Comprehensive analysis of ECHDC3 as a potential biomarker and therapeutic target for acute myeloid leukemia: Bioinformatic analysis and experimental verification.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36172164", "kind": "science-record", "revision": 4, "content_hash": "sha256:3c95f9c05c486a4b5a58b966ab541a802a9d80acef5d0009c6a25c51b2cde622", "created_at": "2026-11-01T18:15:27Z", "updated_at": "2026-11-01T22:07:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comprehensive analysis of ECHDC3 as a potential biomarker and therapeutic target for acute myeloid leukemia: Bioinformatic analysis and experimental verification.", "doi": "10.3389/fonc.2022.947492", "pmid": "36172164", "authors": ["Yijing Zhao", "Li-Ting Niu", "Li-Juan Hu", "Meng Lv"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Background: Enoyl-CoA hydratase domain containing 3 (ECHDC3) increased in CD34+ progenitor cells of acute myeloid leukemia (AML) cells after chemotherapy. However, the prognostic significance and function of ECHDC3 in AML remain to be clarified.\nMethods: In the training cohort, 24 AML (non-acute promyelocytic leukemia, APL) patients were enrolled in Peking University People's Hospital and tested for ECHDC3 in enriched CD34+ cells at diagnosis. In the validation set, 351 bone marrow RNA-seq data of non-APL AML were obtained by two independent online datasets (TCGA-LAML and BEAT-AML). LASSO regression model was conducted to a new prediction model of ECHDC3-related genes. In addition, the ECHDC3 signature was further explored by GO, KEGG, GSEA, and immuno-infiltration analysis. By RNA interference, the function of ECHDC3 in mitochondrial DNA (mt-DNA) transcriptome and chemoresistance was further explored, and the GSE52919 database re-verified the ECHDC3 chemoresistance feature.\nResults: By Kaplan-Meier analysis, patients with ECHDC3high demonstrated inferior overall survival (OS) compared to those with ECHDC3low both in the training (2-year OS, 55.6% vs. 100%, p = 0.011) and validation cohorts (5-year OS, 9.6% vs. 24.3%, p = 0.002). In addition, ECHDC3high predicted inferior OS in the subgroup of patients with ELN 2017 intermediated (int) risk (5-year OS, 9.5% vs. 26.3%, p = 0.039) or FLT3+NPM1- adverse (adv) risk (4-year OS, 6.4% vs. 31.8%, p = 0.003). In multivariate analysis, ECHDC3 was an independent risk factor of inferior OS (HR 1.159, 95% CI 1.013-1.326, p = 0.032). In the prediction model combining ECHDC3 and nine selected genes (RPS6KL1, RELL2, FAM64A, SPATS2L, MEIS3P1, CDCP1, CD276, IL1R2, and OLFML2A) by Lasso regression, patients with high risk showed inferior 5-year OS (9.3% vs. 23.5%, p < 0.001). Bioinformatic analysis suggested that ECHDC3 alters the bone marrow microenvironment by inducing NK, resting mast cell, and monocyte differentiation. Knocking down ECHDC3 in AML cells by RNAi promoted the death of leukemia cells with cytarabine and doxorubicin.\nConclusion: These bioinformatic analyses and experimental verification indicated that high ECHDC3 expression might be a poor prognostic biomarker for non-APL AML, which might be a potential target for reverting chemoresistance.", "claims": [{"id": "claim-method", "statement": "Next, the prognostic model was established with Lasso regression to improve the predicted accuracy for overall survival in AML.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36172164", "locator": "sections[15] \"The combination of ECHDC3 and other risk factors improves the accuracy of survival prediction\" paragraph 4", "quote": "Next, the prognostic model was established with Lasso regression to improve the predicted accuracy for overall survival in AML."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material . 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Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In the prediction model combining ECHDC3 and nine selected genes (RPS6KL1, RELL2, FAM64A, SPATS2L, MEIS3P1, CDCP1, CD276, IL1R2, and OLFML2A) by Lasso regression, patients with high risk showed inferior 5-year OS (9.3% vs. 23.5%, p < 0.001).", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "In the prediction model combining ECHDC3 and nine selected genes (RPS6KL1, RELL2, FAM64A, SPATS2L, MEIS3P1, CDCP1, CD276, IL1R2, and OLFML2A) by Lasso regression, patients with high risk showed inferior 5-year OS (9.3% vs. 23.5%, p < 0.001)."}, "evidence": [{"asset": "source:pmid:36172164", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In the prediction model combining ECHDC3 and nine selected genes (RPS6KL1, RELL2, FAM64A, SPATS2L, MEIS3P1, CDCP1, CD276, IL1R2, and OLFML2A) by Lasso regression, patients with high risk showed inferior 5-year OS (9.3% vs. 23.5%, p < 0.001)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "In the prediction model combining ECHDC3 and nine selected genes (RPS6KL1, RELL2, FAM64A, SPATS2L, MEIS3P1, CDCP1, CD276, IL1R2, and OLFML2A) by Lasso regression, patients with high risk showed inferior 5-year OS (9.3% vs. 23.5%, p < 0.001).", "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:36172164"], "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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