scholarly journals Comprehensive Analysis of the Prognostic Significance of Hsa-miR-100-5p and Its Related Gene Signature in Stomach Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Gaoming Wang ◽  
Ludi Yang ◽  
Miao Hu ◽  
Renhao Hu ◽  
Yongkun Wang ◽  
...  
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Tumor Development ◽  
Risk Groups ◽  
Gene Signature ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Stomach Adenocarcinoma

Stomach adenocarcinoma (STAD) is one of the most common cancers in the world. However, the prognosis of STAD remains poor, and the therapeutic effect of chemotherapy and immunotherapy varies from person to person. MicroRNAs (miRNAs) play vital roles in tumor development and metastasis and can be used for cancer diagnosis and prognosis. In this study, hsa-miR-100-5p was identified as the only dysregulated miRNA in STAD samples through an analysis of three miRNA expression matrices. A weighted gene co-expression network analysis (WGCNA) was performed to select hsa-miR-100-5p-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a miR-100-5p-related prognostic signature. Kaplan–Meier analyses, nomograms, and univariate and multivariate Cox regression analyses were used to evaluate the prognostic signature, which was subsequently identified as an independent risk factor for STAD patients. We investigated the tumor immune environment between low- and high-risk groups and found that, among component types, M2 macrophages contributed the most to the difference between these groups. A drug sensitivity analysis suggested that patients with high-risk scores may be more sensitive to docetaxel and cisplatin chemotherapy and that patients in the low-risk group may be more likely to benefit from immunotherapy. Finally, external cohorts were evaluated to validate the robustness of the prognostic signature. In summary, this study may provide new ideas for developing more individualized therapeutic strategies for STAD patients.

scholarly journals An Immune-Related Prognostic Signature for Predicting Clinical Outcomes and Immune Landscape in IDH-Mutant Lower-Grade Gliomas

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Gang Xiao ◽  
Xuan Gao ◽  
Lifeng Li ◽  
Chao Liu ◽  
Zhiyuan Liu ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Outcomes ◽  
Immune Cell ◽  
Cox Regression ◽  
Immune Escape ◽  
Gene Signature ◽  
Individual Therapy ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Background. IDH mutation is the most common in diffuse LGGs, correlated with a favorable prognosis. However, the IDH-mutant LGGs patients with poor prognoses need to be identified, and the potential mechanism leading to a worse outcome and treatment options needs to be investigated. Methods. A six-gene immune-related prognostic signature in IDH-mutant LGGs was constructed based on two public datasets and univariate, multivariate, and LASSO Cox regression analysis. Patients were divided into low- and high-risk groups based on the median risk score in the training and validation sets. We analyzed enriched pathways and immune cell infiltration, applying the GSEA and the immune evaluation algorithms. Results. Stratification and multivariate Cox analysis unveiled that the six-gene signature was an independent prognostic factor. The signature (0.806/0.795/0.822) showed a remarkable prognostic performance, with 1-, 3-, and 5-year time-dependent AUC, higher than for grade (0.612/0.638/0.649) and 1p19q codeletion status (0.606/0.658/0.676). High-risk patients had higher infiltrating immune cells. However, the specific immune escape was observed in the high-risk group after immune activation, owing to increasing immunosuppressive cells, inhibitory cytokines, and immune checkpoint molecules. Moreover, a novel nomogram model was developed to evaluate the survival in IDH-mutant LGGs patients. Conclusion. The six-gene signature could be a promising prognostic biomarker, which is promising to promote individual therapy and improve the clinical outcomes of IDH-mutant gliomas. The study also refined the current classification system of IDH-mutant gliomas, classifying patients into two subtypes with distinct immunophenotypes and overall survival.

scholarly journals Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

2021 ◽  
Author(s):  
Jixiang Cao ◽  
Xi Chen ◽  
Guang Lu ◽  
Haowei Wang ◽  
Xinyu Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Signature ◽  
Risk Scores ◽  
Tumor Infiltration ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

scholarly journals Development and validation of an immune-related prognostic signature in cervical cancer

2021 ◽  
Author(s):  
Rongjia Su ◽  
Chengwen Jin ◽  
Hualei Bu ◽  
Xiaoyun Wang ◽  
Menghua Kuang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Clinical Information ◽  
Risk Groups ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Gynecological Malignancy ◽  
Immune Infiltration

Abstract Background Cervical cancer is the fourth most frequently gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods Raw data and clinical information of cervical cancer samples were download from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results There were 8 immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing PD-L1 expression and PD-1 checkpoint pathway differences between high risk and low risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusion The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

scholarly journals Development and Validation of an Immune-related Prognostic Signature in Cervical Cancer

2021 ◽  
Author(s):  
Rongjia Su ◽  
Chengwen Jin ◽  
Hualei Bu ◽  
Xiaoyun Wang ◽  
Menghua Kuang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Clinical Information ◽  
Risk Groups ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Gynecological Malignancy ◽  
Immune Infiltration

Abstract Background: Cervical cancer is the fourth most frequently gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods: Raw data and clinical information of cervical cancer samples were download from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results: There were 8 immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing PD-L1 expression and PD-1 checkpoint pathway differences between high risk and low risk groups. Conclusions: The 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Furthermore, the 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

scholarly journals Identification of a Seven-Gene Signature and Establishment of a Nomogram Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Haige Zheng ◽  
Xiangkun Wu ◽  
Huixian Liu ◽  
Yumin Lu ◽  
Hengguo Li
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Carcinoma ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Ten gene microarray datasets were obtained from the gene expression omnibus (GEO) database. Level 3 mRNA expression and clinical data were obtained in The Cancer Genome Atlas (TCGA) database. We identified highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in nine GEO and TCGA datasets using Robust Rank Aggregation (RRA) method. Univariate Cox regression analysis and lasso Cox regression analysis were performed to identify DEGs related to the Overall-survival (OS) and to construct a prognostic gene signature. External validation was performed using GSE65858. Moreover, gene set enrichment analyses (GSEA) analysis was used to analyze significantly rich pathways in high-risk and low-risk groups, and tumor immunoassays were used to clarify immune correlation of the prognostic gene. Finally, integrate multiple forecast indicators were used to build a nomogram using the TCGA-HNSCC dataset. Kaplan–Meier analysis, receiver operating characteristic (ROC), a calibration plot, Harrell’s concordance index (C-index), and decision curve analysis (DCA) were used to test the predictive capability of the seven genetic signals and the nomogram. Results: A novel seven-gene signature (including SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) was established to predict overall survival in HNSCC patients. ROC curve performed well in the training and validation data sets. Kaplan–Meier analysis demonstrated that low-risk groups had a longer survival time. The nomogram containing seven genetic markers and clinical prognostic factors was a good predictor of HNSCC survival and showed a certain net clinical benefit through the DCA curve. Further research demonstrated that the infiltration degree of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group.Conclusion: Our analysis established a seven-gene model and nomogram to accurately predict the prognosis status of HNSCC patients, immune relevance was also described, which may provide a new possibility for individual treatment and medical decision-making.

scholarly journals A Novel Ferroptosis-related Gene Signature for Prognosis Prediction in Glioma

2021 ◽  
Author(s):  
Tian Lan ◽  
Die Wu ◽  
Wei Quan ◽  
Donghu Yu ◽  
Sheng Li ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
High Risk Group ◽  
Differential Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background: Glioma is a fatal brain tumor characterized by invasive nature, rapidly proliferation and tumor recurrence. Despite aggressive surgical resection followed by concurrent radiotherapy and chemotherapy, the overall survival (OS) of Glioma patients remains poor. Ferroptosis is a unique modality to regulate programmed cell death and associated with multiple steps of tumorigenesis of a variety of tumors.Methods: In this study, ferroptosis-related genes model was identified by differential analysis and Cox regression analysis. GO, KEGG and GSVA analysis were used to detect the potential biological functions and signaling pathway. The infiltration of immune cells was quantified by Cibersort.Results: The patients’ samples are stratified into two risk groups based on 4-gene signature. High-risk group has poorer overall survival. The results of functional analysis indicated that the extracellular matrix-related biologic functions and pathways were enriched in high-risk group, and that the infiltration of immunocytes is different in two groups.Conclusion: In summary, a novel ferroptosis-related gene signature can be used for prognostic prediction in glioma. The filtered genes related to ferroptosis in clinical could be a potential extra method to assess glioma patients’ prognosis and therapeutic.

scholarly journals Development of a Gene Signature Associated with Iron Metabolism in Lung Adenocarcinoma

2021 ◽  
Author(s):  
Junqi Qin ◽  
Zhanyu Xu ◽  
Fanglu Qin ◽  
Jiangbo Wei ◽  
Liqiang Yuan ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Iron Metabolism ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background: There are few studies on the role of iron metabolism genes in predicting the prognosis of lung adenocarcinoma (LUAD). Our research aims to screen key genes and to establish a prognostic signature that can predict the overall survival rate of lung adenocarcinoma patients. Methods: Genes related to iron metabolism were downloaded from the GeneCards database; in addition, RNA-Seq data and corresponding clinical materials of 594 adenocarcinoma patients from The Cancer Genome Atlas(TCGA) were downloaded. GSE42127 of Gene Expression Omnibus (GEO) database was also further verified. The multi-gene prognostic signature was constructed by the Cox regression model of the Least Absolute Shrinkage and Selection Operator (LASSO). The clinical applicability of the model and its connection with immune cell infiltration was then analyzed. Results: We constructed a prediction signature with 12 genes (HAVCR1, SPN, GAPDH, ANGPTL4, PRSS3, KRT8, LDHA, HMMR, SLC2A1, CYP24A1, LOXL2, TIMP1) in the TCGA test set, and counted the patient's risk value based on this 12-gene signature; patients were split into high and low-risk groups. The survival graph results revealed that the survival prognosis between the high and low-risk groups was significantly different (TCGA: P <0.001, GEO: P = 0.001). Univariate and multivariate Cox regression analysis confirmed that the risk value is a predictor of patient OS (P<0.001). The area under the time-dependent ROC curve (AUC) indicated that our signature had a relatively high true positive rate when predicting the 1-year, 3-year, and 5-year OS of the TCGA cohort, which was 0.735, 0.711, and 0.601, respectively. The analysis of the nomogram and calibration curve showed the predictive ability of the gene model. In addition, immune-related pathways were highlighted in the functional enrichment analysis, and immune response between the two risk groups was observed to be significantly different. All of the results proved the reliability of our iron metabolism-related gene risk prognostic model. Conclusion: We developed and verified a 12-gene prognostic signature, which can help predict the prognosis of lung adenocarcinoma and offer a variety of targeted options for the precise treatment of lung cancer.

scholarly journals Development and Validation of a Hypoxia-Associated Prognostic Signature Related to Osteosarcoma Metastasis and Immune Infiltration

2021 ◽  
Vol 9 ◽  
Author(s):  
Yucheng Fu ◽  
Qiyuan Bao ◽  
Zhuochao Liu ◽  
Guoyu He ◽  
Junxiang Wen ◽  
...  
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
Survival Rates ◽  
Gene Signature ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Messenger Rnas ◽  
Prognostic Signature ◽  
Immune Infiltration

BackgroundIncreasing evidence has shown that hypoxia microenvironment relates to tumor initiation and progression. However, no studies focus on the application of hypoxia-associated genes in predicting osteosarcoma patients’ prognosis. This research aims to identify the hypoxia-associated genes related to osteosarcoma metastasis and construct a gene signature to predict osteosarcoma prognosis.MethodsThe differentially expressed messenger RNAs (DEmRNAs) related to osteosarcoma metastasis were identified from Therapeutically Applicable Research to Generate Effective Treatments (Target) database. Univariate and multivariate cox regression analyses were performed to develop the hypoxia-associated prognostic signature. The Kaplan–Meier (KM) survival analyses of patients with high and low hypoxia risk scores were conducted. The nomogram was constructed and the gene signature was validated in the external Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate the relationships between immune infiltration and gene signature.ResultsTwo genes, including decorin (DCN) and prolyl 4-hydroxylase subunit alpha 1 (P4HA1), were involved in the hypoxia-associated gene signature. In training and testing datasets, patients with high-risk scores showed lower survival rates and the gene signature was identified as the independent prognostic factor. Receiver operating characteristic (ROC) curves demonstrated the robustness of signature. Functional analyses of DEmRNAs among high- and low-risk groups revealed that immune-associated functions and pathways were significantly enriched. Furthermore, ssGSEA showed that five immune cells (DCs, macrophages, neutrophils, pDCs, and TIL) and three immune features (CCR, APC co inhibition, and Check-point) were down-regulated in the high-risk group.ConclusionThe current study established and validated a novel hypoxia-associated gene signature in osteosarcoma. It could act as a prognostic biomarker and serve as therapeutic guidance in clinical applications.

scholarly journals A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Chunmei Zhu ◽  
Shuyuan Zhang ◽  
Di Liu ◽  
Qingqing Wang ◽  
Ningning Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Dna Methylation ◽  
High Risk ◽  
Cox Regression ◽  
Methylation Status ◽  
Gene Signature ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
The Impact

Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC).Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database.Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset.Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.

scholarly journals Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

2021 ◽  
Vol 9 ◽  
Author(s):  
Wei Jiang ◽  
Jiameng Xu ◽  
Zirui Liao ◽  
Guangbin Li ◽  
Chengpeng Zhang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Training Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
External Test

ObjectiveTo screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC.MethodsThe GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs.ResultsWe identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues.ConclusionThis prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

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