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pmid:39223584

A novel super-enhancer-related risk model for predicting prognosis and guiding personalized treatment in hepatocellular carcinoma.

Qing Wu · Ping Li · Xuan Tao · Nan Lin · BinBin Mao · Xianhe Xie

BMC cancer · 2024 doi:10.1186/s12885-024-12874-7

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Experiment

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Data

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pmid:39223584
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2026-11-01T20:29:21Z
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2026-11-01T22:12:36Z
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title
A novel super-enhancer-related risk model for predicting prognosis and guiding personalized treatment in hepatocellular carcinoma.
doi
10.1186/s12885-024-12874-7
pmid
39223584
authors array · 6 items
[0]
Qing Wu
[1]
Ping Li
[2]
Xuan Tao
[3]
Nan Lin
[4]
BinBin Mao
[5]
Xianhe Xie
journal
BMC cancer
year
2024
abstract
BACKGROUND: Our research endeavored to develop a robust predictive signature grounded in super-enhancer-related genes (SERGs), with the dual objectives of forecasting survival outcomes and evaluating the tumor immune microenvironment (TiME) in hepatocellular carcinoma (HCC). METHODS: HCC RNA-sequencing data were retrieved from The Cancer Genome Atlas (TCGA), and 365 patients were randomly assigned to training or testing sets in 1:1 ratio. SERGs of HCC were downloaded from Super-Enhancer Database (SEdb). On the basis of training set, a SERGs signature was identified, and its prognostic value was confirmed by internal and external validation (GSE14520) sets. We subsequently examined the model for potential functional enrichment and the degree of tumor immune infiltration. Additionally, we carried out in vitro experiments to delve into the biological functions of CBX2 gene. RESULTS: An SE-related prognostic model including CBX2, TPX2, EFNA3, DNASE1L3 and SOCS2 was established and validated. According to this risk model, patients in the high-risk group had a significantly worse prognosis, and their immune cell infiltration was significantly different from that of low-risk group. Moreover, the high-risk group exhibited a significant enrichment of tumor-associated pathological pathways. The SERGs signature can generally be utilized to screen HCC patients who are likely to respond to immunotherapy, as there is a positive correlation between the risk score and the Tumor Immune Dysfunction and Exclusion (TIDE) score. Furthermore, the downregulation of the CBX2 gene expression was found to inhibit HCC cell viability, migration, and cell cycle progression, while simultaneously promoting apoptosis. CONCLUSIONS: We developed a novel HCC prognostic model utilizing SERGs, indicating that patients with high-risk score not only face a poorer prognosis but also may exhibit a diminished therapeutic response to immune checkpoint inhibitors (ICIs). This model is designed to tailor personalized treatment strategies to the individual needs of each patient, thereby improving the overall clinical outcomes for HCC patients. Furthermore, CBX2 is a promising candidate for therapeutic intervention in HCC.
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Utilizing the training dataset, we conducted Least Absolute Shrinkage and Selection Operator (LASSO) regression, complemented by tenfold cross-validation [34].
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sections[6] "Construction and validation of the novel SERGs signature" paragraph 2
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Utilizing the training dataset, we conducted Least Absolute Shrinkage and Selection Operator (LASSO) regression, complemented by tenfold cross-validation [34].
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The datasets used and/or analyzed in this study are available from the cancer genome database (TCGA-LIHC) (https://portal.gdc.cancer.gov), SEdb 2.0 (https://www.licpathway.net/sedb/) (Sample_02_1299), NCBI Gene Expression Omnibus (GEO: dataset of GSE14520) (https://www.ncbi.nlm.nih.gov/geo/), Human Protein Atlas (HPA) database (http://www.proteinatlas.org/), KEGG pathway database (https://www.kegg.jp/kegg/kegg1.html), STRING (http://string-db.org) (version 11.5), MethSurv database (https://biit.cs.ut.ee/methsurv/), ImmuCellAI database (http://bioinfo.life.hust.edu.cn/ImmuCellAI), Tumor Immune Dysfunction and Exclusion (TIDE) (http://tide.dfci.harvard.edu/), GSEA (http://software.broadinstitute.org/gsea/index.jsp), “ggplot2” package (https://ggplot2.tidyverse.org), TIMER (http://timer.cistrome.org/), Cancer Cell Line Encyclopedia (CCLE) (https://sites.broadinstitute.org/ccle).
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sections[38] "Availability of data and materials" paragraph 2
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The datasets used and/or analyzed in this study are available from the cancer genome database (TCGA-LIHC) (https://portal.gdc.cancer.gov), SEdb 2.0 (https://www.licpathway.net/sedb/) (Sample_02_1299), NCBI Gene Expression Omnibus (GEO: dataset of GSE14520) (https://www.ncbi.nlm.nih.gov/geo/), Human Protein Atlas (HPA) database (http://www.proteinatlas.org/), KEGG pathway database (https://www.kegg.jp/kegg/kegg1.html), STRING (http://string-db.org) (version 11.5), MethSurv database (https://biit.cs.ut.ee/methsurv/), ImmuCellAI database (http://bioinfo.life.hust.edu.cn/ImmuCellAI), Tumor Immune Dysfunction and Exclusion (TIDE) (http://tide.dfci.harvard.edu/), GSEA (http://software.broadinstitute.org/gsea/index.jsp), “ggplot2” package (https://ggplot2.tidyverse.org), TIMER (http://timer.cistrome.org/), Cancer Cell Line Encyclopedia (CCLE) (https://sites.broadinstitute.org/ccle).
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An SE-related prognostic model including CBX2, TPX2, EFNA3, DNASE1L3 and SOCS2 was established and validated.
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sections[0] "Abstract" paragraph 3
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An SE-related prognostic model including CBX2, TPX2, EFNA3, DNASE1L3 and SOCS2 was established and validated.
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An SE-related prognostic model including CBX2, TPX2, EFNA3, DNASE1L3 and SOCS2 was established and validated.
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The datasets used and/or analyzed in this study are available from the cancer genome database (TCGA-LIHC) (https://portal.gdc.cancer.gov), SEdb 2.0 (https://www.licpathway.net/sedb/) (Sample_02_1299), NCBI Gene Expression Omnibus (GEO: dataset of GSE14520) (https://www.ncbi.nlm.nih.gov/geo/), Human Protein Atlas (HPA) database (http://www.proteinatlas.org/), KEGG pathway database (https://www.kegg.jp/kegg/kegg1.html), STRING (http://string-db.org) (version 11.5), MethSurv database (https://biit.cs.ut.ee/methsurv/), ImmuCellAI database (http://bioinfo.life.hust.edu.cn/ImmuCellAI), Tumor Immune Dysfunction and Exclusion (TIDE) (http://tide.dfci.harvard.edu/), GSEA (http://software.broadinstitute.org/gsea/index.jsp), “ggplot2” package (https://ggplot2.tidyverse.org), TIMER (http://timer.cistrome.org/), Cancer Cell Line Encyclopedia (CCLE) (https://sites.broadinstitute.org/ccle).
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Gene expression data of human hepatocellular carcinoma (HCC)
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Utilizing the training dataset, we conducted Least Absolute Shrinkage and Selection Operator (LASSO) regression, complemented by tenfold cross-validation [34].
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The datasets used and/or analyzed in this study are available from the cancer genome database (TCGA-LIHC) (https://portal.gdc.cancer.gov), SEdb 2.0 (https://www.licpathway.net/sedb/) (Sample_02_1299), NCBI Gene Expression Omnibus (GEO: dataset of GSE14520) (https://www.ncbi.nlm.nih.gov/geo/), Human Protein Atlas (HPA) database (http://www.proteinatlas.org/), KEGG pathway database (https://www.kegg.jp/kegg/kegg1.html), STRING (http://string-db.org) (version 11.5), MethSurv database (https://biit.cs.ut.ee/methsurv/), ImmuCellAI database (http://bioinfo.life.hust.edu.cn/ImmuCellAI), Tumor Immune Dysfunction and Exclusion (TIDE) (http://tide.dfci.harvard.edu/), GSEA (http://software.broadinstitute.org/gsea/index.jsp), “ggplot2” package (https://ggplot2.tidyverse.org), TIMER (http://timer.cistrome.org/), Cancer Cell Line Encyclopedia (CCLE) (https://sites.broadinstitute.org/ccle).
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hepatocellular carcinoma (HCC). METHODS: HCC RNA-sequencing data were retrieved from The Cancer Genome A
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progression, while simultaneously promoting apoptosis. CONCLUSIONS: We developed a novel HCC progn
[1]
lular migration, cell cycle progression, and apoptosis in HepG2 cells. Cell lines and cell culture
[2]
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roadinstitute.org/ccle) and verified through RT-qPCR assays. Concurrently, the protein expression
[1]
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TCC® (Manassas, VA), respectively. Both were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM; Me
[1]
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[2]
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CBX2 was investigated by siRNA-mediated gene knockdown, focusing on its influence on cellular viabi
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unctional impact of CBX2 was investigated by siRNA-mediated gene knockdown, focusing on its inf
[2]
. 8E). Moreover, we knocked down the CBX2 by siRNA (Fig. 8F, G). Subsequently, the outcomes of
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39223584", "kind": "science-record", "revision": 3, "content_hash": "sha256:e73f8c0149a2b1b95108093906880055b90bb3940425594a9e39eecbbaafcbfe", "created_at": "2026-11-01T20:29:21Z", "updated_at": "2026-11-01T22:12:36Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel super-enhancer-related risk model for predicting prognosis and guiding personalized treatment in hepatocellular carcinoma.", "doi": "10.1186/s12885-024-12874-7", "pmid": "39223584", "authors": ["Qing Wu", "Ping Li", "Xuan Tao", "Nan Lin", "BinBin Mao", "Xianhe Xie"], "journal": "BMC cancer", "year": 2024, "abstract": "BACKGROUND: Our research endeavored to develop a robust predictive signature grounded in super-enhancer-related genes (SERGs), with the dual objectives of forecasting survival outcomes and evaluating the tumor immune microenvironment (TiME) in hepatocellular carcinoma (HCC).\nMETHODS: HCC RNA-sequencing data were retrieved from The Cancer Genome Atlas (TCGA), and 365 patients were randomly assigned to training or testing sets in 1:1 ratio. SERGs of HCC were downloaded from Super-Enhancer Database (SEdb). On the basis of training set, a SERGs signature was identified, and its prognostic value was confirmed by internal and external validation (GSE14520) sets. We subsequently examined the model for potential functional enrichment and the degree of tumor immune infiltration. Additionally, we carried out in vitro experiments to delve into the biological functions of CBX2 gene.\nRESULTS: An SE-related prognostic model including CBX2, TPX2, EFNA3, DNASE1L3 and SOCS2 was established and validated. According to this risk model, patients in the high-risk group had a significantly worse prognosis, and their immune cell infiltration was significantly different from that of low-risk group. Moreover, the high-risk group exhibited a significant enrichment of tumor-associated pathological pathways. The SERGs signature can generally be utilized to screen HCC patients who are likely to respond to immunotherapy, as there is a positive correlation between the risk score and the Tumor Immune Dysfunction and Exclusion (TIDE) score. Furthermore, the downregulation of the CBX2 gene expression was found to inhibit HCC cell viability, migration, and cell cycle progression, while simultaneously promoting apoptosis.\nCONCLUSIONS: We developed a novel HCC prognostic model utilizing SERGs, indicating that patients with high-risk score not only face a poorer prognosis but also may exhibit a diminished therapeutic response to immune checkpoint inhibitors (ICIs). This model is designed to tailor personalized treatment strategies to the individual needs of each patient, thereby improving the overall clinical outcomes for HCC patients. 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"network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: wilcoxon."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE14520"], "resolved_accessions": ["GSE14520"], "statements": ["The datasets used and/or analyzed in this study are available from the cancer genome database (TCGA-LIHC) (https://portal.gdc.cancer.gov), SEdb 2.0 (https://www.licpathway.net/sedb/) (Sample_02_1299), NCBI Gene Expression Omnibus (GEO: dataset of GSE14520) (https://www.ncbi.nlm.nih.gov/geo/), Human Protein Atlas (HPA) database (http://www.proteinatlas.org/), KEGG pathway database (https://www.kegg.jp/kegg/kegg1.html), STRING (http://string-db.org) (version 11.5), MethSurv database (https://biit.cs.ut.ee/methsurv/), ImmuCellAI database (http://bioinfo.life.hust.edu.cn/ImmuCellAI), Tumor Immune Dysfunction and Exclusion (TIDE) (http://tide.dfci.harvard.edu/), GSEA (http://software.broadinstitute.org/gsea/index.jsp), “ggplot2” package (https://ggplot2.tidyverse.org), TIMER (http://timer.cistrome.org/), Cancer Cell Line Encyclopedia (CCLE) (https://sites.broadinstitute.org/ccle)."], "basis": "The paper names GSE14520 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:39223584", "data:geo:GSE14520"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "group_comparison_wilcoxon", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE14520"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:30:16Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 19823, "completion_tokens": 2106, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:39223584", "type": "paper", "label": "pmid:39223584"}, {"id": "source:pmid:39223584", "type": "manuscript", "label": "PMID 39223584 full text"}, {"id": "data:geo:GSE14520", "type": "dataset", "label": "GEO GSE14520"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:39223584", "type": "paper", "label": "pmid:39223584"}, {"id": "source:pmid:39223584", "type": "manuscript", "label": "PMID 39223584 full text"}, {"id": "data:geo:GSE14520", "type": "dataset", "label": "GEO GSE14520"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-39223584", "type": "extraction", "used": ["source:pmid:39223584"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE14520", "type": "accession_resolution", "used": ["data:geo:GSE14520"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:39223584", "parent_hash": "sha256:c899310f473e0a35ba40babfa3bf99b1bc61397552a52b09e4bbe2651d5b90be", "fork_of": null, "generation": 2}, "gaps": ["asset:data:geo:GSE14520 licence not read", "asset:data:geo:GSE14520 not downloaded", "execution.not_run", "method.group_comparison_wilcoxon", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:group_comparison_wilcoxon"], "assays": [{"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["hepatocellular carcinoma (HCC). METHODS: HCC RNA-sequencing data were retrieved from The Cancer Genome A", "gnostic value. The expression of CBX2 at the transcriptome level in HCC cell lines was displayed using"], "order_basis": "paper_text_order:375", "id": "assay:1"}, {"role": "data_generation", "definition_id": "apoptosis_assay", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["progression, while simultaneously promoting apoptosis. CONCLUSIONS: We developed a novel HCC progn", "lular migration, cell cycle progression, and apoptosis in HepG2 cells. Cell lines and cell culture", "ay (Fig. 8I), cell cycle assay (Fig. 8J) and apoptosis assay (Fig. 8K) demonstrated that the suppre", "ell cycle progression in HCC, while promoted apoptosis. A higher expression level of CBX2 was assoc"], "order_basis": "paper_text_order:1792", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["roadinstitute.org/ccle) and verified through RT-qPCR assays. Concurrently, the protein expression", "yed using the database of CCLE (Fig. 8A) and RT-qPCR (Fig. 8B). As predicted by transcriptional r", "HCC cell lines (HepG2 and C3A) determined by RT-qPCR; C ICC for determining the subcellular locat", "epG2 cells treated with siNC or siCBX2 via F RT-qPCR and G cropped Western Blot; H the cell viabi"], "order_basis": "paper_text_order:2587", "id": "assay:3"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ained in both cell lines and tissues through Western Blot and IHC analyses. Furthermore, the presence", "med at the protein level in cell lines using Western Blot (Fig. 8D). To confirm the dysregulation of C", "lines (HepG2 and C3A) determined by cropped Western Blot; E the representative images of H&E and IHC", "h siNC or siCBX2 via F RT-qPCR and G cropped Western Blot; H the cell viability of HepG2 cells treated"], "order_basis": "paper_text_order:2711", "id": "assay:4"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["l lines and tissues through Western Blot and IHC analyses. Furthermore, the presence and loca", ". 8D). To confirm the dysregulation of CBX2, IHC analysis was also performed on 20 pairs of H", "Blot; E the representative images of H&E and IHC for CBX2 expressed on HCC and peritumoral ti", "pairs of tumoral and peritumoral tissues by IHC; the mRNA and protein expression levels of C"], "order_basis": "paper_text_order:2728", "id": "assay:5"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["TCC® (Manassas, VA), respectively. Both were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM; Me", "were elucidated in human A-431, osteosarcoma U2OS, and U-251MG cell lines via immunocytochemis", "on, cell cycle progression, and apoptosis in HepG2 cells. Cell lines and cell culture The norma", "mperature of 37 °C. The human HCC cell lines HepG2 and C3A were procured from Procell Life Scie"], "order_basis": "paper_text_order:2846", "id": "assay:6"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["CBX2 was investigated by siRNA-mediated gene knockdown, focusing on its influence on cellular viabi", "unctional impact of CBX2 was investigated by siRNA-mediated gene knockdown, focusing on its inf", ". 8E). Moreover, we knocked down the CBX2 by siRNA (Fig. 8F, G). Subsequently, the outcomes of"], "order_basis": "paper_text_order:3080", "id": "assay:7"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Wilcoxon"], "order_basis": "consumes_earlier_output", "id": "assay:8"}]}