Combining Telomerase Reverse Transcriptase Genetic Variant rs2736100 with Epidemiologic Factors in the Prediction of Lung Cancer Susceptibility

Mechanisms underlying the effects, and clinical applications, of oral microbiota in lung cancer: current challenges and prospects

The oral cavity contains a site-specific microbiota that interacts with host cells to regulate many physiological processes in the human body. Emerging evidence has suggested that changes in the oral microbiota can increase the risk of lung cancer (LC), and the oral microbiota is also altered in patients with LC. Human and animal studies have shown that oral microecological disorders and/or specific oral bacteria may play an active role in the occurrence and development of LC through direct and/or indirect mechanisms. These studies support the potential of oral microbiota in the clinical treatment of LC. Oral microbiota may therefore be used in the prevention and treatment of LC and to improve the side effects of anticancer therapy by regulating the balance of the oral microbiome. Specific oral microbiota in LC may also be used as screening or predictive biomarkers. This review summarizes the main findings in research on oral microbiome-related LC and discusses current challenges and future research directions.

[Construction of a Risk Prediction Model for Lung Cancer Based on Lifestyle Behaviors in the UK Biobank Large-Scale Population Cohort]

Objective

To identify the risk factors related to lifestyle behaviors that affect the incidence of lung cancer, to build a lung cancer risk prediction model to identify, in the population, individuals who are at high risk, and to facilitate the early detection of lung cancer.

Methods

The data used in the study were obtained from the UK Biobank, a database that contains information collected from 502 389 participants between March 2006 and October 2010. Based on domestic and international guidelines for lung cancer screening and high-quality research literature on lung cancer risk factors, high-risk population identification criteria were determined. Univariate Cox regression was performed to screen for risk factors of lung cancer and a multifactor lung cancer risk prediction model was constructed using Cox proportional hazards regression. Based on the comparison of Akaike information criterion and Schoenfeld residual test results, the optimal fitted model assuming proportional hazards was selected. The multiple factor Cox proportional hazards regression was performed to consider the survival time and the population was randomly divided into a training set and a validation set by a ratio of 7:3. The model was built using the training set and the performance of the model was internally validated using the validation set. The area under the receiver operating characteristic (ROC) curve ( AUC) was used to evaluate the efficacy of the model. The population was categorized into low-risk, moderate-risk, and high-risk groups based on the probability of occurrence of 0% to <25%, 25% to <75%, and 75% to 100%. The respective proportions of affected individuals in each risk group were calculated.

Results

The study eventually covered 453 558 individuals, and out of the cumulative follow-up of 5 505 402 person-years, a total of 2 330 cases of lung cancer were diagnosed. Cox proportional hazards regression was performed to identify 10 independent variables as predictors of lung cancer, including age, body mass index (BMI), education, income, physical activity, smoking status, alcohol consumption frequency, fresh fruit intake, family history of cancer, and tobacco exposure, and a model was established accordingly. Internal validation results showed that 8 independent variables (all the 10 independent variables screened out except for BMI and fresh fruit intake) were significant influencing factors of lung cancer ( P<0.05). The AUC of the training set for predicting lung cancer occurrence at one year, five years, and ten years were 0.825, 0.785, and 0.777, respectively. The AUC of the validation set for predicting lung cancer occurrence at one year, five years, and ten years were 0.857, 0.782, and 0.765, respectively. 68.38% of the individuals who might develop lung cancer in the future could be identified by screening the high-risk population.

Conclusion

We established, in this study, a model for predicting lung cancer risks associated with lifestyle behaviors of a large population. Showing good performance in discriminatory ability, the model can be used as a tool for developing standardized screening strategies for lung cancer.

Ethnicity-specific association between TERT rs2736100 (A > C) polymorphism and lung cancer risk: a comprehensive meta-analysis

The rs2736100 (A > C) polymorphism of the second intron of Telomerase reverse transcriptase (TERT) has been confirmed to be closely associated with the risk of Lung cancer (LC), but there is still no unified conclusion on the results of its association with LC. This study included Genome-wide association studies (GWAS) and case-control studies reported so far on this association between TERT rs2736100 polymorphism and LC to clarify such a correlation with LC and the differences in it between different ethnicities and different types of LC. Relevant literatures published before May 7, 2022 on 'TERT rs2736100 polymorphism and LC susceptibility' in PubMed, EMbase, CENTRAL, MEDLINE databases were searched through the Internet, and data were extracted. Statistical analysis of data was performed in Revman5.3 software, including drawing forest diagrams, drawing funnel diagrams and so on. Sensitivity and publication bias analysis were performed in Stata 12.0 software. The C allele of TERT rs2736100 was associated with the risk of LC (Overall population: [OR] = 1.21, 95%CI [1.17, 1.25]; Caucasians: [OR] = 1.11, 95%CI [1.06, 1.17]; Asians: [OR] = 1.26, 95%CI [1.21, 1.30]), and Asians had a higher risk of LC than Caucasians (C vs. A: Caucasians: [OR] = 1.11 /Asians: [OR]) = 1.26). The other gene models also showed similar results. The results of stratified analysis of LC patients showed that the C allele was associated with the risk of Non-small-cell lung carcinoma (NSCLC) and Lung adenocarcinoma (LUAD), and the risk of NSCLC and LUAD in Asians was higher than that in Caucasians. The C allele was associated with the risk of Lung squamous cell carcinoma (LUSC) and Small cell lung carcinoma(SCLC) in Asians but not in Caucasians. NSCLC patients ([OR] = 1.27) had a stronger correlation than SCLC patients ([OR] = 1.03), and LUAD patients ([OR] = 1.32) had a stronger correlation than LUSC patients ([OR] = 1.09).In addition, the C allele of TERT rs2736100 was associated with the risk of LC, NSCLC and LUAD in both smoking groups and non-smoking groups, and the risk of LC in non-smokers of different ethnic groups was higher than that in smokers. In the Asians, non-smoking women were more at risk of developing LUAD. The C allele of TERT rs2736100 is a risk factor for LC, NSCLC, and LUAD in different ethnic groups, and the Asian population is at a more common risk. The C allele is a risk factor for LUSC and SCLC in Asians but not in Caucasians. And smoking is not the most critical factor that causes variation in TERT rs2736100 to increase the risk of most LC (NSCLC, LUAD). Therefore, LC is a multi-etiological disease caused by a combination of genetic, environmental and lifestyle factors.

Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study

BACKGROUND

It is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally.

OBJECTIVE

The study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women.

METHODS

Based on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set.

RESULTS

In sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk-predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination.

CONCLUSIONS

We designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women.

Construction and Validation of a Lung Cancer Risk Prediction Model for Non-Smokers in China

Background

About 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China.

Methods

A large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set.

Results

A total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk.

Conclusions

We developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers.

A risk prediction model for selecting high-risk population for computed tomography lung cancer screening in China

OBJECTIVE

Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer.

MATERIALS AND METHODS

Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282,254 participants including 126,445 males and 155,809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively.

RESULTS

By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100,000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups.

CONCLUSIONS

We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention.

TERT Gene rs2736100 and rs2736098 Polymorphisms are Associated with Increased Cancer Risk: A Meta-Analysis

Abnormal telomerase activity plays a key role in the development of carcinogenesis. The variants rs2736100 and rs2736098 of the telomerase reverse transcriptase (TERT) gene, which encodes the telomerase catalytic subunit, are associated with the risk of different types of cancers. However, the results remain controversy. We conducted a meta-analysis to more precisely assess this association. We comprehensively searched the PubMed and Web of Science databases up to June 1, 2020, and retrieved a total of 103 studies in 82 articles, including 89,320 cases and 121,654 controls. Among these studies, 69 published studies including 75,274 cases and 10,3248 controls were focused on rs2736100, and 34 published studies including 14,046 cases and 18,362 controls were focused on rs2736098. The results showed a strong association between variant rs2736100 and cancer risk in all populations. (G vs. T: OR 1.18, 95% CI 1.12-1.24; TG+GG vs. TT: OR 1.23, 95% CI 1.15-1.31; GG vs. TG+TT: OR 1.25, 95% CI 1.16-1.36); the variant rs2736098 was associated with cancer risk in all populations as well (A vs. G: OR 1.13, 95% CI 1.05-1.22; GA+AA vs. GG: OR 1.15, 95% CI 1.04-1.27; AA vs. GA+GG: OR 1.22, 95% CI 1.10-1.38). Stratified analysis based on the cancer type indicated that rs2736100 was associated with an increased risk of thyroid cancer, bladder cancer, lung cancer, glioma, and myeloproliferative neoplasms. rs2736098 only increased the risk of bladder cancer and lung cancer. Moreover, the TERT variants rs2736100 and rs2736098 were associated with a decreased risk of breast cancer and colorectal cancer. The variants rs2736098 and rs2736100 located in 5p15.33 around TERT were associated with increased cancer risk in all populations. These two variants had bidirectional effects in different tumors.

Risk prediction model for lung cancer incorporating metabolic markers: Development and internal validation in a Chinese population

BACKGROUND

Low-dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false-positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high-risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population.

METHODS

During 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person-years of 976 663. Stepwise multivariable-adjusted logistic regressions with P_entry  = .15 and P_stay  = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high-sensitivity C-reactive protein (hsCRP) and low-density lipoprotein cholesterol (LDL-C) into the prediction model. We used the C-statistic to evaluate discrimination, and Hosmer-Lemeshow tests for calibration. Tenfold cross-validation was conducted for internal validation to assess the model's stability.

RESULTS

A total of 984 lung cancer cases were identified during the follow-up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C-statistic of 0.731. The full model additionally included hsCRP and LDL-C showed significantly better discrimination (C-statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C-statistic in younger participants (<50 years, 0.709), females (0.726), and former or current smokers (0.742). The model calibrated well across the deciles of predicted risk in both the overall population (P_HL  = .689) and all subgroups.

CONCLUSIONS

We developed and internally validated an easy-to-use risk prediction model for lung cancer among the Chinese population that could provide guidance for screening and surveillance.

Genetic polymorphisms, mRNA expression levels of telomere-binding proteins, and associates with telomere damage in PAHs-Exposure workers

Coke oven emissions (COEs), confirmed human carcinogens, are mainly composed of polycyclic aromatic hydrocarbons (PAHs). Telomere shortening in blood leukocytes has been associated with COEs, and polymorphisms in metabolic enzymes. However, the relationship between polymorphisms in telomere related genes and telomere shortening in COEs exposed workers has never been evaluated. Therefore, we measured telomere length and mRNA expression levels of telomere-binding proteins (TBPs) by qPCR method in leucocyte from 544 COEs exposed workers and 238 office staffs (referents). Flight mass spectrometry was used to perform the genotyping of selected functional and susceptible SNPs. The results showed that the telomere length in the exposure group 0.75(0.51,1.08) was significantly shorter than that in the control group 1.05(0.76,1.44) (P < 0.001). The mRNA expression levels of TPP1, TERF1 and TERF2 genes in the exposure group were significantly lower than those in the control group (P < 0.05), the mRNA expression level of POT1 in the exposure group was significantly higher than that in the control group (P < 0.05). We used the wild homozygous genotype as a reference, subjects carrying TERT rs2736109 AA, TERT rs3215401 CC and TERT rs2736100 GT + GG genotypes had significantly longer telomere length in the exposure group (P < 0.05). In conclusion, the workers exposed to COEs had shorter telomere length, which was regulated by the TPP1, TERF1, TERF2 and POT1 genes expression levels, and the gene polymorphisms of TERT gene were associated with the telomere length among PAHs-exposure workers.

Functional genetic variant of WW domain containing oxidoreductase gene associated with urothelial cell carcinoma clinicopathologic characteristics and long-term survival

OBJECTIVES

In Taiwan, urothelial cell carcinoma (UCC) is a common malignancy of urinary tract that is associated with genetic and environmental carcinogens. WW domain-containing oxidoreductase (WWOX) has been identified as a tumor suppressor gene that associated with several cancers development and progression. The study aimed to explore the impact of WWOX gene polymorphisms on the clinicopathological status and prognosis of patients with UCC.

MATERIALS AND METHODS

A total of 1,293 participants, including 431 patients with UCC and 862 healthy controls, were recruited for this study. Five polymorphisms of the WWOX gene were examined by a real-time PCR assay.

RESULTS

We found that individuals carrying TT polymorphism at rs11545028 and at least 1 G allele at rs3764340 associated with more susceptible to UCC. At least 1 A allele at rs12918952 associated with more advance disease and high grade tumor. Patients with T allele at rs11545028 associated with worse relapse free survival in all patients and worse disease specific survival (DSS) in male. Patients with A allele at rs12918952 associated with worse DSS in all patients and worse relapse free survival, DSS and overall survival in male.

CONCLUSIONS

This is the first reported correlation between WWOX polymorphisms and UCC risk and clinicopathologic feature. Genetic variants of WWOX contribute to the pathologic staging, grading, and prognosis. The findings regarding these biomarkers provided a potential prediction of UCC progression.

The TERT rs2736100 polymorphism increases cancer risk: A meta-analysis

Abnormal telomerase activity is implicated in cancer initiation and development. The rs2736100 T > G polymorphism in the telomerase reverse transcriptase (TERT) gene, which encodes the telomerase catalytic subunit, has been associated with increased cancer risk. We conducted a meta-analysis to more precisely assess this association. After a comprehensive literature search of the PubMed and EMBASE databases up to November 1, 2016, 61 articles with 72 studies comprising 108,248 cases and 161,472 controls were included in our meta-analysis. Studies were conducted on various cancer types. The TERT rs2736100 polymorphism was associated with increased overall cancer risk in five genetic models [homozygous model (GG vs. TT): odds ratio (OR) = 1.39, 95% confidence interval (95% CI) = 1.26-1.54, P < 0.001; heterozygous model (TG vs. TT): OR = 1.16, 95% CI = 1.11-1.23, P < 0.001; dominant model (TG + GG vs. TT): OR = 1.23, 95% CI = 1.15-1.31, P < 0.001; recessive model (GG vs. TG + TT): OR = 1.25, 95% CI = 1.16-1.35, P < 0.001; and allele contrast model (G vs. T): OR = 1.17, 95% CI = 1.12-1.23, P < 0.001]. A stratified analysis based on cancer type associated the polymorphism with elevated risk of thyroid cancer, bladder cancer, lung cancer, glioma, myeloproliferative neoplasms, and acute myeloid leukemia. Our results confirm that the TERT rs2736100 polymorphism confers increased overall cancer risk.

Association between WWOX and the risk of malignant tumor, especially among Asians: evidence from a meta-analysis

Purpose

Many studies have been carried out to examine whether there are associations between WWOX polymorphisms (rs3764340 C>G, rs12918952 G>A, and rs383362 G>T) and malignant tumor risk, but the results from these studies remained inconsistent and even controversial. In the present study, we performed a meta-analysis to evaluate the relationships comprehensively.

Methods

Published reports were searched in PubMed, Google Scholar, and Chinese National Knowledge Infrastructure databases. Eight eligible case–control studies were included in the final analysis. In the analysis, pooled odds ratios (ORs) with corresponding 95% CIs were calculated in five genetic models to assess the genetic risk. Egger’s regression and Begg’s funnel plots test were conducted to appraise the publication bias.

Results

We found that rs12918952 G>A and rs383362 G>T polymorphisms were not associated with the susceptibility of malignant tumor. However, a significant correlation was found between WWOX rs3764340 C>G and malignant tumor risk in three genetic models (CG vs CC: OR=1.31, 95% CI: 1.12–1.53, P=0.031; GG/CG vs CC: OR=1.31, 95% CI: 1.11–1.54, P=0.014; G vs C: OR=1.28, 95% CI: 1.09–1.50, P=0.009). Furthermore, when stratified by source of control, the results were significant especially in population-based control for rs3764340.

Conclusion

In general, our results first indicated that the rs3764340 C>G polymorphism in WWOX gene can increase the susceptibility of tumor, while the others cannot. However, large, well-designed epidemiological studies are required to verify our findings.