scholarly journals Comprehensive Characterization of RNA Processing Factors in Gastric Cancer Identifies a Prognostic Signature for Predicting Clinical Outcomes and Therapeutic Responses

2021 ◽  
Vol 12 ◽  
Author(s):  
Shenghan Lou ◽  
Fanzheng Meng ◽  
Xin Yin ◽  
Yao Zhang ◽  
Bangling Han ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Alternative Splicing ◽  
Clinical Outcomes ◽  
Rna Processing ◽  
Prognostic Value ◽  
Drug Response ◽  
External Validation ◽  
Low Risk ◽  
Prognostic Signature ◽  
Processing Factors

RNA processing converts primary transcript RNA into mature RNA. Altered RNA processing drives tumor initiation and maintenance, and may generate novel therapeutic opportunities. However, the role of RNA processing factors in gastric cancer (GC) has not been clearly elucidated. This study presents a comprehensive analysis exploring the clinical, molecular, immune, and drug response features underlying the RNA processing factors in GC. This study included 1079 GC cases from The Cancer Genome Atlas (TCGA, training set), our hospital cohort, and two other external validation sets (GSE15459, GSE62254). We developed an RNA processing-related prognostic signature using Cox regression with the least absolute shrinkage and selection operator (LASSO) penalty. The prognostic value of the signature was evaluated using a multiple-method approach. The genetic variants, pathway activation, immune heterogeneity, drug response, and splicing features associated with the risk signature were explored using bioinformatics methods. Among the tested 819 RNA processing genes, we identified five distinct RNA processing patterns with specific clinical outcomes and biological features. A 10-gene RNA processing-related prognostic signature, involving ZBTB7A, METTL2B, CACTIN, TRUB2, POLDIP3, TSEN54, SUGP1, RBMS1, TGFB1, and PWP2, was further identified. The signature was a powerful and robust prognosis factor in both the training and validation datasets. Notably, it could stratify the survival of patients with GC in specific tumor-node-metastasis (TNM) classification subgroups. We constructed a composite prognostic nomogram to facilitate clinical practice by integrating this signature with other clinical variables (TNM stage, age). Patients with low-risk scores were characterized with good clinical outcomes, proliferation, and metabolism hallmarks. Conversely, poor clinical outcome, invasion, and metastasis hallmarks were enriched in the high-risk group. The RNA processing signature was also involved in tumor microenvironment reprogramming and regulating alternative splicing, causing different drug response features between the two risk groups. The low-risk subgroup was characterized by high genomic instability, high alternative splicing and might benefit from the immunotherapy. Our findings highlight the prognostic value of RNA processing factors for patients with GC and provide insights into the specific clinical and molecular features underlying the RNA processing-related signature, which may be important for patient management and targeting treatment.

scholarly journals Construction and Validation of a Universal Applicable Prognostic Signature for Gastric Cancer Based on Seven Immune-Related Gene Correlated With Tumor Associated Macrophages

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Critical Role ◽  
External Validation ◽  
Low Risk ◽  
Prognostic Signature ◽  
Tumor Associated Macrophages ◽  
Cox Regression Analysis ◽  
Significant Difference

BackgroundTumor-associated macrophages (TAMs) play a critical role in the progression of malignant tumors, but the detailed mechanism of TAMs in gastric cancer (GC) is still not fully explored.MethodsWe identified differentially expressed immune-related genes (DEIRGs) between GC samples with high and low macrophage infiltration in The Cancer Genome Atlas datasets. A risk score was constructed based on univariate Cox analysis and Lasso penalized Cox regression analysis in the TCGA cohort (n=341). The optimal cutoff determined by the 5-year time-dependent receiver operating characteristic (ROC) curve was considered to classify patients into groups with high and low risk. We conducted external validation of the prognostic signature in four independent cohorts (GSE84437, n=431; GSE62254, n=300; GSE15459, n=191; and GSE26901, n=109) from the Gene Expression Omnibus (GEO) database.ResultsThe signature consisting of 7 genes (FGF1, GRP, AVPR1A, APOD, PDGFRL, CXCR4, and CSF1R) showed good performance in predicting overall survival (OS) in the 5 independent cohorts. The risk score presented an obviously positive correlation with macrophage abundance (cor=0.7, p<0.001). A significant difference was found between the high- and low-risk groups regarding the overall survival of GC patients. The high-risk group exhibited a higher infiltration level of M2 macrophages estimated by the CIBERSORT algorithm. In the five independent cohorts, the risk score was highly positively correlated with the stromal cell score, suggesting that we can also evaluate the infiltration of stromal cells in the tumor microenvironment according to the risk score.ConclusionOur study developed and validated a general applicable prognostic model for GC from the perspective of TAMs, which may help to improve the precise treatment strategy of GC.

Evaluation of the prognostic value of guanylyl cyclase C (GCC) lymph node (LN) classification in patients with stage II colon cancer: A pooled analysis.

2012 ◽  
Vol 30 (4_suppl) ◽  
pp. 443-443
Author(s):  
Daniel J. Sargent ◽  
Qian Shi ◽  
Murray B. Resnick ◽  
Stephen Lyle ◽  
Michael O. Meyers ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Clinical Outcomes ◽  
Prognostic Value ◽  
Disease Free Survival ◽  
Strong Relationship ◽  
Low Risk ◽  
Stage Ii ◽  
Risk Status ◽  
Cox Models

443 Background: Identification of a sensitive and specific prognostic marker would aid in the management of patients (pts) with standard histopathology node negative colon cancer (CC). We conducted a pooled individual pt data analysis to confirm the prognostic value of GCC for disease recurrence in untreated stage II CC. Methods: GCC mRNA was quantified by RT-qPCR using formalin-fixed LN from 310 stage II pts diagnosed from 1991-2006 enrolled in two studies (Sargent 2011 [study1] and Haince 2009 [study2]). Patients were classified by GCC LN ratio (LNR) (high risk: LNR ≥ 0.1; low risk: LNR < 0.1), with LNR defined as number of GCC positive LN divided by number of informative LNs. Clinical outcomes included time to recurrence (TTR), overall survival (OS), disease-specific survival (DSS) and disease-free survival (DFS). Stratified log-rank tests and multivariate Cox models assessed the association between clinical outcomes and GCC LN status. Results: The 5-year recurrence rate in study 1 (n=241) was 15.8%, 24.9% in study 2 (n=69). GCC LNR high risk pts had significantly higher risk of TTR, OS, DSS and DFS, which remained after adjusting for age, T stage, grade, number of LNs examined, and presence of lymphovascular invasion ( Table ). In a secondary analysis of low risk stage II pts (T3, ≥12 LNs examined, and negative surgical margins, n=241), a strong relationship between GCC LNR and each endpoint remained (TTR HR=4.34, 95% CI=2.07 – 9.13, p<0.001). Conclusions: Pts with GCC LNR high risk status have significantly poorer outcomes compared to pts with low risk status, particularly among those traditionally considered to be low risk. [Table: see text]

Prognostic and predictive value of immunoscore signature in gastric cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e15594-e15594 ◽  
Author(s):  
Xiaolong Qi ◽  
Yuming Jiang ◽  
Qi Zhang ◽  
Yanfeng Hu ◽  
Tuanjie Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Adjuvant Chemotherapy ◽  
Predictive Value ◽  
Prognostic Value ◽  
Cox Regression ◽  
External Validation ◽  
Disease Free Survival ◽  
Stage Ii ◽  
Predictive Tool

e15594 Background: TNM staging system is not adequate to define the prognosis of patients with gastric cancer (GC). This system is also unable to predict whether the GC patients are likely to benefit from adjuvant chemotherapy. We postulated that ImmunoScore of GC (ISGC) could markedly improve the prediction of postsurgical survival and adjuvant chemotherapeutic benefits. Methods: 125 GC patients were enrolled as a training cohort to detect the expression of 27 immune features using immunohistochemistry and then constructed a five-feature-based ISGC using the LASSO Cox regression model. Internal validation cohort (126 specimens) and two external validation cohorts (628 specimens) were utilized to validate the prognostic and predictive value of ISGC. Results: We established the ISGC classifier based on the five features: CD3invasive margin (IM), CD3center of tumor (CT), CD8IM, CD45ROCT, and CD66bIM. The ISGC classifier could distinguish GC patients with high-ISGC from those with low-ISGC with significant differences in 5-year disease-free survival (45.0% v.s. 4.4%, p < 0.001) and 5-year overall survival (48.8% v.s. 6.7%, p < 0.001). According to the multivariate analysis, the ISGC classifier was proved to be an independent prognostic factor. A combination of ISGC and TNM had better prognostic value than TNM stage alone. In a further analysis, stage II and III GC patients with high-ISGC exhibited a favorable response to adjuvant chemotherapy. To provide a quantitative method to predict stage II and III GC patients’ probability of 3- and 5-year overall survival, we constructed two nomograms that integrated the ISGC and clinicopathological risk factors. Calibration plots showed that the nomograms performed well compared with an ideal model. The predictive accuracy and clinical usefulness of the nomograms were also demonstrated. Conclusions: The ISGC classifier could effectively predict recurrence and survival of GC, and complemented the prognostic value to TNM system. Moreover, the classifier might be a useful predictive tool to identify candidates with stage II and III GC who would benefit from adjuvant chemotherapy. Therefore, the ISGC might facilitate the counseling and personalize the postoperative management of GC patients.

scholarly journals Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
T Cells ◽  
Risk Score ◽  
External Validation ◽  
Disease Free Survival ◽  
Low Risk ◽  
Immune Gene ◽  
Free Survival ◽  
Gene Pairs

Abstract Background Growing attention have been paid to the relationship between TP53 and tumor immunophenotype, but there are still lacking enough search on the field of gastric cancer (GC). Materials and methods We identified differential expressed immune-related genes (DEIRGs) between the TP53-altered GC samples (n = 183) and without TP53-altered GC samples (n = 192) in The Cancer Genome Atlas and paired them. In the TCGA cohort (n = 350), a risk score was determined through univariate and multivariate cox regression and Lasso regression analysis. Patients were divided into two groups, high-risk and low-risk, based on the median risk score. Four independent cohorts (GSE84437,n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26901, n = 100) from the Gene Expression Omnibus (GEO) database were used to validate the reliability and universal applicability of the model. Results The signature contained 11 gene pairs showed good performance in predicting progression-free survival (PFS), disease-free survival (DFS), disease special survival (DSS), and the overall survival (OS) for GC patients in the TCGA cohort. The subgroup analysis showed that the signature was suitable for GC patients with different characteristics. The signature could capable of distinguish GC patients with good prognosis and poor prognosis in all four independent external validation cohorts. The high- and low-risk groups differed significantly in the proportion of several immune cell infiltration, especially for the T cells memory resting, T cells memory activated and follicular helper, and Macrophage M0, which was also related to the prognosis of GC patients. Conclusion The present work proposed an innovative system for evaluating the prognosis of gastric cancer. Considering its stability and general applicability, which may become a widely used tool in clinical practice.

scholarly journals Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
M2 Macrophages ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Training Cohort

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300 ; GSE15459, n = 191 ; and GSE26901, n = 109 ). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort ( n = 600 ) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432 ; GSE84437, n = 431 ; and TCGA, n = 336 ). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort ( AUC > 0.7 ). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC ( p < 0.001 ). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group ( p < 0.001 ), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor ( p = 0.011 ). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score ( p = 0.00085 ). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

scholarly journals A signature of 24 aging‑related gene pairs predict overall survival in gastric cancer

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Yankai Zhang ◽  
Yichao Yan ◽  
Ning Ning ◽  
Zhanlong Shen ◽  
Yingjiang Ye
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Operating Characteristic ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Related Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Aging is the major risk factor for most human cancers. We aim to develop and validate a reliable aging-related gene pair signature (ARGPs) to predict the prognosis of gastric cancer (GC) patients. Methods The mRNA expression data and clinical information were obtained from two public databases, The Cancer Genome Atlas (TCGA) dataset, and Gene Expression Omnibus (GEO) dataset, respectively. The best prognostic signature was established using Cox regression analysis (univariate and least absolute shrinkage and selection operator). The optimal cut-off value to distinguish between high- and low-risk patients was found by time-dependent receiver operating characteristic (ROC). The prognostic ability of the ARGPS was evaluated by a log‐rank test and a Cox proportional hazards regression model. Results The 24 ARGPs were constructed for GC prognosis. Using the optimal cut-off value − 0.270, all patients were stratified into high risk and low risk. In both TCGA and GEO cohorts, the results of Kaplan–Meier analysis showed that the high-risk group has a poor prognosis (P < 0.001, P = 0.002, respectively). Then, we conducted a subgroup analysis of age, gender, grade and stage, and reached the same conclusion. After adjusting for a variety of clinical and pathological factors, the results of multivariate COX regression analysis showed that the ARGPs is still an independent prognostic factor of OS (HR, 4.919; 95% CI 3.345–7.235; P < 0.001). In comparing with previous signature, the novel signature was superior, with an area under the receiver operating characteristic curve (AUC) value of 0.845 vs. 0.684 vs. 0.695. The results of immune infiltration analysis showed that the abundance of T cells follicular helper was significantly higher in the low-risk group, while the abundance of monocytes was the opposite. Finally, we identified and incorporated independent prognostic factors and developed a superior nomogram to predict the prognosis of GC patients. Conclusion Our study has developed a robust prognostic signature that can accurately predict the prognostic outcome of GC patients.

scholarly journals Construction of an immune-related gene signature to predict survival and treatment outcome in gastric cancer

2021 ◽  
Vol 104 (1) ◽  
pp. 003685042199728
Author(s):  
Shuairan Zhang ◽  
Zhi Li ◽  
Hang Dong ◽  
Peihong Wu ◽  
Yang Liu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Drug Resistance ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Differentially Expressed ◽  
Drug Resistant ◽  
Prognostic Signature ◽  
Resistant Gene

Immune cells have emerged as key regulators in the occurrence and development of multiple tumor types. However, it is unclear whether immune-related genes (IRGs) and the tumor immune microenvironment can predict prognosis for patients with gastric cancer (GC). The mRNA expression data in GC tissues ( n = 368) were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs in patients with GC were determined using a computational difference algorithm. A prognostic signature was constructed using COX regression and random survival forest (RSF) analyses. In addition, datasets related to “gemcitabine resistance” and “trastuzumab resistance” (GSE58118 and GSE77346) were obtained for GEO database, and DEGs associated with drug-resistance were screened. Then, we analyzed correlations between gene expression and cancer immune infiltrates via Tumor Immune Estimation Resource (TIMER) site. The cBioportal database was used to analyze drug-resistant gene mutation status and survival. One hundred and fifty-five differentially expressed IRGs were screened between GC and normal tissues, and a prognostic signature consisting of four IRGs (NRP1, PPP3R1, IL17RA, and FGF16) was closely related to the overall survival (OS). According to cutoff value of risk score, patients were divided into high-risk and low-risk group. Patients in the high-risk group had shorter OS compared to the low-risk group in both the training ( p < 0.0001) and testing sets ( p = 0.0021). In addition, we developed a 5-IRGs (LGR6, DKK1, TNFRSF1B, NRP1, and CXCR4) signature which may participate in drug resistance processes in GC. Survival analysis showed that patients with drug-resistant gene mutations had shorter OS ( p = 0.0459) and DFS ( p < 0.001). We constructed four survival-related IRGs and five IRGs related to drug resistance which may contribute to predict the prognosis of GC.

scholarly journals A Novel Pyroptosis-related Genes Prognostic Signature Affects the Immune Infiltration Patterns in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Yiqun Jin ◽  
Bai. Xue-song
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Checkpoint ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment

Abstract PurposePyroptosis is an inflammatory form of cell death associated with tumorigenesis and progression. However, the prognostic value of pyroptosis-related genes (PRGs) in hepatocellular carcinoma (HCC) have not been elucidated.MethodsWe downloaded mRNA expression profiles and clinical information from TCGA and ICGC database. Then, differently expressed PRGs were screened to construct a multigene prognostic signature by least absolute contraction and selection operator (LASSO) Cox regression method in TCGA cohort. Date from ICGC was used to validate the robustness of this signature. Kaplan-Meier analysis was used to compare overall survival (OS) between high- and low-risk group. Univariate and multivariate Cox analysis were performed to identify the independent prognostic value of the signature. Gene set enrichment analysis (GSEA) was utilized to conduct GO and KEGG analysis. Single-sample gene set enrichment analysis was implemented to assess the immune cell infiltration and immune-related function. TIDE algorithm evaluated the significance of this signature in predicting immunotherapeutic sensitivity. ResultsAn 8-PRGs prognostic model was established. The OS of low-risk group was significantly increased compared to high-risk group. Receiver operating characteristic curve showed the model had a good prognostic predictive accuracy. Cox regression analysis proved the model an independent predictor for OS in HCC. GSEA indicated that the risk score was associated with immune response. Furthermore, different subgroups exhibited different immunoinfiltration patterns, different immune-checkpoint levels and different potential responses for immune-checkpoint blockade therapy.ConclusionAn 8-PRGs signature can predict the prognosis of HCC patients and may act as an immunotherapeutic potential target for HCC.

scholarly journals Identification and external validation of a prognostic signature associated with DNA repair genes in gastric cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shimin Chen ◽  
Wenbo Liu ◽  
Yu Huang
Keyword(s):  
Dna Repair ◽  
Immune Cell ◽  
Risk Group ◽  
External Validation ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Analysis ◽  
Repair Genes

AbstractThe aim of this study was to construct and validate a DNA repair-related gene signature for evaluating the overall survival (OS) of patients with gastric cancer (GC). Differentially expressed DNA repair genes between GC and normal gastric tissue samples obtained from the TCGA database were identified. Univariate Cox analysis was used to screen survival-related genes and multivariate Cox analysis was applied to construct a DNA repair-related gene signature. An integrated bioinformatics approach was performed to evaluate its diagnostic and prognostic value. The prognostic model and the expression levels of signature genes were validated using an independent external validation cohort. Two genes (CHAF1A, RMI1) were identified to establish the prognostic signature and patients ware stratified into high- and low-risk groups. Patients in high-risk group presented significant shorter survival time than patients in the low-risk group in both cohorts, which were verified by the ROC curves. Multivariate analysis showed that the prognostic signature was an independent predictor for patients with GC after adjustment for other known clinical parameters. A nomogram incorporating the signature and known clinical factors yielded better performance and net benefits in calibration plot and decision curve analyses. Further, the logistic regression classifier based on the two genes presented an excellent diagnostic power in differentiating early HCC and normal tissues with AUCs higher than 0.9. Moreover, Gene Set Enrichment Analysis revealed that diverse cancer-related pathways significantly clustered in the high-risk and low-risk groups. Immune cell infiltration analysis revealed that CHAF1A and RMI1 were correlated with several types of immune cell subtypes. A prognostic signature using CHAF1A and RMI1 was developed that effectively predicted different OS rates among patients with GC. This risk model provides new clinical evidence for the diagnostic accuracy and survival prediction of GC.

Development and validation of an immune-related gene-based prognostic signature in gastric cancer

2020 ◽  
Author(s):  
Penglei Ge ◽  
Xiaofang Chen ◽  
Yang Wu ◽  
Yubin Fu ◽  
Chunbo Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Risk Score ◽  
Clinical Characteristics ◽  
Expression Profiles ◽  
Low Risk ◽  
Survival Prediction ◽  
Related Gene ◽  
Prognostic Signature ◽  
Immune Related Gene

Abstract Background: Gastric cancer is a common lethal cancer worldwide. We aimed to develop a reliable, individualized, immune-related prognostic signature that can be used to stratify and estimate prognosis in patients with gastric cancer. Methods: This retrospective study analyzed the gene expression profiles of gastric cancer with tumor tissue samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, which included 676 cases in total. Immune genes from the InnateDB database were selected to develop and validate an immune-related prognostic model for gastric cancer patients. Results: An immune-related gene pair (IRGP) model was constructed that enabled us to stratify patients into high- and low-risk immune risk groups in the training set. Patients with a low risk score had a significantly longer median survival time than those with a high risk score. Further, we compared the predictive accuracy of the IRGP model with clinical characteristics, including TNM, grade, age, and stage. The results showed that the model had the highest mean C-index (0.69) compared with grade (0.55) or stage (0.60) in survival prediction. Then, we constructed a nomogram that integrated the IRGP model with independent clinical characteristics, which showed the best prognostic accuracy compared with other signatures. Conclusion: A clinical-immune signature based on IRGP is a promising prognostic biomarker in gastric cancer. Prospective studies are needed to further validate its accuracy and to test its clinical utility in individualized treatment.

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