scholarly journals TYROBP, TLR4 and ITGAM regulated macrophages polarization and immune checkpoints expression in osteosarcoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuo Liang ◽  
Jiarui Chen ◽  
GuoYong Xu ◽  
Zide Zhang ◽  
Jiang Xue ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Risk Model ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Kegg Pathways ◽  
Gene Sets ◽  
Macrophages Polarization ◽  
Immune Related Genes

AbstractWe established a relationship among the immune-related genes, tumor-infiltrating immune cells (TIICs), and immune checkpoints in patients with osteosarcoma. The gene expression data for osteosarcoma were downloaded from UCSC Xena and GEO database. Immune-related differentially expressed genes (DEGs) were detected to calculate the risk score. “Estimate” was used for immune infiltrating estimation and “xCell” was used to obtain 64 immune cell subtypes. Furthermore, the relationship among the risk scores, immune cell subtypes, and immune checkpoints was evaluated. The three immune-related genes (TYROBP, TLR4, and ITGAM) were selected to establish a risk scoring system based on their integrated prognostic relevance. The GSEA results for the Hallmark and KEGG pathways revealed that the low-risk score group exhibited the most gene sets that were related to immune-related pathways. The risk score significantly correlated with the xCell score of macrophages, M1 macrophages, and M2 macrophages, which significantly affected the prognosis of osteosarcoma. Thus, patients with low-risk scores showed better results with the immune checkpoints inhibitor therapy. A three immune-related, gene-based risk model can regulate macrophage activation and predict the treatment outcomes the survival rate in osteosarcoma.

Epithelial-mesenchymal transition interaction with CD8+ T cell, dendritic cell and immune checkpoints in the development of melanoma

2021 ◽  
pp. 1-17
Author(s):  
Tuo Liang ◽  
Jiarui Chen ◽  
Guoyong Xu ◽  
Zide Zhang ◽  
Jiang Xue ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Epithelial Mesenchymal Transition ◽  
Metastatic Potential ◽  
Low Risk ◽  
Immune Checkpoints ◽  
Survival Prediction ◽  
Mesenchymal Transition ◽  
Gene Sets ◽  
The Relationship

BACKGROUND: Melanoma is fatal cancer originating from melanocytes, whose high metastatic potential leads to an extremely poor prognosis. OBJECTIVE: This study aimed to reveal the relationship among EMT, TIICs, and immune checkpoints in melanoma. METHODS: Gene expression data and clinical data of melanoma were downloaded from TCGA, UCSC Xena and GEO databases. EMT-related DEGs were detected for risk score calculation. “ESTIMATE” and “xCell” were used for estimating TIICs and obtaining 64 immune cell subtypes, respectively. Moreover, we evaluated the relationship between the risk score and immune cell subtypes and immune checkpoints. RESULTS: Seven EMT-related genes were selected to establish a risk scoring system because of their integrated prognostic relevance. The results of GSEA revealed that most of the gene sets focused on immune-related pathways in the low-risk score group. The risk score was significantly correlated with the xCell score of some TIICs, which significantly affected the prognosis of melanoma. Patients with a low-risk score may be associated with a better response to ICI therapy. CONCLUSION: The individualized risk score could effectively conduct risk stratification, overall survival prediction, ICI therapy prediction, and TME judgment for patients with melanoma, which would be conducive to patients’ precise treatment.

scholarly journals Alternative Splicing Events in Tumor Immune Infiltration in Colorectal Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jian-yu Shi ◽  
Yan-yan Bi ◽  
Bian-fang Yu ◽  
Qing-feng Wang ◽  
Dan Teng ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Alternative Splicing ◽  
High Risk ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Model ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Cell Infiltration ◽  
Immune Infiltration

Despite extensive research, the exact mechanisms involved in colorectal cancer (CRC) etiology and pathogenesis remain unclear. This study aimed to examine the correlation between tumor-associated alternative splicing (AS) events and tumor immune infiltration (TII) in CRC. We analyzed transcriptome profiling and clinical CRC data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq and Innate databases, respectively to develop and validate a risk model of differential AS events and subsequently a TII risk model. We then conducted a two-factor survival analysis to study the association between TII and AS risk and evaluated the associations between immune signatures and six types of immune cells based on the TIMER database. Subsequently, we studied the distribution of six types of TII cells in high- and low-risk groups for seven AS events and in total. We obtained the profiles of AS events/genes for 484 patients, which included 473 CRC tumor samples and 41 corresponding normal samples, and detected 22581 AS events in 8122 genes. Exon Skip (ES) (8446) and Mutually Exclusive Exons (ME) (74) exhibited the most and fewest AS events, respectively. We then classified the 433 patients with CRC into low-risk (n = 217) and high-risk (n = 216) groups based on the median risk score in different AS events. Compared with patients with low-risk scores (mortality = 11.8%), patients with high-risk scores were associated with poor overall survival (mortality = 27.6%). The risk score, cancer stage, and pathological stage (T, M, and N) were closely correlated with prognosis in patients with CRC (P < 0.001). We identified 6479 differentially expressed genes from the transcriptome profiles of CRC and intersected 468 differential immune-related signatures. High-AS-risk and high-TII-risk predicted a poor prognosis in CRC. Different AS types were associated with different TII risk characteristics. Alternate Acceptor site (AA) and Alternate Promoter (AP) events directly affected the concentration of CD4T cells, and the level of CD8T cells was closely correlated with Alternate Terminator (AT) and Exon Skip (ES) events. Thus, the concentration of CD4T and CD8T cells in the CRC immune microenvironment was not specifically modulated by AS. However, B cell, dendritic cell, macrophage, and neutrophilic cell levels were strongly correlated with AS events. These results indicate adverse associations between AS event risk levels and immune cell infiltration density. Taken together, our findings show a clear association between tumor-associated alternative splicing and immune cell infiltration events and patient outcome and could form a basis for the identification of novel markers and therapeutic targets for CRC and other cancers in the future.

scholarly journals Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Feng ◽  
Jiahui Zhao ◽  
Dechao Wei ◽  
Pengju Guo ◽  
Xiaobing Yang ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Predictive Model ◽  
Risk Score ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Survival Outcomes ◽  
Prognostic Signature ◽  
Therapeutic Benefits

BackgroundRenal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis.MethodsHerein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.ResultsThe immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.ConclusionsThe immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

scholarly journals Immune signature-based risk stratification and prediction of immune checkpoint inhibitor’s efficacy for lung adenocarcinoma

2021 ◽  
Author(s):  
Ming Yi ◽  
Anping Li ◽  
Linghui Zhou ◽  
Qian Chu ◽  
Suxia Luo ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Checkpoint Inhibitors ◽  
Tumor Infiltrating Lymphocytes ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Signature ◽  
Follicular Helper ◽  
Immune Related Genes

Abstract Background Lung adenocarcinoma (LUAD) is a common pulmonary malignant disease with a poor prognosis. There were limited studies investigating the influences of the tumor immune microenvironment on LUAD patients’ survival and response to immune checkpoint inhibitors (ICIs). Methods Based on TCGA-LUAD dataset, we constructed a prognostic immune signature and validated its predictive capability in the internal as well as total datasets. Then, we explored the differences of tumor-infiltrating lymphocytes, tumor mutation burden, and patients’ response to ICI treatment between the high-risk score group and low-risk score group. Results This immune signature consisted of 17 immune-related genes, which was an independent prognostic factor for LUAD patients. In the low-risk score group, patients had better overall survival. Although the differences were non-significant, patients with low-risk scores had more tumor-infiltrating follicular helper T cells and fewer macrophages (M0), which were closely related to clinical outcomes. Additionally, the total TMB was markedly decreased in the low-risk score group. Using immunophenoscore as a surrogate of ICI response, we found that patients with low-risk scores had significantly higher immunophenoscore. Conclusion The 17-immune-related genes signature may have prognostic and predictive relevance with ICI therapy but needs prospective validation.

scholarly journals Immunogenomic Analyses of the Prognostic Predictive Model for Patients with Renal Cancer

2021 ◽  
Author(s):  
Tao Feng ◽  
Jiahui Zhao ◽  
Dechao Wei ◽  
Pengju Guo ◽  
Xiaobing Yang ◽  
...  
Keyword(s):  
Predictive Model ◽  
Risk Score ◽  
Immune Cell ◽  
Predictive Ability ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Survival Outcomes ◽  
Prognostic Signature ◽  
Therapeutic Benefits ◽  
Oncogenic Pathways

Abstract Background: Renal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients.Methods: Herein, an immune prognostic signature was developed, and its predictive ability was confirmed in the cohorts based on TCGA-KIRC dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.Results: The immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.Conclusions: The immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

scholarly journals Expression of immune-related genes as prognostic biomarkers for the assessment of osteosarcoma clinical outcomes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Junjie Guo ◽  
Xiaoyang Li ◽  
Shen Shen ◽  
Xuejian Wu
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Survival Prediction ◽  
Promising Therapeutic Approach ◽  
Score Model ◽  
Immune Related Genes

AbstractCancer immunotherapy is a promising therapeutic approach, but the prognostic value of immune-related genes in osteosarcoma (OS) is unknown. Here, Target-OS RNA-seq data were analyzed to detect differentially expressed genes (DEGs) between OS subgroups, followed by functional enrichment analysis. Cox proportional risk regression was performed for each immune-related gene, and a risk score model to predict the prognosis of patients with OS was constructed. The risk scores were calculated using the risk signature to divide the training set into high-risk and low-risk groups, and validation was performed with GSE21257. We identified two immune-associated clusters, C1 and C2. C1 was closely related to immunity, and the immune score was significantly higher in C1 than in C2. Furthermore, we validated 6 immune cell hub genes related to the prognosis of OS: CD8A, KIR2DL1, CD79A, APBB1IP, GAL, and PLD3. Survival analysis revealed that the prognosis of the high-risk group was significantly worse than that of the low-risk group. We also explored whether the 6-gene prognostic risk model was effective for survival prediction. In conclusion, the constructed a risk score model based on immune-related genes and the survival of patients with OS could be a potential tool for targeted therapy.

scholarly journals An immune-related long non-coding RNA signature predicts prognosis in glioblastoma patients

2021 ◽  
Author(s):  
Xiangdong Lu ◽  
Siquan Zhu ◽  
Shouqing Zhang ◽  
Feng Si ◽  
Yunfeng Ma ◽  
...  
Keyword(s):  
Risk Score ◽  
Risk Model ◽  
Univariate Analysis ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Risk Groups ◽  
Low Risk ◽  
Risk Scores ◽  
Normal Brain ◽  
Vesicle Cycle

IntroductionThis study aimed to explore the prognostic value of immune-related long non-coding RNAs (lncRNAs) in glioblastoma (GBM).Material and methodsExpression and clinical data were acquired, including GSE111260 dataset: 67 GBM and 3 normal brain samples; GSE103227 dataset: 5 GBM and 5 normal brain samples; and TCGA data: 187 GBM samples. Immune-related genes were retrieved from ImmPort database. Immune-related differentially expressed genes (DEGs) and lncRNAs were screened. Prognostic lncRNAs were then screened to establish prognostic risk score model. Survival analysis and differential expression analysis were performed in high- vs. low-risk groups, followed by protein-protein interaction network and lncRNA-mRNA co-expression network.ResultsA total of 251 immune-related DEGs were screened. After correlation analysis, 387 immune-related lncRNAs that co-expressed with 140 immune-related DEGs were screened. Univariate analysis identified 18 lncRNAs that were significantly associated with prognosis. The prognostic risk score could be able to stratify GBM patients into high- and low-risk groups, and patients with high risk scores displayed worse outcomes than those with low risk scores in both training set and validation set. A total of 272 genes had abnormal expression between high- and low-risk groups. Of which, 22 genes were immune-related, such as SNAP25, SNAP91, SNCB, and RAB3A. These genes were mainly enriched in synaptic vesicle cycle/exocytosis and insulin secretion. The co-expression network contained 22 genes and 11 lncRNAs, and lncRNA LINC01574 co-expressed with the great number of mRNAs.ConclusionsWe identified 18 immune-related prognostic lncRNAs, and the established lncRNAs-based prognostic risk model could stratify GBM patients into different risks.

scholarly journals A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ruixiang Luo ◽  
Mengjun Huang ◽  
Yinhuai Wang
Keyword(s):  
Prostate Cancer ◽  
Transcription Factors ◽  
High Risk ◽  
Risk Score ◽  
Drug Sensitivity ◽  
Risk Model ◽  
Low Risk ◽  
Risk Scores ◽  
Risk Genes ◽  
Therapeutic Drugs

Background. Prostate cancer (PC) is one of the most critical cancers affecting men’s health worldwide. The development of many cancers involves dysregulation or mutations in key transcription factors. This study established a transcription factor-based risk model to predict the prognosis of PC and potential therapeutic drugs. Materials and Methods. In this study, RNA-sequencing data were downloaded and analyzed using The Cancer Genome Atlas dataset. A total of 145 genes related to the overall survival rate of PC patients were screened using the univariate Cox analysis. The Kdmist clustering method was used to classify prostate adenocarcinoma (PRAD), thereby determining the cluster related to the transcription factors. The support vector machine-recursive feature elimination method was used to identify genes related to the types of transcription factors and the key genes specifically upregulated or downregulated were screened. These genes were further analyzed using Lasso to establish a model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the functional analysis. The TIMER algorithm was used to quantify the abundance of immune cells in PRAD samples. The chemotherapy response of each GBM patient was predicted based on the public pharmacogenomic database, Genomics of Drug Sensitivity in Cancer (GDSC, http://www.cancerrxgene.org). The R package “pRRophetic” was applied to drug sensitivity (IC50) value prediction. Results. We screened 10 genes related to prognosis, including eight low-risk genes and two high-risk genes. The receiver operating characteristic (ROC) curve was 0.946. Patients in the high-risk score group had a poorer prognosis than those in the low-risk score group. The average area under the curve value of the model at different times was higher than 0.8. The risk score was an independent prognostic factor. Compared with the low-risk score group, early growth response-1 (EGR1), CACNA2D1, AC005831.1, SLC52A3, TMEM79, IL20RA, CRACR2A, and FAM189A2 expressions in the high-risk score group were decreased, while AC012181.1 and TRAPPC8 expressions were increased. GO and KEGG analyses showed that prognosis was related to various cancer signaling pathways. The proportion of B_cell, T_cell_CD4, and macrophages in the high-risk score group was significantly higher than that in the low-risk score group. A total of 25 classic immune checkpoint genes were screened out to express abnormally high-risk scores, and there were significant differences. Thirty mutant genes were identified; in the high- and low-risk score groups, SPOP, TP53, and TTN had the highest mutation frequency, and their mutations were mainly missense mutations. A total of 36 potential drug candidates for the treatment of PC were screened and identified. Conclusions. Ten genes of both high-and low-risk scores were associated with the prognosis of PC. PC prognosis may be related to immune disorders. SPOP, TP53, and TTN may be potential targets for the prognosis of PC.

scholarly journals Hypoxic Characteristic Genes Predict Response to Immunotherapy for Urothelial Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Shuo Hong ◽  
Yueming Zhang ◽  
Manming Cao ◽  
Anqi Lin ◽  
Qi Yang ◽  
...  
Keyword(s):  
Urothelial Carcinoma ◽  
Immune Cell ◽  
Risk Model ◽  
Low Risk ◽  
Cell Infiltration ◽  
Risk Scores ◽  
Immune Cell Infiltration ◽  
Training Set ◽  
Metastatic Urothelial Carcinoma ◽  
Testing Set

Objective: Resistance to immune checkpoint inhibitors (ICIs) has been a massive obstacle to ICI treatment in metastatic urothelial carcinoma (MUC). Recently, increasing evidence indicates the clinical importance of the association between hypoxia and immune status in tumor patients. Therefore, it is necessary to investigate the relationship between hypoxia and prognosis in metastatic urothelial carcinoma.Methods: Transcriptomic and clinical data from 348 MUC patients who underwent ICI treatment from a large phase 2 trial (IMvigor210) were investigated in this study. The cohort was randomly divided into two datasets, a training set (n = 213) and a testing set (n = 135). Data of hypoxia-related genes were downloaded from the molecular signatures database (MSigDB), and screened by univariate and multivariate Cox regression analysis to construct a prognosis-predictive model. The robustness of the model was evaluated in two melanoma cohorts. Furthermore, an external validation cohort, the bladder cancer cohort, from the Cancer Genome Atlas (TCGA) database, was t used to explore the mechanism of gene mutation, immune cell infiltration, signaling pathway enrichment, and drug sensitivity.Results: We categorized patients as the high- or low- risk group using a four-gene hypoxia risk model which we constructed. It was found that patients with high-risk scores had significantly worse overall survival (OS) compared with those with low-risk scores. The prognostic model covers 0.71 of the area under the ROC curve in the training set and 0.59 in the testing set, which is better than the survival prediction of MUC patients using the clinical characteristics. Mutation analysis results showed that deletion mutations in RB1, TP53, TSC1 and KDM6A were correlated with hypoxic status. Immune cell infiltration analysis illustrated that the infiltration T cells, B cells, Treg cells, and macrophages was correlated with hypoxia. Functional enrichment analysis revealed that a hypoxic microenvironment activated inflammatory pathways, glucose metabolism pathways, and immune-related pathways.Conclusion: In this investigation, a four-gene hypoxia risk model was developed to evaluate the degree of hypoxia and prognosis of ICI treatment, which showed a promising clinical prediction value in MUC. Furthermore, the hypoxia risk model revealed a close relationship between hypoxia and the tumor immune microenvironment.

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.

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