scholarly journals A Hypoxia Gene-Based Signature to Predict the Survival and Affect the Tumor Immune Microenvironment of Osteosarcoma in Children

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Feng Jiang ◽  
Xiao-Lin Miao ◽  
Xiao-Tian Zhang ◽  
Feng Yan ◽  
Yan Mao ◽  
...  
Keyword(s):  
High Risk ◽  
Poor Prognosis ◽  
Gene Signature ◽  
Response Strength ◽  
Gene Expression Omnibus ◽  
Immune Microenvironment ◽  
Prognostic Predictor ◽  
Molecular Features ◽  
Tumor Immune Microenvironment ◽  
Immunosuppressive Microenvironment

Osteosarcoma is a quickly developing, malignant cancer of the bone, which is associated with a bad prognosis. In osteosarcoma, hypoxia promotes the malignant phenotype, which results in a cascade of immunosuppressive processes, poor prognosis, and a high risk of metastasis. Nonetheless, additional methodologies for the study of hyperoxia in the tumor microenvironment also need more analysis. We obtained 88 children patients with osteosarcoma from the Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database and 53 children patients with RNA sequence and clinicopathological data from the Gene Expression Omnibus (GEO). We developed a four-gene signature related to hypoxia to reflect the immune microenvironment in osteosarcoma that predicts survival. A high-risk score indicated a poor prognosis and immunosuppressive microenvironment. The presence of the four-gene signature related to hypoxia was correlated with clinical and molecular features and was an important prognostic predictor for pediatric osteosarcoma patients. In summary, we established and validated a four-gene signature related to hypoxia to forecast recovery and presented an independent prognostic predictor representing overall immune response strength within the osteosarcoma microenvironment.

scholarly journals A Hypoxia Signature for Predicting Prognosis and Tumor Immune Microenvironment in Adrenocortical Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xi Chen ◽  
Lijun Yan ◽  
Yu Lu ◽  
Feng Jiang ◽  
Ni Zeng ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Adrenocortical Carcinoma ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Immune Microenvironment ◽  
Gene Sets ◽  
Tumor Immune Microenvironment ◽  
Hypoxia Signature

Adrenocortical carcinoma (ACC) is a rare malignancy with dismal prognosis. Hypoxia is one of characteristics of cancer leading to tumor progression. For ACC, however, no reliable prognostic signature on the basis of hypoxia genes has been built. Our study aimed to develop a hypoxia-associated gene signature in ACC. Data of ACC patients were obtained from TCGA and GEO databases. The genes included in hypoxia risk signature were identified using the Cox regression analysis as well as LASSO regression analysis. GSEA was applied to discover the enriched gene sets. To detect a possible connection between the gene signature and immune cells, the CIBERSORT technique was applied. In ACC, the hypoxia signature including three genes (CCNA2, COL5A1, and EFNA3) was built to predict prognosis and reflect the immune microenvironment. Patients with high-risk scores tended to have a poor prognosis. According to the multivariate regression analysis, the hypoxia signature could be served as an independent indicator in ACC patients. GSEA demonstrated that gene sets linked to cancer proliferation and cell cycle were differentially enriched in high-risk classes. Additionally, we found that PDL1 and CTLA4 expression were significantly lower in the high-risk group than in the low-risk group, and resting NK cells displayed a significant increase in the high-risk group. In summary, the hypoxia risk signature created in our study might predict prognosis and evaluate the tumor immune microenvironment for ACC.

scholarly journals Analysis of Immune–Stromal Score-Based Gene Signature and Molecular Subtypes in Osteosarcoma: Implications for Prognosis and Tumor Immune Microenvironment

2021 ◽  
Vol 12 ◽  
Author(s):  
Dingzhao Zheng ◽  
Kaichun Yang ◽  
Xinjiang Chen ◽  
Yongwu Li ◽  
Yongchun Chen
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Immune Cell ◽  
Molecular Subtypes ◽  
Gene Signature ◽  
Risk Scores ◽  
Survival Duration ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Risk Patients

Objective: Infiltrating immune and stromal cells are essential for osteosarcoma progression. This study set out to analyze immune–stromal score-based gene signature and molecular subtypes in osteosarcoma.Methods: The immune and stromal scores of osteosarcoma specimens from the TARGET cohort were determined by the ESTIMATE algorithm. Then, immune-stromal score-based differentially expressed genes (DEGs) were screened, followed by univariate Cox regression analysis. A LASSO regression analysis was applied for establishing a prognostic model. The predictive efficacy was verified in the GSE21257 dataset. Associations between the risk scores and chemotherapy drug sensitivity, immune/stromal scores, PD-1/PD-L1 expression, immune cell infiltrations were assessed in the TARGET cohort. NMF clustering analysis was employed for characterizing distinct molecular subtypes based on immune-stromal score-based DEGs.Results: High immune/stromal scores exhibited the prolonged survival duration of osteosarcoma patients. Based on 85 prognosis-related stromal–immune score-based DEGs, a nine-gene signature was established. High-risk scores indicated undesirable prognosis of osteosarcoma patients. The AUCs of overall survival were 0.881 and 0.849 in the TARGET cohort and GSE21257 dataset, confirming the well predictive performance of this signature. High-risk patients were more sensitive to doxorubicin and low-risk patients exhibited higher immune/stromal scores, PD-L1 expression, and immune cell infiltrations. Three molecular subtypes were characterized, with distinct clinical outcomes and tumor immune microenvironment.Conclusion: This study developed a robust prognostic gene signature as a risk stratification tool and characterized three distinct molecular subtypes for osteosarcoma patients based on immune–stromal score-based DEGs, which may assist decision-making concerning individualized therapy and follow-up project.

scholarly journals Signatures of Multi-Omics Reveal Distinct Tumor Immune Microenvironment Contributing to Immunotherapy in Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Ziqi Huang ◽  
Baihui Li ◽  
Yan Guo ◽  
Lei Wu ◽  
Fan Kou ◽  
...  
Keyword(s):  
T Cells ◽  
High Risk ◽  
Cd8 T Cells ◽  
Poor Prognosis ◽  
Molecular Subtypes ◽  
Risk Group ◽  
High Risk Group ◽  
Omics Data ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

BackgroundLung adenocarcinoma (LUAD) contains a variety of genomic and epigenomic abnormalities; the effective tumor markers related to these abnormalities need to be further explored.MethodsClustering analysis was performed based on DNA methylation (MET), DNA copy number variation (CNV), and mRNA expression data, and the differences in survival and tumor immune microenvironment (TIME) between subtypes were compared. Further, we evaluated the signatures in terms of both prognostic value and immunological characteristics.ResultsThere was a positive correlation between MET and CNV in LUAD. Integrative analysis of multi-omics data from 443 samples determined molecular subtypes, iC1 and iC2. The fractions of CD8+ T cells and activated CD4+ T cells were higher, the fraction of Tregs was lower, and the expression level of programmed death-ligand 1 (PD-L1) was higher in iC2 with a poor prognosis showing a higher TIDE score. We selected PTTG1, SLC2A1, and FAM83A as signatures of molecular subtypes to build a prognostic risk model and divided patients into high-risk group and low-risk group representing poor prognosis and good prognosis, respectively, which were validated in 180 patients with LUAD. Further, the low-risk group with lower TIDE score had more infiltrating immune cells. In 100 patients with LUAD, the high-risk group with an immunosuppressive state had a higher expression of PD-L1 and lower counts of CD8+ T cells and dendritic cells.ConclusionsThese findings demonstrated that combined multi-omics data could determine molecular subtypes with significant differences of prognosis and TIME in LUAD and suggested potent utility of the signatures to guide immunotherapy.

scholarly journals Novel Epigenetic Eight-Gene Signature Predictive of Poor Prognosis and MSI-Like Phenotype in Human Metastatic Colorectal Carcinomas

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 158
Author(s):  
Valentina Condelli ◽  
Giovanni Calice ◽  
Alessandra Cassano ◽  
Michele Basso ◽  
Maria Grazia Rodriquenz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Poor Prognosis ◽  
Cpg Island ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cpg Island Methylator Phenotype ◽  
Prognostic Information ◽  
Global Methylation

Epigenetics is involved in tumor progression and drug resistance in human colorectal carcinoma (CRC). This study addressed the hypothesis that the DNA methylation profiling may predict the clinical behavior of metastatic CRCs (mCRCs). The global methylation profile of two human mCRC subgroups with significantly different outcome was analyzed and compared with gene expression and methylation data from The Cancer Genome Atlas COlon ADenocarcinoma (TCGA COAD) and the NCBI GENE expression Omnibus repository (GEO) GSE48684 mCRCs datasets to identify a prognostic signature of functionally methylated genes. A novel epigenetic signature of eight hypermethylated genes was characterized that was able to identify mCRCs with poor prognosis, which had a CpG-island methylator phenotype (CIMP)-high and microsatellite instability (MSI)-like phenotype. Interestingly, methylation events were enriched in genes located on the q-arm of chromosomes 13 and 20, two chromosomal regions with gain/loss alterations associated with adenoma-to-carcinoma progression. Finally, the expression of the eight-genes signature and MSI-enriching genes was confirmed in oxaliplatin- and irinotecan-resistant CRC cell lines. These data reveal that the hypermethylation of specific genes may provide prognostic information that is able to identify a subgroup of mCRCs with poor prognosis.

scholarly journals From bench to bed: the tumor immune microenvironment and current immunotherapeutic strategies for hepatocellular carcinoma

2019 ◽  
Vol 38 (1) ◽  
Author(s):  
Yaojie Fu ◽  
Shanshan Liu ◽  
Shan Zeng ◽  
Hong Shen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Small Cell Lung ◽  
Immune Microenvironment ◽  
Liver Malignancy ◽  
The Third ◽  
Clinical Investigations ◽  
Tumor Immune Microenvironment ◽  
Novel Strategy ◽  
Immunosuppressive Microenvironment ◽  
Related Mortality

Abstract Hepatocellular carcinoma (HCC) ranks the most common primary liver malignancy and the third leading cause of tumor-related mortality worldwide. Unfortunately, despite advances in HCC treatment, less than 40% of HCC patients are eligible for potentially curative therapies. Recently, cancer immunotherapy has emerged as one of the most promising approaches for cancer treatment. It has been proven therapeutically effective in many types of solid tumors, such as non-small cell lung cancer and melanoma. As an inflammation-associated tumor, it’s well-evidenced that the immunosuppressive microenvironment of HCC can promote immune tolerance and evasion by various mechanisms. Triggering more vigorous HCC-specific immune response represents a novel strategy for its management. Pre-clinical and clinical investigations have revealed that various immunotherapies might extend current options for needed HCC treatment. In this review, we provide the recent progress on HCC immunology from both basic and clinical perspectives, and discuss potential advances and challenges of immunotherapy in HCC.

scholarly journals Prognostic Modeling of Patients with Metastatic Melanoma Based on Tumor Immune Microenvironment Characteristics

2021 ◽  
Author(s):  
Jing Liu ◽  
Ting Ye ◽  
Xue fang Zhang ◽  
Yong jian Dong ◽  
Wen feng Zhang ◽  
...  
Keyword(s):  
High Risk ◽  
Metastatic Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Microenvironment ◽  
Immune Infiltration ◽  
Tumor Immune Microenvironment

Abstract Most of the malignant melanomas are already in the middle and advanced stages when they are diagnosed, which is often accompanied by the metastasis and spread of other organs.Besides, the prognosis of patients is bleak. The characteristics of the local immune microenvironment in metastatic melanoma have important implications for both tumor progression and tumor treatment. In this study, data on patients with metastatic melanoma from the TCGA and GEO datasets were selected for immune, stromal, and estimate scores, and overlapping differentially expressed genes (DEGs) were screened. A nine-IRGs prognostic model (ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) was established by univariate COX regression, LASSO and multivariate COX regression. Receiver operating characteristic (ROC) curves were used to test the predictive accuracy of the model. Immune infiltration was analyzed by using CIBERSORT, Xcell and ssGSEA in high-risk and low-risk groups. The immune infiltration of the high-risk group was significantly lower than that of the low-risk group. Immune checkpoint analysis revealed that the expression of PDCD1, CTLA4, TIGIT, CD274, HAVR2 and LAG3 were significantly different in groups with different levels of risk scores. WGCNA analysis found that the yellow-green module contained seven genes (ALOX5AP, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22) from the nine-IRG prognostic model, of which the yellow-green module had the highest correlation with risk scores. The results of GO and KEGG suggested that the genes in the yellow-green module were mainly enriched in immune-related biological processes. Finally, we analyzed the prognostic ability and expression characteristics of ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22 in metastatic melanoma. Overall, a prognostic model for metastatic melanoma based on the characteristics of the tumor immune microenvironment was established, which was helpful for further studies.It could function well in helping people to understand the characteristics of the immune microenvironment in metastatic melanoma and to find possible therapeutic targets.

scholarly journals Long Non-Coding RNAs in the Tumor Immune Microenvironment: Biological Properties and Therapeutic Potential

2021 ◽  
Vol 12 ◽  
Author(s):  
Ya-Nan Pi ◽  
Wen-Cai Qi ◽  
Bai-Rong Xia ◽  
Ge Lou ◽  
Wei-Lin Jin
Keyword(s):  
Therapeutic Potential ◽  
Immune Surveillance ◽  
Biological Properties ◽  
Immune Microenvironment ◽  
Protein Coding ◽  
Innate And Adaptive Immunity ◽  
Non Coding Rna ◽  
Tumor Immune Microenvironment ◽  
Immunosuppressive Microenvironment ◽  
Long Non Coding Rna

Cancer immunotherapy (CIT) is considered a revolutionary advance in the fight against cancer. The complexity of the immune microenvironment determines the success or failure of CIT. Long non-coding RNA (lncRNA) is an extremely versatile molecule that can interact with RNA, DNA, or proteins to promote or inhibit the expression of protein-coding genes. LncRNAs are expressed in many different types of immune cells and regulate both innate and adaptive immunity. Recent studies have shown that the discovery of lncRNAs provides a novel perspective for studying the regulation of the tumor immune microenvironment (TIME). Tumor cells and the associated microenvironment can change to escape recognition and elimination by the immune system. LncRNA induces the formation of an immunosuppressive microenvironment through related pathways, thereby controlling the escape of tumors from immune surveillance and promoting the development of metastasis and drug resistance. Using lncRNA as a therapeutic target provides a strategy for studying and improving the efficacy of immunotherapy.

scholarly journals Characterization of the m6A-Associated Tumor Immune Microenvironment in Prostate Cancer to Aid Immunotherapy

2021 ◽  
Vol 12 ◽  
Author(s):  
Zezhen Liu ◽  
Jiehui Zhong ◽  
Jie Zeng ◽  
Xiaolu Duan ◽  
Jianming Lu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Poor Prognosis ◽  
Response Rate ◽  
Checkpoint Inhibitors ◽  
Cell Infiltration ◽  
Intratumor Heterogeneity ◽  
Immune Microenvironment ◽  
Th2 Cell ◽  
Tumor Immune Microenvironment

The aim of this study was to elucidate the correlation between m6A modification and the tumor immune microenvironment (TIME) in prostate cancer (PCa) and to identify the m6A regulation patterns suitable for immune checkpoint inhibitors (ICIs) therapy. We evaluated the m6A regulation patterns of PCa based on 24 m6A regulators and correlated these modification patterns with TIME characteristics. Three distinct m6A regulation patterns were determined in PCa. The m6A regulators cluster with the best prognosis had significantly increased METTL14 and ZC3H13 expression and was characterized by low mutation rate, tumor heterogeneity, and neoantigens. The m6A regulators cluster with a poor prognosis had markedly high KIAA1429 and HNRNPA2B1 expression and was characterized by high intratumor heterogeneity and Th2 cell infiltration, while low Th17 cell infiltration and Macrophages M1/M2. The m6Ascore was constructed to quantify the m6A modification pattern of individual PCa patients based on m6A-associated genes. We found that the low-m6Ascore group with poor prognosis had a higher immunotherapeutic response rate than the high-m6Ascore group. The low-m6Ascore group was more likely to benefit from ICIs therapy. This study was determined that immunotherapy is more effective in low-m6Ascore PCa patients with poor prognosis.

scholarly journals A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jia Li ◽  
Huiyu Wang ◽  
Zhaoyan Li ◽  
Chenyue Zhang ◽  
Chenxing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Egfr Mutations ◽  
Kaplan Meier ◽  
Tumor Immune Microenvironment ◽  
Nsclc Patients

Purpose. Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME). Methods and Results. There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P<0.001 and P=0.03). Pathway enrichment results which were obtained by performing Gene Set Variation Analysis (GSVA) showed that the high-risk group identified by the 5-gene signature had metastatic-related gene expression, resulting in lower survival rates. Kaplan–Meier survival results showed that patients of the high-risk group had shorter disease-free survival (DFS) and overall survival (OS) than those of the low-risk group in the training set (P=0.0012 and P<0.001). The sensitivity and specificity of the gene signature were better and more sensitive to prognosis than TNM (tumor/lymph node/metastasis) staging, in spite of being not statistically significant (P=0.154). Furthermore, Kaplan–Meier survival showed that patients of the high-risk group had shorter OS and PFS than those of the low-risk group (P=0.0035, P<0.001, and P<0.001) in the validating set (GSE31210, GSE41271, and TCGA). At last, univariate and multivariate Cox proportional hazard regression analyses were used to evaluate independent prognostic factors associated with survival, and the gene signature, lymphovascular invasion, pleural invasion, chemotherapy, and radiation were employed as covariates. The 5-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses (P<0.001) (hazard ratio (HR): 3.93, 95% confidence interval CI (2.17–7.1), P=0.001, (HR) 5.18, 95% CI (2.6995–9.945), P<0.001), respectively. Our 5-gene signature was also related to EGFR mutations (P=0.0111), and EGFR mutations were mainly enriched in low-risk group, indicating that EGFR mutations affect the survival rate of patients. Conclusion. The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.

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