Long non-coding (lnc) RNAs serve crucial functions in human cancers. However, the involvement of the lncRNA B4GALT1-antisense RNA 1 (AS1) in non-small cell lung cancer (NSCLC) has not been extensively studied. Reverse transcription-quantitative PCR was performed to detect B4GALT1-AS1 levels in NSCLC tissues and cell lines. Potential influences of B4GALT1-AS1 on biological functions of NSCLC were assessed through a series of in vitro experiments, and the molecular mechanism was determined via RNA immunoprecipitation (RIP) and bioinformatics analyses. The results of the present study demonstrated that knockdown of B4GALT1-AS1 significantly attenuated the proliferative ability and clonality of H1299 and A549 cells. In the present study, B4GALT1-AS1 competed as an endogenous RNA by sequestering microRNA-30e (miR-30e) leading to an enhanced expression of SRY-box transcription factor 9 (SOX9). The effects of silencing B4GALT1-AS1 on NSCLC cells proliferation could be ameliorated by inhibiting miR-30e or restoring SOX9. Hence, B4GALT1-AS1 acted as a lncRNA that drives tumor progression in NSCLC via the regulation of the miR-30e/SOX9 axis. The findings of the present study indicated that the B4GALT1-AS1/miR-30e/SOX9 axis maybe an effective target for NSCLC treatment and management.
Background: Prognostic signatures based on autophagy genes have been proposedfor esophageal squamous cell carcinoma (ESCC). Autophagy genes are closely associated with m6A genes. Our purpose is to identify m6A-related autophagy genes in ESCC and develop a survival prediction model. Methods: Differential expression analyses for m6A genes and autophagy genes were performed based on TCGA and HADd databases followed by constructing a co-expression network. Uni-variable Cox regression analysis was performed for m6Arelated autophagy genes. Using the optimal combination of feature genes by LASSO Cox regression model, a prognostic score (PS) model was developed and subsequently validated in an independent dataset. Results:The differential expression of 13 m6A genes and 107 autophagy genes was observed between ESCC and normal samples. The co-expression network contained 13 m6A genes and 96 autophagy genes. Of the 12 m6A-related autophagy genes that were significantly related to survival, DAPK2, DIRAS3, EIF2AK3, ITPR1, MAP1LC3C, and TP53 were used to construct a PS model, which split the training set into two risk groups with significant different survival ratios (p = 0.015, 1-year, 3-year, and 5-year AUC = 0.873, 0.840, and 0.829). Consistent results of GSE53625 dataset confirmed predictive ability of the model (p = 0.024, 1-year, 3-year, and 5-year AUC = 0.793, 0.751, and 0.744). The six-gene PS score was an independent prognostic factor from clinical factors (HR, 2.362; 95% CI, 1.390-7.064; p-value = 0.012). Conclusion:Our study recommends 6 m6A-related autophagy genes as promising prognostic biomarkers and develops a PS model to predict survival in ESCC.
Through the dynamic detection of peripheral blood circulating tumor cells (CTCs) and the correlation analysis of DNA methylation, the application of relevant indicators of peripheral blood circulating tumor cells (CTCs) in patients with early lung cancer (LC). Double antibody (EpCAM and EGFR antibodies) modified nano-PLGA magnetic beads have been used for CTC sorting. Functional characterization and analysis were performed to confirm that the prepared nano-PLGA MB had good stability and specificity. Furthermore, the separation and identification of CTCs from lung cancer patients were realized by double-antibody nano-PLGA MB (Ep+ER nano-PLGA MB), suggesting that the system had high separation efficiency, with a positive rate of separation of >80%. Meanwhile, methylation-specific PCR was conducted following the extraction DNA from peripheral blood to analyze the methylation level of p16, MGMT and RASSF1A. Corresponding results revealed that the level of p16 methylation could be used as an important index for lung cancer screening. In addition, the association between CTC-DNA methylation and early screening of tumor was analyzed by integrating the clinical information and related indexes of patients. To sum up, in the screening and identification of lung cancer patients, auxiliary effect can be provided through dynamic monitoring of CTC count in peripheral blood and analysis of CTC-DNA methylation level. It is expected to provide scientific basis for screening, therapeutic strategy formulation, gene correlation analysis and prognosis monitoring of LC.
Previous studies reported a dysregulation of micro (mi)R-208b-5p expression level in various types of human cancer; however, the role of miR-208-5p in non-small cell lung cancer (NSCLC) remains unclear. Therefore, the present study aimed to determine whether miR-208b-5p could regulate NSCLC progression. A total of 62 pairs of primary tumor and adjacent normal tissues were collected from patients with NSCLC. miR-208b-5p expression level was determined by reverse transcription-quantitative polymerase chain reaction. Furthermore, miR-208b-5p mimics was transfected into NSCLC A549 and H1299 cells in order to upregulate miR-208b-5p expression. Dual-luciferase reporter assay was utilized to investigate the associations between miR-208b-5p and IL9 mRNA. The results demonstrated that miR-208b-5p expression decreased in NSCLC tissues and cell lines. Furthermore, miR-208b-5p overexpression inhibited A549 and H1299 cell proliferation and invasiveness. miR-208b-5p was demonstrated to bind directly to the 3' untranslated region of interleukin-9 (IL-9) and therefore decreased its expression. In the NSCLC-derived cell lines, miR-208b-5p inactivated IL-9/signal transducer and activator of transcription 3 (STAT3) signaling pathway. Furthermore, enhanced IL-9 level decreased the miR-208b-5p-mediated suppression of epithelial-mesenchymal transition in NSCLC cells by inactivating the STAT3 signaling pathway. In conclusion, the findings from this study demonstrated that miR-208b-5p inhibited migration and invasion of NSCLC cells. The anti-tumor activity of miR-208b-5p may be mediated by IL-9 and STAT-3 pathway.
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