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pmid:41376822
Integrative analysis of tissue and circulating miRNAs as biomarkers for progression and survival in hepatocellular carcinoma.
Non-coding RNA research · 2026 doi:10.1016/j.ncrna.2025.11.002
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Experiment
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Data
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Complete file
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Interpretation column provides biologically meaningful insight for each miRNA.", "structure": {"pmcid": "PMC12686728", "section_index": 24, "section_title": "Association of circulating miRNAs with AFP levels and cancer stage", "row_count": 10, "column_count": 5}, "table": {"label": "Table 3", "caption": "Tabular presentation showing AFP association: Positive/Negative/None = direction of correlation, Stage/Child-Pugh association: Strong/Moderate/Significant/None = relative significance from Kruskal-Wallis tests. Interpretation column provides biologically meaningful insight for each miRNA.", "rows": [["miRNA", "AFP Association", "Stage Association", "Child-Pugh Association", "Interpretation"], ["hsa-miR-3619-3p", "Negative", "Moderate", "Moderate", "Strong inverse correlation with AFP; expression decreases with tumor progression."], ["hsa-miR-1290", "Negative", "Strong", "Strong", "Consistently lower in higher stages; mirrors tissue dysregulation."], ["hsa-miR-1185-2-3p", "Positive", "Strong", "Moderate", "Higher expression in advanced stages; potential biomarker for progression."], ["hsa-miR-422a", "Positive", "Strong", "Significant", "Upregulated with stage; may reflect liver function (Child-Pugh)."], ["hsa-miR-4687-3p", "Negative", "Moderate", "Significant", "Downregulated in higher Child-Pugh classes; may indicate disease severity."], ["hsa-miR-204-3p", "Positive", "Moderate", "Significant", "Expression tracks AFP and liver function."], ["hsa-miR-5100", "None", "Strong", "Strong", "Stage- and Child-Pugh-dependent; less informative for AFP."], ["hsa-miR-195-5p", "None", "Strong", "Significant", "Reflects cancer stage; minimal AFP correlation."], ["hsa-miR-6132", "None", "None", "None", "No clear clinical associations; may be HCC-specific but not stage-dependent."]], "row_count": 10, "column_count": 5, "numeric_cells": 9, "header_rows": 1, "locator": "section[24]/table-wrap[1]#tbl3"}}, {"id": "asset:table:pmid:41376822:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:85adcad6d6258881fc0a0f4dfae8f74d9544d4884cb167dd8532e974de22ea00", "bytes": 2759, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[27]/table-wrap[1]#tbl4", "version": null, "note": "Differentially expressed miRNAs identified in hepatocellular carcinoma (HCC) tissue and serum. The table shows expression direction, key clinical associations (KM and multivariable Cox analyses), concordance with prior literature, proposed molecular roles or pathways, and validating references.", "structure": {"pmcid": "PMC12686728", "section_index": 27, "section_title": "Circulating miRNAs and clinical translation", "row_count": 11, "column_count": 7}, "table": {"label": "Table 4", "caption": "Differentially expressed miRNAs identified in hepatocellular carcinoma (HCC) tissue and serum. The table shows expression direction, key clinical associations (KM and multivariable Cox analyses), concordance with prior literature, proposed molecular roles or pathways, and validating references.", "rows": [["miRNA", "Source (this study)", "Direction in HCC (tumor vs normal or HCC vs non-HCC serum)", "Key clinical association in this study", "Concordance with prior literature", "Proposed role/pathways", "Validating refs"], ["hsa-miR-187", "Tissue", "↑", "Independent risk in multivariable Cox (HR 1.40; p < 0.001); KM significant", "Nonconcurrent with our study Reported to be tumor suppressor and downregulated in HCC", "miR-187 acted as a tumor suppressor in HCC progression via direct targeting of IGF-1R.", "[17]; Present study"], ["hsa-miR-6718", "Tissue", "↑", "Independent risk in multivariable Cox (HR 1.29; p = 0.045)", "Novel/under-reported in HCC", "Putative cell-cycle/MAPK regulation (by enrichment)", "Present study"], ["hsa-miR-5589", "Tissue", "↓", "Concurrent with previous study; Protective in multivariable Cox (HR 0.88; p = 0.038)", "under-reported in HCC", "Can upregulate CDK1 and CCNA2 and promote oncogenesis", "[21]; Present study"], ["hsa-miR-106b", "Tissue", "↑ (T3/T4 > T1/T2)", "Stage-linked upregulation (advanced T); marker of aggressiveness", "Consistent with previous study on HCC and poorer outcomes in epithelial cancers", "Inhibits RUNX3", "[22]; Present study"], ["hsa-miR-124 family (-1/-2/-3)", "Tissue", "↓ (M1 < M0)", "Down in metastatic tumors; metastasis suppressor signal", "Concordant with HCC data showing miR-124-3p suppresses invasion via CRKL/ERK", "ERK/CRKL, anti-metastatic", "[30]; Present study"], ["hsa-miR-33b", "Tissue", "variable", "KM significant; not independent in Cox", "Prior HCC work links miR-33b to proliferation and mobility", "Notch1/Fli-1 axis", "[31]; Present study"], ["hsa-miR-424 (-3p/-5p)", "Tissue", "variable", "KM significant; not independent in Cox", "Mixed reports in HCC (pro- or anti-metastatic depending on isoform/context)", "SRF-STAT1/2; CHEK1/cell cycle", "[32,33]; Present study"], ["miR-21", "Tissue & serum (literature)", "↑", "–", "Robust diagnostic/prognostic signal across cancers and HCC; correlates with advanced stage and poorer survival", "PTEN, PDCD4; apoptosis evasion", "[34]"], ["miR-122", "Tissue & serum", "↓", "–", "Liver-specific tumor suppressor; consistently down in HCC", "Metabolism, cell-cycle control", "[16]"], ["miR-221/222", "Tissue & serum", "↑", "–", "Recurrent oncogenic pair in HCC; progression/recurrence associations", "CDKN1B/p27, CDKN1C/p57", "[16]"]], "row_count": 11, "column_count": 7, "numeric_cells": 28, "header_rows": 1, "locator": "section[27]/table-wrap[1]#tbl4"}}], "gaps": ["asset:source:pmid:41376822 not verified"]}, "analysis": {"state": "absent", "methods": [], "targets": [], "gaps": ["method", "targets"]}, "capability": {"state": "absent", "requirements": [], "gaps": ["capability"]}, "readiness": {"state": "partial", "tier": null, "blocking_axis": null, "blocked_on": null, "axes": {}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:41376822", "asset:table:pmid:41376822:1", "asset:table:pmid:41376822:2", "asset:table:pmid:41376822:3", "asset:table:pmid:41376822:4"], "steps": ["read_published_estimate"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": ["xml"], "capabilities": [], "data_gaps": ["source:pmid:41376822"]}, "gaps": ["corpus_record_absent"]}, "review": {"state": "partial", "machine_critique": {"status": "blocked", "providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "full-text-reading-v1", "prompt_hash": "f380d52616ddc458d9f19f64209efde8d9db7c241aec86a45b21a0bfc61788fd", "source_hash": "59f1130ccae1528afbe456a634081733a0d993a38e7f7e81ade93bc591d56ff7", "raw_response_sha256": "sha256:25c0c79d5c259e64196d90e027ef41394430ab01f3bc05317559ecc3f2048b3f", "raw_response_chars": 3937}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "full-text-reading-v1", "prompt_hash": "d690be862bb161c09e7a529b93f68a51d3bf4bfbc3d4fbc4df12ca39c5716276", "source_hash": "59f1130ccae1528afbe456a634081733a0d993a38e7f7e81ade93bc591d56ff7", "raw_response_sha256": "sha256:3d069d90d38bb022738ec8409d419eab27bc53279a9f2dc7459570a1ccfc0fb6", "raw_response_chars": 56166}], "target_providers": [{"provider": "minimax", "model": "MiniMax-M3.1-Flash", "prompt_version": "replication-target-v2", "prompt_hash": "b212cb6973878fda470df1fdca9106f79afecc650022324648905d286c299470", "source_hash": "59f1130ccae1528afbe456a634081733a0d993a38e7f7e81ade93bc591d56ff7", "raw_response": "{\"parent_value\":16,\"unit\":\"miRNAs\",\"evidence\":\"We identified 16 significantly dysregulated miRNAs in HCC tissues, including hsa-miR-187 and hsa-miR-6718, which were associated with poor survival, and hsa-miR-5589, which showed a protective effect.\",\"target_result\":\"16 significantly dysregulated miRNAs in HCC tissues\",\"number_stated\":true}", "reading": {"parent_value": 16, "unit": "miRNAs", "evidence": "We identified 16 significantly dysregulated miRNAs in HCC tissues, including hsa-miR-187 and hsa-miR-6718, which were associated with poor survival, and hsa-miR-5589, which showed a protective effect.", "target_result": "16 significantly dysregulated miRNAs in HCC tissues", "number_stated": true, "source_locator": "chars 750-950 of source sha256 59f1130ccae1528afbe456a634081733a0d993a38e7f7e81ade93bc591d56ff7"}, "recorded_at": "2026-10-31T15:15:54.764185+00:00"}, {"provider": "minimax_alt", "model": "MiniMax-M3", "prompt_version": "replication-target-v2", "prompt_hash": "46a78e8a68d8504902cc04a79c537a896cc92ed598799c58558836f497c5cedf", "source_hash": "59f1130ccae1528afbe456a634081733a0d993a38e7f7e81ade93bc591d56ff7", "raw_response": "<think>We need reason carefully, output JSON only. Need extract single numerical result that replication registered target main finding headline number, stated by paper, unit printed. Need determine what is headline numerical result. Source study integrative tissue/serum miRNAs HCC, main findings 16 dysregulated miRNAs, hsa-miR-187 significant survival p=.0324, Cox HR 1.25 etc. User asking single numerical result paper states for registered target: \"the headline numerical result the paper states for its main finding\". Need infer likely target 16 (number of significantly dysregulated miRNAs) from abstract and result. Need decide. We have ample budget but must not overanalyze? Need parse user intent exactly.\n\n\"Read the supplied paper text and find the single numerical result that a replication of the registered target has to reproduce. Report the headline number only: the value the paper itself states for the registered target, not a value you calculated, converted, averaged or reconstructed. The unit must be the unit the paper prints. The evidence must be an exact, contiguous quotation from SOURCE TEXT that contains the number; do not shorten it, do not add an ellipsis and do not paraphrase it. Attribution may be carried by the text's own wording: a number may be attached to the target by a parenthetical list, by the word 'respectively', or by words that name the target directly. When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves. If the text states no number for the registered target, set number_stated to false, leave parent_value at 0, leave unit and evidence empty, and say in target_result what the text does state instead. Never invent a source.\"\n\nREGISTERED TARGET: \"the headline numerical result the paper states for its main finding\"\n\nThey expect identify main finding's headline numerical result from abstract perhaps 16. Source abstract \"We identified 16 significantly dysregulated miRNAs in HCC tissues, including hsa-miR-187 and hsa-miR-6718, which were associated with poor survival, and hsa-miR-5589, which showed a protective effect.\" Main numerical result 16. Unit likely \"miRNAs\" (paper prints). Evidence exact contiguous quote contains 16 and target. Could quote abstract whole sentence. parent_value 16, unit \"miRNAs\", target_result e.g. \"16 significantly dysregulated miRNAs in HCC tissues\" number_stated true. Need consider if headline number is hsa-miR-187 HR 1.25 etc. The registered target generic likely mapped to headline numerical result of main finding. In biomedical paper often key outcome HR, sample size? Need discern expected based task templates. Need reason what \"single numerical result\" means and \"parent_value\" schema odd. Could be from replication check numerical result. They want \"headline number\" not multiple. For abstract of computational paper main finding is 16 DE miRNAs. Source begins abstract identifies 16. Conclusion focuses hsa-miR-187/6718 no number. Likely answer 16 miRNAs with evidence exact sentence. But need ensure \"unit must be unit paper prints\" if no unit? Count \"miRNAs\". They may consider \"16 significantly dysregulated miRNAs\" unit \"miRNAs\" acceptable. They might expect unit empty because no explicit unit? Many similar tasks have unit like \"%\" \"cases\" etc. Count miRNAs is natural unit.\n\nLet's consider alternative interpretation: registered target could mean survival association and headline numerical result = HR=1.25? \"the headline numerical result the paper states for its main finding\" main finding prognostic biomarkers. The main claim abstract \"identified 16 significantly dysregulated miRNAs in HCC tissues\" with 16 main finding. Usually in extraction evaluation target not explicitly named beyond \"headline numerical result\". They expect pick the single key count in abstract. We need output only JSON object. We can ensure evidence whole clause abstract with 16: \"We identified 16 significantly dysregulated miRNAs in HCC tissues, including hsa-miR-187 and hsa-miR-6718, which were associated with poor survival, and hsa-miR-5589, which showed a protective effect.\" That is exact. Unit \"miRNAs\". target_result \"16 significantly dysregulated miRNAs in HCC tissues.\" Parent 16. No invented.\n\nNeed examine if 16 percent 0.85 % candidate? Single number 16. Good.\n\nLet's not overcomplicate. Need fulfill developer \"You extract claims only from supplied source text. Reply with JSON object.\" Only JSON final. Should target_result maybe \"The study identified 16 significantly dysregulated miRNAs in HCC tissues.\" Unit \"miRNAs\". Evidence exact quote sentence. Is headline result a count not value? Yes.\n\nCould parent_value 16 unit \"\"? They expressly \"unit must be the unit the paper prints.\" Text prints 16 miRNAs, so use miRNAs. No need percent.\n\nLet's inspect source abstract: \"We identified 16 significantly dysregulated miRNAs in HCC tissues, including hsa-miR-187 and hsa-miR-6718, which were associated with poor survival, and hsa-miR-5589, which showed a protective effect.\" Exactly. Good.\n\nWe should maybe choose evidence shorter contiguous clause \"We identified 16 significantly dysregulated miRNAs in HCC tissues\" but user \"When that is how the text links them, quote the whole clause so a reader can check the attribution for themselves.\" Whole sentence is safe and exact. It includes other numbers? No other numbers in sentence besides 16. hsa names have numbers, but those aren't numeric results. Fine.\n\ntarget_result may not need say calculated. \"The paper reports 16 significantly dysregulated miRNAs in HCC tissues as its headline main finding.\" That is a claim direct from source, acceptable. Could simpler \"16 significantly dysregulated miRNAs in HCC tissues.\" We should ensure schema fields all included, no extra. Use valid JSON double quotes. number_stated true. final.</think>\n\n{\"parent_value\":16,\"unit\":\"miRNAs\",\"evidence\":\"We identified 16 significantly dysregulated miRNAs in HCC tissues, including hsa-miR-187 and hsa-miR-6718, which were associated with poor survival, and hsa-miR-5589, which showed a protective effect.\",\"target_result\":\"16 significantly dysregulated miRNAs in HCC tissues.\",\"number_stated\":true}", "reading": {"parent_value": 16, "unit": "miRNAs", "evidence": "We identified 16 significantly dysregulated miRNAs in HCC tissues, including hsa-miR-187 and hsa-miR-6718, which were associated with poor survival, and hsa-miR-5589, which showed a protective effect.", "target_result": "16 significantly dysregulated miRNAs in HCC tissues.", "number_stated": true, "source_locator": "chars 750-950 of source sha256 59f1130ccae1528afbe456a634081733a0d993a38e7f7e81ade93bc591d56ff7"}, "recorded_at": "2026-10-31T15:16:18.213215+00:00"}], "agreement": false, "disagreements": {"target_result": {"minimax": "16 miRNAs were significantly dysregulated between HCC tumor and non-tumor tissues.", "minimax_alt": "Using this threshold, we found a focused set of 16 miRNAs that showed significant expression changes between tumour and non-tumour tissues (Fig. 1).", "decisive": false}, "analysis_methods": {"minimax": ["Differential expression analysis using limma-trend", "Kaplan-Meier survival analysis with log-rank tests", "Univariate and multivariate Cox proportional hazards regression", "DAVID Gene Ontology and KEGG functional enrichment analysis", "Clinical subgroup differential expression analysis"], "minimax_alt": ["limma-trend (v3.50.0) differential expression analysis", "adjusted-p-value and absolute-log2-fold-change filtering", "DAVID GO and KEGG functional enrichment analysis", "Kaplan–Meier survival analysis with log-rank tests", "univariate and multivariate Cox proportional hazards regression", "clinical subgroup differential expression analysis"], "decisive": false}, "statistical_method": {"minimax": "limma-trend", "minimax_alt": "limma-trend (v3.50.0)", "decisive": true}}, "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": []}}, {"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:83acc78b554a872818b0562a56e3703db03ff814ea6f5fc3423b52186b55d6e9", "type": "sep_package", "label": "SEP v1 package for pmid:41376822"}], "activities": [{"id": "activity:conversion:pmid:41376822", "type": "conversion", "used": ["package:83acc78b554a872818b0562a56e3703db03ff814ea6f5fc3423b52186b55d6e9"], "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": "pmid:41376822", "parent_hash": "sha256:66f1f39fbfec3c8f57aee75e3d13e7b0d9154a4e2cf74410b3e5de3490cb6382", "fork_of": null, "generation": 2}, "gaps": ["abstract", "asset:source:pmid:41376822 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:41376822' does not resolve to a declared asset", "targets"]}