Study on the relationship between immune cell infiltration pattern and clinical characteristics and prognosis of cervical cancer based on TCGA database

Objective:To investigate the correlation between immune cell infiltration pattern and clinical features and prognosis of cervical carcinoma.Methods:All cervical cancer transcript data and related clinical data were downloaded from the public database Cancer Genome Atlas(TCGA),and the relative proportions of 22 invasive immune cell types were calculated by Cibersort software.Perl was used to assess the correlation between the pattern of immune cell invasion and clinical characteristics(age,clinical stage,tumor grade)in cervical cancer,and the correlation between the pattern of immune cell invasion and survival in cervical cancer was calculated by the K-M Log-Rank method.Result:The distribution of immune cells in 306 cases of cervical cancer and 3 cases of normal tissues was assessed using Cibersort.Compared with normal tissues,the contents of resting dendritic cells,activated dendritic cells,M1 macrophages and activated CD4+memory T cells were higher;the contents of M2 macrophages,neutrophils,regulatory T cells and activated mast cells were lower in cervical cancer tissues.The contents of M1 macrophages,unactivated CD4+memory T cells,andγδT cells were positively correlated with patient age(P<0.05).The contents of follicular helper T cells,activated and unactivated natural killer(NK)cells,and naive CD4 T cells were negatively correlated with patient age(P<0.05).Those with high resting dendritic cell composition had shorter overall survival,while those with high follicular helper T cell composition had longer overall survival(P<0.05).Conclusion:Compared with normal tissues,the composition of immune cells in cervical cancer tissues has certain specificity,which can provide reference for the early screening and diagnosis of the disease.Patients in different age groups may have different immune cell infiltration patterns,which can be used as a basis to explore drug targets in clinical practice.Resting dendritic cells and follicular helper T cells in cervical cancer can be used as possible efficacy predictors of clinical immunotherapy for cervical cancer.

The Role of ZNF207 in Liver Hepatocellular Carcinoma:Expression Analysis and Prognostic Implications

Objective:To analyze the expression and clinical significance of the zinc finger protein ZNF207 gene in liver hepatocellular carcinoma(LIHC)based on The Cancer Genome Atlas(TCGA)database.Methods:The mRNA sequencing data of 371 cases of primary liver cancer,50 cases of normal tissues,and 3 cases of recurrent liver cancer were downloaded from the TCGA database.The corresponding clinical information of the 371 cases of hepatocellular carcinoma was subsequently analyzed.The difference in ZNF207 expression between normal and tumor tissues was analyzed using the UALCAN online database.The impact of ZNF207 expression on survival prognosis was assessed using the Kaplan-Meier method in R software.The GO and KEGG pathways of ZNF207 were analyzed.The Cox proportional hazards model was used to evaluate the prognostic factors of patients with LIHC.RT-qPCR was employed to verify the expression of ZNF207 in LIHC cells.Results:ZNF207 was highly expressed in LIHC tissues and HepG2 cells,with a significant difference(P<0.05).Multivariate Cox regression analysis revealed that patients with high ZNF207 expression had a significantly shorter overall survival time compared to those with low ZNF207 expression(HR=1.466,95%CI:1.011-2.126,P<0.05).GO enrichment analysis suggested that ZNF207 may influence the onset and progression of hepatocellular carcinoma by regulating mRNA splicing and mRNA transcription processing through the spliceosome.KEGG pathway enrichment analysis indicated that ZNF207 might affect the onset and progression of hepatocellular carcinoma through mitophagy,mRNA surveillance,homologous recombination,spliceosome,and nuclear-cytoplasmic transport.Conclusion:The expression of ZNF207 may be an independent predictor of the prognosis of patients with LIHC and could influence the development of hepatocellular carcinoma through various gene functions and pathways.It has the potential to serve as a novel molecular marker for predicting the prognosis of hepatocellular carcinoma.

Sex differences in the scored Patient-Generated Subjective Global Assessment in 19,528 cancer patients

Background:The scored Patient-Generated Subjective Global Assessment(PG-SGA)has been widely used to assess the nutritional status of cancer patients.The purpose of this study is to compare the differences in PG-SGA scores and the 7 domain scores of the PG-SGA in male and female cancer patients.Methods:This study was conducted at 72 hospitals from July 2013 to December 2018,a part of the Investigation on Nutritional Status and its Clinical Outcomes of Common Cancers.The PG-SGA was recorded to evaluate the nutritional status of patients.A total of 19,528 patients with 13 common malignancies were included in this study.Student t test and the χ^(2) test were applied to analyze the sex diferences in the 7 domain scores.The Cancer Genome Atlas(TCGA)database was used to analyze the expression levels of symptom-related genes.Results:There were significant sex dfferences in the PG-SGA(P=0.032),notably in patients with gastric cancer(male vs female:9.09±4.86 vs 9.58±5.07,P=0.005)and esophageal cancer(9.64±4.90 vs 10.46±4.96,P=0.011)and the average total PG-SGA of female patients was slightly higher than that of male patients(7.64±4.98 vs 7.77±5.14).The differences were mainly related to the weight,eating,symptom,as well as activity and physical function scores in the stratified analysis.Possible causes of the sex differences were the rates of nausea,vomiting,dry mouth,and other symptoms,in both gastric and esophageal cancer patients.Analysis of the TCGA database suggested that most of the related genes were sex neutral,except for genes related to dysphagia in gastric cancer(VEGFC was higher in female patients,VEGFA and VEGFB higher in male patients).Conclusions:There are sex differences in the PG-SGA scores in patients with various tumor types(female patients generally had higher scores than male patients),with differences mainly in the weight,eating,symptom,as well as activity and physical function scores.The sex differences in PG-SGA scores might be due to the differences in the clinical manifestations of the disease,and further studies should be carried out to investigate other factors influencing the PG-SGA scores in cancer patients.This study provides basic data supporting the individualized nutritional treatment of cancer patients in clinical practice.

Molecular subtypes based on DNA methylation predict prognosis in patients with renal clear cell carcinoma

Background:Tumor heterogeneity is closely related to the occurrence,progression and recurrence of renal clear cell carcinoma(ccRCC),making early diagnosis and effective treatment difficult.DNA methylation is an important regulator of gene expression and can affect tumor heterogeneity.Methods:In this study,we investigated the prognostic value of subtypes based on DNA methylation status in 506 ccRCC samples with paired clinical data from the TCGA database.Differences in DNA methylation levels were associated with differences in T,N and M categories,age,stage and prognosis.Finally,the samples were divided into the training group and the testing group according to 450K and 27K.Univariate and multivariate Cox regression analysis was used to construct the prediction model in the training group,and the model was verified and evaluated in the testing group.Results:By univariate Cox regression analysis,21,122 methylation sites and 6,775 CpG sites were identified as potential DNA methylation biomarkers for overall survival of ccRCC patients(P<0.05).3,050 CpG sites independently associated with prognosis were identified with T,N,M,stage and age as covariables.Consensus cluster of 3,050 potential prognostic methylation sites was used to identify different DNA methylation subsets of ccRCC for prognostic purposes.We performed functional enrichment analysis on these 3,640 genes and identified 75 significantly enriched pathways(P<0.05).We then researched the expression of methylated genes in subgroups.Verifing with the training set,suggesting that DNA methylation levels generally reflect the expression of these genes.Conclusion:Based on TCGA database and a series of bioinformatics methods,We identified prognostic specific methylation sites and established prognostic prediction models for ccRCC patients.This model helps to identify novel biomarkers,precision drug targets and disease molecular subtypes in patients with ccRCC.Therefore,this model may be useful in predicting the prognosis,clinical diagnosis and management of patients with different epigenetic subtypes of ccRCC.

Construction and validation of an immune-related lncRNA prognostic model for rectal adenocarcinomas

Objective This study aimed to construct a prognostic model for rectal adenocarcinomas based on immune-related long noncoding RNAs(lncRNAs)and verify its prediction efficiency.Methods Transcript data and clinical data of rectal adenocarcinomas were downloaded from The Cancer Genome Atlas(TCGA)database.Perl software(strawberry version)and R language(version 3.6.1)were used to analyze the immune-related genes and immune-related lncRNAs of rectal adenocarcinomas,and the differentially expressed immune-related lncRNAs were screened according to the criteria|log2FC|>1 and P<0.05.The key immune-related lncRNAs were screened using single-factor Cox regression analysis and lasso regression analysis.Multivariate Cox regression analysis was performed to construct an immune-related lncRNA prognostic model using the risk scores.Next,we evaluated the effectiveness of the model through Kaplan-Meier(K-M)survival analysis,ROC curve analysis,and independent prognostic analysis of clinical features.In addition,prognostic biomarkers of immune-related lncRNAs in the model were analyzed by K-M survival analysis.Results In this study,we obtained gene expression profile matrices of 89 rectal adenocarcinomas and 2 paracancerous specimens from TCGA database and applied immunologic signatures to these transcripts.Through R and Perl software analysis,we obtained 847 immune-related lncRNAs and 331 protein-encoded immune-related genes in rectal adenocarcinomas.Eight important immune-related lncRNAs related to the prognosis of rectal adenocarcinomas were identified using univariate Cox regression and lasso regression analysis.Furthermore,four immune-related lncRNAs were identified as prognostic markers of rectal adenocarcinomas via multivariate Cox regression analysis.The prognostic risk model was as follows:risk score=(-4.084)*expression LINC01871+(3.112)*expression AL158152.2+(7.616)*expression PXN-AS1+(-0.867)*expression HCP5.The independent prognostic effect of the rectal adenocarcinoma risk score model was revealed through K-M analysis,ROC curve analysis,and univariate,and multivariate Cox regression analysis(P=0.035).LINC01871(P=0.006),PXN-AS1(P=0.008),and AL158152.2(P=0.0386)were closely correlated with the prognosis of rectal adenocarcinomas through the K-M survival analysis.Conclusion We constructed a prognostic model of rectal adenocarcinomas based on four immune-related lncRNAs by analyzing the data based on TCGA database,with high prediction accuracy.We also identified two biomarkers with poor prognosis(PXN-AS1 and AL158152.2)and one biomarker with good prognosis(LINC01871).

Prognostic risk model construction and prognostic biomarkers identification in esophageal adenocarcinoma based on immune-related long noncoding RNA

Objective The aim of this study was to construct a prognostic model of esophageal adenocarcinoma(EAC)based on immune-related long noncoding RNAs(immune-related lncRNAs)and identify prognostic biomarkers using the Cancer Genome Atlas(TCGA)database.Methods Whole genomic mRNA expression and clinical data of esophageal adenocarcinoma were obtained from the TCGA database.The software Strawberry Perl,R and R packets were used to identify the immune-related genes and lncRNAs of esophageal adenocarcinoma,and for data processing and analysis.The differentially expressed lncRNAs were detected while comparing esophageal adenocarcinoma and normal tissue samples.The key immune-related lncRNAs were screened using lasso regression analysis and univariate cox regression analysis,and used to construct the prognostic model using multivariate cox regression analysis.To evaluate the accuracy of the risk prognostic model,all esophageal adenocarcinomas were divided into high-risk and low-risk groups according to the median risk score,after which Kaplan-Meier(K-M)survival curves,operating characteristic(ROC)curve and independent prognostic analysis of clinical traits were created.In addition,statistically significant immune-related lncRNAs and potential prognostic biomarkers were identified using the prognostic model and multifactor cox regression analysis for k-m survival analysis.Results A total of 1322 differentially expressed immune-related lncRNAs were identified,28 of which were associated with prognosis via univariate cox regression analysis.In addition,K-M survival analysis showed that the total survival time of the higher risk group was significantly shorter than that of the lower risk group(P=1.063e-10).The area under the ROC curve of 5-year total survival rate was 0.90.The risk score showed independent prognostic risk for esophageal adenocarcinoma via single factor and multifactorial independent prognostic analyses.In addition,the HR and 95%CI of each key immune-related lncRNA were calculated using multivariate Cox regression.Using k-m survival analysis,we found that 5 out of 12 key significant immune-related lncRNAs had independent prognostic value[AL136115.1(P=0.006),AC079684.1(P=0.008),AC07916394.1(P=0.0386),AC087620.1(P=0.041)and MIRLET7BHG(P=0.044)].Conclusion The present study successfully constructed a prognostic model of esophageal adenocarcinoma based on the TCGA database,with moderate predictive accuracy.The model consisted of the expression level of 12 immune-related lncRNAs.Furthermore,the study identified one favorable prognostic biomarker,MIRLET7BHG,and four poor prognostic biomarkers(AL136115.1,AC079684.1,AC016394.1,and AC087620.1).

The mRNA–miRNA–lncRNA Regulatory Network and Factors Associated with Prognosis Prediction of Hepatocellular Carcinoma

The aim of this study was to identify novel prognostic mRNA and microRNA(miRNA)biomarkers for hepatocellular carcinoma(HCC)using methods in systems biology.Differentially expressed mRNAs,miRNAs,and long non-coding RNAs(lncRNAs)were compared between HCC tumor tissues and normal liver tissues in The Cancer Genome Atlas(TCGA)database.Subsequently,a prognosis-associated mRNA co-expression network,an mRNA–miRNA reg-ulatory network,and an mRNA–miRNA–lncRNA regulatory network were constructed to identify prognostic biomarkers for HCC through Cox survival analysis.Seven prognosis-associated mRNA co-expression modules were obtained by analyzing these differentially expressed mRNAs.An expression module including 120 mRNAs was significantly corre-lated with HCC patient survival.Combined with patient survival data,several mRNAs and miRNAs,including CHST4,SLC22A8,STC2,hsa-miR-326,and hsa-miR-21 were identified from the network to predict HCC patient prognosis.Clinical significance was investigated using tissue microarray analysis of samples from 258 patients with HCC.Functional annotation of hsa-miR-326 and hsa-miR-21-5p indicated specific associations with several cancer-related pathways.The present study provides a bioinformatics method for biomarker screening,leading to the identification of an integrated mRNA–miRNA–lncRNA regulatory network and their co-expression patterns in relation to predicting HCC patient survival.