Multiple Gene Expression Profiles Research Articles

Biological characteristics of molecular subtypes of ulcerative colitis characterized by ferroptosis and neutrophil infiltration

Clinical ulcerative colitis (UC) is a heterogeneous condition. Moreover, medical interventions are nonspecific, and thus, treatment responses are inconsistent. The aim of this study was to explore the molecular subtypes and biological characteristics of UC based on ferroptosis and neutrophil gene sets. Multiple intestinal mucosa gene expression profiles of UC patients in the Gene Expression Omnibus (GEO) database were downloaded. Unsupervised clustering methods were used to identify potential molecular subtypes based on ferroptosis and neutrophil gene sets. Multiple immune infiltration algorithms were used to evaluate the biological characteristics of the molecular subtypes. Machine learning identifies hub genes for molecular subtypes and analyses their diagnostic efficacy for UC and predictive performance for drug therapy. The relevant conclusions were verified by clinical samples and animal experiments. Four molecular subtypes were identified according to the ferroptosis and neutrophil gene sets: neutrophil, ferroptosis, mixed and quiescent. The subtypes have different biological characteristics and immune infiltration levels. Multiple machine learning methods jointly identified four hub genes (FTH1, AQP9, STEAP3 and STEAP4). Receiver operating characteristic (ROC) curve analysis revealed that the four hub genes could be used as diagnostic markers for UC. The clinical response profile data of infliximab treatment patients showed that AQP9 and STEPA4 were reliable predictors of infliximab treatment response. In human samples the AQP9 and STEAP4 protein were shown to be increased in UC intestinal samples. In animal experiments, the ferroptosis and neutrophil phenotype were confirmed. Dual analysis of ferroptosis and neutrophil gene expression revealed four subgroups of UC patients. The molecular subtype-associated hub genes can be used as diagnostic markers for UC and predict infliximab treatment response.

Drug repositioning using multiple gene expression profiles

Drug repositioning using multiple gene expression profiles Chuo University’s Professor Y-h. Taguchi places focus on drug repositioning using multiple gene expression profiles. In my previous manuscripts (1-3), I introduced our studies of in silico drug repositioning using gene expression profiles. Nevertheless, in these studies, we could use the single gene expression profile to perform in silico drug repositioning. In this manuscript, the last one in this series, I introduce our recent study (4) in which we could use multiple gene expression profiles. Please see my previous article for more details about the in silico drug repositioning. (5)In the studies (1-3) introduced previously, I used tensor decomposition (TD) based unsupervised feature extraction (FE) (6,7), which was a mathematical/computational method for data processing and was recently implemented in Bioconductor packages. (8)

A tensor decomposition-based integrated analysis applicable to multiple gene expression profiles without sample matching.

The integrated analysis of multiple gene expression profiles previously measured in distinct studies is problematic since missing both sample matches and common labels prevent their integration in fully data-driven, unsupervised training. In this study, we propose a strategy to enable the integration of multiple gene expression profiles among multiple independent studies with neither labeling nor sample matching using tensor decomposition unsupervised feature extraction. We apply this strategy to Alzheimer's disease (AD)-related gene expression profiles that lack precise correspondence among samples, including AD single-cell RNA sequence (scRNA-seq) data. We were able to select biologically reasonable genes using the integrated analysis. Overall, integrated gene expression profiles can function analogously to prior- and/or transfer-learning strategies in other machine-learning applications. For scRNA-seq, the proposed approach significantly reduces the required computational memory.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

Salinity stress alters the expression patterns of novel miR‐2008 and its target gene PLEKHA3 in the sea cucumber Apostichopus japonicus

AbstractThe responses of sea cucumbers to salinity changes are reflected in the expression profiles of multiple genes and microRNAs. The miR‐2008 and Pleckstrin Homology Domain Containing A3 (PLEKHA3) were previously shown to be involved in responses to salinity changes in sea cucumbers. To better understand the relationship between miR‐2008 and PLEKHA3, the miR‐2008 and PLEKHA3 predicted interaction was investigated using luciferase reporter assays and gene knockdowns. Dual‐luciferase reporter gene analysis indicated that miR‐2008 overexpression decreased luciferase activity in the PLEKHA3‐WT group. The miR‐2008 expression in coelomocytes was increasingly downregulated in the agomir group with salinity acclimation. PLEKHA3 expression patterns in the miR‐2008 agomir group were upregulated after 6 and 48 h and downregulated after 24 h of low‐salinity stress. The miR‐2008 expression in the miR‐2008 agomir group peaked after 24 h of low‐salinity exposure in sea cucumbers. miR‐2008 overexpression and PLEKHA3 inhibition affected ion concentrations, osmotic pressure, Na+/K+‐ATPase (NKA) activity, and ROS levels. The changes in ion concentrations, osmotic pressure, and NKA activity after 6–24 h of salinity stress were observed. The results indicated that 6–24 h after the initiation of salinity stress may be critical to the salinity‐acclimation process in sea cucumbers.

Integrated Analysis of Tissue-Specific Gene Expression in Diabetes by Tensor Decomposition Can Identify Possible Associated Diseases.

In the field of gene expression analysis, methods of integrating multiple gene expression profiles are still being developed and the existing methods have scope for improvement. The previously proposed tensor decomposition-based unsupervised feature extraction method was improved by introducing standard deviation optimization. The improved method was applied to perform an integrated analysis of three tissue-specific gene expression profiles (namely, adipose, muscle, and liver) for diabetes mellitus, and the results showed that it can detect diseases that are associated with diabetes (e.g., neurodegenerative diseases) but that cannot be predicted by individual tissue expression analyses using state-of-the-art methods. Although the selected genes differed from those identified by the individual tissue analyses, the selected genes are known to be expressed in all three tissues. Thus, compared with individual tissue analyses, an integrated analysis can provide more in-depth data and identify additional factors, namely, the association with other diseases.

Reproducibility of Gene Expression Signatures in Diffuse Large B-Cell Lymphoma.

Simple SummaryMultiple gene expression signatures with biological or prognostic subgroups have been published in diffuse large B-cell lymphoma (DLBCL). With exception of the cell of origin (COO) classifier, these were not validated in independent cohorts. The aim of the study was to reproduce four gene expression signatures capturing multiple biological subgroups using the NanoString platform. In addition, we aimed to identify potential associations between the signatures and portray the heterogeneity of DLBCL. We show that, in an independent cohort of clinically well-defined patients, these signatures can co-occur in the same patient and that each classifier captures a different aspect of the biological heterogenous panorama of DLBCL. Beside COO, there is clear evidence of different immune and MYC signatures. A direct comparison in our cohort showed that these signatures reflect independent biological features. More comparative studies with gene expression profiles need to be conducted to enable a further integration and to help develop new taxonomy systems for clinical utility.Multiple gene expression profiles have been identified in diffuse large B-cell lymphoma (DLBCL). Besides the cell of origin (COO) classifier, no signatures have been reproduced in independent studies or evaluated for capturing distinct aspects of DLBCL biology. We reproduced 4 signatures in 175 samples of the HOVON-84 trial on a panel of 117 genes using the NanoString platform. The four gene signatures capture the COO, MYC activity, B-cell receptor signaling, oxidative phosphorylation, and immune response. Performance of our classification algorithms were confirmed in the original datasets. We were able to validate three of the four GEP signatures. The COO algorithm resulted in 94 (54%) germinal center B-cell (GCB) type, 58 (33%) activated B-cell (ABC) type, and 23 (13%) unclassified cases. The MYC-classifier revealed 77 cases with a high MYC-activity score (44%) and this MYC-high signature was observed more frequently in ABC as compared to GCB DLBCL (68% vs. 32%, p < 0.00001). The host response (HR) signature of the consensus clustering was present in 55 (31%) patients, while the B-cell receptor signaling, and oxidative phosphorylation clusters could not be reproduced. The overlap of COO, consensus cluster and MYC activity score differentiated six gene expression clusters: GCB/MYC-high (12%), GCB/HR (16%), GCB/non-HR (27%), COO-Unclassified (13%), ABC/MYC-high (25%), and ABC/MYC-low (7%). In conclusion, the three validated signatures identify distinct subgroups based on different aspects of DLBCL biology, emphasizing that each classifier captures distinct molecular profiles.

Investigation of genes and pathways involved in breast cancer subtypes through gene expression meta-analysis

BackgroundMolecular-based studies have revealed heterogeneity in Breast cancer BC while also improving classification and treatment. However, efforts are underway to distinguish between distinct subtypes of breast cancer. In this study, the results of several microarray studies were combined to identify genes and pathways specific to each BC subtype. MethodsMeta-analysis of multiple gene expression profile datasets was screened to find differentially expressed genes (DEGs) across subtypes of BC and normal breast tissue samples. Protein-protein interaction network and gene set enrichment analysis were used to identify critical genes and pathways associated with BC subtypes. The differentially expressed genes from meta-analysis was validated using an independent comprehensive breast cancer RNA-sequencing dataset obtained from the Cancer Genome Atlas (TCGA). ResultsWe identified 110 DEGs (13 DEGs in all and 97 DEGs in each subtype) across subtypes of BC. All subtypes had a small set of shared DEGs enriched in the Chemokine receptor bind chemokine pathway. Luminal A specific were enriched in the translational elongation process in mitochondria, and the enhanced process in luminal B subtypes was interferon-alpha/beta signaling. Cell cycle and mitotic DEGs were enriched in the basal-like group. All subtype-specific DEG genes (100%) were successfully validated for Luminal A, Luminal B, ERBB2, and Normal-like. However, the validation percentage for Basal-like group was 77.8%. ConclusionIntegrating researches such as a meta-analysis of gene expression might be more effective in uncovering subtype-specific DEGs and pathways than a single-study analysis. It would be more beneficial to increase the number of studies that use matched BC subtypes along with GEO profiling approaches to reach a better result regarding DEGs and reduce probable biases. However, achieving 77.8% overlap in basal-specific genes and complete concordance in specific genes related to other subtypes can implicate the strength of our analysis for discovering the subtype-specific genes.

The Impact of the Central Carbon Energy Metabolism Transcriptome in the Pathogenesis and Outcomes of Multiple Myeloma

The Impact of the Central Carbon Energy Metabolism Transcriptome in the Pathogenesis and Outcomes of Multiple Myeloma

Prediction of Biochemical Recurrence-Free Survival of Prostate Cancer Patients Leveraging Multiple Gene Expression Profiles in Tumor Microenvironment.

Tumor-adjacent normal (TAN) tissues, which constitute tumor microenvironment and are different from healthy tissues, provide critical information at molecular levels that can be used to differentiate aggressive tumors from indolent tumors. In this study, we analyzed 52 TAN samples from the Cancer Genome Atlas (TCGA) prostate cancer patients and developed a 10-gene prognostic model that can accurately predict biochemical recurrence-free survival based on the profiles of these genes in TAN tissues. The predictive ability was validated using TAN samples from an independent cohort. These 10 prognostic genes in tumor microenvironment are different from the prognostic genes detected in tumor tissues, indicating distinct progression-related mechanisms in two tissue types. Bioinformatics analysis showed that the prognostic genes in tumor microenvironment were significantly enriched by p53 signaling pathway, which may represent the crosstalk tunnels between tumor and its microenvironment and pathways involving cell-to-cell contact and paracrine/endocrine signaling. The insight acquired by this study has advanced our knowledge of the potential role of tumor microenvironment in prostate cancer progression.

PD43-06 TUMOR STRATIFICATION OF UPPER TRACT UROTHELIAL CARCINOMA BY GENE EXPRESSION PROFILING AND GENETIC VARIATION

INTRODUCTION AND OBJECTIVE:While lower tract urothelial carcinoma has undergone multiple large gene expression profiling initiatives, upper tract urothelial carcinoma (UTUC) has had relatively litt...

SC-JNMF: single-cell clustering integrating multiple quantification methods based on joint non-negative matrix factorization.

Single-cell RNA-sequencing is a rapidly evolving technology that enables us to understand biological processes at unprecedented resolution. Single-cell expression analysis requires a complex data processing pipeline, and the pipeline is divided into two main parts: The quantification part, which converts the sequence information into gene-cell matrix data; the analysis part, which analyzes the matrix data using statistics and/or machine learning techniques. In the analysis part, unsupervised cell clustering plays an important role in identifying cell types and discovering cell diversity and subpopulations. Identified cell clusters are also used for subsequent analysis, such as finding differentially expressed genes and inferring cell trajectories. However, single-cell clustering using gene expression profiles shows different results depending on the quantification methods. Clustering results are greatly affected by the quantification method used in the upstream process. In other words, even if the original RNA-sequence data is the same, gene expression profiles processed by different quantification methods will produce different clusters. In this article, we propose a robust and highly accurate clustering method based on joint non-negative matrix factorization (joint-NMF) by utilizing the information from multiple gene expression profiles quantified using different methods from the same RNA-sequence data. Our joint-NMF can extract common factors among multiple gene expression profiles by applying each NMF under the constraint that one of the factorized matrices is shared among multiple NMFs. The joint-NMF determines more robust and accurate cell clustering results by leveraging multiple quantification methods compared to conventional clustering methods, which use only a single gene expression profile. Additionally, we showed the usefulness of discovering marker genes with the extracted features using our method.

Novel lncRNA lncRNA001074 participates in the low salinity-induced response in the sea cucumber Apostichopus japonicus by targeting the let-7/NKAα axis.

Salinity fluctuations have severe impacts on sea cucumbers and therefore important consequences in sea cucumber farming. The responses of sea cucumbers to salinity changes are reflected in the expression profiles of multiple genes and non-coding RNAs (ncRNAs). The microRNA (let-7) which is a developmental regulator, the ion transporter gene sodium potassium ATPase gene (NKAα), and the long ncRNA lncRNA001074 were previously shown to be involved in responses to salinity changes in various marine species. To better understand the relationship between ncRNAs and target genes, the let-7/NKAα/lncRNA001074 predicted interaction was investigated in this study using luciferase reporter assays and gene knockdowns in the sea cucumber Apostichopus japonicus. The results showed that NKAα was the target gene of let-7 and NKAα expression levels were inversely correlated with let-7 expression based on the luciferase reporter assays and western blots. The let-7 abundance was negatively regulated by lncRNA001074 and NKAα both in vitro and in vivo. Knockdown of lncRNA001074 led to let-7 overexpression. These results demonstrated that lncRNA001074 binds to the 3'-UTR binding site of let-7 in a regulatory manner. Furthermore, the expression profiles of let-7, NKAα, and lncRNA001074 were analyzed in sea cucumbers after the knockdown of each of these genes. The results found that lncRNA001074 competitively bound let-7 to suppress NKAα expression under low salinity conditions. The downregulation of let-7, in conjunction with the upregulation of lncRNA001074 and NKAα, may be essential for the response to low salinity change in sea cucumbers. Therefore, the dynamic balance of the lncRNA001074, NKAα, and let-7 network might be a potential response mechanism to salinity change in sea cucumbers.

Integrating genome-wide association and transcriptome prediction model identifies novel target genes for osteoporosis

SummaryIn this study, we integrated large-scale GWAS summary data and used the predicted transcriptome-wide association study method to discover novel genes associated with osteoporosis. We identified 204 candidate genes, which provide novel clues for understanding the genetic mechanism of osteoporosis and indicate potential therapeutic targets.IntroductionOsteoporosis is a highly polygenetic disease characterized by low bone mass and deterioration of the bone microarchitecture. Our objective was to discover novel candidate genes associated with osteoporosis.MethodsTo identify potential causal genes of the associated loci, we investigated trait-gene expression associations using the transcriptome-wide association study (TWAS) method. This method directly imputes gene expression effects from genome-wide association study (GWAS) data using a statistical prediction model trained on GTEx reference transcriptome data. We then performed a colocalization analysis to evaluate the posterior probability of biological patterns: associations characterized by a single causal variant or multiple distinct causal variants. Finally, a functional enrichment analysis of gene sets was performed using the VarElect and CluePedia tools, which assess the causal relationships between genes and a disease and search for potential gene’s functional pathways. The osteoporosis-associated genes were further confirmed based on the differentially expressed genes profiled from mRNA expression data of bone tissue.ResultsOur analysis identified 204 candidate genes, including 154 genes that have been previously associated with osteoporosis, 50 genes that have not been previously discovered. A biological function analysis found that 20 of the candidate genes were directly associated with osteoporosis. Further analysis of multiple gene expression profiles showed that 15 genes were differentially expressed in patients with osteoporosis. Among these, SLC11A2, MAP2K5, NFATC4, and HSP90B1 were enriched in four pathways, namely, mineral absorption pathway, MAPK signaling pathway, Wnt signaling pathway, and PI3K-Akt signaling pathway, which indicates a causal relationship with the occurrence of osteoporosis.ConclusionsWe demonstrated that transcriptome fine-mapping identifies more osteoporosis-related genes and provides key insight into the development of novel targeted therapeutics for the treatment of osteoporosis.

A Clinicogenetic Prognostic Classifier for Prediction of Recurrence and Survival in Asian Breast Cancer Patients.

BackgroundSeveral prognostic factors affect the recurrence of breast cancer in patients who undergo mastectomy. Assays of the expression profiles of multiple genes increase the probability of overexpression of certain genes and thus can potentially characterize the risk of metastasis.MethodsWe propose a 20-gene classifier for predicting patients with high/low risk of recurrence within 5 years. Gene expression levels from a quantitative PCR assay were used to screen 473 luminal breast cancer patients treated at Taiwan Hospital (positive for estrogen and progesterone receptors, negative for human epidermal growth factor receptor 2). Gene expression scores, along with clinical information (age, tumor stage, and nodal stage), were evaluated for risk prediction. The classifier could correctly predict patients with and without relapse (logistic regression, P<0.05).ResultsA Cox proportional hazards regression analysis showed that the 20-gene panel was prognostic with hazard ratios of 5.63 (95% confidence interval 2.77-11.5, univariate) and 5.56 (2.62-11.8, multivariate) for the “genetic” model, and of 8.02 (3.52-18.3, univariate) and 19.8 (5.96-65.87, multivariate) for the “clinicogenetic” model during a 5-year follow-up.ConclusionsThe proposed 20-gene classifier can successfully separate the patients into two risk groups, and the two risk group had significantly different relapse rate and prognosis. This 20-gene classifier can provide better estimation of prognosis, which can help physicians to make better personalized treatment plans.

Identification of biomarkers associated with cervical lymph node metastasis in papillary thyroid carcinoma: Evidence from an integrated bioinformatic analysis.

Integrated analysis of accumulated data is an effective way to obtain reliable potential diagnostic molecular of cervical lymph node metastases (LNM) in papillary thyroid carcinoma (PTC). The benefits of prophylactic lymph node dissection (PLND) for these clinically node-negative (cN0) patients remained considerable controversies. Hence, elucidation of the mechanisms of LNM and exploration of potential biomarkers and prognostic indicators are essential for accurate diagnosis of LNM in PTC patients. Up to date, advanced microarray and bioinformatics analysis have advanced an understanding of the molecular mechanisms of disease occurrence and development, which are necessary to explore genetic changes and identify potential diagnostic biomarkers. In present study, we performed a comprehensive analysis of the differential expression, biological functions, and interactions of LNM-related genes. Two publicly available microarray datasets GSE60542 and GSE129562 were available from Gene Expression Omnibus (GEO) database. Differentially expressed genes between clinically node-positive (cN1) and cN0 PTC samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Our results identified 48 differentially expressed genes (DEGs) genetically associated with LNM in PTC patients. Gene ontology (GO) analyses revealed the changes in the modules were mostly enriched in the regulation of MHC class II receptor activity, the immune receptor activity, and the peptide antigen binding. Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis of DEGs displayed the intestinal immune network for IgA production, staphylococcus aureus infection, and cell adhesion molecules (CAMs). To screen core genes related to LNM of PTC from the protein-protein interaction network, top 10 hub genes were identified with highest scores. Our results help us understand the exact mechanisms underlying the metastasis of cervical LNM in PTC tissues and pave an avenue for the progress of precise medicine for individual patients.

Identification of candidate biomarkers correlated with the pathogenesis and prognosis of breast cancer via integrated bioinformatics analysis.

Background:This study was carried out to identify potential key genes associated with the pathogenesis and prognosis of breast cancer (BC).Methods:Seven GEO datasets (GSE24124, GSE32641, GSE36295, GSE42568, GSE53752, GSE70947, GSE109169) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between BC and normal breast tissue samples were screened by an integrated analysis of multiple gene expression profile datasets. Hub genes related to the pathogenesis and prognosis of BC were verified by employing protein–protein interaction (PPI) network.Results:Ten hub genes with high degree were identified, including CDK1, CDC20, CCNA2, CCNB1, CCNB2, BUB1, BUB1B, CDCA8, KIF11, and TOP2A. Lastly, the Kaplan–Meier plotter (KM plotter) online database demonstrated that higher expression levels of these genes were related to lower overall survival. Experimental validation showed that all 10 hub genes had the same expression trend as predicted.Conclusion:The findings of this research would provide some directive significance for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy of BC, which could be used as a new biomarker for diagnosis and to guide the combination medicine of BC.

Osteogenic differentiation of bone marrow mesenchymal stem cells on chitosan/gelatin scaffolds: gene expression profile and mechanical analysis

In the present study we explore the extracellular matrix (ECM) produced by human bone marrow mesenchymal stem/stromal cells (BM-MSCs) induced to undergo osteogenic differentiation within porous chitosan/gelatin (CS:Gel) scaffolds by investigating their multiple gene expression profile and mechanical behavior. Initially, the efficiency of the BM-MSCs osteogenic differentiation within the constructs was confirmed by the significant rise in the expression of the osteogenesis associated genes DLX5, RUNX2, ALP and OSC. In line with these findings, OSC and Col1A1 protein expression was also detected in BM-MSCs on the CS:Gel scaffolds at day 14 of osteogenic differentiation. We then profiled, for the first time, the expression of 84 cell adhesion and ECM molecules using PCR arrays. The arrays, which were conducted at day 14 of osteogenic differentiation, demonstrated that 49 genes including collagens, integrins, laminins, ECM proteases, catenins, thrombospondins, ECM protease inhibitors and cell-cell adhesion molecules were differentially expressed in BM-MSCs seeded on scaffolds compared to tissue culture polystyrene control. Moreover, we performed dynamic mechanical analysis of the cell-loaded scaffolds on days 0, 7 and 14 to investigate the correlation between the biological results and the mechanical behavior of the constructs. Our data demonstrate a significant increase in the stiffness of the constructs with storage modulus values of 2 MPa on day 7, compared to 0.5 MPa on day 0, following a drop of the stiffness at 0.8 MPa on day 14, that may be attributed to the significant increase of specific ECM protease gene expression such as MMP1, MMP9, MMP11 and MMP16 at this time period.

Comprehensive Analysis of lncRNAs Associated with the Pathogenesis and Prognosis of Gastric Cancer.

Integrated analysis of accumulated data is an effective way to obtain reliable potential diagnostic molecular in gastric cancer (GC). The study aimed to identify potential lncRNAs associated with the pathogenesis and prognosis in GC. Raw noncoding RNA microarray data (GSE53137, GSE95667, and GSE111762) was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes between GC and adjacent normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Differentially expressed genes were further confirmed by the cancer genome atlas (TCGA) database. Competing endogenous RNA (ceRNA) network, survival analysis, and gene set enrichment analysis (GSEA) were extensively applied to identify hub lncRNAs and discover potential biomarkers related to diagnosis and prognosis of GC. qPCR was applied to confirm hub lncRNA expression levels in GC tissues. In total, 17 integrated differential lncRNAs were obtained after intersections of differential genes between GEO and TCGA database. Four lncRNAs (HMGA1P4, UBE2Q1-AS1, MAGI2-AS3, MIR22HG) concentrated in ceRNA network were validated by qPCR in GC tissues, which were consistent with informatics results. The clinicopathological association revealed that four lncRNAs might be effective in GC progression. Further study revealed that GC patients with lower MAGI2-AS3 expression was evidently longer than those with higher MAGI2-AS3 expression (p = 0.015). Multivariate analysis revealed MAGI2-AS3 was independently associated with overall survival in GC. GSEA showed GC samples were differentially enriched in pyrimidine metabolism, RNA degradation, cell cycle, oxidative phosphorylation etc., and most significantly enriched in ribosome pathway in MAGI2-AS3 low expression phenotype. Four lncRNAs, including HMGA1P4, UBE2Q1-AS1, MAGI2-AS3, and MIR22HG may contribute to GC development, and MAGI2-AS3 might be associated with the prognosis of GC.

Genomic Landscape of Sex Disparities in Multiple Myeloma

Background While survival of patients with multiple myeloma (MM) continues to improve, disparities in care are widely prevalent. While sex-based differences in cancer outcomes are apparent in several malignancies, these have not been studied as extensively in MM and consequently, the biology underlying these differences are unknown. Methods We utilized the Multiple Myeloma Research Foundation (MMRF) CoMMpass database (IA11) to evaluate the outcomes of MM by sex. The CoMMpass database includes over 1000 newly-diagnosed MM patients with enriched tumor samples analyzed using RNA sequencing (RNA-seq). Differentially expressed genes (DEGs) were predicted from RNA-seq data using the limma-voom method after TMM normalization, then gene set enrichment analyses using GSEA and WebGestalt and ClueGo were performed to assess for highly enriched pathways. We then performed multiple linear regression analysis using sex as the dependent variable. Other known high-risk prognostic markers were used as independent categorical or numeric variables. Categorical variables included age (&lt;/&gt;65 years), beta2 microglobulin (&gt;5.5 mg/L), elevated LDH (&gt;ULN), presence of del 17p, t(4;14), t(14;16), gain chr 1q, del 1p and hyperdiploid status. Numeric variables included genes from the EMC-92 gene signature. Overall survival (OS) was estimated by Kaplan Meier method and log-rank test. Results Among patients with available data, females accounted for 44% (N=384) and males 56% (N=487) patients. Male sex is associated with inferior overall survival, with median survival (men 55 months vs women NR) (p=0.00024). GSEA detected 310 out of 668 gene sets to be up-regulated in the female cohort of which 69 gene sets were significantly up-regulated (FDR q &lt;25%). While in males, 358 out of the 668 gene sets queried were up-regulated and no gene set was significantly upregulated at FDR q&lt;25%. Compared to males, significantly enriched gene sets in females were those involved oxidative metabolism (oxidative phosphorylation, respiratory electron transfer, and TCA cycle) which have been shown to be a function of B cell differentiation to support increased antibody production and may suggest a more differentiated plasma cell phenotype in women. We then performed WebGestalt over-representation analysis (ORA) using up and down DEGs which showed that cell-cell junction, ECM proteoglycans, IFN gamma signaling, L1CAM interactions and axon guidance pathways were affected when comparing by sex. Using the limma-voom method to predict DEGs (after TMM normalization), the up-regulated genes in women included those involved in protective processes such as inhibition of JAK/STAT mediated signaling (SOCS3), increased glutathione S transferase to detoxify products of oxidative stress (GSTM1), promotion of autophagy (DEPP1) as well as development of long term T cell immunity (TNFRSF4). Downregulated genes in women include those associated with increased proliferative potential such as cell-cell adhesion and signaling (LPHN2, EPHB1, LSAMP, NCAM1, CADM1, CD44, ANK3, CD99), cell cycle regulation (ZNF256, TEAD1, PDGFD), cytoskeletal proteins (MAP9, HYDIN, TMSB4X, TIAM1) and angiogenesis (PDE3B, VASH2). Multiple linear regression analysis with sex as dependent variable showed no differences in the independent categorical variables tested between the male and female groups. 14 genes of the EMC-92 gene signature were also noted to significantly differ between the female and male groups (EHBP1L1, FGFR3, C1S, GRB14, HMGB3, ITM2B, LBR, MRPL41, RAB2A, RPS28, RPS4X, SPATS2L, TOP2A, TMEM97) along with genes expressed in multiple high-risk gene expression profiling signatures (TMEM97, ITM2B). These genes have been associated with cell survival, proliferation, differentiation and angiogenesis which may explain the differences in survival outcomes. Conclusion Female sex was associated with improved overall survival in the MMRF CoMMpass database. Over expression of several protective signatures and under expression of genes associated with increased proliferative signal may explain the differences in disease biology. Validation of these results in an independent cohort will help clarify these interesting and novel findings. Disclosures Girnius: Celgene: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Takeda: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Genentech: Membership on an entity's Board of Directors or advisory committees. Landau:Caelum: Membership on an entity's Board of Directors or advisory committees; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Karyopharm: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Research Funding; Prothena: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Takeda: Membership on an entity's Board of Directors or advisory committees, Research Funding. Costa:GSK: Consultancy, Honoraria, Research Funding; Abbvie: Consultancy; Karyopharm: Consultancy; Fujimoto Pharmaceutical Corporation Japan: Other: Advisor; Janssen: Research Funding, Speakers Bureau; Celgene: Consultancy, Honoraria, Research Funding; Amgen: Consultancy, Honoraria, Research Funding, Speakers Bureau; Sanofi: Consultancy, Honoraria, Speakers Bureau.