LncRNA MITA1 promotes gefitinib resistance by inducing autophagy in lung cancer cells.

Lung cancer is a major health challenge worldwide. Gefitinib, a tyrosine kinase inhibitor (TKI), is the common therapeutic drug used in advanced non-small-cell lung cancer (NSCLC). However, it is eventually bound to face the problem of acquired drug resistance. In this work, we investigated the role of lncRNA MITA1 in the acquisition of gefitinib resistance in NSCLC and uncovered the possible underlying molecular mechanism of the same. Experiments were carried out using the HCC827 and HCC827GR cells. These were transfected with pcDNA-MITA1 or si-MITA1 and treated with gefitinib. Subsequently, lncRNA MITA1 mediated effect on cell viability and apoptosis were studied using the MTT and flow cytometry assays. Furthermore, using qRT-PCR, Western blotting, and immunofluorescence assays, the regulatory association between lncRNA MITA1 and markers of autophagy (LC3, Beclin-1, and p62) were examined by estimating their cellular protein levels. Also, these results were verified in the presence of an autophagy inhibitor bafilomycin A1. We found that MITA1 was highly upregulated in the gefitinib-resistant NSCLC cells, indicating the regulatory role of MITA1 in gefitinib resistance. Mechanistically, upregulated MITA1 led to gefitinib resistance by suppressing apoptosis, increasing cell viability, and inducing autophagy. Furthermore, these results were true when tested in the presence of bafilomycin A1. Our results suggest that MITA1 by inducing autophagy could be a key regulator of gefitinib resistance in NSCLC.

Cytochrome P450 1A1 exon 7 polymorphism and susceptibility to lung cancer in the Chinese population: an updated meta-analysis and review.

BACKGROUND: Although many epidemiologic studies have investigated the cytochrome P450 1A1 (CYP1A1) exon 7 gene polymorphism and its association with lung cancer (LC), definitive conclusions cannot be drawn. OBJECTIVE: To clarify the effects of CYP1A1 exon 7 polymorphism on the risk of LC, an updated meta-analysis was performed in the Chinese population. METHODS: Related studies were identified from PubMed, Springer Link, Ovid, the Chinese Wanfang Data Knowledge Service Platform, Chinese National Knowledge Infrastructure (CNKI), and the Chinese Biology Medicine (CBM) databases until October 2014. Odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of the associations. RESULTS: A total of 25 articles including 3,540 LC cases and 5,284 controls were included in this meta-analysis. Overall, significant association was found between CYP1A1 exon 7 polymorphism and LC risk when all studies in the Chinese population were pooled into this meta-analysis (GG versus AA: OR = 1.71, 95% CI: 1.46-2.01; GG versus AG: OR = 1.41, 95% CI: 1.21-1.64; GG + AG versus AA: OR = 1.37, 95% CI: 1.16-1.62; GG versus AA + AG: OR = 1.52, 95% CI: 1.32-1.76). In subgroup analyses stratified by ethnicity, source of controls, and geographical locations, significantly increased risk was found in Chinese Han people, in population-based studies, in hospital-based studies, in South China, and in North China. CONCLUSION: This meta-analysis provides the evidence that CYP1A1 exon 7 polymorphism may contribute to LC development in the Chinese population, and studies with a larger sample size and wider population spectrum are warranted to verify this finding.

A preliminary analysis of non-small cell lung cancer biomarkers in serum.

OBJECTIVE: To identify potential serum biomarkers that could be used to discriminate lung cancers from normal. METHODS: Proteomic spectra of twenty-eight serum samples from patients with non-small cell lung cancer and twelve from normal individuals were generated by SELDI (Surfaced Enhanced Laser Desorption/Ionization) Mass Spectrometry. Anion-exchange columns were used to fractionate the sera into 6 designated pH groups. Two different types of protein chip arrays, IMAC-Cu and WCX2, were employed. Samples were examined in PBSII Protein Chip Reader (Ciphergen Biosystem Inc) and the discriminatory profiling between cancer and normal samples was analyzed with Biomarker Pattern software. RESULTS: Five distinct potential lung cancer biomarkers with higher sensitivity and specificity were found, with four common biomarkers in both IMAC-Cu and WCX2 chip; the remaining biomarker occurred only in WCX2 chip. Two biomarkers were up-regulated while three biomarkers were down-regulated in the serum samples from patients with non-small cell lung cancer. The sensitivities provided by the individual biomarkers were 75%-96.43% and specificities were 75%-100%. CONCLUSIONS: The preliminary results suggest that serum is a capable resource for detecting specific non-small cell lung cancer biomarkers. SELDI mass spectrometry is a useful tool for the detection and identification of new potential biomarker of non-small cell lung cancer in serum.