scholarly journals A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xin Zhong ◽  
Feichao Xuan ◽  
Yun Qian ◽  
Junhai Pan ◽  
Suihan Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Clinical Performance ◽  
Preoperative Evaluation ◽  
Functional Enrichment ◽  
Genomic Signature ◽  
Diagnostic Efficiency ◽  
Lasso Regression ◽  
Clinical Value ◽  
Therapeutic Decisions

Abstract Background Preoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignature based model to predict the possibility of LN metastasis in GC patients. Methods We used the RNA profile retrieving strategy and performed 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 the R software. GC samples in GSE62254 were randomly allocated into a learning set (n = 210) and a verification set (n = 90). By using the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signatures were established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic efficiency, as well as the clinical performance of this model were assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERs. Results Based on the genomic signature, we established a nomogram that 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 performed using bioinformatics methods which revealed that these DERs were involved in several lymphangiogenesis-correlated cascades. Conclusions In this study, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be utilized to estimate the probability of LN metastasis with good performance in GC. The functional analysis of the DERs reveals the prospective biogenesis of LN metastasis in GC.

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.

The usefulness of CT for the preoperative evaluation of lymph node in patients with gastric cancer

1992 ◽  
Vol 28 (5) ◽  
pp. 728
Author(s):  
Kyung Myung Son ◽  
Hyun Kwon Ha ◽  
Eun Suk Cha ◽  
Cho Hyun Park ◽  
In Chul Kim ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Preoperative Evaluation

scholarly journals 5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Ke Wang ◽  
Weibo Zhong ◽  
Zining Long ◽  
Yufei Guo ◽  
Chuanfan Zhong ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Biochemical Recurrence ◽  
Prognostic Model ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Clinical Value ◽  
Cox Regression Analysis

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.

scholarly journals Preoperative Evaluation of Lymph Node Metastases in Patients with Gastric Cancer: An Analysis of Imaging Methods

2018 ◽  
Vol 51 (3) ◽  
pp. 276-282
Author(s):  
Yusuf Günay ◽  
Emrah Çağlar ◽  
Esin Korkmaz ◽  
Rabiye Uslu Erdemir ◽  
İlhan Taşdöven ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Lymph Node Metastases ◽  
Preoperative Evaluation ◽  
Imaging Methods ◽  
Node Metastases

scholarly journals Genome-wide profiling of alternative splicing in glioblastoma and their clinical value

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Youwei Li ◽  
Dongsheng Guo
Keyword(s):  
Alternative Splicing ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Functional Enrichment ◽  
Correlation Network ◽  
Splicing Factors ◽  
Lasso Regression ◽  
Clinical Value ◽  
Cox Analysis

Abstract Background Alternative splicing (AS), one of the main post-transcriptional biological regulation mechanisms, plays a key role in the progression of glioblastoma (GBM). Systematic AS profiling in GBM is limited and urgently needed. Methods TCGA SpliceSeq data and the corresponding clinical data were downloaded from the TCGA data portal. Survival-related AS events were identified through Kaplan–Meier survival analysis and univariate Cox analysis. Then, splicing correlation network was constructed based on these AS events and associated splicing factors. LASSO regression followed by multivariate Cox analysis was performed to validate independent AS biomarkers and to construct a risk prediction model. Enrichment analysis was subsequently conducted to explore potential signaling pathways of these AS events. Results A total of 132 TCGA GBM samples and 45,610 AS events were included in our study, among which 416 survival-related AS events were identified. An AS correlation network, including 54 AS events and 94 splicing factors, was constructed, and further functional enrichment was performed. Moreover, the novel risk prediction model we constructed displayed moderate performance (the area under the curves were > 0.7) at both one, two and three years. Conclusions Survival-related AS events may be vital factors of both biological function and prognosis. Our findings in this study can deepen the understanding of the complicated mechanisms of AS in GBM and provide novel insights for further study. Moreover, our risk prediction model is ready for preliminary clinical applications. Further verification is required.

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