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1.
Dan Li William Yang Carolyn Arthur Jun S. Liu Carolina Cruz-Niera Mary Qu Yang 《BMC systems biology》2018,12(7):117
Background
Adenocarcinoma in situ (AIS) is a pre-invasive lesion in the lung and a subtype of lung adenocarcinoma. The patients with AIS can be cured by resecting the lesion completely. In contrast, the patients with invasive lung adenocarcinoma have very poor 5-year survival rate. AIS can develop into invasive lung adenocarcinoma. The investigation and comparison of AIS and invasive lung adenocarcinoma at the genomic level can deepen our understanding of the mechanisms underlying lung cancer development.Results
In this study, we identified 61 lung adenocarcinoma (LUAD) invasive-specific differentially expressed genes, including nine long non-coding RNAs (lncRNAs) based on RNA sequencing techniques (RNA-seq) data from normal, AIS, and invasive tissue samples. These genes displayed concordant differential expression (DE) patterns in the independent stage III LUAD tissues obtained from The Cancer Genome Atlas (TCGA) RNA-seq dataset. For individual invasive-specific genes, we constructed subnetworks using the Genetic Algorithm (GA) based on protein-protein interactions, protein-DNA interactions and lncRNA regulations. A total of 19 core subnetworks that consisted of invasive-specific genes and at least one putative lung cancer driver gene were identified by our study. Functional analysis of the core subnetworks revealed their enrichment in known pathways and biological progresses responsible for tumor growth and invasion, including the VEGF signaling pathway and the negative regulation of cell growth.Conclusions
Our comparison analysis of invasive cases, normal and AIS uncovered critical genes that involved in the LUAD invasion progression. Furthermore, the GA-based network method revealed gene clusters that may function in the pathways contributing to tumor invasion. The interactions between differentially expressed genes and putative driver genes identified through the network analysis can offer new targets for preventing the cancer invasion and potentially increase the survival rate for cancer patients.2.
Background
Clinical statement alone is not enough to predict the progression of disease. Instead, the gene expression profiles have been widely used to forecast clinical outcomes. Many genes related to survival have been identified, and recently miRNA expression signatures predicting patient survival have been also investigated for several cancers. However, miRNAs and their target genes associated with clinical outcomes have remained largely unexplored.Methods
Here, we demonstrate a survival analysis based on the regulatory relationships of miRNAs and their target genes. The patient survivals for the two major cancers, ovarian cancer and glioblastoma multiforme (GBM), are investigated through the integrated analysis of miRNA-mRNA interaction pairs.Results
We found that there is a larger survival difference between two patient groups with an inversely correlated expression profile of miRNA and mRNA. It supports the idea that signatures of miRNAs and their targets related to cancer progression can be detected via this approach.Conclusions
This integrated analysis can help to discover coordinated expression signatures of miRNAs and their target mRNAs that can be employed for therapeutics in human cancers.3.
Objectives
To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.Results
From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.Conclusions
These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.4.
Swee Ling Lim Zhunan Jia Yonghai Lu Hui Zhang Cheng Teng Ng Boon Huat Bay Han Ming Shen Choon Nam Ong 《Metabolomics : Official journal of the Metabolomic Society》2018,14(9):118
Introduction
Histologically lung cancer is classified into four major types: adenocarcinoma (Ad), squamous cell carcinoma (SqCC), large cell carcinoma (LCC), and small cell lung cancer (SCLC). Presently, our understanding of cellular metabolism among them is still not clear.Objectives
The goal of this study was to assess the cellular metabolic profiles across these four types of lung cancer using an untargeted metabolomics approach.Methods
Six lung cancer cell lines, viz., Ad (A549 and HCC827), SqCC (NCl-H226 and NCl-H520), LCC (NCl-H460), and SCLC (NCl-H526), were analyzed using liquid chromatography quadrupole time-of-flight mass spectrometry, with normal human small airway epithelial cells (SAEC) as the control group. The principal component analysis (PCA) was performed to identify the metabolic signatures that had characteristic alterations in each histological type. Further, a metabolite set enrichment analysis was performed for pathway analysis.Results
Compared to the SAEC, 31, 27, 34, 34, 32, and 39 differential metabolites mainly in relation to nucleotides, amino acid, and fatty acid metabolism were identified in A549, HCC827, NCl-H226, NCl-H520, NCl-H460, and NCl-H526 cells, respectively. The metabolic signatures allowed the six cancerous cell lines to be clearly separated in a PCA score plot.Conclusion
The metabolic signatures are unique to each histological type, and appeared to be related to their cell-of-origin and mutation status. The changes are useful for assessing the metabolic characteristics of lung cancer, and offer potential for the establishment of novel diagnostic tools for different origin and oncogenic mutation of lung cancer.5.
Jue Wang Zhimin Geng Jiakan Weng Longjie Shen Ming Li Xueli Cai Chengchao Sun Maoping Chu 《BMC developmental biology》2016,16(1):41
Background
Long non-coding RNAs (LncRNAs) have been identified to play important roles in epigenetic processes that underpin organogenesis. However, the role of LncRNAs in the regulation of transition from fetal to adult life of human heart has not been evaluated.Methods
Immunofiuorescent staining was used to determine the extent of cardiac cell proliferation. Human LncRNA microarrays were applied to define gene expression signatures of the fetal (13–17 weeks of gestation, n?=?4) and adult hearts (30–40 years old, n?=?4). Pathway analysis was performed to predict the function of differentially expressed mRNAs (DEM). DEM related to cell proliferation were selected to construct a lncRNA-mRNA co-expression network. Eight lncRNAs were confirmed by quantificational real-time polymerase chain reaction (n?=?6).Results
Cardiac cell proliferation was significant in the fetal heart. Two thousand six hundred six lncRNAs and 3079 mRNAs were found to be differentially expressed. Cell cycle was the most enriched pathway in down-regulated genes in the adult heart. Eight lncRNAs (RP11-119 F7.5, AX747860, HBBP1, LINC00304, TPTE2P6, AC034193.5, XLOC_006934 and AL833346) were predicted to play a central role in cardiac cell proliferation.Conclusions
We discovered a profile of lncRNAs differentially expressed between the human fetal and adult heart. Several meaningful lncRNAs involved in cardiac cell proliferation were disclosed.6.
Background
Evidences have increasingly indicated that lncRNAs (long non-coding RNAs) are deeply involved in important biological regulation processes leading to various human complex diseases. Experimental investigations of these disease associated lncRNAs are slow with high costs. Computational methods to infer potential associations between lncRNAs and diseases have become an effective prior-pinpointing approach to the experimental verification.Results
In this study, we develop a novel method for the prediction of lncRNA-disease associations using bi-random walks on a network merging the similarities of lncRNAs and diseases. Particularly, this method applies a Laplacian technique to normalize the lncRNA similarity matrix and the disease similarity matrix before the construction of the lncRNA similarity network and disease similarity network. The two networks are then connected via existing lncRNA-disease associations. After that, bi-random walks are applied on the heterogeneous network to predict the potential associations between the lncRNAs and the diseases. Experimental results demonstrate that the performance of our method is highly comparable to or better than the state-of-the-art methods for predicting lncRNA-disease associations. Our analyses on three cancer data sets (breast cancer, lung cancer, and liver cancer) also indicate the usefulness of our method in practical applications.Conclusions
Our proposed method, including the construction of the lncRNA similarity network and disease similarity network and the bi-random walks algorithm on the heterogeneous network, could be used for prediction of potential associations between the lncRNAs and the diseases.7.
Emily G. Armitage Andrew D. Southam 《Metabolomics : Official journal of the Metabolomic Society》2016,12(9):146
Introduction
Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.Objectives
Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.Methods
A literature search of the current relevant primary research was performed.Results
Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.Conclusion
Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.8.
9.
10.
Background
Hsp90-beta has been investigated to be correlated with the occurrence and development of tumor. The intention of this research was to test the level of Hsp90-beta in malignant pleural effusion (MPE) of patients with lung cancer and disclose the clinical significance of Hsp90-beta as a potential tumor marker for differential diagnosis of pleural effusion caused by lung cancer.Methods
The level of Hsp90-beta was determined using enzyme-linked immunosorbent assay. Calculations of the Hsp90-beta threshold, the sensitivity and specificity for distinguishing MPE from benign pleural effusion were performed using receiver operator characteristic curve.Results
The level of Hsp90-beta in MPE of lung cancer patients was higher than that in control individuals (P <?0.05) and increased MPE Hsp90-beta was correlated with the pathological differentiation, tumor size and lymphatic metastasis (P <?0.05). The cutoff value of Hsp90-beta produced by receiver operator characteristic curve for distinguishing lung cancer from control individuals were 1.659?ng/mL and the sensitivity and specificity were 93.46 and 79%.Conclusions
Increased Hsp90-beta in MPE was correlated with malignant biological behavior of lung cancer patients, indicating that the level of Hsp90-beta could be a tool of referential value for differential diagnosis of pleural effusion caused by lung cancer.11.
Meng-Yao Sun Jian-Yong Zhu Chun-Yan Zhang Miao Zhang Ya-Nan Song Khalid Rahman Li-Jun Zhang Hong Zhang 《Biotechnology letters》2017,39(10):1477-1484
Objectives
To identify whether lncRNAs (long non-coding RNA) participate in the regulation of cisplatin-resistant induced autophagy in endometrial cancer cells.Results
Autophagy activity was significantly boosted in cisplatin-resistant Ishikawa cells, a human endometrial cancer cell line, compared with that in parental Ishikawa cells. After analyzing the overall long noncoding RNA (lncRNA) profiling, a meaningful lncRNA, HOTAIR, was identified. It was down-regulated simultaneously in cisplatin-resistant Ishikawa cells and parental Ishikawa cells treated with cisplatin. RNA interference of HOTAIR reduced the proliferation of cisplatin-resistant Ishikawa cells and enhanced the autophagy activity of cisplatin-resistant Ishikawa cells with or without cisplatin treatment, in addition, beclin-1, multidrug resistance (MDR), and P-glycoprotein (P-gp) were mediated by lncRNA HOTAIR.Conclusions
It is clear that lncRNAs, specifically HOTAIR, can regulate the cisplatin-resistance ability of human endometrial cancer cells through the regulation of autophagy by influencing Beclin-1, MDR, and P-gp expression.12.
Background
Human cancers are complex ecosystems composed of cells with distinct molecular signatures. Such intratumoral heterogeneity poses a major challenge to cancer diagnosis and treatment. Recent advancements of single-cell techniques such as scRNA-seq have brought unprecedented insights into cellular heterogeneity. Subsequently, a challenging computational problem is to cluster high dimensional noisy datasets with substantially fewer cells than the number of genes.Methods
In this paper, we introduced a consensus clustering framework conCluster, for cancer subtype identification from single-cell RNA-seq data. Using an ensemble strategy, conCluster fuses multiple basic partitions to consensus clusters.Results
Applied to real cancer scRNA-seq datasets, conCluster can more accurately detect cancer subtypes than the widely used scRNA-seq clustering methods. Further, we conducted co-expression network analysis for the identified melanoma subtypes.Conclusions
Our analysis demonstrates that these subtypes exhibit distinct gene co-expression networks and significant gene sets with different functional enrichment.13.
14.
Eric B Meltzer William T Barry Thomas A D'Amico Robert D Davis Shu S Lin Mark W Onaitis Lake D Morrison Thomas A Sporn Mark P Steele Paul W Noble 《BMC medical genomics》2011,4(1):70
Background
The accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is a major clinical challenge. We developed a model to diagnose IPF by applying Bayesian probit regression (BPR) modelling to gene expression profiles of whole lung tissue.Methods
Whole lung tissue was obtained from patients with idiopathic pulmonary fibrosis (IPF) undergoing surgical lung biopsy or lung transplantation. Controls were obtained from normal organ donors. We performed cluster analyses to explore differences in our dataset. No significant difference was found between samples obtained from different lobes of the same patient. A significant difference was found between samples obtained at biopsy versus explant. Following preliminary analysis of the complete dataset, we selected three subsets for the development of diagnostic gene signatures: the first signature was developed from all IPF samples (as compared to controls); the second signature was developed from the subset of IPF samples obtained at biopsy; the third signature was developed from IPF explants. To assess the validity of each signature, we used an independent cohort of IPF and normal samples. Each signature was used to predict phenotype (IPF versus normal) in samples from the validation cohort. We compared the models' predictions to the true phenotype of each validation sample, and then calculated sensitivity, specificity and accuracy.Results
Surprisingly, we found that all three signatures were reasonably valid predictors of diagnosis, with small differences in test sensitivity, specificity and overall accuracy.Conclusions
This study represents the first use of BPR on whole lung tissue; previously, BPR was primarily used to develop predictive models for cancer. This also represents the first report of an independently validated IPF gene expression signature. In summary, BPR is a promising tool for the development of gene expression signatures from non-neoplastic lung tissue. In the future, BPR might be used to develop definitive diagnostic gene signatures for IPF, prognostic gene signatures for IPF or gene signatures for other non-neoplastic lung disorders such as bronchiolitis obliterans.15.
Jialin Wang Tao Zhang Xiaotao Shen Jia Liu Deli Zhao Yawen Sun Lu Wang Yingjun Liu Xiaoyun Gong Yanxun Liu Zheng-Jiang Zhu Fuzhong Xue 《Metabolomics : Official journal of the Metabolomic Society》2016,12(7):116
Introduction
Previous metabolomics studies have revealed perturbed metabolic signatures in esophageal squamous cell carcinoma (ESCC) patients, however, most of these studies included mainly late-staged ESCC patients due to the difficulties of collecting the early-staged samples from asymptotic ESCC subjects.Objectives
This study aims to explore the early-staged ESCC metabolic signatures and potential of serum metabolomics to diagnose ESCC at early stages.Methods
Serum samples of 97 ESCC patients (stage 0, 39 cases; stage I, 17 cases; stage II, 11 cases, stage III, 30 cases) and 105 healthy controls (HC) were enrolled and randomly separated into training data (77 ESCCs, 84 HCs) and validation data (20 ESCCs, 21 HCs). Untargeted metabolomics was performed to identify ESCC-related metabolic signatures.Results
The global metabolomics profiles could clearly distinguish ESCC from HC in training data. 16 ascertained metabolites were found to be disturbed in the metabolic pathways characterized by dysregulated fatty acid biosynthesis, glycerophospholipid metabolism, choline metabolism in cancer and linoleic acid metabolism. The AUC value in validation data was 0.895, with sensitivity 85.0 % and specificity 90.5 %. Good diagnostic performances were also achieved for early stage ESCC, with the values of area under the curve (AUC) 0.881 for the ESCC patients in both stage 0 and I–II. In addition, six metabolites were found to discriminate ESCC stages. Among them, three biomarkers, dodecanoic acid, LysoPA(18:1), and LysoPC(14:0), exhibited clear trend for ESCC progression.Conclusion
These findings suggest serum metabolomics, performed in a minimally noninvasive and convenient manner, may possess great potential for early diagnosis of ESCC patients.16.
Background
Almost 16,000 human long non-coding RNA (lncRNA) genes have been identified in the GENCODE project. However, the function of most of them remains to be discovered. The function of lncRNAs and other novel genes can be predicted by identifying significantly enriched annotation terms in already annotated genes that are co-expressed with the lncRNAs. However, such approaches are sensitive to the methods that are used to estimate the level of co-expression.Results
We have tested and compared two well-known statistical metrics (Pearson and Spearman) and two geometrical metrics (Sobolev and Fisher) for identification of the co-expressed genes, using experimental expression data across 19 normal human tissues. We have also used a benchmarking approach based on semantic similarity to evaluate how well these methods are able to predict annotation terms, using a well-annotated set of protein-coding genes.Conclusion
This work shows that geometrical metrics, in particular in combination with the statistical metrics, will predict annotation terms more efficiently than traditional approaches. Tests on selected lncRNAs confirm that it is possible to predict the function of these genes given a reliable set of expression data. The software used for this investigation is freely available.17.
Sanaya Bamji-Stocke Victor van Berkel Donald M. Miller Hermann B. Frieboes 《Metabolomics : Official journal of the Metabolomic Society》2018,14(6):81
Introduction
Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection has proven essential to extend survival. Genomic and proteomic advances have provided impetus to the effort dedicated to detect and diagnose the disease at an earlier stage. Recently, the study of metabolites associated with tumor formation and progression has inaugurated the era of cancer metabolomics to aid in this effort.Objectives
This review summarizes recent work regarding novel metabolites with the potential to serve as biomarkers for early lung tumor detection, evaluation of disease progression, and prediction of patient outcomes.Method
We compare the metabolite profiling of cancer patients with that of healthy individuals, and the metabolites identified in tissue and biofluid samples and their usefulness as lung cancer biomarkers. We discuss metabolite alterations in tumor versus paired non-tumor lung tissues, as well as metabolite alterations in different stages of lung cancers and their usefulness as indicators of disease progression and overall survival. We evaluate metabolite dysregulation in different types of lung cancers, and those associated with lung cancer versus other lung diseases. We also examine metabolite differences between lung cancer patients and smokers/risk-factor individuals.Result
Although an extensive list of metabolites has been evaluated to distinguish between these cases, refinement of methods is further required for adequate patient diagnosis and treatment.Conclusion
We conclude that with technological advancement, metabolomics may be able to replace more invasive and costly diagnostic procedures while also providing the means to more effectively tailor treatment to patient-specific tumors.18.
19.
Background
Recent studies demonstrated that long non-coding RNAs (lncRNAs) could be intricately implicated in cancer-related molecular networks, and related to cancer occurrence, development and prognosis. However, clinicopathological and molecular features for these cancer-related lncRNAs, which are very important in bridging lncRNA basic research with clinical research, fail to well settle to integration.Results
After manually reviewing more than 2500 published literature, we collected the cancer-related lncRNAs with the experimental proof of functions. By integrating from literature and public databases, we constructed CRlncRNA, a database of cancer-related lncRNAs. The current version of CRlncRNA embodied 355 entries of cancer-related lncRNAs, covering 1072 cancer-lncRNA associations regarding to 76 types of cancer, and 1238 interactions with different RNAs and proteins. We further annotated clinicopathological features of these lncRNAs, such as the clinical stages and the cancer hallmarks. We also provided tools for data browsing, searching and download, as well as online BLAST, genome browser and gene network visualization service.Conclusions
CRlncRNA is a manually curated database for retrieving clinicopathological and molecular features of cancer-related lncRNAs supported by highly reliable evidences. CRlncRNA aims to provide a bridge from lncRNA basic research to clinical research. The lncRNA dataset collected by CRlncRNA can be used as a golden standard dataset for the prospective experimental and in-silico studies of cancer-related lncRNAs. CRlncRNA is freely available for all users at http://crlnc.xtbg.ac.cn.20.