Kewei Ma

Bmi-1 is critical for the proliferation and invasiveness of gastric carcinoma cells

Background and Aim:  Bmi‐1 is a transcriptional repressor belonging to the Polycomb group and is associated with the cell proliferation and carcinogenesis of a variety of human cancers. The level of Bmi‐1 expression correlates with the aggressiveness of many cancers, and is considered an important marker for cancer diagnosis. However, its role in gastric carcinoma is unknown.

MDM2 rs2279744 and TP53 rs1042522 Polymorphisms Associated with Etoposide- and Cisplatin-Induced Grade III/IV Neutropenia in Chinese Extensive-Stage Small-Cell Lung Cancer Patients

Background/Aims: Etoposide and cisplatin (EP) chemotherapy is the most frequently used regimen in extensive-stage small-cell lung cancer (SCLC) patients, although the side effects (e.g., neutropenia) are high. This study investigates the association of the MDM2 rs2279744 and TP53 rs1042522 single-nucleotide polymorphisms (SNPs) with EP-induced grade III/IV neutropenia and with response to EP in extensive-stage SCLC patients. Methods: Blood samples from 119 extensive-stage SCLC patients were subjected to genotyping of these 2 SNPs, using the allele-specific matrix-assisted laser desorption ionization-time-of-flight (MALDI-TOF) mass spectrometry for determining the association with neutropenia in the patients. Results: The data showed that patients carrying the MDM2 rs2279744-GG genotype were associated with a lower incidence of grade III/IV neutropenia in the recessive and additive models, while the TP53 rs1042522-CC genotype was associated with a higher incidence in the recessive model. Furthermore, the combination of the MDM2 rs2279744-TT+TG and the TP53 rs1042522-CC genotype was associated with a significantly higher incidence of grade III/IV neutropenia. And the combination of the MDM2 rs2279744-GG and the TP53 rs1042522-GG+GC genotype was associated with the lowest incidence of grade III/IV neutropenia. Conclusions: MDM2 rs2279744 and TP53 rs1042522 SNPs were associated with EP-induced high-grade neutropenia in extensive-stage SCLC patients. Further studies are needed to investigate the underlying mechanisms.

An individual risk prediction model for lung cancer based on a study in a Chinese population.

Aims and Background Early detection and diagnosis remains an effective yet challenging approach to improve the clinical outcome of patients with cancer. Low-dose computed tomography screening has been suggested to improve the diagnosis of lung cancer in high-risk individuals. To make screening more efficient, it is necessary to identify individuals who are at high risk. Methods and Study design We conducted a case-control study to develop a predictive model for identification of such high-risk individuals. Clinical data from 705 lung cancer patients and 988 population-based controls were used for the development and evaluation of the model. Associations between environmental variants and lung cancer risk were analyzed with a logistic regression model. The predictive accuracy of the model was determined by calculating the area under the receiver operating characteristic curve and the optimal operating point. Results Our results indicate that lung cancer risk factors included older age, male gender, lower education level, family history of cancer, history of chronic obstructive pulmonary disease, lower body mass index, smoking cigarettes, a diet with less seafood, vegetables, fruits, dairy products, soybean products and nuts, a diet rich in meat, and exposure to pesticides and cooking emissions. The area under the curve was 0.8851 and the optimal operating point was obtained. With a cutoff of 0.35, the false positive rate, true positive rate, and Youden index were 0.21, 0.87, and 0.66, respectively. Conclusions The risk prediction model for lung cancer developed in this study could discriminate high-risk from low-risk individuals.

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

Genetic variants from a considerable number of susceptibility loci have been identified in association with cancer risk, but their interaction with epidemiologic factors in lung cancer remains to be defined. We sought to establish a forecasting model for identifying individuals with high-risk of lung cancer by combing gene single-nucleotide polymorphisms with epidemiologic factors. Genotyping and clinical data from 500 lung cancer cases and 500 controls were used for developing the logistic regression model. We found that lung cancer was associated with telomerase reverse transcriptase (TERT) rs2736100 single-nucleotide polymorphism. The TERT rs2736100 model was still significantly associated with lung cancer risk when combined with environmental and lifestyle factors, including lower education, lower BMI, COPD history, heavy cigarettes smoking, heavy cooking emission, and dietary factors (over-consumption of meat and deficiency in fish/shrimp, vegetables, dairy products, and soybean products). These data suggest that combining TERT SNP and epidemiologic factors may be a useful approach to discriminate high and low-risk individuals for lung cancer.

Mouse double minute-2 homolog (MDM2)-rs2279744 polymorphism associated with lung cancer risk in a Northeastern Chinese population.

Altered expression or function of mouse double minute‐2 (MDM2) protein could contribute to lung carcinogenesis; thus, this study investigated MDM2‐rs2279744 polymorphism together with other epidemiologic factors for their association with lung cancer risk.

Real-world EGFR testing in patients with stage IIIB/IV non-small-cell lung cancer in North China: A multicenter, non-interventional study: Real-world EGFR testing in North China

Before tyrosine kinase inhibitor (TKI) therapy can be administered in patients with advanced non‐small cell lung cancer (NSCLC), EGFR mutation testing is required. However, few studies have evaluated the extent of EGFR testing in real‐world practice in China.

Systematic Correlation Analyses of Circulating Tumor Cells with Clinical Variables and Tumor Markers in Lung Cancer Patients

Measurement of circulating tumor cells (CTC) offers promise as a clinical biomarker to monitor disease status, therapeutic response, and progression in cancer patients. However, its clinical value in lung cancer patients has not been fully explored. We systematically evaluate the association of CTCs with clinical variables and tumor markers in a cohort of lung cancer patients. Using the CELLSEARCH System, CTCs were detected in both small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) patients prior to therapy. Univariate analysis revealed that detection of CTC was related to histology, stage, tumor size, invasiveness, and lymphatic metastasis. CTCs were associated with distant metastases in NSCLC, but not in SCLC. Using multivariate analysis, we found that CTCs were independently correlated with disease stage, SCLC, and elevated serum neuron-specific enolase (NSE). These data suggest that CTCs are more likely to be detected in patients with stage IV disease and with SCLC, and that elevated serum NSE predicts the presence of CTCs.