Identification of an Immune Gene Signature Based on Tumor Microenvironment Characteristics in Colon Adenocarcinoma.
Ontology highlight
ABSTRACT:
SUBMITTER: Chen Y
PROVIDER: S-EPMC8020110 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundColon adenocarcinoma (COAD) is a common gastrointestinal tumor and often occurs in the left colon with a poor prognosis. The progression of COAD is closely related to the tumor microenvironment, especially the hypoxia. Currently, few studies have reported the correlation between hypoxia-related genes and the prognosis of COAD patients. Furthermore, we constructed a prognostic model using four hypoxia-related genes to predict the prognosis of COAD patients.MethodsThe mRNA expression profiles and corresponding clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The string online analysis tool was used to construct a protein-protein interaction network (PPI) of hypoxia-related genes. Kaplan-Meier curve was used to analyze the relationship of hypoxia risk score and the overall survival of COAD patients, and the receiver operating characteristic (ROC) curve was used to assess the reliability.ResultsWe screened out four hypoxia genes, including TKTL1 (transketolase like 1), SLC2A3 (solute carrier family 2 member 3), ALDOB (aldolase, fructose-bisphosphate B) and ENO3 (enolase 3), which were used to construct a hypoxia risk model to predict the overall survival of COAD patients. Besides, we also found that the hypoxia risk score was correlated with the immunosuppression of tumor microenvironment.ConclusionThe model we constructed with four survival-related hypoxia genes, including TKTL1, SLC2A3, ALDOB and ENO3, could be used to predict the overall survival of COAD patients with high stability.
| S-EPMC8687688 | biostudies-literature
Project description:BackgroundThe current study probed prognosis-related potential for m6A-related lncRNAs signatures within colon tumor immune microenvironment (TIM).MethodsAfter downloading transcriptomic datasets for colon cancer (CC) patients from The Cancer Genome Atlas (TCGA), they were divided, in a 1:1 ratio, within training or test datasets. m6A-related lncRNAs were then scrutinized across such dataset using Pearson correlation assessment before generating a m6A-related lncRNAs prognosis-related model using the training dataset. The latter was then validated with the test and the whole dataset. In addition, we compared the differences of TIM and the estimated IC50 of drug response between the high- and low-risk groups.ResultsOverall survival (OS) resulted as linked with 11 m6A-related lncRNAs, while within the developed prognosis-related model, areas-under-curves were as follows: within training dataset, values at 3-, 4-, and 5-years were 0.777, 0.819, and 0.805, accordingly, and for test one, they were 0.697, 0.682, and 0.706, respectively. Finally, the values for the whole dataset were 0.675 (3-year), 0.682 (4-years), and 0.679 (5-years), accordingly. Moreover, CC cases categorized within low-risk cohort demonstrated enhanced OS (p < .0001), lower metastasis (p = 2e-06) and lower T stage (p = .0067), more instability for microsatellite status (p = .012), and downregulation for PD-L1, PD-1, CTLA-4, LAG3, and HAVCR2 (p < .05). In addition, risk scorings were significantly linked to the degree of infiltrative intensity for CD8 and CD4 (memory resting) T-cells, T-regulatory (Tregs), and Mast cells triggering (p < .05). Patients with low infiltrative propensity for CD4 T-cells also had better OS (p = .016). Moreover, six representative drugs were found to be sensitive for treating CC patients.ConclusionA robust m6A-related prognostic model with great performances was developed before exploring the TIM characteristics and its potential therapeutic drugs, which might improve the prognosis and therapeutic efficacy.
| S-EPMC10242659 | biostudies-literature
Project description:The composition and relative abundances of immune cells in the tumor microenvironment are key factors affecting the progression of lung adenocarcinomas (LUADs) and the efficacy of immunotherapy. Using the cancer gene expression dataset from The Cancer Genome Atlas (TCGA) program, we scored stromal and immune cells for tumor purity prediction by CIBERSORT and ESTMATE. Differential expression analysis was employed to identify 374 genes between the high-score group and the low-score group, which were utilized to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Protein-protein interaction (PPI) and Cox regression analysis were performed on the differentially expressed genes (DEGs) to identify four key tumor microenvironment (TME) -related genes (CCR2, CCR4, P2RY12, and P2RY13). The expression levels of the four DEGs differed significantly among LUAD patients of different ages, genders, and TNM stages. We found that the infiltration of resting memory CD4+ T cells, memory B cells, and M0 macrophages into the TME was co-regulated by these four DEGs. These four genes were closely related to the prognosis of LUAD and affected the infiltration of immune cells into the TME, which had predictive prognostic value in LUAD.
| S-EPMC7880407 | biostudies-literature
Project description:Background Gastrointestinal malignant cancers affect many sites in the intestinal tract, including the colon. In this study, we purposed to improve prognostic predictions for colon cancer (CC) patients by establishing a novel biosignature of immune-related genes (IRGs) based on the tumor microenvironment (TME). Methods Using the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, we calculated the stromal and immune scores of every CC patient extracted from The Cancer Genome Atlas (TCGA). We then identified 4 immune-related messenger RNA (mRNA) biosignatures through a Cox and least absolute shrinkage and selection operator (LASSO) univariate analysis, and a Cox multivariate analysis. Relationships between tumor immune infiltration and the risk score were evaluated through the CIBERSORT algorithm and Tumor Immune Estimation Resource (TIMER) database. Results Our studies showed that individuals who had a high immune score (P=0.017) and low stromal score (P=0.041) had a favorable overall survival (OS) rate. By comparing high/low scores cohort, 220 differentially expressed genes (DEGs) were determined. Then an immune-related four-mRNA biosignature, including PDIA2, NAFTC1, VEGFC, and CD1B was identified. Kaplan-Meier, calibration, and receiver operating characteristic (ROC) curves verified the model’s performance. By using univariate and multivariate Cox analyses, we found each biosignature was an independent risk factor for assessing a CC patient’s survival. Three external GEO cohorts validated its good efficiency in estimating OS among individuals with CC. Moreover, the signature was also related to infiltration of several cells of the immune system in the tumor microenvironment. Conclusions The resultant model in our study included 4 IRGs associated with the TME. These IRGs can be utilized as an auxiliary variable to estimate and help improve the prognosis of individuals with CC.
| S-EPMC8576251 | biostudies-literature
Project description:BackgroundColon adenocarcinoma (COAD) is one of the most common gastrointestinal tumors worldwide, and immunotherapy is one of the most promising treatments for it. Identifying immune genes involved in the development and maintenance of cancer is key to the use of tumor immunotherapy. This study aimed to determine the prognostic value of immune genes in patients with COAD and to establish an immune-related gene signature. Differentially expressed genes, immune-related genes (DEIGs), and transcription factors (DETFs) were screened using the following databases: Cistrome, The Cancer Genome Atlas (TCGA), the Immunology Database and Analysis Portal, and InnateDB. We constructed a network showing the regulation of DEIGs by DETFs. Using weighted gene co-expression network analysis, we prepared 5 co-expressed gene modules; 6 hub genes (CD1A, CD1B, FGF9, GRP, SERPINE1, and F2RL2) obtained using univariate and multivariate regression analysis were used to construct a risk model. Patients from TCGA database were divided into high- and low-risk groups based on whether their risk score was greater or less than the mean; the public dataset GSE40967, which contains gene expression profiles of 566 colon cancer patients, was used for validation.ResultsSurvival analysis, somatic gene mutations, and tumor-infiltrating immune cells differed significantly between the high- and low-risk groups.ConclusionsThis immune-related gene signature could play an important role in guiding treatment, making prognoses, and potentially developing future clinical applications.
| S-EPMC9552517 | biostudies-literature
Project description:The implication of the Estimation of Stromal and Immune cells in Malignant tumor tissues using expression data (ESTIMATE) method to determine the tumor microenvironment (TME) and tumor immune score including tumor purity represents an efficient method to identify and assess biomarkers for immunotherapy response in precision medicine. In this study we utilized a machine learning algorithm to analyze the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) lung adenocarcinoma (LUAD) transcriptome data to evaluate the association between TME and tumor purity. Furthermore, we investigated whether fewer TME components or a few dominant genes can infer tumor purity. The results indicated that the 29 immune infiltrating components determined by the ssGSEA method could screen the 5 TME components [chemokine C-C-Motif receptor (CCR), T-helper-cells, Check-point, Treg, and tumor-infiltrating lymphocytes (TIL)] that significantly contributed the most to tumor purity prediction through regression tree and random forest regression methods. The findings revealed that higher activity of these five immune infiltrating components significantly lowered the tumor purity. Moreover, 5 TME components contributed significantly to the improvement of Mean Square Error (MES); therefore, we selected these five sets' genes and analyzed survival data to establish a prognostic model. We screened out 11 prognostic-related genes and constructed a risk model comprising 11 genes with good predictive value for patients' prognosis. Furthermore, we obtained four genes (GIMAP6, CD80, IL16, and CCR2) that had predictive advantages for tumor purity using random forest classification and random forest regression. The comprehensive score of genes for tumor purity prediction (CSGTPP) was obtained by least absolute shrinkage and selection operator (LASSO) regression indicated that four genes could be successfully used to classify high and low CSGTPP samples and that tumor purity was negatively correlated with CSGTPP. Survival analysis revealed that the higher the CSGTPP, the better the prognosis of patients. The association between a cluster of differentiation 274 (CD274) and CSGTPP revealed a higher expression of CD274 in the high CSGTPP group. Collectively, we speculated that CSGTPP could serve as a predictor of the response to immunotherapy and a promising indicator of immunotherapy effect.
| S-EPMC8916235 | biostudies-literature
Project description:Background: Lung cancer is the leading cause of cancer-related death globally. Hypoxia can suppress the activation of the tumor microenvironment (TME), which contributes to distant metastasis. However, the role of hypoxia-mediated TME in predicting the diagnosis and prognosis of lung adenocarcinoma (LUAD) patients remains unclear. Methods: Both RNA and clinical data from the LUAD cohort were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Both univariate and multivariate Cox regression analyses were used to further screen prognosis-related hypoxia gene clusters. Time-dependent receiver operation characteristic (ROC) curves were established to evaluate the predictive sensitivity and specificity of the hypoxia-related risk signature. The characterization of gene set enrichment analysis (GSEA) and TME immune cell infiltration were further explored to identify hypoxia-related immune infiltration. Results: Eight hypoxia-related genes (LDHA, DCN, PGK1, PFKP, FBP1, LOX, ENO3, and CXCR4) were identified and established to construct a hypoxia-related risk signature. The high-risk group showed a poor overall survival compared to that of the low-risk group in the TCGA and GSE68465 cohorts (p < 0.0001). The AUCs for 1-, 3-, and 5-year overall survival were 0.736 vs. 0.741, 0.656 vs. 0.737, and 0.628 vs. 0.649, respectively. The high-risk group was associated with immunosuppression in the TME. Conclusion: The hypoxia-related risk signature may represent an independent biomarker that can differentiate the characteristics of TME immune cell infiltration and predict the prognosis of LUAD.
| S-EPMC8634728 | biostudies-literature
Project description:The tumor microenvironment (TME) has been shown to be involved in angiogenesis, tumor metastasis, and immune response, thereby affecting the treatment and prognosis of patients. This study aims to identify genes that are dysregulated in the TME of patients with colon adenocarcinoma (COAD) and to evaluate their prognostic value based on RNA omics data. We obtained 512 COAD samples from the Cancer Genome Atlas (TCGA) database and 579 COAD patients from the independent dataset (GSE39582) in the Gene Expression Omnibus (GEO) database. The immune/stromal/ESTIMATE score of each patient based on their gene expression was calculated using the ESTIMATE algorithm. Kaplan-Meier survival analysis, Cox regression analysis, gene functional enrichment analysis, and protein-protein interaction (PPI) network analysis were performed. We found that immune and stromal scores were significantly correlated with COAD patients' overall survival (log rank p < 0.05). By comparing the high immune/stromal score group with the low score group, we identified 688 intersection differentially expressed genes (DEGs) from the TCGA dataset (663 upregulated and 25 downregulated). The functional enrichment analysis of intersection DEGs showed that they were mainly enriched in the immune process, cell migration, cell motility, Toll-like receptor signaling pathway, and PI3K-Akt signaling pathway. The hub genes were revealed by PPI network analysis. Through Kaplan-Meier and Cox analysis, four TME-related genes that were significantly related to the prognosis of COAD patients were verified in GSE39582. In addition, we uncovered the relationship between the four prognostic genes and immune cells in COAD. In conclusion, based on the RNA expression profiles of 1091 COAD patients, we screened four genes that can predict prognosis from the TME, which may serve as candidate prognostic biomarkers for COAD.
| S-EPMC8762242 | biostudies-literature
Project description:BackgroundThe extracellular matrix (ECM) plays a vital role in progression, expansion, and prognosis of malignancies. In this study, we aimed to explore a novel ECM-based prognostic model for patients with colon cancer (CC).MethodsECM-related genes were obtained from Molecular Signatures database. Differential expression analysis was performed using the CC dataset from The Cancer Genome Atlas (TCGA) database. Four ECM-related genes related to overall survival were identified using the Cox regression and LASSO analysis. Then an ECM-related signature was developed and verified in three independent CC cohorts (GSE33882, GSE39582 and GSE29621) from the Gene Expression Omnibus (GEO). A prognostic nomogram was developed incorporating the ECM-related gene signature with clinical risk factors. CIBERSORT was used to explore the immune cell infiltration level. Human Protein Atlas (HPA) database was utilized to validate the expression levels of identified prognostic ECM genes.ResultsFour ECM-related genes (CXCL13, CXCL14, SFRP5 and THBS4) were identified to develop an ECM-based gene signature and demarcated CC patients into the high- and low-risk groups. In training and validation datasets, patients in the low-risk group had better overall survival outcomes than those in the high-risk group (log-rank P<0.001). In addition, ECM-related signature was significantly associated with consensus molecular subtype 4 (CMS4) as well as other known clinical risk factors such as a higher Tumor, Nodal Involvement, Metastasis (TNM) stage. Moreover, the risk score derived from the ECM-based gene signature could be utilized as an independent prognostic factor for CC patients. A nomogram including the ECM-related gene signature, age and stage was developed to serve clinical practice. CIBERSORT analysis showed immune cell infiltration was different between high- and low-risk groups. The immunohistochemical results derived from HPA indicated differential expression of prognosis-related ECM genes in CC and normal tissues.ConclusionsIn the present study, a novel risk model based on ECM-signature could effectively reflect individual risk classification and provide potential therapeutic targets for CC patients. Moreover, the prognostic nomogram may help predict individualized survival.
| S-EPMC10007896 | biostudies-literature
Project description:Hypoxia, a typical hallmark of numerous tumors, indicates poor infiltration of antitumor lymphocytes, as well as facilitates the development, progression, and drug resistance of malignant cells. Here, the present research was performed to identify novel hypoxia-related molecular markers and their correlation to the tumor immune microenvironment (TIME) in colon cancer. The expression of hypoxia-related gene signature was extracted from The Cancer Genome Atlas (TCGA) COAD cohort. Based on this signature, a risk score model was constructed using the Lasso regression model. Its discrimination ability and stability were validated in another independent cohort (GSE17536) from Gene Expression Omnibus (GEO) database. Moreover, molecular biology experiments (quantitative real-time PCR and multiple immunohistochemistry) were performed to validate the results of bioinformatics analyses. Three hub genes, including PPFIA4, SERPINE1, and STC2, were chosen to build the risk score model. All of these genes were increasingly expressed in the hypoxia subgroup (HS). Compared with the normoxia subgroup (NS), HS had worse pathological features (T, N, M, and stage) and overall survival (OS), more expression of immune checkpoint molecules, poorer infiltration of some pro-inflammation immune cells (CD4+ T cells and CD8+ T cells), and enriched infiltration of M0/M2 macrophages. After the risk model was proven to be valuable and stable, a nomogram was built based on this model and some clinicopathological factors. Moreover, it had been identified that three hub genes were all increasingly expressed in hypoxic conditions by quantitative real-time PCR (qPCR). The results of multiple immunohistochemistry (mIHC) also showed that higher expression of hub genes was associated with poorer infiltration of pro-inflammation immune cells (CD8+ T cells and M1 macrophages) and richer infiltration of anti-inflammation immune cells (Treg cells and M2 macrophages). In conclusion, the present study uncovered the relations among hypoxia, TIME, and clinicopathological features of colon cancer. It might provide new insight and a potential therapeutic target for immunotherapy.
| S-EPMC9208084 | biostudies-literature