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 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 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 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 Role of ferroptosis-related genes in prognostic prediction and tumor immune microenvironment in colorectal carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11745
Author(s):  
Chao Yang ◽  
Shuoyang Huang ◽  
Fengyu Cao ◽  
Yongbin Zheng
Keyword(s):  
High Risk ◽  
Complete Information ◽  
High Risk Group ◽  
Consensus Clustering ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Risk Patients ◽  
Prognostic Prediction ◽  
Public Datasets

Background and Aim Colorectal cancer (CRC) ranks the second most common cause of cancer-related mortality worldwide. Ferroptosis, a recently discovered form of programmed cell death different from other, raises promising novel opportunities for therapeutic intervention of CRC. This study intended to systematically assess the prognosis value and multiple roles of the ferroptosis-related genes in the tumor immune microenvironment of CRC. Materials and Methods Of 1,192 CRC patients with complete information from the public datasets (TCGA CRC, GEO GSE39582 and GSE17538 cohorts) were selected for analysis. Firstly, K-means consensus clustering was performed to identify ferroptosis-associated subtypes in CRC patients. Subsequently, we constructed a risk signature based on ferroptosis-related genes in TCGA cohort and acquired its validation in two GEO cohorts. Additionally, we established a nomogram integrating the risk signature and clinical factors to improve risk assessment of CRC patients. Results Five molecular subtypes were identified by consensus clustering for ferroptosis-related genes. There were significant differences in the overall survival, immune cells infiltration status and PD1/PD-L1 mRNA among the five clusters. Then, a risk signature based on the ten-gene was constructed which could distinguish effectively high-risk group among CRC patients in both training and validation sets. The high-risk patients were more likely to have an inhibitory immune microenvironment and lower stemness features. A prognostic nomogram integrated risk signature and clinicopathological features could be used as a more accurate prognostic prediction visualization tool than TNM stage alone. Conclusion This ferroptosis risk signature may accurately differentiate between different risk populations and predict the prognosis of CRC. Besides, this study elucidated the crucial role of ferroptosis in tumor immune microenvironment.

scholarly journals Comprehensive Analysis of Inflammatory Response–Related Genes, and Prognosis and Immune Infiltration in Patients With Low-Grade Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Han ◽  
Zhifan Zuo ◽  
Meilin Qu ◽  
Yinghui Zhou ◽  
Qing Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Grade ◽  
Immune Microenvironment ◽  
Cox Regression Analysis

Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG.Methods: We downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognosis-related differentially expressed inflammatory genes with the difference analysis and single-factor Cox regression analysis. The prognostic risk model was constructed by LASSO Cox regression analysis, which enables us to compare the overall survival rate of high- and low-risk groups in the model by Kaplan–Meier analysis and subsequently draw the risk curve and survival status diagram. We analyzed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. The CellMiner database was used to study drug sensitivity.Results: In this study, 3 genes (CALCRL, MMP14, and SELL) were selected from 9 prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model was better. The age, grade, and risk score can be used as independent prognostic factors (p < 0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. We found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method (p < 0.05). Finally, we explored the relationship between the genes in the model and the susceptibility of drugs.Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.

scholarly journals Prognostic modeling of patients with metastatic melanoma based on tumor immune microenvironment characteristics

2021 ◽  
Vol 19 (2) ◽  
pp. 1448-1470
Author(s):  
Jing Liu ◽  
◽  
Xuefang Zhang ◽  
Ting Ye ◽  
Yongjian Dong ◽  
...  
Keyword(s):  
High Risk ◽  
Metastatic Melanoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Microenvironment ◽  
Immune Infiltration ◽  
Tumor Immune Microenvironment

<abstract> <p>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 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 curves were used to test the predictive accuracy of the model. Immune infiltration was analyzed by using CIBERSORT and Xcell 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 demonstrated the visible difference in groups with different levels of risk scores. WGCNA analysis found that the yellow-green module contained seven genes from the nine-IRG prognostic model, and 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, the expression characteristics of ALOX5AP, ARHGAP15, CCL8, FCER1G, GBP4, HCK, MMP9, RARRES2 and TRIM22 were analyzed between metastatic melanoma and normal samples. Overall, a prognostic model for metastatic melanoma based on the tumor immune microenvironment characteristics was established, which left plenty of space for further studies. It could function well in helping people to understand characteristics of the immune microenvironment in metastatic melanoma.</p> </abstract>

scholarly journals Identification of Metastasis-Related Signature For Predicting Survival In Osteosarcoma

2021 ◽  
Author(s):  
Song Shi ◽  
Shuaijie Yang ◽  
Zhenyu Zhou ◽  
Kai Sun ◽  
Ran Tao ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Gene Signature ◽  
Low Risk ◽  
Risk Scores ◽  
Activation Markers

Abstract BackgroundRNA sequencing has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. In this study, we aimed to develop a signature to improve the prognostic predictions of osteosarcoma.Materials and methodsBy comparing the expression profiles between metastatic and non-metastatic samples, we obtained 57 metastatic-related gene signatures. Then we constructed a 3‐gene signature to predict the prognostic risk of osteosarcoma patients by the Cox proportional hazards regression model. The risk score derived from this signature could successfully stratify osteosarcoma patients into subgroups with different survival outcomes.ResultsPatients in the low-risk group showed more prolonged overall survival than those in the high-risk group. And the performance was validated with another independent dataset. Multivariate cox regression revealed that the risk score served as an independent risk factor. Besides, we found that patients with low-risk scores had higher expression levels of immune-related signatures, suggesting an active immune status in those patients. Using the CIBERSORT database, we further systematically analyzed the relationships between the risk score and immune cell infiltration levels, as well as the immune activation markers. Higher infiltration of immune cells (CD8 T cells, monocytes, M2 macrophages, and memory B cells) and higher levels of immune cytotoxic markers (GZMA, GMZB, IFNG, and TNF) were observed in patients in the low-risk group.ConclusionsIn summary, this 3-gene signature could be a reliable marker for prognostic evaluation and help clinicians identify high‐risk patients.

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 Development And Validation of A Pyroptosis -Related Signature And Immune Microenvironment Infiltration In Cutaneous Melanoma

2021 ◽  
Author(s):  
Wei Dalong ◽  
Xiaoling Lan ◽  
Qiang Tang ◽  
Zhiqun Huang ◽  
Qianli Tang
Keyword(s):  
High Risk ◽  
Cutaneous Melanoma ◽  
Immune Cell ◽  
Risk Model ◽  
Risk Group ◽  
External Validation ◽  
High Risk Group ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Abstract Background: Cutaneous melanoma is cancer that is both malignant and aggressive, with a poor prognosis. Pyroptosis can affect the prognosis of cancer patients by controlling tumor cell growth, migration, and metastasis, as well as is closely related to the tumor immune microenvironment. The significance of pyroptosis-related genes (PRGs) in cutaneous melanoma, however, is unknown. Methods: The training set and external validation sets were cutaneous melanoma samples from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO), respectively. By using univariate Cox regression analysis and selection operator (Lasso) regression model, prognostic genes for overall survival (OS) were found. Candidate genes that were screened were used to calculate risk scores and construct a PRG risk model. The Kaplan Meier curve, time-dependent receiver operating characteristic (ROC) curve, and area under the curve (AUC)were used to assess the functional and prognostic usefulness of gene signatures in the risk model. Furthermore, to speculate on the activity of immune cell infiltration and immune-related pathways in the tumor immune microenvironment and calculate corresponding scores, a single sample gene set enrichment analysis (ssGSEA) was used.Results: An eight PRGs risk signature (AIM2, CASP4, CASP5, CASP8, IL18, NLRC4, NLRP6, PRKACA) were conducted and divided all cutaneous melanoma patients in the TCGA cohort into two groups: Low-risk and High-risk. Both the training and external validation sets showed that patients in the low-risk group showed a significantly higher likelihood of survival than those in the high-risk group (p < 0.001). Except for PRKACA, all the other eight PRGs in our study appeared to be longer survival times for patients. The results of ssGSEA in terms of 16 types of immune cells and the activity of 13 immune-related pathways showed that the High-risk group had lower immune pathway activity and lower levels of immune cell infiltration. In conclusion, the PRG-signature may be a significant predictor of prognosis and may play an essential role in UM patients' tumor immunity.

scholarly journals Development of a Toll-Like Receptor-Based Gene Signature That Can Predict Prognosis, Tumor Microenvironment, and Chemotherapy Response for Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Lixia Liu ◽  
Bin Liu ◽  
Jie Yu ◽  
Dongyun Zhang ◽  
Jianhong Shi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Immune Checkpoint ◽  
Immune Cell ◽  
Gene Signature ◽  
Chemotherapy Response ◽  
Toll Like Receptor ◽  
Risk Patients ◽  
Immune Score

Objective: Emerging evidence highlights the implications of the toll-like receptor (TLR) signaling pathway in the pathogenesis and therapeutic regimens of hepatocellular carcinoma (HCC). Herein, a prognostic TLR-based gene signature was conducted for HCC.Methods: HCC-specific TLRs were screened in the TCGA cohort. A LASSO model was constructed based on prognosis-related HCC-specific TLRs. The predictive efficacy, sensitivity, and independency of this signature was then evaluated and externally verified in the ICGC, GSE14520, and GSE76427 cohorts. The associations between this signature and tumor microenvironment (stromal/immune score, immune checkpoint expression, and immune cell infiltrations) and chemotherapy response were assessed in HCC specimens. The expression of TLRs in this signature was verified in HCC and normal liver tissues by Western blot. Following si-MAP2K2 transfection, colony formation and apoptosis of Huh7 and HepG2 cells were examined.Results: Herein, we identified 60 HCC-specific TLRs. A TLR-based gene signature (MAP2K2, IRAK1, RAC1, TRAF3, MAP3K7, and SPP1) was conducted for HCC prognosis. High-risk patients exhibited undesirable outcomes. ROC curves confirmed the well prediction performance of this signature. Multivariate Cox regression analysis demonstrated that the signature was an independent prognostic indicator. Also, high-risk HCC was characterized by an increased immune score, immune checkpoint expression, and immune cell infiltration. Meanwhile, high-risk patients displayed higher sensitivity to gemcitabine and cisplatin. The dysregulation of TLRs in the signature was confirmed in HCC. MAP2K2 knockdown weakened colony formation and elevated apoptosis of Huh7 and HepG2 cells.Conclusion: Collectively, this TLR-based gene signature might assist clinicians to select personalized therapy programs for HCC patients.

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