Altered molecular pathways and prognostic markers in active systemic juvenile idiopathic arthritis: integrated bioinformatic analysis

Systemic juvenile idiopathic arthritis (SJIA) is a severe childhood-onset inflammatory disease characterized by arthritis accompanied by systemic auto-inflammation and extra-articular symptoms. While recent advances have unraveled a range of risk factors, the pathomechanisms involved in SJIA and potential prognostic markers for treatment success remain partly unknown. In this study, we included 70 active SJIA and 55 healthy control patients from the National Center for Biotechnology Information to analyze for differentially expressed genes (DEGs) using R. Functional enrichment analysis, protein-protein interaction (PPI), and gene module construction were performed for DEGs and hub gene set. We additionally examined immune system cell composition with CIBERSORT and predicted prognostic markers and potential treatment drugs for SJIA. In total, 94 upregulated and 24 downregulated DEGs were identified. Two specific modules of interest and eight hub genes (ARG1, DEFA4, HP, MMP8, MMP9, MPO, OLFM4, PGLYRP1) were screened out. Functional enrichment analysis suggested that complex neutrophil-related functions play a decisive role in the disease pathogenesis. CIBERSORT indicated neutrophils, M0 macrophages, CD8+ T cells, and naïve B cells to be relevant drivers of disease progression. Additionally, we identified TPM2 and GZMB as potential prognostic markers for treatment response to canakinumab. Moreover, sulindac sulfide, (-)-catechin, and phenanthridinone were identified as promising treatment agents. This study provides a new insight into molecular and cellular pathogenesis of active SJIA and highlights potential targets for further research.

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Application of Weighted Gene Co-expression Network Analysis to Identify Key Modules and Hub Genes in Systemic Juvenile Idiopathic Arthritis

10.21203/rs.3.rs-254358/v1 ◽

2021 ◽

Author(s):

Mi Zhou ◽

Ruru Guo ◽

Yongfei Wang ◽

Wanling Yang ◽

Rongxiu Li ◽

...

Keyword(s):

Juvenile Idiopathic Arthritis ◽

Network Analysis ◽

Immune Responses ◽

Systemic Juvenile Idiopathic Arthritis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Data Sets ◽

Hub Genes ◽

Highly Correlated

Abstract Background: Systemic juvenile idiopathic arthritis (sJIA) is a severe autoinflammatory disorder whose molecular mechanism is still not clearly defined. To better understand the disease using scattered datasets from public domains, we performed a weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes underlying sJIA pathogenesis.Methods: Two gene expression datasets, GSE7753 and GSE13501, were used to construct WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were applied to the entirety of genes and the hub genes in the sJIA modules. Cytoscape was used to screen and visualize the hub genes. We further compared the hub genes with the GWAS genes and used a consensus WGCNA analysis to prove that our conclusions are conservative and reproducible across multiple independent data sets. Results: A total of 5414 genes were obtained for WGCNA, from which highly correlated genes were divided into 17 modules. The red module demonstrated the highest correlation with the sJIA module (r =0.8, p=3e−29), while the green-yellow module was found to be closely related to the non-sJIA module (r =0.62, p=1e−14). Functional enrichment analysis demonstrated that the red module was largely enriched in activation of immune responses, infection, nucleosome and erythrocyte, the green-yellow module was mostly enriched in immune responses and inflammation. Additionally, the hub genes in the red module were highly enriched in erythrocyte differentiation, including ALAS2, AHSP, TRIM10, TRIM58 and KLF1. The hub genes from the green-yellow module were mainly associated with immune responses, exemplified by genes such as KLRB1, KLRF1, CD160, KIRs etc.Conclusion: We identified sJIA-related modules and several hub genes that might be associated with the development of sJIA. The two modules may help understand the mechanisms of sJIA and the hub genes may become biomarkers and therapeutic targets of sJIA in the future.

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A Novel Metastasis-related Genes Based Signature for Predicting the Progression-free Interval of Patients With Papillary Thyroid Carcinoma

10.21203/rs.3.rs-1221000/v1 ◽

2022 ◽

Author(s):

Rui Liu ◽

Zhen Cao ◽

Meng-wei Wu ◽

Xiao-bin Li ◽

Hong-wei Yuan ◽

...

Keyword(s):

Papillary Thyroid Carcinoma ◽

Thyroid Carcinoma ◽

Cell Lines ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Bioinformatic Analysis ◽

Functional Enrichment ◽

Clinical Parameters ◽

Papillary Thyroid ◽

Free Interval

Abstract Background: We aimed to build a novel model with metastasis-related genes (MTGs) signature and relevant clinical parameters for predicting progression-free interval (PFI) after surgery for papillary thyroid carcinoma (PTC).Methods: We performed a bioinformatic analysis of integrated PTC datasets with the MTGs to identify differentially expressed MTGs (DE-MTGs). Then we generated PFI-related DE-MTGs and established a novel MTGs based signature. After that, we validated the signature on multiple datasets and PTC cell lines. Further, we carried out uni- and multivariate analysis to identify independent prognostic characters. Finally, we established a signature and clinical parameters-based nomogram for predicting the PFI of PTC. Results: We identified 155 DE-MTGs related to PFI in PTC. The functional enrichment analysis showed that the DE-MTGs were associated with an essential oncogenic process. Consequently, we found a novel 10-gene signature and could distinguish patients with poorer prognoses and predicted PFI accurately. The novel signature had a C-index of 0.76 and the relevant nomogram had a C-index of 0.80. Also, it was closely related to pivotal clinical characters of datasets and invasiveness of cell lines. And the signature was confirmed a significant independent prognostic factor in PTC. Finally, we built a nomogram by including the signature and relevant clinical factors. Validation analysis showed that the nomogram's efficacy was satisfying in predicting PTC’s PFI. Conclusions: The MTG signature and nomogram were closely associated with PTC prognosis and may help clinicians improve the individualized prediction of PFI, especially for high-risk patients after surgery.

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Identification of potential prognostic markers for osteosarcoma by integrated analysis

10.21203/rs.3.rs-52760/v2 ◽

2020 ◽

Author(s):

Yuxiang Ge ◽

Wang Ding ◽

Chong Bian ◽

Huijie Gu ◽

Jun Xu ◽

...

Keyword(s):

Prognostic Markers ◽

Bone Development ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Great Promise ◽

Integrated Analysis ◽

Hub Genes ◽

Hub Gene ◽

Gene Modules

Abstract Background: Osteosarcoma (OS), one of the utmost common and malignant cancer, accounts for over 30% among skeletal sarcomas. Although great efforts have been made, the mechanism of OS still remains largely unknown. Here, we intend to identify gene modules and candidate biomarkers for clinical diagnosis of patients with OS, and reveal the mechanisms of OS progression.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to build a co-expression network and investigate the relationship between modules and clinical traits. Functional enrichment analysis was performed on module genes. Protein-protein interaction (PPI) network was constructed to identify the hub gene and the expression level of hub genes was validated based on another dataset.Results: A total of 9854 genes were included in WGCNA, and 17 gene modules were constructed. Gene module related with OS in sacrum was mainly enriched in skeletal system development, bone development and extracellular structure organization. Furthermore, we screened the top 10 hub genes and further validated 5 of the 10 (MMP13, DCN, GNG2, PCOLCE and RUNX2), the expression of which were upregulated as compared with normal tissues.Conclusion: The hub gene we identified show great promise as prognostic markers for the management of OS and our findings also provide new insight for molecular mechanism of OS.

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Identification of a seven-long non-coding RNA signature associated with Jab1/CSN5 in predicting hepatocellular carcinoma

Cell Death Discovery ◽

10.1038/s41420-021-00560-7 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Weijie Ma ◽

Ye Yao ◽

Gang Xu ◽

Xiaoling Wu ◽

Jinghua Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Tumors ◽

Strong Association ◽

Critical Role ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Bioinformatic Analysis ◽

Functional Enrichment ◽

Clinical Value

AbstractHepatocellular carcinoma (HCC) is a leading cause of cancer death worldwide, accounting for over 700,000 deaths each year. The lack of predictive and prognostic biomarkers for HCC, with effective therapy, remains a significant challenge for HCC management. Long non-coding RNAs (lncRNAs) play a key role in tumorigenesis and have clinical value as potential biomarkers in the early diagnosis and prediction of HCC. Jun activation domain-binding protein 1 (Jab1, also known as COP9 signalosome subunit 5, CSN5) is a potential oncogene that plays a critical role in the occurrence of HCC. Here, we performed a comprehensive analysis for Jab1/CSN5-associated lncRNAs to predict the prognosis of HCC. The differentially expressed (DE) lncRNAs between in HCC were analyzed based on the TCGA RNA-seq data. We detected 1031 upregulated lncRNAs in 371 HCC tissues and identified a seven-lncRNA signature strongly correlated with Jab1/CSN5 (SNHG6, CTD3065J16.9, LINC01604, CTD3025N20.3, KB-1460A1.5, RP13-582O9.7, and RP11-29520.2). We further evaluated the prognostic significance of these lncRNAs by GEPIA (http://gepia.cancer-pku.cn/). The expression data in 364 liver tumors indicated that this seven-lncRNA signature could better predict worse survival in HCC patients. Moreover, 35 clinical HCC samples were evaluated to assess the validity and reproducibility of the bioinformatic analysis. We found that the targeted lncRNAs were upregulated, with a strong association with Jab1/CSN5 and prognostic value in HCC. Functional enrichment analysis by Gene Ontology (GO) showed that these seven prognostic lncRNAs exhibit oncogenic properties and are associated with prominent hallmarks of cancer. Overall, our findings demonstrate the clinical implication of Jab1/CSN5 with the seven‐lncRNAs in predicting survival for patients with HCC.

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Screening and Identification of Potential Biomarkers Associated with Cutaneous Squamous Cell Carcinoma

10.21203/rs.3.rs-823635/v1 ◽

2021 ◽

Author(s):

Wenxing Su ◽

Biao Huang ◽

Qingyi Zhang ◽

Wei Han ◽

Lu An ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Bioinformatic Analysis ◽

Cutaneous Squamous Cell Carcinoma ◽

Functional Enrichment ◽

Hub Genes

Abstract Cutaneous squamous cell carcinoma (cSCC) is the leading cause of death in patients with non-melanoma skin cancers (NMSC). However, unclear pathogenesis of cSCC limits the application of molecular targeted therapy. We downloaded three microarray data (GSE2503, GSE45164 and GSE66359) from Gene Expression Omnibus (GEO) and screened out their common difference genes between tumor and non-tumor tissues. Functional enrichment analysis was performed using DAVID. The STRING online website was used to construct a protein-protein interaction network (PPI), and then Cytoscape performed module analysis and degree calculation.A total of 146 DEGs was identified with significant differences, including 113 up-regulated genes and 33 down-regulated genes. The enriched functions and pathways of the DEGs include microtubule-based movement, ATP binding, cell cycle, p53 signaling pathway, oocyte meiosis and PLK1 signaling events. Nine hub genes were identified, namely CDK1, AURKA, RRM2, CENPE, CCNB1, KIAA0101, ZWINT, TOP2A, ASPM. The differential expression of these genes has been verified in other data sets. By integrated bioinformatic analysis, the hub genes identified in this study elucidated the molecular mechanism of the pathogenesis and progression of cSCC, and are expected to become future biomarkers or therapeutic targets.

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Bioinformatic analysis identifies potential key genes of epilepsy

PLoS ONE ◽

10.1371/journal.pone.0254326 ◽

2021 ◽

Vol 16 (9) ◽

pp. e0254326

Author(s):

Yike Zhu ◽

Dan Huang ◽

Zhongyan Zhao ◽

Chuansen Lu

Keyword(s):

Network Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Bioinformatic Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

P Value ◽

Ppi Network ◽

Functional Roles ◽

Protein Protein Interaction

Background Epilepsy is one of the most common brain disorders worldwide. It is usually hard to be identified properly, and a third of patients are drug-resistant. Genes related to the progression and prognosis of epilepsy are particularly needed to be identified. Methods In our study, we downloaded the Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE143272. Differentially expressed genes (DEGs) with a fold change (FC) >1.2 and a P-value <0.05 were identified by GEO2R and grouped in male, female and overlapping DEGs. Functional enrichment analysis and Protein-Protein Interaction (PPI) network analysis were performed. Results In total, 183 DEGs overlapped (77 ups and 106 downs), 302 DEGs (185 ups and 117 downs) in the male dataset, and 750 DEGs (464 ups and 286 downs) in the female dataset were obtained from the GSE143272 dataset. These DEGs were markedly enriched under various Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. 16 following hub genes were identified based on PPI network analysis: ADCY7, C3AR1, DEGS1, CXCL1 in male-specific DEGs, TOLLIP, ORM1, ELANE, QPCT in female-specific DEGs and FCAR, CD3G, CLEC12A, MOSPD2, CD3D, ALDH3B1, GPR97, PLAUR in overlapping DEGs. Conclusion This discovery-driven study may be useful to provide a novel insight into the diagnosis and treatment of epilepsy. However, more experiments are needed in the future to study the functional roles of these genes in epilepsy.

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Bioinformatic Analysis of Key Genes Related to Tumor Microenvironment in Hepatocellular Carcinoma

10.21203/rs.3.rs-508119/v1 ◽

2021 ◽

Author(s):

Jingxu Zhang ◽

Hao Liu ◽

Keyi Zhao ◽

Zhiye Bao ◽

Zhishuo Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Bioinformatic Analysis ◽

Functional Enrichment ◽

Expression Data ◽

Hub Genes ◽

Oncomine Database ◽

Key Genes ◽

Low Expression

Abstract Background: Tumor microenvironment (TME) plays important roles in the development of different types of cancer. However, the critical regulatory members of TME related to hepatocellular carcinoma (HCC) remain unclear. In this study, a bioinformatic analysis based on Cancer Genome Atlas (TCGA) and Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) datasets was conducted to predict the key genes affecting TME in HCC.Material and Methods: First, 340 patients and 20531 genes’ expression data with ESTIMATE scores were filtered and combined to identify differentially expressed genes. Next, protein-protein interaction (PPI) network and functional enrichment analysis were conducted to find hub genes. Then, log-rank test and functional enrichment analysis were conducted on the consensus genes and hub genes. Finally, Kaplan-Meier curves of the hub genes were drawn. As verification, those genes were searched on Oncomine database.Results: Among all differentially expressed genes, 916 genes were expressed in both the immune and stromal groups. The Gene Ontology (GO) terms they enriched were T cell activation, leukocyte migration, collagen-containing matrix, external side of plasma membrane, receptor ligand and activator activity. Cytokine-cytokine receptor interaction was the most significant Kyoto Encyclopedia of Genes and Genomes (KEGG) term. Furthermore, cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 hub genes were low expressed in 304 patients, participating in a variety of responses including immune response−activating cell surface receptor signaling, immune response−activating signal transduction, clathrin−coated vesicle membrane, immune receptor activity， peptide binding and amide binding pathways. Their low expression was also verified on Oncomine database.Conclusion: cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 participated in many aspects related to TME, and their low expression constructs a signature, may predict a poor 5 years’ survival in hepatocellular carcinoma.

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Identification of potential prognostic markers for osteosarcoma by integrated analysis

10.21203/rs.3.rs-52760/v1 ◽

2020 ◽

Author(s):

Yuxiang Ge ◽

Wang Ding ◽

Chong Bian ◽

Huijie Gu ◽

Jun Xu ◽

...

Keyword(s):

Prognostic Markers ◽

Bone Development ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Great Promise ◽

Integrated Analysis ◽

Hub Genes ◽

Hub Gene ◽

Gene Modules

Abstract Background: Osteosarcoma (OS) is the most common type of musculoskeletal malignant tumor, accounting for over 30% of primary skeletal sarcomas. Although great efforts have been made, the mechanism of OS still remains largely unknown. In this study, we aim to identify gene modules and representative candidate biomarkers for clinical diagnosis of patients with OS, and reveal the mechanisms of OS progression.Methods: Weighted gene co-expression network analysis (WGCNA) was conducted to construct a co-expression network and investigate the relationship between modules and clinical traits. Functional enrichment analysis was performed on module genes. Protein-protein interaction (PPI) network was constructed to identify the hub gene and the expression level of hub genes was validated based on another dataset.Results: A total of 9854 genes were included in WGCNA, and 17 gene modules were constructed. Gene module related with OS in sacrum was mainly enriched in skeletal system development, bone development and extracellular structure organization. Furthermore, we screened the top 10 hub genes and further validated 5 of the 10 (MMP13, DCN, GNG2, PCOLCE and RUNX2), the expression of which were upregulated as compared with normal tissues.Conclusion: The hub gene we identified show great promise as prognostic markers for the management of OS and our findings also provide new insight for molecular mechanism of OS.

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Systems Biology Approaches Reveal a Multi-stress Responsive WRKY Transcription Factor and Stress Associated Gene Co-expression Networks in Chickpea

Current Bioinformatics ◽

10.2174/1574893614666190204152500 ◽

2019 ◽

Vol 14 (7) ◽

pp. 591-601 ◽

Cited By ~ 1

Author(s):

Aravind K. Konda ◽

Parasappa R. Sabale ◽

Khela R. Soren ◽

Shanmugavadivel P. Subramaniam ◽

Pallavi Singh ◽

...

Keyword(s):

Expression Pattern ◽

Gene Networks ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Wrky Transcription Factor ◽

Functional Enrichment ◽

Differentially Expressed ◽

Callose Deposition ◽

Significant Probability

Background: Chickpea is a nutritional rich premier pulse crop but its production encounters setbacks due to various stresses and understanding of molecular mechanisms can be ascribed foremost importance. Objective: The investigation was carried out to identify the differentially expressed WRKY TFs in chickpea in response to herbicide stress and decipher their interacting partners. Methods: For this purpose, transcriptome wide identification of WRKY TFs in chickpea was done. Behavior of the differentially expressed TFs was compared between other stress conditions. Orthology based cofunctional gene networks were derived from Arabidopsis. Gene ontology and functional enrichment analysis was performed using Blast2GO and STRING software. Gene Coexpression Network (GCN) was constructed in chickpea using publicly available transcriptome data. Expression pattern of the identified gene network was studied in chickpea-Fusarium interactions. Results: A unique WRKY TF (Ca_08086) was found to be significantly (q value = 0.02) upregulated not only under herbicide stress but also in other stresses. Co-functional network of 14 genes, namely Ca_08086, Ca_19657, Ca_01317, Ca_20172, Ca_12226, Ca_15326, Ca_04218, Ca_07256, Ca_14620, Ca_12474, Ca_11595, Ca_15291, Ca_11762 and Ca_03543 were identified. GCN revealed 95 hub genes based on the significant probability scores. Functional annotation indicated role in callose deposition and response to chitin. Interestingly, contrasting expression pattern of the 14 network genes was observed in wilt resistant and susceptible chickpea genotypes, infected with Fusarium. Conclusion: This is the first report of identification of a multi-stress responsive WRKY TF and its associated GCN in chickpea.

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Structural variations in papaya genomes

BMC Genomics ◽

10.1186/s12864-021-07665-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zhenyang Liao ◽

Xunxiao Zhang ◽

Shengcheng Zhang ◽

Zhicong Lin ◽

Xingtan Zhang ◽

...

Keyword(s):

Agronomic Traits ◽

Enrichment Analysis ◽

Copy Number Variant ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Structural Variations ◽

Reproduction Traits ◽

Depth Study ◽

Environmental Adaptability ◽

The Impact

Abstract Background Structural variations (SVs) are a type of mutations that have not been widely detected in plant genomes and studies in animals have shown their role in the process of domestication. An in-depth study of SVs will help us to further understand the impact of SVs on the phenotype and environmental adaptability during papaya domestication and provide genomic resources for the development of molecular markers. Results We detected a total of 8083 SVs, including 5260 deletions, 552 tandem duplications and 2271 insertions with deletion being the predominant, indicating the universality of deletion in the evolution of papaya genome. The distribution of these SVs is non-random in each chromosome. A total of 1794 genes overlaps with SV, of which 1350 genes are expressed in at least one tissue. The weighted correlation network analysis (WGCNA) of these expressed genes reveals co-expression relationship between SVs-genes and different tissues, and functional enrichment analysis shows their role in biological growth and environmental responses. We also identified some domesticated SVs genes related to environmental adaptability, sexual reproduction, and important agronomic traits during the domestication of papaya. Analysis of artificially selected copy number variant genes (CNV-genes) also revealed genes associated with plant growth and environmental stress. Conclusions SVs played an indispensable role in the process of papaya domestication, especially in the reproduction traits of hermaphrodite plants. The detection of genome-wide SVs and CNV-genes between cultivated gynodioecious populations and wild dioecious populations provides a reference for further understanding of the evolution process from male to hermaphrodite in papaya.

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