scholarly journals Excavation of a Novel Transcription Factors-related Prognostic Signature for Osteosarcoma

2021 ◽  
Author(s):  
Long Cheng ◽  
Yi Liu ◽  
Xu Huang ◽  
Jiting Zhang ◽  
Bo Wu ◽  
...  
Keyword(s):  
Clinical Decision Making ◽  
Cox Regression ◽  
Clinical Decision ◽  
Significant Negative Correlation ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Prognosis Group ◽  
Risk Prognosis ◽  
Independent Cohort

Abstract BackgroundTranscription factors (TFs) are involved in the initiation and development of many cancers, regulating cancer-related activities. However, the significance of TF-related genes in predicting the prognosis of osteosarcoma (OS) patients is not yet clear. Risk stratification using prognostic markers can facilitate clinical decision-making and effect in the treatment of cancer.Material and methodsIn the study, we aimed to establish an optimal TF signature for predicting the prognosis of OS patients. We identified 24 differentially expressed TFs in metastatic and non-metastatic OS samples from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Subsequently, we performed univariate and multivariate cox regression analysis to built a TFs-related prognostic signature (TRPS) confirmed in an independent cohort (GSE39055). The ESTIMATE algorithm was used to estimate the immune/stromal cell score.ResultsWe built a TRPS for OS patients, including MESP1 and ZNF597. Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) curve both confirmed the accuracy of the signature. Multivariate analysis proved that this TRPS was an independent prognostic predictor of OS, and it was further confirmed in the GSE39055 dataset and multiple clinical subtypes. In addition, we found a significant negative correlation between stromal score and risk score. Moreover, the relative abundance of NK cells in the low-risk prognosis group was notably higher than that in the high-risk prognosis group. ConclusionIn summary, we established a TFs-related prognostic signature with high diagnostic, prognostic efficacy in OS patients, which may optimize the prognostic management of osteosarcoma patients and help achieve individualized treatment.

Clinical Significance of Prognostic Alternative Splicing Signature in Tumor Immune Environment and Immune Therapy of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Qianhui Xu ◽  
Hao Xu ◽  
Rongshan Deng ◽  
Nanjun Li ◽  
Ruiqi Mu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Alternative Splicing ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Immune Therapy ◽  
Immune Checkpoint Blockade ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Prognostic Prediction

Abstract Background: Hepatocellular carcinoma (HCC) ranks the sixth prevalent tumors with high mortality globally. Alternative splicing (AS) drives protein diversity, the imbalance of which might act an important factor in tumorigenesis. We aimed to construct of AS-based prognostic signature and elucidate the role in tumor immune microenvironment (TIME) and immunotherapy in HCC.Methods: To determine the prognosis-related AS events, univariate Cox regression analysis was performed, followed by the development of prognostic signatures. The prognosis predictive ability of risk signature was validated and a predictive nomogram was constructed. To uncover the context of TIME in HCC, ESTIMATE R package, ssGSEA algorithm and CIBERSORT method and TIMER database exploration were performed. And the correlation of AS events with immune checkpoint blockade (ICB)-related genes was analyzed.Results: A total of 3294 AS events associated with survival of HCC patients were screened. Based on splicing subtypes, we then constructed eight AS prognostic signature with robust prognostic predictive accuracy. Furthermore, a quantitative nomogram exhibited robust validity in prognostic prediction of HCC. Besides, the consolidated signature was significantly correlated with TIME diversity and ICB-related genes. Finally, the splicing regulatory network uncovered the potential functions of splicing factors (SFs) in HCC.Conclusion: Herein, the AS events may contribute novel and robust indicators for the prognostic prediction of HCC. The AS-SF networks could open up new approach for investigation of potential regulatory mechanisms. And we revealed the pivotal player of AS events in context of TIME and immunotherapy, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

scholarly journals Identification of a five-gene prognostic signature related to B cells infiltration in pancreatic adenocarcinoma

2021 ◽  
Author(s):  
Shaomei Tang ◽  
Xiaoliang Huang ◽  
Haixing Jiang ◽  
Shanyu Qin
Keyword(s):  
B Cells ◽  
Pancreatic Adenocarcinoma ◽  
Immune Cells ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Cell Components

Abstract Background: Pancreatic adenocarcinoma (PAAD) is an extremely malignant cancer. Immunotherapy is a promising avenue for elevating survival time of PAAD patients.Methods: The RNA sequencing and clinical data of PAAD were downloaded from the TCGA database. The ssGSEA method and weighted gene co-expression network analysis were used to calculate the relative abundance of tumor-infiltrated immune cells and identified the immune cells closely related module. Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis were used to construct a prognostic model. MCPcounter and EPIC were also applied to assess the immune cell components using gene expression profile.Results: The B cells closely related module was identified and five genes including ARID5A, CLEC2B, MICAL1, MZB1 and RAPGEF1 were ultimately selected to establish the prognostic signature for calculating risk scores of PAAD patients. Kaplan-Meier curves presented a worse survival in the high-risk patients (p<0.05) and the area under the Receiver operating characteristic (ROC) curve of risk score for 1-year and 3-year survival were 0.78 and 0.80 based on the training set. Also, similar results were verified in the validated and combined sets. Interestingly, low-risk group presented significantly elevated immune, stroma scores and proportion of B cells and associations between these five genes and B cells were identified by using multiple methods including ssGSEA, MCPcounter and EPIC. Conclusions: This is the first attempt to study a B cells related prognostic signature, which is instrumental in exploration of novel prognostic biomarkers in PAAD.

scholarly journals Systematic Analysis of the Functions and Prognostic Values of Rna Binding Protein in Head and Neck Squamous Cell Carcinoma

2020 ◽  
Author(s):  
song jukun ◽  
Feng Liu ◽  
Bo Liu ◽  
Xianlin Cheng ◽  
Xinhai Yin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Head And Neck ◽  
Clinical Decision Making ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Clinical Decision ◽  
The Cancer Genome Atlas ◽  
Systematic Analysis ◽  
Cox Regression Analysis

Abstract Background: Dysregulation of RNA-binding proteins (RBPs) playsan important role in controlling processes in cancer development.However, the function of RBPs has not been thoroughly and systematically documented in head and neck cancer.We aim to explore the role of RPB in the pathogenesis of HNSC.Methods: We obtained HNSC gene expression data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and the GEO databases, andidentified aberrantly expressed RBPs between tumors and normal tissues.Meanwhile, we performed a series of bioinformatics to explore the function and prognostic value of these RBPs.Results: A total of 249 abnormally expressed RBPs were identified, including 101 down-regulated RBPs and 148 up-regulated RBPs.Using least absolute shrinkage and selection operator (LASSO) and univariate Cox regression analysis, the fifteen RPBs were identified as hub genes. With the fifteen RPBS, the prognostic prediction model was constructed.Further analysis showed that the high-risk score of the patients expressed a better survival outcome. The prediction model was validated in another HNSC dataset, and similar findings were observed. Conclusions: Our results provide novel insights into the pathogenesis of HNSC. The fifteen RBP gene signature exhibited the predictive value of moderate HNSC prognosis, and have potential application value in clinical decision-making and individualized treatment.

scholarly journals Prognostic Signature of Osteosarcoma Based on 14 Autophagy-Related Genes

2021 ◽  
Vol 27 ◽  
Author(s):  
Wei Qi ◽  
Qian Yan ◽  
Ming Lv ◽  
Delei Song ◽  
Xianbin Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Immune Cell ◽  
Cox Regression ◽  
Clinical Decision ◽  
Cell Types ◽  
Survival Outcome ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: Osteosarcoma is a common malignancy of bone with inferior survival outcome. Autophagy can exert multifactorial influence on tumorigenesis and tumor progression. However, the specific function of genes related to autophagy in the prognosis of osteosarcoma patients remains unclear. Herein, we aimed to explore the association of genes related to autophagy with the survival outcome of osteosarcoma patients.Methods: The autophagy-associated genes that were related to the prognosis of osteosarcoma were optimized by LASSO Cox regression analysis. The survival of osteosarcoma patients was forecasted by multivariate Cox regression analysis. The immune infiltration status of 22 immune cell types in osteosarcoma patients with high and low risk scores was compared by using the CIBERSORT tool.Results: The risk score model constructed according to 14 autophagy-related genes (ATG4A, BAK1, BNIP3, CALCOCO2, CCL2, DAPK1, EGFR, FAS, GRID2, ITGA3, MYC, RAB33B, USP10, and WIPI1) could effectively predict the prognosis of patients with osteosarcoma. A nomogram model was established based on risk score and metastasis.Conclusion: Autophagy-related genes were identified as pivotal prognostic signatures, which could guide the clinical decision making in the treatment of osteosarcoma.

scholarly journals Development and validation of an immune gene-set based prognostic signature for soft tissue sarcoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rui Shen ◽  
Bo Liu ◽  
Xuesen Li ◽  
Tengbo Yu ◽  
Kuishuai Xu ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Cox Regression ◽  
Survival Curve ◽  
The Cancer Genome Atlas ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Meier Survival Curve

Abstract Background Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs. Methods We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed. Results A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis (P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcoma patients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs. Conclusions In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcoma patients and provide potential novel immunotherapy targets.

scholarly journals A Innovative Prognostic Symbol Based on Autophagy-Related Long Noncoding RNAs Signature Predicts in Low-Grade Glioma

2021 ◽  
Author(s):  
Aierpati Maimaiti ◽  
Mirezhati Tuerhong ◽  
Yongxin Wang ◽  
Maimaitili Aisha ◽  
Lei Jiang ◽  
...  
Keyword(s):  
Survival Analysis ◽  
High Risk ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Degradation Process ◽  
Clinical Decision ◽  
Low Risk ◽  
Low Grade ◽  
Kaplan Meier ◽  
Selection Operator

Abstract Autophagy is a highly conserved lysosomal degradation process essential in tumorigenesis. However, the involvement of autophagy-related lncRNA in low-grade gliomas (LGG) remains a pending question. Efforts were made to establish an autophagy-related lncRNA signature prognostic in LGG patients, and to explore the behind potential function. We used Univariate Cox, Least Absolute Shrinkage and Selection Operator (Lasso), and Multivariate Cox regression models were designed to establish an autophagy-related lncRNA prognostic signature. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, nomogram, C-index, calibration curve and clinical decision-making curve were adopted to assess the predictive capability of the identified signature.A signature comprising 9 autophagy-related lncRNAs (AL136964.1, ARHGEF26-AS1, PCED1B-AS1, AS104072.1, PRKCQ-AS1, LINC00957, AS125616.1, PSMB8-AS1, and AC087741.1) was identified as a prognostic model. LGG patients were allocated into high- and low-risk cohorts depending on the median model-based Riskscore. The survival analysis showed a 10-year survival rate of 9.3% (95% CI: 1.91-45.3%) in high-risk LGG patients and 48.4% (95% CI: 24.7-95.0%) in low-risk individuals in the training set. While those of patients in the validation set were 13.48% (95% CI: 4.52-40.2%) for high-risk LGG patients and 48.4% (95% CI: 28.04-83.4%) for low-risk LGG patients, respectively. This suggested a relatively low survival in high-risk cases compared to low-risk individuals. In addition, LncRNA signature was independently prognostic and potentially associated with the progression of LGG. Taken together, our constructed 9 autophagy-related lncRNA signature may play a crucial part in the diagnosis and treatment for LGG, which may guide to open up a new avenues for tumor targeted therapy.

scholarly journals Development and validation of an immune gene-set based prognostic signature for soft tissue sarcoma

2020 ◽  
Author(s):  
Rui Shen ◽  
Xiangying Meng ◽  
Jianyi Li ◽  
Tengbo Yu ◽  
Kuishuai Xu
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Cox Regression ◽  
Survival Curve ◽  
The Cancer Genome Atlas ◽  
Immune Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Meier Survival Curve

Abstract Background Sarcomas is a group of heterogeneous malignant tumors originated from mesenchymal tissue and different types of sarcomas have disparate outcomes. The present study aims to identify the prognostic value of immune-related genes (IRGs) in sarcoma and establish a prognostic signature based on IRGs. Methods We collected the expression profile and clinical information of 255 soft tissue sarcoma samples from The Cancer Genome Atlas (TCGA) database and 2498 IRGs from the ImmPort database. The LASSO algorithm and Cox regression analysis were used to identify the best candidate genes and construct a signature. The prognostic ability of the signature was evaluated by ROC curves and Kaplan-Meier survival curves and validated in an independent cohort. Besides, a nomogram based on the IRGs and independent prognostic clinical variables was developed. Results A total of 19 IRGs were incorporated into the signature. In the training cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.938, 0.937 and 0.935, respectively. The Kaplan-Meier survival curve indicated that high-risk patients were significantly worse prognosis(P < 0.001). In the validation cohort, the AUC values of signature at 1-, 2-, and 3-years were 0.730, 0.717 and 0.647, respectively. The Kaplan-Meier survival curve also showed significant distinct survival outcome between two risk groups. Furthermore, a nomogram based on the signature and four prognostic variables showed great accuracy in whole sarcoma patients and subgroup analyses. More importantly, the results of the TF regulatory network and immune infiltration analysis revealed the potential molecular mechanism of IRGs. Conclusions In general, we identified and validated an IRG-based signature, which can be used as an independent prognostic signature in evaluating the prognosis of sarcoma patients and provide potential novel immunotherapy targets.

scholarly journals Immunological significance of prognostic alternative splicing signature in hepatocellular carcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qianhui Xu ◽  
Hao Xu ◽  
Rongshan Deng ◽  
Nanjun Li ◽  
Ruiqi Mu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Alternative Splicing ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Gene Set Enrichment Analysis ◽  
Splicing Factors ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Prognostic Prediction

Abstract Background Hepatocellular carcinoma (HCC) ranks the sixth prevalent tumors with high mortality globally. Alternative splicing (AS) drives protein diversity, the imbalance of which might act an important factor in tumorigenesis. This study aimed to construct of AS-based prognostic signature and elucidate the role in tumor immune microenvironment (TIME) and immunotherapy in HCC. Methods Univariate Cox regression analysis was performed to determine the prognosis-related AS events and gene set enrichment analysis (GSEA) was employed for functional annotation, followed by the development of prognostic signatures using univariate Cox, LASSO and multivariate Cox regression. K-M survival analysis, proportional hazards model, and ROC curves were conducted to validate prognostic value. ESTIMATE R package, ssGSEA algorithm and CIBERSORT method and TIMER database exploration were performed to uncover the context of TIME in HCC. Quantitative real-time polymerase chain reaction was implemented to detect ZDHHC16 mRNA expression. Cytoscape software 3.8.0 were employed to visualize AS-splicing factors (SFs) regulatory networks. Results A total of 3294 AS events associated with survival of HCC patients were screened. Based on splicing subtypes, eight AS prognostic signature with robust prognostic predictive accuracy were constructed. Furthermore, quantitative prognostic nomogram was developed and exhibited robust validity in prognostic prediction. Besides, the consolidated signature was significantly correlated with TIME diversity and ICB-related genes. ZDHHC16 presented promising prospect as prognostic factor in HCC. Finally, the splicing regulatory network uncovered the potential functions of splicing factors (SFs). Conclusion Herein, exploration of AS patterns may provide novel and robust indicators (i.e., risk signature, prognostic nomogram, etc.,) for prognostic prediction of HCC. The AS-SF networks could open up new approach for investigation of potential regulatory mechanisms. And pivotal players of AS events in context of TIME and immunotherapy efficiency were revealed, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

scholarly journals Identification of lncRNA prognostic signature and analysis of their functions for HCV-related hepatocellular carcinoma

2020 ◽  
Author(s):  
Kun Wang ◽  
Wenxin Li ◽  
Yefu Liu ◽  
Zhiqiang Hao ◽  
Xiangdong Hua ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Clinical Variables ◽  
Kaplan Meier ◽  
Underlying Mechanisms

Abstract Background Hepatitis C virus (HCV) infection is a main contribution to the increase in hepatocellular carcinoma (HCC) incidence and patients’ death recently, but prognostic biomarkers for HCV-related HCC remain rarely reported. This study was to identify an lncRNA prognostic signature for HCV-HCC patients and explore their underlying function mechanisms. Methods In total, 102 HCV-HCC samples and 50 normal control samples were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis were conducted to screen an lncRNA signature that could predict overall survival (OS) and then, the risk score was calculated using this signature. The prognostic potential of this risk score was evaluated by drawing Kaplan-Meier, receiver operating characteristic (ROC) curves and performing multivariate Cox regression analyses with clinical variables. Furthermore, a co-expression and competing endogenous RNA (ceRNA) networks were constructed to explore the functional mechanisms of lncRNAs. Results Multivariate Cox regression showed six lncRNAs (SLC16A1-AS1, ZFPM2-AS1, JARID2-AS1, LINC01426, USP3-AS1 and LYPLAL1-AS1) were significantly associated with OS of HCV-HCC patients. These six lncRNAs were used to establish a risk score model, which displayed a higher prognosis prediction accuracy [area under the ROC curve (AUC) = 0.95 for training set; AUC = 0.885 for testing; AUC = 0.907 for entire set]. Also, this was independent of various clinical variables. The crucial co-expression (LINC01426/SLC16A1-AS1-AURKA/SFN/CCNB1, ZFPM2-AS1/LYPLAL1-AS1/JARID2-AS1-TSSK6) or ceRNA (USP3-AS1-hsa-miR-383-SFN) interaction axes were identified. Conclusion Our study identified a novel six-lncRNA prognosis signature for HCV-HCC patients and indicated their underlying mechanisms for HCC progression.

Prognostic role of aberrant mTOR activation in patients with stage II and III colorectal cancer

2020 ◽  
Vol 14 (12) ◽  
pp. 1127-1137
Author(s):  
Tong-Tong Zhang ◽  
Yi-Qing Zhu ◽  
Hong-Qing Cai ◽  
Jun-Wen Zheng ◽  
Jia-Jie Hao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Stage Ii ◽  
Poor Prognostic Factor ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
Risk Predictor

Aim: This study aimed to develop an effective risk predictor for patients with stage II and III colorectal cancer (CRC). Materials & methods: The prognostic value of p-mTOR (Ser2448) levels was analyzed using Kaplan–Meier survival analysis and Cox regression analysis. Results: The levels of p-mTOR were increased in CRC specimens and significantly correlated with poor prognosis in patients with stage II and III CRC. Notably, the p-mTOR level was an independent poor prognostic factor for disease-free survival and overall survival in stage II CRC. Conclusion: Aberrant mTOR activation was significantly associated with the risk of recurrence or death in patients with stage II and III CRC, thus this activated proteins that may serve as a potential biomarker for high-risk CRC.

Sign in / Sign up

Export Citation Format

Share Document