Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA-mRNA network mining and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37538797", "kind": "science-record", "revision": 4, "content_hash": "sha256:58ce63da463e0988ed06b9dde963bc7fa8d8d9a355383b42cf98ca6b1d69e502", "created_at": "2026-11-01T19:02:19Z", "updated_at": "2026-11-01T22:52:27Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA-mRNA network mining and machine learning.", "doi": "10.3389/fendo.2023.1174911", "pmid": "37538797", "authors": ["Xixia Zhang", "Xiao Li", "Caixia Wang", "Shuang Wang", "Yuan Zhuang", "Bing Liu", "Xin Lian"], "journal": "Frontiers in endocrinology", "year": 2023, "abstract": "Background: Nasopharyngeal cancer (NPC) has a high incidence in Southern China and Asia, and its survival is extremely poor in advanced patients. MiRNAs play critical roles in regulating gene expression and serve as therapeutic targets in cancer. This study sought to disclose key miRNAs and target genes responsible for NPC prognosis and endocrine metabolism.\nMaterials and methods: Three datasets (GSE32960, GSE70970, and GSE102349) of NPC samples came from Gene Expression Omnibus (GEO). Limma and WGCNA were applied to identify key prognostic miRNAs. There were 12 types of miRNA tools implemented to study potential target genes (mRNAs) of miRNAs. Univariate Cox regression and stepAIC were introduced to construct risk models. Pearson analysis was conducted to analyze the correlation between endocrine metabolism and RiskScore. Single-sample gene set enrichment analysis (ssGSEA), MCP-counter, and ESTIMATE were performed for immune analysis. The response to immunotherapy was predicted by TIDE and SubMap analyses.\nResults: Two key miRNAs (miR-142-3p and miR-93) were closely involved in NPC prognosis. The expression of the two miRNAs was dysregulated in NPC cell lines. A total of 125 potential target genes of the key miRNAs were screened, and they were enriched in autophagy and mitophagy pathways. Five target genes (E2F1, KCNJ8, SUCO, HECTD1, and KIF23) were identified to construct a prognostic model, which was used to divide patients into high group and low group. RiskScore was negatively correlated with most endocrine-related genes and pathways. The low-risk group manifested higher immune infiltration, anticancer response, more activated immune-related pathways, and higher response to immunotherapy than the high-risk group.\nConclusions: This study revealed two key miRNAs that were highly contributable to NPC prognosis. We delineated the specific links between key miRNAs and prognostic mRNAs with miRNA-mRNA networks. The effectiveness of the five-gene model in predicting NPC prognosis as well as endocrine metabolism provided a guidance for personalized immunotherapy in NPC patients.", "claims": [{"id": "claim-method", "statement": "Limma and WGCNA were applied to identify key prognostic miRNAs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37538797", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Limma and WGCNA were applied to identify key prognostic miRNAs."}], "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/ Supplementary Material .", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37538797", "locator": "sections[27] \"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/ Supplementary Material ."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Two key miRNAs (miR-142-3p and miR-93) were closely involved in NPC prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37538797", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Two key miRNAs (miR-142-3p and miR-93) were closely involved in NPC prognosis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Two key miRNAs (miR-142-3p and miR-93) were closely involved in NPC prognosis.", "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:37538797"], "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:GSE102349"], "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:GSE102349"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37538797", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2023.1174911", "sha256": "sha256:7515f4cdeae0db29678bdfbcfcff7b7b8fe031a7c29a9f2edfaf45b8af946708", "bytes": 60939, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2023.1174911", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10396331", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE102349", "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=GSE102349", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE102349", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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