scholarly journals The Identification and Analysis of a Novel Model Based on Ferroptosis-Related Genes for Predicting the Prognosis of Diffuse Large B-Cell Lymphomas

2021 ◽  
Author(s):  
Jiayi Wang ◽  
Hongling Peng ◽  
Guangsen Zhang ◽  
Yunxiao Xu ◽  
Wenzhe Yan
Keyword(s):  
B Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Survival Curve ◽  
B Cell Lymphoma ◽  
B Cell Lymphomas ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Large B Cell

Abstract Background Diffuse large B-cell lymphomas (DLBCLs) are the most common B-cell lymphoma featured as phenotypically and genetically heterogeneous. Ferroptosis is a new found programmed cell death and have a crucial role in the chemoresistance of tumor. We aim to build a ferroptosis-related genes (FRGs) prognostic signature to predict the outcome of DLBCLs. Methods Our study retrospectively investigated the mRNA expression level and clinical data of 604 DLBCL patients from 3 GEO public datasets. A series of bioinformatic approaches including Cox regression analysis, function enrich analysis, immune infiltration analysis, ROC curve analysis, Kaplan–Meier survival curve and the least absolute shrinkage and selection operator (LASSO) method by the corresponding R packages in R statistical software were combined to explored the heterogenicity of FRG based clusters and to build prognostic model. Immunohistochemistry was used to exam the protein expression of six FRGs in different molecular type of DLBCL. Results We first identified 19 FRGs with potential prognostic values and classfied the patients into cluster 1 and cluster 2, Results indicated that cluster 1 tend to have a shorter overall survival (OS) time, while patients in the two clusters have different patterns of infiltrating immune cells among. Furthermore, the LASSO was used to generated a six-genes (GCLC, LPCAT3, NFE2L2, ABCC1, SLC1A5, and GOT1) risk signature which constructed a risk score formula and prognostic model for the OS of DLBCL patients. Kaplan–Meier survival analysis proved that poorer OS was exhibited in higher risk patients stratified by the prognostic model in both the training cohort and test cohort. In addition, we constructed nomograms to predict the OS of DLBCL patients. Both the decision curve(DCA) and the calibration plots showed that the nomogram had good agreement between predicted results and actual observation. Finally, the validation by immunohistochemistry indicated the GCLC, LPCAT3, ABCC1, SLC1A5, and GOT1 were high expressed in DLBCL with various prognostic adverse molecular factor. Conclusion In sum, we built a new FRG-based prognostic model which will help improve diagnosis and treatment for DLBCL patients.

scholarly journals The Identification and Analysis of a Novel Model Based on Ferroptosis-Related Genes for Predicting the Prognosis of Diffuse Large B-Cell Lymphomas

2021 ◽  
Author(s):  
Jiayi Wang ◽  
Hongling Peng ◽  
Guangsen Zhang ◽  
Yunxiao Xu ◽  
Wenzhe Yan
Keyword(s):  
B Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Function Analysis ◽  
Differential Expression Analysis ◽  
B Cell Lymphomas ◽  
Cox Regression Analysis ◽  
Cell Death Pathway ◽  
Kaplan Meier ◽  
Large B Cell

Abstract Backgroud: Diffuse large B-cell lymphomas (DLBCLs) are featured as phenotypically and genetically heterogeneous. Ferroptosis is a newly discovered regulated cell death pathway that plays a crucial role in the occurrence and progression of tumors. We aim to identify a ferroptosis-related gene (FRG) prognostic signature for DLBCLs by systematic analysis of transcriptional profiles. Methods: This study retrospectively analysed the transcriptome profiles and clinical parameters of 604 DLBCL patients from 3 public datasets. A series of bioinformatic approaches including univariate and multivariate Cox regression analysis, function analysis, immune infiltration analysis, differential expression analysis, ROC curve analysis, Kaplan–Meier survival curve and the least absolute shrinkage and selection operator (LASSO) method by the corresponding R packages in R software were combined to explored the heterogenicity of FRG based clusters and to built prognostic model. Immunohistochemistry was used to exam the protein expression of six FRGs in different type of DLBCL.Results: We first identified 19 FRGs with potential prognostic values and classfied the patients into two subgroups (named cluster 1 and cluster 2), Results showed that there were different patterns of immune cell infiltration among patients in the two clusters. Furthermore, the LASSO was used to generated a six genes (GCLC, LPCAT3, NFE2L2, ABCC1, SLC1A5, and GOT1) risk signature which constructed a risk score formula and prognostic model for the overall survival (OS) of DLBCL patients. Kaplan–Meier survival analysis proved that poorer OS was exhibited in higher risk patients stratified by the prognostic model in both the training cohort and test cohort. In addtion, we constructed nomograms to predict the OS of DLBCL patients. Both the decision curve(DCA) and the calibration plots showed that the nomogram had good predictive performance. Finally, the validation by immunohistochemistry indicated the GCLC, LPCAT3, NFE2L2, SLC1A5, and GOT1 were high expressed in DLBCL with various prognostic adverse molecular factor. Conclusion: In sum, we built a new FRG-based prognostic model which will help improve diagnosis and treatment for DLBCL patients.

scholarly journals High ETV6 Levels Support Aggressive B Lymphoma Cell Survival and Predict Poor Outcome in Diffuse Large B-Cell Lymphoma Patients

Cancers ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Dario Marino ◽  
Marco Pizzi ◽  
Iuliia Kotova ◽  
Ronny Schmidt ◽  
Christoph Schröder ◽  
...  
Keyword(s):  
B Cell ◽  
Cox Regression ◽  
Cell Lymphoma ◽  
Expression Profiles ◽  
Inverse Correlation ◽  
B Cell Lymphoma ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Large B Cell Lymphoma ◽  
Large B Cell

The identification of prognostic factors for aggressive B-cell lymphomas still represents an unmet clinical need. We used forward phase protein arrays (FFPA) to identify proteins associated with overall survival (OS) from diagnostic formalin-fixed paraffin-embedded material of diffuse large B-cell lymphoma (DLBCL) patients (n = 47). Univariate Cox regression analysis identified numerous proteins, including immune check-point molecules (PDCD1, PDCD2 and PD1L2) and BCL2 to be significantly associated with OS. However, only ETV6 and PIM2 proteins persisted following multivariate Cox analysis. Independent validation studies by immunohistochemistry and analysis of public gene expression profiles of DLBCL confirmed a prognostic role for high ETV6 and ETV6/PIM2 ratios in DLBCL. ETV6 is a recurrently mutated/deleted gene in DLBCL for which its function in this disease entity is currently unknown. We find that ETV6 is upregulated during oncogenic transformation of germinal center B-cells and that it regulates DLBCL survival, as its acute loss results in marked apoptosis. Fluctuations in survivin (BIRC5) expression levels were associated with this phenomenon. Furthermore, an inverse correlation between ETV6 and BIRC5 expression levels was found and correlated with a response to the BIRC5 inhibitor, YM155. In conclusion, we present evidence for an oncogenic function of ETV6 in DLBCL.

scholarly journals Identification of Aging-Related Genes Associated with Prognostic Value and Immune Microenvironment Characteristics in Diffuse Large B-Cell Lymphoma

2022 ◽  
Vol 2022 ◽  
pp. 1-30
Author(s):  
Cancan Luo ◽  
Han Nie ◽  
Li Yu
Keyword(s):  
B Cell ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Diffuse large B-cell lymphoma (DLBCL) is a complex invasive tumour that occurs mainly among the elderly. Therefore, we analysed the relationship between ageing-related genes (AG) and DLBCL prognosis. Datasets related to DLBCL and human AGs were downloaded and screened from the Gene Expression Omnibus (GEO) database and HAGR website, respectively. LASSO and Cox regression were used to analyse AGs in the dataset and construct an AG predictive model related to DLBCL prognosis. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes enrichment were used to analyse the function of the AG predictive model. The immune microenvironment and immune cell infiltration in DLBCL and their relationship with the AG prediction model were also analysed. After the analysis, 118 AGs were identified as genes related to DLBCL prognosis. Using the LASSO and Cox regression analyses, 9 AGs (PLAU, IL7R, MYC, S100B, IGFBP3, NR3C1, PTK2, TBP, and CLOCK) were used to construct an AG prognostic model. In the training and verification sets, this model exhibited excellent predictive ability for the prognosis of patients with DLBCL who have different clinical characteristics. Further analysis revealed that the high- and low-risk groups of the AG prognostic model were significantly correlated with immune cell infiltration and tumour microenvironment in DLBCL. Functional enrichment analysis also showed that the genes in the AG model were associated with immune-related functions and pathways. In conclusion, we constructed an AG model with a strong predictive function in DLBCL, with the ability to predict the prognosis of patients with different clinical features. This model provides new ideas and potential therapeutic targets for the study of the pathogenesis of DLBCL.

scholarly journals The role of рАКТ1 expression in diffuse large B-cell lymphoma

2021 ◽  
Vol 20 (3) ◽  
pp. 13-20
Author(s):  
E. V. Vaneeva ◽  
V. A. Rosin ◽  
D. A. Diakonov ◽  
A. S. Luchinin ◽  
S. V. Samarina ◽  
...  
Keyword(s):  
B Cell ◽  
Tumor Cells ◽  
Cox Regression ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Cox Regression Analysis ◽  
Large B Cell Lymphoma ◽  
Long Term Treatment ◽  
The Impact ◽  
Large B Cell

Aim. To evaluate the prognostic value of pAKT1 expression by tumor cells in patients with diffuse large B-cell lymphoma.Materials and methods. The study included 90 patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL), who were treated at the clinic of Kirov Research Institute of Hematology and Blood Transfusion from 2014 to 2017 and received standard first-line polychemotherapy according to the R-CHOP regimen. Using immunohistochemical and morphometric methods, the relative number of tumor cells expressing pAKT1 was determined. Using the two-sided Fisher’s exact test, the relationship of different levels of marker expression with clinical and laboratory parameters of patients and long-term treatment results was analyzed. The impact of pAKT1 on the risk of an adverse event was assessed using the Cox regression analysis.Results. Overexpression of pAKT1 is associated with unfavorable clinical characteristics of patients with DLBCL, excessive expression of the BCL2 and c-Myc oncoproteins, as well as with low rates of overall and progressive survival. Overexpression of pAKT1 is an independent prognostic factor and statistically significantly affects the risk of an adverse outcome in DLBCL.Conclusion. The degree of pAKT1 expression is an informative criterion that allows to predict the course of diffuse large B-cell lymphoma. It is advisable to use the indicated marker when stratifying patients into risk groups.

scholarly journals Central nervous system relapse in diffuse large B-cell lymphoma: analysis of risk factors and a new prognostic model

2015 ◽  
Vol 26 ◽  
pp. vii85
Author(s):  
Yusuke Kanemasa ◽  
Tatsu Shimoyama ◽  
Yuki Sasaki ◽  
Sawada Takeshi ◽  
Eisaku Sasaki ◽  
...  
Keyword(s):  
Risk Factors ◽  
Central Nervous System ◽  
Nervous System ◽  
B Cell ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Central Nervous System Relapse ◽  
Large B Cell Lymphoma ◽  
Large B Cell

scholarly journals Value of PD-L1 Protein Expression in the Combined Prognostic Model of Diffuse Large B-Cell Lymphoma

2021 ◽  
Vol 14 (3) ◽  
pp. 308-314
Author(s):  
Svetlana Valerevna Samarina ◽  
N.Yu. Semenova ◽  
N.V. Minaeva ◽  
D.A. Dyakonov ◽  
V.A. Rosin ◽  
...  
Keyword(s):  
Protein Expression ◽  
B Cell ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Large B Cell Lymphoma ◽  
L1 Protein ◽  
Large B Cell

scholarly journals An NCCN-IPI based immune-related gene prognostic model for diffuse large B-cell lymphoma

2021 ◽  
Author(s):  
Shidai Mu ◽  
Deyao Shi ◽  
Lisha Ai ◽  
Fengjuan Fan ◽  
Fei Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
B Cell ◽  
Prognostic Model ◽  
Cell Lymphoma ◽  
International Prognostic Index ◽  
Prognostic Significance ◽  
B Cell Lymphoma ◽  
Large B Cell Lymphoma ◽  
Risk Patients ◽  
Large B Cell

AbstractBackgroundAn enhanced International Prognostic Index (NCCN-IPI) was built to better discriminate diffuse large B-cell lymphoma (DLBCL) patients in the rituximab era. However, there is an urgent need to identify novel valuable biomarkers in the context of targeted therapies, such as immune checkpoint blockade (ICB) therapy.MethodsGene expression data and clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. 73 immune-related hub genes in DLBCL patients with different IPI levels were identified by weighted gene co-expression network analysis (WGCNA), and 4 genes were selected to construct an IPI-based immune-related prognostic model (IPI-IPM). Afterward, the genetic, somatic mutational and molecular profiles of IPI-IPM subgroups were analyzed, as well as the potential clinical response of ICB in different IPI-IPM subgroups.ResultsThe IPI-IPM was constructed base on the expression of LCN2, CD5L, NLRP11 and SERPINB2, where high-risk patients had shorter overall survival (OS) than low-risk patients, consistent with the results in the GEO cohorts. The comprehensive results showed that a high IPI-IPM risk score was correlated with immune-related signaling pathways, high KMT2D and CD79B mutation rates, high infiltration of CD8+ T cells and macrophages (M1, M2), as well as up-regulation of inhibitory immune checkpoints including PD-L1, LAG3 and BTLA, indicating more potential response to ICB therapy.ConclusionThe IPI-IPM has independent prognostic significance for DLBCL patients, which provides an immunological perspective to elucidate the mechanisms on tumor progression and drug resistance, also sheds a light on developing immunotherapy for DLBCL.

scholarly journals Factors predicting recurrence after curative resection for rectal cancer: a 16-year study

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Waad Farhat ◽  
Mohamed Azzaza ◽  
Abdelkader Mizouni ◽  
Houssem Ammar ◽  
Mahdi ben Ltaifa ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Recurrence Rate ◽  
Cox Regression ◽  
Survival Curve ◽  
Rectal Adenocarcinoma ◽  
Distal Margin ◽  
Curative Surgery ◽  
Factors Affecting ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Abstract Background The recurrence after curative surgery of the rectal adenocarcinoma is a serious complication, considered as a failure of the therapeutic strategy. The aim of this study was to identify the different prognostic factors affecting the recurrence of adenocarcinoma of the rectum. Methods A retrospective analysis of patients operated for adenocarcinoma of the rectum between January 2000 and December 2015 was conducted. The study of the recurrence rate and prognostic factors was performed through the Kaplan Meier survival curve and the Cox regression analysis. Results During the study period, 188 patients underwent curative surgery for rectal adenocarcinoma, among which 53 had a recurrence. The recurrence rate was 44.6% at 5 years. The multivariate analysis identified four parameters independently associated with the risk of recurrence after curative surgery: a distal margin ≤ 2 cm (HR = 6.8, 95% CI 2.7–16.6, 6), extracapsular invasion of lymph node metastasis (HR = 4.4, 95% CI 1.3–14), tumor stenosis (HR = 4.3, 95% CI 1.2–15.2), and parietal invasion (pT3/T4 disease) (HR = 3, 95% CI 1.1–9.4). Conclusion The determination of the prognostic factors affecting the recurrence of rectal adenocarcinoma after curative surgery allows us to define the high-risk patients for recurrence. Trial registration ClinicalTrials.gov Identifier: NCT03899870. Registered on 2 February 2019, retrospectively registered.

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