Esophageal cancer lymph node metastasis–associated gene signature optimizes overall survival prediction of esophageal cancer

Cai, Weiyang; Li, Yanyan; Huang, Bo; Hu, Changyuan

doi:10.1002/jcb.27416

Cited by 14 publications

(11 citation statements)

References 36 publications

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“…Univariate Cox regression was used for preliminary identification of prognostic DEGs ( Wu et al, 2020 ), followed by multivariate analysis to estimate the prognostic value of the identified DEGs ( Zhang R. et al, 2020 ). To construct a risk assessment system, the risk score for each patient was calculated according to regression coefficients and mRNA expression levels ( Cai et al, 2019 ). Based on the median risk score, patients were allocated to high- and low-risk groups ( Qian et al, 2020 ).…”

Section: Functional Enrichment Analysis Of 342 Intersecting Genesmentioning

confidence: 99%

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Guo

Wang

et al. 2020

Front. Genet.

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Ovarian cancer (OC) is the deadliest gynecological cancer in women. Immune cell infiltration has a critical role in regulating carcinogenesis and prognosis in OC. To identify prognostic genes relevant to the tumor microenvironment in OC, we investigated the association between OC and gene expression profiles. Results obtained with the ESTIMATE R tool showed that immune score and stromal score were correlated with lymphatic invasion, and high immune score predicted a favorable prognosis. A total of 342 common differentially expressed genes were identified according to the two scores; these genes were mainly involved in immune response, extracellular region, and serine-type endopeptidase activity. Three immune-related prognostic genes were selected by univariate and multivariate Cox regression analysis. We further established a prognostic model and validated the prognostic value of three hub genes in different databases; our results showed that this model could accurately predict survival and evaluate prognosis independent of clinical characteristics. Three hub genes have prognostic value in OC. TIMER analysis revealed that the three genes were correlated with different immune cells. Low levels of macrophage infiltration and high levels of CD4+ T cell infiltration were associated with favorable survival outcomes. Arm-level gain of GYPC was correlated with neutrophils and dendritic cells. These findings indicate that CXCR4, GYPC, and MMP12 modulate prognosis via effects on the infiltration of immune cells. Thus, these genes represent potential targets for immune therapy in OC.

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Section: Functional Enrichment Analysis Of 342 Intersecting Genesmentioning

confidence: 99%

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Guo

Wang

et al. 2020

Front. Genet.

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“…Genes and proteins related to DNA repair have become therapeutic targets in prostate cancer and ovarian cancer [13,14]. For the recent study, plenty of gene sets have been reported as a guidance in optimizing the treatment and play an important role as a biomarker in various cancers [15][16][17][18][19]. Multigene prognostic signatures from tumor tissue of patients can predict the prognosis of cancer patients more accurately than a single gene.…”

Section: Introductionmentioning

confidence: 99%

Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer

Zhu

Zhang

Liu

et al. 2020

Preprint

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Background: Liver cancer (LC) is one of the most fatal cancers throughout the world. More efficient and sensitive gene signatures that could accurately predict survival in LC patients are vitally needed to promote a better individualized and effective treatment. Material/Methods: 422 LC patients with both RNA-Seq and clinical data in TCGA were embedded in our study. Gene set enrichment analysis (GSEA) was applied to identify genes and hallmark gene sets that are more valuable for liver cancer therapy. Cox regression analysis was used to identify genes related to overall survival (OS) and build the prediction model. cBioPortal database was used to examine the alterations of the panel mRNA signature. ROC curves and Kaplan–Meier curves were used to validate the prediction model. Besides, the expression of the genes in the model were validated using quantitative real-time PCR in clinical tissue specimens. Results: The panel of DNA repair-related mRNA signature consisted of seven mRNAs: RFC4, ZWINT, UPF3B, NCBP2, ADA, SF3A3, and GTF2H1. On-line analysis of cBioPortal database found that the expression of the panel mRNA has a wide variation ranging from 7% to 10%. All the mRNAs were significantly upregulated in LC tissues compared to normal tissues (P < 0.05). The risk model is closely related to the OS of LC patients. The hazard ratio (HR) is 2.184 (95%CI [confidence interval]:1.523-3.132) and log-rank P-value<0.0001. For clinical specimen validation, we found that all of the genes in the model upregulated in liver cancer tissues versus normal liver tissues, which was consistent with the results predicted. Conclusions: Our study demonstrated a mRNA signature including seven mRNA for prognosis prediction of LC. This panel gene signature provides a new criterion for accurate diagnosis and therapeutic target of LC.

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“…For instance, Xue et al searched for key factors by constructing a specific competitive endogenous RNA network in ESCA [9]. The 8-mRNA-based risk score model developed by Cai et al successfully predicted the survival of ESCA [10]. Ushiku et al confirmed that promoter DNA hypermethylation of CDO1 could be an independent prognostic factor in ESCC [11].…”

Section: Introductionmentioning

confidence: 99%

Development of Multiscale Transcriptional Regulatory Network in Esophageal Cancer Based on Integrated Analysis

Zhang

et al. 2020

BioMed Research International

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Objective. To explore multiscale integrated analysis methods in identifying key regulators of esophageal cancer (ESCA). Methods. We downloaded the ESCA dataset from The Cancer Genome Atlas (TCGA) database, which contained RNA-seq data, miRNA-seq data, methylation data, and clinical phenotype information. Then, we combined ESCA-related genes from the NCBI-GENE and OMIM databases and RNA-seq dataset from TCGA to analyze differentially expressed genes (DEGs). Meanwhile, differentially expressed miRNAs (DEmiRNAs) and genes with differential methylation levels were identified. The pivot–module pairs were established using the RAID v2.0 database and TRRUST v2 database. Next, the multifactor-regulated functional network was constructed based on the above information. Additionally, gene corresponding targeted drug information was obtained from the DrugBank database. Moreover, we further screened regulators by assessing their diagnostic value and prognostic value, especially the value of distinguishing patients at TNM I stage from normal patients. In addition, the external database from the Gene Expression Omnibus (GEO) database was used for validation. Lastly, gene set enrichment analysis (GSEA) was performed to explore the potential biological functions of key regulators. Results. Our study indicated that CXCL8, CYP2C8, and E2F1 had excellent diagnostic and prognostic values, which may be potential regulators of ESCA. At the same time, the good early diagnosis ability of the three regulators also provided new insights for the diagnosis and early treatment of ESCA patients. Conclusion. We develop a multiscale integrated analysis and suggest that CXCL8, CYP2C8, and E2F1 are promising regulators with good diagnostic and prognostic values in ESCA.

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Esophageal cancer lymph node metastasis–associated gene signature optimizes overall survival prediction of esophageal cancer

Cited by 14 publications

References 36 publications

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Three Genes Predict Prognosis in Microenvironment of Ovarian Cancer

Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer

Development of Multiscale Transcriptional Regulatory Network in Esophageal Cancer Based on Integrated Analysis

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