scholarly journals Development and validation of genomic predictors of radiation sensitivity using preclinical data

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Venkata S. K. Manem
Keyword(s):  
Large Scale ◽  
Enrichment Analysis ◽  
Radiation Sensitivity ◽  
Continuous Variable ◽  
Gene Set Enrichment Analysis ◽  
Radiation Response ◽  
Biological Processes ◽  
Learning Approaches ◽  
Clinical Value ◽  
Rank Gene

Abstract Background Radiation therapy is among the most effective and commonly used therapeutic modalities of cancer treatments in current clinical practice. The fundamental paradigm that has guided radiotherapeutic regimens are ‘one-size-fits-all’, which are not in line with the dogma of precision medicine. While there were efforts to build radioresponse signatures using OMICS data, their ability to accurately predict in patients is still limited. Methods We proposed to integrate two large-scale radiogenomics datasets consisting of 511 with 23 tissues and 60 cancer cell lines with 9 tissues to build and validate radiation response biomarkers. We used intrinsic radiation sensitivity, i.e., surviving fraction of cells (SF2) as the radiation response indicator. Gene set enrichment analysis was used to examine the biological determinants driving SF2. Using SF2 as a continuous variable, we used five different approaches, univariate, rank gene ensemble, rank gene multivariate, mRMR and elasticNet to build genomic predictors of radiation response through a cross-validation framework. Results Through the pathway analysis, we found 159 pathways to be statistically significant, out of which 54 and 105 were positively and negatively enriched with SF2. More importantly, we found cell cycle and repair pathways to be enriched with SF2, which are inline with the fundamental aspects of radiation biology. With regards to the radiation response gene signature, we found that all multivariate models outperformed the univariate model with a ranking based approach performing well compared to other models, indicating complex biological processes underpinning radiation response. Conclusion To summarize, we found biological processes underpinning SF2 and systematically compared different machine learning approaches to develop and validate predictors of radiation response. With more patient data available in the future, the clinical value of these biomarkers can be assessed that would allow for personalization of radiotherapy.

scholarly journals Integrated lncRNA and mRNA Transcriptome Analyses in the Ovary of Cynoglossus semilaevis Reveal Genes and Pathways Potentially Involved in Reproduction

2021 ◽  
Vol 12 ◽  
Author(s):  
Yani Dong ◽  
Likang Lyu ◽  
Daiqiang Zhang ◽  
Jing Li ◽  
Haishen Wen ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Mapk Signaling ◽  
Cynoglossus Semilaevis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Biological Processes ◽  
Tongue Sole ◽  
Oocyte Meiosis

Long non-coding RNAs (lncRNAs) have been reported to be involved in multiple biological processes. However, the roles of lncRNAs in the reproduction of half-smooth tongue sole (Cynoglossus semilaevis) are unclear, especially in the molecular regulatory mechanism driving ovarian development and ovulation. Thus, to explore the mRNA and lncRNA mechanisms regulating reproduction, we collected tongue sole ovaries in three stages for RNA sequencing. In stage IV vs. V, we identified 312 differentially expressed (DE) mRNAs and 58 DE lncRNAs. In stage V vs. VI, we identified 1,059 DE mRNAs and 187 DE lncRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DE mRNAs were enriched in ECM-receptor interaction, oocyte meiosis and steroid hormone biosynthesis pathways. Furthermore, we carried out gene set enrichment analysis (GSEA) to identify potential reproduction related-pathways additionally, such as fatty metabolism and retinol metabolism. Based on enrichment analysis, DE mRNAs with a potential role in reproduction were selected and classified into six categories, including signal transduction, cell growth and death, immune response, metabolism, transport and catabolism, and cell junction. The interactions of DE lncRNAs and mRNAs were predicted according to antisense, cis-, and trans-regulatory mechanisms. We constructed a competing endogenous RNA (ceRNA) network. Several lncRNAs were predicted to regulate genes related to reproduction including cyp17a1, cyp19a1, mmp14, pgr, and hsd17b1. The functional enrichment analysis of these target genes of lncRNAs revealed that they were involved in several signaling pathways, such as the TGF-beta, Wnt signaling, and MAPK signaling pathways and reproduction related-pathways such as the progesterone-mediated oocyte maturation, oocyte meiosis, and GnRH signaling pathway. RT-qPCR analysis showed that two lncRNAs (XR_522278.2 and XR_522171.2) were mainly expressed in the ovary. Dual-fluorescence in situ hybridization experiments showed that both XR_522278.2 and XR_522171.2 colocalized with their target genes cyp17a1 and cyp19a1, respectively, in the follicular cell layer. The results further demonstrated that lncRNAs might be involved in the biological processes by modulating gene expression. Taken together, this study provides lncRNA profiles in the ovary of tongue sole and further insight into the role of lncRNA involvement in regulating reproduction in tongue sole.

scholarly journals A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Dengliang Lei ◽  
Yue Chen ◽  
Yang Zhou ◽  
Gangli Hu ◽  
Fang Luo
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Gene Set Enrichment

BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.MethodsThe mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.

scholarly journals Explore the Protective Role of Obesity in the Progression of Myocardial Infarction

2021 ◽  
Vol 8 ◽  
Author(s):  
Siyuan Zhao ◽  
Rongyuan Cao ◽  
Shuhua Zhang ◽  
Yan Kang
Keyword(s):  
Myocardial Infarction ◽  
Blood Pressure ◽  
Regulatory Networks ◽  
Large Scale ◽  
Enrichment Analysis ◽  
Protective Role ◽  
Gene Set Enrichment Analysis ◽  
Shortest Path Analysis ◽  
Acute Mi

Obesity has been shown as a risk factor to increase the incidence of myocardial infarction (MI). However, obesity has also been linked to the decreased mortality of acute MI with unknown mechanisms. Here, we firstly used large-scale literature data mining to identify obesity downstream targets and MI upstream regulators with polarity, based on which an obesity-MI regulatory network was constructed. Then, a gene set enrichment analysis was conducted to explore the functional profile of the genes involved in the obesity-MI regulatory networks. After that, a mega-analysis using MI RNA expression datasets was conducted to test the expression of obesity-specific genes in MI patients, followed by a shortest-path analysis to explore any potential gene-MI association. Our results suggested that obesity could inhibit 11 MI promoters, including NPPB, NPPA, IRS1, SMAD3, MIR155, ADRB1, AVP, MAPK14, MC3R, ROCK1, and COL3A1, which were mainly involved in blood pressure-related pathways. Our study suggested that obesity could influence MI progression by driving multiple genes associated with blood pressure regulation. Moreover, PTH could be a novel obesity driven gene associated with the pathogenesis of MI, which needs further validation.

scholarly journals GPNMB contributes to a vicious circle for chronic obstructive pulmonary disease

2020 ◽  
Vol 40 (6) ◽  
Author(s):  
Xi-Juan Zhang ◽  
Zhong-Hua Cui ◽  
Yan Dong ◽  
Xiu-Wen Liang ◽  
Yan-Xin Zhao ◽  
...  
Keyword(s):  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Large Scale ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Chronic Obstructive ◽  
P Value ◽  
Obstructive Pulmonary Disease ◽  
Copd Patients ◽  
Size Population

Abstract Osteoporosis (OP) is significant and debilitating comorbidity of chronic obstructive pulmonary disease (COPD). We hypothesize that genetic variance identified with OP may also play roles in COPD. We have conducted a large-scale relation data analysis to explore the genes implicated with either OP or COPD, or both. Each gene linked to OP but not to COPD was further explored in a mega-analysis and partial mega-analysis of 15 independently collected COPD RNA expression datasets, followed by gene set enrichment analysis (GSEA) and literature-based pathway analysis to explore their functional linked to COPD. A multiple linear regression (MLR) model was built to study the possible influence of sample size, population region, and study date on the gene expression data in COPD. At the first step of the analysis, we have identified 918 genes associated with COPD, 581 with OP, and a significant overlap (P<2.30e-140; 210 overlapped genes). Partial mega-analysis showed that, one OP gene, GPNMB presented significantly increased expression in COPD patients (P-value = 0.0018; log fold change = 0.83). GPNMB was enriched in multiple COPD pathways and plays roles as a gene hub formulating multiple vicious COPD pathways included gene MMP9 and MYC. GPNMB could be a novel gene that plays roles in both COPD and OP. Partial mega-analysis is valuable in identify case-specific genes for COPD.

scholarly journals Identification of IL10RA by Weighted Correlation Network Analysis and in vitro Validation of Its Association With Prognosis of Metastatic Melanoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Si Cheng ◽  
Zhe Li ◽  
Wenhao Zhang ◽  
Zhiqiang Sun ◽  
Zhigang Fan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metastatic Melanoma ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion

Skin cutaneous melanoma (SKCM) is the major cause of death for skin cancer patients, its high metastasis often leads to poor prognosis of patients with malignant melanoma. However, the molecular mechanisms underlying metastatic melanoma remain to be elucidated. In this study we aim to identify and validate prognostic biomarkers associated with metastatic melanoma. We first construct a co-expression network using large-scale public gene expression profiles from GEO, from which candidate genes are screened out using weighted gene co-expression network analysis (WGCNA). A total of eight modules are established via the average linkage hierarchical clustering, and 111 hub genes are identified from the clinically significant modules. Next, two other datasets from GEO and TCGA are used for further screening of biomarker genes related to prognosis of metastatic melanoma, and identified 11 key genes via survival analysis. We find that IL10RA has the highest correlation with clinically important modules among all identified biomarker genes. Further in vitro biochemical experiments, including CCK8 assays, wound-healing assays and transwell assays, have verified that IL10RA can significantly inhibit the proliferation, migration and invasion of melanoma cells. Furthermore, gene set enrichment analysis shows that PI3K-AKT signaling pathway is significantly enriched in metastatic melanoma with highly expressed IL10RA, indicating that IL10RA mediates in metastatic melanoma via PI3K-AKT pathway.

scholarly journals Increased IGFBP7 Expression Correlates with Poor Prognosis and Immune Infiltration in Gastric Cancer: An Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Qiaoyun Zhao ◽  
Rulin Zhao ◽  
Conghua Song ◽  
Huan Wang ◽  
Jianfang Rong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Immune Cell ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Coexpressed Genes

Abstract Background Insulin-like growth factor binding protein-7 (IGFBP7) contributes to multiple biological processes in various tumors. However, the role of IGFBP7 in gastric cancer (GC) is still undetermined. The study aims to explore the role of IGFBP7 in GC via an integrated bioinformatics analysis.Methods IGFBP7 expression levels in GC and its normal gastric tissues were analyzed using multiple databases, including the Tumor Immune Estimation Resource (TIMER), Oncomine, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The methylation analysis was conducted with MEXPRESS, UALCAN and Xena online tools. The survival analysis was conducted using the Kaplan-Meier Plotter and Gene Expression Profiling Interactive Analysis (GEPIA) databases. Coexpressed genes of IGFBP7 were selected with the cBioPortal tool and enrichment analysis was conducted with the clusterProfiler package in R software. Gene set enrichment analysis (GSEA) was performed to explore the IGFBP7-related biological processes involved in GC. Correlations between IGFBP7 and immune cell infiltrates were analyzed using the TIMER database.Results IGFBP7 expression was significantly upregulated in GC and correlated with stage, grade, tumor status and Helicobacter pylori infection. High IGFBP7 expression and low IGFBP7 methylation levels were significantly associated with short survival of patients with GC. Univariate and multivariate analyses revealed that IGFBP7 was an independent risk factor for GC. The coexpressed genes LHFPL6, SEPTIN4, HSPB2, LAYN and GGT5 predicted unfavorable outcomes of GC. Enrichment analysis showed that the coexpressed genes were involved in extracellular matrix (ECM)-related processes. GSEA indicated that IGFBP7 was positively related to ECM and inflammation-related pathways. TIMER analysis indicated that the IGFBP7 expression level was strongly correlated with genes related to various infiltrating immune cells in GC, especially with gene markers of tumor associated macrophages (TAMs).Conclusions We demonstrate that increased IGFBP7 expression correlates with poor prognosis and immune cell infiltration in GC. IGFBP7 might be a potential biomarker for the diagnosis and targeted therapy for GC.

scholarly journals Newborn infant skin gene expression: Remarkable differences versus adults

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0258554
Author(s):  
Marty O. Visscher ◽  
Ping Hu ◽  
Andrew N. Carr ◽  
Charles C. Bascom ◽  
Robert J. Isfort ◽  
...  
Keyword(s):  
Ultraviolet Radiation ◽  
Immune Function ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Hair Cycle ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Adult Skin ◽  
Skin Samples

At birth, human infants are poised to survive in harsh, hostile conditions. An understanding of the state of newborn skin development and maturation is key to the maintenance of health, optimum response to injury, healing and disease. The observational study collected full-thickness newborn skin samples from 27 infants at surgery and compared them to skin samples from 43 adult sites protected from ultraviolet radiation exposure, as the standard for stable, mature skin. Transcriptomics profiling and gene set enrichment analysis were performed. Statistical analysis established over 25,000 differentially regulated probe sets, representing 10,647 distinct genes, in infant skin compared to adult skin. Gene set enrichment analysis showed a significant increase in 143 biological processes (adjusted p < 0.01) in infant skin, versus adult skin samples, including extracellular matrix (ECM) organization, cell adhesion, collagen fibril organization and fatty acid metabolic process. ECM organization and ECM structure organization were the biological processes in infant skin with the lowest adjusted P-value. Genes involving epidermal development, immune function, cell differentiation, and hair cycle were overexpressed in adults, representing 101 significantly enriched biological processes (adjusted p < 0.01). The processes with the highest significant difference were skin and epidermal development, e.g., keratinocyte differentiation, keratinization and cornification intermediate filament cytoskeleton organization and hair cycle. Enriched Gene Ontology (GO) biological processes also involved immune function, including antigen processing and presentation. When compared to ultraviolet radiation-protected adult skin, our results provide essential insight into infant skin and its ability to support the newborn’s preparedness to survive and flourish, despite the infant’s new environment laden with microbes, high oxygen tension and potential irritants. This fundamental knowledge is expected to guide strategies to protect and preserve the features of unperturbed, young skin.

scholarly journals BMPR1A signalling is linked to tumour progression in dedifferentiated liposarcomas: lessons for rhBMP2 use in spinal surgery

2016 ◽  
Author(s):  
Hannah L O'Neill ◽  
Amy P Cassidy ◽  
Olivia B Harris ◽  
John W Cassidy
Keyword(s):  
Large Scale ◽  
Tumour Progression ◽  
Soft Tissue Sarcomas ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Receptor Expression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Tissue Type ◽  
Type I

Bone Morphogenic Protein 2 (BMP2) is a multipurpose cytokine, important in the development of bone and cartilage, and with a role in tumour initiation and progression. Because of BMP2’s osteogenic properties, a recombinant human version (rhBMP2) has found utility as an adjuvant therapy during surgery for spinal fusions. However, the results of large-scale meta-analysis has highlighted the potential of rhBMP2 to promote new tumour formation, leading to an FDA black box warning. BMP2 signal transduction is dependent on two distinct classes of serine/threonine kinase known as the type I and type II receptors. Although the type I receptors (BMPR1A and BMPR1B) are largely thought to have overlapping functions, we find tissue and cellular compartment specific patterns of expression, suggesting potential for distinct BMP2 signalling outcomes dependent on tissue type. Herein, we utilise large publicly available datasets from The Cancer Genome Atlas (TCGA) and Protein Atlas to define a novel role for BMPR1A-biased BMP2 signalling in soft tissue sarcomas. Using disease free survival as our primary endpoint, we find this BMPR1A-biased BMP2 signalling confers poor overall prognosis compared both to patients with BMPR1B-biased and to the sarcoma dataset as a whole. Through further annotation of the TCGA sarcoma dataset, we localise this effect to dedifferentiated liposarcomas but find overall BMP2/BMP receptor expression is equal across subsets. Finally, through gene set enrichment analysis we link this effect to increased transcriptional activity of the matrisome and general extracellular matrix remodelling. Our study highlights the importance of continued research into the tumorigenic properties of BMP2, the need for extensive patient follow-up and the potential disadvantages of rhBMP2 use. For the first time, we identify BMPR1A-biased BMP2 signalling as a biomarker of disease relapse in dedifferentiated liposarcomas.

Integrative Analysis Reveals the Prognostic Value and Functions of Splicing Factors Implicated in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Yue Wang ◽  
Fan Yang ◽  
Jiaqi Shang ◽  
Haitao He ◽  
Qing Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Splicing Factors ◽  
Biological Functions ◽  
Clinical Value ◽  
Multiple Cancer ◽  
Protein Coding ◽  
Protein Coding Genes

Abstract Splicing factors (SFs) play critical roles in the pathogenesis of various cancers through regulating tumor-associated alternative splicing (AS) events. However, the clinical value and biological functions of SFs in hepatocellular carcinoma (HCC) remain obscure. In this study, we identified 40 dysregulated SFs in HCC and established a prognostic model composed of four SFs (DNAJC6, ZC3H13, IGF2BP3, DDX19B). The predictive efficiency and independence of the prognostic model were confirmed to be satisfactory. Gene Set Enrichment Analysis (GSEA) illustrated the risk score calculated by our prognostic model was significantly associated with multiple cancer-related pathways and metabolic processes. Furthermore, we constructed the SFs-AS events regulatory network and extracted 108 protein-coding genes from the network for following functional explorations. Protein-protein interaction (PPI) network delineated the potential interactions among these 108 protein-coding genes. GO and KEGG pathway analyses investigated ontology gene sets and canonical pathways enriched by these 108 protein-coding genes. Overlapping the results of GSEA and KEGG, seven pathways were identified to be potential pathways regulated by our prognostic model through triggering aberrant AS events in HCC. In conclusion, the present study established an effective prognostic model based on SFs for HCC patients. Functional explorations of SFs and SFs-associated AS events provided directions to explore biological functions and mechanisms of SFs in HCC tumorigenesis.

scholarly journals simGWAS: a fast method for simulation of large scale case–control GWAS summary statistics

2018 ◽  
Vol 35 (11) ◽  
pp. 1901-1906 ◽  
Author(s):  
Mary D Fortune ◽  
Chris Wallace
Keyword(s):  
Large Scale ◽  
Simulated Data ◽  
Enrichment Analysis ◽  
R Package ◽  
Gene Set Enrichment Analysis ◽  
Supplementary Information ◽  
Intermediate Step ◽  
Fast Method ◽  
Summary Statistics ◽  
Causal Variants

Abstract Motivation Methods for analysis of GWAS summary statistics have encouraged data sharing and democratized the analysis of different diseases. Ideal validation for such methods is application to simulated data, where some ‘truth’ is known. As GWAS increase in size, so does the computational complexity of such evaluations; standard practice repeatedly simulates and analyses genotype data for all individuals in an example study. Results We have developed a novel method based on an alternative approach, directly simulating GWAS summary data, without individual data as an intermediate step. We mathematically derive the expected statistics for any set of causal variants and their effect sizes, conditional upon control haplotype frequencies (available from public reference datasets). Simulation of GWAS summary output can be conducted independently of sample size by simulating random variates about these expected values. Across a range of scenarios, our method, produces very similar output to that from simulating individual genotypes with a substantial gain in speed even for modest sample sizes. Fast simulation of GWAS summary statistics will enable more complete and rapid evaluation of summary statistic methods as well as opening new potential avenues of research in fine mapping and gene set enrichment analysis. Availability and implementation Our method is available under a GPL license as an R package from http://github.com/chr1swallace/simGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

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