[Human chromosome 8p11 (CHRNB3-CHRNA6) region gene polymorphisms and susceptibility to lung cancer in Chinese Han population]

Identify potential driver genes for PAX-FOXO1 fusion-negative rhabdomyosarcoma through frequent gene co-expression network mining

Background

Rhabdomyosarcoma (RMS) is a soft tissue sarcoma usually originated from skeletal muscle. Currently, RMS classification based on PAX-FOXO1 fusion is widely adopted. However, compared to relatively clear understanding of the tumorigenesis in the fusion-positive RMS, little is known for that in fusion-negative RMS (FN-RMS).

Methods

We explored the molecular mechanisms and the driver genes of FN-RMS through frequent gene co-expression network mining (fGCN), differential copy number (CN) and differential expression analyses on multiple RMS transcriptomic datasets.

Results

We obtained 50 fGCN modules, among which five are differentially expressed between different fusion status. A closer look showed 23% of Module 2 genes are concentrated on several cytobands of chromosome 8. Upstream regulators such as MYC, YAP1, TWIST1 were identified for the fGCN modules. Using in a separate dataset we confirmed that, comparing to FP-RMS, 59 Module 2 genes show consistent CN amplification and mRNA overexpression, among which 28 are on the identified chr8 cytobands. Such CN amplification and nearby MYC (also resides on one of the above cytobands) and other upstream regulators (YAP1, TWIST1) may work together to drive FN-RMS tumorigenesis and progression. Up to 43.1% downstream targets of Yap1 and 45.8% of the targets of Myc are differentially expressed in FN-RMS vs. normal comparisons, which also confirmed the driving force of these regulators.

Discussion

We discovered that copy number amplification of specific cytobands on chr8 and the upstream regulators MYC, YAP1 and TWIST1 work together to affect the downstream gene co-expression and promote FN-RMS tumorigenesis and progression. Our findings provide new insights for FN-RMS tumorigenesis and offer promising targets for precision therapy. Experimental investigation about the functions of identified potential drivers in FN-RMS are in progress.

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.

Connections of nicotine to cancer

This Opinion article discusses emerging evidence of direct contributions of nicotine to cancer onset and growth. The list of cancers reportedly connected to nicotine is expanding and presently includes small-cell and non-small-cell lung carcinomas, as well as head and neck, gastric, pancreatic, gallbladder, liver, colon, breast, cervical, urinary bladder and kidney cancers. The mutagenic and tumour-promoting activities of nicotine may result from its ability to damage the genome, disrupt cellular metabolic processes, and facilitate growth and spreading of transformed cells. The nicotinic acetylcholine receptors (nAChRs), which are activated by nicotine, can activate several signalling pathways that can have tumorigenic effects, and these receptors might be able to be targeted for cancer therapy or prevention. There is also growing evidence that the unique genetic makeup of an individual, such as polymorphisms in genes encoding nAChR subunits, might influence the susceptibility of that individual to the pathobiological effects of nicotine. The emerging knowledge about the carcinogenic mechanisms of nicotine action should be considered during the evaluation of regulations on nicotine product manufacturing, distribution and marketing.

Combined effect between CHRNB3-CHRNA6 region gene variant (rs6474412) and smoking in psoriasis vulgaris severity

BACKGROUND

Many factors associated with causing psoriasis have been reported, such as the genetic and environmental factors. Smoking is one of the well-established environmental risk factors for psoriasis and also associated with the disease severity. In addition, several studies of psoriasis and psoriatic arthritis have documented gene-environment interactions involving smoking behavior. Although gene polymorphisms on nicotinic acetylcholine receptor subunits CHRNB3-CHRNA6 region gene have been found to correlate with smoking behavior and lung cancer susceptibility in Chinese Han population, the combined effect between the smoking-related genetic variants and smoking behavior on psoriasis vulgaris (PV) has been unreported.

OBJECTIVE

To evaluate the combined effect of the smoking-related (rs6474412-C/T) polymorphism on CHRNB3-CHRNA6 region gene and smoking behavior on PV risk and clinic traits in Chinese Han population.

METHODS

A hospital-based case-control study including 672 subjects (355 PV cases and 317 controls) was conducted. The variant of rs6474412 was typed by SNaPshot Multiplex Kit (Applied Biosystems Co., USA).

RESULTS

The higher body mass index (BMI≥25), smoking behavior and alcohol consumption were risk factors for PV, and the estimated ORs were 1.55 (95% CI, 1.09-2.29), 1.74 (95% CI, 1.22-2.49) and 1.81 (95% CI, 1.25-2.62) respectively. The smoking patients had more severe conditions than non-smokers (OR=1.71, 95% CI, 1.08-2.70, P=0.020). The alleles and genotypes of rs6474412 were not associated with risk of PV, but the combined effect of rs6474412 genotype (TT) and smoking behavior increased severity of PV (OR=5.95; 95% CI, 1.39-25.31; P<0.05; adjusted OR=2.20; 95% CI, 1.55-3.14; P<0.001).

CONCLUSIONS

Our results demonstrate that the combined effect of rs6474412-C/T polymorphism in smoking-related CHRNB3-CHRNA6 region gene and smoking behavior may not confer risk to PV, but may have impact on PV severity in Chinese Han population.