scholarly journals Construction and Validation of a Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

2021 ◽  
Vol 28 ◽  
pp. 107327482110271
Author(s):  
Shilong Li ◽  
Zongxian Zhao ◽  
Huaxiang Yang ◽  
Daohan Wang ◽  
Weilin Sun ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Node Metastasis

Background: Increasing evidence indicated that the tumor microenvironment (TME) plays a critical role in tumor progression. This study aimed to identify and evaluate mRNA signature involved in lymph node metastasis (LNM) in TME for gastric cancer (GC). Methods: Gene expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA). The ESTIMATE algorithm was used to evaluate the TME of GC. The heatmap and Venn plots were applied for visualizing and screening out intersect differentially expressed genes (DEGs) involved in LNM in TME. Functional enrichment analysis, gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network were also conducted. Furthermore, binary logistic regression analysis were employed to develop a 4-mRNAs signature for the LNM prediction. ROC curves were applied to validate the LNM predictive ability of the riskscore. Nomogram was constructed and calibration curve was plotted to verify the predictive power of nomogram. Results: A total of 88 LNM related DEGs were identified. Functional enrichment analysis and GSEA implied that those genes were associated with some biological processes, such as ion transportation, lipid metabolism and thiolester hydrolase activity. After univariate and multivariate logistic regression analysis, 4 mRNAs (RASSF2, MS4A2, ANKRD33B and ADH1B) were eventually screened out to develop a predictive model. ROC curves manifested the good performance of the 4-mRNAs signature. The proportion of patients with LNM in high-risk group was significantly higher than that in low-risk group. The C-index of nomogram from training and test cohorts were 0.865 and 0.765, and the nomogram was well calibrated. Conclusions: In general, we identified a 4-mRNAs signature that effectively predicted LNM in GC patients. Moreover, the 4-mRNAs signature and nomogram provide a guidance for the preoperative evaluation and postoperative treatment of GC patients.

scholarly journals Bioinformatics method combined with logistic regression analysis reveal potentially important miRNAs in ischemic stroke

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Zhiqiang Wei ◽  
Xingdi Qi ◽  
Yan Chen ◽  
Xiaoshuang Xia ◽  
Boyu Zheng ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Ischemic Stroke ◽  
Logistic Regression Analysis ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Bioconductor Package ◽  
Diagnostic Signature

Abstract Purpose: The present study aimed to investigate the comprehensive differential expression profile of microRNAs (miRNAs) by screening for miRNA expression in ischemic stroke and normal samples. Methods: Differentially expressed miRNA (DEM) analysis was conducted using limma R Bioconductor package. Target genes of DEMs were identified from TargetScanHuman and miRTarBase databases. Functional enrichment analysis of the target genes was performed using clusterProfiler R Bioconductor package. The miRNA-based ischemic stroke diagnostic signature was constructed via logistic regression analysis. Results: Compared with the normal cohort, a total of 14 DEMs, including 5 up-regulated miRNAs and 9 down-regulated miRNAs, were identified in ischemic stroke patients. These DEMs have 1600 regulatory targets. Using a logistic regression model, the top five miRNAs were screened for constructing an miRNA-based ischemic stroke diagnostic signature. Using the miRNA–mRNA interaction pairs, two target genes (specificity protein 1 (SP1) and Argonaute 1 (AGO1)) were speculated to be the primary genes of ischemic stroke. Discussion and conclusion: Here, several potential miRNAs biomarkers were identified and an miRNA-based diagnostic signature for ischemic stroke was established, which can be a valuable reference for future clinical researches.

scholarly journals A Novel RNA-Seq-Based Model for Preoperative Prediction of Lymph Node Metastasis in Oral Squamous Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Bo Qiao ◽  
Min Zhao ◽  
Jing Wu ◽  
Huan Wu ◽  
Yiming Zhao ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Seq ◽  
Node Metastasis ◽  
Preoperative Prediction

Objective. To develop and validate a novel RNA-seq-based nomogram for preoperative prediction of lymph node metastasis (LNM) for patients with oral squamous cell carcinoma (OSCC). Methods. RNA-seq data for 276 OSCC patients (including 157 samples with LNM and 119 without LNM) were downloaded from TCGA database. Differential expression analysis was performed between LNM and non-LNM of OSCC. These samples were divided into a training set and a test set by a ratio of 9 : 1 while the relative proportion of the non-LNM and LNM groups was kept balanced within each dataset. Based on clinical features and seven candidate RNAs, we established a prediction model of LNM for OSCC using logistic regression analysis. Tenfold crossvalidation was utilized to examine the accuracy of the nomogram. Decision curve analysis was performed to evaluate the clinical utility of the nomogram. Results. A total of 139 differentially expressed RNAs were identified between LNM and non-LNM of OSCC. Seven candidate RNAs were screened based on FPKM values, including NEURL1, AL162581.1 (miscRNA), AP002336.2 (lncRNA), CCBE1, CORO6, RDH12, and AC129492.6 (pseudogene). Logistic regression analysis revealed that the clinical N stage (p<0.001) was an important factor to predict LNM. Moreover, three RNAs including RDH12 (p value < 0.05), CCBE1 (p value < 0.01), and AL162581.1 (p value < 0.05) could be predictive biomarkers for LNM in OSCC patients. The average accuracy rate of the model was 0.7661, indicating a good performance of the model. Conclusion. Our findings constructed an RNA-seq-based nomogram combined with clinicopathology, which could potentially provide clinicians with a useful tool for preoperative prediction of LNM and be tailored for individualized therapy in patients with OSCC.

scholarly journals Long Noncoding RNA RP11-357H14.17 Plays an Oncogene Role in Gastric Cancer by Activating ATF2 Signaling and Enhancing Treg Cells

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Tang Xiaoli ◽  
Wang Wenting ◽  
Zhang Meixiang ◽  
Zuo Chunlei ◽  
Hu Chengxia
Keyword(s):  
Gastric Cancer ◽  
Noncoding Rna ◽  
Prognostic Value ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Treg Cells ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Biological Behavior

Background. Gastric cancer (GC) is one of the most common malignant tumors in the world. The potential functions and mechanisms of long noncoding RNAs (lncRNAs) in GC development are still unclear. It is of great significance to explore the prognostic value of LncRNA signatures for GC. Methods. LncRNAs differently expressed in GC and their prognostic value were studied based on The Cancer Genome Atlas (TCGA) database. The functional regulatory network and immune infiltration of RP11-357H14.17 were further studied using a variety of bioinformatics tools and databases. Results. We found that the high expression of RP11-357H14.17 was closely associated with shortened overall survival (OS) and poor prognosis in gastric cancer patients. We also found that its expression was related to clinical features including tumor volume, metastasis, and differentiation. Functional enrichment analysis revealed that RP11-357H14.17 is closely related to enhanced DNA replication and metabolism; ssGSEA analysis implied the oncogenic roles of RP11-357H14.17 was related to ATF2 signaling and Treg cell differentiation. Furthermore, we verified such link by using real-time PCR and IHC staining in human GC samples. Conclusion. We demonstrate that RP11-357H14.17 may play a crucial role in the occurrence, development, and malignant biological behavior of gastric cancer as a potential prognostic marker for gastric cancer.

scholarly journals Identification of an m6A-Related lncRNA Signature for Predicting the Prognosis in Patients With Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
JunJie Yu ◽  
WeiPu Mao ◽  
Si Sun ◽  
Qiang Hu ◽  
Can Wang ◽  
...  
Keyword(s):  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Enrichment Analysis ◽  
Roc Curves ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Prognostic Signature ◽  
Training Set ◽  
Testing Set

PurposeThis study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.MethodsThe KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan–Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.ResultsWe constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.ConclusionsWe constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.

scholarly journals The predictive prognostic factors and risk factors of lymph node metastasis in patients with early gastric cancer

2019 ◽  
Author(s):  
hongliang zu ◽  
huiling Wang ◽  
yan MA ◽  
yingwei xue
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prognostic Factors ◽  
Treatment Strategies ◽  
Multivariate Logistic Regression Analysis ◽  
Clinicopathological Features ◽  
Multivariate Logistic Regression ◽  
Node Metastasis

Abstract Background: Determining the prognosis of early gastric cancer (EGC) is very important for the selection of preoperative treatment strategies. The purpose of this paper was to investigate the clinicopathological features and prognostic factors in EGC and the related risk factors of lymph node metastasis (LNM). Methods: From March 2007 through December 2010, 1004 patients who underwent gastrectomy at Harbin Medical University were retrospectively identified; 120 patients were diagnosed with EGC. The clinicopathological features and prognostic factors were analysed by univariate and multivariate analyses. Multivariate logistic regression analysis was used to discern risk factors for LNM in EGC. Results: The incidence of EGC was 11.96%. A univariate analysis showed that age, preoperative haemoglobin (Hb) level, prealbumin level, tumour size and LNM were significant prognostic factors. A multivariate analysis showed that the preoperative Hb level and LNM were independent prognostic factors. A multivariate logistic regression analysis revealed that age, Ca-199 level and macroscopic tumour type were independent risk factors for LNM in EGC. Conclusions: Preoperative Hb level and LNM were both independent prognostic factors for EGC. These factors may help surgeons implement appropriate treatment strategies during the perioperative period.

scholarly journals Molecular characterization of metabolic subtypes of gastric cancer based on metabolism-related lncRNA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lingdi Li ◽  
Jianfei Ma
Keyword(s):  
Gastric Cancer ◽  
Metabolic Pathways ◽  
Metabolic Regulation ◽  
Protective Factor ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Underlying Mechanism ◽  
Functional Enrichment ◽  
Biological Processes

AbstractIncreasing evidence has demonstrated that lncRNAs are critical regulators in diverse biological processes, but the function of lncRNA in metabolic regulation remains largely unexplored. In this study, we evaluated the association between lncRNA and metabolic pathways and identified metabolism-related lncRNAs. Gastric cancer can be mainly subdivided into 2 clusters based on these metabolism-related lncRNA regulators. Comparative analysis shows that these subtypes are found to be highly consistent with previously identified subtypes based on other omics data. Functional enrichment analysis shows that they are enriched in distinct biological processes. Mutation analysis shows that ABCA13 is a protective factor in subtype C1 but a risk factor in C2. Analysis of chemotherapeutic and immunotherapeutic sensitivity shows that these subtypes tend to display distinct sensitivity to the same chemical drugs. In conclusion, these findings demonstrated the significance of lncRNA in metabolic regulation. These metabolism-related lncRNA regulators can improve our understanding of the underlying mechanism of lncRNAs and advance the research of immunotherapies in the clinical management of gastric cancer.

scholarly journals A Genomic-Clinicopathologic Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

2020 ◽  
Author(s):  
Xin Zhong ◽  
Feichao Xuan ◽  
Yun Qian ◽  
Junhai Pan ◽  
Suihan Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Clinical Performance ◽  
Preoperative Evaluation ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Genomic Signature ◽  
Lasso Regression ◽  
Clinical Value ◽  
Therapy Strategy

Abstract Background: Preoperative evaluation of the lymph node (LN) state is of pivotal significance for therapy strategy decisions in gastric cancer (GC) patients. However, there is lack of noninvasive method that can identify such status preoperatively. We aimed at developing a genomic biosignature based model to forecast the possibility of LN metastasis in GC patients.Methods: We employed the RNA profile retrieving strategy and conducted RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using R software. The GC samples in GSE62254 were randomly divided into a learning set (n = 210) and a verification set (n = 90). By performing the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signature was established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic effectiveness of this model was assessed, as well as the clinical performance subsequently assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERsResults: Based on this genomic signature, we established a nomogram which robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833–0.999) and verification sets (AUC = 0.775, 95% CI 0.647–0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was conducted using bioinformatics methods which posited that these DERs were involved with several lymphangiogenesis-correlated cascades.Conclusions: Here, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be readily utilized to estimate the possibility of LN metastasis with good performance in GC. The uncovering of the DERs reveals the prospective biogenesis of LN metastasis in GC.

scholarly journals Detecting Hub Genes Associated With Lauren Subtype of Gastric Cancer by Weighted Gene Co-Expression Network Analysis

2020 ◽  
Author(s):  
XU LIU ◽  
Li Yao ◽  
Jingkun Qu ◽  
Lin Liu ◽  
XU LIU ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Network Analysis ◽  
Cancer Survival ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Diffuse Gastric Cancer ◽  
Hub Genes ◽  
Gene Modules

Abstract Background Gastric cancer is a rather heterogeneous type of malignant tumor. Among the several classification system, Lauren classification can reflect biological and pathological differences of different gastric cancer.Method to provide systematic biological perspectives, we employ weighted gene co-expression network analysis to reveal transcriptomic characteristics of gastric cancer. GSE15459 and TCGA STAD dataset were downloaded. Co-expressional network was constructed and gene modules were identified. Result Two key modules blue and red were suggested to be associated with diffuse gastric cancer. Functional enrichment analysis of genes from the two modules was performed. Validating in TCGA STAD dataset, we propose 10 genes TNS1, PGM5, CPXM2, LIMS2, AOC3, CRYAB, ANGPTL1, BOC and TOP2A to be hub-genes for diffuse gastric cancer. Finally these ten genes were associated with gastric cancer survival. Conclusion More attention need to be paid and further experimental study is required to elucidate the role of these genes.

scholarly journals The prognostic value of tumor size in penile cancer: A Surveillance,Epidemiology, and End Results database study

2020 ◽  
Author(s):  
Wenwen Zheng ◽  
Zhiyu Zhang ◽  
Xilei Xie ◽  
Weiwei Zhu ◽  
Kangqi Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Lymph Node ◽  
Lymph Node Metastasis ◽  
Logistic Regression Analysis ◽  
Tumor Size ◽  
Prognostic Value ◽  
Penile Cancer ◽  
Node Metastasis ◽  
End Results

Abstract Background: The objective of this study was to investigate the prognostic value of tumor size on cancer-specific mortality (CSM) and lymph node metastasis for patients with penile squamous cell carcinoma (PSCC).Method: The patients diagnosed with PSCC between 2004 and 2015 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Restricted cubic spline functions were calculated to characterize the association between tumor size and the risk of CSM. The competing-risks model was used to evaluate the impact of tumor size on the cumulative incidence of CSM. The logistic regression analysis was performed to examine the association between tumor size and lymph node metastasis.Results: Totally, 1365 PSCC patients were analyzed, with 52.3% having tumors ≤30 mm, and 47.7% >30 mm. The restricted cubic splines showed that the risks of CSM increased as tumors enlarged. Following adjustment of competing events, the PSCC patients with tumors >30 mm were more likely to succumb to CSM in comparison with those with tumors ≤30 mm (hazard ratio [HR]=1.57, 95% confidence interval [CI]: 1.23-2.01, P<0.001). In subgroup analyses, tumor size >30 mm was significantly associated with an increased risk of CSM relative to tumor size ≤30 mm among patients with T1 (HR=1.56, 95%CI: 1.03-2.37, P=0.036) and T3 (HR=2.51, 95%CI: 1.41-4.45, P=0.002) classifications. On logistic regression analysis, tumors >30 mm were significantly associated with lymph node metastasis (odds ratio [OR]=1.46, 95% CI: 1.03-2.07, P=0.034).Conclusion: Larger tumors (>30 mm) were significantly associated with higher risks of CSM and increased likelihood of lymph node metastasis for PSCC patients, which could be integrated into the development of a staging system for penile cancer.

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