Incorporating deep learning and multi-omics autoencoding for analysis of lung adenocarcinoma prognostication

Lung cancer is the most occurring cancer type, and its mortality rate is also the highest, among them lung adenocarcinoma (LUAD) accounts for about 40 % of lung cancer. There is an urgent need to develop a prognosis prediction model for lung adenocarcinoma. Previous LUAD prognosis studies only took single-omics data, such as mRNA or miRNA, into consideration. To this end, we proposed a deep learning-based autoencoding approach for combination of four-omics data, mRNA, miRNA, DNA methylation and copy number variations, to construct an autoencoder model, which learned representative features to differentiate the two optimal patient subgroups with a significant difference in survival (P = 4.08e-09) and good consistency index (C-index = 0.65). The multi-omics model was validated though four independent datasets, i.e. GSE81089 for mRNA (n = 198, P = 0.0083), GSE63805 for miRNA (n = 32, P = 0.018), GSE63384 for DNA methylation (n = 35, P = 0.009), and TCGA independent samples for copy number variations (n = 94, P = 0.0052). Finally, a functional analysis was performed on two survival subgroups to discover genes involved in biological processes and pathways. This is the first study incorporating deep autoencoding and four-omics data to construct a robust survival prediction model, and results show the approach is useful at predicting LUAD prognostication.     

Genome-wide identification and characterization of long non-coding RNAs involved in acquired resistance to gefitinib in non-small-cell lung cancer

Acquired resistance is a major obstacle to the therapeutic efficacy of gefitinib in non-small-cell lung cancer (NSCLC). Current knowledge about the role of long non-coding RNAs (lncRNAs) in this phenomenon is insufficient. In this study, we searched RNA sequencing data for lncRNAs associated with acquired resistance to gefitinib in NSCLC, and constructed a functional lncRNA-mRNA co-expression network and protein-protein interaction (PPI) network to analyze their putative target genes and biological functions. The expression levels of 14 outstanding dysregulated lncRNAs and mRNA were verified using real-time PCR. Changes in the expression levels of 39 lncRNAs and 121 mRNAs showed common patterns in our two pairs of gefitinib-sensitive and gefitinib-resistant NSCLC cell lines. The co-expression network included 1235 connections among these common differentially expressed lncRNAs and mRNAs. The significantly enriched signaling pathways based on dysregulated mRNAs were mainly involved in the Hippo signaling pathway; proteoglycans in cancer; and valine, leucine, and isoleucine biosynthesis. The results show that LncRNAs play an important part in acquired gefitinib resistance in NSCLC by regulating mRNA expression and function, and may represent potential new molecular biomarkers and therapeutic targets for gefitinib-resistant NSCLC.     

Cereblon contributes to the development of pulmonary fibrosis via inactivation of adenosine monophosphate-activated protein kinase α1

Pulmonary fibrosis is a progressive and lethal lung disease characterized by the proliferation and differentiation of lung fibroblasts and the accumulation of extracellular matrices. Since pulmonary fibrosis was reported to be associated with adenosine monophosphate-activated protein kinase (AMPK) activation, which is negatively regulated by cereblon (CRBN), we aimed to determine whether CRBN is involved in the development of pulmonary fibrosis. Therefore, we evaluated the role of CRBN in bleomycin (BLM)-induced pulmonary fibrosis in mice and in transforming growth factor-beta 1 (TGF-β1)-induced differentiation of human lung fibroblasts. BLM-induced fibrosis and the mRNA expression of collagen and fibronectin were increased in the lung tissues of wild-type (WT) mice; however, they were significantly suppressed in Crbn knockout (KO) mice. While the concentrations of TGF-β1/2 in bronchoalveolar lavage fluid were increased via BLM treatment, they were similar between BLM-treated WT and Crbn KO mice. Knockdown of CRBN suppressed TGF-β1-induced activation of small mothers against decapentaplegic 3 (SMAD3), and overexpression of CRBN increased it. TGF-β1-induced activation of SMAD3 increased α-smooth muscle actin (α-SMA) and collagen levels. CRBN was found to be colocalized with AMPKα1 in lung fibroblasts. CRBN overexpression inactivated AMPKα1. When cells were treated with metformin (an AMPK activator), the CRBN-induced activation of SMAD3 and upregulation of α-SMA and collagen expression were significantly suppressed, suggesting that increased TGF-β1-induced activation of SMAD3 via CRBN overexpression is associated with AMPKα1 inactivation. Taken together, these data suggest that CRBN is a profibrotic regulator and maybe a potential target for treating lung fibrosis.Subject terms: Pathogenesis, Biochemistry     

Web-based tools for mining the NCI databases for anticancer drug discovery

In this paper, we describe the development of a set of integrated Web-based tools for mining the National Cancer Institute's (NCI) anticancer databases for anticancer drug discovery. For data mining, three different correlation algorithms were implemented, which included the commonly used Pearson's correlation algorithm available from the NCI's COMPARE program, the Spearman's and Kendall's correlation algorithms. In addition, we implemented the p-value test to evaluate the significance of the correlation results. These Web-based data mining tools allow robust analysis of the correlation between the in vitro anticancer activity of the drugs in the NCI anticancer database, the protein levels and mRNA levels of molecular targets (genes) in the NCI 60 human cancer cell lines for identification of potential lead compounds for a specific molecular target and for study of the molecular mechanism action of a drug. Examples were provided to identify PKC ligands using a lead compound and to identify potential ErbB-2 inhibitors using the mRNA levels of ErbB-2 in the NCI 60 tumor cell lines.     

Crucial role of HMGA1 in the self-renewal and drug resistance of ovarian cancer stem cells

Cancer stem cells are a subpopulation of cancer cells characterized by self-renewal ability, tumorigenesis and drug resistance. The aim of this study was to investigate the role of HMGA1, a chromatin remodeling factor abundantly expressed in many different cancers, in the regulation of cancer stem cells in ovarian cancer. Spheroid-forming cancer stem cells were isolated from A2780, SKOV3 and PA1 ovarian cancer cells by three-dimensional spheroid culture. Elevated expression of HMGA1 was observed in spheroid cells along with increased expression of stemness-related genes, such as SOX2, KLF4, ALDH, ABCB1 and ABCG2. Furthermore, spheroid A2780 cells, compared with adherent cells, showed higher resistance to chemotherapeutic agents such as paclitaxel and doxorubicin. HMGA1 knockdown in spheroid cells reduced the proliferative advantage and spheroid-forming efficiency of the cells and the expression of stemness-related genes. HMGA1 overexpression in adherent A2780 cells increased cancer stem cell properties, including proliferation, spheroid-forming efficiency and the expression of stemness-related genes. In addition, HMGA1 regulated ABCG2 promoter activity through HMGA1-binding sites. Knockdown of HMGA1 in spheroid cells reduced resistance to chemotherapeutic agents, whereas the overexpression of HMGA1 in adherent ovarian cancer cells increased resistance to chemotherapeutic agents in vitro. Furthermore, HMGA1-overexpressing A2780 cells showed a significant survival advantage after chemotherapeutic agent treatment in a xenograft tumorigenicity assay. Together, our results provide novel insights regarding the critical role of HMGA1 in the regulation of the cancer stem cell characteristics of ovarian cancer cells, thus suggesting that HMGA1 may be an important target in the development of therapeutics for ovarian cancer patients.     

Extracellular matrix alterations in low‐grade lung adenocarcinoma compared with normal lung tissue by imaging mass spectrometry

Lung adenocarcinoma (LUAD) is the second most common cancer, affecting both men and women. Fibrosis is a hallmark of LUAD occurring throughout progression with excess production of extracellular matrix (ECM) components that lead to metastatic cell processes. Understanding the ECM cues that drive LUAD progression has been limited due to a lack of tools that can access and report on ECM components within the complex tumor microenvironment. Here, we test whether low‐grade LUAD can be distinguished from normal lung tissue using a novel ECM imaging mass spectrometry (ECM IMS) approach. ECM IMS analysis of a tissue microarray with 20 low‐grade LUAD tissues and 20 normal lung samples from 10 patients revealed 25 peptides that could discriminate between normal and low‐grade LUAD using area under the receiver‐operating curve (AUC) ≥0.7, P value ≤.001. Principal component analysis demonstrated that 62.4% of the variance could be explained by sample origin from normal or low‐grade tumor tissue. Additional work performed on a wedge resection with moderately differentiated LUAD demonstrated that the ECM IMS analytical approach could distinguish LUAD spectral features from spectral features of normal adjacent lung tissue. Conventional liquid chromatography with tandem mass spectrometry (LC‐MS/MS) proteomics demonstrated that specific sites of hydroxylation of proline (HYP) were a main collagen post translational modification that was readily detected in LUAD. A distinct peptide from collagen 3A1 modified by HYP was increased 3.5 fold in low‐grade LUAD compared with normal lung tissue (AUC 0.914, P value <.001). This suggests that regulation of collagen proline hydroxylation could be an important process during early LUAD fibrotic deposition. ECM IMS is a useful tool that may be used to define fibrotic deposition in low‐grade LUAD.     

Protective Effect of Cudrania tricuspidata Extract against High-Fat Diet Induced Nonalcoholic Fatty Liver Disease through Nrf-2/HO-1 Pathway

Nonalcoholic fatty liver disease is the most common chronic disease affecting a wide range of the world’s population and associated with obesity-induced metabolic syndrome. It is possibly emerging as a leading cause of life-threatening liver diseases for which a drug with a specific therapeutic target has not been developed yet. Previously, there have been reports on the benefits of Cudrania tricuspidata (CT) for treating obesity and diabetes via regulation of metabolic processes, such as lipogenesis, lipolysis, and inflammation. In this study, we investigated the ameliorative effect of orally administered 0.25% and 0.5% (w/w) CT mixed with high-fat diet (HFD) to C57BL/6J mice for 7 weeks. It was found that body weight, fat mass, hepatic mass, serum glucose level, and liver cholesterol levels were significantly reduced after CT treatment. In CT-treated HFD-fed mice, the mRNA expression levels of hepatic lipogenic and inflammatory cytokine-related genes were markedly reduced, whereas the expression level of epididymal lipogenic genes was increased. The mRNA expression level of beta-oxidation and Nrf-2/HO-1 genes significantly increased in CT-treated obese mice livers. We propose that CT alleviates hepatic steatosis by reducing oxidative stress and inflammation.     

PAK1 as a Potential Therapeutic Target in Male Smokers with EGFR-Mutant Non-Small Cell Lung Cancer

P21-activated kinases (PAKs) are serine/threonine protein kinases that contribute to several cellular processes. Here, we aimed to determine the prognostic value of PAK1 and its correlation with the clinicopathological characteristics and five-year survival rates in patients with non-small cell lung cancer (NSCLC). We evaluated PAK1 mRNA and protein expression in NSCLC cells and resected tumor specimens, as well as in healthy human bronchial epithelial cells and adjacent healthy lung tissues, respectively, for effective comparison. Immunohistochemical tissue microarray analysis of 201 NSCLC specimens showed the correlation of PAK1 expression with clinicopathological characteristics. The mRNA and protein expression of PAK1 were 2.9- and 4.3-fold higher in six of seven NSCLC cell types and human tumors (both, p < 0.001) than in healthy human bronchial epithelial BEAS-2B cells and adjacent healthy lung tissues, respectively. Decreased survival was significantly associated with PAK1 overexpression in the entire cohort (χ² = 8.48, p = 0.0036), men (χ² = 17.1, p < 0.0001), and current and former smokers (χ² = 19.2, p < 0.0001). Notably, epidermal growth factor receptor (EGFR) mutation-positive lung cancer patients with high PAK1 expression showed higher mortality rates than those with low PAK1 expression (91.3% vs. 62.5%, p = 0.02). Therefore, PAK1 overexpression could serve as a molecular target for the treatment of EGFR mutation-positive lung cancer, especially among male patients and current/former smokers.     

1,3-取代吲唑类低氧诱导因子l抑制剂的设计合成及其抗肝癌活性

低氧诱导因子l(HIF-1)与肿瘤细胞的生长、侵袭和耐药密切相关,在肿瘤细胞内HIF-1高度表达,因此新型的HIF-1抑制剂可作为潜在的抗肿瘤药物。 本文合成了9个1,3-取代吲唑衍生物。 通过蛋白质印迹(Western Blot)法及实时定量荧光PCR(Real time-PCR)等方法检测了其对HIF-1及其靶基因血管内皮生长因子(VEGF)表达水平的影响,并以3-(5'-羟甲基-2'-呋喃基)-1-苯甲基吲唑(YC-1)为阳性对照药物初步评价了其体外抗肝癌细胞增殖的生物活性。 实验发现化合物7b可显著抑制HIF-1及其下游靶基因VEGF的表达,且体外抗肝癌增殖生物活性优于YC-1,半抑制浓度(IC₅₀)值为10.37 μmol/L。 研究结果表明,3-(5'-羟甲基-2'-呋喃)-1-(1″-对甲苯磺酰基)吲唑具有靶向抑制HIF及良好的抗肝癌活性作用。     

Network pharmacology analysis of pharmacological mechanisms underlying the anti-type 2 diabetes mellitus effect of guava leaf

The current study aimed to explore the anti-type 2 diabetes mellitus (T2DM) mechanism of guava leaf based on network pharmacology. The compounds contained in guava leaf was summarized from the literature, and a series of databases was used to identify the active components and corresponding potential targets. The intersection between diabetes-associated genes searched in the GeneCard database and the predicted targets of guava leaf active components was defined as target genes, which were then used to construct a “compound-active components-target genes” pharmacological network. The biological functions and pathway enrichment analyses of target genes were performed in KOBAS 3.0. The differential expression analysis of GSE76894 was performed to obtain the differential expressed genes (DEGs) in T2DM patients by comparing non-diabetic controls. Finally, the intersection between DEGs and target genes were named key genes, and the representative pathways in which these genes were involved were drawn through KEGG Mapper. We found that the active components of guava leaf may regulate the PI3K-AKT signaling pathway, T2DM regulation process, and insulin resistance pathway, which was evidenced by KEGG pathway analysis of key genes. These results implied that guava leaf has a potential anti-T2DM property and its mode of action may be exerted via regulating insulin secretion and reducing blood sugar level.     

Excessive mechanical strain accelerates intervertebral disc degeneration by disrupting intrinsic circadian rhythm

Night shift workers with disordered rhythmic mechanical loading are more prone to intervertebral disc degeneration (IDD). Our results showed that circadian rhythm (CR) was dampened in degenerated and aged NP cells. Long-term environmental CR disruption promoted IDD in rats. Excessive mechanical strain disrupted the CR and inhibited the expression of core clock proteins. The inhibitory effect of mechanical loading on the expression of extracellular matrix genes could be reversed by BMAL1 overexpression in NP cells. The Rho/ROCK pathway was demonstrated to mediate the effect of mechanical stimulation on CR. Prolonged mechanical loading for 12 months affected intrinsic CR genes and induced IDD in a model of upright posture in a normal environment. Unexpectedly, mechanical loading further accelerated the IDD in an Light-Dark (LD) cycle-disrupted environment. These results indicated that intrinsic CR disruption might be a mechanism involved in overloading-induced IDD and a potential drug target for night shift workers.Subject terms: Osteoarthritis, Cytoskeleton, Ageing     

Let-7i miRNA and platinum loaded nano-graphene oxide platform for detection/reversion of drug resistance and synergetic chemical-photothermal inhibition of cancer cell

The drug resistance of chemotherapy is a major challenge to overcome for antineoplastic agents and the reverse of drug resistant is essential for cancer therapy. Herein, we developed a drug delivery system which can simultaneously detect/reverse the drug resistance and perform synergetic treatment of cancer. In this work, we integrated cyanine5(Cy5) modified mi RNA(let-7 i)(Cy5-mi RNA) and platinum onto nano-graphene oxide(NGO)(30-50 nm) platform to achieve simultaneously detection/reversion of ...     

Modelling the role of dual specificity phosphatases in herceptin resistant breast cancer cell lines

BackgroundBreast cancer remains the most lethal type of cancer for women. A significant proportion of breast cancer cases are characterised by overexpression of the human epidermal growth factor receptor 2 protein (HER2). These cancers are commonly treated by Herceptin (Trastuzumab), but resistance to drug treatment frequently develops in tumour cells. Dual-specificity phosphatases (DUSPs) are thought to play a role in the mechanism of resistance, since some of them were reported to be overexpressed in tumours resistant to Herceptin.ResultsWe used a systems biology approach to investigate how DUSP overexpression could favour cell proliferation and to predict how this mechanism could be reversed by targeted inhibition of selected DUSPs. We measured the expression of 20 DUSP genes in two breast cancer cell lines following long-term (6 months) exposure to Herceptin, after confirming that these cells had become resistant to the drug. We constructed several Boolean models including specific substrates of each DUSP, and showed that our models correctly account for resistance when overexpressed DUSPs were kept activated. We then simulated inhibition of both individual and combinations of DUSPs, and determined conditions under which the resistance could be reversed.ConclusionsThese results show how a combination of experimental analysis and modelling help to understand cell survival mechanisms in breast cancer tumours, and crucially enable us to generate testable predictions potentially leading to new treatments of resistant tumours.