scholarly journals A Novel Ferroptosis-Related Gene Signature Predicts Overall Survival of Breast Cancer Patients

Biology ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 151
Author(s):  
Haifeng Li ◽  
Lu Li ◽  
Cong Xue ◽  
Riqing Huang ◽  
Anqi Hu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Prediction Model ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Prediction ◽  
Clinical Information ◽  
Risk Groups ◽  
Gene Signature ◽  
Breast Cancer Patients

Breast cancer is the second leading cause of death in women, thus a reliable prognostic model for overall survival (OS) in breast cancer is needed to improve treatment and care. Ferroptosis is an iron-dependent cell death. It is already known that siramesine and lapatinib could induce ferroptosis in breast cancer cells, and some ferroptosis-related genes were closely related with the outcomes of treatments regarding breast cancer. The relationship between these genes and the prognosis of OS remains unclear. The data of gene expression and related clinical information was downloaded from public databases. Based on the TCGA-BRCA cohort, an 8-gene prediction model was established with the least absolute shrinkage and selection operator (LASSO) cox regression, and this model was validated in patients from the METABRIC cohort. Based on the median risk score obtained from the 8-gene model, patients were stratified into high- or low-risk groups. Cox regression analyses identified that the risk score was an independent predictor for OS. The findings from CIBERSORT and ssGSEA presented noticeable differences in enrichment scores for immune cells and pathways between the abovementioned two risk groups. To sum up, this prediction model has potential to be widely applied in future clinical settings.

scholarly journals The Landscape of Glycosyltransferase Gene Signature for Overall Survival Prediction in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Qiang Cai ◽  
Shizhe Yu ◽  
Jian Zhao ◽  
Duo Ma ◽  
Long Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Regression Analyses ◽  
Risk Patients

Abstract Background: Hepatocellular carcinoma (HCC) is heterogeneous disease occurring in the background of chronic liver diseases. The role of glycosyltransferase (GT) genes have recently been the focus of research associating with the development of tumors. However, the prognostic value of GT genes in HCC remains not elucidated. This study aimed to demonstrate the GT genes related to the prognosis of HCC through bioinformatics analysis.Methods: The GT genes signatures were identified from the training set of The Cancer Genome Atlas (TCGA) dataset using univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then, we analyzed the prognostic value of GT genes signatures related to the overall survival (OS) of HCC patients. A prognostic model was constructed, and the risk score of each patient was calculated as formula, which divided HCC patients into high- and low-risk groups. Kaplan-Meier (K-M) and Receiver operating characteristic (ROC) curves were used to assess the OS of HCC patients. The prognostic value of GT genes signatures was further investigated in the validation set of TCGA database. Univariate and multivariate Cox regression analyses were performed to demonstrate the independent factors on OS. Finally, we utilized the gene set enrichment analysis (GSEA) to annotate the function of these genes between the two risk categories. Results: In this study, we identified and validated 4 GT genes as the prognostic signatures. The K-M analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 4 gene signatures could predict OS for 3-, 5-, and 7-year in patients with HCC, revealing the prognostic ability of these gene signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for HCC. Functional analysis further revealed that immune-related pathways were enriched, and immune status in HCC were different between the two risk groups.Conclusion: In conclusion, a novel GT genes signature can be used for prognostic prediction in HCC. Thus, targeting GT genes may be a therapeutic alternative for HCC.

scholarly journals A Novel Glycolysis-Related Four-mRNA Signature for Predicting the Survival of Patients With Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaolu Zhang ◽  
Jia Wang ◽  
Jing Zhuang ◽  
Cun Liu ◽  
Chundi Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Clinical Information ◽  
Risk Groups ◽  
Gene Signature ◽  
Training Cohort ◽  
Cox Regression Analysis ◽  
Entire Cohort ◽  
Cancer Prediction

Background: Glycolysis is critical in the occurrence and development of tumors. Owing to the biological and clinical heterogeneity of patients with BRCA, the traditional predictive classification system is far from satisfactory. Survival and prognosis biomarkers related to glycolysis have broad application prospects for assessing the risk of patients and guiding their individualized treatment.Methods: The mRNA expression profiles and clinical information of patients with BRCA were obtained from TCGA database, and glycolysis-related genes were obtained by GSEA. Patients with BRCA were randomly divided into the training cohort and testing cohort. Univariate and multivariate Cox analyses were used to establish and validate a new mRNA signature for predicting the prognosis of patients with BRCA.Results: We established a four-gene breast cancer prediction signature that included PGK1, SDHC, PFKL, and NUP43. The patients with BRCA in the training cohort and testing cohort were divided into high-risk and low-risk groups based on the signature. The AUC values were 0.74 (training cohort), 0.806 (testing cohort) and 0.769 (entire cohort), thereby showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that four-gene signature could independently predict the prognosis of BRCA patients without being affected by clinical factors.Conclusion: We constructed a four-gene signature to predict the prognosis of patients with BRCA. This signature will aid in the early diagnosis and personalized treatment of breast cancer, but the specific associated biological mechanism requires further study.

scholarly journals Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Feng Jiang ◽  
Chuyan Wu ◽  
Ming Wang ◽  
Ke Wei ◽  
Jimei Wang
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Single Gene ◽  
Prediction Method ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Models ◽  
Normal Tissues ◽  
Predictive Risk

AbstractOne of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan–Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.

Integrated immune-related gene signature to predict clinical outcome for patients with luminal B breast cancer.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12526-e12526
Author(s):  
Xiaying Kuang ◽  
Du Cai ◽  
Ying Lin ◽  
Feng Gao
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Validation Cohort ◽  
Risk Groups ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Training Cohort ◽  
Luminal B ◽  
Immune Related Gene ◽  
Immune Related Genes

e12526 Background: Luminal B breast cancer is always routinely treated with chemotherapy and endocrine therapy but heterogeneous with respect to sensitivity to treatment, identification of patients who may most benefit remains a matter of controversy. Immune-related genes (IRGs) was found to be associated with the prognosis of breast cancer. The aim of this study is to evaluate the impact of IRGs in predicting the outcome of luminal B breast cancer patients. Methods: According to the Metabric microarray dataset also as a training cohort, 488 luminal B breast cancer patients were selected for generation of immune-related gene signature (IRGS). Another independent dataset (n=250) of patients with complete prognostic information was analyzed as a validation cohort. Prognostic analysis was assessed to test the predictive value of IRGS. Results: A model of prognostic IRGS containing 12 immune-related genes was developed. In both training and validation cohorts, IRGS significantly stratified luminal B breast cancer patients into immune low- and high-risk groups in terms of disease free survival (DFS, HR=4.95, 95% CI=3.22-7.62, P<0.001 in training cohort, HR=2.47, 95% CI=1.29-4.75, P<0.001 in validation cohort). Multivariate analysis revealed IRGS as an independent prognostic factor (HR=4.96, 95% CI=3.00-8.18, P<0.001 in training cohort, HR=2.56, 95% CI=1.28-5.09, P=0.007 in validation cohort). Furthermore, those 12 genes mostly related with response to chemical, and the expression levels of them were completely opposite in patients of immune low- and high-risk groups. Conclusions: The proposed IRGS is a satisfactory prognostic model for estimating DFS of luminal B breast cancer patients. Further studies are needed to assess the clinical effectiveness of this system in predicting prognosis and treatment options for luminal B breast cancer patients. This work was supported by National Natural Science Foundation of China (No. 81602520), Natural Science Foundation of Guangdong Province (No. 2017A030313596).

Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

2021 ◽  
Vol 16 ◽  
Author(s):  
Dongqing Su ◽  
Qianzi Lu ◽  
Yi Pan ◽  
Yao Yu ◽  
Shiyuan Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

scholarly journals Breast Subtypes and Prognosis of Breast Cancer Patients With Initial Bone Metastasis: A Population-Based Study

2020 ◽  
Vol 10 ◽  
Author(s):  
Deyue Liu ◽  
Jiayi Wu ◽  
Caijin Lin ◽  
Lisa Andriani ◽  
Shuning Ding ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Prognostic Factors ◽  
Metastatic Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Metastatic Breast ◽  
Risk Groups ◽  
Population Based ◽  
Breast Cancer Patients

BackgroundMetastatic breast cancer (MBC) is a highly heterogeneous disease and bone is one of the most common metastatic sites. This retrospective study was conducted to investigate the clinical features, prognostic factors and benefits of surgery of breast cancer patients with initial bone metastases.MethodsFrom 2010 to 2015, 6,860 breast cancer patients diagnosed with initial bone metastasis were analyzed from Surveillance, Epidemiology, and End Results (SEER) database. Univariate and Multivariable analysis were used to identify prognostic factors. A nomogram was performed based on the factors selected from cox regression result. Survival curves were plotted according to different subtypes, metastatic burdens and risk groups differentiated by nomogram.ResultsHormone receptor (HR) positive/human epidermal growth factor receptor 2 (HER2) positive patients showed the best outcome compared to other subtypes. Patients of younger age (&lt;60 years old), white race, lower grade, lower T stage (&lt;=T2), not combining visceral metastasis tended to have better outcome. About 37% (2,249) patients received surgery of primary tumor. Patients of all subtypes could benefit from surgery. Patients of bone-only metastases (BOM), bone and liver metastases, bone and lung metastases also showed superior survival time if surgery was performed. However, patients of bone and brain metastasis could not benefit from surgery (p = 0.05). The C-index of nomogram was 0.66. Cutoff values of nomogram point were identified as 87 and 157 points, which divided all patients into low-, intermediate- and high-risk groups. Patients of all groups showed better overall survival when receiving surgery.ConclusionOur study has provided population-based prognostic analysis in patients with initial bone metastatic breast cancer and constructed a predicting nomogram with good accuracy. The finding of potential benefit of surgery to overall survival will cast some lights on the treatment tactics of this group of patients.

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

2021 ◽  
Author(s):  
Menglin He ◽  
Cheng Hu ◽  
Jian Deng ◽  
Hui Ji ◽  
Weiqian Tian
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

scholarly journals Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhen Tan ◽  
Yubin Lei ◽  
Bo Zhang ◽  
Si Shi ◽  
Jiang Liu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Ductal Adenocarcinoma ◽  
Rank Test ◽  
Cancer Center ◽  
Cox Regression Analysis

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed an existing certain curative effect in treating PDAC. The aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients.MethodIn this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO (Gene Expression Omnibus) datasets, and identify modules related to CD4+ T cells. LASSO Cox regression analysis and Kaplan–Meier curve were applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and the Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analyses were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset.ResultsA four-gene signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verified by a Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset.ConclusionWe constructed a novel four-gene signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC 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.

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