Multi-Omics Integrative Bioinformatics Analyses Reveal Long Non-coding RNA Modulates Genomic Integrity via Competing Endogenous RNA Mechanism and Serves as Novel Biomarkers for Overall Survival in Lung Adenocarcinoma

Wang, Zhong Lin; Ren, Ziyuan; Li, Rui; Ge, Junpeng; Zhang, Guoming; Xin, Yaodong; Qu, Yi-Qing

doi:10.3389/fcell.2021.691540

Cited by 6 publications

(2 citation statements)

References 49 publications

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“…Based on cell-cluster-markers and TAMs-related-genes, TOP 8 genes (C1QTNF6, CCNB1, FSCN1, HMMR, KPNA2, PRC1, RRM2, and TK1) significantly associated with prognosis were obtained (Figure 2). These have obvious benefits to clinicians for the assessment of patient prognosis (36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49). The same data were used to construct a risk score model containing 9 factors (C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, and GPR65) for prognostic evaluation (Figure 2) (50)(51)(52)(53)(54)(55).…”

Section: Discussionmentioning

confidence: 99%

Significance of macrophage infiltration in the prognosis of lung adenocarcinoma patients evaluated by scRNA and bulkRNA analysis

Zhu¹,

Zheng²,

Liu³

et al. 2022

Front. Immunol.

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PurposeTo investigate the significance of macrophage infiltration to the prognosis of lung adenocarcinoma.MethodsR language bioinformatics analysis technology, was used to obtain macrophage infiltration-related module genes through WGCNA (Weighted Gene Co-Expression Network Analysis). Marker genes of macrophage subtypes were identified using single-cell sequencing of lung adenocarcinoma tissue. Risk score models were constructed and validated using external data cohorts and clinical samples.ResultsAnalysis of cohorts TCGA-LUAD, GSE11969, GSE31210, GSE50081, GSE72094 and GSE8894, revealed a negative correlation between macrophage infiltration and survival. Immunohistochemical analyses of clinical samples were consistent with these data. Based on cell-cluster-markers and TAMs-related-genes, TOP8 genes were obtained (C1QTNF6, CCNB1, FSCN1, HMMR, KPNA2, PRC1, RRM2, and TK1) with a significant association to prognosis. Risk score models including 9 factors (C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, GPR65) for prognosis were constructed. The efficacy, stability and generalizability of the risk score models were validated using multiple data cohorts (GSE19188, GSE26939, GSE31210, GSE50081, GSE42127, and GSE72094).ConclusionsMacrophage infiltration negatively correlates with prognosis in patients with lung adenocarcinoma. Based on cell-cluster-markers and TAMs-related-genes, both TOP8 genes (C1QTNF6, CCNB1, FSCN1, HMMR, KPNA2, PRC1, RRM2, TK1) and risk score models using C1QTNF6, FSCN1, KPNA2, GLI2, TYMS, BIRC3, RBBP7, KRT8, GPR65 could predict disease prognosis.

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Section: Discussionmentioning

confidence: 99%

Significance of macrophage infiltration in the prognosis of lung adenocarcinoma patients evaluated by scRNA and bulkRNA analysis

Zhu¹,

Zheng²,

Liu³

et al. 2022

Front. Immunol.

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“…WGCNA can identify gene coexpression network modules, determine the correlation between modules and phenotypes, and then discover important genes that regulate key biological processes [ 14 ]. This method has helped researchers achieve many remarkable results in numerous areas, including cancers [ 15 ], the nervous system [ 16 ], and the immune system [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis to Identify Key Genes Involved in Advanced Atherosclerosis

Huo

Wang

2021

Disease Markers

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Background. The study was aimed at finding accurate and effective therapeutic targets and deepening our understanding of the mechanisms of advanced atherosclerosis (AA). Methods. We downloaded the gene expression datasets GSE28829, GSE120521, and GSE43292 from Gene Expression Omnibus. Weighted gene coexpression network analysis (WGCNA) was performed for GSE28829, and functional enrichment analysis and protein–protein interaction network analysis were conducted on the key module. Significant genes in the key module were analyzed by molecular complex detection, and genes in the most important subnetwork were defined as hub genes. Multiple dataset analyses for hub genes were conducted. Genes that overlapped between hub genes and differentially expressed genes (DEGs) of GSE28829 and GSE120521 were defined as key genes. Further validation for key genes was performed using GSE28829 and GSE43292. Gene set enrichment analysis (GSEA) was applied to key genes. Results. A total of 77 significant genes in the key module of GSE28829 were screened out that were mainly associated with inflammation and immunity. The subnetwork was obtained from significant genes, and 18 genes in this module were defined as hub genes, which were related to immunity and expressed in multiple diseases, particularly systemic lupus erythematosus. Some hub genes were regulated by SPI1 and associated with the blood, spleen, and lung. After overlapping with DEGs of GSE28829 and GSE120521, a total of 10 genes (HCK, ITGAM, CTSS, TYROBP, LAPTM5, FCER1G, ITGB2, NCF2, AIF1, and CD86) were identified as key genes. All key genes were validated and evaluated successfully and were related to immune response pathways. Conclusion. Our study suggests that the key genes related to immune and inflammatory responses are involved in the development of AA. This may deepen our understanding of the mechanisms of and provide valuable therapeutic targets for AA.

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Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma

Zhu,

Zheng,

Chen

et al. 2024

CCHTS

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aims: Genomic instability can drive clonal evolution, continuous modification of tumor genomes, and tumor genomic heterogeneity. The molecular mechanism of genomic instability still needs further investigation. This study aims to identify novel genome instabilityassociated lncRNAs (GI-lncRNAs) and investigate the role of genome instability in pan-Renal cell carcinoma (RCC). Materials and Methods:: A mutator hypothesis was employed, combining the TCGA database of somatic mutation (SM) information, to identify GI-lncRNAs. Subsequently, a training cohort (n = 442) and a testing cohort (n = 439) were formed by randomly dividing all RCC patients. Based on the training cohort dataset, a multivariate Cox regression analysis lncRNAs risk model was created. Further validations were performed in the testing cohort, TCGA cohort, and different RCC subtypes. To confirm the relative expression levels of lncRNAs in HK-2, 786-O, and 769-P cells, qPCR was carried out. Functional pathway enrichment analyses were performed for further investigation. Results:: A total of 170 novel GI-lncRNAs were identified. The lncRNA prognostic risk model was constructed based on LINC00460, AC073218.1, AC010789.1, and COLCA1. This risk model successfully differentiated patients into distinct risk groups with significantly different clinical outcomes. The model was further validated in multiple independent patient cohorts. Additionally, functional and pathway enrichment analyses revealed that GI-lncRNAs play a crucial role in GI. Furthermore, the assessments of immune response, drug sensitivity, and cancer stemness revealed a significant relationship between GI-lncRNAs and tumor microenvironment infiltration, mutational burden, microsatellite instability, and drug resistance. Conclusions:: In this study, we discovered four novel GI-lncRNAs and developed a novel signature that effectively predicted clinical outcomes in pan-RCC. The findings provide valuable insights for pan-RCC immunotherapy and shed light on potential underlying mechanisms. conclusion: We developed and validated a novel signature that performed well in predicting clinical outcomes of pan-RCC and provides references for pan-RCC immunotherapy and potential mechanisms.

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Multi-Omics Integrative Bioinformatics Analyses Reveal Long Non-coding RNA Modulates Genomic Integrity via Competing Endogenous RNA Mechanism and Serves as Novel Biomarkers for Overall Survival in Lung Adenocarcinoma

Cited by 6 publications

References 49 publications

Significance of macrophage infiltration in the prognosis of lung adenocarcinoma patients evaluated by scRNA and bulkRNA analysis

Significance of macrophage infiltration in the prognosis of lung adenocarcinoma patients evaluated by scRNA and bulkRNA analysis

Comprehensive Analysis to Identify Key Genes Involved in Advanced Atherosclerosis

Exploring Novel Genome Instability-associated lncRNAs and their Potential Function in Pan-Renal Cell Carcinoma

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