A three-gene expression signature model to predict neo-chemoradiotherapy response of esophageal squamous cell carcinomas.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. e15135-e15135
Author(s):  
Jing Wen ◽  
Hong Yang ◽  
Kongjia Luo ◽  
Yi Hu ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prediction Model ◽  
Differentially Expressed Genes ◽  
Predictive Power ◽  
Predictive Accuracy ◽  
Quantitative Polymerase Chain Reaction ◽  
Gene Expression Signature ◽  
Differentially Expressed ◽  
Linear Discriminant ◽  
Crt Response

e15135 Background: Preoperative chemoradiotherapy (CRT) followed by surgery has been proved to improve survival in comparison with surgery alone. However, the outcomes of CRT are heterogeneous, and no clinical or pathological method could prediction CRT response. In this study, we aim to identify mRNA markers for ESCC CRT-response prediction. Methods: Gene expression analyses were performed on pretreatment cancer biopsies from 28 ESCCs who received neoadjuvant CRT and surgery. Surgical specimens were assessed for the pathological response to CRT. The identified differentially expressed genes were validated by real-time quantitative polymerase chain reaction (qPCR), based on which a classifying model was built up by Fisher’s linear discriminant analysis. The predictive power of this model was further assessed in another set of 32 ESCCs. Results: The profiling of the 28 ESCCs identified 10 differentially expressed genes with more than 2-fold changes between pathological complete responsers (pCRs) and less than pCRs (<pCRs), among which 6 genes (LIMCH1, SDPR, Clorf226, SLC9A9, GSTM3, and IGSF10) were down-regulated and 4 genes (MMP9, MMP1, MMP12 and OASL) up-regulated in pCRs versus <pCRs. A prediction model based on qPCR values of 3 genes was built up, Y=-10.388 - 0.343 × MMP1 + 0.642 × LIMCH1 + 0.921 × Clorf226 with a cut-off value of 0.607. It provided a predictive accuracy of 85.7% with leave-one-out cross-validation. Further, the predictive power of this model was validated in another set of 32 ESCCs, among which a predictive accuracy of 81.3% was achieved. Conclusions: The combination of three genes by qPCR identified by microarrays in our study provides possibility for ESCC CRT prediction, which will facilitate individualization of ESCC treatment. Further perspective validation in larger independent cohorts is warranted to fully assess the predictive power of this prediction model.

scholarly journals Transcriptome Analysis Reveals Dynamic Cultivar-Dependent Patterns of Gene Expression in Potato Spindle Tuber Viroid-Infected Pepper

Plants ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 2687
Author(s):  
Nikol Hadjieva ◽  
Elena Apostolova ◽  
Vesselin Baev ◽  
Galina Yahubyan ◽  
Mariyana Gozmanova
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Differentially Expressed Genes ◽  
Transcriptome Analysis ◽  
Quantitative Polymerase Chain Reaction ◽  
Potato Spindle Tuber Viroid ◽  
Fine Tuning ◽  
Differentially Expressed ◽  
Gene Ontology Term ◽  
Ontology Term

Potato spindle tuber viroid (PSTVd) infects various plants. PSTVd pathogenesis is associated with interference with the cellular metabolism and defense signaling pathways via direct interaction with host factors or via the transcriptional or post-transcriptional modulation of gene expression. To better understand host defense mechanisms to PSTVd infection, we analyzed the gene expression in two pepper cultivars, Capsicum annuum Kurtovska kapia (KK) and Djulunska shipka (DS), which exhibit mild symptoms of PSTVd infection. Deep sequencing-based transcriptome analysis revealed differential gene expression upon infection, with some genes displaying contrasting expression patterns in KK and DS plants. More genes were downregulated in DS plants upon infection than in KK plants, which could underlie the more severe symptoms seen in DS plants. Gene ontology enrichment analysis revealed that most of the downregulated differentially expressed genes in both cultivars were enriched in the gene ontology term photosynthesis. The genes upregulated in DS plants fell in the biological process of gene ontology term defense response. We validated the expression of six overlapping differentially expressed genes that are involved in photosynthesis, plant hormone signaling, and defense pathways by quantitative polymerase chain reaction. The observed differences in the responses of the two cultivars to PSTVd infection expand the understanding of the fine-tuning of plant gene expression that is needed to overcome the infection.

Gene Expression Profiling Differentiates Childhood Acute Lymphoblastic Leukemia in Down Syndrome Versus Non-Down Syndrome Patients.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 1203-1203
Author(s):  
Karen R. Rabin ◽  
Jinhua Wang ◽  
Anna Tsimelzon ◽  
Debra Morrison ◽  
Amos S. Gaikwad ◽  
...  
Keyword(s):  
Gene Expression ◽  
Down Syndrome ◽  
Acute Lymphoblastic Leukemia ◽  
Oxidative Phosphorylation ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Signature ◽  
Differentially Expressed ◽  
P Values

Abstract Children with Down syndrome (DS) and acute lymphoblastic leukemia (ALL) form a unique biological subset. These patients have generally inferior outcomes in many studies, and an increased incidence of treatment-related toxicities. Cases of DS ALL have a much lower frequency of recurrent prognostically significant chromosomal abnormalities than cases of ALL in the general pediatric population. Global gene expression profiling provides an opportunity to gain insights into pathogenesis and potential therapeutic targets in DS ALL. We performed microarray analysis of RNA from bone marrow samples obtained at diagnosis in 30 DS ALL and 24 non-DS ALL cases using the Affymetrix Human Genome U133 Plus 2.0 array. Unsupervised hierarchical clustering separated cases into two main groups, one of which included 21 of 30 DS samples (Fisher’s exact test, p = 0.013), suggesting inherent biologic similarities. Non-DS samples clustered according to known cytogenetic features. Consistent with recently published data, a subset (13%) of our DS ALL cases were found to have JAK2 mutations. Cases of DS ALL bearing JAK2 mutations did not form a distinct subcluster, suggesting that the JAK2 pathway may be dysregulated via other events in cases of DS ALL with wild-type JAK2. Two-sample comparison of DS versus non-DS ALL cases demonstrated differential expression of 513 genes with p values &lt;0.001 (Figure 1). Oxidative phosphorylation pathway genes were most over-represented among differentially expressed genes, with 35 of 115 genes in this pathway demonstrating down-regulation in DS compared to non-DS ALL (Bonferroni corrected p value &lt; 1 ×10−9), including several cytochrome c oxidase and ubiquinone subunits. Our data indicate that DS ALL blasts may utilize oxidative phosphorylation to a lesser extent than non-DS ALL, a feature which could be exploited therapeutically. The top 513 genes differentially expressed in DS versus non-DS ALL (Benjamini-Hochberg corrected p values &lt; 0.001) are displayed in a heatmap where genes relatively overexpressed in DS ALL are depicted in yellow, and relatively underexpressed in DS ALL in red. DS ALL cases are indicated by red circles and non-DS ALL cases by white circles. Four DS ALL cases bearing a JAK2 mutation at arginine 683 are indicated by black stars. Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) versus non-Down syndrome acute lymphoblastic leukemia (ALL). Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) versus non-Down syndrome acute lymphoblastic leukemia (ALL).

Abstract 19792: Prediction of Right Ventricular Failure After Cardiac Transplantation : A Recipient Transcriptomic Study

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Damien Guijarro ◽  
Jean-Pierre Gueffet ◽  
Marja Steenman ◽  
Jean-Christian Roussel ◽  
Jean-Noel Trochu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Right Ventricle ◽  
Right Ventricular ◽  
Differentially Expressed Genes ◽  
Cardiac Transplantation ◽  
Gene Expression Signature ◽  
Differentially Expressed ◽  
Cardiac Transplant ◽  
Characteristic Analysis ◽  
Post Transplantation

Introduction: Right ventricle failure (RVF) is a frequent and severe complication after cardiac transplantation. However, risk stratification for RVF is poorly achieved. Development of transcriptomic biomarkers for outcome prediction in cardio-vascular diseases is promising. Hypothesis: Our aim was to identify right ventricular gene expression signature associated to RVF and to define a transcriptomic biomarker that could predict post-transplantation RVF. Methods: Recipient RV myocardial samples of 44 patients transplanted from February 1998 to November 2002 in our center were collected. We retrospectively identified patients with (RVF group) and without (CTL group) post-transplantation RVF. A 4035-gene expression profile was obtained for all patients. Differentially expressed genes between RVF and CTL groups were identified and a molecular RVF predictor was used to determine for each patient a RVF prediction score (RVFs). Results: 9 (20%) and 18 (41%) patients were classified in RVF and CTL groups respectively. As compared to CTL group, RVF patients showed higher pre-operative bilirubin level and higher post-operative death rate. Molecular RVF predictor included 75 differentially expressed genes. Using this predictor, risk for post-transplantation RVF was 2.8-fold greater if RVFs was >0.5 (CI 95%: 1.243-6.305). Sensitivity and specificity of RVFs were 0.778 and 0.889, respectively. Using receiver operating characteristic analysis, RVFs area under curve (AUC) was significantly greater than AUC of commonly used RVF predictors (pulmonary vascular resistance, trans-pulmonary gradient). Conclusions: Gene expression profiling of recipient right ventricle could be used to predict post-transplantation RVF. Transcriptomic biomarkers should be further investigated as a new tool for selection of cardiac transplant candidates.

scholarly journals Microarray and Growth Analyses Identify Differences and Similarities of Early Corn Response to Weeds, Shade, and Nitrogen Stress

Weed Science ◽  
2012 ◽  
Vol 60 (2) ◽  
pp. 158-166 ◽  
Author(s):  
Janet Moriles ◽  
Stephanie Hansen ◽  
David P. Horvath ◽  
Graig Reicks ◽  
David E. Clay ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Quantitative Polymerase Chain Reaction ◽  
Expression Patterns ◽  
Light Stress ◽  
Growth Yield ◽  
Binding Activity ◽  
Differentially Expressed ◽  
Growth And Yield ◽  
Light Competition

Weed interference with crop growth is often attributed to water, nutrient, or light competition; however, specific physiological responses to these stresses are not well described. This study's objective was to compare growth, yield, and gene expression responses of corn to nitrogen (N), low light (40% shade), and weed stresses. Corn vegetative parameters from V2 to V12 stages, yield parameters, and gene expression using transcriptome (2008) and quantitative polymerase chain reaction (qPCR) (2008/09) analyses at V8 were compared among the stresses and with nonstressed corn. N stress did not affect vegetative parameters, although grain yield was reduced by 40% compared with nonstressed plants. Shade, present until V2, reduced biomass and leaf area > 50% at V2, and recovering plants remained smaller than nonstressed plants at V12. However, grain yields of shade-stressed and nonstressed plants were similar, unless shade remained until V8. Weed stress reduced corn growth and yield in 2008 when weeds remained until V6. In 2009, weed stress until V2 reduced corn vegetative growth, but yield reductions occurred only if weed stress remained until V6 or later. Principle component analysis of differentially expressed genes indicated that shade and weed stress had more similar gene expression patterns to each other than they did to nonstressed or N-stressed tissues. However, corn grown in N-stressed conditions shared 252 differentially expressed genes with weed-stressed plants. Ontologies associated with light/photosynthesis, energy conversion, and signaling were down-regulated in response to all three stresses. Shade and weed stress clustered most tightly together, based on gene expression, but shared only three ontologies, O-METHYLTRANSFERASE activity (lignification processes), POLY(U)-BINDING activity (posttranscriptional gene regulation), and stomatal movement. Based on morphologic and genomic observations, weed stress to corn was not explained by individual effects of N or light stress. Therefore, we hypothesize that these stresses share limited signaling mechanisms.

Gene Expression Profiling in Down Syndrome Acute Lymphoblastic Leukemia Identifies Distinct Profiles Associated with CRLF2 Expression Status.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 2389-2389
Author(s):  
Karen R. Rabin ◽  
Jinhua Wang ◽  
Julia Meyer ◽  
Michael G. Loudin ◽  
Deepa Bhojwani ◽  
...  
Keyword(s):  
Gene Expression ◽  
Down Syndrome ◽  
Acute Lymphoblastic Leukemia ◽  
Differentially Expressed Genes ◽  
Lymphoblastic Leukemia ◽  
Gene Expression Signature ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Gene Set Enrichment

Abstract Abstract 2389 Poster Board II-366 Children with Down syndrome (DS) have a 10 to 20-fold increased risk of developing acute lymphoblastic leukemia (ALL), and they have experienced poorer outcomes on recent major protocols worldwide. The cytogenetic abnormalities which are generally common in childhood ALL and contribute to risk-based treatment assignment are markedly less frequent in children with DS-ALL. Recently, activating mutations in Janus kinase 2 (JAK2) have been identified in approximately 20% of DS-ALL, and interstitial deletions involving cytokine receptor-like factor 2 (CRLF2) in approximately 50% of DS-ALL. Global gene expression profiling may provide insights into the biologic consequences of these molecular lesions. We performed microarray analysis of RNA from diagnostic bone marrow samples in 23 DS-ALL and 26 non-DS ALL cases using the Affymetrix Human Genome U133 Plus 2.0 array. CRLF2 expression was high in 10 of the 23 DS-ALL cases, 3 of which also bore JAK2 mutations, and in a single non-DS ALL case. Unsupervised hierarchical clustering analysis demonstrated clustering of non-DS ALL cases belonging to known cytogenetic subgroups such as E2A-PBX1, MLL rearrangement, and high hyperdiploidy. In contrast, neither DS-ALL cases overall nor the JAK2-mutated or high CRLF2 expressing cases formed a cohesive cluster. Supervised analysis identified 43 genes that were differentially expressed between CRLF2 high versus low cases with a false discovery rate <10%. Several of the most highly differentially expressed genes were validated by quantitative real-time PCR. These included three genes with high expression in CRLF2-high cases: chemokine (C-C motif) ligand 17 (CCL17) (p=0.01), V-yes-1 Yamaguchi sarcoma viral oncogene homolog 1 (YES1) (p=0.007), and Iroquois homeobox 2 (IRX2) (p=0.008); and one gene with expression inversely correlated with CRLF2 expression: dual specificity phosphatase 6 (DUSP6) (p=0.0015). Our findings suggest that DS-ALL does not form a single distinct biologic subgroup, but nearly half of DS-ALL cases are defined by high CRLF2 expression, a substantial enrichment for this lesion compared to its prevalence in non-DS ALL. Identification of downstream pathways may identify opportunities for targeted intervention, including interactions with other cytokines and activation of the JAK-STAT pathway. Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) acute lymphoblastic leukemia (ALL) cases with high versus low CRLF2 expression. Each column indicates a case, with CRLF2-high cases depicted in gray and CRLF2-low cases in gold. Each row indicates one of the top 100 differentially expressed genes as determined by Gene Set Enrichment Analysis. Figure 1. Gene expression signature of top differentially expressed genes in Down syndrome (DS) acute lymphoblastic leukemia (ALL) cases with high versus low CRLF2 expression. Each column indicates a case, with CRLF2-high cases depicted in gray and CRLF2-low cases in gold. Each row indicates one of the top 100 differentially expressed genes as determined by Gene Set Enrichment Analysis. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Differential gene expression analysis of ankylosing spondylitis shows deregulation of the HLA-DRB, HLA-DQB, ITM2A, and CTLA4 genes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rowan AlEjielat ◽  
Anas Khaleel ◽  
Amneh H. Tarkhan
Keyword(s):  
Gene Expression ◽  
Ankylosing Spondylitis ◽  
Differentially Expressed Genes ◽  
Gene Network ◽  
Disease Diagnosis ◽  
Receptor Activation ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Inflammatory Disorder ◽  
Data Set

Abstract Background Ankylosing spondylitis (AS) is a rare inflammatory disorder affecting the spinal joints. Although we know some of the genetic factors that are associated with the disease, the molecular basis of this illness has not yet been fully elucidated, and the genes involved in AS pathogenesis have not been entirely identified. The current study aimed at constructing a gene network that may serve as an AS gene signature and biomarker, both of which will help in disease diagnosis and the identification of therapeutic targets. Previously published gene expression profiles of 16 AS patients and 16 gender- and age-matched controls that were profiled on the Illumina HumanHT-12 V3.0 Expression BeadChip platform were mined. Patients were Portuguese, 21 to 64 years old, were diagnosed based on the modified New York criteria, and had Bath Ankylosing Spondylitis Disease Activity Index scores > 4 and Bath Ankylosing Spondylitis Functional Index scores > 4. All patients were receiving only NSAIDs and/or sulphasalazine. Functional enrichment and pathway analysis were performed to create an interaction network of differentially expressed genes. Results ITM2A, ICOS, VSIG10L, CD59, TRAC, and CTLA-4 were among the significantly differentially expressed genes in AS, but the most significantly downregulated genes were the HLA-DRB6, HLA-DRB5, HLA-DRB4, HLA-DRB3, HLA-DRB1, HLA-DQB1, ITM2A, and CTLA-4 genes. The genes in this study were mostly associated with the regulation of the immune system processes, parts of cell membrane, and signaling related to T cell receptor and antigen receptor, in addition to some overlaps related to the IL2 STAT signaling, as well as the androgen response. The most significantly over-represented pathways in the data set were associated with the “RUNX1 and FOXP3 which control the development of regulatory T lymphocytes (Tregs)” and the “GABA receptor activation” pathways. Conclusions Comprehensive gene analysis of differentially expressed genes in AS reveals a significant gene network that is involved in a multitude of important immune and inflammatory pathways. These pathways and networks might serve as biomarkers for AS and can potentially help in diagnosing the disease and identifying future targets for treatment.

scholarly journals Behavioral sensitization induced by methamphetamine causes differential alterations in gene expression and histone acetylation of the prefrontal cortex in rats

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Hui Li ◽  
Jing-An Chen ◽  
Qian-Zhi Ding ◽  
Guan-Yi Lu ◽  
Ning Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prefrontal Cortex ◽  
Histone Acetylation ◽  
Differentially Expressed Genes ◽  
Behavioral Sensitization ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Adult Rats ◽  
Mrna Microarray ◽  
H3 Acetylation

Abstract Background Methamphetamine (METH) is one of the most widely abused illicit substances worldwide; unfortunately, its addiction mechanism remains unclear. Based on accumulating evidence, changes in gene expression and chromatin modifications might be related to the persistent effects of METH on the brain. In the present study, we took advantage of METH-induced behavioral sensitization as an animal model that reflects some aspects of drug addiction and examined the changes in gene expression and histone acetylation in the prefrontal cortex (PFC) of adult rats. Methods We conducted mRNA microarray and chromatin immunoprecipitation (ChIP) coupled to DNA microarray (ChIP-chip) analyses to screen and identify changes in transcript levels and histone acetylation patterns. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, were performed to analyze the differentially expressed genes. We then further identified alterations in ANP32A (acidic leucine-rich nuclear phosphoprotein-32A) and POU3F2 (POU domain, class 3, transcription factor 2) using qPCR and ChIP-PCR assays. Results In the rat model of METH-induced behavioral sensitization, METH challenge caused 275 differentially expressed genes and a number of hyperacetylated genes (821 genes with H3 acetylation and 10 genes with H4 acetylation). Based on mRNA microarray and GO and KEGG enrichment analyses, 24 genes may be involved in METH-induced behavioral sensitization, and 7 genes were confirmed using qPCR. We further examined the alterations in the levels of the ANP32A and POU3F2 transcripts and histone acetylation at different periods of METH-induced behavioral sensitization. H4 hyperacetylation contributed to the increased levels of ANP32A mRNA and H3/H4 hyperacetylation contributed to the increased levels of POU3F2 mRNA induced by METH challenge-induced behavioral sensitization, but not by acute METH exposure. Conclusions The present results revealed alterations in transcription and histone acetylation in the rat PFC by METH exposure and provided evidence that modifications of histone acetylation contributed to the alterations in gene expression caused by METH-induced behavioral sensitization.

scholarly journals Impact of aging on gene expression response to x-ray irradiation using mouse blood

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Constantinos G. Broustas ◽  
Axel J. Duval ◽  
Sally A. Amundson
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Differentially Expressed ◽  
X Rays ◽  
Gene Expression Response ◽  
X Ray ◽  
The Impact ◽  
Old Mice ◽  
Radiation Biodosimetry ◽  
Young Mice

AbstractAs a radiation biodosimetry tool, gene expression profiling is being developed using mouse and human peripheral blood models. The impact of dose, dose-rate, and radiation quality has been studied with the goal of predicting radiological tissue injury. In this study, we determined the impact of aging on the gene expression profile of blood from mice exposed to radiation. Young (2 mo) and old (21 mo) male mice were irradiated with 4 Gy x-rays, total RNA was isolated from whole blood 24 h later, and subjected to whole genome microarray analysis. Pathway analysis of differentially expressed genes revealed young mice responded to x-ray exposure by significantly upregulating pathways involved in apoptosis and phagocytosis, a process that eliminates apoptotic cells and preserves tissue homeostasis. In contrast, the functional annotation of senescence was overrepresented among differentially expressed genes from irradiated old mice without enrichment of phagocytosis pathways. Pathways associated with hematologic malignancies were enriched in irradiated old mice compared with irradiated young mice. The fibroblast growth factor signaling pathway was underrepresented in older mice under basal conditions. Similarly, brain-related functions were underrepresented in unirradiated old mice. Thus, age-dependent gene expression differences should be considered when developing gene signatures for use in radiation biodosimetry.

scholarly journals Construction of a competitive endogenous RNA network and analysis of potential regulatory axis targets in glioblastoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kai Yu ◽  
Huan Yang ◽  
Qiao-li Lv ◽  
Li-chong Wang ◽  
Zi-long Tan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Differentially Expressed Genes ◽  
Pearson Correlation ◽  
Differentially Expressed ◽  
Migration And Invasion ◽  
Glioblastoma Cells ◽  
Network Chain ◽  
Cerna Network ◽  
Potential Marker

Abstract Background Glioblastoma is the most common primary malignant brain tumor. Because of the limited understanding of its pathogenesis, the prognosis of glioblastoma remains poor. This study was conducted to explore potential competing endogenous RNA (ceRNA) network chains and biomarkers in glioblastoma by performing integrated bioinformatics analysis. Methods Transcriptome expression data from The Cancer Genome Atlas database and Gene Expression Omnibus were analyzed to identify differentially expressed genes between glioblastoma and normal tissues. Biological pathways potentially associated with the differentially expressed genes were explored by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, and a protein-protein interaction network was established using the STRING database and Cytoscape. Survival analysis using Gene Expression Profiling Interactive Analysis was based on the Kaplan–Meier curve method. A ceRNA network chain was established using the intersection method to align data from four databases (miRTarBase, miRcode, TargetScan, and lncBace2.0), and expression differences and correlations were verified by quantitative reverse-transcription polymerase chain reaction analysis and by determining the Pearson correlation coefficient. Additionally, an MTS assay and the wound-healing and transwell assays were performed to evaluate the effects of complement C1s (C1S) on the viability and migration and invasion abilities of glioblastoma cells, respectively. Results We detected 2842 differentially expressed (DE) mRNAs, 2577 DE long non-coding RNAs (lncRNAs), and 309 DE microRNAs (miRNAs) that were dysregulated in glioblastoma. The final ceRNA network consisted of six specific lncRNAs, four miRNAs, and four mRNAs. Among them, four DE mRNAs and one DE lncRNA were correlated with overall survival (p < 0.05). C1S was significantly correlated with overall survival (p= 0.015). In functional assays, knockdown of C1S inhibited the proliferation and invasion of glioblastoma cell lines. Conclusions We established four ceRNA networks that may influence the occurrence and development of glioblastoma. Among them, the MIR155HG/has-miR-129-5p/C1S axis is a potential marker and therapeutic target for glioblastoma. Knockdown of C1S inhibited the proliferation, migration, and invasion of glioblastoma cells. These findings clarify the role of the ceRNA regulatory network in glioblastoma and provide a foundation for further research.

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