Matrix Metalloproteinase-7 Promoter Site Single Nucleotide Polymorphism (-181A>G) in Epithelial Ovarian Cancer in the Eastern Indian Population.

BACKGROUND: Matrix metalloproteinase-7 (MMP7) plays multiple roles in different stages of tumor development. Elevated MMP7 activity has been reported in ovarian cancer. Single nucleotide polymorphism (SNP) of promoter sites of the MMP7 gene has been shown to cause alteration in gene expression, hence resulting in changes in susceptibility to various diseases and tumor development. METHODS: The current study evaluated the association of epithelial ovarian cancer risk with MMP7 promoter site -181A>G polymorphism in the population of eastern India. The present case-control study included 64 histopathologically confirmed cases of epithelial ovarian cancer and 100 control subjects. The MMP7 -181A/G polymorphism was identified using polymerase chain reaction-restriction fragment length polymorphism. The association between genotypes and epithelial ovarian cancer risk was analyzed by odds ratio (OR) with a 95% confidence interval. RESULTS: The frequencies of AA, AG, and GG genotypes in ovarian cancer cases were 37.5%, 46.9%, and 15.6%, respectively, while that of control subjects were 56%, 36%, and 8%, respectively, in the study population. By taking the wild-type AA genotype as a reference, it was found that genotype GG was associated with a significant risk for epithelial ovarian cancer (OR: 2.92). Frequency distribution of genotypes did not show any significant association with tumor characteristics like the International Federation of Gynecology and Obstetrics (FIGO) stage, histology, lymph node status, and distant metastasis. CONCLUSION: The present study demonstrated the association of MMP7 promoter site -181 GG genotype and the G allele with increased risk for epithelial ovarian cancer in the eastern Indian population.

Machine learning predictive models of LDL-C in the population of eastern India and its comparison with directly measured and calculated LDL-C.

BACKGROUND: LDL-C is a strong risk factor for cardiovascular disorders. The formulas used to calculate LDL-C showed varying performance in different populations. Machine learning models can study complex interactions between the variables and can be used to predict outcomes more accurately. The current study evaluated the predictive performance of three machine learning models-random forests, XGBoost, and support vector Rregression (SVR) to predict LDL-C from total cholesterol, triglyceride, and HDL-C in comparison to linear regression model and some existing formulas for LDL-C calculation, in eastern Indian population. METHODS: The lipid profiles performed in the clinical biochemistry laboratory of AIIMS Bhubaneswar during 2019-2021, a total of 13,391 samples were included in the study. Laboratory results were collected from the laboratory database. 70% of data were classified as train set and used to develop the three machine learning models and linear regression formula. These models were tested in the rest 30% of the data (test set) for validation. Performance of models was evaluated in comparison to best six existing LDL-C calculating formulas. RESULTS: LDL-C predicted by XGBoost and random forests models showed a strong correlation with directly estimated LDL-C (r = 0.98). Two machine learning models performed superior to the six existing and commonly used LDL-C calculating formulas like Friedewald in the study population. When compared in different triglycerides strata also, these two models outperformed the other methods used. CONCLUSION: Machine learning models like XGBoost and random forests can be used to predict LDL-C with more accuracy comparing to conventional linear regression LDL-C formulas.