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2021 ◽  
Rui Geng ◽  
Tian Chen ◽  
Zihang Zhong ◽  
Senmiao Ni ◽  
Jianling Bai ◽  

Abstract Background: OV is the most lethal gynecological malignancy. M6A and lncRNAs have great influence on OV development and patients' immunotherapy response. Here, we decided to establish a reliable signature in the light of mRLs. Method: The lncRNAs associated with m6A in OV were analyzed and obtained by co-expression analysis in the light of TCGA-OV database. Univariate, LASSO and multivariate Cox regression analyses were employed to establish the model in the light of the mRLs. K-M analysis, PCA, GSEA, and nomogram based on the TCGA-OV and GEO database were conducted to prove the predictive value and independence of the model. The underlying relationship between the model and TME and cancer stemness properties were further investigated through immune features comparison, consensus clustering analysis, and Pan-cancer analysis.Results: A prognostic signature comprising four mRLs: WAC-AS1, LINC00997, DNM3OS, and FOXN3-AS1, was constructed and verified for OV according to TCGA and GEO database. The expressions of the four mRLs were confirmed by qRT-PCR in clinical samples. Applying this signature, people can identify patients more effectively. All the sample were assigned into two clusters, and the clusters had different overall survival, clinical features, and tumor microenvironment. Finally, Pan-cancer analysis further demonstrated the four mRLs significantly related to immune infiltration, TME and cancer stemness properties in various cancer types. Conclusion: This study provided an accurate prognostic signature for patients with OV and elucidated the potential mechanism of the mRLs in immune modulation and treatment response, giving new insights into identifying new therapeutic targets.

2021 ◽  
Vol 12 ◽  
Xin Jin ◽  
Jun Wang ◽  
Lina Ge ◽  
Qing Hu

Objective: Sciatica pertains to neuropathic pain that has been associated with inflammatory response. We aimed to identify significant immune-related biomarkers for sciatica in peripheral blood.Methods: We utilized the GSE150408 expression profiling data from the Gene Expression Omnibus (GEO) database as the training dataset and extracted immune-related genes for further analysis. Differentially expressed immune-related genes (DEIRGs) between healthy controls and patients with sciatica were selected using the “limma” package and verified in clinical specimens by quantitative reverse transcription PCR (RT-qPCR). A diagnostic immune-related gene signature was established using the training model and random forest (RF), generalized linear model (GLM), and support vector machine (SVM) models. Sciatica patient subtypes were identified using the consensus clustering method.Results: Thirteen significant DEIRGs were acquired, of which five (CRP, EREG, FAM19A4, RLN1, and WFIKKN1) were selected to establish a diagnostic immune-related gene signature according to the most appropriate training model, namely, the RF model. A clinical application nomogram model was established based on the expression level of the five DEIRGs. The sciatica patients were divided into two subtypes (C1 and C2) according to the consensus clustering method.Conclusions: Our research established a diagnostic five immune-related gene signature to discriminate sciatica and identified two sciatica subtypes, which may be beneficial to the clinical diagnosis and treatment of sciatica.

2021 ◽  
Vol 12 ◽  
Yuhang Liu ◽  
Changjiang Liu ◽  
Hao Zhang ◽  
Xinzeyu Yi ◽  
Aixi Yu

Background: Soft tissue sarcoma (STS) is a group of tumors with a low incidence and a complex type. Therefore, it is an arduous task to accurately diagnose and treat them. Glycolysis-related genes are closely related to tumor progression and metastasis. Hence, our study is dedicated to the development of risk characteristics and nomograms based on glycolysis-related genes to assess the survival possibility of patients with STS.Methods: All data sets used in our research include gene expression data and clinical medical characteristics in the Genomic Data Commons Data Portal (National Cancer Institute) Soft Tissue Sarcoma (TCGA SARC) and GEO database, gene sequence data of corresponding non-diseased human tissues in the Genotype Tissue Expression (GTEx).Next, transcriptome data in TCGA SARC was analyzed as the training set to construct a glycolysis-related gene risk signature and nomogram, which were confirmed in external test set.Results: We identified and verified the 7 glycolysis-related gene signature that is highly correlated with the overall survival (OS) of STS patients, which performed excellently in the evaluation of the size of AUC, and calibration curve. As well as, the results of the analysis of univariate and multivariate Cox regression demonstrated that this 7 glycolysis-related gene characteristic acts independently as an influence predictor for STS patients. Therefore, a prognostic-related nomogram combing 7 gene signature with clinical influencing features was constructed to predict OS of patients with STS in the training set that demonstrated strong predictive values for survival.Conclusion: These results demonstrate that both glycolysis-related gene risk signature and nomogram were efficient prognostic indicators for patients with STS. These findings may contribute to make individualize clinical decisions on prognosis and treatment.

2021 ◽  
Jun-wei LIANG ◽  
Wen-jun BAI ◽  
Xiao-yan WANG ◽  
Li-li CHI

Abstract Background:Many studies on long chain non-coding RNAs (lncRNAs) are published in recent years. But the roles of lncRNAs in diarrhea irritable bowel syndrome (IBS-D) are still unclear and should be further examined. The present work focused on determining the molecular mechanisms underlying lncRNAs regulation in IBS-D on the basis of the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network.Methods:This study collected the mRNAs (GSE36701) expression data within human tissue samples with IBS-D group and normal group based on Gene Expression Omnibus (GEO) database and collected the differentially expressed lncRNAs (DELs) and differentially expressed miRNAs (DEmiRs) based on PubMed.Functional enrichment analysis of DEGs was performed on the DAVID database. Then the interaction network was constructed and visualized using STRING database and Cytoscape.Results: This study identified 3192 DEmRNAs (1437 with up-regulation and 1755 with down-regulation),29 DEmiRs (18 upregulated and 11 downregulated)and 2 DELs(one upregulated and one downregulated) between IBS-D and control samples.Furthermore,we constructed a lncRNA-miRNA-mRNA network through two DELs (lncRNA TUG1 with up-regulation and lncRNA H19 with down-regulation), four DemiRs (hsa-miR-148a-3p,hsa-miR-342-3p,hsa-miR-149-5p with up-regulation and hsa-miR-219a-5p with down-regulation)and 24 DEGs (4 with up-regulation and 20 with down-regulation) with 42 axes. Simultaneously, we conducted functional enrichment and pathway analyses on genes within the as-constructed ceRNA network. According to our PPI/ceRNA network and functional enrichment analysis results, two critical genes were found (BCL2L11 and QKI). Conclusion:In conclusion, the ceRNA interaction axis we identified is a potentially critical target for treating IBS-D.BCL2L11 axis(LncH19-hsa-miR-148a-3p-BCL2L11) may via interaction with PI3K/AKT pathways in IBS-D.Our results shed more lights on the possible pathogenic mechanism in IBS-D using a lncRNA-associated ceRNA network.

2021 ◽  
Miao Lv ◽  
Wanting He ◽  
Tian Liang ◽  
Jialei Yang ◽  
Xiaolan Huang ◽  

Abstract Stroke was the third leading cause of global disability-adjusted life years and its treatment drugs were limited. Researchers still need to find reliable diagnostic biomarkers and therapeutic targets. This study aimed to explore hub genes associated with ischemic stroke (IS) through bioinformatics analysis and identify its potential diagnostic biomarkers and therapeutic targets. The microarray data sets of GSE58294 and GSE16561 related to IS were downloaded from the Gene Expression Omnibus (GEO) database. Weighted co-expression network analysis (WGCNA) and differential expression analysis were integrated to find overlapping genes, and then hub genes were screened by Least absolute shrinkage and selection operator (LASSO) regression. NetworkAnalyst database was used to construct the TF-gene network and miRNA-TF regulatory network of the hub genes. DGIdb and CMap databases were used to predict targeted drugs and small molecule compounds. Through the Comprehensive analysis of GSE58294 and GSE16561, 10 hub genes were screened by LASSO regression, namely ARG1, LY96, ABCA1, SLC22A4, CD163, TPM2, SLC25A42, ID3, FAM102A and CD79B. NetworkAnalyst database analysis showed that ABCA1 was the hub gene most regulated by miRNA and TF. TRRUST and NetworkAnalyst databases showed that SP1 (P=0.0246) was a key transcription factor for hub genes. Finally, DGIdb database analysis identified 7 therapeutic drugs targeted with ABCA1 and SLC22A4, and 4 small molecule compounds targeted with ID3 and SLC22A4 were obtained from CMap database. We identified IS-related diagnostic biomarkers and therapeutic drugs, which can provide new insights for the diagnosis and treatment of IS.

2021 ◽  
Vol 22 (1) ◽  
Ying Jiang ◽  
Yi Shen ◽  
Liyan Ding ◽  
Shengli Xia ◽  
Liying Jiang

Abstract Backgrounds As osteoarthritis (OA) disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized. Here, we aim to identify miRNA signatures in patients to fully elucidate regulatory mechanism of OA pathogenesis and advance in basic understanding of the genetic etiology of OA. Methods Six participants (3 OA and 3 controls) were recruited and serum samples were assayed through RNA sequencing (RNA-seq). And, RNA-seq dataset was analysed to identify genes, pathways and regulatory networks dysregulated in OA. The overlapped differentially expressed microRNAs (DEMs) were further screened in combination with the microarray dataset GSE143514. The expression levels of candidate miRNAs were further validated by quantitative real-time PCR (qRT-PCR) based on the GEO dataset (GSE114007). Results Serum samples were sequenced interrogating 382 miRNAs. After screening of independent samples and GEO database, the two comparison datasets shared 19 overlapped candidate micRNAs. Of these, 9 up-regulated DEMs and 10 down-regulated DEMs were detected, respectively. There were 236 target genes for up-regulated DEMs and 400 target genes for those down-regulated DEMs. For up-regulated DEMs, the top 10 hub genes were KRAS, NRAS, CDC42, GDNF, SOS1, PIK3R3, GSK3B, IRS2, GNG12, and PRKCA; for down-regulated DEMs, the top 10 hub genes were NR3C1, PPARGC1A, SUMO1, MEF2C, FOXO3, PPP1CB, MAP2K1, RARA, RHOC, CDC23, and CREB3L2. Mir-584-5p-KRAS, mir-183-5p-NRAS, mir-4435-PIK3R3, and mir-4435-SOS1 were identified as four potential regulatory pathways by integrated analysis. Conclusions We have integrated differential expression data to reveal putative genes and detected four potential miRNA-target gene pathways through bioinformatics analysis that represent new mediators of abnormal gene expression and promising therapeutic targets in OA.

2021 ◽  
Vol 21 (1) ◽  
Jia-xin Li ◽  
Xun-jie Cao ◽  
Yuan-yi Huang ◽  
Ya-ping Li ◽  
Zi-yuan Yu ◽  

Abstract Introduction Staphylococcus aureus is a gram-positive bacterium that causes serious infection. With the increasing resistance of bacteria to current antibiotics, it is necessary to learn more about the molecular mechanism and cellular pathways involved in the Staphylococcus aureus infection. Methods We downloaded the GSE33341 dataset from the GEO database and applied the weighted gene co-expression network analysis (WGCNA), from which we obtained some critical modules. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were applied to illustrate the biological functions of genes in these modules. We constructed the protein-protein interaction (PPI) network by Cytoscape and selected five candidate hub genes. Five potential hub genes were validated in GSE30119 by GraphPad Prism 8.0. The diagnostic values of these genes were calculated and present in the ROC curve based on the GSE13670 dataset. Their gene functions were analyzed by Gene Set Enrichment Analysis (GSEA). Results A co-expression network was built with 5000 genes divided into 11 modules. The genes in green and turquoise modules demonstrated a high correlation. According to the KEGG and GO analyses, genes in the green module were closely related to ubiquitination and autophagy. Subsequently, we picked out the top five hub genes in the green module. And UBB was determined as the hub gene in the GSE30119 dataset. The expression level of UBB, ASB, and MKRN1 could significantly differentiate between Staphylococcus aureus infection and healthy controls based on the ROC curve. The GSEA analysis indicated that lower expression levels of UBB were associated with the P53 signal pathway. Conclusions We identified some hub genes and significant signal enrichment pathways in Staphylococcus aureus infection via bioinformatics analysis, which may facilitate the development of potential clinical therapeutic strategies.

Jianwen Hu ◽  
Yanpeng Yang ◽  
Yongchen Ma ◽  
Yingze Ning ◽  
Guowei Chen ◽  

Gastric cancer is one of the most heterogeneous tumors with multi-level molecular disturbances. Sustaining proliferative signaling and evading growth suppressors are two important hallmarks that enable the cancer cells to become tumorigenic and ultimately malignant, which enable tumor growth. Discovering and understanding the difference in tumor proliferation cycle phenotypes can be used to better classify tumors, and provide classification schemes for disease diagnosis and treatment options, which are more in line with the requirements of today’s precision medicine. We collected 691 eligible samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, combined with transcriptome data, to explore different heterogeneous proliferation cycle phenotypes, and further study the potential genomic changes that may lead to these different phenotypes in this study. Interestingly, two subtypes with different clinical and biological characteristics were identified through cluster analysis of gastric cancer transcriptome data. The repeatability of the classification was confirmed in an independent Gene Expression Omnibus validation cohort, and consistent phenotypes were observed. These two phenotypes showed different clinical outcomes, and tumor mutation burden. This classification helped us to better classify gastric cancer patients and provide targeted treatment based on specific transcriptome data.

2021 ◽  
Vol 8 ◽  
Xing Liu ◽  
Shiyue Xu ◽  
Ying Li ◽  
Qian Chen ◽  
Yuanyuan Zhang ◽  

Background: Inflammatory activation and immune infiltration play important roles in the pathologic process of heart failure (HF). The current study is designed to investigate the immune infiltration and identify related biomarkers in heart failure patients due to ischemic cardiomyopathy.Methods: Expression data of HF due to ischemic cardiomyopathy (CM) samples and non-heart failure (NF) samples were downloaded from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between CM and NF samples were identified. Single sample gene set enrichment analysis (ssGSEA) was performed to explore the landscape of immune infiltration. Weighted gene co-expression network analysis (WGCNA) was applied to screen the most relevant module associated with immune infiltration. The diagnostic values of candidate genes were evaluated by receiver operating curves (ROC) curves. The mRNA levels of potential biomarkers in the peripheral blood mononuclear cells (PBMCs) isolated from 10 CM patients and 10 NF patients were analyzed to further assess their diagnostic values.Results: A total of 224 DEGs were identified between CM and NF samples in GSE5406, which are mainly enriched in the protein processing and extracellular matrix related biological processes and pathways. The result of ssGSEA showed that the abundance of dendritic cells (DC), mast cells, natural killer (NK) CD56dim cells, T cells, T follicular helper cells (Tfh), gammadelta T cells (Tgd) and T helper 2 (Th2) cells were significantly higher, while the infiltration of eosinophils and central memory T cells (Tcm) were lower in CM samples compared to NF ones. Correlation analysis revealed that Calumenin (CALU) and palladin (PALLD) were negatively correlated with the abundance of DC, NK CD56dim cells, T cells, Tfh, Tgd and Th2 cells, but positively correlated with the level of Tcm. More importantly, CALU and PALLD were significantly lower in PBMCs from CM patients compared to NF ones.Conclusion: Our study revealed that CALU and PALLD are potential biomarkers associated with immune infiltration in heart failure due to ischemic cardiomyopathy.

2021 ◽  
Jianxing Ma ◽  
Chen Wang

Abstract This study is to establish NMF (nonnegative matrix factorization) typing related to the tumor microenvironment (TME) of colorectal cancer (CRC) and to construct a gene model related to prognosis to be able to more accurately estimate the prognosis of CRC patients. NMF algorithm was used to classify samples merged clinical data of differentially expressed genes (DEGs) of TCGA that are related to the TME shared in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and survival differences between subtype groups were compared. By using createData Partition command, TCGA database samples were randomly divided into train group and test group. Then the univariate Cox analysis, Lasso regression and multivariate Cox regression models were used to obtain risk model formula, which is used to score the samples in the train group, test group and GEO database, and to divide the samples of each group into high-risk and low-risk groups, according to the median score of the train group. After that, the model was validated. Patients with CRC were divided into 2, 3, 5 subtypes respectively. The comparison of patients with overall survival (OS) and progression-free survival (PFS) showed that the method of typing with the rank set to 5 was the most statistically significant (p=0.007, p<0.001, respectively). Moreover, the model constructed containing 14 immune-related genes (PPARGC1A, CXCL11, PCOLCE2, GABRD, TRAF5, FOXD1, NXPH4, ALPK3, KCNJ11, NPR1, F2RL2, CD36, CCNF, DUSP14) can be used as an independent prognostic factor, which is superior to some previous models in terms of patient prognosis. The 5-type typing of CRC patients and the 14 immune-related genes model constructed by us can accurately estimate the prognosis of patients with CRC.

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