scholarly journals Downregulation of STOX1 is a novel prognostic biomarker for glioma patients

2021 ◽  
Vol 16 (1) ◽  
pp. 1164-1174
Author(s):  
Fei-qin Jin ◽  
Lei Jin ◽  
Yan-ling Wang
Keyword(s):  
Signaling Pathway ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Wnt Signaling Pathway ◽  
Clinicopathological Characteristics ◽  
Idh1 Mutation ◽  
Chi Square ◽  
Who Grade ◽  
Cox Regression Analysis

Abstract Storkhead box 1 (STOX1) is a winged helix transcription factor structurally and functionally related to the forkhead family of transcription factors. Recent studies have highlighted its role in the central nervous system and revealed hints in the development of glioma. However, the expression profiles of STOX1, its association with clinicopathological characteristics, and potential functions in glioma remain unknown. In this study, we analyzed three publicly available datasets including CGGA, TCGA, and Rembrandt and revealed a grade-dependent reduction in STOX1 expression in glioma (P < 0.001). Chi-square test demonstrated that low STOX1 expression was significantly associated with older age at initial diagnosis (P < 0.001), less IDH1 mutation (P < 0.001), and advanced WHO grade (P < 0.001). Moreover, multivariate Cox regression analysis showed that STOX1 expression may serve as a novel independent prognostic biomarker in glioma patients. Bioinformatic functional analysis (GSEA) predicted that STOX1 was related to many key cancer pathways including P53 signaling pathway (P < 0.01), DNA replication (P < 0.05), homologous recombination (P < 0.05), and Wnt signaling pathway (P < 0.05). Taken together, these findings suggested that STOX1 may be used as a novel predictive molecular biomarker for glioma grading and overall patient survival. Further investigations on the functional roles and therapeutic value of STOX1 in glioma are warranted.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

scholarly journals Incidence and Survival Trends of Bladder Rhabdomyosarcoma:A SEER-based Analysis of 125 Cases

2020 ◽  
Author(s):  
Pin Li ◽  
Huixia Zhou ◽  
Hualin Cao ◽  
Tao Guo ◽  
Weiwei Zhu ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Survival Time ◽  
Clinical Outcomes ◽  
Cox Regression ◽  
Survival Curve ◽  
Clinicopathological Characteristics ◽  
Chi Square ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Significant Factors

Abstract Background To elucidate the bladder rhabdomyosarcoma clinicopathological characteristics and reveal the prognostic factors. Methods We screened data from SEER database (1975-2016) stratified by age group, evaluated the differences between groups with Chi-square and Fisher’s test, conducted the Kaplan-Meier survival analysis and plotted the survival curve. The significant factors were brought into Cox regression analysis and calculated the HR(95%CI). Results About half of the patients who develop bladder RMS will be younger than 2 years of age. Embryonal RMS account for 76% of all histopathology types. Age at diagnosis more than 16-y (HR=6.595,95%CI:3.62-12.01, p=7.04e-10), NOT embryonal rhabdomyosarcoma (HR=3.61, 95%CI:1.99-6.549, p =4.1e-06), without radiotherapy combined or surgery alone (HR=4.382, 95%CI:1.99-6.549, p =2.4e-05) and not performed the surgery (HR=2.982,95%CI:1.263-7.039, p =0.0126) were negatively correlated with 5-year survival time, while race( p =0.341), whether performed the lymphadenectomy( p =0.722) showed no influence on survival time. Cox regression results show that age, histology, SEER stage, treatment combined or alone influence the clinical outcomes. Conclusions We demonstrated the demographic and characteristic of bladder rhabdomyosarcoma, identified and excluded the prognostic factors for the 5-year overall survival and clinical outcomes.

High amphiregulin mRNA expression is a strong prognostic biomarker with response to cetuximab in FIRE-1, CIOX, and FIRE-3.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 4026-4026
Author(s):  
Arndt Stahler ◽  
Sebastian Stintzing ◽  
Dominik Paul Modest ◽  
Ingrid Ricard ◽  
Christine Kapaun ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Progression Free Survival ◽  
Housekeeping Genes ◽  
Threshold Values ◽  
Chi Square ◽  
Cox Regression Analysis ◽  
Chi Square Test ◽  
Treatment Table

4026 Background: Amphiregulin ( AREG) and epiregulin ( EREG) were discussed as biomarkers for treatment of metastatic colorectal cancer (mCRC). Data from randomized controlled trials (RCT) are limited. Methods: AREG and EREG mRNA expression by RTqPCR in relation to housekeeping genes were available from 688 patients of three RCT (FIRE-1, n = 192, FUFIRI vs. mIrOx; CIOX, n = 113, cetuximab + CAPIRI/CAPOX; FIRE-3, n = 383, FOLFIRI+cetuximab/bevacizumab) and were normalized to their respective range of each trial with median and 3rd quartile as threshold values. Kaplan-Meier estimated overall survival (OS) and progression-free survival (PFS). Cox regression analysis calculated hazard ratio (HR) and 95% confidence interval (95% CI). Overall response rate (ORR) was compared by chi square test. Results: Across all trials, high AREG mRNA expression appeared as strong prognostic biomarker for OS, PFS and ORR for all threshold values. In RAS wildtype patients, high AREG expression was associated with better OS and PFS for cetuximab but not bevacizumab treatment. (Table) No effects were seen for epiregulin when all trials were analysed together. Conclusions: High AREG mRNA expression appeared as strong prognostic biomarker in mCRC. Positive predictive information might exist for cetuximab treatment. [Table: see text]

scholarly journals The lncRNAs RP1-261G23.7, RP11-69E11.4 and SATB2-AS1 are a novel clinical signature for predicting recurrent osteosarcoma

2020 ◽  
Vol 40 (1) ◽  
Author(s):  
Tang Ying ◽  
Jin-ling Dong ◽  
Cen Yuan ◽  
Peng Li ◽  
Qingshan Guo
Keyword(s):  
Regression Analysis ◽  
Signaling Pathway ◽  
Cox Regression ◽  
Wnt Signaling Pathway ◽  
Cox Proportional Hazards ◽  
Recurrence Time ◽  
Rank Test ◽  
Recurrence Rates ◽  
Cox Regression Analysis ◽  
Log Rank Test

Abstract Background: Osteosarcoma is the most common primary bone malignancy in children and adolescents. In order to find factors related to its recurrence, and thus improve recovery prospects, a powerful clinical signature is needed. Long noncoding RNAs (lncRNAs) are essential in osteosarcoma processes and development, and here we report significant lncRNAs to aid in earlier diagnosis of osteosarcoma. Methods: A univariate Cox proportional hazards regression analysis and a multivariate Cox regression analysis were used to analyze osteosarcoma patients’ lncRNA expression data from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET), a public database. Results: A lncRNA signature consisting of three lncRNAs (RP1-261G23.7, RP11-69E11.4 and SATB2-AS1) was selected. The signature was used to sort patients into high-risk and low-risk groups with meaningful recurrence rates (median recurrence time 16.80 vs. &gt;128.22 months, log-rank test, P&lt;0.001) in the training group, and predictive ability was validated in a test dataset (median 16.32 vs. &gt;143.80 months, log-rank test, P=0.006). A multivariate Cox regression analysis showed that the significant lncRNA was an independent prognostic factor for osteosarcoma patients. Functional analysis suggests that these lncRNAs were related to the PI3K-Akt signaling pathway, the Wnt signaling pathway, and the G-protein coupled receptor signaling pathway, all of which have various, important roles in osteosarcoma development. The significant 3-lncRNA set could be a novel prediction biomarker that could aid in treatment and also predict the likelihood of recurrence of osteosarcoma in patients.

scholarly journals The Ferroptosis-Related Noncoding RNA Signature as a Novel Prognostic Biomarker in the Tumor Microenvironment, Immunotherapy, and Drug Screening of Gastric Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xinming Chen ◽  
Zheng Zhu ◽  
Xiaoling Li ◽  
Xinyue Yao ◽  
Lianxiang Luo
Keyword(s):  
Tumor Microenvironment ◽  
Gastric Adenocarcinoma ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Risk Group ◽  
Expression Profiles ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

BackgroundFerroptosis is a new type of cell death different from apoptosis, necrosis, autophagy, and pyroptosis. This study aimed to explore the relationship between ferroptosis-related noncoding RNA (ncRNA) and gastric adenocarcinoma with regard to immunity and prognosis.MethodsFerroptosis-related ncRNA expression profiles and clinical pathology and overall survival information were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database. The ferroptosis-related ncRNA signature was identified by Cox regression analysis and the least absolute shrinkage and selection operator analysis. The survival analysis, receiver operating characteristic (ROC) analysis, and decision curve analysis were adopted to evaluate the prognostic prediction performance of the signature. The correlation between risk and multiple clinical characteristics was analyzed using the chi-square test. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were used for mining functions and pathways. The CIBERSORT, ssGSEA, and ESTIMATE algorithms were used to assess immune infiltration and the tumor microenvironment. The response of immunotherapy was predicted using the Submap algorithm, and the Connectivity Map and the ridge regression model were used to screen and evaluate drugs.ResultsA carcinogenic risk signature was constructed using five ferroptosis-related ncRNAs. It showed an extraordinary ability to predict the prognoses of patients with gastric adenocarcinoma [area under the ROC curve (AUC) after 6 years = 0.689; GSE84426, AUC after 6 years = 0.747]. The lower ferroptosis potential level and lower tumor mutation burden were related to the poor prognoses of patients. The high-risk group had more immune cell recruitment, and the overall effect of the anti-immune checkpoint immunotherapy was not as good as that of the low-risk group. The high- and low-risk groups were enriched in tumor- and immune-related pathways, respectively. The screened antitumor drugs, such as genistein, guanabenz, and betulinic acid, improved the survival of the patients.ConclusionsThe ferroptosis-related ncRNA signature is a potential carcinogenic prognostic biomarker of gastric adenocarcinoma.

PCAT-1 Expression is Associated With The Prognosis in Prostate Cancer.

2020 ◽  
Author(s):  
Sheng Li ◽  
Xiaolan Ruan ◽  
Tongzu Liu
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Univariate Analysis ◽  
Prognostic Significance ◽  
Survival Curves ◽  
Chi Square ◽  
Cox Regression Analysis ◽  
Chi Square Test ◽  
Cox Analysis

Abstract Purpose: In the study, we aimed to estimate the prognostic significance of PCAT-1 in patients with prostate cancer (PCa).Methods: The expression of PCAT-1 in paired PCa tissues and normal controls was examined via quantitative real-time polymerase chain reaction (qRT-PCR). The influence of PCAT-1 level on clinical features was assessed using Chi-square test. The survival curves were plotted to estimate the prognosis of patients. And the Cox analysis was carried out to find promising predictive factors for patients.Results: The expression level of PCAT-1 in PCa tissues was significantly elevated compared with the adjacent normal control (P<0.0001). The increased expression of PCAT-1 was affected by high Gleason score (P=0.017), positive serum PSA (P=0.011) and advanced TNM stage (P=0.003). The Kaplan-Meier survival curves showed that the overall survival rate of patients with high PCAT-1 expression was significantly lower than those with low PCAT-1 expression (P<0.001). Both univariate analysis (P=0.000, HR=10.623, 95%CI=5.798-19.464) and multivariate Cox regression analysis (P=0.000, HR=10.996, 95%CI=5.896-20.507) revealed that PCAT-1 could act as a prognostic biomarker for PCa patients.Conclusion: Taken together, overexpression of PCAT-1 is involved in PCa progression and could be a potential prognostic biomarker for PCa patients.

scholarly journals Evaluation of the Role of hsa-mir-124 in Predicting Clinical Outcome in Breast Invasive Carcinoma Based on Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Tongbao Feng ◽  
Ping Zhang ◽  
Yingxin Sun ◽  
Xiu Han ◽  
Jichun Tong ◽  
...  
Keyword(s):  
Overall Survival ◽  
Invasive Carcinoma ◽  
Cox Regression ◽  
Human Cancer ◽  
Expression Profiles ◽  
Clinicopathological Characteristics ◽  
The Cancer Genome Atlas ◽  
Molecular Phenotype ◽  
Chi Square ◽  
Chi Square Test

Purpose. Breast invasive carcinoma (BRCA) is the most common malignant tumor. MiR-124 plays a tumor-suppressive role in human cancer. However, the clinical significance of miR-124 in BRCA remains unclear. The aim of this study was to evaluate the association of hsa-mir-124 expression and the clinicopathological characteristics in BRCA using database analysis. Methods. The clinical data and expression profiles of hsa-mir-124 were obtained from the cancer genome atlas for BRCA (TCGA_BRCA). Then, the prognostic value of hsa-mir-124 in BRCA was investigated using the Cox Regression test, and the association of hsa-mir-124 and pathology TNM stages and pathologic stages were measured by the Kruskal–Wallis test and Wilcox. test. In addition, the association of hsa-mir-124 and tumor molecular phenotypes was performed using the Chi-Square test. Results. We found that the overall survival of patients with high expression of hsa-mir-124-1 and hsa-mir-124-2 was better than that of patients with low expression of hsa-mir-124-1 and hsa-mir-124-2. And the expression of hsa-mir-124-1, hsa-mir-124-2, and hsa-mir-124-3 was mainly enriched in T1/T2 stages, NO/N1 stages, and M0 stages. Then, the expression of hsa-mir-124-1, hsa-mir-124-2, and hsa-mir-124-3 was negatively associated with tumor lymph node metastasis. Moreover, the expression of hsa-mir-124 was associated with tumor molecular phenotype in breast invasive carcinoma. Conclusion. Our findings indicated that hsa-mir-124 expressions were associated with overall survival, TNM stages, pathologic characteristics, and tumor molecular phenotype in BRCA via TCGA_BRCA database, providing a new biomarker and a potential therapeutic target for BRCA patients.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer. Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an . Functional enrichment analysis was performed by Metascape. Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR , MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1 ). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000). Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

scholarly journals Calumenin contributes to epithelial-mesenchymal transition and predicts poor survival in glioma

2021 ◽  
Vol 12 (1) ◽  
pp. 67-75
Author(s):  
Ying Yang ◽  
Jin Wang ◽  
Shihai Xu ◽  
Fei Shi ◽  
Aijun Shan
Keyword(s):  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Malignant Gliomas ◽  
Strongly Correlated ◽  
Who Grade ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis ◽  
R Language ◽  
Biological Adhesion ◽  
Tcga Dataset

Abstract Background Calumenin (CALU) has been reported to be associated with invasiveness and metastasis in some malignancies. However, in glioma, the role of CALU remains unclear. Methods Clinical and transcriptome data of 998 glioma patients, including 301 from CGGA and 697 from TCGA dataset, were included. R language was used to perform statistical analyses. Results CALU expression was significantly upregulated in more malignant gliomas, including higher grade, IDH wildtype, mesenchymal, and classical subtype. Gene Ontology analysis revealed that CALU-correlated genes were mainly enriched in cell/biological adhesion, response to wounding, and extracellular matrix/structure organization, all of which were strongly correlated with the epithelial-mesenchymal transition (EMT) phenotype. GSEA further validated the profound involvement of CALU in EMT. Subsequent GSVA suggested that CALU was particularly correlated with three EMT signaling pathways, including TGFβ, PI3K/AKT, and hypoxia pathway. Furthermore, CALU played synergistically with EMT key markers, including N-cadherin, vimentin, snail, slug, and TWIST1. Survival and Cox regression analysis showed that higher CALU predicted worse survival, and the prognostic value was independent of WHO grade and age. Conclusions CALU was correlated with more malignant phenotypes in glioma. Moreover, CALU seemed to serve as a pro-EMT molecular target and could contribute to predict prognosis independently in glioma.

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

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