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Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are widely used for patients with EGFR-mutated lung cancer. Despite its initial therapeutic efficacy, most patients eventually develop drug resistance, which leads to a poor prognosis in lung cancer patients. Previous investigations have proved that non-coding RNAs including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and microRNAs (miRNAs) contribute to drug resistance by various biological functions, whereas how they regulate EGFR-TKI resistance remains unclear. In this study, we examined gene expression using the microarray technology on gefitinib-resistant NSCLC cells to obtain differentially expressed (DE) lncRNAs and mRNAs. A total of 45 DE-lncRNAs associated with overall survival and 1799 target DE-mRNAs were employed to construct a core lncRNA–miRNA–mRNA network to illustrate underlying molecular mechanisms of how EGFR-TKI resistance occurs in NSCLC. We found that target DE-mRNAs were mainly enriched in pathways involved in EGFR-TKI resistance, especially the target DE-mRNAs regulated by LINC01128 were significantly enriched in the PI3K/Akt signaling pathway, where the synergy of these target DE-mRNAs may play a key role in EGFR-TKI resistance. In addition, downregulated LINC01128, acting as a specific miRNA sponge, decreases PTEN via sponging miR-25-3p. Furthermore, signaling reactions caused by the downregulation of PTEN would activate the PI3K/Akt signaling pathway, which may lead to EGFR-TKI resistance. In addition, a survival analysis indicated the low expression of LINC01128, and PTEN is closely related to poor prognosis in lung adenocarcinoma (LUAD). Therefore, the LINC01128/miR-25-3p/PTEN axis may promote EGFR-TKI resistance via the PI3K/Akt signaling pathway, which provides new insights into the underlying molecular mechanisms of drug resistance to EGFR-TKIs in NSCLC. In addition, our study sheds light on developing novel therapeutic approaches to overcome EGFR-TKI resistance in NSCLC.

Background There are sudden deterioration phenomena during the progression of many complex diseases, including most cancers; that is, the biological system may go through a critical transition from one stable state (the normal state) to another (the disease state). It is of great importance to predict this critical transition or the so-called pre-disease state so that patients can receive appropriate and timely medical care. In practice, however, this critical transition is usually difficult to identify due to the high nonlinearity and complexity of biological systems. Methods In this study, we employed a model-free computational method, local network entropy (LNE), to identify the critical transition/pre-disease states of complex diseases. From a network perspective, this method effectively explores the key associations among biomolecules and captures their dynamic abnormalities. Results Based on LNE, the pre-disease states of ten cancers were successfully detected. Two types of new prognostic biomarkers, optimistic LNE (O-LNE) and pessimistic LNE (P-LNE) biomarkers, were identified, enabling identification of the pre-disease state and evaluation of prognosis. In addition, LNE helps to find “dark genes” with nondifferential gene expression but differential LNE values. Conclusions The proposed method effectively identified the critical transition states of complex diseases at the single-sample level. Our study not only identified the critical transition states of ten cancers but also provides two types of new prognostic biomarkers, O-LNE and P-LNE biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Background: The dry body of the Tokay Gecko (Gekko gecko) is the source of a valuable traditional Chinese medicine, it is therefore listed as a Class II protected animal species in China. Due to increasing market demand and a declining supply of the species, a considerable number of adulterants have emerged in the market. Thus, it is necessary to establish an accurate and rapid method of identification for distinguishing G. gecko from its adulterants and for separating it from highly processed products.Methods: A total of 274 COI sequences were analyzed by using MEGA 5.0 software. Several specific primers were designed to amplify mini-barcode regions and identify G. gecko from its counterfeits and products.Results: 274 COI sequences of 16 G. gecko species and adulterants were analyzed. G. gecko could be distinguished from its adulterants through BLAST analysis, intra- and inter-specific distance analyses, and an NJ tree based on COI sequences. Two pairs of specific primers designed for this study, COISF2/COISR2 and COISF3/COISR3, amplified 200- and 133-bp fragments of the COI region, respectively, both of which were suitable for the identification of G. gecko and its adulterants. Furthermore, COISF3/COISR3 detected G. gecko in 15 batches of products.Conclusion: Therefore, the specific DNA mini-barcoding method developed here may be a powerful tool for the identification of G. gecko and counterfeits, and may also be used to distinguish G. gecko from its highly processed by-products.

Artemisinin is currently the most effective ingredient in the treatment of malaria, which is thus of great significance to study the genetic regulation of Artemisia annua. Alternative splicing (AS) is a regulatory process that increases the complexity of transcriptome and proteome. The most common mechanism of alternative splicing (AS) in plant is intron retention (IR). However, little is known about whether the IR isoforms produced by light play roles in regulating biosynthetic pathways. In this work we would explore how the level of AS in A. annua responds to light regulation. We obtained a new dataset of AS by analyzing full-length transcripts using both Illumina- and single molecule real-time (SMRT)-based RNA-seq as well as analyzing AS on various tissues. A total of 5,854 IR isoforms were identified, with IR accounting for the highest proportion (48.48%), affirming that IR is the most common mechanism of AS. We found that the number of up-regulated IR isoforms (1534/1378, blue and red light, respectively) was more than twice that of down-regulated (636/682) after treatment of blue or red light. In the artemisinin biosynthetic pathway, 10 genes produced 16 differentially expressed IR isoforms. This work demonstrated that the differential expression of IR isoforms induced by light has the potential to regulate sesquiterpenoid biosynthesis. This study also provides high accuracy full-length transcripts, which can be a valuable genetic resource for further research of A. annua, including areas of development, breeding, and biosynthesis of active compounds.