Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma
BackgroundDiffuse large B-cell lymphoma (DLBCL) is a heterogeneous group with varied pathophysiological, genetic, and clinical features, accounting for approximately one-third of all lymphoma cases worldwide. Notwithstanding that unprecedented scientific progress has been achieved over the years, th...
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| Format: | Article |
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Frontiers Media S.A.
2022-04-01
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| Series: | Frontiers in Endocrinology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.846357/full |
| _version_ | 1818524178854182912 |
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| author | Jiaqin Yan Wei Yuan Wei Yuan Wei Yuan Junhui Zhang Ling Li Lei Zhang Xudong Zhang Mingzhi Zhang |
| author_facet | Jiaqin Yan Wei Yuan Wei Yuan Wei Yuan Junhui Zhang Ling Li Lei Zhang Xudong Zhang Mingzhi Zhang |
| author_sort | Jiaqin Yan |
| collection | DOAJ |
| description | BackgroundDiffuse large B-cell lymphoma (DLBCL) is a heterogeneous group with varied pathophysiological, genetic, and clinical features, accounting for approximately one-third of all lymphoma cases worldwide. Notwithstanding that unprecedented scientific progress has been achieved over the years, the survival of DLBCL patients remains low, emphasizing the need to develop novel prognostic biomarkers for early risk stratification and treatment optimization.MethodIn this study, we screened genes related to the overall survival (OS) of DLBCL patients in datasets GSE117556, GSE10846, and GSE31312 using univariate Cox analysis. Survival-related genes among the three datasets were screened according to the criteria: hazard ratio (HR) >1 or <1 and p-value <0.01. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to optimize and establish the final gene risk prediction model. The TCGA-NCICCR datasets and our clinical cohort were used to validate the performance of the prediction model. CIBERSORT and ssGSEA algorithms were used to estimate immune scores in the high- and low-risk groups.ResultsWe constructed an eight-gene prognostic signature that could reliably predict the clinical outcome in training, testing, and validation cohorts. Our prognostic signature also performed distinguished areas under the ROC curve in each dataset, respectively. After stratification based on clinical characteristics such as cell-of-origin (COO), age, eastern cooperative oncology group (ECOG) performance status, international prognostic index (IPI), stage, and MYC/BCL2 expression, the difference in OS between the high- and low-risk groups was statistically significant. Next, univariate and multivariate analyses revealed that the risk score model had a significant prediction value. Finally, a nomogram was established to visualize the prediction model. Of note, we found that the low-risk group was enriched with immune cells.ConclusionIn summary, we identified an eight-gene prognostic prediction model that can effectively predict survival outcomes of patients with DLBCL and built a nomogram to visualize the perdition model. We also explored immune alterations between high- and low-risk groups. |
| first_indexed | 2024-12-11T05:53:56Z |
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| id | doaj.art-b507b9f4e7934cd4972992fba4d8e4f9 |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-12-11T05:53:56Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Endocrinology |
| spelling | doaj.art-b507b9f4e7934cd4972992fba4d8e4f92022-12-22T01:18:44ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-04-011310.3389/fendo.2022.846357846357Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell LymphomaJiaqin Yan0Wei Yuan1Wei Yuan2Wei Yuan3Junhui Zhang4Ling Li5Lei Zhang6Xudong Zhang7Mingzhi Zhang8Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaThe Academy of Medical Sciences, Zhengzhou University, Zhengzhou, ChinaState Key Laboratory of Esophageal Cancer Prevention and Treatment, Zhengzhou University, Zhengzhou, ChinaOtorhinolaryngology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaDepartment of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, ChinaBackgroundDiffuse large B-cell lymphoma (DLBCL) is a heterogeneous group with varied pathophysiological, genetic, and clinical features, accounting for approximately one-third of all lymphoma cases worldwide. Notwithstanding that unprecedented scientific progress has been achieved over the years, the survival of DLBCL patients remains low, emphasizing the need to develop novel prognostic biomarkers for early risk stratification and treatment optimization.MethodIn this study, we screened genes related to the overall survival (OS) of DLBCL patients in datasets GSE117556, GSE10846, and GSE31312 using univariate Cox analysis. Survival-related genes among the three datasets were screened according to the criteria: hazard ratio (HR) >1 or <1 and p-value <0.01. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to optimize and establish the final gene risk prediction model. The TCGA-NCICCR datasets and our clinical cohort were used to validate the performance of the prediction model. CIBERSORT and ssGSEA algorithms were used to estimate immune scores in the high- and low-risk groups.ResultsWe constructed an eight-gene prognostic signature that could reliably predict the clinical outcome in training, testing, and validation cohorts. Our prognostic signature also performed distinguished areas under the ROC curve in each dataset, respectively. After stratification based on clinical characteristics such as cell-of-origin (COO), age, eastern cooperative oncology group (ECOG) performance status, international prognostic index (IPI), stage, and MYC/BCL2 expression, the difference in OS between the high- and low-risk groups was statistically significant. Next, univariate and multivariate analyses revealed that the risk score model had a significant prediction value. Finally, a nomogram was established to visualize the prediction model. Of note, we found that the low-risk group was enriched with immune cells.ConclusionIn summary, we identified an eight-gene prognostic prediction model that can effectively predict survival outcomes of patients with DLBCL and built a nomogram to visualize the perdition model. We also explored immune alterations between high- and low-risk groups.https://www.frontiersin.org/articles/10.3389/fendo.2022.846357/fullprediction modelimmune cell infiltrationnomogramstratification analysesdiffuse large B-cell lymphoma |
| spellingShingle | Jiaqin Yan Wei Yuan Wei Yuan Wei Yuan Junhui Zhang Ling Li Lei Zhang Xudong Zhang Mingzhi Zhang Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma Frontiers in Endocrinology prediction model immune cell infiltration nomogram stratification analyses diffuse large B-cell lymphoma |
| title | Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma |
| title_full | Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma |
| title_fullStr | Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma |
| title_full_unstemmed | Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma |
| title_short | Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma |
| title_sort | identification and validation of a prognostic prediction model in diffuse large b cell lymphoma |
| topic | prediction model immune cell infiltration nomogram stratification analyses diffuse large B-cell lymphoma |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.846357/full |
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