The role of telomerase in the etiology of primary spontaneous pneumothorax

Background

This study aims to investigate the role of telomerase activity in the risk of primary spontaneous pneumothorax, which is most frequently encountered in the practice of thoracic surgery.

Methods

A total of 61 patients (56 males, 5 females; median age: 29.4 years; range, 17 to 43 years) who underwent treatment for primary spontaneous pneumothorax and 19 age- and sex-matched healthy controls (10 males, 9 females; median age: 29.1 years; range, 23 to 43 years) were included in this prospective study between January 2018 - August 2018. Telomerase activity was evaluated with enzyme-linked immunosorbent assay. The correlation between telomerase activity and clinical and demographic parameters was examined.

Results

The mean serum telomerase level was 3.4±0.6 ng/mL in the primary spontaneous pneumothorax group and 1.9±0.5 ng/mL in the control group, indicating significantly higher levels in the patient group (p<0.001). There was no significant association between the telomerase levels and presence of blebs and/or bullae on thoracic computed tomography, extent of pneumothorax, laterality (right, left, or bilateral), and pack years of cigarette smoking.

Conclusion

Telomerase levels of patients with primary spontaneous pneumothorax are significantly higher than healthy individuals. Future genetic studies may ultimately clarify a potential relationship between primary spontaneous pneumothorax and short telomere syndrome.

Identification and analysis of key lncRNAs in malignant-transformed BEAS-2B cells induced with coal tar pitch by microarray analysis

This study aims to explore the key and differentially expressed long non-coding RNAs (lncRNAs) and elucidates their possible mechanisms in malignant-transformed Human bronchial epithelial (BEAS-2B) cells induced by coal tar pitch extracts (CTPE). BEAS-2B cells were stimulated with 2.4 μg/ml CTPE, then passaged for three times which were named CTPE1 and then passaged until passage 30 (CTPE30). The results showed that cells of CTPE30 appeared abnormal morphology. Furthermore, migration, clonality and proliferation of cells in CTPE group were significantly increased compared with those in control groups. However, the apoptosis of cells in CTPE group was inhibited. A total of 569 differentially expressed mRNAs and 707 differentially expressed lncRNAs were screened out, among which four lncRNAs were validated and were consistent with the microarray results. 32 target genes were screened out by Co-expression network. The study suggests that differentially expressed lncRNAs may play a potential role in lung carcinogenesis.

Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer

The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length. Real-time quantitative methylation-specific PCR was used to detect the levels of three-gene promoter methylation, and real-time PCR method was applied to determine the relative telomere length. BP neural network and Fisher discrimination analysis were used to establish the discrimination diagnosis model. The levels of three-gene promoter methylation in patients with lung cancer were significantly higher than those of the normal controls. The values of Z(P) in two groups were 2.641 (0.008), 2.075 (0.038) and 3.044 (0.002), respectively. The relative telomere lengths of patients with lung cancer (0.93 ± 0.32) were significantly lower than those of the normal controls (1.16 ± 0.57), t = 4.072, P < 0.001. The areas under the ROC curve (AUC) and 95 % CI of prediction set from Fisher discrimination analysis and BP neural network were 0.670 (0.569-0.761) and 0.760 (0.664-0.840). The AUC of BP neural network was higher than that of Fisher discrimination analysis, and Z(P) was 0.76. Four biomarkers are associated with lung cancer. BP neural network model for the prediction of lung cancer is better than Fisher discrimination analysis, and it can provide an excellent and intelligent diagnosis tool for lung cancer.