Systematic analyses of a novel circRNA-related miRNAs prognostic signature for Cervical Cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35766420", "kind": "science-record", "revision": 4, "content_hash": "sha256:855dc6cd9e7b4adc2ea186ad0c8524b2e9ffd4887a820b89e0b72a1df790fcba", "created_at": "2026-11-01T18:14:48Z", "updated_at": "2026-11-01T22:06:54Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Systematic analyses of a novel circRNA-related miRNAs prognostic signature for Cervical Cancer.", "doi": "10.1590/1678-4685-GMB-2021-0405", "pmid": "35766420", "authors": ["Shasha Wang", "Songying Zhang"], "journal": "Genetics and molecular biology", "year": 2022, "abstract": "Accumulating evidences shed light on the important roles of Circular RNAs (circRNAs) acting as competing endogenous RNAs (ceRNAs) in cervical cancer (CC) biology. The present study aimed to identify a novel circRNA-related prognostic signature for CC. The expression data and clinical information of CC were downloaded from the Gene Expression Omnibus (GEO) datasets to identify the differential circRNAs expression. Based on the targeted miRNA prediction, circRNA-related miRNAs were detected in training group and validation group of The Cancer Genome Atlas (TCGA) dataset to construct the novel prognostic signature of CC with least absolute shrinkage and selection operator (LASSO). Moreover, the Kaplan-Meier (K-M) analysis was applied to test the model. In the present study, three differentially expressed circRNAs (hsa_circ_0001498, hsa_circ_0066147, and hsa_circ_0006948) were identified in GSE102686 and GSE107472. Then, with the criteria 25 predicted miRNAs were analyzed in TCGA datasets to calculate the prognostic signature. Furthermore, we developed a six-miRNA signature (hsa-miR-217, hsa-miR-30b-3p, hsa-miR-136-5p, hsa-miR-185-3p, hsa-miR-501-5p and hsa-miR-658) based on their expression level and coefficients. We performed a Pearson correlation analysis to screen 47 mRNAs which are negatively regulated by these six miRNAs. Functional enrichment analysis indicated these mRNAs were mainly enriched in cancer-related biology, such as regulation of transcription, signal transduction, and cell cycle. The present study provides novel insight for better understanding of circRNA-related ceRNA network in CC and facilitates the identification of potential biomarkers for prognosis.", "claims": [{"id": "claim-method", "statement": "In the training group, Cox regression analysis with least absolute shrinkage and selection operator (LASSO) were performed to construct risk score to indicate the relative CC progression hazard for each case (Mao et al., 2019).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35766420", "locator": "sections[4] \"Construction of the CC-specific circRNA-related miRNAs prognostic signature\" paragraph 3", "quote": "In the training group, Cox regression analysis with least absolute shrinkage and selection operator (LASSO) were performed to construct risk score to indicate the relative CC progression hazard for each case (Mao et al., 2019)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "CircRNA High-throughput sequencing and microarray data from 10 pairs CC tissues and paired-paracancerous cervical tissues were downloaded from the two public microarray CC GEO datasets (GSE102686 and GSE107472).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35766420", "locator": "sections[3] \"Identification of RNA in GEO and TCGA dataset\" paragraph 2", "quote": "CircRNA High-throughput sequencing and microarray data from 10 pairs CC tissues and paired-paracancerous cervical tissues were downloaded from the two public microarray CC GEO datasets (GSE102686 and GSE107472)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study developed a six-miRNA prognostic signature for cervical cancer comprising hsa-miR-217, hsa-miR-30b-3p, hsa-miR-136-5p, hsa-miR-185-3p, hsa-miR-501-5p and hsa-miR-658.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35766420", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Furthermore, we developed a six-miRNA signature (hsa-miR-217, hsa-miR-30b-3p, hsa-miR-136-5p, hsa-miR-185-3p, hsa-miR-501-5p and hsa-miR-658) based on their expression level and coefficients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study developed a six-miRNA prognostic signature for cervical cancer comprising hsa-miR-217, hsa-miR-30b-3p, hsa-miR-136-5p, hsa-miR-185-3p, hsa-miR-501-5p and hsa-miR-658.", "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:35766420"], "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:GSE102686"], "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:GSE102686"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35766420", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1590/1678-4685-GMB-2021-0405", "sha256": "sha256:81a806cca5c0decac8e33f717809fb760d360c404ad6f9480db2e111d499deb3", "bytes": 39079, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1590/1678-4685-GMB-2021-0405", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9241030", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE102686", "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=GSE102686", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE102686", "version": null, "note": "CircRNA High-throughput sequencing and microarray data from 10 pairs CC tissues and paired-paracancerous cervical tissues were downloaded from the two public microarray CC GEO datasets (GSE102686 and GSE107472).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "CircRNA High-throughput sequencing and microarray data from 10 pairs CC tissues and paired-paracancerous cervical tissues were downloaded from the two public microarray CC GEO datasets (GSE102686 and GSE107472).", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM2742920", "GSM2742921", "GSM2742922", "GSM2742923", "GSM2742924", "GSM2742925", "GSM2742926", "GSM2742927", "GSM2742928", "GSM2742929"], "sample_count": 10, "group_sizes": null, "group_sizes_basis": null, "platform": "19978", "taxon": "Homo sapiens", "title": "Circular RNA profile of human cervical squamous cell carcinoma", "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:GSE107472", "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=GSE107472", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE107472", "version": null, "note": "CircRNA High-throughput sequencing and microarray data from 10 pairs CC tissues and paired-paracancerous cervical tissues were downloaded from the two public microarray CC GEO datasets (GSE102686 and GSE107472).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "CircRNA High-throughput sequencing and microarray data from 10 pairs CC tissues and paired-paracancerous cervical tissues were downloaded from the two public microarray CC GEO datasets (GSE102686 and GSE107472).", "access_statement_kind": "repository_route_statement", "sample_accessions": ["GSM2868030", "GSM2868031", "GSM2868032", "GSM2868033", "GSM2868034", "GSM2868035", "GSM2868036", "GSM2868037", "GSM2868038", "GSM2868039"], "sample_count": 10, "group_sizes": null, "group_sizes_basis": null, "platform": "16791", "taxon": "Homo sapiens", "title": "Circular RNAs are super abundant in cervical tumor and plasma detected by high throughput microarray [RNA-Seq]", "resolution_checked_at": "2026-11-01T18:06:43Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE102686 not downloaded", "asset:data:geo:GSE102686 licence not read", "asset:data:geo:GSE107472 not downloaded", "asset:data:geo:GSE107472 licence not read"]}, "analysis": {"state": "partial", "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": "In the training group, Cox regression analysis with least absolute shrinkage and selection operator (LASSO) were performed to construct risk score to indicate the relative CC progression hazard for each case (Mao et al., 2019)."}}, {"id": "method-2", "family": "group_comparison", "specific": "student_t_test", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["student_t"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Student's t test"}}, {"id": "method-3", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Analysis of Variance"}}, {"id": "method-4", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-5", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.7, "band": "medium", "quote": "Pearson correlation"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": ["GSE102686", "GSE107472"], "resolved_accessions": ["GSE102686", "GSE107472"], "statements": ["CircRNA High-throughput sequencing and microarray data from 10 pairs CC tissues and paired-paracancerous cervical tissues were downloaded from the two public microarray CC GEO datasets (GSE102686 and GSE107472)."], "basis": "The paper names GSE102686, GSE107472 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": ["cox_ph"], "assets": ["source:pmid:35766420", "data:geo:GSE102686"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE102686"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:52:51Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": null}, "calls": 5, "usage": {"prompt_tokens": 12294, "completion_tokens": 1249, "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:35766420", "type": "paper", "label": "pmid:35766420"}, {"id": "source:pmid:35766420", "type": "manuscript", "label": "PMID 35766420 full text"}, {"id": "data:geo:GSE102686", "type": "dataset", "label": "GEO GSE102686"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35766420", "type": "paper", "label": "pmid:35766420"}, {"id": "source:pmid:35766420", "type": "manuscript", "label": "PMID 35766420 full text"}, {"id": "data:geo:GSE102686", "type": "dataset", "label": "GEO GSE102686"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35766420", "type": "extraction", "used": ["source:pmid:35766420"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE102686", "type": "accession_resolution", "used": ["data:geo:GSE102686"], "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:35766420", "parent_hash": "sha256:94848c02dc530dc2ab98386044bb51dabd4db81a34c44e5e304257c9c2739d73", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE102686 licence not read", "asset:data:geo:GSE102686 not downloaded", "asset:data:geo:GSE107472 licence not read", "asset:data:geo:GSE107472 not downloaded", "execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["In the training group, Cox regression analysis with least absolute shrinkage and selection operator (LASSO) were performed to construct risk score to indicate the relative CC progression hazard for each case (Mao et al., 2019)."], "order_basis": "analysis_only", "id": "assay:1"}]}