scholarly journals Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jinyuan Shi ◽  
Pu Wu ◽  
Lei Sheng ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Papillary Thyroid ◽  
Related Gene ◽  
Normal Thyroid

Abstract Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. Methods A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. Results LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature’s genes with the influence of ferroptosis induced by sorafenib. Conclusions We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients’ prognoses and targeting ferroptosis may be an alternative for PTC’s therapy.

scholarly journals Ferroptosis-Related Gene Signature Predicts The Prognosis of Papillary Thyroid Carcinoma

2021 ◽  
Author(s):  
Jinyuan Shi ◽  
Pu Wu ◽  
Lei Sheng ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
High Risk ◽  
Immune Cells ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Papillary Thyroid ◽  
Related Gene ◽  
Normal Thyroid

Abstract Background: Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. Methods: A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. Results: LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group(p<0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. Conclusions: We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients’ prognoses and targeting ferroptosis may be an alternative for PTC’s therapy.

scholarly journals A novel defined pyroptosis-related gene signature for predicting the prognosis of ovarian cancer

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Ying Ye ◽  
Qinjin Dai ◽  
Hongbo Qi
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Differentially Expressed ◽  
Related Gene

AbstractOvarian cancer (OC) is a highly malignant gynaecological tumour that has a very poor prognosis. Pyroptosis has been demonstrated in recent years to be an inflammatory form of programmed cell death. However, the expression of pyroptosis-related genes in OC and their correlations with prognosis remain unclear. In this study, we identified 31 pyroptosis regulators that were differentially expressed between OC and normal ovarian tissues. Based on these differentially expressed genes (DEGs), all OC cases could be divided into two subtypes. The prognostic value of each pyroptosis-related gene for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 7-gene signature was built and classified all OC patients in the TCGA cohort into a low- or high-risk group. OC patients in the low-risk group showed significantly higher survival possibilities than those in the high-risk group (P < 0.001). Utilizing the median risk score from the TCGA cohort, OC patients from a Gene Expression Omnibus (GEO) cohort were divided into two risk subgroups, and the low-risk group had increased overall survival (OS) time (P = 0.014). Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the OS of OC patients. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses indicated that immune-related genes were enriched and that the immune status was decreased in the high-risk group. In conclusion, pyroptosis-related genes play important roles in tumour immunity and can be used to predict the prognosis of OCs.

scholarly journals A Ferroptosis-associated Gene Signature for the Prediction of Prognosis and Therapeutic Response in Luminal-type Breast Carcinoma

2020 ◽  
Author(s):  
Yang Peng ◽  
Haochen Yu ◽  
Yingzi Zhang ◽  
Zhenrong Tang ◽  
Chi Qu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Breast Carcinoma ◽  
Risk Score ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Related Gene ◽  
Luminal Type

Abstract Background: Ferroptosis is a new form of regulated cell death (RCD), and its emergence has provided a new approach to the progression and drug resistance of breast cancer (BRCA). However, there is still a great gap in the study of ferroptosis-related genes in BRCA, especially luminal-type BRCA patients.Methods: We downloaded the mRNA expression profiles and corresponding clinical data of BRCA patients from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and The Cancer Genome Atlas (TCGA) databases. Then, we built a prognostic multigene signature with ferroptosis-related differentially expressed genes (DEGs) in the METABRIC cohort and validated it in the TCGA cohort. The predictive value of this signature was investigated in terms of mutations, copy number variations (CNVs), the immune microenvironment, tumor purity, related pathway and the probability of a response to immunotherapy and chemotherapy.Findings: The patients were divided into a high-risk group and a low-risk group by the ferroptosis-associated gene signature, and the high-risk group had a worse overall survival (OS). The risk score based on the 10 ferroptosis-related gene-based signature was determined to be an independent prognostic predictor in both the METABRIC and TCGA cohorts (HR, 1.41, 95% CI, 1.14-1.76, P = 0.002; HR, 2.19, 95% CI, 1.13-4.26, P= 0.02). Gene set enrichment analysis indicated that the term “cytokine-cytokine receptor interaction” was enriched in the high risk score subgroup. Moreover, the immune infiltration scores of most immune cells were significantly different between the two groups, and the low-risk group was much more sensitive to immunotherapy and chemotherapy. In addition, we found that amplifications on chromosome 12 accompanied by the deletion of chromosome 21 were enriched in the high-risk subgroup. Pathway score results suggest that the ferroptosis-related gene-based signature show differences in most breast cancer-associated phenotypes. Finally, a nomogram incorporating a classifier based on the 10 ferroptosis-related genes, tumor stage, age and histologic grade was established. This nomogram showed a favorable discriminating ability and might contribute to clinical decision-making for luminal-type breast carcinoma.

scholarly journals A Novel Ferroptosis-Related Gene Signature Robustly Predicts Clinical Prognosis And Tumor Immunity in Patients With Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Wei Song ◽  
Weiting Kang ◽  
Qi Zhang
Keyword(s):  
High Risk ◽  
Clear Cell ◽  
Risk Group ◽  
Gene Signature ◽  
Renal Clear Cell Carcinoma ◽  
High Risk Group ◽  
Risk Scores ◽  
Related Gene ◽  
Clinical Prognosis ◽  
Validation Set

Abstract Objective: This study aimed to construct a ferroptosis-related gene signature to predict clinical prognosis and tumor immunity in patients with kidney renal clear cell carcinoma (KIRC).Methods: The mRNA expression profiles and corresponding clinical data of KIRC patients were downloaded from The Cancer Genome Atlas (TCGA), which were randomly divided into training (398 patients) and validation set (132 patients). The iron death related (IDR) prediction model was constructed based on training set and 60 ferroptosis-related genes from previous literatures, followed by prognostic performance evaluation and verification using the validation set. Moreover, functional enrichment, immune cell infiltration, metagene clusters correlation, and TIDE scoring analyses were performed. Results: In total, 23 ferroptosis-related genes were significantly associated with overall survival (OS). The IDR prediction model (a 10-gene signature) was then constructed to stratify patients into two risk groups. The OS of KIRC patients with high-risk scores was significantly shorter than those with low-risk scores. Moreover, the risk score was confirmed as an independent prognostic predictor for OS. The positive and negative correlated genes with this model were significantly enriched in p53 signaling pathway, and cGMP-PKG signaling pathway. The patients in the high-risk group had higher ratios of plasma cells, T cells CD8, and T cells regulatory Tregs. Furthermore, IgG, HCK, LCK, and Interferson metagenes were significantly correlated with risk score. By TIDE score analysis, patients in the high-risk group could benefit from immunotherapy.Conclusions: The identified ferroptosis-related gene signature is significantly correlated with clinical prognosis and immune immunity in KIRC patients.

scholarly journals Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8128 ◽  
Author(s):  
Cheng Yue ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Differentially Expressed ◽  
Lasso Regression

Background Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient’s outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. Methods The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. Results A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals Identification of an Autophagy-Related Pair Signature for Predicting Prognoses and Immune Activity in Pancreatic Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Qian Zhang ◽  
Liping Lv ◽  
Ping Ma ◽  
Yangyang Zhang ◽  
Jiang Deng ◽  
...  
Keyword(s):  
High Risk ◽  
Pancreatic Adenocarcinoma ◽  
Small Molecule ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
M2 Macrophage ◽  
Related Gene ◽  
Significant Difference

BackgroundPancreatic adenocarcinoma (PAAD) spreads quickly and has a poor prognosis. Autophagy research on PAAD could reveal new biomarkers and targets for diagnosis and treatment.MethodsAutophagy-related genes were translated into autophagy-related gene pairs, and univariate Cox regression was performed to obtain overall survival (OS)-related IRGPs (P&lt;0.001). LASSO Cox regression analyses were performed to construct an autophagy-related gene pair (ARGP) model for predicting OS. The Cancer Genome Atlas (TCGA)-PAAD cohort was set as the training group for model construction. The model predictive value was validated in multiple external datasets. Receiver operating characteristic (ROC) curves were used to evaluate model performance. Tumor microenvironments and immune infiltration were compared between low- and high-risk groups with ESTIMATE and CIBERSORT. Differentially expressed genes (DEGs) between the groups were further analyzed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and used to identify potential small-molecule compounds in L1000FWD.ResultsRisk scores were calculated as follows: ATG4B|CHMP4C×(-0.31) + CHMP2B|MAP1LC3B×(0.30) + CHMP6|RIPK2 ×(-0.33) + LRSAM1|TRIM5×(-0.26) + MAP1LC3A|PAFAH1B2×(-0.15) + MAP1LC3A|TRIM21×(-0.08) + MET|MFN2×(0.38) + MET|MTDH×(0.47) + RASIP1|TRIM5×(-0.23) + RB1CC1|TPCN1×(0.22). OS was significantly shorter in the high-risk group than the low-risk group in each PAAD cohort. The ESTIMATE analysis showed no difference in stromal scores but a significant difference in immune scores (p=0.0045) and ESTIMATE scores (p=0.014) between the groups. CIBERSORT analysis showed higher naive B cell, Treg cell, CD8 T cell, and plasma cell levels in the low-risk group and higher M1 and M2 macrophage levels in the high-risk group. In addition, the results showed that naive B cells (r=-0.32, p&lt;0.001), Treg cells (r=-0.31, p&lt;0.001), CD8 T cells (r=-0.24, p=0.0092), and plasma cells (r=-0.2, p&lt;0.026) were statistically correlated with the ARGP risk score. The top 3 enriched GO-BPs were signal release, regulation of transsynaptic signaling, and modulation of chemical synaptic transmission, and the top 3 enriched KEGG pathways were the insulin secretion, dopaminergic synapse, and NF-kappa B signaling pathways. Several potential small-molecule compounds targeting ARGs were also identified.ConclusionOur results demonstrate that the ARGP-based model may be a promising prognostic indicator for identifying drug targets in patients with PAAD.

scholarly journals Validation of an Immune-related Gene Pair Index as a Prognostic Marker of Early-Stage Lung Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Zihao Wang ◽  
Xuan Xiang ◽  
Xiaoshan Wei ◽  
Linlin Ye ◽  
Yiran Niu ◽  
...  
Keyword(s):  
High Risk ◽  
Validation Cohort ◽  
Risk Group ◽  
Early Stage ◽  
Lung Squamous Cell Carcinoma ◽  
High Risk Group ◽  
Low Risk ◽  
Data Sets ◽  
Related Gene ◽  
Immune Related Gene

Abstract Background. Lung squamous cell carcinoma (LUSC) is one of the subtypes of non-small-cell lung cancer (NSCLC) and accounts for approximately 20 to 30% of all lung cancers.Methods. In this study, we developed an immune-related gene pair index (IRGPI) for early-stage LUSC from 3 public LUSC data sets, including The Cancer Genome Atlas LUSC cohort and Gene Expression Omnibus data sets, and explored whether IRGPI could act as a prognostic marker to identify patients with early-stage LUSC at high risk.Results. IRGPI was constructed by 68 gene pairs consisting of 123 unique immune-related genes from TCGA LUSC cohort. In the derivation cohort, the hazard of death among high-risk group was 10.51 times that of the low-risk group (HR, 10.51; 95%CI, 6.96-15.86; p<0.001). The hazard of death among the high-risk group was 2.26 times that of the low-risk group (HR, 2.26; 95%CI, 1.2-4.25; p=0.009) in the GSE37745 validation cohort and was 3.2 times that of low-risk group (HR, 3.2; 95%CI, 0.98-10.4; p=0.042) in the GSE41271 validation cohort. The infiltrations of CD8+ T cells and T follicular helper cells were lower in the high-risk group, as compared with the low-risk group in the TCGA cohort (6.94% vs 9.63%, p=0.004; 2.15% vs 3%, p=0.002, respectively). The infiltrations of neutrophils, activated mast cells and monocytes were higher in the high-risk group, as compared with the low-risk group in the TCGA cohort (1.63% vs 0.72%, p=0.001; 1.64% vs 1.02%, p=0.007; 0.57% vs 0.35%, p=0.041, respectively).Conclusions. IRGPI is a significant prognostic biomarker for predicting overall survival in early-stage LUSC patients.

scholarly journals A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jia Li ◽  
Huiyu Wang ◽  
Zhaoyan Li ◽  
Chenyue Zhang ◽  
Chenxing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Egfr Mutations ◽  
Kaplan Meier ◽  
Tumor Immune Microenvironment ◽  
Nsclc Patients

Purpose. Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME). Methods and Results. There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P<0.001 and P=0.03). Pathway enrichment results which were obtained by performing Gene Set Variation Analysis (GSVA) showed that the high-risk group identified by the 5-gene signature had metastatic-related gene expression, resulting in lower survival rates. Kaplan–Meier survival results showed that patients of the high-risk group had shorter disease-free survival (DFS) and overall survival (OS) than those of the low-risk group in the training set (P=0.0012 and P<0.001). The sensitivity and specificity of the gene signature were better and more sensitive to prognosis than TNM (tumor/lymph node/metastasis) staging, in spite of being not statistically significant (P=0.154). Furthermore, Kaplan–Meier survival showed that patients of the high-risk group had shorter OS and PFS than those of the low-risk group (P=0.0035, P<0.001, and P<0.001) in the validating set (GSE31210, GSE41271, and TCGA). At last, univariate and multivariate Cox proportional hazard regression analyses were used to evaluate independent prognostic factors associated with survival, and the gene signature, lymphovascular invasion, pleural invasion, chemotherapy, and radiation were employed as covariates. The 5-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses (P<0.001) (hazard ratio (HR): 3.93, 95% confidence interval CI (2.17–7.1), P=0.001, (HR) 5.18, 95% CI (2.6995–9.945), P<0.001), respectively. Our 5-gene signature was also related to EGFR mutations (P=0.0111), and EGFR mutations were mainly enriched in low-risk group, indicating that EGFR mutations affect the survival rate of patients. Conclusion. The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.

scholarly journals Prognostic Value of Immune-Related genes in the Tumor Microenvironment of Glioma - Analyzed by Integrated Bioinformatics

2021 ◽  
Author(s):  
Yali Zhong ◽  
Xiaobin Luo ◽  
Fubing Yang ◽  
Xinling Song
Keyword(s):  
T Cells ◽  
High Risk ◽  
Risk Prediction ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Immune Related Genes

Abstract Object: Immune related genes play an important role in the process of tumor genesis and development. Therefore, we aim to find the Immune genes which are related to the prognosis of glioma patients, and to explore the infiltration of Immune cells in glioma microenvironment. Methods We downloaded the data of the glioma samples from the CGGA database, and performed batch correction to screen the primary glioma samples for subsequent analysis. Then the ESTIMATE algorithm was used to deal with the Stromal scores and Immune scores of the primary glioma samples, and the difference was analyzed. Then the common Immune related genes (IRGs) were obtained by intersecting with the Immune genes in the ImmPort database. Moreover, we used common IRGs to construct protein-protein interaction (PPI) networks, from which we screened the top 30 genes with high connectivity, and Lasso regression was used to screen the IRGs. Lastly, we obtained the combined genes, which were overlapped both in the top 30 high-connection genes and Lasso regression genes. The final genes were used to construct COX risk prediction models. The accuracy of the model were verified by the TCGA glioma data, and the model genes were analyzed for Immune-related pathways, as well as the Hallmark and KEGG enrichment. Additionally, we used CIBERSOFT algorithm to estimate the Immune cell content of the samples, and analyzed the differences, correlations and survival of the Immune cells in high and low risk groups. Results Firstly, a total of 117 IRGs were obtained from the gene sets, which were overlapped in the data of Stromal score, Immune score and ImmPort database. Secondly, the top 30 genes were selected after the PPI network, and another 26 genes were screened out after the Lasso regression algorithm. And then, six coexist IRGs were obtained from the intersecting sets. Furthermore, the COX risk prediction model was constructed and tested, showing that the overall survival rate of the high-risk group was about 50% of that of the low-risk group. We observed that the high-risk group were enriched in Immune response and Immune process. Most importantly, in KEGG pathways, the high-risk groups were mainly enriched in p53 signaling pathway, JAK-STAT signaling pathway, pathways in cancer and cell cycle. By estimating the Immune cell contents, we also found that the Immune cell Plasma cells, T cells CD8, T cells CD4 naïve, T cells regulatory (Tregs), Macrophages M0 and Neutrophils were higher in high-risk groups, when compared to the low-risk group, with significant difference. Finally, the correlation analysis showed that the degree of Immune infiltration in high-risk groups was related to T cells regulatory (Tregs), Macrophages M0 and Neutrophils. Conclusion A COX risk prediction model of 6 genes was successfully constructed, which was enriched in Immune-related pathways. Meanwhile, survival analysis and TCGA data validation revealed significant differences in the model genes in the overall survival of the glioma patients, and the degree of Immune infiltration in the model was associated with T cells regulatory (Tregs), Macrophages M0 and Neutrophils.

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