| Objective: The aim of this study was to evaluate the prognostic value of the number of examined lymph nodes(ELNs)and propose a novel biomarker screening method.Based on prognostic related markers,ELNs risk signature model and nomogram model were constructed to predict Overall Survival(OS)of patients with gastric cancer,with a view to identifying high-risk groups and predicting treatment effects.Furthermore,the potential molecular mechanisms and prognostic effects of CDKN2B-AS1 gene expression in the ELNs risk signature model in pan-cancer were investigated and analyzed.Methods: 1)This initial cohort study included 19,317 GC patients from the Surveillance,Epidemiology,and End Results(SEER)database in the United States.The database data were randomly divided into training sets and internal validation sets by 2: 1.The receiver operating characteristic curve(ROC)was used to determine the optimal threshold of the number of ELNs.GC patients were divided into high ELNs group and low ELNs group based on the optimal threshold.Kaplan-Meier(K-M)curve was used to evaluate the prognostic value of ELNs group.Use the training set to build the nomograph prediction model,and then use the internal SEER data,the Cancer Genome Atlas(TCGA)data set(396 patients with STAD)and the Cancer Hospital affiliated to Xinjiang Medical University(471 patients with GC)to verify,and pass the consistency index(C-index),calibration map ROC curve and decision curve analysis(DCA).Take PSM analysis as perceptual analysis.2)At the molecular level,based on the transcriptome data of TCGA-STAD patients,DERNAs(DElnc RNA,DEmi RNA and DEm RNA)and tumor infiltrating immune cells(TIICs)related to the number of ELNs were identified.In the TCGA-STAD cohort,the signature of DERNAs and TIICs related to ELNs was constructed using LASSO-Cox regression analysis.K-M analysis is used to compare OS between high and low ELNs signature groups.The nomogram is constructed based on the ELNs signature and other clinical features.The nomogram model is evaluated by C-index,calibration curve,ROC curve and DCA.Meta analysis,GEPIA database and reverse transcription-quantitative PCR(RT-q PCR)were used to verify the expression and abundance of prognostic genes and TIICs between GC tissue and normal gastric tissue,respectively.In addition,ELNs signature was used to predict the therapeutic efficacy of patients and analyze the relevant mechanisms.3)Download cancer and normal tissue sequencing data from the UCSC Xena database for the TCGA pan-cancer cohort.Mann-Whitney U test was used to evaluate the difference of CDKN2B-AS1 expression between cancer and non-cancer tissues.Univariate Cox regression and K-M curve analysis were used to evaluate the prognostic effect of CDKN2B-AS1 on the specific prognostic types of each cancer(total survival,progression-free survival,disease-free survival and disease-specific survival).The c Bio Portal tool was used to analyze the mutation characteristics of CDKN2B-AS1 in different cancers.EWAS database was used to analyze the relationship between survival time and methylation level of CDKN2B-AS1 promoter.The analysis of immune-related characteristics of CDKN2B-AS1 includes the evaluation of the correlation between the expression of CDKN2B-AS1 and the immune score,matrix score and estimated score of various cancer types through Spearman correlation analysis.Based on the TIMER2.0 database,the TIICs level evaluated by different algorithms was obtained,and the relationship between CDKN2B-AS1 expression and them was explored.Spearman correlation analysis was used to study the relationship between CDKN2B-AS1 expression and microsatellite instability(MSI)score and tumor mutation burden(TMB)value in different tumors in TCGA cohort.In addition,the correlation between CDKN2B-AS1 expression and other immune-related genes(including chemokine,chemokine receptor,MHC gene,immunosuppressant,immunostimulator and immune checkpoint gene)in multiple tumors was explored through Sanger Box 3.0 website.Bio GRID database and GEPIA2.0 database are used to obtain genes related to CDKN2B-AS1.Based on CDKN2B-AS1 related genes,we performed Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis The TCGA data of 33 kinds of cancer were used to analyze the correlation between CDKN2B-AS1 and all genes,calculate the Spearman correlation coefficient in batches,and perform Gene Set Enrichment Analysis(GSEA).Cancer SEA database and TISCH database were used to evaluate the tumor biology-related functional status of CDKN2B-AS1 and the expression level of CDKN2B-AS1 in each cell type at the single cell sequencing level.In this study,ICBatlas database was used to evaluate the effect of CDKN2B-AS1 expression on immunotherapy response.Furthermore,the prognostic value of CDKN2B-AS1 was analyzed by IMvigor210 cohort.Finally,the tumor immune dysfunction and exclusion(TIDE)algorithm was used to predict the immune response.Patients with high TIDE score showed higher chance of tumor immune escape,thus showing a lower response rate to immunotherapy.Results: 1)Based on the SEER training set,the optimal threshold of the number of ELNs is 16.Multivariate Cox regression analysis showed that compared with low ELNs,high ELNs improved OS[hazard ratio(HR)=0.659,95% confidence intervals(CIs): 0.626-0.694,P<0.001].A nomograph model containing ELNs was constructed using the training set,and it was proved that it has good calibration and discrimination ability(C-index [95% CI],0.714[0.710-0.718]).This model was verified in the internal validation set(C-index [95% CI],0.720 [0.714-0.726])TCGA-STAD data set(C-index [95% CI],0.693 [0.662-0.724])and tumor hospital data set(C-index [95% CI],0.750 [0.720-0.782])were also verified.PSM results showed that ELNs were independent risk factors for OS in GC patients.2)The ELNs related signature model based on ELNs group,regulatory T cells(Tregs),neutrophils,CDKN2B-AS1,H19,HOTTIP,LINC00643,MIR663 AHG,TMEM236,ZNF705 A and hsa-mi R-135a-5p was constructed by LASSO-Cox regression analysis.The results showed that the OS of patients with high ELNs signature was significantly worse than that of patients with low ELNs signature(HR=2.418,95% CI=1.804-3.241,P<0.001).This feature has good performance in predicting 1-year,3-year and 5-year survival rates(AUCs [95% CI] = 0.688 [0.612,0.763],0.744 [0.659,0.830] and 0.778[0.647,0.909],respectively).Univariate and multivariate Cox regression analysis showed that the ELNs signature was an independent predictor of OS in patients with GC.The nomogram model constructed by ELNs signature combined with other independent clinicopathologic prognostic factors is used to predict the area under the ROC curve of the patients with GC for 1,3 and 5 years,AUC are: AUCs [95% CI] = 0.742 [0.675,0.808],0.768 [0.686,0.849] and 0.813 [0.692,0.934],C-index is 0.710(95% CI:0.680-0.740),and the calibration chart and DCA show that the nomogram has good prediction performance and clinical applicability.Meta analysis,GEPIA database and RT-q PCR results showed that the expression of prognostic genes(LINC00643,TMEM236 and hsa-mi R-135a-5p)in ELNs signature was different between GC tissues and adjacent non-tumor tissues.In addition,ELNs signature was significantly correlated with N stage and T stage.The results of treatment efficacy evaluation suggest that GC patients in the low ELNs signature group are more sensitive to most chemotherapy drugs,and the TIDE score of GC patients in the low ELNs signature group is lower,indicating that the low ELNs signature group may have better immune treatment response.Enrichment analysis showed that cell development and proliferation-related pathways were significantly enriched in patients with high ELNs,while immune-related pathways such as interferon α Responses were significantly enriched in patients with low ELNs signature.3)CDKN2B-AS1 is highly expressed in a variety of tumors.It was significantly associated with the tumor stage of six human tumors,including ACC,KICH,KIRC,LIHC,THCA,and UVM.The results of Cox regression analysis of CDKN2B-AS1 expression level and four prognostic indicators of OS,DSS,DFS and PFS in 33 tumors showed that in OS analysis,CDKN2B-AS1 expression was negatively correlated with OS in STAD,ACC,KICH,KIRC,LIHC,PCPG,THCA and UCEC.In DSS analysis,high CDKN2B-AS1 expression was associated with poor prognosis in patients with ACC,KICH,KIRC,PRAD,THCA,and UCEC.High CDKN2B-AS1 expression is associated with poor DFS in KIRP and LIHC patients.In PFS analysis,high expression of CDKN2B-AS1 was a risk factor for patients with ACC,KICH,KIRC,LICH,THCA,UCEC,and UVM.Cancer patients with CDKN2B-AS1 mutations have worse outcomes in OS,DSS,DFS,and PFS.CDKN2B-AS1 promoter methylation levels correlate with CDKN2B-AS1 expression in a variety of cancers.In addition,the expression of CDKN2B-AS1 was significantly positively correlated with most immune-related genes and immune checkpoint(ICPs)genes in a variety of tumors,as well as with TMB and MSI.The mechanism analysis shows that the higher expression of CDKN2B-AS1 is related to the immune activation and proliferation of cancer.Conclusion: The first part of the study is based on the large clinical big data of gastric cancer in SEER at the macro level,with ELNs as the target variable for the study.The patients with gastric cancer are divided into two different phenotypic groups through ELNs,and a nomograph prognostic model including ELNs is established.At the same time,other clinical indicators of the prognosis of gastric cancer are also explored.The nomograph model shows good predictive effect in the internal validation set,TCGA-STAD data set and the gastric cancer cohort in this study.In the second part,based on TCGA,we constructed the ELNs signature related to the prognosis of GC at the molecular level.The results show that this feature is an effective predictor of GC patients,which can identify high-risk groups and evaluate the treatment effect.GEO,GEPIA database and RT-q PCR experiments were used to explore the correlation between the key prognostic indicators(genes or/and tumor immune cells)of ELNs signature and the prognosis of gastric cancer patients,and to verify the applicability and reliability of ELNs signature.This feature may contain potential biomarkers used to predict the treatment response of patients with GC.In addition,this study also identified a novel and robust nomogram combining the signature of ELNs and clinical factors to predict the 1-year,3-year and 5-year survival rates of GC patients,which will help the personalized survival prediction and clinical decision-making of GC patients.In the third part,we used 33 kinds of cancer included in TCGA to analyze CDKN2B-AS1 for the first time,and discussed the expression level,clinical prognosis,genetic changes and immune regulation of CDKN2B-AS1 in pan-cancer.The results showed that the overexpression of CDKN2B-AS1 usually predicted the poor prognosis of cancer patients,was a robust prognostic biomarker of pan-cancer,and effectively predicted the response of immunotherapy,and was expected to become the target of tumor immunotherapy.At the same time,the potential mechanism of CDKN2B-AS1 in tumor was also discussed.The research of the three parts has established the prediction model of gastric cancer prognosis by using bioinformatics technology at the macro and molecular levels,in order to explore the clinical indicators and new biomarkers of gastric cancer prognosis,and comprehensively and gradually understand the pathogenesis of gastric cancer. |
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