Significance of Gastrokine-1 Polymorphism Rs4254535 as a Prognostic Marker and its Association with Clinical Characteristics in Chinese Lung Cancer Patients.

Background: The single nucleotide polymorphism (SNP) of Gastrokine-1 (GKN1) is associated with lung cancer but its association with prognosis is not clear. Methods: Genomic DNA was extracted from the blood samples of 888 patients with lung cancer. The association between GKN1 polymorphism rs4254535 and prognostic was analyzed by the Kaplan-Meier (KM) method, Log-rank test, and Cox proportional hazards model. Results: In females and patients diagnosed with late-stage lung cancer, the CC genotype (CC vs TT, adjusted odds ratio [HR] = 0.57, 95% Confidence Interval [CI]: 0.33-0.99, P = 0.045; HR = 0.66, 95% CI: 0.48-0.92, P = 0.014) and recessive CC genotype (CC vs TT + TC, HR = 0.55, 95% CI: 0.32-0.94, P = 0.028; HR = 0.64, 95% CI: 0.47-0.89, P = 0.006) of rs4254535 conferred a better prognosis, compared with the TT and TT + TC genotype. Rs4254535 dominate TC + CC genotype, recessive CC genotype, and C allele who were adenocarcinoma patients had a significantly better prognosis. The recessive CC genotype of non-smoking patients has a better prognosis, compared to the TT + TC genotype. Additionally, in the dominant TT + TC genotype and C allele, no family history patients had a significantly better prognosis, compared to the TT genotype. Conclusion: For lung cancer patients, GKN1 polymorphism rs4254535 may be a protective genetic marker and predicts the prognosis of lung cancer patients.

Prognostic implications of the EGFR polymorphism rs763317 and clinical variables among young Chinese lung cancer population.

The 5-year survival rate for patients with lung cancer, the world's second most frequent malignant tumor, is less than 20%, and its prognosis cannot be clearly predicted. Our aim was to analyze the epidermal growth factor receptor (EGFR) rs763317 (G>A) single nucleotide polymorphism and its association with prognosis in Chinese Han lung cancer patients. 839 patients with primary lung cancer were recruited, and genomic DNA was extracted and genotyped by SNPscan. Kaplan-Meier technique and multivariate Cox proportional hazards model were used to analyze the association between prognosis and EGFR polymorphism rs763317. A significant association after stratification by age, significantly increased lung cancer risk was associated with the AA homozygous genotype of rs763317 (adjusted hazard ratio = 2.53, 95% CI: 1.31-4.88, p=0.005), and conferred a poor survival for lung cancer patients (MST: median survival time: 13.6 months) compared with GG genotype (MST: 41.5 months), and in the recessive model AA genotype (AA vs. GG + GA; adjusted hazard ratio = 2.57, 95% CI: 1.34-4.93, p=0.004) who were young (<60 years) had a significantly increased risk of death. The EGFR polymorphism rs763617 might serve as a significant genetic marker for predicting the prognosis of lung cancer.

Prognostic value of GPC5 polymorphism rs2352028 and clinical characteristics in Chinese lung cancer patients.

Background: GPC5 rs2352028 is associated with the risk of lung cancer, but its relationship with lung cancer prognosis is unclear. Materials & methods: The authors collected blood samples from 888 patients with lung cancer and used a Cox proportional hazards model to analyze the association between prognosis and GPC5 polymorphism rs2352028. Results: GPC5 rs2352028 C > T was associated with a better prognosis. Patients with CT genotype had longer overall survival than those with CC genotype. Additionally, older and early-stage patients with CT + TT genotype had a lower risk of death than those with CC genotype. Conclusion: GPC5 rs2352028 C > T may play a protective role in patients with lung cancer and GPC5 rs2352028 may be a potential genetic marker for lung cancer prognosis.

Prognosis of lung cancer with simple brain metastasis patients and establishment of survival prediction models: a study based on real events.

OBJECTIVES: The aim of this study was to explore risk factors for the prognosis of lung cancer with simple brain metastasis (LCSBM) patients and to establish a prognostic predictive nomogram for LCSBM patients. MATERIALS AND METHODS: Three thousand eight hundred and six cases of LCSBM were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015 using SEER Stat 8.3.5. Lung cancer patients only had brain metastasis with no other organ metastasis were defined as LCSBM patients. Prognostic factors of LCSBM were analyzed with log-rank method and Cox proportional hazards model. Independent risk and protective prognostic factors were used to construct nomogram with accelerated failure time model. C-index was used to evaluate the prediction effect of nomogram. RESULTS AND CONCLUSION: The younger patients (18-65 years old) accounted for 54.41%, while patients aged over 65 accounted for 45.59%.The ratio of male: female was 1:1. Lung cancer in the main bronchus, upper lobe, middle lobe and lower lobe were accounted for 4.91%, 62.80%, 4.47% and 27.82% respectively; and adenocarcinoma accounted for 57.83% of all lung cancer types. The overall median survival time was 12.2 months. Survival rates for 1-, 3- and 5-years were 28.2%, 8.7% and 4.7% respectively. We found female (HR = 0.81, 95% CI 0.75-0.87), the married (HR = 0.80; 95% CI 0.75-0.86), the White (HR = 0.90, 95% CI 0.84-0.95) and primary site (HR = 0.45, 95% CI 0.39-0.52) were independent protective factors while higher age (HR = 1.51, 95% CI 1.40-1.62), advanced grade (HR = 1.19, 95% CI 1.12-1.25) and advanced T stage (HR = 1.09, 95% CI 1.05-1.13) were independent risk prognostic factors affecting the survival of LCSBM patients. We constructed the nomogram with above independent factors, and the C-index value was 0.634 (95% CI 0.622-0.646). We developed a nomogram with seven significant LCSBM independent prognostic factors to provide prognosis prediction.

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.

Associations Between CAMKK1 Polymorphism rs7214723 and the Prognosis of Patients With Lung Cancer.

BACKGROUND: The 5-year survival rate of patients with lung cancer in China is less than 20% and predicting their prognosis is challenging. We investigated the association between a common non-synonymous single nucleotide polymorphism (SNP), rs7214723, in the Ca2+/calmodulin-dependent protein kinase kinase 1 (CAMKK1) gene and the prognosis of patients with lung cancer. METHODS: Genomic DNA was extracted from the blood samples of 839 patients with lung cancer, recruited from Changhai Hospital (n = 536) and Taizhou Institute of Health Sciences (n = 352), and genotyped using the SNPscan technique. The association between patient prognosis and the genotypic data for CAMKK1 was analyzed using a multivariate Cox proportional hazards model adjusted for multiple potential confounders. The CRISPR/Cas9 gene-editing system was used to introduce point mutations in the CAMKK1 rs7214723 of A549 and NCI-H358 cells. Subsequently, Cell proliferation and migration ability were assessed with the Cell Counting Kit-8 and scratch assay. The Annexin V-FITC apoptosis detection kit was used to detect cell apoptosis. RESULTS: The CAMKK1 rs7214723 recessive CC genotype conferred significantly better overall survival (CC vs. TT + TC: adjusted hazard ratio = 0.78, 95% confidence interval [CI], 0.61-1.00, P = 0.049) than the TT + TC genotypes. Stratified analysis showed that the CAMKK1 rs7214723 CC genotype and recessive CC genotype conferred a significantly decreased risk of death in patients who were male, had a smoking history, or had stage III + IV cancer, compared with the TT and TT + TC genotypes. Relative to the TT + TC genotypes, the rs7214723 recessive CC genotype was also associated with a decreased risk of death in patients aged < 60 years (CC vs. TT + TC: adjusted hazard ratio = 0.59, 95% CI, 0.37-0.93, P = 0.024) and patients with squamous cell carcinoma (CC vs. TT + TC: adjusted hazard ratio = 0.65, 95% CI, 0.44-0.98, P = 0.038). Remarkably, CRISPR/Cas9-guided single nucleotide editing demonstrated that CAMKK1 rs7214723 T > C mutation significantly inhibits cell proliferation and migration and promotes cell apoptosis. CONCLUSIONS: CAMKK1 SNP rs7214723 may be a significant prognostic factor for the risk of death among patients with lung cancer.