Synergistic association of CYP1A1 polymorphisms with increased susceptibility to squamous cell lung cancer in north Indian smokers

Synergistic polymorphic interactions of phase II metabolizing genes and their association toward lung cancer susceptibility in North Indians

Lung cancer is a multifactorial carcinoma with diverse heterogeneity. Genetic variations in drug-metabolizing enzymes may lead to defective detoxification and clearance of carcinogenic compounds. The high-order gene-gene interaction has been carried out between different genotypes of Phase II detoxification genes (NQO1, SULT1A1, NAT2, and EPHX1). Our results depict the genetic combination of SULT1A1 R213H with NAT2 × 5B L161L, SULT1A1 R213H with NAT2 × 5C K268R, EPHX1 H139R and NAT2 × 5B L161L exhibit a protective effect towards lung cancer risk. Further, the triple combinations of NQO1 P187S, EPHX1 Y113H, and EPHX1 H139R; NQO1 P187S, EPHX1 Y113H, and NAT2 × 6 R197Q; NQO1 P187S, EPHX1 Y113H, and NAT2 × 7 G286E; SULT1A1 R213H, EPHX1 H139R, and NAT2 × 7 G286E suggested a two-fold increased risk of lung cancer for subjects. Genetic polymorphisms of phase II detoxifying genes (NAT2, NQO1, EPHX1, SULT1A1) are prognostic markers for lung cancer.

Independent and Interactive Effect of CYPs and GSTs Genetic Variants and Tobacco Smoking on the Risk of Non-Small Cell Lung Carcinoma

BACKGROUND

CYP and GST gene families detoxify tobacco carcinogens and have been linked to the risk of non-small cell lung carcinoma (NSCLC).

AIM

Independent and combined effects of CYP and GST genetic variations and smoking on the risk of non-small cell lung carcinoma (NSCLC) and its sub-histological types.

METHODS

We modelled an epistatic interaction via the effects of particular genotypes in two genes as OR (odds ratio), OR1, and OR2, a combination of both genotypes were characterized as OR_combine. In contrast, the two ORs' epistatic interaction for the individual genotypes has been represented as OR_interaction = OR_combine/(OR1 × OR2).

RESULTS

The variant genotypes of CYP2A6 (OR:4.2, p <0.001), GSTT1 (OR:3.9, p <0.001), and GSTM1 (OR: 4.5, p <0.001) were showed a significant risk with NSCLC. GSTM1 (del.)/CYP2A6 (variant) genotype was associated with a higher risk of NSCLC (OR:12.5, p <0.001). GSTM1 (del.)/CYP2A6 (Ser/Pro+Pro/Pro) and GSTM1 (del.)/CYP2A13 (CT+TT) interacted redundantly (OR_intraction = 0.66 and 0.64). A co-suppressive interaction was observed between GSTT1 (del.)/CYP2A6 (Ser/Pro+Pro/Pro) (OR_intraction = 0.41). Simultaneously, both GSTT1/GSTM1 del. genotype was associated with a significantly higher risk to NSCLC. In contrast, GSTT1 del./GSTM1 del. genotype interaction displayed a co-suppressive effect (OR_intraction = 0.15). CYP1A1(TC+CC)/CYP2A13(CT+TT)mutually interacted synergistically (OR_intraction = 1.27).CYP1A1 (TC+CC)/GSTP1 (Val/Val+Ile/Val) genotype demonstrated an additive (OR_intraction = 1) effect. GSTP1(Val/Val+Ile/Val) interacts with GSTT1 (del.) genotype exerted a suppressive effect (OR_intraction = 0.69). CYP2A6 in smokers increased risk by 4.2 (p = 0.001) to 5.6 fold (p <0.001), while GSTM1 and GSTT1 were independent of smoking.

CONCLUSION

Epistatic interactions revealed that CYPs/GSTs might follow a web of the interactions to modify the risk of NSCLC.

Integrated Analysis of Multiple Microarray Studies to Identify Novel Gene Signatures in Non-alcoholic Fatty Liver Disease

Background: Non-alcoholic fatty liver disease (NAFLD) is a well-known cause of liver dysfunction and has become a common chronic liver disease in many countries. However, the intrinsic molecular mechanisms underlying the pathogenesis of NAFLD have not yet been fully elucidated. Methods: We obtained the gene expression datasets of NAFLD through the Gene Expression Omnibus (GEO) database. Subsequently, robust rank aggregation (RRA) method was used to identify differentially expressed genes (DEGs) between NAFLD patients and controls. Gene functional annotation and PPI network analysis were performed to explore the potential function of the DEGs. Finally, we used a sequencing dataset GSE126848 to validate our results. Results: In this study, GSE48452, GSE66676, GSE72756, GSE63067, GSE89632, and GSE107231 were included, including 125 NAFLD patients and 116 control patients. The RRA integrated analysis determined 96 significant DEGs (50 up-regulated and 46 down-regulated) and the most significant gene aberrantly expressed in NAFLD was ENO3 (P-value = 7.17E-05), followed by CYP7A1 (P-value = 9.04E-05), and P4HA1 (P-value = 1.67E-04). Carboxylic acid metabolic process (GO:0019752; P-value = 1.39E-03) was the most significantly enriched for biological process in GO (gene ontology) analysis. KEGG pathway enrichment analysis showed that steroid hormone biosynthesis (hsa00140; P-value = 6.68E-03) and PPAR signaling pathway (hsa03320; P-value = 9.95E-03) were significantly enriched. Based on the results of the PPI and the results of the RRA, we finally defined the four most critical genes as the hub genes, including ENO3, CYP7A1, P4HA1, and CYP1A1. Conclusions: Our integrated analysis identified novel gene signatures and will contribute to the understanding of comprehensive molecular changes in NAFLD.

Development of diagnostic model of lung cancer based on multiple tumor markers and data mining

Objective

To develop early intelligent discriminative model of lung cancer and evaluate the efficiency of diagnosis value.

Methods

Based on the genetic polymorphism profile of CYP1A1-rs1048943, GSTM1, mEH-rs1051740, XRCC1-rs1799782 and XRCC1-rs25489 and the methylations of p16 and RASSF1A gene, and the length of telomere in the peripheral blood from 200 lung cancer patients and 200 health persons, the discriminative model was established through decision tree and ANN technique.

Results

ACU of the discriminative model based on multiple tumour markers increased by about 10%; The accuracy rate of decision tree model and ANN model for testing set were 93.00% and 89.62% respectively. The ROC analysis showed the decision tree model’s AUC is 0.929 (0.894∼0.964), the ANN model’s AUC is 0.894 (0.853∼0.935). However, the classify accuracy rate and AUC of Fisher discriminatory analysis model are all about 0.7.

Conclusion

The early intelligent discriminative model of lung cancer based on multiple tumor markers and data mining techniques has a higher accuracy rate and might be useful for early diagnosis of lung cancer.