Immune Infiltration-Related ceRNA Network Revealing Potential Biomarkers for Prognosis of Head and Neck Squamous Cell Carcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36097539", "kind": "science-record", "revision": 4, "content_hash": "sha256:b840e095d530885e6ab23c9f51b2322af212b6a31934a032bd0c3ea81495c135", "created_at": "2026-11-01T18:15:20Z", "updated_at": "2026-11-01T22:07:32Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune Infiltration-Related ceRNA Network Revealing Potential Biomarkers for Prognosis of Head and Neck Squamous Cell Carcinoma.", "doi": "10.1155/2022/1014347", "pmid": "36097539", "authors": ["Shuai Zhao", "Mengle Peng", "Zhongquan Wang", "Jingjing Cao", "Xinyu Zhang", "Ruijing Yu", "Tao Huang", "Wenping Lian"], "journal": "Disease markers", "year": 2022, "abstract": "Background: Head and neck squamous cell carcinoma (HNSCC) is a frequently lethal malignancy, and the mortality is considerably high. The tumor microenvironment (TME) has been identified as a critical participation in cancer development, treatment, and prognosis. However, competing endogenous RNA (ceRNA) networks grouping with immune/stromal scores of HNSCC patients need to be further illustrated. Therefore, our study aimed to provide clues for searching promising prognostic markers of TME in HNSCC.\nMaterials and Methods: ESTIMATE algorithm was used to calculate immune scores and stromal scores of the enrolled HNSCC patients. Differentially expressed genes (DEGs), lncRNAs (DELs), and miRNAs (DEMs) were identified by comparing the expression difference between high and low immune/stromal scores. Then, a ceRNA network and protein-protein interaction (PPI) network were constructed for selecting hub regulators. In addition, survival analysis was performed to access the association between immune scores, stromal scores, and differentially expressed RNAs in the ceRNA network and the overall survival (OS) of HNSCC patients. Then, the GSE65858 datasets from Gene Expression Omnibus (GEO) database was used for verification. At last, the difference between the clinical characteristics and immune cell infiltration in different expression groups of IL10RA, PRF1, and IL2RA was analyzed.\nResults: Survival analysis showed a better OS in the high immune score group, and then we constructed a ceRNA network composed of 97 DEGs, 79 DELs and 22 DEMs. Within the ceRNA network, FOXP3, IL10RA, STAT5A, PRF1, IL2RA, miR-148a-3p, miR-3065-3p, and lncRNAs, including CXCR2P1, HNRNPA1P21, CTA-384D8.36, and IGHV1OR15-2, were closely correlated with the OS of HNSCC patients. Especially, using the data from GSE65858, we successfully verified that IL10RA, PRF1, and IL2RA were not only significantly upregulated in patients high immune scores, but also their high expressions were associated with longer survival time. In addition, stratified analysis showed that PRF1 and IL2RA might be involved in the mechanism of tumor progress.\nConclusion: In conclusion, we constructed a ceRNA network related to the TME of HNSCC, which provides candidates for therapeutic intervention and prognosis evaluation.", "claims": [{"id": "claim-data-access", "statement": "Datasets used in this study were obtained from open public databases (https://tcga-data.nci.nih.gov/tcga/, https://www.ncbi.nlm.nih.gov/geo/), but the datasets analyzed in this study are available from the corresponding authors on request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36097539", "locator": "sections[20] \"Data Availability\" paragraph 2", "quote": "Datasets used in this study were obtained from open public databases (https://tcga-data.nci.nih.gov/tcga/, https://www.ncbi.nlm.nih.gov/geo/), but the datasets analyzed in this study are available from the corresponding authors on request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Survival analysis identified 11 RNAs in the ceRNA network whose expression was associated with overall survival in HNSCC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36097539", "locator": "sections[16] \"3.6. PPI Network Construction and Survival Analysis\" paragraph 2", "quote": "The survival curves of five DEGs (FOXP3, IL10RA, STAT5A, PRF1, and IL2RA), two DEMs (miR-148a-3p and miR-3065-3p), and four DELs (CXCR2P1, HNRNPA1P21, CTA-384D8.36, and IGHV1OR15-2) are exhibited in Figures 4(a)–4(k)."}, {"asset": "source:pmid:36097539", "locator": "sections[16] \"3.6. PPI Network Construction and Survival Analysis\" paragraph 2", "quote": "High expression levels of FOXP3, IL10RA, STAT5A, PRF1, IL2RA, miR-148a-3p, CXCR2P1, HNRNPA1P21, CTA-384D8.36, and IGHV1OR15-2 and low expression levels of miR-3065-3p were associated with longer OS in HNSCC patients (Figure 4)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Survival analysis identified 11 RNAs in the ceRNA network whose expression was associated with overall survival in HNSCC patients.", "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:36097539"], "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:GSE65858"], "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:GSE65858"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36097539", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/2022/1014347", "sha256": 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