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pmid:37342196

Identification of a novel MYC target gene set signature for predicting the prognosis of osteosarcoma patients.

Deliang Gong · Qingzhong Zhao · Jun Liu · Shibing Zhao · Chengfeng Yi · Jianwei Lv · Hang Yu · Erbao Bian · Dasheng Tian

Frontiers in oncology · 2023 doi:10.3389/fonc.2023.1169430

Paper

complete

Experiment

complete

Data

partial

Analysis

complete

Execution

absent

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paper object · 10 fields
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title
Identification of a novel MYC target gene set signature for predicting the prognosis of osteosarcoma patients.
doi
10.3389/fonc.2023.1169430
pmid
37342196
authors array · 9 items
[0]
Deliang Gong
[1]
Qingzhong Zhao
[2]
Jun Liu
[3]
Shibing Zhao
[4]
Chengfeng Yi
[5]
Jianwei Lv
[6]
Hang Yu
[7]
Erbao Bian
[8]
Dasheng Tian
journal
Frontiers in oncology
year
2023
abstract
Osteosarcoma is a primary malignant tumor found mainly in teenagers and young adults. Patients have very little long-term survival. MYC controls tumor initiation and progression by regulating the expression of its target genes; thus, constructing a risk signature of osteosarcoma MYC target gene set will benefit the evaluation of both treatment and prognosis. In this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. Then, a risk signature consisting of 10 MYC target genes was developed using Cox regression analysis. The signature indicates that patients in the high-risk group performed poorly. After that, we verified it in the GSE21257 dataset. In addition, the difference in tumor immune function among the low- and high-risk populations was compared by single sample gene enrichment analysis. Immunotherapy and prediction of response to the anticancer drug have shown that the risk signature of the MYC target gene set was positively correlated with immune checkpoint response and drug sensitivity. Functional analysis has demonstrated that these genes are enriched in malignant tumors. Finally, STX10 was selected for functional experimentation. STX10 silence has limited osteosarcoma cell migration, invasion, and proliferation. Therefore, these findings indicated that the MYC target gene set risk signature could be used as a potential therapeutic target and prognostic indicator in patients with osteosarcoma.
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Moreover, we utilized the LASSO Cox regression analysis to build an MTG signature of prognosis.
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sections[17] "Construction and evaluation of the MYC target gene set signature" paragraph 2
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Moreover, we utilized the LASSO Cox regression analysis to build an MTG signature of prognosis.
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sections[24] "Data availability statement" paragraph 2
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The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors.
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The MYC target gene set signature achieved 1-, 3-, and 5-year AUC values of 0.806, 0.833, and 0.899, respectively.
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sections[17] "Construction and evaluation of the MYC target gene set signature" paragraph 2
quote
The results indicated that the AUC values of 1 year, 3 years, and 5 years were 0.806, 0.833, and 0.899, respectively ( Figure 2C ), implying that the risk prediction model we built was particular and sensitive.
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The MYC target gene set signature achieved 1-, 3-, and 5-year AUC values of 0.806, 0.833, and 0.899, respectively.
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Genome-wide gene expression profiling on pre-chemotherapy biopsies of osteosarcoma patients who developed metastases within 5yrs (n=34) and patients who did not develop metastases within 5yrs (n=19)
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Integrative analysis reveals relationships of genetic and epigenetic alterations in osteosarcoma [methylation]
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Different promoter affinities account for specificity in MYC-dependent gene regulation
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this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. T
[1]
nes were obtained by downloading the data of ChIP-seq of MYC with GEO data. Subsequently, 87 diffe
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Materials and methods Cell culture and transfection The human osteosarcoma cell lines MG-63, HOS
[1]
dium containing FBS (10%) was introduced for transfection. Then, the cells were transfected with JetPr
[2]
duced for transfection. Then, the cells were transfected with JetPrime (Poly Plus-transformation ®).
[3]
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sarcoma cell lines MG-63, HOS, 143B, SAOS-2, U2OS, and R-1059D, and the normal osteoblast cell
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Moreover, we utilized the LASSO Cox regression analysis to build an MTG signature of prognosis.
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37342196", "kind": "science-record", "revision": 2, "content_hash": "sha256:a0a3c7e2cd58817b9b161e00f86bb4c2e7eb5b650c73968384287de8f7d5379e", "created_at": "2026-11-01T19:02:05Z", "updated_at": "2026-11-01T21:31:01Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a novel MYC target gene set signature for predicting the prognosis of osteosarcoma patients.", "doi": "10.3389/fonc.2023.1169430", "pmid": "37342196", "authors": ["Deliang Gong", "Qingzhong Zhao", "Jun Liu", "Shibing Zhao", "Chengfeng Yi", "Jianwei Lv", "Hang Yu", "Erbao Bian", "Dasheng Tian"], "journal": "Frontiers in oncology", "year": 2023, "abstract": "Osteosarcoma is a primary malignant tumor found mainly in teenagers and young adults. Patients have very little long-term survival. MYC controls tumor initiation and progression by regulating the expression of its target genes; thus, constructing a risk signature of osteosarcoma MYC target gene set will benefit the evaluation of both treatment and prognosis. In this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. Then, a risk signature consisting of 10 MYC target genes was developed using Cox regression analysis. The signature indicates that patients in the high-risk group performed poorly. After that, we verified it in the GSE21257 dataset. In addition, the difference in tumor immune function among the low- and high-risk populations was compared by single sample gene enrichment analysis. Immunotherapy and prediction of response to the anticancer drug have shown that the risk signature of the MYC target gene set was positively correlated with immune checkpoint response and drug sensitivity. Functional analysis has demonstrated that these genes are enriched in malignant tumors. Finally, STX10 was selected for functional experimentation. STX10 silence has limited osteosarcoma cell migration, invasion, and proliferation. Therefore, these findings indicated that the MYC target gene set risk signature could be used as a potential therapeutic target and prognostic indicator in patients with osteosarcoma.", "claims": [{"id": "claim-method", "statement": "Moreover, we utilized the LASSO Cox regression analysis to build an MTG signature of prognosis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37342196", "locator": "sections[17] \"Construction and evaluation of the MYC target gene set signature\" paragraph 2", "quote": "Moreover, we utilized the LASSO Cox regression analysis to build an MTG signature of prognosis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37342196", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The MYC target gene set signature achieved 1-, 3-, and 5-year AUC values of 0.806, 0.833, and 0.899, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37342196", "locator": "sections[17] \"Construction and evaluation of the MYC target gene set signature\" paragraph 2", "quote": "The results indicated that the AUC values of 1 year, 3 years, and 5 years were 0.806, 0.833, and 0.899, respectively ( Figure 2C ), implying that the risk prediction model we built was particular and sensitive."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The MYC target gene set signature achieved 1-, 3-, and 5-year AUC values of 0.806, 0.833, and 0.899, 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:37342196"], "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:GSE21257"], "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:GSE21257"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37342196", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fonc.2023.1169430", "sha256": "sha256:980607a15000ea5480ef440a9d15060d6ae449f3fe40268ce3c7328ee94414ce", "bytes": 42195, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fonc.2023.1169430", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10277635", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE21257", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21257", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE21257", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM530667", "GSM530899", "GSM531283", "GSM531284", "GSM531285", "GSM531286", "GSM531287", "GSM531288", "GSM531289", "GSM531290", "GSM531291", "GSM531292", "GSM531293", "GSM531294", "GSM531295", "GSM531296", "GSM531297", "GSM531298", "GSM531299", "GSM531300", "GSM531301", "GSM531302", "GSM531303", "GSM531304", "GSM531305", "GSM531306", "GSM531307", "GSM531308", "GSM531309", "GSM531310", "GSM531311", "GSM531312", "GSM531313", "GSM531314", "GSM531319", "GSM531320", "GSM531321", "GSM531322", "GSM531323", "GSM531324", "GSM531325", "GSM531326", "GSM531327", "GSM531328", "GSM531329", "GSM531330", "GSM531331", "GSM531332", "GSM531333", "GSM531334", "GSM531335", "GSM531351", "GSM531352"], "sample_count": 53, "group_sizes": null, "group_sizes_basis": null, "platform": "10295", "taxon": "Homo sapiens", "title": "Genome-wide gene expression profiling on pre-chemotherapy biopsies of osteosarcoma patients who developed metastases within 5yrs (n=34) and patients who did not develop metastases within 5yrs (n=19)", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE36002", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE36002", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE36002", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM879181", "GSM879182", "GSM879183", "GSM879184", "GSM879185", "GSM879186", "GSM879187", "GSM879188", "GSM879189", "GSM879190", "GSM879191", "GSM879192", "GSM879193", "GSM879194", "GSM879195", "GSM879196", "GSM879197", "GSM879198", "GSM879199", "GSM879200", "GSM879201", "GSM879202", "GSM879203", "GSM879204", "GSM879205"], "sample_count": 25, "group_sizes": null, "group_sizes_basis": null, "platform": "8490", "taxon": "Homo sapiens", "title": "Integrative analysis reveals relationships of genetic and epigenetic alterations in osteosarcoma [methylation]", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE77356", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE77356", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE77356", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2050623", "GSM2050624", "GSM2050625", "GSM2050626", "GSM2050627", "GSM2050628", "GSM2050629", "GSM2050630", "GSM2050631", "GSM2050632", "GSM2050633", "GSM2050634", "GSM2050635", "GSM2050636", "GSM2050637", "GSM2050638", "GSM2050639", "GSM2050640", "GSM2050641", "GSM2131215", "GSM2131216", "GSM2131217"], "sample_count": 22, "group_sizes": null, "group_sizes_basis": null, "platform": "10999;18573", "taxon": "Homo sapiens", "title": "Different promoter affinities account for specificity in MYC-dependent gene regulation", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE21257 not downloaded", "asset:data:geo:GSE21257 licence not read", "asset:data:geo:GSE36002 not downloaded", "asset:data:geo:GSE36002 licence not read", "asset:data:geo:GSE77356 not downloaded", "asset:data:geo:GSE77356 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Moreover, we utilized the LASSO Cox regression analysis to build an MTG signature of prognosis."}}, {"id": "method-2", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "GO enrichment"}}, {"id": "method-3", "family": "contingency_analysis", "specific": "chi_square", "label": "Categorical (contingency table) analysis", "dsl_operations": ["chi_square_test"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["categorical_cross_tabulation"], "evidence": {"score": 0.8, "band": "high", "quote": "chi-square"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "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": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "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": ["GSE21257", "GSE36002", "GSE77356"], "resolved_accessions": ["GSE21257", "GSE36002", "GSE77356"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article material. Further inquiries can be directed to the corresponding authors."], "basis": "The paper names GSE21257, GSE36002, GSE77356 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["cox_ph"], "assets": ["source:pmid:37342196", "data:geo:GSE21257"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE21257"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:44:24Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 11411, "completion_tokens": 1483, "cached_tokens": 264}}, "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:37342196", "type": "paper", "label": "pmid:37342196"}, {"id": "source:pmid:37342196", "type": "manuscript", "label": "PMID 37342196 full text"}, {"id": "data:geo:GSE21257", "type": "dataset", "label": "GEO GSE21257"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37342196", "type": "paper", "label": "pmid:37342196"}, {"id": "source:pmid:37342196", "type": "manuscript", "label": "PMID 37342196 full text"}, {"id": "data:geo:GSE21257", "type": "dataset", "label": "GEO GSE21257"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37342196", "type": "extraction", "used": ["source:pmid:37342196"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE21257", "type": "accession_resolution", "used": ["data:geo:GSE21257"], "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:37342196", "parent_hash": "sha256:dd5bb46f562c92bb06048b6485cd0c1b8876e0596e5fab760d85055de0a78b4e", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE21257 licence not read", "asset:data:geo:GSE21257 not downloaded", "asset:data:geo:GSE36002 licence not read", "asset:data:geo:GSE36002 not downloaded", "asset:data:geo:GSE77356 licence not read", "asset:data:geo:GSE77356 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "chip_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["this paper, we used GEO data to download the ChIP-seq data of MYC to obtain the MYC target gene. T", "nes were obtained by downloading the data of ChIP-seq of MYC with GEO data. Subsequently, 87 diffe"], "order_basis": "paper_text_order:475", "id": "assay:1"}, {"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": ["Materials and methods Cell culture and transfection The human osteosarcoma cell lines MG-63, HOS", "dium containing FBS (10%) was introduced for transfection. Then, the cells were transfected with JetPr", "duced for transfection. Then, the cells were transfected with JetPrime (Poly Plus-transformation ®).", "ed by General Biol (Anhui, China), to target knockdown MYC and STX10. The MYC siRNA consisted of on"], "order_basis": "paper_text_order:1577", "id": "assay:2"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["sarcoma cell lines MG-63, HOS, 143B, SAOS-2, U2OS, and R-1059D, and the normal osteoblast cell"], "order_basis": "paper_text_order:1644", "id": "assay:3"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Moreover, we utilized the LASSO Cox regression analysis to build an MTG signature of prognosis."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}