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pmid:35677537
Identification of an Autophagy-Related Signature Based on Whole Bone Marrow Sequencing for the Prognosis and Immune Microenvironment Characterization of Multiple Myeloma.
Licheng Li · Ting Chen · Jishi Wang · Mengxing Li · Qinshan Li
Journal of immunology research · 2022 doi:10.1155/2022/3922739
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Experiment
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Data
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Analysis
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Complete file
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Autophagy performs an integral function in homeostasis, survival, and drug resistance in multiple myeloma (MM). Therefore, the purpose of the present research was to identify potential autophagy-related genes (ARGs) in patients with MM. We downloaded the transcriptomic data (GSE136400) of patients with MM, as well as the corresponding clinical data from the Gene Expression Omnibus (GEO); the patients were classified at random into two groups in a ratio of 6: 4, with 212 samples in the training dataset and 142 samples in the test dataset. Both multivariate and univariate Cox regression analyses were performed to identify autophagy-related genes. The univariate Cox regression analysis demonstrated that 26 ARGs had a significant correlation with overall survival (OS). We constructed an autophagy-related risk prognostic model based on six ARGs: EIF2AK2 (ENSG00000055332), KIF5B (ENSG00000170759), MYC (ENSG00000136997), NRG2 (ENSG00000158458), PINK1 (ENSG00000158828), and VEGFA (ENSG00000112715) using LASSO-Cox regression analysis to predict risk outcomes, which revealed substantially shortened OS duration in the high-risk cohort in contrast with that in the low-risk cohort. Therefore, the ARG-based model significantly predicted the MM patients' prognoses and was verified in an internal test set. Differentially expressed genes were found to be predominantly enriched in pathways associated with inflammation and immune regulation. Immune infiltration of tumor cells resulted in the formation of a strong immunosuppressive microenvironment in high-risk patients. The potential therapeutic targets of ARGs were subsequently analyzed via protein-drug network analysis. Therefore, a prognostic model for MM was established via a comprehensive analysis of ARGs, through using the clinical models; we have further revealed the molecular landscape features of multiple myeloma.", "claims": [{"id": "claim-method", "statement": "To prevent these performances to be over-fitted, we used LASSO-Cox regression [14] analysis in the training set to determine key OS-related ARGs for establishing a prognostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35677537", "locator": "sections[8] \"2.6. Establishment and Verification of an ARG-Related Prognostic Model for MM\" paragraph 2", "quote": "To prevent these performances to be over-fitted, we used LASSO-Cox regression [14] analysis in the training set to determine key OS-related ARGs for establishing a prognostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The manuscript contains the actual data provided in the present research; additional inquiries can be sent to the respective authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35677537", "locator": "sections[26] \"Data Availability\" paragraph 2", "quote": "The manuscript contains the actual data provided in the present research; additional inquiries can be sent to the respective authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The six-ARG prognostic model predicted overall survival in MM, with one-, three-, and five-year AUCs of 0.610, 0.704, and 0.752, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35677537", "locator": "sections[16] \"3.3. Evaluation of the Prognostic Features of OS-Related ARGs in Patients with MM\" paragraph 2", "quote": "Furthermore, the AUC values for anticipating OS over one, three, and five years were shown to be 0.610, 0.704, and 0.752, indicating that the signature had a high predictive power (Figure 3(b))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The six-ARG prognostic model predicted overall survival in MM, with one-, three-, and five-year AUCs of 0.610, 0.704, and 0.752, respectively.", "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:35677537"], "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:GSE136400"], "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:GSE136400"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35677537", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/3922739", "sha256": 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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": ["GSE136400"], "resolved_accessions": ["GSE136400"], "statements": ["The manuscript contains the actual data provided in the present research; additional inquiries can be sent to the respective authors."], "basis": "The paper names GSE136400 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. 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