Stromal Score Groups Research Articles

Comprehensive bioinformatics analysis of the role of VWF in the tumor microenvironment of malignant mesothelioma.

To explore the influence and effect of tumor microenvironment on the development of malignant mesothelioma using machine learning methods. 87 open cases were downloaded from the Cancer Genome Atlas database including transcriptome data, clinical data, and mutation data. The immune, stromal, and estimate scores were calculated for each case by using the ESTIMATE algorithm, and then the cases were grouped according to high and low stromal scores to predict all-cause survival in malignant mesothelioma cases. Their mutation data were analyzed to reveal the differences in mutated genes between the 2 groups, and then the von Willebrand factor (VWF) and FCRL3 genes were identified according to the intersection of DEGs and high-frequency mutated genes. Lastly, the correlation between VWF and the immune checkpoint of 22 kinds of immune cells was analyzed by using the CIBERSORT package of R software. A significant difference was found in the survival time of patients between the high and low stromal score groups. High expression of the VWF gene was negatively correlated with the prognosis of malignant mesothelioma, and the expression of VWF was positively correlated with naive B cells and activated CD4 memory T cells and negatively correlated with NK cells. The results revealed that high expression of VWF may involve in the development of malignant mesothelioma, and the anti-CTLA4 immune checkpoint treatment may have certain efficacy.

Exploring the correlation analysis of immune microenvironment, mutation burden and prognosis of papillary thyroid carcinoma based on Estimate algorithm.

This study aimed to investigate the correlation between immune infiltration and tumor mutational burden (TMB) in papillary thyroid carcinoma. Transcriptome sequencing data sets, gene mutation data sets, and clinical data sets were downloaded from The Cancer Genome Atlas (TCGA) database. The immune and papillary carcinoma stromal scores were calculated using the "Estimate" package of R software. The relationship between the immune score, stromal score, TMB, and papillary thyroid carcinoma progression-free survival was analyzed. Pearson's test was used to analyze the correlation between the immune score, stromal score, and TMB. The stromal score and immune score of papillary thyroid carcinoma tissue were lower than those of normal thyroid tissue (P=0.008 and P<0.001, respectively). There was no significant difference in progression-free survival between the high stromal and low stromal score groups (P=0.075). The progression-free survival of the high immune score group was better than that of the low immune score group (P=0.029), and the progression-free survival of the low TMB group was better than that of the high TMB group (P<0.001). The high immune score, low TMB group had the best prognosis (P=0.003). Univariate Cox analysis showed that age, pathological stage, and TMB were risk factors for progression-free survival [hazard ratio (HR) >1, P<0.05], and that the immune score was a protective factor for progression-free survival (HR <1, P<0.05). Multivariate Cox analysis showed that age and TMB were independent risk factors for progression-free survival (HR >1, P<0.05), and that the immune score was an independent protective factor for progression-free survival (HR <1, P<0.05). Correlation analysis showed that the immune and stromal score were both negatively correlated with TMB (r=-0.26, P=0.031 and r=-0.41, P=0.028, respectively). The immune and stromal scores of papillary thyroid carcinoma were negatively correlated with TMB. Thyroid cancer gene mutations inhibit immune cell infiltration and alter the thyroid cancer microenvironment. The immune score was an independent protective factor for progression-free survival, while TMB was an independent risk factor, both of which can be used for clinical prognosis assessment. Combined immunological and genomic analysis of papillary thyroid carcinoma can reveal potential prognostic markers and therapeutic targets and provide clues for the tumor immune escape mechanism.

Assessment of the targeted effect of Sijunzi decoction on the colorectal cancer microenvironment via the ESTIMATE algorithm

Sijunzi decoction (SJZD) was used to treat patients with colorectal cancer (CRC) as an adjuvant method. The aim of the study was to investigate the therapeutic targets and pathways of SJZD towards the tumor microenvironment of CRC via network pharmacology and the ESTIMATE algorithm. The ESTIMATE algorithm was used to calculate immune and stromal scores to predict the level of infiltrating immune and stromal cells. The active targets of SJZD were searched in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and UniProt database. The core targets were obtained by matching the differentially expressed genes in CRC tissues and the targets of SJZD. Then, GO, KEGG and validation in TCGA were carried out. According to the ESTIMATE algorithm and survival analysis, the median survival time of the low stromal score group was significantly higher than that of the high stromal score group (P = 0.018), while the patients showed no significant difference of OS between different immune groups (P = 0.19). A total of 929 genes were upregulated and 115 genes were downregulated between the stromal score groups (|logFC| > 2, adjusted P < 0.05); 357 genes were upregulated and 472 genes were downregulated between the immune score groups. The component-target network included 139 active components and 52 related targets. The core targets were HSPB1, SPP1, IGFBP3, and TGFB1, which were significantly associated with poor prognosis in TCGA validation. GO terms included the response to hypoxia, the extracellular space, protein binding and the TNF signaling pathway. Immunoreaction was the main enriched pathway identified by KEGG analysis. The core genes (HSPB1, SPP1, IGFBP3 and TGFB1) affected CRC development and prognosis by regulating hypoxia, protein binding and epithelial-mesenchymal transition in the extracellular matrix.

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma.

Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM.

Prognostic Value of Tumor-microenvironment-associated Genes in Ovarian Cancer

Abstract Background: Ovarian cancer (OV) is the fifth leading cause of cancer death among women. Growing evidence supports a key role of the tumor microenvironment in the growth, progression, and metastasis of OV. However, the prognostic effects of gene expression signatures associated with the OV microenvironment have not been well established. This study was aimed at applying the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to identify tumor-microenvironment-associated genes that predict outcomes in patients with OV. Methods: The gene expression profiles of OV samples were downloaded from The Cancer Genome Atlas database. The immune and stromal scores of 469 OV samples on the basis of the ESTIMATE algorithm were available. To better understand the effects of gene expression signatures associated with the OV microenvironment on prognosis, we categorized these samples into groups with high and low ESTIMATE scores. A different OV cohort from the Gene Expression Omnibus (GEO) database and immunohistochemistry from The Human Protein Atlas database were used for external validation. Results: The molecular subtypes of patients with OV correlated with the stromal scores, and the mesenchymal subtype had the highest stromal scores. Patients with higher stromal scores had lower 5-year overall survival; 449 differentially expressed genes in the stromal score group were identified, 26 of which were significantly associated with poor prognosis in patients with OV (p &lt; 0.05). In another OV cohort from the Gene Expression Omnibus database, six genes were further validated to be significantly associated with poor prognosis. Immunohistochemistry data from The Human Protein Atlas database confirmed the overexpression of CX3CR1, GFPT2, NBL1, TFPI2, and ZFP36 in OV tissues compared with normal tissues. Conclusion: Our findings suggest that CX3CR1, GFPT2, NBL1, TFPI2, and ZFP36 may be promising biomarkers for OV prognosis, with clinical implications for therapeutic strategies.

N6-Methylandenosine-Related lncRNAs in Tumor Microenvironment Are Potential Prognostic Biomarkers in Colon Cancer.

BackgroundLncRNA dysregulation and the tumor microenvironment (TME) have been shown to play a vital role in the progression and prognosis of colon cancer (CC). We aim to reveal the potential molecular mechanism from the perspective of lncRNA in the TME and provide the candidate biomarkers for CC prognosis.MethodsESTIMATE analysis was used to divide the CC patients into high and low immune or stromal score groups. The expression array of lncRNA was re-annotated by Seqmap. Microenvironment-associated lncRNAs were filtered through differential analysis. The m6A-associated lncRNAs were screened by Pearson correlation analysis. Lasso Cox regression analyses were performed to construct the m6A- and tumor microenvironment-related lncRNA prognostic model (m6A-TME-LM). Survival analysis was used to assess the prognostic efficacy of candidate lncRNAs. Enrichment analyses annotated the candidate genes’ functions.ResultsWe obtained 25 common differentially expressed lncRNAs (DELs) associated with immune microenvironment and m6A-related genes for subsequent lasso analysis. Four out of these DELs were selected for the m6A-TME-LM. All the four lncRNAs were related to overall survival, and a test set testified the result. Further stratification analysis of the m6A-TME-LM retained its ability to predict OS for male and chemotherapy adjuvant patients and performed an excellent prognostic efficacy in the TNM stage III and IV subgroups. Network analysis also found the four lncRNAs mediated co-expression network was associated with tumor development.ConclusionWe constructed the m6A-TME-LM, which could provide a better prognostic prediction of CC.

Identification of P2RY13 as an immune-related prognostic biomarker in lung adenocarcinoma: A public database-based retrospective study.

BackgroundLung adenocarcinoma (LUAD) is the leading histological subtype of non-small cell lung cancer (NSCLC).MethodsIn the present study, the gene matrixes of LUAD were downloaded from The Cancer Genome Atlas to infer immune and stromal scores with the ‘Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data’ (ESTIMATE) algorithm and identified immune-related differentially expressed genes (DEGs) between the high- and low-stromal/immune score groups. Next, all DEGs were subjected to univariate Cox regression and survival analyses to screen out prognostic biomarkers in the tumor microenvironment (TME), and were validated in the Gene Expression Omnibus database. Single-sample gene set enrichment analysis (ssGSEA) was performed to assess the level of tumor-infiltrating immune cells (TIICs) and immune functions, and GSEA was used to identified pathways altered by prognostic biomarkers.ResultsSurvival analysis showed that LUAD in the high-immune and stromal score group had a better clinical prognosis. A total of 303 immune-related DEGs were detected. Univariate Cox regression and survival analyses revealed that P2Y purinoceptor 13 (P2RY13) was a favorable factor for the prognosis of LUAD. ssGSEA and Spearman correlation analysis demonstrated that P2RY13 was highly correlated with various TIICs and immune functions. Several immune-associated pathways were enriched between the high- and low-expression P2RY13 groups.ConclusionP2RY13 may be a potential prognostic indicator and is highly associated with the TME in LUAD. However, further experimental studies are required to validate the present findings.

Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment.

BackgroundOvarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgentlyMethodsGene expression profile for OSC patients was obtained from the TCGA database. The ESTIMATE algorithm was used to calculate immune scores and stromal scores of expression data of ovarian serous cystadenocarcinoma samples. Survival results between high and low groups of immune and stromal score were compared and differentially expressed genes (DEGs) were screened out by limma package. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the protein-protein interaction (PPI) network analysis were performed with the g:Profiler database, the Cytoscape and Search Tool for the Retrieval of Interacting Genes (STRING-DB). Survival results between high and low immune and stromal score groups were compared. Kaplan-Meier plots based on TCGA follow up information were generated to evaluate patients’ overall survival.ResultsEighty-six upregulated DEGs and one downregulated DEG were identified. Three modules, which included 49 nodes were chosen as important networks. Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were considered to be correlated with poor overall survival.ConclusionSeven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were correlated with poor overall survival in our study. This new set of genes can become strong predictor of survival, individually or combined. Further investigation of these genes is needed to validate the conclusion to provide novel understanding of tumor microenvironment with ovarian serous cystadenocarcinoma prognosis and treatment.

Bioinformatics analysis reveals the competing endogenous RNA (ceRNA) coexpression network in the tumor microenvironment and prognostic biomarkers in soft tissue sarcomas

ABSTRACT Soft tissue sarcomas (STSs) are rare, heterogeneous mesenchymal neoplasias. Understanding the tumor microenvironment (TME) and identifying potential biomarkers for prognosis associated with the TME of STS might provide effective clues for immune therapy. We evaluated the immune scores and stromal scores of STS patients by using the RNA sequencing dataset from The Cancer Genome Atlas (TCGA) database and the ESTIMATE algorithm. Then, the differentially expressed mRNAs (DEGs), miRNAs (DEMs) and lncRNAs (DELs) were identified after comparing the high- and low-score groups. Next, we established a competing endogenous RNA (ceRNA) network and explored the prognostic values of biomarkers involved in the network with the help of bioinformatics analysis. High immune score was significantly associated with favorable overall survival in STS patients. A total of 328 DEGs, 18 DEMs and 67 DELs commonly regulated in the immune and stromal score groups were obtained. A ceRNA network and protein–protein interaction (PPI) network identified some hub nodes with considerable importance in the network. Kaplan–Meier survival analysis demonstrated that nine mRNAs, two miRNAs and three lncRNAs were closely associated with overall survival of STS patients. Gene set enrichment analysis (GSEA) suggested that these three lncRNAs were mainly involved in immune response-associated pathways in STS patients. Finally, the expression levels of five mRNAs (APOL1, EFEMP1, LYZ, RARRES1 and TNFAIP2) were verified, which were consistent with the results of the TCGA cohort. The results of our study confirmed the prognostic value of immune scores for STS patients. We also identified several TME-related biomarkers that might contribute to prognostic prediction and immune therapy.

Microenvironment-related prognostic genes in esophageal cancer

BackgroundEsophageal cancer is one of the most common malignant tumors. The role of tumor microenvironment in esophageal cancer is unclear.MethodsThe gene expression profiles and clinical data of 158 patients with esophageal cancer were extracted from The Cancer Genome Atlas database. Immune scores and stromal scores were calculated based on ESTIMATE algorithm. According to different immune/stromal scores, differentially expressed genes (DEGs) were identified. The function enrichment, protein interactions of shared DEGs and their associations with overall survival were analyzed.ResultsIn regard to the association of the immune/stromal scores and disease stage, pathological type and overall survival, only the stromal scores among the different stages were significantly different (P=0.015). In the high immune and stromal score groups, 603 shared up-regulated genes were found. The related function and pathways included regulation of lymphocyte activation, cytokine binding and chemokine signaling pathway. Protein-protein interaction analysis showed that ITGAM had the most connections, followed by CXCL10 and CCR2. High expression of 11 genes, including MS4A7, TMIGD3, MS4A4A, EVI2A, MS4A6A, FCER1G, AIF1, GNGT2, LCP2, DNAJC5B and RNASE6, were found to be associated with shorter overall survival.ConclusionsMicroenvironment-associated functions and pathways were analyzed in esophageal cancer, and 11 microenvironment-associated genes were correlated to poor prognoses. Further studies on these genes may be helpful to understand the tumor microenvironment and provide new therapies for esophageal cancer.

Development and validation of an immune and stromal prognostic signature in uveal melanoma to guide clinical therapy.

The tumor microenvironment is known to play an important role in uveal melanoma. Reliable prognostic signatures are needed to aid high risk patients and improve prognosis. Uveal melanoma tissues from three public datasets were analyzed. RNA sequence data of uveal melanoma and corresponding clinical features were obtained from The Cancer Genome Atlas database. Immune and stromal scores were calculated by applying the “ESTIMATE” algorithm. The samples were divided into high and low immune or stromal score groups. We constructed prognostic models by using the ‘lasso’ package and tested them for 500 iterations. The cell signature was validated in another GSE44295 and GSE84976 datasets. We found that the median survival time of the low immune/stromal score group is longer than that of the high-score group. Thirteen immune cells and one stromal cell were concerned significant in predicting poor overall survival rate. Finally, a four-cell model was identified. Further validation revealed that the low-risk group has a significantly better survival than the high-risk group in another two datasets (P < 0.05). Moreover, the high-risk group is more sensitive to immunotherapy and chemotherapy. Summarizing, the proposed immune cells signature is a promising biomarker for estimating overall survival in uveal melanoma.

Mining the potential prognostic value of synaptosomal-associated protein 25 (SNAP25) in colon cancer based on stromal-immune score.

BackgroundColon cancer is one of the deadliest tumors worldwide. Stromal cells and immune cells play important roles in cancer biology and microenvironment across different types of cancer. This study aimed to identify the prognostic value of stromal/immune cell-associated genes for colon cancer in The Cancer Genome Atlas (TCGA) database using bioinformatic technology.MethodsThe gene expression data and corresponding clinical information of colon cancer were downloaded from TCGA database. Stromal and immune scores were estimated based on the ESTIMATE algorithm. Sanger software was used to identify the differentially expressed genes (DEGs) and prognostic DEGs based on stromal and immune scores. External validation of prognostic biomarkers was conducted in Gene Expression Omnibus (GEO) database. Gene ontology (GO) analysis, pathway enrichment analysis, and gene set enrichment analysis (GSEA) were used for functional analysis. STRING and Cytoscape were used to assess the protein-protein interaction (PPI) network and screen hub genes. Quantitative real-time PCR (qRT-PCR) was used to validate the expression of hub genes in clinical tissues. Synaptosomal-associated protein 25 (SNAP25) was selected for analyzing its correlations with tumor-immune system in the TISIDB database.ResultsWorse overall survivals of colon cancer patients were found in high stromal score group (2963 vs. 1930 days, log-rank test P = 0.038) and high immune score group (2894 vs. 2230 days, log-rank test P = 0.076). 563 up-regulated and 9 down-regulated genes were identified as stromal-immune score-related DEGs. 70 up-regulated DEGs associated with poor outcomes were identified by COX proportional hazard regression model, and 15 hub genes were selected later. Then, we verified aquaporin 4 (AQP4) and SNAP25 as prognostic biomarkers in GEO database. qRT-PCR results revealed that AQP4 and SNAP25 were significantly elevated in colon cancer tissues compared with adjacent normal tissues (P = 0.003, 0.001). GSEA and TISIDB suggested that SNAP25 involved in cancer-related signaling pathway, immunity and metabolism progresses.ConclusionSNAP25 is a microenvironment-related and immune-related gene that can predict poor outcomes in colon cancer.

Identification of Novel Tumor-Microenvironment-Regulating Factor That Facilitates Tumor Immune Infiltration in Colon Cancer

Colon cancer is one of the most common malignancies causing death worldwide. It is well known that the cells of the tumor microenvironment contribute to the progression and prognosis of colon cancer. However, the gene alterations and potential remodeling mechanisms in the tumor microenvironment of colon cancer remain largely unknown. In this study, immune scores from the ESTIMATE algorithm were used to discriminate between patients with high or low immune-cell infiltration. There were 42 immune differentially expressed genes (DEGs) of prognostic value identified in this study. Among them, KCNJ5 is a key factor in promoting M2 macrophage recruitment and tumor immune infiltration in colon cancer. These findings may provide novel insights for decoding the complicated interplay between cancer cells and the tumor microenvironment as well as for developing new avenues for therapeutic intervention in colon cancer.

Identification and validation of stromal-tumor microenvironment-based subtypes tightly associated with PD-1/PD-L1 immunotherapy and outcomes in patients with gastric cancer

BackgroundImmunotherapies targeting programmed cell death 1 (PD-1) and programmed death-ligand 1 (PD-L1) have been approved for gastric cancer (GC) patients. However, a large proportion of patients with T-cell-inflamed tumor microenvironment do not respond to the PD-1/PD-L1 blockade. The stromal component of the tumor microenvironment has been associated with immunotherapy. This study aims to explore the clinical significance of the non-immune cells in the tumor microenvironment and their potential as biomarkers for immunotherapy.MethodsA total of 383 patients with GC from the Cancer Genome Atlas (TCGA) cohort, 300 patients with GC from the GSE62254 cohort in Gene Expression Omnibus (GEO) were included in the study. A stromal score was generated using the ESTIMATE algorithm, and the likelihood of response to PD-1/PD-L1 immunotherapy of GC patients was predicted using the TIDE algorithm. The prognostic value of the stromal score from GC cases was evaluated by the Kaplan–Meier method and Cox regression analysis. Gene set enrichment analysis (GSEA) was also conducted.ResultsThe stromal score showed significant differences in different molecular subtypes and T stages. Multivariate analyses further confirmed that the stromal score was an independent indicator of overall survival (OS) in the two cohorts. The low stromal score group showed higher tumor mutation burden (TMB) and micro-satellite instability (MSI), and was more sensitive to immune checkpoint inhibitor according to the TIDE algorithm. Activation of the transforming growth factor and epithelial–mesenchymal transition were observed in the high stromal score subtype, which is associated with T-cell suppression, and may be responsible for resistance to PD-1/PD-L1 therapy. BPIFB2 was confirmed as a hub gene relevant to immunotherapy.ConclusionThe stromal score was associated with cancer progression and molecular subtypes, and may serve as a novel biomarker for predicting the prognosis and response to immunotherapy in patients with GC.

Identification of Genes with Prognostic Value in the Breast Cancer Microenvironment Using Bioinformatics Analysis.

BackgroundStromal and immune cells play essential roles in the development of breast cancer (BC). This study was conducted to identify prognosis-related genes from the tumor microenvironment.Material/MethodsThe gene expression profiles of 622 BC samples were downloaded from TCGA (The Cancer Genome Atlas) database. Stromal and immune scores were calculated by using the ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumours using Expression data) algorithm. Then, differentially expressed genes (DEGs) between the high score group and the low score group were screened. The intersecting DEGs were selected through Venn diagrams, and survival analysis was conducted. Functional and pathway enrichment analyses were performed using the DAVID (Database for Annotation, Visualization and Integrated Discovery), and a protein–protein interaction (PPI) network was constructed with the STRING database and Cytoscape. These genes were validated for prognostic value by use of the KM (Kaplan-Meier) plotter tool.ResultsThe low immune score group was associated with a poor prognosis. However, there was no difference in the prognosis between the high and low stromal score groups. A total of 248 intersecting DEGs were found in BC, and 61 genes were significantly associated with the prognosis of BC patients in the TCGA database. These genes were enriched in the immune response, components of the plasma membrane, and receptor activity. Furthermore, in the validation group, 31 of 61 genes were significantly associated with prognosis.ConclusionsOur bioinformatics analysis identified 31 tumor microenvironment-related genes as potential prognostic predictors for breast cancer patients. Some of these genes that have not been widely investigated previously, such as CXCL9, GPR18, S1PR4, SASH3, and PYH1N1, might be additional predictive factors for BC patients.

Deciphering Immune-Associated Genes to Predict Survival in Clear Cell Renal Cell Cancer.

Background To elucidate the correlations between tumor microenvironment and clinical characteristics as well as prognosis in clear cell renal cell cancer (ccRCC) and investigate the immune-associated genes by a comprehensive analysis of The Cancer Genome Atlas (TCGA) database. Methods We collected mRNA expression profiles of 537 ccRCC samples from the TCGA database. Immune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. The differentially expressed genes (DEGs) were screened between high immune/stromal score and low immune/stromal score groups by the cutoff of |log (fold change)| > 1, P value <0.05 by using package “limma” in R. Functional enrichment analysis was performed by DAVID, and the protein-protein interaction network of intersected DEGs between stromal score and immune score groups was conducted using the STRING database. The Kaplan–Meier method was used to explore DEGs with predictive values in overall survival, and the prognostic DEGs were further validated in a Gene Expression Omnibus (GEO) dataset GSE29609. Results A higher immune score was associated with T3/4 (vs. T1/2, P < 0.001), N1 (vs. N0, P=0.05), M1 (vs. M0, P=0.004), G3/4 (vs. G1/2, P < 0.001), advanced AJCC stage (P < 0.001), and shorter overall survival (P=0.04). Intersected DEGs between immune and stromal score groups were 48 upregulated and 47 downregulated genes, with 43 DEGs associated with overall survival in ccRCC. After validation by a cohort of 39 ccRCC cases with detailed follow-up information from GSE29609, six immune-associated DEGs including CASP5, HSD11B1, VSIG4, HMGCS2, HSD11B2, and OGDHL were demonstrated to be predictive of prognosis in ccRCC. Conclusions Our study elucidated tight associations between immune score and clinical characteristics as well as prognosis in ccRCC. Moreover, six DEGs were explored and validated to exert predictive values in overall survival of ccRCC.

Genetic landscape of prognostic value in pancreatic ductal adenocarcinoma microenvironment.

The prognosis of pancreatic ductal adenocarcinoma (PDAC) remains dismally poor and is widely considered as an intricate genetic disorder. The mutational landscape of PDAC may directly reflect cancer immunogenicity and dictate the extent and phenotype of immune cell infiltration. In adverse, the microenvironment may also effect the gene expression of cancer cells, which is associated with clinical prognosis. Thus, it is crucial to identify genomic alterations in PDAC microenvironment and its impacts on clinical prognosis. The gene expression profiles, mutation data and clinical information of 179 pancreatic cancer patients with an initial pathologic diagnosis ranging from 2001 to 2013 were retrieved from The Cancer Genome Atlas (TCGA) database. The MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm for calculating immune scores and stromal scores and Tumor IMmune Estimation Resource (TIMER) resource for cell infiltrations were applied in this study. The average immune score or stromal score of PDAC subtype was significantly higher than that of other specific subtypes. KRAS mutant cases had significantly lower immune scores (P=0.001) and stromal scores (P=0.007), in concert with lower immune scores in TP53 mutant cases (P=0.030). However, no significant difference was found in SMAD4 and CDKN2A mutations. In the cohort OS/RFS, the infiltration levels of CD8+ T cells, B cells, Macrophages, Neutrophils and DCs in high stromal score group were higher than those in the low score group (all P<0.001), as well as in immune score groups except for Macrophages in the cohort RFS. In the cohort OS/RFS, 317/379 upregulated genes and 9/6 downregulated genes were observed in the high immune score group, while 227/205 upregulated genes and 17/6 downregulated genes in the high stromal score group. With the analysis for prognostic value of DEGs, 82 and 58 DEGs respectively in the high immune and stromal score group were verified to be significantly associated with better OS (P<0.05), while 53 and 17 DEGs respectively with longer RFS (P<0.05). Functional enrichment analysis showed genes of prognostic values were significantly related to immune response. A list of genes with prognostic value in PDAC microenvironment were obtained from functional enrichment analysis based on immune and stromal scores, which indicates a series of potential auxiliary prognostic biomarkers for PDAC are available. Further research on these genes may be valuable and helpful to understand the crosstalk between tumor and microenvironment, new immune evasion mechanisms and underlying novel therapeutic targets in an integrated manner.