Identification of possible genetic polymorphisms involved in cancer cachexia: a systematic review

Cancer cachexia is a polygenic and complex syndrome. Genetic variations in regulation of the inflammatory response, muscle and fat metabolic pathways, and pathways in appetite regulation are likely to contribute to the susceptibility or resistance to developing cancer cachexia. A systematic search of Medline and EmBase databases, covering 1986–2008 was performed for potential candidate genes/genetic polymorphisms relating to cancer cachexia. Related genes were then identified using pathway functional analysis software. All candidate genes were reviewed for functional polymorphisms or clinically significant polymorphisms associated with cachexia using the OMIM and GeneRIF databases. Genes with variants which had functional or clinical associations with cachexia and replicated in at least one study were entered into pathway analysis software to reveal possible network associations between genes. A total of 184 polymorphisms with functional or clinical relevance to cancer cachexia were identified in 92 candidate genes. Of these, 42 polymorphisms (in 33 genes) were replicated in more than one study with 13 polymorphisms found to influence two or more hallmarks of cachexia (i.e. inflammation, loss of fat mass and/or lean mass and reduced survival). Thirty-three genes were found to be significantly interconnected in two major networks with four genes (ADIPOQ, IL6, NFKB1 and TLR4) interlinking both networks. Selection of candidate genes and polymorphisms is a key element of multigene study design. The present study provides an initial framework to select genes/ polymorphisms for further study in cancer cachexia, and to develop their potential as susceptibility biomarkers of developing cachexia.     

Systematic prediction of key genes for ovarian cancer by co-expression network analysis

Ovarian cancer (OC) is the most lethal gynaecological malignancy, characterized by high recurrence and mortality. However, the mechanisms of its pathogenesis remain largely unknown, hindering the investigation of the functional roles. This study sought to identify key hub genes that may serve as biomarkers correlated with prognosis. Here, we conduct an integrated analysis using the weighted gene co-expression network analysis (WGCNA) to explore the clinically significant gene sets and identify candidate hub genes associated with OC clinical phenotypes. The gene expression profiles were obtained from the MERAV database. Validations of candidate hub genes were performed with RNASeqV2 data and the corresponding clinical information available from The Cancer Genome Atlas (TCGA) database. In addition, we examined the candidate genes in ovarian cancer cells. Totally, 19 modules were identified and 26 hub genes were extracted from the most significant module (R² = .53) in clinical stages. Through the validation of TCGA data, we found that five hub genes (COL1A1, DCN, LUM, POSTN and THBS2) predicted poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that these five genes exhibited diagnostic efficiency for early-stage and advanced-stage cancer. The protein expression of these five genes in tumour tissues was significantly higher than that in normal tissues. Besides, the expression of COL1A1 was associated with the TAX resistance of tumours and could be affected by the autophagy level in OC cell line. In conclusion, our findings identified five genes could serve as biomarkers related to the prognosis of OC and may be helpful for revealing pathogenic mechanism and developing further research.     

Molecular insight into cotton leaf curl geminivirus disease resistance in cultivated cotton (Gossypium hirsutum)

Cultivated cotton (Gossypium hirsutum) is the most important fibre crop in the world. Cotton leaf curl disease (CLCuD) is the major limiting factor and a threat to textile industry in India and Pakistan. All the local cotton cultivars exhibit moderate to no resistance against CLCuD. In this study, we evaluated an exotic cotton accession Mac7 as a resistance source to CLCuD by challenging it with viruliferous whiteflies and performing qPCR to evaluate the presence/absence and relative titre of CLCuD‐associated geminiviruses/betasatellites. The results indicated that replication of pathogenicity determinant betasatellite is significantly attenuated in Mac7 and probably responsible for resistance phenotype. Afterwards, to decipher the genetic basis of CLCuD resistance in Mac7, we performed RNA sequencing on CLCuD‐infested Mac7 and validated RNA‐Seq data with qPCR on 24 independent genes. We performed co‐expression network and pathway analysis for regulation of geminivirus/betasatellite‐interacting genes. We identified nine novel modules with 52 hubs of highly connected genes in network topology within the co‐expression network. Analysis of these hubs indicated the differential regulation of auxin stimulus and cellular localization pathways in response to CLCuD. We also analysed the differential regulation of geminivirus/betasatellite‐interacting genes in Mac7. We further performed the functional validation of selected candidate genes via virus‐induced gene silencing (VIGS). Finally, we evaluated the genomic context of resistance responsive genes and found that these genes are not specific to A or D sub‐genomes of G. hirsutum. These results have important implications in understanding CLCuD resistance mechanism and developing a durable resistance in cultivated cotton.     

A novel algorithm based on bi-random walks to identify disease-related lncRNAs

Background

It’s a very urgent task to identify cancer genes that enables us to understand the mechanisms of biochemical processes at a biomolecular level and facilitates the development of bioinformatics. Although a large number of methods have been proposed to identify cancer genes at recent times, the biological data utilized by most of these methods is still quite less, which reflects an insufficient consideration of the relationship between genes and diseases from a variety of factors.

Results

In this paper, we propose a two-rounds random walk algorithm to identify cancer genes based on multiple biological data (TRWR-MB), including protein-protein interaction (PPI) network, pathway network, microRNA similarity network, lncRNA similarity network, cancer similarity network and protein complexes. In the first-round random walk, all cancer nodes, cancer-related genes, cancer-related microRNAs and cancer-related lncRNAs, being associated with all the cancer, are used as seed nodes, and then a random walker walks on a quadruple layer heterogeneous network constructed by multiple biological data. The first-round random walk aims to select the top score k of potential cancer genes. Then in the second-round random walk, genes, microRNAs and lncRNAs, being associated with a certain special cancer in corresponding cancer class, are regarded as seed nodes, and then the walker walks on a new quadruple layer heterogeneous network constructed by lncRNAs, microRNAs, cancer and selected potential cancer genes. After the above walks finish, we combine the results of two-rounds RWR as ranking score for experimental analysis. As a result, a higher value of area under the receiver operating characteristic curve (AUC) is obtained. Besides, cases studies for identifying new cancer genes are performed in corresponding section.

Conclusion

In summary, TRWR-MB integrates multiple biological data to identify cancer genes by analyzing the relationship between genes and cancer from a variety of biological molecular perspective.

     

Integrated genomics of chemotherapy resistant ovarian cancer: A role for extracellular matrix,TGFbeta and regulating microRNAs

Epithelial ovarian cancer is the sixth most common cancer in women worldwide and the most important cause of death from gynaecological cancers in the Western world.Our explorative pathway analysis on seven published gene-sets associated with platinum resistance in ovarian cancer reveals TP53 and transforming growth factor beta as key genes. Furthermore, the extracellular matrix was associated with chemotherapy resistance in ovarian cancer as well as endocrine resistance in breast cancer. Pathway analysis again revealed transforming growth factor beta as a key gene regulating extracellular matrix gene expression. A model is presented based on literature linking transforming growth factor beta, extracellular matrix, integrin signalling, epithelial to mesenchymal transition and regulating microRNAs with a (bivalent) role in chemotherapy response.     

Identification of potential functional variants underlying ovine resistance to gastrointestinal nematode infection by using RNA-Seq

In dairy sheep flocks from Mediterranean countries, replacement and adult ewes are the animals most affected by gastrointestinal nematode (GIN) infections. In this study, we have exploited the information derived from an RNA-Seq experiment with the aim of identifying potential causal mutations related to GIN resistance in sheep. Considering the RNA-Seq samples from 12 ewes previously classified as six resistant and six susceptible animals to experimental infection by Teladorsagia circumcincta, we performed a variant calling analysis pipeline using two different types of software, gatk version 3.7 and Samtools version 1.4. The variants commonly identified by the two packages (high-quality variants) within two types of target regions – (i) QTL regions previously reported in sheep for parasite resistance based on SNP-chip or sequencing technology studies and (ii) functional candidate genes selected from gene expression studies related to GIN resistance in sheep – were further characterised to identify mutations with a potential functional impact. Among the genes harbouring these potential functional variants (930 and 553 respectively for the two types of regions), we identified 111 immune-related genes in the QTL regions and 132 immune-related genes from the initially selected candidate genes. For these immune-related genes harbouring potential functional variants, the enrichment analyses performed highlighted significant GO terms related to apoptosis, adhesion and inflammatory response, in relation to the QTL related variants, and significant disease-related terms such as inflammation, adhesion and necrosis, in relation to the initial candidate gene list. Overall, the study provides a valuable list of potential causal mutations that could be considered as candidate causal mutations in relation to GIN resistance in sheep. Future studies should assess the role of these suggested mutations with the aim of identifying genetic markers that could be directly implemented in sheep breeding programmes considering not only production traits, but also functional traits such as resistance to GIN infections.     

Construction of miRNA‐lncRNA‐mRNA co‐expression network affecting EMT‐mediated cisplatin resistance in ovarian cancer

Platinum resistance is one of the major concerns in ovarian cancer treatment. Recent evidence shows the critical role of epithelial–mesenchymal transition (EMT) in this resistance. Epithelial‐like ovarian cancer cells show decreased sensitivity to cisplatin after cisplatin treatment. Our study prospected the association between epithelial phenotype and response to cisplatin in ovarian cancer. Microarray dataset {"type":"entrez-geo","attrs":{"text":"GSE47856","term_id":"47856"}}GSE47856 was acquired from the GEO database. After identifying differentially expressed genes (DEGs) between epithelial‐like and mesenchymal‐like cells, the module identification analysis was performed using weighted gene co‐expression network analysis (WGCNA). The gene ontology (GO) and pathway analyses of the most considerable modules were performed. The protein–protein interaction network was also constructed. The hub genes were specified using Cytoscape plugins MCODE and cytoHubba, followed by the survival analysis and data validation. Finally, the co‐expression of miRNA‐lncRNA‐TF with the hub genes was reconstructed. The co‐expression network analysis suggests 20 modules relating to the Epithelial phenotype. The antiquewhite4, brown and darkmagenta modules are the most significant non‐preserved modules in the Epithelial phenotype and contain the most differentially expressed genes. GO, and KEGG pathway enrichment analyses on these modules divulge that these genes were primarily enriched in the focal adhesion, DNA replication pathways and stress response processes. ROC curve and overall survival rate analysis show that the co‐expression pattern of the brown module''s hub genes could be a potential prognostic biomarker for ovarian cancer cisplatin resistance.     

Resistance to paclitaxel in a cisplatin-resistant ovarian cancer cell line is mediated by P-glycoprotein

The IGROVCDDP cisplatin-resistant ovarian cancer cell line is also resistant to paclitaxel and models the resistance phenotype of relapsed ovarian cancer patients after first-line platinum/taxane chemotherapy. A TaqMan low-density array (TLDA) was used to characterise the expression of 380 genes associated with chemotherapy resistance in IGROVCDDP cells. Paclitaxel resistance in IGROVCDDP is mediated by gene and protein overexpression of P-glycoprotein and the protein is functionally active. Cisplatin resistance was not reversed by elacridar, confirming that cisplatin is not a P-glycoprotein substrate. Cisplatin resistance in IGROVCDDP is multifactorial and is mediated in part by the glutathione pathway and decreased accumulation of drug. Total cellular glutathione was not increased. However, the enzyme activity of GSR and GGT1 were up-regulated. The cellular localisation of copper transporter CTR1 changed from membrane associated in IGROV-1 to cytoplasmic in IGROVCDDP. This may mediate the previously reported accumulation defect. There was decreased expression of the sodium potassium pump (ATP1A), MRP1 and FBP which all have been previously associated with platinum accumulation defects in platinum-resistant cell lines. Cellular localisation of MRP1 was also altered in IGROVCDDP shifting basolaterally, compared to IGROV-1. BRCA1 was also up-regulated at the gene and protein level. The overexpression of P-glycoprotein in a resistant model developed with cisplatin is unusual. This demonstrates that P-glycoprotein can be up-regulated as a generalised stress response rather than as a specific response to a substrate. Mechanisms characterised in IGROVCDDP cells may be applicable to relapsed ovarian cancer patients treated with frontline platinum/taxane chemotherapy.     

ALDH1A1 Maintains Ovarian Cancer Stem Cell-Like Properties by Altered Regulation of Cell Cycle Checkpoint and DNA Repair Network Signaling

Objective

Aldehyde dehydrogenase (ALDH) expressing cells have been characterized as possessing stem cell-like properties. We evaluated ALDH+ ovarian cancer stem cell-like properties and their role in platinum resistance.

Methods

Isogenic ovarian cancer cell lines for platinum sensitivity (A2780) and platinum resistant (A2780/CP70) as well as ascites from ovarian cancer patients were analyzed for ALDH+ by flow cytometry to determine its association to platinum resistance, recurrence and survival. A stable shRNA knockdown model for ALDH1A1 was utilized to determine its effect on cancer stem cell-like properties, cell cycle checkpoints, and DNA repair mediators.

Results

ALDH status directly correlated to platinum resistance in primary ovarian cancer samples obtained from ascites. Patients with ALDH^HIGH displayed significantly lower progression free survival than the patients with ALDH^LOW cells (9 vs. 3 months, respectively p<0.01). ALDH1A1-knockdown significantly attenuated clonogenic potential, PARP-1 protein levels, and reversed inherent platinum resistance. ALDH1A1-knockdown resulted in dramatic decrease of KLF4 and p21 protein levels thereby leading to S and G2 phase accumulation of cells. Increases in S and G2 cells demonstrated increased expression of replication stress associated Fanconi Anemia DNA repair proteins (FANCD2, FANCJ) and replication checkpoint (pS317 Chk1) were affected. ALDH1A1-knockdown induced DNA damage, evidenced by robust induction of γ-H2AX and BAX mediated apoptosis, with significant increases in BRCA1 expression, suggesting ALDH1A1-dependent regulation of cell cycle checkpoints and DNA repair networks in ovarian cancer stem-like cells.

Conclusion

This data suggests that ovarian cancer cells expressing ALDH1A1 may maintain platinum resistance by altered regulation of cell cycle checkpoint and DNA repair network signaling.     

PARP inhibitors in ovarian cancer: Sensitivity prediction and resistance mechanisms

Poly (ADP‐ribose) polymerase (PARP) inhibitors have provided great clinical benefits to ovarian cancer patients. To date, three PARP inhibitors, namely, olaparib, rucaparib and niraparib have been approved for the treatment of ovarian cancer in the United States. Homologous recombination deficiency (HRD) and platinum sensitivity are prospective biomarkers for predicting the response to PARP inhibitors in ovarian cancers. Preclinical data have focused on identifying the gene aberrations that might generate HRD and induce sensitivity to PARP inhibitors in vitro in cancer cell lines or in vivo in patient‐derived xenografts. Clinical trials have focused on genomic scar analysis to identify biomarkers for predicting the response to PARP inhibitors. Additionally, researchers have aimed to investigate mechanisms of resistance to PARP inhibitors and strategies to overcome this resistance. Combining PARP inhibitors with HR pathway inhibitors to extend the utility of PARP inhibitors to BRCA‐proficient tumours is increasingly foreseeable. Identifying the population of patients with the greatest potential benefit from PARP inhibitor therapy and the circumstances under which patients are no longer suited for PARP inhibitor therapy are important. Further studies are required in order to propose better strategies for overcoming resistance to PARP inhibitor therapy in ovarian cancers.     

Global risk transformative prioritization for prostate cancer candidate genes in molecular networks

Understanding the pathogenesis of complex diseases is aided by precise identification of the genes responsible. Many computational methods have been developed to prioritize candidate disease genes, but coverage of functional annotations may be a limiting factor for most of these methods. Here, we introduce a global candidate gene prioritization approach that considers information about network properties in the human protein interaction network and risk transformative contents from known disease genes. Global risk transformative scores were then used to prioritize candidate genes. This method was introduced to prioritize candidate genes for prostate cancer. The effectiveness of our global risk transformative algorithm for prioritizing candidate genes was evaluated according to validation studies. Compared with ToppGene and random walk-based methods, our method outperformed the two other candidate gene prioritization methods. The generality of our method was assessed by testing it on prostate cancer and other types of cancer. The performance was evaluated using standard leave-one-out cross-validation.     

Network-based method for mining novel HPV infection related genes using random walk with restart algorithm

The human papillomavirus (HPV), a common virus that infects the reproductive tract, may lead to malignant changes within the infection area in certain cases and is directly associated with such cancers as cervical cancer, anal cancer, and vaginal cancer. Identification of novel HPV infection related genes can lead to a better understanding of the specific signal pathways and cellular processes related to HPV infection, providing information for the development of more efficient therapies. In this study, several novel HPV infection related genes were predicted by a computation method based on the known genes involved in HPV infection from HPVbase. This method applied the algorithm of random walk with restart (RWR) to a protein-protein interaction (PPI) network. The candidate genes were further filtered by the permutation and association tests. These steps eliminated genes occupying special positions in the PPI network and selected key genes with strong associations to known HPV infection related genes based on the interaction confidence and functional similarity obtained from published databases, such as STRING, gene ontology (GO) terms and KEGG pathways. Our study identified 104 novel HPV infection related genes, a number of which were confirmed to relate to the infection processes and complications of HPV infection, as reported in the literature. These results demonstrate the reliability of our method in identifying HPV infection related genes.This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.     

Role of Jagged1/STAT3 signalling in platinum‐resistant ovarian cancer

Jagged1, the essential ligand of the Notch signalling pathway, is highly expressed in metastatic prostate cancer, and its high expression in breast cancer is linked to poor survival rates. However, the mechanism of Jagged1′s involvement in platinum‐resistant ovarian cancer has not been thoroughly elucidated to date. The purpose of the present study was to investigate the roles of Jagged1 in the platinum resistance of ovarian cancer and its possible mechanisms. Compared with a platinum responsive group of ovarian epithelial cell carcinomas, we found the positive staining intensity of Notch1, Notch2, Jagged1, STAT3 and Epithelial‐mesenchymal transition (EMT) proteins were lower in a platinum‐resistant group. The DDP‐resistant ovarian cancer cell line (C13K) had a higher IC50 of DDP than its parental cell line (OV2008) (P < 0.05) and acquired an EMT phenotype and invasive characteristics. Inhibiting or knockdown of Jagged1 expression could not only reduce its capacity of migration and invasion but also reverse EMT and down‐regulate the expression of serine 727‐phosphorylated STAT3 (pS727) at the protein level but not total STAT3 or tyrosine 705‐phosphorylated STAT3 (pY705) in C13K cells. Furthermore, it was found that crosstalk between the Jagged1/Notch and JAK/STAT3 signalling pathways were involved in Jagged1‐promoting EMT in C13K cells. Experiments in vivo showed a reduced micrometastatic tumour burden in the lung, liver and spleen of mice implanted with C13K cells with knocked‐down Jagged1 compared with mice implanted with control cells. All of these results demonstrate that Jagged1 can crosstalk with the JAK/STAT3 pathway, and they all cooperate to promote the aberrant occurrence of EMT, further reinforcing the abilities of invasion and migration of platinum‐resistant ovarian cancer in vivo and in vitro.     

Confirmation and fine‐mapping of clinical mastitis and somatic cell score QTL in Nordic Holstein cattle

A genome‐wide association study of 2098 progeny‐tested Nordic Holstein bulls genotyped for 36 387 SNPs on 29 autosomes was conducted to confirm and fine‐map quantitative trait loci (QTL) for mastitis traits identified earlier using linkage analysis with sparse microsatellite markers in the same population. We used linear mixed model analysis where a polygenic genetic effect was fitted as a random effect and single SNPs were successively included as fixed effects in the model. We detected 143 SNP‐by‐trait significant associations (P < 0.0001) on 20 chromosomes affecting mastitis‐related traits. Among them, 21 SNP‐by‐trait combinations exceeded the genome‐wide significant threshold. For 12 chromosomes, both the present association study and the previous linkage study detected QTL, and of these, six were in the same chromosomal locations. Strong associations of SNPs with mastitis traits were observed on bovine autosomes 6, 13, 14 and 20. Possible candidate genes for these QTL were identified. Identification of SNPs in linkage disequilibrium with QTL will enable marker‐based selection for mastitis resistance. The candidate genes identified should be further studied to detect candidate polymorphisms underlying these QTL.     

Calpain-2 expression is associated with response to platinum based chemotherapy, progression-free and overall survival in ovarian cancer

Ovarian cancer is routinely treated with surgery and platinum‐based chemotherapy. Resistance is a major obstacle in the efficacy of this chemotherapy regimen and the ability to identify those patients at risk of developing resistance is of considerable clinical importance. The expression of calpain‐1, calpain‐2 and calpastatin were determined using standard immunohistochemistry on a tissue microarray of 154 primary ovarian carcinomas from patients subsequently treated with platinum‐based adjuvant chemotherapy. High levels of calpain‐2 expression was significantly associated with platinum resistant tumours (P = 0.031). Furthermore, high expression of calpain‐2 was significantly associated with progression‐free (P = 0.049) and overall survival (P = 0.006) in this cohort. The association between calpain‐2 expression and overall survival remained significant in multivariate analysis accounting for tumour grade, stage, optimal debulking and platinum sensitivity (hazard ratio = 2.174; 95% confidence interval = 1.144–4.130; P = 0.018). The results suggest that determining calpain‐2 expression in ovarian carcinomas may allow prognostic stratification of patients treated with surgery and platinum‐based chemotherapy. The findings of this study warrant validation in a larger clinical cohort.     

Integrated Analysis of Gene Expression Profiles Associated with Response of Platinum/Paclitaxel-Based Treatment in Epithelial Ovarian Cancer

Purpose

This study aims to explore gene expression signatures and serum biomarkers to predict intrinsic chemoresistance in epithelial ovarian cancer (EOC).

Patients and Methods

Gene expression profiling data of 322 high-grade EOC cases between 2009 and 2010 in The Cancer Genome Atlas project (TCGA) were used to develop and validate gene expression signatures that could discriminate different responses to first-line platinum/paclitaxel-based treatments. A gene regulation network was then built to further identify hub genes responsible for differential gene expression between the complete response (CR) group and the progressive disease (PD) group. Further, to find more robust serum biomarkers for clinical application, we integrated our gene signatures and gene signatures reported previously to identify secretory protein-encoding genes by searching the DAVID database. In the end, gene-drug interaction network was constructed by searching Comparative Toxicogenomics Database (CTD) and literature.

Results

A 349-gene predictive model and an 18-gene model independent of key clinical features with high accuracy were developed for prediction of chemoresistance in EOC. Among them, ten important hub genes and six critical signaling pathways were identified to have important implications in chemotherapeutic response. Further, ten potential serum biomarkers were identified for predicting chemoresistance in EOC. Finally, we suggested some drugs for individualized treatment.

Conclusion

We have developed the predictive models and serum biomarkers for platinum/paclitaxel response and established the new approach to discover potential serum biomarkers from gene expression profiles. The potential drugs that target hub genes are also suggested.     

基于蛋白互作网络识别非小细胞肺癌相关基因功能模块

本研究对非小细胞肺癌(non-small cell lung carcinoma,NSCLC)基因表达数据进行差异表达分析,并与蛋白质相互作用网络(PPIN)数据进行整合,进一步利用Heinz搜索算法识别NSCLC相关的基因功能模块,并对模块中的基因进行功能(GO term)和通路(KEGG)富集分析,旨在探究肺癌发病分子机制。蛋白互作网络分析得到一个包含96个基因和117个相互作用的功能模块,以及8个对NSCLC的发生和发展起到关键作用候选基因标志物。富集分析结果表明,这些基因主要富集于基因转录催化及染色质调控等生物学过程,并在基础转录因子、黏着连接、细胞周期、Wnt信号通路及HTLV-Ⅰ感染等生物学通路中发挥重要作用。本研究对非小细胞肺癌相关的基因和生物学通路进行预测,可用于肺癌的早期诊断和早期治疗,以降低肺癌死亡率。     

基于随机游走的风险致病基因预测研究进展

风险致病基因预测有助于揭示癌症等复杂疾病发生、发展机理,提高现有复杂疾病检测、预防及治疗水平,为药物设计提供靶标.全基因组关联分析(GWAS)和连锁分析等传统方法通常会产生数百种候选致病基因,采用生物实验方法进一步验证这些候选致病基因往往成本高、费时费力,而通过计算方法预测风险致病基因,并对其进行排序,可有效减少候选致病基因数量,帮助生物学家优化实验验证方案.鉴于目前随机游走算法在风险致病基因预测方面的卓越表现,本文从单元分子网络、多重分子网络和异构分子网络出发,对基于随机游走预测风险致病基因研究进展进行较全面的综述分析,讨论其所存在的计算问题,展望未来可能的研究方向.