scholarly journals Identification of genes associated with lymph node metastasis in papillary thyroid carcinoma by weighted gene co-expression network analysis

2020 ◽  
Author(s):  
Zhen Liu ◽  
Mingyue Guo ◽  
Lidong Wang ◽  
Chenxi Liu ◽  
Yonglian Huang
Keyword(s):  
Lymph Node ◽  
Network Analysis ◽  
Lymph Node Metastasis ◽  
Biological Significance ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Data Set ◽  
Node Metastasis ◽  
Gene Modules ◽  
Differential Genes

Abstract Background: Thyroid cancer (TC) is the most common endocrine malignant tumor, in which thyroid papillary carcinoma has the highest incidence of (PTC). However, some patients will relapse or even die because of tumor metastasis. Therefore, for the treatment of PTC, it is extremely important to find gene targets and their potential biomarkers. We obtained the RNA sequence matrix of PTC and the clinical information of the corresponding samples in the TCGA database. Methods: The weighted gene co-expression network analysis was utilized to construct the co-expression network, and the gene modules with high correlation among genes and the relationship between genes and clinical traits were established. Results: We utilized the screened 5271 genes for WGCNA analysis and constructed 19 gene modules. Genes in the magenta module with the highest correlation with lymph node metastasis were selected for enrichment analysis to construct a protein interaction network. After that, we selected 12 hub genes of this module by using the intersection of the differential genes in the data set of cytoscape and GEO database. 12 genes were visualized in GEPIA. After that, the gene expression was verified on the oncomine page, and 8 genes with high correlation with TC lymph metastasis (ARNTL, CDH3, CLDN1, COL8A1, COL8A2, IL1RAP, LPAR5) were screened. Conclusion: In this paper, the differential genes are screened by combining the datasets of TCGA, GEO and Oncomine in TC for the first time, which makes the screened differential genes have more biological significance. These genes may offer new insights for the treatment of PTC lymph node metastasis.

scholarly journals miR-224, miR-147b and miR-31 associated with lymph node metastasis and prognosis for lung adenocarcinoma by regulating PRPF4B, WDR82 or NR3C2

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9704
Author(s):  
Yan Wang ◽  
Shengtao Shang ◽  
Kun Yu ◽  
Hongbin Sun ◽  
Wenduan Ma ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Lung Adenocarcinoma ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Node Metastasis

Background The present study is to screen lymph node metastasis-related microRNAs (miRNAs) in lung adenocarcinoma (LUAD) and uncover their underlying mechanisms. Methods The miRNA microarray dataset was collected from the Gene Expression Omnibus database under accession number GSE64859. The differentially expressed miRNAs (DEMs) were identified using a t-test. Target genes of DEMs were predicted through the miRWalk2.0 database. The function of these target genes was annotated with the clusterProfiler and the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools. Protein-protein interaction network was established using the STRING database to extract hub target genes. The expressions and associations with survival and lymph node metastasis of miRNAs and target genes were validated by analysis of The Cancer Genome Atlas (TCGA) dataset. Results Eight DEMs were identified between lymph node metastasis and non-metastasis samples of GSE64859 dataset. miRNA-target gene pairs were predicted between six DEMs and 251 target genes (i.e. hsa-miR-224-PRPF4B, hsa-miR-147b-WDR82 and hsa-miR-31-NR3C2). The clusterProfiler analysis showed WDR82 was involved in the mRNA surveillance pathway, while the GO enrichment analysis using the DAVID database indicated PRPF4B participated in the protein phosphorylation and NR3C2 was related with the transcription, DNA-templated. WDR82 and PRPF4B may be hub genes because they could interact with others. Two DEMs (miR-31-5p and miR-31-3p) and 45 target genes (including PRPF4B and NR3C2) were significantly associated with overall survival. The expressions of miR-224 and miR-147b were validated to be upregulated, while WDR82, PRPF4B and NR3C2 were downregulated in lymph node metastasis samples of TCGA datasets compared with non-metastasis samples. Also, there were significantly negative expression correlations between miR-147b and WDR82, between miR-224 and PRPF4B, as well as between miR-31 and NR3C2 in LUAD samples. Conclusions The present study identified several crucial miRNA-mRNA interaction pairs, which may provide novel explanations for the lymph node metastasis and poor prognosis for LUAD patients.

scholarly journals Noninvasive CT radiomic model for preoperative prediction of lymph node metastasis in early cervical carcinoma

2020 ◽  
Vol 93 (1108) ◽  
pp. 20190558
Author(s):  
Jiaming Chen ◽  
Bingxi He ◽  
Di Dong ◽  
Ping Liu ◽  
Hui Duan ◽  
...  
Keyword(s):  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cervical Carcinoma ◽  
Ct Images ◽  
International Federation ◽  
Clinical Feature ◽  
Data Set ◽  
Node Metastasis ◽  
Gynecology And Obstetrics ◽  
Model Combining

Objective: To build and validate a CT radiomic model for pre-operatively predicting lymph node metastasis in early cervical carcinoma. Methods and materials: A data set of 150 patients with Stage IB1 to IIA2 cervical carcinoma was retrospectively collected from the Nanfang hospital and separated into a training cohort (n = 104) and test cohort (n = 46). A total of 348 radiomic features were extracted from the delay phase of CT images. Mann–Whitney U test, recursive feature elimination, and backward elimination were used to select key radiomic features. Ridge logistics regression was used to build a radiomic model for prediction of lymph node metastasis (LNM) status by combining radiomic and clinical features. The area under the receiver operating characteristic curve (AUC) and κ test were applied to verify the model. Results: Two radiomic features from delay phase CT images and one clinical feature were associated with LNM status: log-sigma-2–0 mm-3D_glcm_Idn (p = 0.01937), wavelet-HL_firstorder_Median (p = 0.03592), and Stage IB (p = 0.03608). Radiomic model was built consisting of the three features, and the AUCs were 0.80 (95% confidence interval: 0.70 ~ 0.90) and 0.75 (95% confidence intervalI: 0.53 ~ 0.93) in training and test cohorts, respectively. The κ coefficient was 0.84, showing excellent consistency. Conclusion: A non-invasive radiomic model, combining two radiomic features and a International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma. This model could serve as a pre-operative tool. Advances in knowledge: A noninvasive CT radiomic model, combining two radiomic features and the International Federation of Gynecology and Obstetrics stage, was built for prediction of LNM status in early cervical carcinoma.

scholarly journals Radiogenomic Analysis of Papillary Thyroid Carcinoma for Prediction of Cervical Lymph Node Metastasis: A Preliminary Study

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuyang Tong ◽  
Peixuan Sun ◽  
Juanjuan Yong ◽  
Hongbo Zhang ◽  
Yunxia Huang ◽  
...  
Keyword(s):  
Lymph Node ◽  
Papillary Thyroid Carcinoma ◽  
Lymph Node Metastasis ◽  
Papillary Thyroid ◽  
Hub Genes ◽  
Normal Thyroid ◽  
Protein Protein Interaction ◽  
Node Metastasis ◽  
Gene Modules ◽  
Deviation Ratio

BackgroundPapillary thyroid carcinoma (PTC) is characterized by frequent metastases to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound, and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumors.MethodsIn total, 270 patients were enrolled in this prospective study, and radiomic features were extracted according to multiple guidelines. A radiomic signature was built with selected features in the training cohort and validated in the validation cohort. The total protein extracted from tumor samples was analyzed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Genes in modules related to metastasis were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and a protein-protein interaction (PPI) network was built to identify the hub genes in the modules. Finally, the screened hub genes were validated by immunohistochemistry analysis.ResultsThe radiomic signature showed good performance for predicting CLN status in training and validation cohorts, with area under curve of 0.873 and 0.831 respectively. A radiogenomic map was created with nine significant correlations between radiomic features and gene modules, and two of them had higher correlation coefficient. Among these, MEmeganta representing the upregulation of telomere maintenance via telomerase and cell-cell adhesion was correlated with ‘Rectlike’ and ‘deviation ratio of tumor tissue and normal thyroid gland’ which reflect the margin and the internal echogenicity of the tumor, respectively. MEblue capturing cell-cell adhesion and glycolysis was associated with feature ‘minimum calcification area’ which measures the punctate calcification. The hub genes of the two modules were identified by protein-protein interaction network. Immunohistochemistry validated that LAMC1 and THBS1 were differently expressed in metastatic and non-metastatic tissues (p=0.003; p=0.002). And LAMC1 was associated with feature ‘Rectlike’ and ‘deviation ratio of tumor and normal thyroid gland’ (p<0.001; p<0.001); THBS1 was correlated with ‘minimum calcification area’ (p<0.001).ConclusionsThe radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow noninvasive identification of the molecular properties of PTC tumors, which might support clinical decision making and personalized management.

Doctor, What Are My Chances of Having a Positive Sentinel Node? A Validated Nomogram for Risk Estimation

2007 ◽  
Vol 25 (24) ◽  
pp. 3670-3679 ◽  
Author(s):  
José Luiz B. Bevilacqua ◽  
Michael W. Kattan ◽  
Jane V. Fey ◽  
Hiram S. Cody ◽  
Patrick I. Borgen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Cancer Center ◽  
Tumor Type ◽  
Data Set ◽  
Specific Patient ◽  
Node Metastasis ◽  
Pathologic Features

Purpose Lymph node metastasis is a multifactorial event. Several variables have been described as predictors of lymph node metastasis in breast cancer. However, it is difficult to apply these data—usually expressed as odds ratios—to calculate the probability of sentinel lymph node (SLN) metastasis for a specific patient. We developed a user-friendly prediction model (nomogram) based on a large data set to assist in predicting the presence of SLN metastasis. Patients and Methods Clinical and pathologic features of 3,786 sequential SLN biopsy procedures were assessed with multivariable logistic regression to predict the presence of SLN metastasis in breast cancer. The model was subsequently applied to 1,545 sequential SLN biopsies. A nomogram was created from the logistic regression model. A computerized version of the nomogram was developed and is available on the Memorial Sloan-Kettering Cancer Center (New York, NY) Web site. Results Age, tumor size, tumor type, lymphovascular invasion, tumor location, multifocality, and estrogen and progesterone receptors were associated with SLN metastasis in multivariate analysis. The nomogram was accurate and discriminating, with an area under the receiver operating characteristic curve of 0.754 when applied to the validation group. Conclusion Newly diagnosed breast cancer patients are increasingly interested in information about their disease. This nomogram is a useful tool that helps physicians and patients to accurately predict the likelihood of SLN metastasis.

scholarly journals Radiogenomic analysis of cervical lymph node metastasis in papillary thyroid carcinoma: A preliminary study

2020 ◽  
Author(s):  
Yuyang Tong ◽  
Peixuan Sun ◽  
Yunxia Huang ◽  
Jin Zhou ◽  
Juanjuan Yong ◽  
...  
Keyword(s):  
Lymph Node ◽  
Papillary Thyroid Carcinoma ◽  
Lymph Node Metastasis ◽  
Thyroid Carcinoma ◽  
Prospective Study ◽  
Molecular Characteristics ◽  
Papillary Thyroid ◽  
Cervical Lymph Nodes ◽  
Node Metastasis ◽  
Gene Modules

Abstract Background Papillary thyroid carcinoma (PTC) is characterized by frequent metastasis to cervical lymph nodes (CLNs), and the presence of lymph node metastasis at diagnosis has a significant impact on the surgical approach. Therefore, we established a radiomic signature to predict the CLN status of PTC patients using preoperative thyroid ultrasound and investigated the association between the radiomic features and underlying molecular characteristics of PTC tumours. Methods A radiogenomic map linking radiomics features to gene modules was constructed, and immunohistochemistry was performed to validate key associations. In all, 180 patients were enrolled in this prospective study, and 47 radiomic features, including tumour size, shape, position, margin, echo pattern and calcification, were extracted. Total protein extracted from 49 tumour samples was analysed with LC/MS and iTRAQ technology. Gene modules acquired by clustering were chosen for their diagnostic significance. A radiogenomic map linking radiomic features to gene modules was constructed with the Spearman correlation matrix. Immunohistochemistry was performed to validate key associations between radiomic features and gene modules. Results The diagnostic performance of radiomics signature was better than that of the ultrasound-based method in predicting CLN status. Weighted gene co-expression network analysis generated 16 gene modules, and a radiogenomic map with nine significant correlations between radiomics features and gene modules was created. For example, the feature ‘minimum calcification area’ was significantly associated with module MEblue, which represents cell-cell adhesion and glycolysis. Immunohistochemistry showed that LAMC1 and THBS1 expression was associated with several radiomics features. Conclusions The radiomic signature proposed here has the potential to noninvasively predict the CLN status in PTC patients. Merging imaging phenotypes with genomic data could allow the noninvasive identification of the molecular properties of PTC tumours, which would support clinical decision making with personalized management. Trail registration: This prospective study was approved by the ethics committee of the Fudan University Shanghai Cancer Centre (1809191-1-NSFC) in July 2018.

scholarly journals Weighted gene correlation network analysis reveals novel biomarkers associated with mesenchymal stromal cell differentiation in early phase

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8907 ◽  
Author(s):  
Bin Xiao ◽  
Guozhu Wang ◽  
Weiwei Li
Keyword(s):  
Bone Marrow ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Ras Signaling ◽  
High Morbidity ◽  
Hub Genes ◽  
Data Set ◽  
Regulatory Pathways ◽  
Gene Modules

Osteoporosis is a major public health problem that is associated with high morbidity and mortality, and its prevalence is increasing as the world’s population ages. Therefore, understanding the molecular basis of the disease is becoming a high priority. In this regard, studies have shown that an imbalance in adipogenic and osteogenic differentiation of bone marrow mesenchymal stem cells (MSCs) is associated with osteoporosis. In this study, we conducted a Weighted Gene Co-Expression Network Analysis to identify gene modules associated with the differentiation of bone marrow MSCs. Gene Ontology and Kyoto Encyclopedia of Genes and Genome enrichment analysis showed that the most significant module, the brown module, was enriched with genes involved in cell cycle regulation, which is in line with the initial results published using these data. In addition, the Cytoscape platform was used to identify important hub genes and lncRNAs correlated with the gene modules. Furthermore, differential gene expression analysis identified 157 and 40 genes that were upregulated and downregulated, respectively, after 3 h of MSCs differentiation. Interestingly, regulatory network analysis, and comparison of the differentially expressed genes with those in the brown module identified potential novel biomarker genes, including two transcription factors (ZNF740, FOS) and two hub genes (FOXQ1, SGK1), which were further validated for differential expression in another data set of differentiation of MSCs. Finally, Gene Set Enrichment Analysis suggested that the two most important candidate hub genes are involved in regulatory pathways, such as the JAK-STAT and RAS signaling pathways. In summary, we have revealed new molecular mechanisms of MSCs differentiation and identified novel genes that could be used as potential therapeutic targets for the treatment of osteoporosis.

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