scholarly journals Identification of an Immunologic Signature of Lung Adenocarcinomas Based on Genome-Wide Immune Expression Profiles

2021 ◽  
Vol 7 ◽  
Author(s):  
Bo Ling ◽  
Guangbin Ye ◽  
Qiuhua Zhao ◽  
Yan Jiang ◽  
Lingling Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
External Validation ◽  
Immune Gene ◽  
Clinical Prognosis ◽  
Resistance Pathway ◽  
Lung Adenocarcinomas

Background: Lung cancer is one of the most common types of cancer, and it has a poor prognosis. It is urgent to identify prognostic biomarkers to guide therapy.Methods: The immune gene expression profiles for patients with lung adenocarcinomas (LUADs) were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The relationships between the expression of 45 immune checkpoint genes (ICGs) and prognosis were analyzed. Additionally, the correlations between the expression of 45 biomarkers and immunotherapy biomarkers, including tumor mutation burden (TMB), mismatch repair defects, neoantigens, and others, were identified. Ultimately, prognostic ICGs were combined to determine immune subgroups, and the prognostic differences between these subgroups were identified in LUAD.Results: A total of 11 and nine ICGs closely related to prognosis were obtained from the GEO and TCGA databases, respectively. CD200R1 expression had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68, and GZMB, indicating that it may cause the disordered expression of adaptive immune resistance pathway genes. Multivariable Cox regression was used to construct a signature composed of four prognostic ICGs (IDO1, CD274, CTLA4, and CD200R1): Risk Score = −0.002*IDO1+0.031*CD274−0.069*CTLA4−0.517*CD200R1. The median Risk Score was used to classify the samples for the high- and low-risk groups. We observed significant differences between groups in the training, testing, and external validation cohorts.Conclusion: Our research provides a method of integrating ICG expression profiles and clinical prognosis information to predict lung cancer prognosis, which will provide a unique reference for gene immunotherapy for LUAD.

scholarly journals Identification of immune prognostic signature of lung carcinoma based on Genome-wide immune expression profile

2020 ◽  
Author(s):  
Bo Ling ◽  
Guangbin Ye ◽  
Qiuhua Zhao ◽  
Yan Jiang ◽  
Lingling Liang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cancer Patients ◽  
Expression Profile ◽  
Immune Checkpoint ◽  
Immune Gene ◽  
Clinical Prognosis ◽  
Checkpoint Gene ◽  
Lung Cancer Patients ◽  
Resistance Pathway

Abstract Background : Lung cancer is one of the most common types of cancer with low early diagnosis rate and poor prognosis. The integration of immune checkpoint gene expression data and patient prognosis information can help identify the immune subtypes of lung cancer and provide reference for individualized gene immunotherapy in patients with lung cancer. Methods : The data of immune gene expression for lung cancer patients were obtained from TCGA and GEO databases. The relationship between the expressions of 45 immune checkpoint genes (ICGs) and prognosis were analysed. In the other hand, the correlation between the expressions of 45 biomarkers , tumor mutation load (TMB), MMRs, neoantigens and other immunotherapy biomarkers were been identified. Ultimately, prognosis-related ICGs were combined with IDO1, CD274, and CTLA4 to divide lung cancer immune subgroups and the prognostic differences between lung cancer immune subgroups were identified. Results: Based on TCGA database and GEO database, 9 and 11 ICGs were obtained respectively, which were closely related to prognosis. There was a certain synergistic relationship between them. The expression of CD200R1 had a significant negative correlation with TMB and neoantigens. CD200R1 showed a significant positive correlation with CD8A, CD68 and GZMB genes, indicating that it may cause the expression disorder of adaptive immune resistance pathway genes. Based on CD200R1 and combination with IDO1, CD274 and CTLA4, the group with high expression of CD200R1 and low expression of IDO1, CD274 and CTLA4 had the best prognosis among the immune subtypes. Conclusion : Our research provides a method of integrating immune checkpoint gene expression profile and clinical prognosis information to identify immune subtypes of lung cancer, which can provide a unique reference for gene immunotherapy of lung cancer patients.

scholarly journals Comprehensive Analysis of Cell Cycle-Related Genes in Patients With Prostate Cancer

2022 ◽  
Vol 11 ◽  
Author(s):  
Zehua Liu ◽  
Rongfang Pan ◽  
Wenxian Li ◽  
Yanjiang Li
Keyword(s):  
Prostate Cancer ◽  
Cell Cycle ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Cancer Center ◽  
Clinical Prognosis ◽  
Normal Tissues

This study aimed to identify critical cell cycle-related genes (CCRGs) in prostate cancer (PRAD) and to evaluate the clinical prognostic value of the gene panel selected. Gene set variation analysis (GSVA) of dysregulated genes between PRAD and normal tissues demonstrated that the cell cycle-related pathways played vital roles in PRAD. Patients were classified into four clusters, which were associated with recurrence-free survival (RFS). Moreover, 200 prognostic-related genes were selected using the Kaplan–Meier (KM) survival analysis and univariable Cox regression. The prognostic CCRG risk score was constructed using random forest survival and multivariate regression Cox methods, and their efficiency was validated in Memorial Sloan Kettering Cancer Center (MSKCC) and GSE70770. We identified nine survival-related genes: CCNL2, CDCA5, KAT2A, CHTF18, SPC24, EME2, CDK5RAP3, CDC20, and PTTG1. Based on the median risk score, the patients were divided into two groups. Then the functional enrichment analyses, mutational profiles, immune components, estimated half-maximal inhibitory concentration (IC50), and candidate drugs were screened of these two groups. In addition, the characteristics of nine hub CCRGs were explored in Oncomine, cBioPortal, and the Human Protein Atlas (HPA) datasets. Finally, the expression profiles of these hub CCRGs were validated in RWPE-1 and three PRAD cell lines (PC-3, C4-2, and DU-145). In conclusion, our study systematically explored the role of CCRGs in PRAD and constructed a risk model that can predict the clinical prognosis and immunotherapeutic benefits.

scholarly journals Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Yun Zhong ◽  
Zhe Liu ◽  
Dangchi Li ◽  
Qinyuan Liao ◽  
Jingao Li
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Survival Rates ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Test Set ◽  
Cox Regression Analysis

Background. An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods. Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results. In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion. In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

scholarly journals Systematic Characterization of Novel Immune Gene Signatures Predicts Prognostic Factors in Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Dafeng Xu ◽  
Yu Wang ◽  
Jincai Wu ◽  
Yuliang Zhang ◽  
Zhehao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Signature ◽  
Clinical Samples ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Related Genes ◽  
Cancer Tissues

Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes.Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples.Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages.Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.

scholarly journals Prognostic Value of Autophagy-related Genes Correlated With Metastasis in Uveal Melanoma Patients

2021 ◽  
Author(s):  
Yan Sun ◽  
Xiaoran Wang ◽  
Baoxin Chen ◽  
Lang Xiong ◽  
Jingqi Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Uveal Melanoma ◽  
Risk Score ◽  
Protein Interactions ◽  
Cox Regression ◽  
Early Stage ◽  
Expression Profiles ◽  
High Expression ◽  
Melanoma Metastasis

Abstract Half of the patients with primary uveal melanoma will develop progressive metastasis, leading to high mortality rate. Autophagy has been demonstrated to engage in metastasis in multiple tumors. Detection, diagnosis and treatment at the early-stage of uveal melanoma may help prevent potential tumor progression and optimize the prognosis. The purpose of our study was to discover autophagy-related genes (ARGs) correlated with uveal melanoma metastasis and determine their prognostic values. We analyzed the gene expression profiles and the clinical data from the Gene Expression Omnibus (GEO) database in uveal melanoma. A total of 14 and 16 differentially expressed ARGs were identified to be related to uveal melanoma metastasis from GSE22138 and GSE27831 sets. The two datasets shared three common genes including RAF1, CDKN1A and WIPI1 that occupied the core positions in the Protein-Protein Interactions (PPI) Network of ARGs. Following that, TCGA was introduced for survival analysis of the three genes. The survival analysis showed that high expression of RAF1 was related to favorable prognosis of uveal melanoma, whereas high expression of CDKN1A and WIPI1 suggested poor prognosis. Then a three-ARG based prognostic risk score model was constructed to predict survival outcomes. Univariate and multivariate Cox regression analyses indicated that the risk score can be considered as an independent prognostic factor for uveal melanoma, exhibiting good accuracy and sensitivity. In summary, we established an autophagy-related prognostic model based on uveal melanoma metastasis, which may contribute to the detection of early metastasis and prediction of prognosis, thereby prolonging survival through early personalized intervention.

scholarly journals Discerning divergent tuberculosis endotypes: A meta-analysis and systematic review of individual patient data

2020 ◽  
Author(s):  
Andrew R DiNardo ◽  
Kimal Rajapakshe ◽  
Tanmay Gandhi ◽  
Sandra Grimm ◽  
Tomoki Nishiguchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Responses ◽  
Clustering Analysis ◽  
Expression Profiles ◽  
Meta Analysis ◽  
External Validation ◽  
Critical Factor ◽  
Type I ◽  
Immune Gene ◽  
Ifn Γ

Rationale: Host response is a critical factor determining susceptibility to tuberculosis (TB). A delicate balance should be maintained between intracellular immunity against Mycobacterium tuberculosis (Mtb) and minimizing detrimental immunopathology. Studies have identified incongruous immune responses that can lead to a similar TB disease phenotype. Instead of envisioning that susceptibility to TB follows a singular path, we propose the hypothesis that varied host endotypes exist within the TB clinical phenotype. Methods and Results: Unbiased clustering analysis from 12 publicly available gene expression datasets consisting of data from 717 TB patients and 527 controls, identified 4 TB patient endotypes with distinct immune responses. The two largest endotypes exhibit divergent metabolic, epigenetic and immune pathways. TB patient endotype A, comprising 333 TB patients (46.4%), is characterized by increased expression of genes important for i) glycolysis, ii) IL-2-STAT5, IL-6-STAT3, Type I and II Interferon IFN-γ and TNF signaling and iii) epigenetic-modifying genes. In contrast, TB patient endotype B, comprising 313 TB patients (43.6%), is characterized by i) upregulated NFAT and hormone metabolism, and ii) decreased glycolysis, IFN-γ and TNF signaling. In silico evaluation suggests therapies beneficial for endotype A could be detrimental to endotype B, and vice versa. Multiplex ELISA completed from an external validation cohort confirmed a TB patient sub-group with decreased immune upregulation. Conclusions: Host immunity to TB is heterogenous. Unbiased clustering analysis identified distinct TB endotypes with divergent metabolic, epigenetic and immune gene expression profiles that may enable stratified or personalized treatment management in the future.

scholarly journals A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xin Xu ◽  
Yida Lu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

scholarly journals Identification and validation of a novel ferroptosis-related gene model for predicting the prognosis of gastric cancer patients

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254368
Author(s):  
Gang Liu ◽  
Jian-ying Ma ◽  
Gang Hu ◽  
Huan Jin
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Expression Profiles ◽  
Critical Role ◽  
Clinicopathological Features ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Background Ferroptosis is a novel form of regulated cell death that plays a critical role in tumorigenesis. The purpose of this study was to establish a ferroptosis-associated gene (FRG) signature and assess its clinical outcome in gastric cancer (GC). Methods Differentially expressed FRGs were identified using gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were performed to construct a prognostic signature. The model was validated using an independent GEO dataset, and a genomic-clinicopathologic nomogram integrating risk scores and clinicopathological features was established. Results An 8-FRG signature was constructed to calculate the risk score and classify GC patients into two risk groups (high- and low-risk) according to the median value of the risk score. The signature showed a robust predictive capacity in the stratification analysis. A high-risk score was associated with advanced clinicopathological features and an unfavorable prognosis. The predictive accuracy of the signature was confirmed using an independent GSE84437 dataset. Patients in the two groups showed different enrichment of immune cells and immune-related pathways. Finally, we established a genomic-clinicopathologic nomogram (based on risk score, age, and tumor stage) to predict the overall survival (OS) of GC patients. Conclusions The novel FRG signature may be a reliable tool for assisting clinicians in predicting the OS of GC patients and may facilitate personalized treatment.

scholarly journals Construction of a novel immune gene-based model for prognosis prediction of colorectal cancer

2021 ◽  
Author(s):  
Bin Xie ◽  
jie lin
Keyword(s):  
Gene Expression ◽  
Cox Regression ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Gene Set Enrichment Analysis ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Risk Of Death ◽  
Immune Related Genes

Abstract Background Colon adenocarcinoma (COAD) is the third leading cause of cancer-related death. Although surgical treatment and chemotherapy of COAD have made significant progress, its immunotherapy also has great potential, nowadays. Methods Gene expression profiles and clinical data of COAD patients were obtained from The Cancer Genome Atlas_Colon Adenocarcinoma (TCGA_COAD) and Gene Expression Omnibus (GEO) databases, which were further detected for immune-related genes. Immune-related genes were downloaded from Immunology Database and Analysis Portal (ImmPort). LASSO Cox regression analysis was utilized to analyze the immune-related prognostic signature. The prognostic value of the signature was validated by ROC curve. To further detected the potential pathway about immune-related genes in COAD patients, Gene Set Enrichment Analysis (GESA) was used to identify the most significant pathways. Results Finally, a total of 436 immune-related mRNA were identified. Eleven prognosis-related genes were selected to establish an immune-related prognostic signature, which divided patients into high and low risk groups. Several biological processes, such as immune response was enriched. Moreover, our prognosis model has better performance in predicting the 1-, 3-, 5- and 8-years overall survival (OS) for patients from the TCGA and GEO cohort. Also, the complicated signature obtained by combining immune-related signatures with clinical factors provides a more accurate OS predicting compared with individual molecular signatures. Conclusion We have established a prognostic signature consisting of 11 immune-related genes, which may be potential biomarkers for identifying COAD with a high risk of death. Then, the possibility including immunotherapy in personalized COAD treatment was evaluated.

scholarly journals Identification of a Seven-lncRNA Immune Risk Signature and Construction of a Predictive Nomogram for Lung Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Donghui Jin ◽  
Yuxuan Song ◽  
Yuan Chen ◽  
Peng Zhang
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Survival Analysis ◽  
High Risk ◽  
Lung Adenocarcinoma ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Clinicopathological Features

Background. The incidence of lung cancer is the highest of all cancers, and it has the highest death rate. Lung adenocarcinoma (LUAD) is a major type of lung cancer. This study is aimed at identifying the prognostic value of immune-related long noncoding RNAs (lncRNAs) in LUAD. Materials and Methods. Gene expression profiles and the corresponding clinicopathological features of LUAD patients were obtained from The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was performed on the prognostic immune-related lncRNAs to calculate the risk scores, and a risk signature was constructed. Survival analysis was performed to assess the prognostic value of the risk signature. A nomogram was also constructed based on the clinicopathological features and risk signature. Results. A total of 437 LUAD patients with gene expression data and clinicopathological features were obtained in this study, which was considered the combination set. They were randomly and equally divided into a training set and a validation set. Seven immune-related lncRNAs (AC092794.1, AL034397.3, AC069023.1, AP000695.1, AC091057.1, HLA-DQB1-AS1, and HSPC324) were identified and used to construct a risk signature. The patients were divided into the low- and high-risk groups based on the median risk score of -0.04074. Survival analysis suggested that patients in the low-risk group had a longer overall survival (OS) than those in the high-risk group (p=1.478e−02). A nomogram was built that could predict the 1-, 3-, and 5-year survival rates of LUAD patients (C-index of the nomogram was 0.755, and the AUCs for the 1-, 3-, and 5-year survivals were 0.826, 0.719, and 0.724, respectively). The validation and combination sets confirmed these results. Conclusion. Our study identified seven novel immune-related lncRNAs and generated a risk signature, as well as a nomogram, that could predict the prognosis of LUAD patients.

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