Co-expression network analysis of placental transcriptome as the basis for searching signal ways and preeclampsia biomarkers

2020 ◽  
pp. 71-72
Author(s):  
А.А. Бабовская ◽  
Е.А. Трифонова ◽  
А.А. Зарубин ◽  
А.В. Марков ◽  
В.А. Степанов
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Early Diagnosis ◽  
Differentially Expressed Genes ◽  
Pathological Process ◽  
Biological Pathways ◽  
Perinatal Morbidity ◽  
Hub Gene ◽  
Significant Gene ◽  
Placental Diseases

Проблема профилактики и ранней диагностики преэклампсии (ПЭ) продолжает оставаться одной из ведущих в акушерстве, поскольку данное осложнение беременности несет большой риск материнской и младенческой смертности. Считается, что основная причина ПЭ - это нарушение этапов формирования плаценты, а регуляции экспрессии генов является значимым механизмом развития плацентарной патологии. Классический подход в транскриптомных исследованиях экспрессии основан на поиске дифференциально-экспрессирующихся генов при заболевании, однако такой подход рассматривает гены изолированно, не учитывая их возможные взаимодействия. Более перспективным подходом является анализ коэкспрессии, который описывает гены, вовлеченные в единые биологические пути патологического процесса, а также позволяет выделять в каждом из кластеров наиболее функционально значимый ген в сети - центральный (hub gene). The problem of prevention and early diagnosis of preeclampsia continues to be one of the leading in obstetrics. It`s a major problem that contributes substantially to maternal and perinatal morbidity and mortality worldwide . Gene expression contributes significantly to the pathogenesis of placental diseases. Traditional methods of studying gene expression are based on the search of differentially expressed genes in a disease, but this approach considers genes in isolation. Coexpression analysis describes the genes involved in the unified biological pathways of the pathological process and also allows you to select in each of the clusters the most functionally significant gene in the network - the hub gene.

scholarly journals Insight into Molecular Profile Changes after Skeletal Muscle Contusion Using Microarray and Bioinformatics Analyses

2020 ◽  
Author(s):  
Na Li ◽  
Ru-feng Bai ◽  
Chun Li ◽  
Li-hong Dang ◽  
Qiu-xiang Du ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Healing Process ◽  
Differentially Expressed ◽  
Molecular Profile ◽  
Wound Age ◽  
Functional Modules

Abstract Background: Muscle trauma frequently occurs in daily life. However, the molecular mechanisms of muscle healing, which partly depend on the extent of the damage, are not well understood. This study aimed to investigate gene expression profiles following mild and severe muscle contusion, and to provide more information about the molecular mechanisms underlying the repair process.Methods: A total of 33 rats were divided randomly into control (n = 3), mild contusion (n = 15), and severe contusion (n = 15) groups; the contusion groups were further divided into five subgroups (1, 3, 24, 48, and 168 h post-injury; n = 3 per subgroup). Then full genome microarray of RNA isolated from muscle tissue was performed to access the gene expression changes during healing process.Results: A total of 2,844 and 2,298 differentially expressed genes were identified in the mild and severe contusion groups, respectively. The analysis of the overlapping differentially expressed genes showed that there are common mechanisms of transcriptomic repair of mild and severe contusion within 48 h post-contusion. This was supported by the results of principal component analysis, hierarchical clustering, and weighted gene co‐expression network analysis of the 1,620 coexpressed genes in mildly and severely contused muscle. From these analyses, we discovered that the gene profiles in functional modules and temporal clusters were similar between the mild and severe contusion groups; moreover, the genes showed time-dependent patterns of expression, which allowed us to identify useful markers of wound age. We then performed an analysis of the functions of genes (including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway annotation, and protein–protein interaction network analysis) in the functional modules and temporal clusters, and the hub genes in each module–cluster pair were identified. Interestingly, we found that genes downregulated within 24−48 h of the healing process were largely associated with metabolic processes, especially oxidative phosphorylation of reduced nicotinamide adenine dinucleotide phosphate, which has been rarely reported. Conclusions: These results improve our understanding of the molecular mechanisms underlying muscle repair, and provide a basis for further studies of wound age estimation.

P0971IDENTIFICATION OF HUB GENES ASSOCIATED WITH THE DEPOSITION OF EXTRACELLULAR MATRIX AND SPECIFIC FOR DIABETIC NEPHROPATHY BY WEIGHTED GENE CO-EXPRESSION NETWORK ANALYSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Songtao Feng ◽  
Linli Lv ◽  
Gao Yueming ◽  
Cao Jingyuan ◽  
Di Yin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Extracellular Matrix ◽  
Diabetic Nephropathy ◽  
Network Analysis ◽  
Expression Level ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Screening ◽  
Gene Modules ◽  
Geo Database

Abstract Background and Aims Diabetic nephropathy (DN) and its most severe manifestation, end-stage renal disease (ESRD), remains one of the leading causes of reduced lifespan in people with diabetes. Identifying novel molecules that are involved in the pathogenesis of DN has both diagnostic and therapeutic implications. The gene co-expression network analysis (WGCNA) algorithm represents a novel systems biology approach that provide the approach of association between gene modules and clinical traits to find the module involvement into the certain phenotypic trait. The goal of this study was to identify hub genes and their roles in DN from the aspect of whole gene transcripts analysis. Method Various types of chronic kidney diseases (CKD), including DN, microarray-based mRNA gene expression data, listed in the Gene Expression Omnibus (GEO) database, were analyzed. Next, we constructed a weighted gene co-expression network and identified modules distinguishing DN from normal or other types of CKD by WGCNA. Functional annotations of the genes in modules specialized for DN were analyzed by Gene Ontology (GO) enrichment analysis. Through protein-protein interaction (PPI) analysis and hub gene screening, the hub genes specific for DN were obtained. Furthermore, we drew ROC curves to determine the diagnosis and differential diagnosis value to DN of hub genes. Finally, another study of microarray in the GEO database was selected to verify the expression level of hub genes and in the “Nephroseq” database, the correlation between the gene expression level and eGFR was analyzed. Results “GSE99339”, glomerular tissue microarray in 187 patients with a total of 10947 genes, was selected for analysis. After excluding the inappropriate cases, a total of 179 specimens were analyzed, including 14 cases of DN, 22 cases of focal segmental glomerulosclerosis (FSGS), 15 cases of hypertensive nephropathy (HT), 26 cases of IgA nephropathy (IgAN), 13 cases of minimal change disease (MCD), 21 cases of membranous nephropathy (MGN), 23 cases of rapidly progressive glomerulonephritis (RPGN), 30 cases of lupus nephritis (LN) and 14 cases of kidney tissue adjacent to tumor. Co-expression network analysis by WGCNA identified 23 distinct gene modules of the total 10947 genes and revealed “MEsaddlegreen” module was strongly correlated with DN (r=0,54), but not with other groups. GO functional annotation showed that these 64 genes in the “MEsaddlegreen” module mainly enriched in the deposition of extracellular matrix, which represents the specific and diagnostic pathophysiological process of DN. Further PPI and hub gene screening analysis revealed that LUM, ELN, FBLN1, MMP2, FBLN5 and FMOD can be served as hub genes, which had been proved to play an important role in the deposition of extracellular matrix. Furthermore, we found that the expression of hub genes was the highest in DN group and for the diagnosis value of DN by each gene, the area under the ROC curve is about 0.75∼0.95. The external verification of another study showed that compared with the normal control group, the expression of these hub genes was the highest in the DN group, and their expression level was negatively correlated with eGFR. Conclusion Using WGCNA and further bioinformatics approach, we identified six hub genes that appear to be identical to DN development. As such, they may represent potential diagnostic biomarkers as well as therapeutic targets with clinical utility.

scholarly journals Weighted gene co-expression network analysis to identify key modules and hub genes associated with paucigranulocytic asthma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Min Li ◽  
Wenye Zhu ◽  
Chu Wang ◽  
Yuanyuan Zheng ◽  
Shibo Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Immune Status ◽  
Expression Patterns ◽  
Gene Expression Pattern ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Asthma is a heterogeneous disease that can be divided into four inflammatory phenotypes: eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma (MGA), and paucigranulocytic asthma (PGA). While research has mainly focused on EA and NA, the understanding of PGA is limited. In this study, we aimed to identify underlying mechanisms and hub genes of PGA. Methods Based on the dataset from Gene Expression Omnibus(GEO), weighted gene coexpression network analysis (WGCNA), differentially expressed genes (DEGs) analysis and protein–protein interaction (PPI) network analysis were conducted to construct a gene network and to identify key gene modules and hub genes. Functional enrichment analyses were performed to investigate the biological process, pathways and immune status of PGA. The hub genes were validated in a separate dataset. Results Compared to non-PGA, PGA had a different gene expression pattern, in which 449 genes were differentially expressed. One gene module significantly associated with PGA was identified. Intersection between the differentially expressed genes (DEGs) and the genes from the module that were most relevant to PGA were mainly enriched in inflammation and immune response regulation. The single sample Gene Set Enrichment Analysis (ssGSEA) suggested a decreased immune infiltration and function in PGA. Finally six hub genes of PGA were identified, including ADCY2, CXCL1, FPRL1, GPR109B, GPR109A and ADCY3, which were validated in a separate dataset of GSE137268. Conclusions Our study characterized distinct gene expression patterns, biological processes and immune status of PGA and identified hub genes, which may improve the understanding of underlying mechanism and provide potential therapeutic targets for PGA.

scholarly journals FCN3 Is a Prognostic Biomarker Correlated with Immune Response in Liver Cancer

2020 ◽  
Author(s):  
Zhongxiao Lu ◽  
Jian Wu ◽  
Yi-ming Li ◽  
Wen-xiang Chen ◽  
Qiang-feng Yu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Liver Cancer ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
R Software ◽  
Hub Gene

Abstract AimLiver cancer is a common malignant tumor whose molecular pathogenesis remains unclear. This study attempts to identify key genes related to liver cancer by bioinformatics analysis and analyze their biological functions.MethodsThe gene expression data of the microarray were downloaded from the Gene Expression Omnibus(GEO) database. The differentially expressed genes (DEGs) were then identified by the R software package “limma” and were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using DAVID. The protein-protein interaction (PPI) network was constructed via String, and the results were visualized in Cytoscape. Modules and hub genes were identified using the MCODE plugin, while the expression of hub genes and its effects were analyzed by GEPIA2. Additionally, the co-expression of the hub gene was explored in String, while the GO results were visualized using the R software. Finally, the targets of the hub gene were predicted through an online website. ResultsIn total, 43 differentially expressed genes were obtained. The GO analysis was mainly concentrated in the redox process and nuclear mitosis, while the KEGG pathway analysis was mainly enriched in retinol metabolism and the cell cycle. Moreover, four hub genes were identified in the PPI network, however, the Kaplan-Meier risk curve showed that only ECT2 and FCN3 affected the survival of liver cancer. ECT2 was found to be high expressed in liver cancer, carrying out signal transduction and targeting hsa-miR-27a-3p. FCN3 was observed to be lowly expressed in liver cancer and related to the immune response, targeting hsa-miR132-5p.ConclusionThe obtained findings suggest that two genes are significantly related to the prognosis of liver cancer, and the analysis of their biological function provided novel insight into the pathogenesis of liver cancer. Furthermore, FCN3 may serve as a promising biomarker for patients with liver cancer.

scholarly journals Gene co-expression networks in whole blood implicate multiple interrelated molecular pathways in obese asthma

2017 ◽  
Author(s):  
Damien C. Croteau-Chonka ◽  
Zhanghua Chen ◽  
Kathleen C. Barnes ◽  
Albino Barraza-Villarreal ◽  
Juan C. Celedón ◽  
...  
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Whole Blood ◽  
Childhood Asthma ◽  
Hedgehog Signaling ◽  
Asthma Management ◽  
Management Program ◽  
Biological Pathways ◽  
Gene Set Enrichment Analysis ◽  
Childhood Asthma Management Program

AbstractBackgroundAsthmatic children who develop obesity have poorer outcomes compared to those that do not, including poorer control, more severe symptoms, and greater resistance to standard treatment. Gene expression networks are powerful statistical tools for characterizing the underpinnings of human disease that leverage the putative co-regulatory relationships of genes to infer biological pathways altered in disease states.ObjectiveThe aim of this study was to characterize the biology of childhood asthma complicated by adult obesity.MethodsWe performed weighted gene co-expression network analysis (WGCNA) of gene expression data in whole blood from 514 adult subjects from the Childhood Asthma Management Program (CAMP). We then performed module preservation and association replication analyses in 418 subjects from two independent asthma cohorts (one pediatric and one adult).ResultsWe identified a multivariate model in which four gene co-expression network modules were associated with incident obesity in CAMP (each P < 0.05). The module memberships were enriched for genes in pathways related to platelets, integrins, extracellular matrix, smooth muscle, NF-κB signaling, and Hedgehog signaling. The network structures of each of the four obese asthma modules were significantly preserved in both replication cohorts (permutation P = 9.999E-05). The corresponding module gene sets were significantly enriched for differential expression in obese subjects in both replication cohorts (each P < 0.05).ConclusionsOur gene co-expression network profiles thus implicate multiple interrelated pathways in the biology of an important endotype of obese asthma.Key MessagesWe hypothesized that individuals with asthma complicated by obesity had distinct blood gene expression signatures.Gene co-expression network analysis implicated several inflammatory biological pathways in one form of obese asthma.Capsule SummaryThis work addresses a knowledge gap about the molecular relationship between asthma and obesity, suggesting that an endotype of obese asthma, known as asthma complicated by obesity, is underpinned by coherent biological mechanisms.AbbreviationsCAMPChildhood Asthma Management ProgramWGCNAweighted gene co-expression network analysisAsthma BRIDGEAsthma BioRepository for Integrative Genomic ExplorationGACRSGenetics of Asthma in Costa Rica StudyCHSSouthern California Children’s Health StudyBMIbody mass indexBICBayes Information CriterionHUGOHuman Genome OrganisationPCprincipal componentGSEAgene set enrichment analysisIL-1interleukin-1Hh signalingHedgehog signaling

scholarly journals Identification of a Key Glioblastoma Candidate Gene, FUBP3, Based on Weighted Gene Co-expression Network Analysis

2022 ◽  
Author(s):  
Jianmin Li ◽  
Zhao Zhang ◽  
Ke Guo ◽  
Shuhua Wu ◽  
Chong Guo ◽  
...  
Keyword(s):  
T Cells ◽  
Survival Analysis ◽  
Network Analysis ◽  
Candidate Gene ◽  
Differentially Expressed Genes ◽  
Normal Tissue ◽  
Venn Diagram ◽  
Adjacent Normal Tissue ◽  
Immune Infiltration ◽  
Significant Gene

Abstract Background: Glioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene.Methods: Two GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was used to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was used for survival analysis to determine the significant gene, FUBP3. OS,DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3. Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis.Results: GSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) from GSE70231 and GSE108474, respectively, were identified. We then performed weighted gene co-expression network analysis (WGCNA) and identified the most downregulated gene modules of GSE70231 and GSE108474, and 659 and 3915 module genes from GSE70231 and GSE108474, respectively, were selected. Five intersection genes (FUBP3, DAD1, CLIC1, ABR, and DNM1) were calculated by Venn diagram. FUBP3 was then identified as the only significant gene by survival analysis using the GEPIA website. OS, DSS, and PFI analyses verified the significance of FUBP3. Immunohistochemical analysis revealed FUBP3 expression in GBM and adjacent normal tissue. KEGG and GO analyses uncovered the possible function of FUBP3 in GBM. Tumor microenvironment analysis showed that FUBP3 may be connected to immune infiltration, and immunohistochemistry identified a positive correlation between immune cells (CD4+ T cells, CD8+ T cells, and macrophages) and FUBP3.Conclusion: FUBP3 is associated with immune surveillance in GBM, indicating that it has a great impact on GBM development and progression. Therefore, interventions involving FUBP3 and its regulatory pathway may be a new approach for GBM treatment.

scholarly journals CLCA1 Has a Prognostic Indicator for the Colorectal Cancer by Weighted Gene Co-Expression Network Analysis

2021 ◽  
Author(s):  
jiajng lin ◽  
lingzhi yang ◽  
suyong lin ◽  
zhihua chen ◽  
shaoqin chen
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Colon Cancer ◽  
Network Analysis ◽  
Mrna Expression ◽  
Disease Free Survival ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Hub Gene

Abstract Colorectal cancer (CRC) has become the second most common digestive tract tumor. Even though the means to treat colon cancer have improved, patients prognosis is low due to the lack of accurate molecular targets. Hence, it urgently demanded better biomarkers for prognosis and progression of colon cancer. This study explores the hub gene associated with the prognosis of colorectal cancer and further analyzes the hub gene function. In this study, all genes mRNA expression data were from the cancer genome atlas (TCGA) colon cancer database and the Gene Expression Omnibus (GEO). These databases were used to screen the differentially expressed co-genes between colon cancer tissue and normal tissue. Weighted Gene Co-expression Network Analysis screened out a total of 103 differential co-expression genes (WGCNA). According to the R cluster profile package annotation analysis, these genes biological functions mainly concentrate on energy metabolism. Moreover, in the protein-protein interaction (PPI) network, the CytoHubba plugin of Cytoscape was used to screen out ten genes (CLCA1, ZG16, GUCA2B, GUCA2A, CLCA4, SLC26A3, MS4A12, GCG, SI, and NR1H4). According to the survival analysis results, high expression of CLCA1has better overall survival and disease-free survival in patients with CRC. Simultaneously, the mRNA expression of CLCA1 in normal tissues was higher than that in CRC tissues. Besides, there were significant differences in the expression of CLCA1 in pathological stage, T stage, and M stage. By using a gene set enrichment analysis, we found several considerable enrichment pathways in the high-groups. CIBERSORT analysis for the proportion of TICs revealed that B-cell naive, dendritic cells, plasma cells, and CD4+ T cells were positively correlated with CLCA1 expression, suggesting that CLCA1 might be responsible for the preservation of immune-dominant status for TME. Finally, in the Human Protein Atlas (HPA) database, the protein level of CLCA1 in the colorectal cancer samples decreased, consistent with the down-regulation of the mRNA expression level CLCA1. To sum up, by integrating WGCNA with differential gene expression analysis, this research generated a significant survival correlative gene called CLCA1 that can predict prognosis prediction in colon cancer.

scholarly journals Identification and validation of hub genes for diabetic retinopathy

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12126
Author(s):  
Li Peng ◽  
Wei Ma ◽  
Qing Xie ◽  
Baihua Chen
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Diabetic Retinopathy ◽  
Network Analysis ◽  
Retinal Microvasculature ◽  
Potential Biomarker ◽  
Significant Gene ◽  
Gene Modules ◽  
Key Genes ◽  
And Oxidative Stress

Background Diabetic retinopathy (DR) is characterized by a gradually progressive alteration in the retinal microvasculature that leads to middle-aged adult acquired persistent blindness. Limited research has been conducted on DR pathogenesis at the gene level. Thus, we aimed to reveal novel key genes that might be associated with DR formation via a bioinformatics analysis. Methods The GSE53257 dataset from the Gene Expression Omnibus was downloaded for gene co-expression analysis. We identified significant gene modules via the Weighted Gene Co-expression Network Analysis, which was conducted by the Protein-Protein Interaction (PPI) Network via Cytoscape and from this we screened for key genes and gene sets for particular functional and pathway-specific enrichments. The hub gene expression was verified by real-time PCR in DR rats modeling and an external database. Results Two significant gene modules were identified. Significant key genes were predominantly associated with mitochondrial function, fatty acid oxidation and oxidative stress. Among all key genes analyzed, six up-regulated genes (i.e., SLC25A33, NDUFS1, MRPS23, CYB5R1, MECR, and MRPL15) were highly and significantly relevant in the context of DR formation. The PCR results showed that SLC25A33 and NDUFS1 expression were increased in DR rats modeling group. Conclusion Gene co-expression network analysis highlights the importance of mitochondria and oxidative stress in the pathophysiology of DR. DR co-expressing gene module was constructed and key genes were identified, and both SLC25A33 and NDUFS1 may serve as potential biomarker and therapeutic target for DR.

scholarly journals NOVA1-Mediated SORBS2 Isoform Promotes Colorectal Cancer Migration by Activating the Notch Pathway

2021 ◽  
Vol 9 ◽  
Author(s):  
Tao Zhang ◽  
Sixia Chen ◽  
Yi Peng ◽  
Changgang Wang ◽  
Xi Cheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Alternative Splicing ◽  
Differentially Expressed Genes ◽  
Tumor Tissue ◽  
Notch Pathway ◽  
Differentially Expressed ◽  
Splicing Factor ◽  
Integrated Analysis ◽  
Hub Gene

Background: Gene expression and alternative splicing (AS) can promote cancer development via complex mechanisms. We aimed to identify and verify the hub AS events and splicing factors associated with the progression of colorectal cancer (CRC).Methods: RNA-Seq data, clinical data, and AS events of 590 CRC samples were obtained from the TCGA and TCGASpliceSeq databases. Cox univariable and multivariable analyses, KEGG, and GO pathway analyses were performed to identify hub AS events and splicing factor/spliceosome genes, which were further validated in five CRCs.Results: In this study, we first compared differentially expressed genes and gene AS events between normal and tumor tissues. Differentially expressed genes were different from genes with differentially expressed AS events. Prognostic analysis and co-expression network analysis of gene expression and gene AS events were conducted to screen five hub gene AS events involved in CRC progression: EPB41L2, CELF2, TMEM130, VCL, and SORBS2. Using qRT-PCR, we also verified that the gene AS events SORBS2 were downregulated in tumor tissue, and gene AS events EPB41L2, CELF2, TMEM130, and VCL were upregulated in tumor tissue. The genes whose mRNA levels were significantly related to the five hub gene AS events were significantly enriched in the GO term of cell division and Notch signaling pathway. Further coexpression of gene AS events and alternative splicing factor genes revealed NOVA1 as a crucial factor regulating the hub gene AS event expression in CRC. Through in vitro experiments, we found that NOVA1 inhibited gene AS event SORBS2, which induced the migration of CRC cells via the Notch pathway.Conclusion: Integrated analysis of gene expression and gene AS events and further experiments revealed that NOVA1-mediated SORBS2 promoted the migration of CRC, indicating its potential as a therapeutic target.

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