Weighted Gene Coexpression Network Analysis of Features That Control Cancer Stem Cells Reveals Prognostic Biomarkers in Lung Adenocarcinoma

Liao, Yi; Wang, Yulei; Cheng, Mengqing; Huang, Chengliang; Fan, Xiaoxuan

doi:10.3389/fgene.2020.00311

Cited by 73 publications

(57 citation statements)

References 56 publications

(53 reference statements)

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“…In term of immunity, the overexpression of SRGs, which was positively related to cancer stemness, was associated with reduced immune infiltration. The findings were in accordance with previous studies (Limagne et al, 2016;Liu et al, 2018;Malta et al, 2018;Miranda et al, 2019;Liao et al, 2020). It has been reported that transcriptome-derived stromal and immune scores have implications for patient survival, metastasis and recurrence (Liu et al, 2018).…”

Section: Discussionsupporting

confidence: 92%

Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma

Zeng

Song

et al. 2020

Front. Genet.

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Section: Discussionsupporting

confidence: 92%

Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma

Zeng

Song

et al. 2020

Front. Genet.

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“…This series of external veri cation fully demonstrated the feasibility and accuracy of our signature (Fig. 6) [23].…”

Section: Validation Of the Signature In Geomentioning

confidence: 55%

Stem Cell-Related Prognostic Signature for Lung Adenocarcinoma

Huang

Shi

Zhou

et al. 2020

Preprint

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Background: Adenocarcinoma is characterized by high infiltration and negative growth, which is easy to invade the walls of blood vessels and lymphatic vessels. The function of the stem cell population is to control and maintain cell regeneration. Therefore, it is necessary to study the prognostic value of stem cells in LUAD.Methods: Stem cells signature construction relies on the univariate, least absolute shrinkage operator (LASSO) and multivariate Cox regression analysis. Risk plot, Kaplan-Meier analysis and the area under ROC (AUC) are verified the accuracy of the signature in GEO (GSE20319 and GSE42127) and TCGA cohort respectively. What's more, to further improve persuasiveness, we conducted nomogram, drug efficacy analysis, immune infiltration analysis, and compared the relationship between mRNA stem cell index (mRNAsi) and signature.Result: The patients were divided into two groups via the cutoff of risk score; it can be seen that the low-risk group has better survival. The robust signature that contains ten stem cells is independent prognostic factors for LUAD (C6orf62, DNER, NELL2, LATS2, LGR5, PTPRO, LRIG1, PABPC1, NT5E and SET). The nomogram showed that 1-year (0.805), 3-year (0.773), and 5-year (0.765) survival rates of the signature we constructed were all greater than 0.7, indicating that our signature was very feasible.Conclusion: The signature can be used as a reliable and convenient tool for lung adenocarcinoma.

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“…If not treated promptly and effectively, RA can seriously reduce the quality of life and even cause disability. There is no doubt that understanding diseases at the molecular level will help to improve their diagnosis and treatment [26,27].…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis

Ren

Du³

et al. 2020

BioMed Research International

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Rheumatoid arthritis (RA) is an autoimmune disease characterized by erosive arthritis, which has not been thoroughly cured yet, and standardized treatment is helpful for alleviating clinical symptoms. Here, various bioinformatics analysis tools were comprehensively utilized, aiming to identify critical biomarkers and possible pathogenesis of RA. Three gene expression datasets profiled by microarray were obtained from GEO database. Dataset GSE55235 and GSE55457 were merged for subsequent analyses. We identified differentially expressed genes (DEGs) in RStudio with limma package, performing functional enrichment analysis based on GSEA software and clusterProfiler package. Next, protein-protein interaction (PPI) network was set up through STRING database and Cytoscape. Moreover, CIBERSORT website was used to assess the inflammatory state of RA. Finally, we validated the candidate hub genes with dataset GSE77298. As a result, we identified 106 DEGs (72 upregulated and 34 downregulated genes). Through GO, KEGG, and GSEA analysis, we found that DEGs were mainly involved in immune response and inflammatory signaling pathway. With the help of Cytoscape software and MCODE plug-in, the most prominent subnetwork was screened out, containing 14 genes and 45 edges. For ROC curve analysis, eight genes with AUC >0.80 were considered as hub genes of RA. In conclusion, compared with healthy controls, the DEGs and their closely related biological functions were analyzed, and we held that chemokines and immune cells infiltration promote the progression of rheumatoid arthritis. Targeting the eight biomarkers we identified may be useful for the diagnosis and treatment of rheumatoid arthritis.

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Weighted Gene Coexpression Network Analysis of Features That Control Cancer Stem Cells Reveals Prognostic Biomarkers in Lung Adenocarcinoma

Cited by 73 publications

References 56 publications

Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma

Stemness Related Genes Revealed by Network Analysis Associated With Tumor Immune Microenvironment and the Clinical Outcome in Lung Adenocarcinoma

Stem Cell-Related Prognostic Signature for Lung Adenocarcinoma

Comprehensive Bioinformatics Analysis Reveals Hub Genes and Inflammation State of Rheumatoid Arthritis

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