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

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

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

scholarly journals The m6A-related gene signature for predicting the prognosis of breast cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11561
Author(s):  
Shanliang Zhong ◽  
Zhenzhong Lin ◽  
Huanwen Chen ◽  
Ling Mao ◽  
Jifeng Feng ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cancer Survival ◽  
Risk Group ◽  
Gene Signature ◽  
Breast Cancer Survival ◽  
Breast Cancer Patients ◽  
Related Gene ◽  
Rna Methylation

N6-methyladenosine (m6A) modification has been shown to participate in tumorigenesis and metastasis of human cancers. The present study aimed to investigate the roles of m6A RNA methylation regulators in breast cancer. We used LASSO regression to identify m6A-related gene signature predicting breast cancer survival with the datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA). RNA-Seq data of 3409 breast cancer patients from GSE96058 and 1097 from TCGA were used in present study. A 10 m6A-related gene signature associated with prognosis was identified from 22 m6A RNA methylation regulators. The signature divided patients into low- and high-risk group. High-risk patients had a worse prognosis than the low-risk group. Further analyses indicated that IGF2BP1 may be a key m6A RNA methylation regulator in breast cancer. Survival analysis showed that IGF2BP1 is an independent prognostic factor of breast cancer, and higher expression level of IGF2BP1 is associated with shorter overall survival of breast cancer patients. In conclusion, we identified a 10 m6A-related gene signature associated with overall survival of breast cancer. IGF2BP1 may be a key m6A RNA methylation regulator in breast cancer.

scholarly journals Identification and validation of prognostic signature for breast cancer based on genes potentially involved in autophagy

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9621
Author(s):  
Shanliang Zhong ◽  
Huanwen Chen ◽  
Sujin Yang ◽  
Jifeng Feng ◽  
Siying Zhou
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Cox Regression ◽  
Risk Group ◽  
Breast Cancer Survival ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Breast Cancer Patients ◽  
Prognostic Signature

We aimed to identify prognostic signature based on autophagy-related genes (ARGs) for breast cancer patients. The datasets of breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) Cox regression was conducted to construct multiple-ARG risk signature. In total, 32 ARGs were identified as differentially expressed between tumors and adjacent normal tissues based on TCGA. Six ARGs (IFNG, TP63, PPP1R15A, PTK6, EIF4EBP1 and NKX2-3) with non-zero coefficient were selected from the 32 ARGs using LASSO regression. The 6-ARG signature divided patients into high-and low-risk group. Survival analysis indicated that low-risk group had longer survival time than high-risk group. We further validated the 6-ARG signature using dataset from GEO and found similar results. We analyzed the associations between ARGs and breast cancer survival in TCGA and nine GEO datasets, and obtained 170 ARGs with significant associations. EIF4EBP1, FOS and FAS were the top three ARGs with highest numbers of significant associations. EIF4EBP1 may be a key ARG which had a higher expression level in patients with more malignant molecular subtypes and higher grade breast cancer. In conclusion, our 6-ARG signature was of significance in predicting of overall survival of patients with breast cancer. EIF4EBP1 may be a key ARG associated with breast cancer survival.

Macrophage Polarization-Related Gene Signature for Risk Stratification and Prognosis of Survival in Gliomas 

2021 ◽  
Author(s):  
Jingwei Zhang ◽  
Shuwang Li ◽  
Fangkun Liu
Keyword(s):  
Essential Role ◽  
Cox Regression ◽  
Macrophage Polarization ◽  
Gene Signature ◽  
High Risk Group ◽  
M2 Macrophage ◽  
Related Gene ◽  
Sequencing Data ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Macrophage polarization plays an essential role in tumor immune cells infiltration and tumor growth. We selected a series of genes distinguishing between M1 and M2 macrophage and explored their prognostic value in gliomas. A total of 170 genes were included in our study. CGGA database was used as the training cohort, and the TCGA database as the validation cohort. The biological processes and functions were identified by GO and KEGG analysis. Kaplan-Meier analysis was used to compare survival differences between groups. Finally, GEPIA was applied to explore immune infiltrates in the tumor microenvironment. Importantly, we re-verified the results using our sequencing data. We build a risk score model using Cox regression analysis based on the CGGA and verified in the TCGA database and our sequencing data. Patients with gliomas in the high-risk group were associated with high grade, IDH WT status, MGMT promoter unmethylation, 1p19q non-codeletion, and prone to have a poor outcome. Moreover, these genes play an essential role in immune infiltrations in LGG and GBM microenvironments. Macrophage polarization-related gene signature can predict the malignancy and outcome of patients with gliomas and might act as a promising target for glioma immunotherapy in the future.

scholarly journals Survival of Women With Breast Cancer in Kaunas Region, Lithuania

Medicina ◽  
2012 ◽  
Vol 48 (5) ◽  
pp. 39 ◽  
Author(s):  
Rugilė Ivanauskienė ◽  
Jurgita Gedminaitė ◽  
Elona Juozaitytė ◽  
Giedrius Vanagas ◽  
Renata Šimoliūnienė ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Survival Rates ◽  
Breast Cancer Survival ◽  
European Countries ◽  
Disease Stage ◽  
Breast Cancer Patients ◽  
Kaplan Meier ◽  
Cox Proportional Hazard Models ◽  
Advanced Disease Stage

Objective. The assessment of breast cancer survival rates and comparison with those of other countries may help to deepen knowledge among decision makers in the health care system and to improve the inequalities in accessibility to early detection and effective treatment. The aim of this study was to evaluate breast cancer survival rates in Kaunas region, Lithuania, and to compare them with those in the selected European countries. Material and Methods. A retrospective study was carried out using medical records and data gathered from the Lithuanian Cancer Registry. A group of 240 patients with primary breast cancer diagnosed in 2008 in Kaunas region was analyzed. All causes of death were included in the analysis. The closing date of follow-up was September 30, 2010. Survival was determined using the life-table method and the Kaplan-Meier method. Cox proportional hazard models were used to estimate the effects of prognostic risk factors on survival. Results. The median age of the patients was 63 years (range, 28–95). The 1-year and 2-year cumulative survival for breast cancer patients in Kaunas region, Lithuania, was 94.2% and 90.1%, respectively. As expected, the survival of patients with diagnosed advanced disease (stage III and IV) was significantly worse than that of patients with stage I (P<0.001) and II (P=0.003) disease. The screening group (aged 50–69 years) showed better survival in comparison with the group older than 69 years. Age, T4 tumor, and distant metastasis were the prognostic factors significantly associated with an increased relative mortality risk of breast cancer. Conclusions. Compared to the European survival rates, the 1-year and 2-year survival of patients with breast cancer in Lithuania was found to be similar to most European countries.

scholarly journals The variant T allele of PvuII in ESR1 gene is a prognostic marker in early breast cancer survival

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Danny Houtsma ◽  
Stefanie de Groot ◽  
Renee Baak-Pablo ◽  
Elma Meershoek -Klein Kranenbarg ◽  
Caroline M. Seynaeve ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Patients ◽  
Prognostic Marker ◽  
Early Breast Cancer ◽  
Cancer Survival ◽  
Breast Cancer Survival ◽  
Breast Cancer Patients ◽  
Available N ◽  
Cox Regression Analysis ◽  
Breast Cancer Outcome

AbstractThe PvuII (rs2234693) Single Nucleotide Polymorphism (SNP) in the gene coding for the estrogen receptor-1 (ESR1), has been found associated with outcome in tamoxifen treated patients with early hormone-receptor positive breast cancer. However, it remains unclear whether this SNP is a predictive marker for tamoxifen efficacy or a prognostic marker for breast cancer outcome. The aim of this study was to examine the prognostic potential of this SNP in postmenopausal early breast cancer patients treated with adjuvant exemestane. Dutch postmenopausal patients randomised to 5 years of adjuvant exemestane of whom tissue was available (N = 807) were selected from the Tamoxifen Exemestane Adjuvant Multinational (TEAM) trial database. The SNP rs2234693 in the ESR1 gene was genotyped on DNA from formalin-fixed paraffin embedded (FFPE) tumor tissue using Taqman assays and related to the primary endpoint disease-free survival (DFS) and secondary endpoint overall survival (OS). Survival analyses were performed using Cox regression analysis. In total 805 patients were included in the analyses (median follow up of 5.22 years) and genotypes were obtained in 97% of the samples. The variant T allele of PvuII in ESR1 (rs2234693) was associated with a better DFS (hazard ratio (HR) 0.689, 95% confidence interval (CI) 0.480–0.989, P = 0.044) in univariate analysis only, and a better OS in both univariate (HR 0.616, 95%, CI 0.411–0.923, P = 0.019) and multivariate analyses (HR 0.571, 95% CI 0.380–0.856, P = 0.007), consistent with a prognostic rather than a predictive drug response effect. Variation of PvuII in the ESR1 gene is related to OS in postmenopausal, early HR + breast cancer patients treated with exemestane in the TEAM study. Variation in the ESR1 gene may therefore be a prognostic marker of early breast cancer survival, and warrants further research.

scholarly journals Survival rates of breast cancer and predictive factors: a hospital-based study from western Amazon area in Brazil

2019 ◽  
Vol 24 (1) ◽  
pp. 261-273 ◽  
Author(s):  
Ruth Helena Pimenta Fujimoto ◽  
Rosalina Jorge Koifman ◽  
Ilce Ferreira da Silva
Keyword(s):  
Breast Cancer ◽  
Cancer Survival ◽  
Cox Regression ◽  
Survival Rates ◽  
Breast Cancer Survival ◽  
Cancer Specific Survival ◽  
Risk Of Death ◽  
Kaplan Meier ◽  
Medical Reports ◽  
Survival Studies

Abstract Breast cancer survival in Latin America countries is below Central European countries. Hospital-based breast cancer survival studies in western Amazon, Brazil, are lacking. This article aims to estimate hospital-based breast cancer survival in Rio Branco, Acre, and predictor factors. Hospital-based cohort study of all women diagnosed with breast cancer (2007-2012) was proceeded. Information were obtained from medical reports, and follow-up was until 2013. One-, 2- and 5- years breast cancer specific-survival were estimated by Kaplan-Meier method. Crude and adjusted Harzards Ratios (HR) were estimated by proportional Cox regression model. One-, 2-, and 5-year overall breast cancer survival were 95.5%, 83.7%, and 87.3% respectively. Surgery combined to radiotherapy significantly affected 1-, 2-, and 5-year survival (99%, 94%, and 90.6%, respectively) as compared to other treatments (77%,57.1%, and 37.5%, respectively). Comparing to surgery combined to radiotherapy treatment, surgery alone increased the risk of death, independently of age and stage (HR = 7.23;95%CI:2.29-22.83). In Rio Branco, Acre, 5-year breast cancer survival is similar to more developed areas in Brazil. Surgery combined to radiotherapy was independently associated to a lower risk of death as compared to surgery alone and other treatment.

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

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

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

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

scholarly journals Establishment and Validation of an MTORC1 Signaling-Related Gene Signature to Predict Overall Survival in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zheng Yao ◽  
Song Wen ◽  
Jun Luo ◽  
Weiyuan Hao ◽  
Weiren Liang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Decision Tree ◽  
Survival Data ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Cancer Hallmarks ◽  
Mtorc1 Signaling

Background. Accurate and effective biomarkers for the prognosis of patients with hepatocellular carcinoma (HCC) are poorly identified. A network-based gene signature may serve as a valuable biomarker to improve the accuracy of risk discrimination in patients. Methods. The expression levels of cancer hallmarks were determined by Cox regression analysis. Various bioinformatic methods, such as GSEA, WGCNA, and LASSO, and statistical approaches were applied to generate an MTORC1 signaling-related gene signature (MSRS). Moreover, a decision tree and nomogram were constructed to aid in the quantification of risk levels for each HCC patient. Results. Active MTORC1 signaling was found to be the most vital predictor of overall survival in HCC patients in the training cohort. MSRS was established and proved to hold the capacity to stratify HCC patients with poor outcomes in two validated datasets. Analysis of the patient MSRS levels and patient survival data suggested that the MSRS can be a valuable risk factor in two validated datasets and the integrated cohort. Finally, we constructed a decision tree which allowed to distinguish subclasses of patients at high risk and a nomogram which could accurately predict the survival of individuals. Conclusions. The present study may contribute to the improvement of current prognostic systems for patients with HCC.

scholarly journals Nomogram Models Based on Gene Expression in Prediction of Breast Cancer Bone Metastasis

2021 ◽  
Author(s):  
Teng-di Fan ◽  
Di-kai Bei ◽  
Song-wei Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Bone Metastasis ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Training Set ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Patient Prognosis ◽  
Kaplan Meier Plotter

Abstract Objective: To design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods: Dataset GSE124647 was used as a training set, and GSE14020 was taken as a validation set. In the training cohort, limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC non-bone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan-Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the prognostic value of hub genes in BC was explored. Results: A total of 1858 DEGs were obtained. WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival. While GJA1, IGFBP6, MDFI, ITGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival. Kaplan-Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion: Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

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