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Immune signature profiling identified prognostic factors for gastric cancer.

ABSTRACT: Objective:Tumor microenvironment, especially the host immune system, plays a pivotal role in tumor initiation and progression. Profiling of immune signature within tumor might uncover biomarkers for targeted therapies and clinical outcomes. However, systematic analysis of immune-related genes in gastric cancer (GC) has not been reported. Methods:Expressions of a total of 718 immune-related genes were generated in 372 stomach adenocarcinoma (STAD) patients from The Cancer Genome Atlas (TCGA) database using RNA-sequencing data. Integrated bioinformatics analyses were performed to identify prognostic factors as well. Results:Survival analyses revealed 73 genes, which were significantly associated with patient's overall survival (OS). Taken together with clinicopathological parameters, we established a predictive model, containing 10 immune-related genes, which were NRP1, C6, CXCR4, LBP, PNMA1, TLR5, ITGA6, MICB, PBK and TNFRSF18, with powerful efficiency in distinguishing satisfactory or poor survival of STAD patients. Moreover, the top 3 ranked prognostic genes, NRP1, TGF?2 and MFGE8, were also significantly associated with patient's OS by an independent validation achieved from Kaplan-Meier plotter database. Conclusions:We profiled prognostic immune signature and established prognostic predictive model for GC, which could reflect immune disorders within tumor microenvironment, and also may provide novel predictive and therapeutic targets for GC patients in the near future.

SUBMITTER: Yang W

PROVIDER: S-EPMC6613504 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Immune signature profiling identified prognostic factors for gastric cancer.

Yang Wenhui W Lai Zhiyong Z Li Yuan Y Mu Jianbing J Yang Mudan M Xie Jun J Xu Jun J

Chinese journal of cancer research = Chung-kuo yen cheng yen chiu 20190601 3

<h4>Objective</h4>Tumor microenvironment, especially the host immune system, plays a pivotal role in tumor initiation and progression. Profiling of immune signature within tumor might uncover biomarkers for targeted therapies and clinical outcomes. However, systematic analysis of immune-related genes in gastric cancer (GC) has not been reported.<h4>Methods</h4>Expressions of a total of 718 immune-related genes were generated in 372 stomach adenocarcinoma (STAD) patients from The Cancer Genome At ...[more]

PMID: 31354215

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Project description:BackgroundAn increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated.MethodsUnivariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300; GSE15459, n = 191; and GSE26901, n = 109). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort (n = 600) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432; GSE84437, n = 431; and TCGA, n = 336). Immune cell infiltration (ICI) was quantified by the CIBERSORT method.ResultsA risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort (AUC > 0.7). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC (p < 0.001). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group (p < 0.001), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor (p = 0.011). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score (p = 0.00085). The patients' risk score increased with the progression of the clinicopathological stage.ConclusionIn this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

Project description:BackgroundAccumulating evidence demonstrated that tumor microenvironmental cells played important roles in predicting clinical outcomes and therapeutic efficacy. We aimed to develop a reliable immune-related gene signature for predicting the prognosis of ovarian cancer (OC).MethodsSingle sample gene-set enrichment analysis (ssGSEA) of immune gene-sets was used to quantify the relative abundance of immune cell infiltration and develop high- and low-abundance immune subtypes of 308 OC samples. The presence of infiltrating stromal/immune cells in OC tissues was calculated as an estimate score. We estimated the correlation coefficients among the immune subtype, clinicopathological feature, immune score, distribution of immune cells, and tumor mutation burden (TMB). The differentially expressed immune-related genes between high- and low-abundance immune subtypes were further used to construct a gene signature of a prognostic model in OC with lasso regression analysis.ResultsThe ssGSEA analysis divided OC samples into high- and low-abundance immune subtypes based on the abundance of immune cell infiltration, which was significantly related to the estimate score and clinical characteristics. The distribution of immune cells was also significantly different between high- and low-abundance immune subtypes. The correlation analysis showed the close relationship between TMB and the estimate score. The differentially expressed immune-related genes between high- and low-abundance immune subtypes were enriched in multiple immune-related pathways. Some immune checkpoints (PDL1, PD1, and CTLA-4) were overexpressed in the high-abundance immune subtype. Furthermore, the five-immune-related-gene-signature prognostic model (CCL18, CXCL13, HLA-DOB, HLA-DPB2, and TNFRSF17)-based high-risk and low-risk groups were significantly related to OC overall survival.ConclusionImmune-related genes were the promising predictors of prognosis and survival, and the comprehensive landscape of tumor microenvironmental cells of OC has potential for therapeutic schedule monitoring.

Project description:BackgroundTranscription factors (TFs) are involved in important molecular biological processes of tumor cells and play an essential role in the occurrence and development of gastric cancer (GC).MethodsCombined The Cancer Genome Atlas Program and Genotype-Tissue Expression database to extract the expression of TFs in GC, analyzed the differences, and weighted gene co-expression network analysis to extract TFs related to GC. The cohort including the training and validation cohort. Univariate Cox, least absolute contraction and selection operator (LASSO) regression, and multivariate Cox analysis was used for screening hub TFs to construct the prognostic signature in the training cohort. The Kaplan-Meier (K-M) and the receiver operating characteristic curve (ROC) was drawn to evaluate the predictive ability of the prognostic signature. A nomogram combining clinical information and prognostic signatures of TFs was constructed and its prediction accuracy was evaluated through various methods. The target genes of the hub TFs was predicted and enrichment analysis was performed to understand its molecular biological mechanism. Clinical samples and public data of GC was collected to verify its expression and prognosis. 5-Ethynyl-2'-deoxyuridine and Acridine Orange/Ethidium Bromide staining, flow cytometry and Western-Blot detection were used to analyze the effects of hub-TF ELK3 on the proliferation and apoptosis of gastric cancer in vitro.ResultsA total of 511 misaligned TFs were obtained and 200 GC-related TFs were exposed from them. After systematic analysis, a prognostic signature composed of 4 TFs (ZNF300, ELK3, SP6, MEF2B) were constructed. The KM and ROC curves demonstrated the good predictive ability in training, verification, and complete cohort. The areas under the ROC curve are respectively 0.737, 0.705, 0.700. The calibration chart verified that the predictive ability of the nomogram constructed by combining the prognostic signature of TFs and clinical information was accurate, with a C-index of 0.714. Enriching the target genes of hub TFs showed that it plays an vital role in tumor progression, and its expression and prognostic verification were consistent with the previous analysis. Among them, ELK3 was proved in vitro, and downregulation of its expression inhibited the proliferation of gastric cancer cells, induced proliferation, and exerted anti-tumor effects.ConclusionsThe 4-TFs prognostic signature accurately predicted the overall survival of GC, and ELK3 may be potential therapeutic targets for GC.

Project description:The prognosis of patients with gastric cancer (GC) is still unsatisfying. Numerous markers of gastric cancer stem cells (GCSCs) have been identified and were thought to be related to cancer aggressiveness. However, the roles of GCSC markers in GC patients' prognosis and immune infiltration remain unknown. Expression of GCSC markers was analyzed using Oncomine and Gene Expression Profiling Interactive Analysis (GEPIA). Their associations with clinicopathological parameters were analyzed using UALCAN and LinkedOmics. Alternations and protein expression of GCSC markers were analyzed by cBioPortal and the Human Protein Atlas databases, respectively. The prognostic significance of GCSC markers was evaluated using Kaplan-Meier plotter. Correlations between the expression of GCSC markers and immune infiltration along with biomarkers of tumor-infiltrating immune cells (TIICs) were assessed combined Tumor Immune Estimation Resource and GEPIA. GeneMANIA was used to discover the interactive genes of GCSC markers, and enrichment analysis was performed using Database for Annotation, Visualization, and Integrated Discovery server. We identified six GCSC markers significantly up-expressed in GC, compared with normal stomach tissues. Among them, the overexpression of ICAM1, THY1, and CXCR4 significantly indicated adverse, while EPCAM indicated beneficial clinicopathological features of GC patients. The up-regulation of CXCR4 showed unfavorable prognostic significance, whereas EPCAM and TFRC showed the opposite. The six GCSC markers were all correlated with the infiltration and activation of distinct TIICs. Especially, ICAM1, THY1, and CXCR4 showed strongly positive correlations with tumor-associated macrophages. Besides, chemokine, Toll-like receptor, NF-kappa B, and HIF-1 signaling pathways might be involved in the regulation of GCSC markers on cancer development. This study proposed that GCSC markers might be promising targets of GC treatment to weaken cancer stem-like properties and strengthen anticancer immunity.

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Immune signature profiling identified prognostic factors for gastric cancer.

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Immune signature profiling identified prognostic factors for gastric cancer.

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