Analysis and Experimental Validation of Rheumatoid Arthritis Innate Immunity Gene CYFIP2 and Pan-Cancer

Zhao, Zhenyu; He, ShaoJie; Yu, XinCheng; Lai, Xiaofeng; Tang, Sheng; M, El Akkawi Mariya; Wang, MoHan; Yan, Hai; Huang, Xing-Qi; Zeng, Shan; Zha, Dingsheng

doi:10.3389/fimmu.2022.954848

Cited by 25 publications

(22 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, studies applied WGCNA were normally combined with multiple machine learning algorithms to identify biomarkers for disease prognosis and diagnosis. Zhao et al (2022) identified four core genes (BTN3A2, CYFIP2, ST8SIA1, and TYMS) as biomarker for diagnosis of rheumatoid arthritis via comprehensive analysis of WGCNA, LASSO, random forest, and support vector machine analysis. By WGCNA, LASSO, and random forest algorithms, Fan et al (2022) obtained five signature genes (UPP1, S100A9, KIF1B, S100A12, SLC26A8) and emerged remarkable diagnostic performance in pediatric septic shock.…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Zhang

Wang²,

Wang³

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

ObjectivesPost-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.MethodsExpression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.ResultsFunctional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P < 0.001) and FERM3 (r = 0.728, P < 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.ConclusionA total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.

show abstract

Section: Discussionmentioning

confidence: 99%

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Zhang

Wang²,

Wang³

et al. 2023

Front. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Furthermore, our previous whole-exome sequencing showed that various genes are frequently mutated in primary tumors and CTCs ( 19 ). Of these mutated genes, some are well-characterized functional oncogenes, such as CYFIP2 , NOP16 , and ZNF117 ( 28 – 30 ), and genetic variations in others are closely associated with other cancers, such as MOB3C and SSPO ( 31 , 32 ). It was also found that non-silent single nucleotide variations (non-silent SNVs) and insertion-deletion mutations were more frequent in CTCs than in primary tumor samples ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

Epithelial-mesenchymal transition classification of circulating tumor cells predicts clinical outcomes in progressive nasopharyngeal carcinoma

et al. 2022

View full text Add to dashboard Cite

BackgroundLiquid biopsy facilitates the enrichment and isolation of circulating tumor cells (CTCs) in various human cancers, including nasopharyngeal carcinoma (NPC). Characterizing CTCs allows observation of the evolutionary process of single tumor cells undergoing blood-borne dissemination, such as epithelial-mesenchymal transition. However, the prognostic value of phenotypic classification of CTCs in predicting the clinical outcomes of NPC remains poorly understood.Patients and methodsA total of 92 patients who met the inclusion criteria were enrolled in the present study. The CanPatrol™ CTC technology platform was employed to isolate CTCs, and an RNA in situ hybridization-based system was used for phenotypic classification. Kaplan–Meier survival curves were used for univariate survival analysis, and the log-rank test was performed for between-group comparisons of the survival curves.ResultsCTCs were detected in 88.0% (81/92) of the enrolled patients with NPC. The total CTC number did not vary between the T and N stages or between Epstein–Barr virus DNA-positive and -negative cases. The numbers of total CTCs and epithelial/mesenchymal (E/M) hybrid CTCs decreased significantly at 3 months post concurrent chemoradiotherapy (P=0.008 and P=0.023, respectively), whereas the numbers of epithelial or mesenchymal CTCs did not decrease. E/M hybrid-predominant cases had lower disease-free survival (P=0.043) and distant metastasis-free survival (P=0.046) rates than non-E/M hybrid-predominant cases.ConclusionCTC classification enables a better understanding of the cellular phenotypic alterations responsible for locoregional invasion and distant metastasis in NPC. E/M hybrid-predominant CTC distribution predicts unfavorable clinical outcomes in patients with progressive NPC.

show abstract

“…gmt" datasets to investigate the various levels of infiltration of immune cell types between SLE samples and normal samples. [21] A single-sample gene set enrichment analysis (ssGSEA) is a particular type of analysis that combines the "immune. gmt" dataset with the ssGSEA method.…”

Section: Immune Infiltration Analysis By Ssgseamentioning

confidence: 99%

Screening of potential biomarkers of system lupus erythematosus based on WGCNA and machine learning algorithms

Li,

Huo,

Wang

2023

Medicine

View full text Add to dashboard Cite

Systemic lupus erythematosus (SLE) is an autoimmune disease involving multiple systems. Its recurrent episodes and fluctuating disease courses have a severe impact on patients. Biomarkers to predict disease prognosis and remission are still lacking in SLE. We downloaded the GSE50772 dataset from the Gene Expression Omnibus database and identified differentially expressed genes (DEGs) between SLE and healthy controls. Weighted gene co-expression network analysis was used to identify key gene modules and corresponding genes in SLE. The overlapped genes in DEGs and key modules are used as key genes for subsequent analysis. These key genes were analyzed using 3 machine learning algorithms, including the least absolute shrinkage and selection operator, support vector machine recursive elimination, and random forest algorithms. The overlapped genes were obtained as potential biomarkers for further analysis, investigating and validating the potential biomarkers’ possible functions, regulatory mechanisms, diagnostic value, and expression levels. And finally studied the differences between groups in level of immune cell infiltration and explored the relationship between potential biomarkers and immunity. A total of 234 overlapped genes in DEGs and key modules are used as key genes for subsequent analysis. After taking the intersection of the key genes obtained by 3 algorithms, we got 4 potential biomarkers (ARID2, CYSTM1, DDIT3, and RNASE1) with high diagnostic values. Finally, further immune infiltration analysis showed differences in various immune cells in the SLE and healthy control samples. ARID2, CYSTM1, DDIT3, and RNASE1 can affect the immune function of SLE patients. ARID2, CYSTM1, DDIT3, and RNASE1 could be used as immune-related potential biomarkers and therapeutic or diagnostic targets for further research.

show abstract

Analysis and Experimental Validation of Rheumatoid Arthritis Innate Immunity Gene CYFIP2 and Pan-Cancer

Cited by 25 publications

References 68 publications

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Epithelial-mesenchymal transition classification of circulating tumor cells predicts clinical outcomes in progressive nasopharyngeal carcinoma

Screening of potential biomarkers of system lupus erythematosus based on WGCNA and machine learning algorithms

Contact Info

Product

Resources

About