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pmid:35498426
Identification and Validation of a Prognostic Prediction Model in Diffuse Large B-Cell Lymphoma.
Jiaqin Yan · Wei Yuan · Junhui Zhang · Ling Li · Lei Zhang · Xudong Zhang · Mingzhi Zhang
Frontiers in endocrinology · 2022 doi:10.3389/fendo.2022.846357
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Experiment
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Complete file
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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.\nMethod: In 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.\nResults: We 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.\nConclusion: In 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.", "claims": [{"id": "claim-method", "statement": "Multivariate Cox regression analysis showed that the risk score model was an independent prognostic factor for poor prognosis (p < 0.0001, HR = 1.380) ( Figure 6B ).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35498426", "locator": "sections[14] \"Validation of the Performance of Our Prediction Model\" paragraph 2", "quote": "Multivariate Cox regression analysis showed that the risk score model was an independent prognostic factor for poor prognosis (p < 0.0001, HR = 1.380) ( Figure 6B )."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets GSE117556, GSE10846 and GSE31312 for this study can be found in the GEO (https://www.ncbi.nlm.nih.gov/geo).The datasets NCICCR for this study can be found at TCGA (https://portal.gdc.cancer.gov/repository).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35498426", "locator": "sections[17] \"Data Availability Statement\" paragraph 2", "quote": "The datasets GSE117556, GSE10846 and GSE31312 for this study can be found in the GEO (https://www.ncbi.nlm.nih.gov/geo).The datasets NCICCR for this study can be found at TCGA (https://portal.gdc.cancer.gov/repository)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Compared with other indicators, the risk score was superior for predicting the patient prognosis (AUC = 0.820) ( Figure 6C ).", "kind": "quantitative", "target": {"value": 0.82, "unit": "auc", "tolerance": null, "source": "Compared with other indicators, the risk score was superior for predicting the patient prognosis (AUC = 0.820) ( Figure 6C )."}, "evidence": [{"asset": "source:pmid:35498426", "locator": "sections[14] \"Validation of the Performance of Our Prediction Model\" paragraph 2", "quote": "Compared with other indicators, the risk score was superior for predicting the patient prognosis (AUC = 0.820) ( Figure 6C )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Compared with other indicators, the risk score was superior for predicting the patient prognosis (AUC = 0.820) ( Figure 6C ).", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35498426"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10846"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10846"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35498426", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2022.846357", "sha256": 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"application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31312", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31312", "version": null, "note": "The datasets GSE117556, GSE10846 and GSE31312 for this study can be found in the GEO (https://www.ncbi.nlm.nih.gov/geo).The datasets NCICCR for this study can be found at TCGA (https://portal.gdc.cancer.gov/repository).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets GSE117556, GSE10846 and GSE31312 for this study can be found in the GEO (https://www.ncbi.nlm.nih.gov/geo).The datasets NCICCR for this study can be found at TCGA (https://portal.gdc.cancer.gov/repository).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM775979", "GSM775980", "GSM775981", "GSM775982", "GSM775983", "GSM775984", "GSM775985", 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE117556", "GSE10846", "GSE31312"], "resolved_accessions": ["GSE117556", "GSE10846", "GSE31312"], "statements": ["The datasets GSE117556, GSE10846 and GSE31312 for this study can be found in the GEO (https://www.ncbi.nlm.nih.gov/geo).The datasets NCICCR for this study can be found at TCGA (https://portal.gdc.cancer.gov/repository)."], "basis": "The paper names GSE117556, GSE10846, GSE31312 in GEO and the accession resolves in the repository. 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