A seven-long noncoding RNA signature predicts relapse in patients with early-stage lung adenocarcinoma.

BACKGROUND: Long noncoding RNA (lncRNA) has been increasingly reported to play crucial roles in cancer development. In this study, we aim to develop a lncRNA-based signature to predict the relapse of early-stage (stage I-II) lung adenocarcinoma (LUAD). METHODS: With a lncRNA-mining strategy, lncRNA expression profiles of three LUAD cohorts were obtained from the Gene Expression Omnibus database. A risk score model was established based on the lncRNAs expression from training set (GSE31210, n = 204) and further validated in two independent testing sets (GSE50081, n = 124; and GSE30219, n = 84). The potential signaling pathways modulated by the prognostic lncRNAs were explored using bioinformatics analysis. RESULTS: In the training set, seven lncRNAs were identified to be significantly correlated with the relapse-free survival (RFS) of early-stage LUAD, and were then aggregated to form a seven-lncRNA prognostic signature to classify patients into high-risk and low-risk groups. Individuals of training set in the high-risk group exhibited a poorer RFS than those in the low-risk group (HR: 7.574, 95% CI: 4.165-13.775; P < 0.001). The similar prognostic powers of the seven-lncRNA signature were also achieved in the two independent testing sets and in stratified analysis. Multivariate Cox regression indicated that the prognostic value of seven-lncRNA signature was independent of other clinical features. Functional enrichment analysis found that the seven-lncRNA signature may be involved in biological pathways such as cell cycle, DNA replication, and p53 signaling pathway. CONCLUSION: Our results indicate that the seven-lncRNA signature may be an innovative biomarker to predict the relapse of early-stage LUAD.

BMCMDA: a novel model for predicting human microbe-disease associations via binary matrix completion.

BACKGROUND: Human Microbiome Project reveals the significant mutualistic influence between human body and microbes living in it. Such an influence lead to an interesting phenomenon that many noninfectious diseases are closely associated with diverse microbes. However, the identification of microbe-noninfectious disease associations (MDAs) is still a challenging task, because of both the high cost and the limitation of microbe cultivation. Thus, there is a need to develop fast approaches to screen potential MDAs. The growing number of validated MDAs enables us to meet the demand in a new insight. Computational approaches, especially machine learning, are promising to predict MDA candidates rapidly among a large number of microbe-disease pairs with the advantage of no limitation on microbe cultivation. Nevertheless, a few computational efforts at predicting MDAs are made so far. RESULTS: In this paper, grouping a set of MDAs into a binary MDA matrix, we propose a novel predictive approach (BMCMDA) based on Binary Matrix Completion to predict potential MDAs. The proposed BMCMDA assumes that the incomplete observed MDA matrix is the summation of a latent parameterizing matrix and a noising matrix. It also assumes that the independently occurring subscripts of observed entries in the MDA matrix follows a binomial model. Adopting a standard mean-zero Gaussian distribution for the nosing matrix, we model the relationship between the parameterizing matrix and the MDA matrix under the observed microbe-disease pairs as a probit regression. With the recovered parameterizing matrix, BMCMDA deduces how likely a microbe would be associated with a particular disease. In the experiment under leave-one-out cross-validation, it exhibits the inspiring performance (AUC = 0.906, AUPR =0.526) and demonstrates its superiority by ~ 7% and ~ 5% improvements in terms of AUC and AUPR respectively in the comparison with the pioneering approach KATZHMDA. CONCLUSIONS: Our BMCMDA provides an effective approach for predicting MDAs and can be also extended to other similar predicting tasks of binary relationship (e.g. protein-protein interaction, drug-target interaction).

ANKRD22 promotes M2 polarization in lung adenocarcinoma macrophages via the glycolytic pathway.

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer with a low 5-year survival rate. ANKRD22 is an ankyrin repeat protein capable of promoting tumor progression, and its mechanism in LUAD remains elusive. Our study aims to investigate the mechanisms underlying the involvement of ANKRD22 in the progression of LUAD. The expression of ANKRD22 in LUAD and its enriched pathway were analyzed by bioinformatics analysis. Meanwhile, the correlation between ANKRD22 and the expression of glycolysis-related genes and M2 macrophage marker genes was analyzed. qRT-PCR was used for determination of the expression of ANKRD22, IL-10 and CCL17, CCK-8 for cell viability, and western blot for expression of ANKRD22, LDHA, HK2, PGK1, and PKM2. Immunofluorescence and flow cytometry were utilized to examine the level of CD163, and kits were used to measure the contents of pyruvic acid, lactate, citrate, and malate. Seahorse XF96 analyzer was employed to determine extracellular acidification rate (ECAR) and oxygen consumption rate (OCR). Mitochondrial membrane potential was assessed using the JC-1 probe. Bioinformatics analysis, qRT-PCR, and western blot showed that ANKRD22 was highly expressed in LUAD, which had a positive connection with M2 marker genes. Knockdown of ANKRD22 considerably attenuated the expression of ANKRD22, IL-10, and CCL17 in M2. ANKRD22 overexpression demonstrated the opposite results. Bioinformatics analysis uncovered that ANKRD22 was enriched in the glycolytic pathway and positively correlated with glycolysis-related genes. The knockdown of ANKRD22 substantially attenuated pyruvic acid, lactate, citrate, malate, and ECAR levels and elevated OCR levels in cells. The knockdown of ANKRD22 also reduced mitochondrial membrane potential. Further, it was discovered that glycolysis-related genes had a positive correlation with M2 marker genes. It was revealed by rescue experiments that the usage of 2-DG, a glycolytic inhibitor, remarkably reversed the facilitating effect of overexpression of ANKRD22 on M2 polarization. This study demonstrates that ANKRD22 can facilitate LUAD M2 polarization through glycolysis, and targeting ANKRD22 to inhibit M2 polarization has the potential to be a new strategy for LUAD treatment.

A nine-long non-coding RNA signature for prognosis prediction of patients with lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) is malignant disease with poor therapeutic response and unfavourable prognosis. OBJECTIVE: This study aims to develop a long non-coding RNA (lncRNA) signature for survival prediction in patients with LUSC. METHODS: We obtained lncRNA expression profiles of 493 LUSC cases from The Cancer Genome Atlas, and randomly divided the samples into a training set (n= 296) and a testing set (n= 197). Univariate Cox regression and random survival forest algorithm were performed to select optimum survival-related lncRNAs. RESULTS: A lncRNA-focused risk score model was then constructed for prognosis prediction in the training set and further validated in the testing set and the entire set. Finally, bioinformatics analysis was carried out to explore the potential signaling pathways associated with the prognostic lncRNAs. A set of 9 lncRNAs were found to be strongly correlated with overall survival of LUSC patients. These 9 lncRNAs were integrated into a prognostic signature, which could separate patients into high- and low-risk groups with significantly different survival times in the training set (median: 30.5 vs. 80.5 months, log-rank P< 0.001). This signature was also confirmed in the testing set and the entire set. Besides, the prognostic value of the 9-lncRNA signature was independent of clinical features and maintained stable in stratified analyses. Functional enrichment study suggested that the 9 lncRNAs may be mainly involved in metabolism-related pathways, phosphatidylinositol signaling system, p53 signaling pathway, and notch signaling pathway. CONCLUSIONS: Our study demonstrated the potential clinical implication of the 9-lncRNA signature for survival prediction of LUSC patients.

(E)-ß-farnesene synthase gene affects aphid behavior in transgenic Medicago sativa.

BACKGROUND: Pea aphid (Acyrthosiphon pisum Harris) is one of the major pests in alfalfa crops, causing significant yield losses. (E)-ß-farnesene (EßF), an alarm pheromone released by pea aphid, is generic to many species of aphids, and is used to minimize potential danger from predators and parasitoids by avoiding the source of the pheromone. RESULTS: In this study, EßF synthase gene was constructed into a plant expression vector, and overexpressed in alfalfa (Medicago sativa L.), with expression among transgenic lines confirmed by qRT-PCR. Subcellular localization analysis showed that EßF synthase gene was expressed in the plasma membrane and nucleus of the leaf. GC/MS of extraction from transgenic alfalfa indicated emission of EßF ranging from 5.92 to 13.09 ng day-1 g-1 fresh tissue. Behavior assays in Y-olfactometers demonstrated that transgenic alfalfa expressing AaEßF gene could repel pea aphids, with aphids taking a significantly longer time to select a transgenic line compared with the control line (P < 0.01). CONCLUSION: We have demonstrated a potentially valuable strategy of using EßF synthase genes for aphid control in alfalfa. © 2018 Society of Chemical Industry.

Predicting combinative drug pairs via multiple classifier system with positive samples only.

BACKGROUND AND OBJECTIVE: Due to the synergistic effects of drugs, drug combination is one of the effective approaches for treating complex diseases. However, the identification of drug combinations by dose-response methods is still costly. It is promising to develop supervised learning-based approaches to predict potential drug combinations on a large scale. Nevertheless, these approaches have the inadequate utilization of heterogeneous features, which causes the loss of information useful to classification. Moreover, they have an intrinsic bias, because they assume unknown drug pairs as non-combinations, of which some could be real drug combinations in practice. METHODS: To address above issues, this work first designs a two-layer multiple classifier system (TLMCS) to effectively integrate heterogeneous features involving anatomical therapeutic chemical codes of drugs, drug-drug interactions, drug-target interactions, gene ontology of drug targets, and side effects. To avoid the bias caused by labelling unknown samples as negative, it then utilizes the one-class support vector machines, (which requires no negative instance and only labels approved drug combinations as positive instances), as the member classifiers in TLMCS. Last, both a 10-fold cross validation (10-CV) and a novel prediction are performed to validate the performance of TLMCS. RESULTS: The comparison with three state-of-the-art approaches under 10-CV exhibits the superiority of TLMCS, which achieves the area under the receiver operating characteristic curve = 0.824 and the area under the precision-recall curve = 0.372. Moreover, the experiment under the novel prediction demonstrates its ability, where 9 out of the top-20 predicted combinative drug pairs are validated by checking the published literature. Furthermore, for each of the newly-validated drug combinations, this work analyses the combining mode of the member drugs and investigates their relationship in terms of drug targeting pathways. CONCLUSIONS: The proposed TLMCS provides an effective framework to integrate those heterogeneous features and is trained by only positive samples such that the bias of taking unknown drug pairs as negative samples can be avoided. Furthermore, its results in the novel prediction reveal five types of drug combinations and three types of drug relationships in terms of pathways.