scholarly journals Identification of Prognostic lncRNA Related to the Immune Microenvironment of Soft Tissue Sarcoma

2021 ◽  
Author(s):  
Wang-Ying Dai ◽  
Bin Wang ◽  
Jian-Yi Li ◽  
Jun-Cheng Zhu ◽  
Zong-ping Luo
Keyword(s):  
Cluster Analysis ◽  
High Risk ◽  
Soft Tissue ◽  
Tumor Microenvironment ◽  
Soft Tissue Sarcoma ◽  
Expression Analysis ◽  
Risk Group ◽  
Immune Microenvironment ◽  
Kaplan Meier ◽  
Unsupervised Cluster Analysis

Abstract Background: Soft tissue sarcoma is a malignant tumor with high degree of malignancy and poor prognosis, originating from mesenchymal tissue. Long non-coding RNAs (lncRNAs) are involved in various biological and pathological processes in the body. They target mRNA through transcription or post-transcription, resulting in the occurrence, invasion, and metastasis of tumors. Therefore, they are highly relevant with regard to early diagnoses and as prognostic indicators.Objective: The objective of the present study was to identify immune-related lncRNAs associated with the tumor microenvironment that can be used to predict soft tissue sarcomas.Methods: Clinical data and follow-up data were obtained from the cBioPortal database, and RNA sequencing data used for the model structure can be accessed from. The Cancer Genome Atlas (TCGA) database. LncRNAs were screened by differential expression analysis and co-expression analysis. The Cox regression model and Kaplan–Meier analysis were used to study the association between lncRNAs and soft tissue sarcoma prognosis in the immune microenvironment. Unsupervised cluster analysis was then completed to discover the impact of screening lncRNAs on disease. Lastly, we constructed an mRNA-lncRNA network by Cytoscape software.Results: Unsupervised cluster analysis revealed that the 210 lncRNAs screened showed strong correlation with the tumor immune microenvironment. Two signatures containing seven and five lncRNAs related to the tumor microenvironment were constructed and used to predict overall survival (OS) and disease-free survival (DFS). The Kaplan–Meier（K-M） survival curve showed that the prognoses of patients in the high-risk and low-risk groups differed significantly, and the prognosis associated with the low-risk group was better than that associated with the high-risk group. Two nomograms with predictive capabilities were established.Conclusion: The results indicate that seven OS- and five DFS-related lncRNAs are correlated with the prognosis of soft tissue sarcoma.

scholarly journals Identification of Tumor Microenvironment-Related Alternative Splicing Events to Predict the Prognosis of Endometrial Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xuan Liu ◽  
Chuan Liu ◽  
Jie Liu ◽  
Ying Song ◽  
Shanshan Wang ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Endometrial Cancer ◽  
Alternative Splicing ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Risk Group ◽  
Gene Signature ◽  
Unsupervised Cluster ◽  
Score Model ◽  
Unsupervised Cluster Analysis

BackgroundEndometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients’ survival, and growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers.MethodsWe downloaded the clinical information and mRNA expression profiles of 542 tumor tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we constructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment.ResultsThe prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group was notably better. Furthermore, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient’s ESTIMATE score and the higher the infiltration of immune cells.ConclusionsWe used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature was combined with tumor microenvironment (TME) and AS events, which allowed a deeper understanding of the immune status of EC patients, and also provided new insights for clinical patients with EC.

scholarly journals A Novel S100 Family-Based Signature Associated with Prognosis and Immune Microenvironment in Glioma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yifang Hu ◽  
Jiahang Song ◽  
Zhen Wang ◽  
Jingbao Kan ◽  
Yaoqi Ge ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Microenvironment ◽  
S100 Family ◽  
Kaplan Meier ◽  
Dismal Prognosis ◽  
Family Based ◽  
Genome Atlas

Background. Glioma is the most common central nervous system (CNS) cancer with a short survival period and a poor prognosis. The S100 family gene, comprising 25 members, relates to diverse biological processes of human malignancies. Nonetheless, the significance of S100 genes in predicting the prognosis of glioma remains largely unclear. We aimed to build an S100 family-based signature for glioma prognosis. Methods. We downloaded 665 and 313 glioma patients, respectively, from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) database with RNAseq data and clinical information. This study established a prognostic signature based on the S100 family genes through multivariate COX and LASSO regression. The Kaplan–Meier curve was plotted to compare overall survival (OS) among groups, whereas Receiver Operating Characteristic (ROC) analysis was performed to evaluate model accuracy. A representative gene S100B was further verified by in vitro experiments. Results. An S100 family-based signature comprising 5 genes was constructed to predict the glioma that stratified TCGA-derived cases as a low- or high-risk group, whereas the significance of prognosis was verified based on CGGA-derived cases. Kaplan–Meier analysis revealed that the high-risk group was associated with the dismal prognosis. Furthermore, the S100 family-based signature was proved to be closely related to immune microenvironment. In vitro analysis showed S100B gene in the signature promoted glioblastoma (GBM) cell proliferation and migration. Conclusions. We constructed and verified a novel S100 family-based signature associated with tumor immune microenvironment (TIME), which may shed novel light on the glioma diagnosis and treatment.

Identification of Novel Tumor Microenvironment-Related Long Non-coding RNAs to Predict the Prognosis for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Dan Li ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Clinicopathological Parameters ◽  
Kaplan Meier ◽  
The Impact

Abstract Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. Tumor microenvironment (TME) plays a vital role in the tumor progression of HCC. Thus, we aimed to analyze the association of TME with HCC prognosis, and construct an TME-related lncRNAs signature for predicting the prognosis of HCC patients.Methods: We firstly assessed the stromal/immune /Estimate scores within the HCC microenvironment using the ESTIMATE algorithm based on TCGA database, and its associations with survival and clinicopathological parameters were also analyzed. Then, different expression lncRNAs were filtered out according to immune/stromal scores. Cox regression was performed to built an TME-related lncRNAs risk signature. Kaplan–Meier analysis was carried out to explored the prognostic values of the risk signature. Furthermore, we explored the biological functions and immune microenvironment feathers in high- and low risk groups. Lastly, we probed the association of the risk signature with the treatment responses to immune checkpoint inhibitors (ICIs) in HCC by comparing the immunophenoscore (IPS).Results: Stromal/immune /Estimate scores of HCC patients were obtained based on the ESTIMATE algorithm. The Kaplan-Meier curve analysis showed the high stromal/immune/ Estimate scores were significantly associated with better prognosis of the HCC patients. Then, six TME-related lncRNAs were screened for constructing the prognosis model. Kaplan-Meier survival curves suggested that HCC patients in high-risk group had worse prognosis than those with low-risk. ROC curve and Cox regression analyses demonstrated the signature could predict HCC survival exactly and independently. Function enrichment analysis revealed that some tumor- and immune-related pathways associated with HCC tumorigenesis and progression might be activated in high-risk group. We also discovered that some immune cells, which were beneficial to enhance immune responses towards cancer, were remarkably upregulated in low-risk group. Besides, there was closely correlation of immune checkmate inhibitors (ICIs) with the risk signature and the signature can be used to predict treatment response of ICIs.Conclusions: We analyzed the impact of the tumor microenvironment scores on the prognosis of patients with HCC. A novel TME-related prognostic risk signature was established, which may improve prognostic predictive accuracy and guide individualized immunotherapy for HCC patients.

scholarly journals Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Shenglan Huang ◽  
Jian Zhang ◽  
Xiaolan Lai ◽  
Lingling Zhuang ◽  
Jianbing Wu
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Kaplan Meier ◽  
Treatment Responses

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

scholarly journals Model based on five tumour immune microenvironment-related genes for predicting hepatocellular carcinoma immunotherapy outcomes

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xinyu Gu ◽  
Jun Guan ◽  
Jia Xu ◽  
Qiuxian Zheng ◽  
Chao Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Prognostic Model ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Low Risk ◽  
Immune Microenvironment ◽  
Kaplan Meier ◽  
Immune Related Genes

Abstract Background Although the tumour immune microenvironment is known to significantly influence immunotherapy outcomes, its association with changes in gene expression patterns in hepatocellular carcinoma (HCC) during immunotherapy and its effect on prognosis have not been clarified. Methods A total of 365 HCC samples from The Cancer Genome Atlas liver hepatocellular carcinoma (TCGA-LIHC) dataset were stratified into training datasets and verification datasets. In the training datasets, immune-related genes were analysed through univariate Cox regression analyses and least absolute shrinkage and selection operator (LASSO)-Cox analyses to build a prognostic model. The TCGA-LIHC, GSE14520, and Imvigor210 cohorts were subjected to time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses to verify the reliability of the developed model. Finally, single-sample gene set enrichment analysis (ssGSEA) was used to study the underlying molecular mechanisms. Results Five immune-related genes (LDHA, PPAT, BFSP1, NR0B1, and PFKFB4) were identified and used to establish the prognostic model for patient response to HCC treatment. ROC curve analysis of the TCGA (training and validation sets) and GSE14520 cohorts confirmed the predictive ability of the five-gene-based model (AUC > 0.6). In addition, ROC and Kaplan–Meier analyses indicated that the model could stratify patients into a low-risk and a high-risk group, wherein the high-risk group exhibited worse prognosis and was less sensitive to immunotherapy than the low-risk group. Functional enrichment analysis predicted potential associations of the five genes with several metabolic processes and oncological signatures. Conclusions We established a novel five-gene-based prognostic model based on the tumour immune microenvironment that can predict immunotherapy efficacy in HCC patients.

Early Prediction of Therapy Outcome in Patients with High-Risk Soft Tissue Sarcoma Using Positron Emission Tomography

Onkologie ◽  
2008 ◽  
Vol 31 (3) ◽  
pp. 107-112 ◽  
Author(s):  
Bernd Kasper ◽  
Sascha Dietrich ◽  
Antonia Dimitrakopoulou-Strauss ◽  
Ludwig G. Strauss ◽  
Uwe Haberkorn ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
High Risk ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Emission Tomography ◽  
Therapy Outcome ◽  
Early Prediction ◽  
Positron Emission

Exploration of Tumor Mutation Burden Combined with Immune Infiltrates in the Prognosis of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Zhenyu Zhao ◽  
Boxue He ◽  
Qidong Cai ◽  
Pengfei Zhang ◽  
Xiong Peng ◽  
...  
Keyword(s):  
High Risk ◽  
Lung Adenocarcinoma ◽  
Clinical Application ◽  
Risk Group ◽  
High Risk Group ◽  
Immune Microenvironment ◽  
Tumor Mutation Burden ◽  
Immune Signature ◽  
Immune Infiltration ◽  
Mutation Burden

Abstract Background: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. A recent study shows that immunotherapy is an effective method of LUAD treatment, and tumor mutation burden (TMB) was associated with the immune microenvironment and affected the immunotherapy. Exploration of the gene signature associated with tumor mutation burden and immune infiltrates in predicting prognosis in lung adenocarcinoma in this study, we explored the correlation of TMB with immune infiltration and prognosis in LUAD.Materials and Methods: In this study, we firstly got mutation data and LUAD RNA-Seq data of the LUAD from The Cancer Genome Atlas (TCGA), and according to the TMB we divided the patients into high/low-TMB levels groups. The gene ontology (GO) pathway enrichment analysis and KOBAS-Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis were utilized to explore the molecular function of the differentially expressed genes (DEGs) between the two groups. The function enrichment analyses of DEGs were related to the immune pathways. Then, the ESTIMATE algorithm, CIBERSORT, and ssGSEA analysis were utilized to identify the relationship between TMB subgroups and immune infiltration. According to the results, Venn analysis was utilized to select the immune-related genes in DEGs. Univariate and Lasso Cox proportional hazards regression analyses were performed to construct the signature which positively associated with the immune infiltration and affected the survival. Finally, we verified the correlation between the signature and immune infiltration. Result: The exploration of the immune infiltration suggested that high-TMB subgroups positively associated with the high level of immune infiltration in LUAD patients. According to the TMB-related immune signature, the patients were divided into High/Low-risk groups, and the high-risk group was positively associated with poor prognostic. The results of the PCA analysis confirmed the validity of the signature. We also verified the effectiveness of the signature in GSE30219 and GSE72094 datasets. The ROC curves and C-index suggested the good clinical application of the TMB-related immune signature in LUAD prognosis. Another result suggested that the patients of the high-risk group were positively associated with higher TMB levels, PD-L1expression, and immune infiltration levels.Conclusion: In conclusion, our signature provides potential biomarkers for studying aspects of the TMB in LUAD such as TMB affected immune microenvironment and prognosis. This signature may provide some biomarkers which could improve the biomarkers of PD-L1 immunotherapy response and were inverted for the clinical application of the TMB in LUAD. LUAD male patients with higher TMB-levels and risk scores may benefit from immunotherapy. The high-risk patients along with higher PD-L1 expression of the signature may suitable for immunotherapy and improve their survival by detecting the TMB of LUAD.

scholarly journals Neoadjuvant Pazopanib Treatment in High-Risk Soft Tissue Sarcoma: A Quantitative Dynamic 18F-FDG PET/CT Study of the German Interdisciplinary Sarcoma Group

Cancers ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 790 ◽  
Author(s):  
Christos Sachpekidis ◽  
Ioannis Karampinis ◽  
Jens Jakob ◽  
Bernd Kasper ◽  
Kai Nowak ◽  
...  
Keyword(s):  
High Risk ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Kinetic Analysis ◽  
Quantitative Evaluation ◽  
Fdg Pet ◽  
Standardized Uptake Value ◽  
Pet Ct ◽  
18F Fdg ◽  
Regression Grade

The outcome of high-risk soft tissue sarcoma (STS) is poor with radical surgery being the only potentially curative modality. Pazopanib is a multikinase inhibitor approved for the treatment of metastatic STS. Herein, in terms of the German Interdisciplinary Sarcoma Group (GISG-04/NOPASS) trial, we evaluate the potential role of kinetic analysis of fludeoxyglucose F-18 (18F-FDG) data derived from the application of dynamic positron emission tomography/computed tomography (PET/CT) in response assessment to pazopanib of STS patients scheduled for surgical resection. Sixteen STS patients treated with pazopanib as neoadjuvant therapy before surgery were enrolled in the analysis. All patients underwent dynamic PET/CT prior to and after pazopanib treatment. Data analysis consisted of visual (qualitative) analysis of the PET/CT scans, semi-quantitative evaluation based on standardized uptake value (SUV) calculations, and quantitative analysis of the dynamic 18F-FDG PET data, based on two-tissue compartment modeling. Resection specimens were histopathologically assessed and the percentage of regression grade was recorded in 14/16 patients. Time to tumor relapse/progression was also calculated. In the follow-up, 12/16 patients (75%) were alive without relapse, while four patients (25%) relapsed, among them one patient died. Median histopathological regression was 20% (mean 26%, range 5–70%). The studied population was dichotomized using a histopathological regression grade of 20% as cut-off. Based on this threshold, 10/14 patients (71%) showed partial remission (PR), while stable disease (SD) was seen in the rest 4 evaluable patients (29%). Semi-quantitative evaluation showed no statistically significant change in the widely used PET parameters, SUVaverage and SUVmax. On the other hand, 18F-FDG kinetic analysis revealed a significant decrease in the perfusion-related parameter K1, which reflects the carrier-mediated transport of 18F-FDG from plasma to tumor. This decrease can be considered as a marker in response to pazopanib in STS and could be due to the anti-angiogenic effect of the therapeutic agent.

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