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pmid:39789627

miR-9-5p/HMMR regulates the tumorigenesis and progression of clear cell renal cell carcinoma through EMT and JAK1/STAT1 signaling pathway.

Journal of translational medicine · 2025 doi:10.1186/s12967-024-05988-w

Paper

partial

Experiment

partial

Data

partial

Analysis

absent

Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:39789627
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science-record
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2026-11-01T10:25:49Z
updated_at
2026-11-01T10:25:49Z
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paper object · 10 fields
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partial
title
miR-9-5p/HMMR regulates the tumorigenesis and progression of clear cell renal cell carcinoma through EMT and JAK1/STAT1 signaling pathway.
doi
10.1186/s12967-024-05988-w
pmid
39789627
authors
null
journal
Journal of translational medicine
year
2025
abstract
null
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the headline numerical result the paper states for its main finding
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null
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null
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null
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null
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abstract
[1]
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experiment object · 8 fields
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the headline numerical result the paper states for its main finding
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Read the published estimate and its interval from the source
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null
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The number a replication must land on, and the sentence it came from.
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[0]
the published estimate, its unit and its interval
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The published result cannot be recomputed without the data the paper analysed.
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the source data with its provenance
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no owned computational capability is bound to this experiment
description
Re-run the paper's analysis on the source data and produce the registered result. | SEP input not carried into the asset list: materials:the source data behind the published result in pmid:39789627
parameters object · 0 fields
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step:run_analysis input 'materials:the source data behind the published result in pmid:39789627' does not resolve to a declared asset
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10.1186/s12967-024-05988-w
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null
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Source read by the package builder: full_text.
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null
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xml
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source:pmid:39789627
gaps array · 1 item
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corpus_record_absent
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The text states qualitatively that HMMR is overexpressed in ccRCC and is associated with tumor progression and poor patient prognosis, but it does not state a headline numerical result for this main finding.
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raw_response
<think>We need answer JSON only. Need parse task: find single numerical result replication registered target (headline numerical result main finding). Source supplied appears abstract/methods no headline numeric result, only sample counts and thresholds etc. Registered target is generic headline numerical result. Paper text states no number for registered target likely false. Need set parent_value 0, unit empty, evidence empty, target_result says what text states instead. Need exact schema. We should determine whether perhaps total 614 samples is headline numerical? They specifically ask "single numerical result that a replication of the registered target has to reproduce" registered target: "the headline numerical result the paper states for its main finding." The source doesn't state one; findings qualitative. Numbers are sample sizes/method thresholds. Thus number_stated false. target_result perhaps "The text states that HMMR is overexpressed in ccRCC ... but reports no headline numerical result." Unit/evidence empty. Ensure parent_value numeric 0. JSON valid. Could the answer target_result include source wording, but not evidence exact quote requirement applies if number. They say "say in target_result what the text does state instead." We can paraphrase perhaps mention qualitative finding. No extra props.</think> {"parent_value":0,"unit":"","evidence":"","target_result":"The text states that HMMR is overexpressed in ccRCC and associated with pathological grade, stage, and patient prognosis, but it gives no headline numerical result for this main finding.","number_stated":false}
reading object · 6 fields
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The text states that HMMR is overexpressed in ccRCC and associated with pathological grade, stage, and patient prognosis, but it gives no headline numerical result for this main finding.
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null
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2026-10-31T15:30:24.603444+00:00
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raw_response
{ "target_result": "HMMR was significantly up-regulated in ccRCC tumor tissues, and elevated HMMR expression was associated with ccRCC progression and adverse patient prognosis.", "work_type": "analysis_plus_physical_validation", "physical_methods": [ "Transwell assays", "CCK-8 assays", "clone formation assays", "subcutaneous xenograft experiments in nude mice", "cellular functional experiments" ], "analysis_methods": [ "The Cancer Genome Atlas (TCGA) expression analysis", "Gene Set Enrichment Analysis (GSEA)", "Genomics of Drug Sensitivity in Cancer (GDSC) drug-sensitivity prediction using the pRRophetic package", "multivariate regression nomogram model", "single-cell analysis using the Seurat package and tSNE algorithm", "SingleR cluster annotation", "CIBERSORT immune-cell infiltration analysis", "differential expression analysis using DESeq2, limma, and edgeR", "ENCORI-predicted HMMR-interacting miRNA analysis" ], "statistical_method": "Student’s t-test for differences between two groups and Pearson correlation analysis for correlations, using p < 0.05 as the threshold for statistical significance.", "method_evidence": "To evaluate the differences between two groups and to analyze correlations, Student’s t-test and Pearson correlation analysis were applied, respectively, with a threshold of p < 0.05 indicating statistical significance.", "repositories": [ { "repository": "The Cancer Genome Atlas (TCGA) database", "accession": "TCGA", "data_description": "Raw mRNA expression data for ccRCC, comprising 614 samples: 72 normal and 542 tumor samples; the database was also used for miRNA expression, immune-cell infiltration, and other ccRCC analyses.", "evidence": "We downloaded the raw mRNA expression data for ccRCC from the TCGA database, encompassing a total of 614 samples—normal (n = 72) and tumor (n = 542).", "publicly_downloadable": false }, { "repository": "NCBI GEO", "accession": "GSE207493", "data_description": "Single-cell data from 19 ccRCC patients, used for pathway enrichment and analysis of HMMR expression across cell subtypes.", "evidence": "Additionally, for single-cell analysis, data for 19 ccRCC patients were extracted from the NCBI GEO public database (GSE207493).", "publicly_downloadable": true }, { "repository": "MsigDB database", "accession": "annotated gene sets version 7.0", "data_description": "Annotated gene sets used with GSE207493 for GSEA between HMMR high-expression and low-expression subtypes.", "evidence": "We downloaded annotated gene sets version 7.0 from the MsigDB database and data from GSE207493 for pathway enrichment analysis between subtypes.", "publicly_downloadable": false }, { "repository": "Genomics of Drug Sensitivity in Cancer (GDSC) database", "accession": "unknown", "data_description": "Drug-sensitivity data used with the pRRophetic package to estimate IC50 values and predict drug sensitivity for tumor samples.", "evidence": "The Genomics of Drug Sensitivity in Cancer (GDSC) database was used, along with the “pRRophetic” package, to predict drug sensitivity for each tumor sample.", "publicly_downloadable": false }, { "repository": "ENCORI database", "accession": "unknown", "data_description": "Predicted low-expressed miRNAs potentially interacting with HMMR, intersected with differentially expressed miRNAs identified by DESeq2, limma, and edgeR.", "evidence": "We then intersected the differentially expressed miRNAs identified by these methods with low-expressed miRNAs predicted to potentially interact with HMMR from the ENCORI database.", "publicly_downloadable": false } ], "availability_statement": "The paper states that raw TCGA mRNA expression data were downloaded and that GSE207493 was extracted from the public NCBI GEO database. It does not explicitly state that the TCGA, MsigDB, GDSC, or ENCORI data can be downloaded without an application, embargo, or request, and it gives no availability statement for newly generated experimental data.", "data_on_request": false, "source_claims": [ "HMMR expression was significantly up-regulated in tumor tissues of ccRCC patients.", "Elevated HMMR expression showed a strong correlation with ccRCC progression and adverse patient prognosis.", "Knocking down HMMR inhibited the proliferative and migratory abilities of ccRCC cells, whereas HMMR overexpression amplified these oncogenic properties.", "Reduced HMMR expression inhibited ccRCC tumor proliferation in a nude-mouse model.", "miR-9-5p overexpression effectively downregulated HMMR expression and impeded ccRCC cell proliferation and migration.", "HMMR might influence ccRCC growth through the EMT and JAK1/STAT1 signaling pathways." ], "missing_information": [ "No single headline numerical estimate, effect size, confidence interval, or exact p-value for the main finding is stated in the supplied text.", "The supplied text does not identify which statistical test was applied specifically to the primary HMMR up-regulation comparison beyond the general statement that Student’s t-test was used for differences between two groups.", "The supplied text does not explicitly describe unrestricted, no-application download conditions for the TCGA, MsigDB, GDSC, or ENCORI repositories.", "No accession is provided for the GDSC or ENCORI data.", "The availability of newly generated experimental data is not stated.", "The numerical results underlying the prognostic association, xenograft measurements, proliferation assays, and migration assays are not provided in the supplied text." ], "evidence_spans": [ "HMMR expression was significantly up-regulated in tumor tissues of ccRCC patients, and elevated HMMR expression level showed a strong correlation with ccRCC progression and adverse prognoses of patients.", "Knocking down HMMR inhibited the proliferative and migratory abilities of ccRCC cells, while its overexpression amplified these oncogenic properties.", "In nude mice model, reduced HMMR expression inhibited ccRCC tumor proliferation in vivo.", "Furthermore, overexpression of an upstream transcriptional regulator, miR-9-5p, effectively downregulated HMMR expression and thus impeded ccRCC cells proliferation and migration.", "HMMR might influence ccRCC growth via the Epithelial-Mesenchymal Transition (EMT) pathway and the Janus Kinase 1/Signal Transducer and Activator of Transcription 1 (JAK1/STAT1) pathway.", "We downloaded the raw mRNA expression data for ccRCC from the TCGA database, encompassing a total of 614 samples—normal (n = 72) and tumor (n = 542).", "Additionally, for single-cell analysis, data for 19 ccRCC patients were extracted from the NCBI GEO public database (GSE207493).", "The statistical analyses of the acquired data were performed using GraphPad Prism 8.0 software and the R programming language (version 4.3.0) for both graphical presentation and the assessment of statistical significance.", "To evaluate the differences between two groups and to analyze correlations, Student’s t-test and Pearson correlation analysis were applied, respectively, with a threshold of p < 0.05 indicating statistical significance." ] }
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step:run_analysis input 'materials:the source data behind the published result in pmid:39789627' does not resolve to a declared asset
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Complete file
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{"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:39789627"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "7827f8360cb3b3210d892137cc00b49e9652464403b79683684f0da384906fe5", "source_hash": "9e70ce552c65cde624f1c00cadc7f6d36fcb0503f4082efa35a4f17d83b0ee53", "raw_response_sha256": "sha256:486591f6c3a520782aa8a75134db20fdd25e4379be3a761243f3aaaa0552c968", "raw_response_chars": 78567}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "95d2735c32b8c5c2d30c09521addf8412191bd46109f8512c468541407451889", "source_hash": "9e70ce552c65cde624f1c00cadc7f6d36fcb0503f4082efa35a4f17d83b0ee53", "raw_response": "{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The text states qualitatively that HMMR is overexpressed in ccRCC and is associated with tumor progression and poor patient prognosis, but it does not state a headline numerical result for this main finding.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "The text states qualitatively that HMMR is overexpressed in ccRCC and is associated with tumor progression and poor patient prognosis, but it does not state a headline numerical result for this main finding.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:30:25.310257+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "5916c2bc3a8382b07e565c89fade77868e75cdb9313236292f59dade819ed129", "source_hash": "9e70ce552c65cde624f1c00cadc7f6d36fcb0503f4082efa35a4f17d83b0ee53", "raw_response": "<think>We need answer JSON only. Need parse task: find single numerical result replication registered target (headline numerical result main finding). Source supplied appears abstract/methods no headline numeric result, only sample counts and thresholds etc. Registered target is generic headline numerical result. Paper text states no number for registered target likely false. Need set parent_value 0, unit empty, evidence empty, target_result says what text states instead. Need exact schema. We should determine whether perhaps total 614 samples is headline numerical? They specifically ask \"single numerical result that a replication of the registered target has to reproduce\" registered target: \"the headline numerical result the paper states for its main finding.\" The source doesn't state one; findings qualitative. Numbers are sample sizes/method thresholds. Thus number_stated false. target_result perhaps \"The text states that HMMR is overexpressed in ccRCC ... but reports no headline numerical result.\" Unit/evidence empty. Ensure parent_value numeric 0. JSON valid.\n\nCould the answer target_result include source wording, but not evidence exact quote requirement applies if number. They say \"say in target_result what the text does state instead.\" We can paraphrase perhaps mention qualitative finding. No extra props.</think>\n\n{\"parent_value\":0,\"unit\":\"\",\"evidence\":\"\",\"target_result\":\"The text states that HMMR is overexpressed in ccRCC and associated with pathological grade, stage, and patient prognosis, but it gives no headline numerical result for this main finding.\",\"number_stated\":false}", "reading": {"parent_value": 0, "unit": "", "evidence": "", "target_result": "The text states that HMMR is overexpressed in ccRCC and associated with pathological grade, stage, and patient prognosis, but it gives no headline numerical result for this main finding.", "number_stated": false, "source_locator": null}, "recorded_at": "2026-10-31T15:30:24.603444+00:00"}], "agreement": false, "disagreements": [], "agreement_basis": {"policy": "decisive_fields", "decisive_fields": ["work_type", "number_stated", "parent_value", "unit"]}, "blinded": true}, "human_critique": {"status": "not_run", "reviewer": null, "signed_at": null, "conflict_checked": false, "commentary": null}, "stages": [{"key": "target_materials", "status": "pending", "reason": null, "evidence": null}, {"key": "independent_readings", "status": "blocked", "reason": "The independent readings are incomplete or disagree; the source evidence must resolve the difference.", "evidence": {"provider_errors": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "reason": "a quoted statement is absent from the supplied text", "raw_response": "{\n  \"target_result\": \"HMMR was significantly up-regulated in ccRCC tumor tissues, and elevated HMMR expression was associated with ccRCC progression and adverse patient prognosis.\",\n  \"work_type\": \"analysis_plus_physical_validation\",\n  \"physical_methods\": [\n    \"Transwell assays\",\n    \"CCK-8 assays\",\n    \"clone formation assays\",\n    \"subcutaneous xenograft experiments in nude mice\",\n    \"cellular functional experiments\"\n  ],\n  \"analysis_methods\": [\n    \"The Cancer Genome Atlas (TCGA) expression analysis\",\n    \"Gene Set Enrichment Analysis (GSEA)\",\n    \"Genomics of Drug Sensitivity in Cancer (GDSC) drug-sensitivity prediction using the pRRophetic package\",\n    \"multivariate regression nomogram model\",\n    \"single-cell analysis using the Seurat package and tSNE algorithm\",\n    \"SingleR cluster annotation\",\n    \"CIBERSORT immune-cell infiltration analysis\",\n    \"differential expression analysis using DESeq2, limma, and edgeR\",\n    \"ENCORI-predicted HMMR-interacting miRNA analysis\"\n  ],\n  \"statistical_method\": \"Student’s t-test for differences between two groups and Pearson correlation analysis for correlations, using p < 0.05 as the threshold for statistical significance.\",\n  \"method_evidence\": \"To evaluate the differences between two groups and to analyze correlations, Student’s t-test and Pearson correlation analysis were applied, respectively, with a threshold of p < 0.05 indicating statistical significance.\",\n  \"repositories\": [\n    {\n      \"repository\": \"The Cancer Genome Atlas (TCGA) database\",\n      \"accession\": \"TCGA\",\n      \"data_description\": \"Raw mRNA expression data for ccRCC, comprising 614 samples: 72 normal and 542 tumor samples; the database was also used for miRNA expression, immune-cell infiltration, and other ccRCC analyses.\",\n      \"evidence\": \"We downloaded the raw mRNA expression data for ccRCC from the TCGA database, encompassing a total of 614 samples—normal (n = 72) and tumor (n = 542).\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"NCBI GEO\",\n      \"accession\": \"GSE207493\",\n      \"data_description\": \"Single-cell data from 19 ccRCC patients, used for pathway enrichment and analysis of HMMR expression across cell subtypes.\",\n      \"evidence\": \"Additionally, for single-cell analysis, data for 19 ccRCC patients were extracted from the NCBI GEO public database (GSE207493).\",\n      \"publicly_downloadable\": true\n    },\n    {\n      \"repository\": \"MsigDB database\",\n      \"accession\": \"annotated gene sets version 7.0\",\n      \"data_description\": \"Annotated gene sets used with GSE207493 for GSEA between HMMR high-expression and low-expression subtypes.\",\n      \"evidence\": \"We downloaded annotated gene sets version 7.0 from the MsigDB database and data from GSE207493 for pathway enrichment analysis between subtypes.\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"Genomics of Drug Sensitivity in Cancer (GDSC) database\",\n      \"accession\": \"unknown\",\n      \"data_description\": \"Drug-sensitivity data used with the pRRophetic package to estimate IC50 values and predict drug sensitivity for tumor samples.\",\n      \"evidence\": \"The Genomics of Drug Sensitivity in Cancer (GDSC) database was used, along with the “pRRophetic” package, to predict drug sensitivity for each tumor sample.\",\n      \"publicly_downloadable\": false\n    },\n    {\n      \"repository\": \"ENCORI database\",\n      \"accession\": \"unknown\",\n      \"data_description\": \"Predicted low-expressed miRNAs potentially interacting with HMMR, intersected with differentially expressed miRNAs identified by DESeq2, limma, and edgeR.\",\n      \"evidence\": \"We then intersected the differentially expressed miRNAs identified by these methods with low-expressed miRNAs predicted to potentially interact with HMMR from the ENCORI database.\",\n      \"publicly_downloadable\": false\n    }\n  ],\n  \"availability_statement\": \"The paper states that raw TCGA mRNA expression data were downloaded and that GSE207493 was extracted from the public NCBI GEO database. It does not explicitly state that the TCGA, MsigDB, GDSC, or ENCORI data can be downloaded without an application, embargo, or request, and it gives no availability statement for newly generated experimental data.\",\n  \"data_on_request\": false,\n  \"source_claims\": [\n    \"HMMR expression was significantly up-regulated in tumor tissues of ccRCC patients.\",\n    \"Elevated HMMR expression showed a strong correlation with ccRCC progression and adverse patient prognosis.\",\n    \"Knocking down HMMR inhibited the proliferative and migratory abilities of ccRCC cells, whereas HMMR overexpression amplified these oncogenic properties.\",\n    \"Reduced HMMR expression inhibited ccRCC tumor proliferation in a nude-mouse model.\",\n    \"miR-9-5p overexpression effectively downregulated HMMR expression and impeded ccRCC cell proliferation and migration.\",\n    \"HMMR might influence ccRCC growth through the EMT and JAK1/STAT1 signaling pathways.\"\n  ],\n  \"missing_information\": [\n    \"No single headline numerical estimate, effect size, confidence interval, or exact p-value for the main finding is stated in the supplied text.\",\n    \"The supplied text does not identify which statistical test was applied specifically to the primary HMMR up-regulation comparison beyond the general statement that Student’s t-test was used for differences between two groups.\",\n    \"The supplied text does not explicitly describe unrestricted, no-application download conditions for the TCGA, MsigDB, GDSC, or ENCORI repositories.\",\n    \"No accession is provided for the GDSC or ENCORI data.\",\n    \"The availability of newly generated experimental data is not stated.\",\n    \"The numerical results underlying the prognostic association, xenograft measurements, proliferation assays, and migration assays are not provided in the supplied text.\"\n  ],\n  \"evidence_spans\": [\n    \"HMMR expression was significantly up-regulated in tumor tissues of ccRCC patients, and elevated HMMR expression level showed a strong correlation with ccRCC progression and adverse prognoses of patients.\",\n    \"Knocking down HMMR inhibited the proliferative and migratory abilities of ccRCC cells, while its overexpression amplified these oncogenic properties.\",\n    \"In nude mice model, reduced HMMR expression inhibited ccRCC tumor proliferation in vivo.\",\n    \"Furthermore, overexpression of an upstream transcriptional regulator, miR-9-5p, effectively downregulated HMMR expression and thus impeded ccRCC cells proliferation and migration.\",\n    \"HMMR might influence ccRCC growth via the Epithelial-Mesenchymal Transition (EMT) pathway and the Janus Kinase 1/Signal Transducer and Activator of Transcription 1 (JAK1/STAT1) pathway.\",\n    \"We downloaded the raw mRNA expression data for ccRCC from the TCGA database, encompassing a total of 614 samples—normal (n = 72) and tumor (n = 542).\",\n    \"Additionally, for single-cell analysis, data for 19 ccRCC patients were extracted from the NCBI GEO public database (GSE207493).\",\n    \"The statistical analyses of the acquired data were performed using GraphPad Prism 8.0 software and the R programming language (version 4.3.0) for both graphical presentation and the assessment of statistical significance.\",\n    \"To evaluate the differences between two groups and to analyze correlations, Student’s t-test and Pearson correlation analysis were applied, respectively, with a threshold of p < 0.05 indicating statistical significance.\"\n  ]\n}"}]}}, {"key": "source_resolution", "status": "pending", "reason": null, "evidence": null}, {"key": "protocol_reconstruction", "status": "pending", "reason": null, "evidence": null}, {"key": "control_execution", "status": "pending", "reason": null, "evidence": null}, {"key": "control_comparison", "status": "pending", "reason": null, "evidence": null}, {"key": "publication", "status": "pending", "reason": null, "evidence": null}], "gaps": ["review.human_critique", "review.machine_critique.blocked"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "package:fcccf8e64b6368b496e0ed624dd9172dd8542111debe5e19bcc155ded6de1346", "type": "sep_package", "label": "SEP v1 package for pmid:39789627"}], "activities": [{"id": "activity:conversion:pmid:39789627", "type": "conversion", "used": ["package:fcccf8e64b6368b496e0ed624dd9172dd8542111debe5e19bcc155ded6de1346"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:minimax", "type": "model", "label": "minimax"}, {"id": "agent:minimax_alt", "type": "model", "label": "minimax_alt"}, {"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}], "source_run_id": "run-20261023T122449232913", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "e42b8804-bde8-4947-972e-edb416bc8734", "parent_hash": "fcccf8e64b6368b496e0ed624dd9172dd8542111debe5e19bcc155ded6de1346", "fork_of": null, "generation": 1}, "gaps": ["abstract", "asset:source:pmid:39789627 not verified", "authors", "capability", "corpus_record_absent", "execution.not_run", "method", "review.human_critique", "review.machine_critique.blocked", "step:obtain_source_data blocked on data:not_downloaded", "step:run_analysis blocked on no owned computational capability is bound to this experiment", "step:run_analysis input 'materials:the source data behind the published result in pmid:39789627' does not resolve to a declared asset", "targets"]}