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Zhang C, Lin Q, Li C, Chen Z, Deng M, Weng H, Zhu X. Analysis of endoplasmic reticulum stress-related gene signature for the prognosis and pattern in diffuse large B cell lymphoma. Sci Rep 2023; 13:13894. [PMID: 37626099 PMCID: PMC10457392 DOI: 10.1038/s41598-023-38568-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 07/11/2023] [Indexed: 08/27/2023] Open
Abstract
Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoma in adults. This study aimed to determine the prognostic significance of endoplasmic reticulum (ER) stress-related genes in DLBCL. ER stress-related genes were obtained from the molecular signatures database. Gene expression data and clinical outcomes from the gene expression omnibus and TCGA datasets were collected, and differentially expressed genes (DEGs) were screened out. Gene ontology enrichment analysis, the kyoto encyclopaedia of genes and genomes pathway analysis, and geneset enrichment analysis were used to analyse the possible biological function of ER stress-related DEGs in DLBCL. Protein-protein interaction network construction using the STRING online and hub genes were identified by cytoHubba on Cytoscape software. The significant prognosis-related genes were screened, and the differential expression was validated. The immune microenvironment assessment of significant genes were evaluated. Next, the nomogram was built using univariate and multivariate Cox regression analysis. 26 ER stress-related DEGs were screened. Functional enrichment analysis showed them to be involved in the regulation of the endoplasmic reticulum mainly. NUPR1 and TRIB3 were identified as the most significant prognostic-related genes by comparison with the GSE10846, GSE11318, and TCGA datasets. NUPR1 was correlated with a good prognosis and immune infiltration in DLBCL; on the other hand, high expression of TRIB3 significantly correlated with a poor prognosis, which was an independent prognostic factor for DLBCL. In summary, we identified NUPR1 and TRIB3 as critical ER stress-related genes in DLBCL. NUPR1 might be involved in immune infiltration in DLBCL, and TRIB3 might serve as a potential therapeutic target and prognostic factor in DLBCL.
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Affiliation(s)
- Chaofeng Zhang
- Department of Hematology and Rheumatology, The Affiliated Hospital of Putian University, Putian, Fujian Province, China
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Qi Lin
- Department of Pharmacy, The Affiliated Hospital of Putian University, Putian, Fujian Province, China
- Pharmaceutical and Medical Technology College, Putian University, Putian, Fujian Province, China
| | - Chaoqi Li
- Pharmaceutical and Medical Technology College, Putian University, Putian, Fujian Province, China
| | - Zhimin Chen
- Department of Nephrology, Blood Purification Research Center, the First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Mengmeng Deng
- Pharmaceutical and Medical Technology College, Putian University, Putian, Fujian Province, China
| | - Huixin Weng
- Pharmaceutical and Medical Technology College, Putian University, Putian, Fujian Province, China
| | - Xiongpeng Zhu
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, China.
- Department of Haematology, Quanzhou First Hospital of Affiliated to Fujian Medical University, Quanzhou, Fujian Province, China.
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Sewak A, Hothorn T. Estimating transformations for evaluating diagnostic tests with covariate adjustment. Stat Methods Med Res 2023; 32:1403-1419. [PMID: 37278185 PMCID: PMC10500951 DOI: 10.1177/09622802231176030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Receiver operating characteristic analysis is one of the most popular approaches for evaluating and comparing the accuracy of medical diagnostic tests. Although various methodologies have been developed for estimating receiver operating characteristic curves and their associated summary indices, there is no consensus on a single framework that can provide consistent statistical inference while handling the complexities associated with medical data. Such complexities might include non-normal data, covariates that influence the diagnostic potential of a test, ordinal biomarkers or censored data due to instrument detection limits. We propose a regression model for the transformed test results which exploits the invariance of receiver operating characteristic curves to monotonic transformations and accommodates these features. Simulation studies show that the estimates based on transformation models are unbiased and yield coverage at nominal levels. The methodology is applied to a cross-sectional study of metabolic syndrome where we investigate the covariate-specific performance of weight-to-height ratio as a non-invasive diagnostic test. Software implementations for all the methods described in the article are provided in the tram add-on package to the R system for statistical computing and graphics.
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Affiliation(s)
- Ainesh Sewak
- Institut für Epidemiologie, Biostatistik und Prävention, Universität Zürich, Zürich, Switzerland
| | - Torsten Hothorn
- Institut für Epidemiologie, Biostatistik und Prävention, Universität Zürich, Zürich, Switzerland
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Martínez-Camblor P. The fundamental role of density functions in the binary classification problem. J STAT COMPUT SIM 2022. [DOI: 10.1080/00949655.2022.2051026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Pablo Martínez-Camblor
- Department of Anesthesiology, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
- Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, Lebanon, NH, USA
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Martínez-Camblor P, MacKenzie TA, Staiger DO, Goodney PP, O'Malley AJ. Summarizing causal differences in survival curves in the presence of unmeasured confounding. Int J Biostat 2020; 17:223-240. [PMID: 32946418 DOI: 10.1515/ijb-2019-0146] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 08/10/2020] [Indexed: 11/15/2022]
Abstract
Proportional hazard Cox regression models are frequently used to analyze the impact of different factors on time-to-event outcomes. Most practitioners are familiar with and interpret research results in terms of hazard ratios. Direct differences in survival curves are, however, easier to understand for the general population of users and to visualize graphically. Analyzing the difference among the survival curves for the population at risk allows easy interpretation of the impact of a therapy over the follow-up. When the available information is obtained from observational studies, the observed results are potentially subject to a plethora of measured and unmeasured confounders. Although there are procedures to adjust survival curves for measured covariates, the case of unmeasured confounders has not yet been considered in the literature. In this article we provide a semi-parametric procedure for adjusting survival curves for measured and unmeasured confounders. The method augments our novel instrumental variable estimation method for survival time data in the presence of unmeasured confounding with a procedure for mapping estimates onto the survival probability and the expected survival time scales.
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Affiliation(s)
- Pablo Martínez-Camblor
- Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA
| | - Todd A MacKenzie
- Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.,The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Lebanon, New Hampshire, USA
| | - Douglas O Staiger
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Lebanon, New Hampshire, USA.,Department of Economics, Dartmouth College, Hanover, New Hampshire, USA
| | - Phillip P Goodney
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Lebanon, New Hampshire, USA.,Section of Vascular Surgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire, USA
| | - A James O'Malley
- Department of Biomedical Data Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire, USA.,The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Lebanon, New Hampshire, USA
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