Polymorphism rs3819102 in thymidylate synthase and environmental factors: effects on lung cancer in Chinese population

Ttc39c is a potential target for the treatment of lung cancer

BACKGROUND

The novel TTC gene, tetratricopeptide repeat domain 39 C (Ttc39c), mainly mediates the interaction between proteins. It is involved in the progression of various tumors. In this study, we determined the effect of Ttc39c on lung adenocarcinoma and found that it might be used as a potential intervention target.

METHODS

We performed a difference analysis of Ttc39c samples from the TCGA database. Transwell experiments were conducted to determine the ability of cell metastasis. Celigo and MTT assays were performed to determine the effect of Ttc39c gene subtraction on cell proliferation. FACS was performed to determine the effect of Ttc39c gene subtraction on apoptosis. Clone-formation experiments were conducted to determine the effect of Ttc39c gene subtraction on cloning ability. Transcriptomics, proteomics, and metabolomics were used to elucidate the enrichment pathway of the Ttc39c gene in the progression of lung adenocarcinoma.

RESULTS

The expression of Ttc39c increased significantly in lung adenocarcinoma. The proliferation, metastasis, and cloning ability of human lung cancer cells were inhibited, while the apoptosis of cells increased significantly after the depletion of Ttc39c. Our results based on the transcriptomics, proteomics, and metabolomics analyses indicated that Ttc39c might be involved in the progression of lung adenocarcinoma (LUAD) mainly through the metabolic pathway and the p53 pathway.

CONCLUSION

To summarize, Ttc39c strongly regulates the proliferation and metastasis of lung adenocarcinoma cells. The main pathways involved in Ttc39c in lung adenocarcinoma include the energy metabolism and p53 pathways.

Prediction of lung cancer risk in Chinese population with genetic-environment factor using extreme gradient boosting

BACKGROUND

Detecting early-stage lung cancer is critical to reduce the lung cancer mortality rate; however, existing models based on germline variants perform poorly, and new models are needed. This study aimed to use extreme gradient boosting to develop a predictive model for the early diagnosis of lung cancer in a multicenter case-control study.

MATERIALS AND METHODS

A total of 974 cases and 1005 controls in Shanghai and Taizhou were recruited, and 61 single nucleotide polymorphisms (SNPs) were genotyped. Multivariate logistic regression was used to calculate the association between signal SNPs and lung cancer risk. Logistic regression (LR) and extreme gradient boosting (XGBoost) algorithms, a large-scale machine learning algorithm, were adopted to build the lung cancer risk model. In both models, 10-fold cross-validation was performed, and model predictive performance was evaluated by the area under the curve (AUC).

RESULTS

After FDR adjustment, TYMS rs3819102 and BAG6 rs1077393 were significantly associated with lung cancer risk (p < 0.05). For lung cancer risk prediction, the model predicted only with epidemiology attained an AUC of 0.703 for LR and 0.744 for XGBoost. Compared with the LR model predicted only with epidemiology, further adding SNPs and applying XGBoost increased the AUC to 0.759 (p < 0.001) in the XGBoost model. BAG6 rs1077393 was the most important predictor among all SNPs in the lung cancer prediction XGBoost model, followed by TERT rs2735845 and CAMKK1 rs7214723. Further stratification in lung adenocarcinoma (ADC) showed a significantly elevated performance from 0.639 to 0.699 (p = 0.009) when applying XGBoost and adding SNPs to the model, while the best model for lung squamous cell carcinoma (SCC) prediction was the LR model predicted with epidemiology and SNPs (AUC = 0.833), compared with the XGBoost model (AUC = 0.816).

CONCLUSION

Our lung cancer risk prediction models in the Chinese population have a strong predictive ability, especially for SCC. Adding SNPs and applying the XGBoost algorithm to the epidemiologic-based logistic regression risk prediction model significantly improves model performance.

Identification of a 5-Gene Metabolic Signature for Predicting Prognosis Based on an Integrated Analysis of Tumor Microenvironment in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a common subtype of lung cancer with a depressing survival rate. The reprogramming of tumor metabolism was identified as a new hallmark of cancer in tumor microenvironment (TME), and we made a comprehensive exploration to reveal the prognostic role of the metabolic-related genes. Transcriptome profiling data of LUAD were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Based on the extracted metabolic-related genes, a novel 5-gene metabolic prognostic signature (including GNPNAT1, LPGAT1, TYMS, LDHA, and PTGES) was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. This signature confirmed its robustness and accuracy by external validation in multiple databases. It could be an independent risk factor for LUAD, and the nomograms possessed moderately accurate performance with the C-index of 0.755 (95% confidence interval: 0.706–0.804) and 0.691 (95% confidence interval: 0.636–0.746) in training set and testing set. This signature could reveal the metabolic features according to the results of gene set enrichment analysis (GSEA) and meanwhile monitor the status of TME through ESTIMATE scores and the infiltration levels of immune cells. In conclusion, this gene signature is a cost-effective tool which could indicate the status of TME to provide more clues in the exploration of new diagnostic and therapeutic strategy.

Association between methylenetetrahydrofolate reductase gene rs1801131 A/C polymorphism and urinary tumors' susceptibility

Background

The methylenetetrahydrofolate reductase (MTHFR) rs1801131 A/C variant results in a decrease in MTHFR enzymatic activity, which may play an important role in folate metabolism and is also an important source of DNA methylation and DNA synthesis. Several case-control studies have been conducted to assess the association of MTHFR rs1801131 polymorphism with the risk of urinary cancers, yet with conflicting conclusions. To derive a more precise estimation of above relationship, the association between the MTHFR rs1801131 A/C polymorphism and the risk of urinary cancer was performed.

Methods

A total of 28 case-control studies was identified. The odds ratios (OR) with 95% confidence intervals (CI) was calculated to assess.

Results

On one hand, we found that the MTHFR rs1801131 A/C polymorphism was associated with increased whole urinary cancers’ risk (for example CA vs. AA: OR = 1.12. 95%CI = 1.01–1.24). On the other hand, we found that the MTHFR rs1801131 A/C polymorphism might increase bladder cancer risk both in Asian (C-allele vs. A-allele: OR = 1.35. 95%CI = 1.15–1.60) and African populations (CA vs. AA: OR = 1.63. 95%CI = 1.17–2.25).

Conclusions

Our current analysis suggested that MTHFR rs1801131 A/C is associated with urinary cancers, especially bladder cancer.

Association Between Genetic Polymorphisms In TYMS And Glioma Risk In Chinese Patients: A Case-Control Study

Background

Thymidylate synthase (TYMS) polymorphisms are reported to be related to susceptibility to some cancers. However, no study exists on TYMS polymorphisms and glioma risk. This study aimed to evaluate the relationship between two common TYMS gene variants (rs1059394 C>T, rs2847153 G>A) and glioma susceptibility.

Methods

This case-control study included 605 patients and 1300 cancer-free individuals. Genotyping was performed using Sequenom Mass-ARRAY. We determined odds ratios (ORs) and their 95% confidence intervals (CIs) to estimate the correlations.

Results

The analysis revealed that rs1059394 TT and CT+TT genotype had significantly low glioma risk (TT to CC: OR = 0.71, 95% CI = 0.52–0.97, P = 0.03; CT+TT to CC: OR = 0.74, 95% CI = 0.55–0.99, P = 0.04). However, no significant difference was found between rs2847153 and glioma risk in any genetic model (P﹥0.05). In high-grade gliomas, the GA and GA+AA genotypes of rs2847153 made the majority of genotypes, compared with GG genotype (GA to GG: OR = 2.01, 95% CI = 1.39–2.91, P < 0.001; GA+AA to GG: OR = 1.78, 95% CI =1.25–2.54, P < 0.001). Moreover, online expression quantitative trait locus (eQTL) analysis indicated that these two polymorphisms may alter TYMS gene expression in transformed fibroblast cells.

Conclusion

Our study provides evidence of the effect of TYMS rs1059394 on the susceptibility of glioma. In high-grade gliomas, compared with GG genotype, the GA and GA+AA genotypes of rs2847153 comprise a larger proportion.