Prognostic Value of Combined Lactate Dehydrogenase, C-Reactive Protein, Cancer Antigen 153 and Cancer Antigen 125 in Metastatic Breast Cancer

Background Breast cancer (BC), especially metastatic BC, is one of the most lethal diseases in women. CA 125 and CA 15-3 are commonly used indicators for diagnosis and prognosis of BC. Some serological indicators, such as lactate dehydrogenase (LDH) and C-reactive protein (CRP), can also be used to assess the prognosis and progression in BC. Methods Univariate Cox regression analysis and LASSO regression analysis were performed to identify prognostic factors and build prognostic models. We distributed the patients into 2 groups based on the median risk score, analyzed prognosis by Kaplan–Meier curve, and screened independent prognostic factors by multivariate Cox regression analysis. Result We identified 4 indicators-LDH, CRP, CA 15-3, and CA 125—related to the prognosis in BC and established a prognostic model. The high LDH group showed worse overall survival (OS) than low LDH group ( P = .017; hazard ratio (HR), 1.528; 95% confidence interval (CI), 1.055-2.215). The high CRP group showed worse OS than low CRP group ( P = .004; HR, 1.666; 95% CI, 1.143-2.429). The high CA153 group showed worse OS than low CA 15-3 group (P=.011; HR, 1.563; 95% CI, 1.075-2.274). The high CA 125 group showed worse OS than low CA 125 group ( P = .021; HR, 1.499; 95% CI, 1.031-2.181). The area under the curve for risk score was .824, Ki-67 was .628, age was .511, and grade was .545. Risk score was found to be an independent prognostic factor using multivariate Cox regression analysis. Conclusion We successfully established an optimization model by combining 4 prognosis-related indicators to assess the prognosis in patients with metastatic BC.

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

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

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.

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Impact of Early C-Reactive Protein/Albumin Ratio on Intra-Hospital Mortality Among Patients with Spontaneous Intracerebral Hemorrhage

Journal of Clinical Medicine ◽

10.3390/jcm9041236 ◽

2020 ◽

Vol 9 (4) ◽

pp. 1236 ◽

Cited By ~ 2

Author(s):

Michael Bender ◽

Kristin Haferkorn ◽

Michaela Friedrich ◽

Eberhard Uhl ◽

Marco Stein

Keyword(s):

Regression Analysis ◽

Intracerebral Hemorrhage ◽

Hospital Mortality ◽

Cox Regression ◽

Spontaneous Intracerebral Hemorrhage ◽

C Reactive Protein ◽

Albumin Ratio ◽

Cox Regression Analysis ◽

Reactive Protein ◽

The Impact

Objective: The impact of increased C-reactive protein (CRP)/albumin ratio on intra-hospital mortality has been investigated among patients admitted to general intensive care units (ICU). However, it was not investigated among patients with spontaneous intracerebral hemorrhage (ICH). This study aimed to investigate the impact of CRP/albumin ratio on intra-hospital mortality in patients with ICH. Patients and Methods: This retrospective study was conducted on 379 ICH patients admitted between 02/2008 and 12/2017. Blood samples were drawn upon admission and the patients’ demographic, medical, and radiological data were collected. The identification of the independent prognostic factors for intra-hospital mortality was calculated using binary logistic regression and COX regression analysis. Results: Multivariate regression analysis shows that higher CRP/albumin ratio (odds ratio (OR) = 1.66, 95% confidence interval (CI) = 1.193–2.317, p = 0.003) upon admission is an independent predictor of intra-hospital mortality. Multivariate Cox regression analysis indicated that an increase of 1 in the CRP/albumin ratio was associated with a 15.3% increase in the risk of intra-hospital mortality (hazard ratio = 1.153, 95% CI = 1.005–1.322, p = 0.42). Furthermore, a CRP/albumin ratio cut-off value greater than 1.22 was associated with increased intra-hospital mortality (Youden’s Index = 0.19, sensitivity = 28.8, specificity = 89.9, p = 0.007). Conclusions: A CRP/albumin ratio greater than 1.22 upon admission was significantly associated with intra-hospital mortality in the ICH patients.

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Identification of Prognosis-related RNA Binding Proteins to Reveal the Role of RNA Binding Proteins in the Progression and Prognosis of Colon Cancer

10.21203/rs.3.rs-54019/v1 ◽

2020 ◽

Author(s):

Qi Zou ◽

Yue Ding ◽

Yuxiang Dong ◽

Dejun Wu ◽

Junyi Wang ◽

...

Keyword(s):

Breast Cancer ◽

Colon Cancer ◽

Regression Analysis ◽

Risk Score ◽

Binding Proteins ◽

Cox Regression ◽

Cox Regression Analysis ◽

Immune Genes ◽

Score Model ◽

Survival Risk

Abstract Background: RNA binding proteins (RBPs) are now under discussion as novel promising bio-markers for patients with colon cancer. The purpose of our study is to identify several RBPs related to the progression and prognosis of colon cancer, and to further investigate the mechanism of their influence on tumor progression. Methods: The transcriptome data of colon cancer as well as clinical characteristics used in this study were downloaded from the The Cancer Genome Atlas (TCGA) database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA) were performed to elucidate the gene functions and relative pathways. Cox and lasso regression analysis were used to analyze the effect of immune genes on the prognosis of breast cancer. Immune risk scoring model was constructed based on the statistical correlation between hub immune genes and survival. Meanwhile, multivariate cox regression analysis was utilized to investigate whether the immune genes risk score model was an independent factor for predicting the prognosis of breast cancer. Nomogram was constructed to comprehensively predict the survival rate of breast cancer. P< 0.05 was considered to be statistically significant. Results: The results of the difference analysis showed that 473 RBPs exhibited differential expression between normal and colon cancer tissues (p<0. 05). Univariate cox regression analysis revealed 25 RBPs statistically correlated with colon cancer related survival risk (P<0.05). In addition, a 10-RBPs based risk scoring model was constructed through multivariate cox regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p<0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC=0.782). Our model showed satisfying AUC and survival correlation in the validation dataset (5-year OS AUC=0.744). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of colon cancer. A nomogram was established to comprehensively predict the survival of colon cancer patients with the results of multivariate cox regression analysis. Finally, we found that 10 RBPs and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways. Conclusion: Collectively, RBPs played an essential role in the progression and prognosis of colon cancer by regulating multiple biological pathways. Furthermore, RBPs risk score was an independent predictive factor of colon cancer, indicating a poor survival.

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Identification of an independent immune-genes prognostic index for breast cancer

10.21203/rs.3.rs-41670/v1 ◽

2020 ◽

Author(s):

Lin Chen ◽

Yuxiang Dong ◽

Yitong Pan ◽

Chen Chen ◽

Junyi Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

R Package ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Immune Genes ◽

Score Model ◽

Survival Risk

Abstract Objective Increasing evidence has indicated an association between immune micro-environment in breast cancer and clinical outcomes. The aim of this research is to comprehensively investigate the effect of tumor immune genes on the prognosis of breast cancer patients. Methods 2498 immune genes were downloaded from ImmPort database. Additionally, we identified and downloaded the transcriptome data of patients with breast cancer from the TCGA database through the R package, as well as relevant clinical information. Survival R package was applied in survival analyses for hub-genes. Cox regression analysis was used to analyze the effect of immune genes on the prognosis of breast cancer. Immune risk scoring model was constructed based on the statistical correlation between hub immune genes and survival. Meanwhile, multivariate cox regression analysis was utilized to investigate whether the immune genes risk score model was an independent factor for predicting the prognosis of breast cancer. Nomogram was constructed to comprehensively predict the survival rate of breast cancer. P < 0.05 was considered to be statistically significant. Results The results of the difference analysis showed that 556 immune genes exhibited differential expression between normal and breast cancer tissues (p < 0. 05). Univariate cox regression analysis revealed 66 immune genes statistically correlated with breast cancer related survival risk, of which 30 were associated with overall survival (P < 0.05). In addition, a 15-genes based immune genes risk scoring model was constructed through lasso COX regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p < 0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC = 0.752). Our model showed satisfying AUC and survival correlation in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of breast cancer. A nomogram was established to comprehensively predict the survival of breast cancer patients with the results of multivariate cox regression analysis. Finally, we found that 15 immune genes and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways. Conclusion Collectively, tumor immune genes played an essential role in the prognosis of breast cancer. Furthermore, immune risk score was an independent predictive factor of breast cancer, indicating a poor survival.

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Development of a Ten-lncRNA Signature Prognostic Model for Breast Cancer Survival: A Study with the TCGA Database

Analytical Cellular Pathology ◽

10.1155/2020/6827057 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Wenqing Zhou ◽

Yongkui Pang ◽

Yunmin Yao ◽

Huiying Qiao

Keyword(s):

Breast Cancer ◽

Gene Ontology ◽

Regression Analysis ◽

Pathway Analysis ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Patient Survival ◽

Receptor Interaction ◽

Cox Regression Analysis

Long noncoding RNA (lncRNA) plays a critical role in the development of tumors. The aim of our study was construction of a lncRNA signature model to predict breast cancer (BRCA) patient survival. We downloaded RNA-seq data and relevant clinical information from the Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNA were computed using the “edgeR” package and subjected to the univariate and multivariate Cox regression analysis. Corresponding protein-coding genes were used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis. Finally, 521 differentially expression lncRNA were obtained. We constructed a ten-lncRNA signature model (LINC01208, RP5-1011O1.3, LINC01234, LINC00989, RP11-696F12.1, RP11-909N17.2, CTC-297N7.9, CTA-384D8.34, CTC-276P9.4, and MAPT-IT1) to predict BRCA patient survival using the multivariate Cox proportional hazard regression model. The C-index was 0.712, and AUC scores of training, test, and entire sets were 0.746, 0.717, and 0.732, respectively. Univariate Cox regression analysis indicated that age, tumor status, N status, M status, and risk score were significantly related to overall survival in patients with BRCA. Further, the multivariate analysis showed that risk score and M status had outstanding independent prognostic values, both with p<0.001. The Gene Ontology (GO) function and KEEG pathway analysis was primarily enriched in immune response, receptor binding, external surface of plasma membrane, signal transduction, cytokine-cytokine receptor interaction, and cell adhesion molecules (CAMs). Finally, we constructed a ten-lncRNA signature model that can serve as an independent prognostic model to predict BRCA patient survival.

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Assessing the prognostic value of stemness-related genes in breast cancer patients

Scientific Reports ◽

10.1038/s41598-020-73164-3 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Wen-Jie Wang ◽

Han Wang ◽

Meng-sen Wang ◽

Yue-Qing Huang ◽

Yu-Yuan Ma ◽

...

Keyword(s):

Breast Cancer ◽

Stem Cells ◽

Regression Analysis ◽

Cancer Stem Cells ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Treatment Resistance ◽

Breast Cancer Patients ◽

Cox Regression Analysis

Abstract Breast cancer (BC) is currently one of the deadliest tumors worldwide. Cancer stem cells (CSCs) are a small group of tumor cells with self-renewal and differentiation abilities and high treatment resistance. One of the reasons for treatment failures is the inability to completely eliminate tumor stem cells. By using the edgeR package, we identified stemness-related differentially expressed genes in GSE69280. Via Lasso-penalized Cox regression analysis and univariate Cox regression analysis, survival genes were screened out to construct a prognostic model. Via nomograms and ROC curves, we verified the accuracy of the prognostic model. We selected 4 genes (PSMB9, CXCL13, NPR3, and CDKN2C) to establish a prognostic model from TCGA data and a validation model from GSE24450 data. We found that the low-risk score group had better OS than the high-risk score group, whether using TCGA or GSE24450 data. A prognostic model including four stemness-related genes was constructed in our study to determine targets of breast cancer stem cells (BCSCs) and improve the treatment effect.

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High-Sensitivity C-Reactive Protein is a Predictor of Subsequent Atrial High-Rate Episodes in Patients with Pacemakers and Preserved Ejection Fraction

Journal of Clinical Medicine ◽

10.3390/jcm9113677 ◽

2020 ◽

Vol 9 (11) ◽

pp. 3677

Author(s):

Min-Tsun Liao ◽

Chun-Kai Chen ◽

Ting-Tse Lin ◽

Li-Ying Cheng ◽

Hung-Wen Ting ◽

...

Keyword(s):

Regression Analysis ◽

Cox Regression ◽

High Sensitivity ◽

Cardiac Biomarkers ◽

High Rate ◽

C Reactive Protein ◽

Cox Regression Analysis ◽

Reactive Protein ◽

Increased Risk ◽

Hs Crp

Atrial fibrillation (AF) is associated with morbidity and mortality. Modern pacemakers can detect atrial high-rate episodes (AHREs) as a surrogate for AF. It remains controversial whether inflammation is a cause or a consequence of AF. This study investigated whether the inflammatory biomarker high-sensitivity C-reactive protein (hs-CRP) can predict subsequent AHREs. This study gathered prospective data from patients with pacemakers and a left ventricle EF ≥ 50% between 2015 and 2019. The hs-CRP and other cardiac biomarkers at baseline and device-detected AHREs, defined as atrial rate ≥ 180 bpm and duration ≥ 6 min, were determined. Cox regression analysis was used to estimate the independent predictors for AHREs. A total of 171 consecutive patients were included. During the median follow-up of 614 days, 66 patients (39%) developed subsequent AHREs. In the univariate Cox regression analysis, sick sinus syndrome (p = 0.005), prior AF (p < 0.001), mitral A velocity (p = 0.008), and hs-CRP (p = 0.013) showed significant association with the increased risk of AHREs. In the multivariate Cox regression model, hs-CRP (HR = 1.121, 95% confidence interval = 1.015–1.238, p = 0.024) retained its significance. Our results suggest that elevated hs-CRP could predict subsequent AHREs and that inflammation could play a role in AF pathogenesis in patients with preserved EF.

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Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽

10.1186/s12885-021-08041-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lin Chen ◽

Yuxiang Dong ◽

Yitong Pan ◽

Yuhan Zhang ◽

Ping Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Validation Dataset ◽

Immune Gene ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

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The Investigation of Associations between TP53 rs1042522, BBC3 rs2032809, CCND1 rs9344, EGFR rs2227983 Polymorphisms and Breast Cancer Phenotype and Prognosis

Diagnostics ◽

10.3390/diagnostics11081419 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1419

Author(s):

Justina Bekampytė ◽

Agnė Bartnykaitė ◽

Aistė Savukaitytė ◽

Rasa Ugenskienė ◽

Erika Korobeinikova ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Prognosis ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Cox Regression Analysis ◽

Pcr Rflp ◽

Oncological Diseases ◽

Cancer Phenotype

Breast cancer is one of the most common oncological diseases among women worldwide. Cell cycle and apoptosis—related genes TP53, BBC3, CCND1 and EGFR play an important role in the pathogenesis of breast cancer. However, the roles of single nucleotide polymorphisms (SNPs) in these genes have not been fully defined. Therefore, this study aimed to analyze the association between TP53 rs1042522, BBC3 rs2032809, CCND1 rs9344 and EGFR rs2227983 polymorphisms and breast cancer phenotype and prognosis. For the purpose of the analysis, 171 Lithuanian women were enrolled. Genomic DNA was extracted from peripheral blood; PCR-RFLP was used for SNPs analysis. The results showed that BBC3 rs2032809 was associated with age at the time of diagnosis, disease progression, metastasis and death. CCND1 rs9344 was associated with tumor size, however an association resulted in loss of significance after Bonferroni correction. In survival analysis, significant associations were observed between BBC3 rs2032809 and OS, PFS and MFS. EGFR rs2227983 also showed some associations with OS and PFS (univariate Cox regression analysis). However, the results were in loss of significance (multivariate Cox regression analysis). In conclusion, BBC3 rs2032809 polymorphism was associated with breast cancer phenotype and prognosis. Therefore, it could be applied as potential markers for breast cancer prognosis.

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Identification of Prognostic RBPs in Osteosarcoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211004918 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110049

Author(s):

Bei Li ◽

Long Fang ◽

Baolong Wang ◽

Zengkun Yang ◽

Tingbao Zhao

Keyword(s):

Regression Analysis ◽

Prognostic Factors ◽

Differential Expression ◽

Ribosome Biogenesis ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Malignant Tumors ◽

Differential Expression Analysis ◽

Cox Regression Analysis

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman’s correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

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