scholarly journals Establishment of A Nomogram for Predicting the Prognosis of Soft Tissue Sarcoma Based on Seven Glycolysis-Related Gene Risk Score

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuhang Liu ◽  
Changjiang Liu ◽  
Hao Zhang ◽  
Xinzeyu Yi ◽  
Aixi Yu
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Cox Regression ◽  
Sequence Data ◽  
Tissue Expression ◽  
Gene Signature ◽  
Prognostic Indicators ◽  
Related Gene ◽  
Training Set ◽  
Predictive Values

Background: Soft tissue sarcoma (STS) is a group of tumors with a low incidence and a complex type. Therefore, it is an arduous task to accurately diagnose and treat them. Glycolysis-related genes are closely related to tumor progression and metastasis. Hence, our study is dedicated to the development of risk characteristics and nomograms based on glycolysis-related genes to assess the survival possibility of patients with STS.Methods: All data sets used in our research include gene expression data and clinical medical characteristics in the Genomic Data Commons Data Portal (National Cancer Institute) Soft Tissue Sarcoma (TCGA SARC) and GEO database, gene sequence data of corresponding non-diseased human tissues in the Genotype Tissue Expression (GTEx).Next, transcriptome data in TCGA SARC was analyzed as the training set to construct a glycolysis-related gene risk signature and nomogram, which were confirmed in external test set.Results: We identified and verified the 7 glycolysis-related gene signature that is highly correlated with the overall survival (OS) of STS patients, which performed excellently in the evaluation of the size of AUC, and calibration curve. As well as, the results of the analysis of univariate and multivariate Cox regression demonstrated that this 7 glycolysis-related gene characteristic acts independently as an influence predictor for STS patients. Therefore, a prognostic-related nomogram combing 7 gene signature with clinical influencing features was constructed to predict OS of patients with STS in the training set that demonstrated strong predictive values for survival.Conclusion: These results demonstrate that both glycolysis-related gene risk signature and nomogram were efficient prognostic indicators for patients with STS. These findings may contribute to make individualize clinical decisions on prognosis and treatment.

Analysis of metabolism-related gene signature for prognosis prediction of clear cell renal cell carcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16580-e16580
Author(s):  
Dalong Cao ◽  
Yuchen Liao ◽  
Guoqiang Wang ◽  
Shangli Cai ◽  
Guohai Shi
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Clear Cell ◽  
Gene Signature ◽  
Related Gene ◽  
Prognostic Signature ◽  
Training Set ◽  
Roc Curve Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Risk Patients

e16580 Background: Clear cell renal cell carcinoma (ccRCC) is characterized by a dysregulation of changes in cellular metabolism. However, the prognostic value of metabolism-related genes in ccRCC have not been systematically profiled. In this study, a candidate prognostic gene signature of metabolism in ccRCC was explored. Methods: The clinical and gene expression profiles of ccRCC patients were downloaded from the TCGA, GEO and three clinical trials (CheckMate 009, CheckMate 010, CheckMate 025), and the metabolism-related gene set was downloaded from MSigDB. Differential expression analysis and LASSO Cox regression with binomial deviance minimization criteria were applied to identify and build a metabolism-based signature. The prognostic significance of the signature was further evaluated with the Receiver Operating Characteristic (ROC) curve analysis. Univariate and multivariate Cox regression analysis was performed to evaluate the impact of each variable on OS. Furthermore, the prediction power of the signature has been validated using different ccRCC cohort. Results: In this study, a signature of 8 metabolism-related genes (ANGPT2, ATP6V1B1, CACNA1E, CD163L1, EPN2, HOXD11, PROS1, SHOX2) was constructed as being significantly associated with overall survival (OS) among patients with ccRCC, which differentiated ccRCC patients into high- and low-risk subgroups. The Kaplan-Meier (KM) analysis showed that the survival rate of the low-risk patients was significantly higher than that of the higher-risk patients (hazard ratio (HR) in training set, 0.25 [95% CI, 0.14-0.44; P < 0.001]; testing set, 0.28 [95% CI, 0.10-0.76; P = 0.008]; validation cohort (clinical trials), 0.47 [95% CI, 0.33-0.68; P < 0 .001]; validation cohort (GSE29609), 0.25, [95% CI, 0.08-0.88; P = 0 .01]). ROC curve analysis of the prognostic signature showed that the areas under curve (AUC) for the 1-, 3-, and 5-year OS in all cohort were more than 0.70 (AUC of the signature for 3 year in the training set and validation cohort were 0.816 and 0.708, respectively, and 0.807 and 0.702, respectively, for the 5- year OS). Further more, a nomogram based on the signature was constructed and showed an accurate prediction for prognosis in ccRCC. Conclusions: Taken together, we identified the key metabolism-related genes and constructed a robust prognostic signature for the prognostic predictor of ccRCC patients, which maybe help personalized management of ccRCC patients.

scholarly journals Correction: Validation of a hypoxia related gene signature in multiple soft tissue sarcoma cohorts

Oncotarget ◽  
2019 ◽  
Vol 10 (20) ◽  
pp. 2007-2007
Author(s):  
Lingjian Yang ◽  
Laura Forker ◽  
Joely J. Irlam ◽  
Nischalan Pillay ◽  
Ananya Choudhury ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Gene Signature ◽  
Related Gene

scholarly journals Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

2021 ◽  
Vol 10 ◽  
Author(s):  
Dai Zhang ◽  
Yi Zheng ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Cox Regression ◽  
Gene Signature ◽  
Breast Cancer Survival ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Kaplan Meier

To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.

scholarly journals Validation of a hypoxia related gene signature in multiple soft tissue sarcoma cohorts

Oncotarget ◽  
2017 ◽  
Vol 9 (3) ◽  
pp. 3946-3955 ◽  
Author(s):  
Lingjian Yang ◽  
Laura Forker ◽  
Joely J. Irlam ◽  
Nischalan Pillay ◽  
Ananya Choudhury ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Gene Signature ◽  
Related Gene

scholarly journals Identification of a novel autophagy-related gene signature for predicting metastasis and survival in patients with osteosarcoma

2020 ◽  
Author(s):  
Guangzhi Zhang ◽  
Yajun Deng ◽  
Zuolong Wu ◽  
Enhui Ren ◽  
Wenhua Yuan ◽  
...  
Keyword(s):  
Muscle Tissue ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Tissue Expression ◽  
Gene Signature ◽  
Related Gene ◽  
Normal Muscle ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Gene Expression Profile Data

Abstract Background: Osteosarcoma (OS) is a bone malignant tumor that occurs in children and adolescents. Due to a lack of reliable prognostic biomarkers, the prognosis of OS patients is often uncertain. This study aimed to construct an autophagy-related gene signature to predict the prognosis of OS patients.Methods: The gene expression profile data of OS and normal muscle tissue samples were downloaded separately from the Therapeutically Applied Research To Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases . The differentially expressed autophagy-related genes (DEARGs) in OS and normal muscle tissue samples were screened using R software, before being subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A protein-protein interaction (PPI) network was constructed and hub autophagy-related genes were screened. Finally, the screened autophagy-related genes were subjected to univariate Cox regression, Lasso Cox regression, survival analysis, and clinical correlation analysis.Results: A total of 120 DEARGs and 10 hub autophagy-related genes were obtained. A prognostic autophagy-related gene signature consisting of 9 genes ( BNIP3 , MYC , BAG1 , CALCOCO2 , ATF4 , AMBRA1 , EGFR , MAPK1 , and PEX ) was constructed. This signature was significantly correlated to the prognosis ( P <0.0001) and distant metastasis of OS patients ( P = 0.013).Conclusion: This signature based on 9 autophagy-related genes could predict metastasis and survival in patients with OS.

scholarly journals Identification and Validation of a Novel Pyroptosis-Related Gene Signature for Prognosis Prediction in Soft Tissue Sarcoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Lin Qi ◽  
Ruiling Xu ◽  
Lu Wan ◽  
Xiaolei Ren ◽  
WenChao Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Risk Score ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Survival Difference ◽  
Related Risk

Soft tissue sarcoma (STS) represents an uncommon and heterogenous group of malignancies, and poses substantial therapeutic challenges. Pyroptosis has been demonstrated to be related with tumor progression and prognosis. Nevertheless, no studies exist that delineated the role of pyroptosis-related genes (PRGs) in STS. In the present study, we comprehensively and systematically analyzed the gene expression profiles of PRGs in STS. The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were utilized to identify differentially expressed PRGs. In total, 34 PRGs were aberrantly expressed between STS and normal tissues. Several PRGs were validated with RT-qPCR. Consensus clustering analysis based on PRGs was conducted to divide STS patients into two clusters, and significant survival difference was observed between two distinct clusters (p = 0.019). Differentially expressed genes (DEGs) were identified between pyroptosis-related clusters. Based on the least absolute shrinkage and selection operator (LASSO) COX regression analysis, the pyroptosis-related gene signature with five key DEGs was constructed. The high pyroptosis-related risk score group of TCGA cohort was characterized by poorer prognosis (p &lt; 0.001), with immune infiltration and function significantly decreased. For external validation, STS patients from Gene Expression Omnibus (GEO) were grouped according to the same cut-off point. The survival difference between two risk groups of GEO cohort was also significant (p &lt; 0.001). With the combination of clinical characteristics, pyroptosis-related risk score was identified to serve as an independent prognostic factor for STS patients. In conclusion, this study provided a comprehensive overview of PRGs in STS and the potential role in prognosis, which could be an important direction for future studies.

scholarly journals PO-0836: A hypoxia related gene signature is independently prognostic in multiple soft tissue sarcoma cohorts

2018 ◽  
Vol 127 ◽  
pp. S436-S437
Author(s):  
L. Yang ◽  
L. Forker ◽  
J.J. Iram ◽  
N. Pillay ◽  
A. Choudhury ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Gene Signature ◽  
Related Gene

scholarly journals A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guichuan Huang ◽  
Jing Zhang ◽  
Ling Gong ◽  
Yi Huang ◽  
Daishun Liu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

scholarly journals Impact of sarcopenia in advanced and metastatic soft tissue sarcoma

2021 ◽  
Author(s):  
Dennis Strassmann ◽  
Bennet Hensen ◽  
Viktor Grünwald ◽  
Katharina Stange ◽  
Hendrik Eggers ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Cox Regression ◽  
Therapy Response ◽  
Prognostic Impact ◽  
Patient Counseling ◽  
Skeletal Muscle Index ◽  
Prognostic Parameter ◽  
Dismal Prognosis ◽  
Metastatic Soft Tissue Sarcoma

Abstract Introduction Advanced or metastatic soft tissue sarcoma (a/mSTS) is associated with a dismal prognosis. Patient counseling on treatment aggressiveness is pivotal to avoid over- or undertreatment. Recently, evaluation of body composition markers like the skeletal muscle index (SMI) became focus of interest in a variety of cancers. This study focuses on the prognostic impact of SMI in a/mSTS, retrospectively. Methods 181 a/mSTS patients were identified, 89 were eligible due to prespecified criteria for SMI assessment. Baseline CT-Scans were analyzed using an institutional software solution. Sarcopenia defining cut-off values for the SMI were established by optimal fitting method. Primary end point was overall survival (OS) and secondary endpoints were progression free survival (PFS), disease control rate (DCR), overall response rate (ORR). Descriptive statistics as well as Kaplan Meier- and Cox regression analyses were administered. Results 28/89 a/mSTS patients showed sarcopenia. Sarcopenic patients were significantly older, generally tended to receive less multimodal therapies (62 vs. 57 years, P = 0.025; respectively median 2.5 vs. 4, P = 0.132) and showed a significantly lower median OS (4 months [95%CI 1.9–6.0] vs. 16 months [95%CI 8.8–23.2], Log-rank P = 0.002). Sarcopenia was identified as independent prognostic parameter of impaired OS (HR 2.40 [95%-CI 1.4–4.0], P < 0.001). Moreover, DCR of first palliative medical treatment was superior in non-sarcopenic patients (49.2% vs. 25%, P = 0.032). Conclusion This study identifies sarcopenia as a prognostic parameter in a/mSTS. Further on, the data suggest that sarcopenia shows a trend of being associated with first line therapy response. SMI is a promising prognostic parameter, which needs further validation.

scholarly journals Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

2021 ◽  
Vol 27 ◽  
Author(s):  
Aoshuang Qi ◽  
Mingyi Ju ◽  
Yinfeng Liu ◽  
Jia Bi ◽  
Qian Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Antigen Processing ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Gene Set ◽  
Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) &lt; 1), and HLA-F was risky (HR &gt; 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

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