinoembryonic antigen levels, tumor differentiation, stage, lymphovascular invasion, and perineural invasion. In conclusion, our results suggest that IL10 rs3021094 might be a valuable prognostic biomarker for colorectal cancer patients.
Aged ; Alleles ; Carcinoembryonic Antigen/blood ; Cell Differentiation ; Colorectal Neoplasms/*genetics/mortality/pathology ; Female ; Genotype ; Homozygote ; Humans ; Interleukin-10/*genetics ; Kaplan-Meier Estimate ; Lymphatic Metastasis ; Male ; Middle Aged ; Neoplasm Staging ; *Polymorphism, Single Nucleotide ; Regression Analysis ; Tumor Markers, Biological/genetics

Background: The risk and benefit of tissue plasminogen activator (tPA) for aged>80 years with acute ischemic stroke (AIS) are controversial. In this study, we investigated the safety and efficacy of tPA in this population and utilized the artificial neural network (ANN) to established outcome predictive models. Methods: We retrospectively reviewed the stroke registry data of patients with AIS, aged >80 years who arrived at the hospital within 3 hours from the onset of symptoms. The characteristics and the outcomes, presented as modified Rankin Scale (mRS), and mortality rate at 3 months between the tPA-treated and non-tPA groups were analyzed. An ANN algorithm was applied to establish predictive models. Results: A total of 80 patients aged>80 years with AIS were identified, and 49 of them received tPA. After adequate training, our ANN models accurately predicted the outcomes with the area under the receiver operating characteristic curves of 0.974, and a low error to predict the mRS score at 3 months. After applying our prediction model to those in the non-tPA group, we demonstrated the potential benefits in those patients if they had undergone tPA therapy. Conclusions: Our results show that ANN can be a potentially useful tool for predicting the treatment outcomes of tPA. Such novel machine learning-based models may help with therapeutic decision making in clinical settings.