Dataset Information

Immune Landscape of Invasive Ductal Carcinoma Tumor Microenvironment Identifies a Prognostic and Immunotherapeutically Relevant Gene Signature.

ABSTRACT: Background: Invasive ductal carcinoma (IDC) is a clinically and molecularly distinct disease. Tumor microenvironment (TME) immune phenotypes play crucial roles in predicting clinical outcomes and therapeutic efficacy. Method: In this study, we depict the immune landscape of IDC by using transcriptome profiling and clinical characteristics retrieved from The Cancer Genome Atlas (TCGA) data portal. Immune cell infiltration was evaluated via single-sample gene set enrichment (ssGSEA) analysis and systematically correlated with genomic characteristics and clinicopathological features of IDC patients. Furthermore, an immune signature was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. A random forest algorithm was applied to identify the most important somatic gene mutations associated with the constructed immune signature. A nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed by multivariate Cox regression. Results: The IDC were clustered into low immune infiltration, intermediate immune infiltration, and high immune infiltration by the immune landscape. The high infiltration group had a favorable survival probability compared with that of the low infiltration group. The low-risk score subtype identified by the immune signature was characterized by T cell-mediated immune activation. Additionally, activation of the interferon-? response, interferon-? response, and TNF-? signaling via the NF?B pathway was observed in the low-risk score subtype, which indicated T cell activation and may be responsible for significantly favorable outcomes in IDC patients. A random forest algorithm identified the most important somatic gene mutations associated with the constructed immune signature. Furthermore, a nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed, revealing that the immune signature was an independent prognostic biomarker. Finally, the relationship of VEGFA, PD1, PDL-1, and CTLA-4 expression with the immune infiltration landscape and the immune signature was analyzed to interpret the responses of IDC patients to immunotherapy. Conclusion: Taken together, we performed a comprehensive evaluation of the immune landscape of IDC and constructed an immune signature related to the immune landscape. This analysis of TME immune infiltration landscape has shed light on how IDC respond to immunotherapy and may guide the development of novel drug combination strategies.

SUBMITTER: Bao X

PROVIDER: S-EPMC6759595 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Immune Landscape of Invasive Ductal Carcinoma Tumor Microenvironment Identifies a Prognostic and Immunotherapeutically Relevant Gene Signature.

Bao Xuanwen X Shi Run R Zhang Kai K Xin Shan S Li Xin X Zhao Yanbo Y Wang Yanfang Y

Frontiers in oncology 20190918

<b>Background:</b> Invasive ductal carcinoma (IDC) is a clinically and molecularly distinct disease. Tumor microenvironment (TME) immune phenotypes play crucial roles in predicting clinical outcomes and therapeutic efficacy. <b>Method:</b> In this study, we depict the immune landscape of IDC by using transcriptome profiling and clinical characteristics retrieved from The Cancer Genome Atlas (TCGA) data portal. Immune cell infiltration was evaluated via single-sample gene set enrichment (ssGSEA) ...[more]

PMID: 31620363

Similar Datasets

Project description:The tumorigenesis of skin cutaneous melanoma (SKCM) remains unclear. The tumor microenvironment (TME) is well known to play a vital role in the onset and progression of SKCM. However, the dynamic mechanisms of immune regulation are insufficient. We conducted a comprehensive analysis of immune cell infiltration in the TME. Based on the differentially expressed genes (DEGs) in clusters grouped by immune infiltration status, a set of hub genes related to the clinical prognosis of SKCM and tumor immune infiltration was explored. Methods: We analyzed immune cell infiltration in two independent cohorts and assessed the relationship between the internal pattern of immune cell infiltration and SKCM characteristics, including clinicopathological features, potential biological pathways, and gene mutations. Genes related to the infiltration pattern of TME immune cells were determined. Furthermore, the unsupervised clustering method (k-means) was used to divide samples into three different categories according to TME, which were defined as TME cluster-A, -B, and -C. DEGs among three groups of samples were analyzed as signature genes. We further distinguished common DEGs between three groups of samples according to whether differences were significant and divided DEGs into the Signature gene-A group with significant differences and the Signature gene-B group with insignificant differences. The Signature gene-A gene set mainly had exon skipping in SKCM, while the Signature gene-B gene set had no obvious alternative splicing form. Subsequently, we analyzed genetic variations of the two signatures and constructed a competing endogenous RNA (ceRNA) regulatory network. LASSO Cox regression was used to determine the immune infiltration signature and risk score of SKCM. Finally, we obtained 13 hub genes and calculated the risk score based on the coefficient of each gene to explore the impact of the high- and low-risk scores on biologically related functions and prognosis of SKCM patients further. The correlation between the risk score and clinicopathological characteristics of SKCM patients indicated that a low-risk score was associated with TME cluster-A classification (p < 0.001) and metastatic SKCM (p < 0.001). Thirteen hub genes also showed different prognostic effects in pan-cancer. The results of univariate and multivariate Cox analyses revealed that risk score could be used as an independent risk factor for predicting the prognosis of SKCM patients. The nomogram that integrated clinicopathological characteristics and immune characteristics to predict survival probability was based on multivariate Cox regression. Finally, 13 hub genes that showed different prognostic effects in pan-cancers were obtained. According to immunohistochemistry staining results, Ube2L6, SRPX2, and IFIT2 were expressed at higher levels, while CLEC4E, END3, and KIR2DL4 were expressed at lower levels in 25 melanoma specimens. Conclusion: We performed a comprehensive assessment of the immune-associated TME. To elucidate the potential development of immune-genomic features in SKCM, we constructed an unprecedented set of immune characteristic genes (EDN3, CLEC4E, SRPX2, KIR2DL4, UBE2L6, and IFIT2) related to the immune landscape of TME. These genes are related to different prognoses and drug responses of SKCM. The immune gene signature constructed can be used as a robust prognostic biomarker of SKCM and a predictor of an immunotherapy effect.

Project description:BackgroundGastric cancer (GC) still represents the third leading cause of cancer-related death worldwide. Peritoneal relapse (PR) is the most frequent metastasis occurring among patients with advanced gastric cancer. Increasingly more evidence have clarified the tumor immune microenvironment (TIME) may predict survival and have clinical significance in GC. However, tumor-transcriptomics based immune signatures derived from immune profiling have not been established for predicting the peritoneal recurrence of the advanced GC.MethodsIn this study, we depict the immune landscape of GC by using transcriptome profiling and clinical characteristics retrieved from GSE62254 of Gene Expression Omnibus (GEO). Immune cell infiltration score was evaluated via single-sample gene set enrichment (ssGSEA) analysis algorithm. The least absolute shrinkage and selection operator (LASSO) Cox regression algorithm was used to select the valuable immune cells and construct the final model for the prediction of PR. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to check the accuracy of PRIs. Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to explore the molecular pathways associated with PRIs.ResultsA peritoneal recurrence related immune score (PRIs) with 10 immune cells was constructed. Compared to the low-PRIs group, the high-PRIs group had a greater risk. The upregulation of the focal adhesion signaling was observed in the high-PRIs subtype by GSEA and KEGG. Multivariate analysis found that both in the internal training cohort and the internal validation cohort, PRIs was a stable and independent predictor for PR. A nomogram that integrated clinicopathological features and PRIs to predict peritoneal relapse was constructed. Subgroup analysis indicated that the PRIs could obviously distinguish peritoneal recurrence in different molecular subtypes, pathological stages and Lauren subtypes, in which PRIs of Epithelial-Mesenchymal Transitions (EMT) subtype, III-IV stage and diffuse subtype are higher respectively.ConclusionOverall, we performed a comprehensive evaluation of the immune landscape of GC and constructed a predictive PR model based on the immune cell infiltration. The PRIs represents novel promising feature of predicting peritoneal recurrence of GC and sheds light on the improvement of the personalized management of GC patients after surgery.

Project description:BackgroundVarious studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression.MethodsWe integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients.ResultsTo describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration.ConclusionsTaken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.

Project description:BackgroundAdvanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.ResultsThree immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.ConclusionsWe identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.

Dataset Information

Immune Landscape of Invasive Ductal Carcinoma Tumor Microenvironment Identifies a Prognostic and Immunotherapeutically Relevant Gene Signature.

Publications

Immune Landscape of Invasive Ductal Carcinoma Tumor Microenvironment Identifies a Prognostic and Immunotherapeutically Relevant Gene Signature.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets