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pmid:35615381
Immune Microenvironment Terms Signature Robustly Predicts the Prognosis and Immunotherapy Response in Bladder Cancer Based on Large Population Cohorts.
Shengjie Liang · Kai Fang · Simin Li · Dong Liu · Qingtong Yi
Frontiers in genetics · 2022 doi:10.3389/fgene.2022.872441
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Complete file
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Open-accessed datasets GSE120736, GSE128959, GSE13507, GSE31684, GSE32548, GSE48075, GSE83586, and The Cancer Genome Atlas (TCGA) database were enrolled in our study. Single-sample gene set enrichment analysis (ssGSEA) was used to quantify 53 immune terms in combined BLCA cohorts. The top 10 important immune terms were identified through random forest algorithm for model establishment. Our model showed satisfactory efficacy in prognosis prediction. Furthermore, we explored clinical and genomic feature differences between high- and low-risk groups. The results indicated that the patients in the high-risk group might be associated with worse clinical features. Gene set enrichment analysis showed that epithelial-mesenchymal translational, mTORC1 signaling, mitotic spindle, glycolysis, E2F target, and G2M checkpoint pathways were aberrantly activated in high-risk patients, partially explaining its worse prognosis. Patients in the low-risk group showed better immunotherapy response according to TIDE and TCIA analysis, indicating that our model could effectively predict the immunotherapy response rate. KCNH4, UGT1A1, TPO, SHANK1, PITX3, MYH1, MYH13, KRT3, DEC1, and OBP2A genes were identified as feature genes in the high- and low-risk patients. CMAP analysis was performed to identify potential compounds targeting the riskscore.", "claims": [{"id": "claim-method", "statement": "In the combined cohort that has been quantified into 53 immune terms, univariate Cox analysis was first performed to identify prognosis-related terms with the threshold of p-value <0.05. Random forest algorithm was then used for dimension reduction (ntree = 1,000), which was performed in R software using the randomForestSRC package (Alaa et al., 2019). 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The top 10 important terms were then identified for multivariate cox analysis and IBS calculation using the following formula: riskscore = coef1*term1 + coef2*term2 + coef3*term3 + … + coefN*termN."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35615381", "locator": "sections[19] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The immune term–based riskscore model showed satisfactory prognostic prediction efficacy in the training, internal validation, and external IMvigor210 cohorts.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35615381", "locator": "sections[11] \"Immune Term-Based Prognosis Model\" paragraph 2", "quote": "In parallel, the ROC curve showed a satisfactory predicted efficacy on patients’ prognosis in the training group (Figure 3F; 1-year AUC = 0.773, 3-year AUC = 0.767, and 5-year AUC = 0.756)."}, {"asset": "source:pmid:35615381", "locator": "sections[11] \"Immune Term-Based Prognosis Model\" paragraph 2", "quote": "Also, our prognosis model showed an effectively predicted efficacy in the internal validation group (Figures 3G–I; 1-year AUC = 0.763, 3-year AUC = 0.700, and 5-year AUC = 0.704) and IMvigor210 external cohort (Figure 3J–L; 1-year AUC = 0.638, 3-year AUC = 0.682, and 5-year AUC = 0.658)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The immune term–based riskscore model showed satisfactory prognostic prediction efficacy in the training, internal validation, and external IMvigor210 cohorts.", "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:35615381"], "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. 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