Mining of prognosis-related genes in cervical squamous cell carcinoma immune microenvironment.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32904067", "kind": "science-record", "revision": 6, "content_hash": "sha256:782817f57a1a601fe411370f138e676d3da266dcc0af4c02bd6dda9123abb7ee", "created_at": "2026-11-01T15:41:10Z", "updated_at": "2026-11-01T21:23:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Mining of prognosis-related genes in cervical squamous cell carcinoma immune microenvironment.", "doi": "10.7717/peerj.9627", "pmid": "32904067", "authors": ["Jiong Ma", "Pu Cheng", "Xuejun Chen", "Chunxia Zhou", "Wei Zheng"], "journal": "PeerJ", "year": 2020, "abstract": "PURPOSE: The aim of this study was to explore the effective immune scoring method and mine the novel and potential immune microenvironment-related diagnostic and prognostic markers for cervical squamous cell carcinoma (CSSC).\nMATERIALS AND METHODS: The Cancer Genome Atlas (TCGA) data was downloaded and multiple data analysis approaches were initially used to search for the immune-related scoring system on the basis of Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) algorithm. Afterwards, the representative genes in the gene modules correlated with immune-related scores based on ESTIMATE algorithm were further screened using Weighted Gene Co-expression Network Analysis (WGCNA) and network topology analysis. Gene functions were mined through enrichment analysis, followed by exploration of the correlation between these genes and immune checkpoint genes. Finally, survival analysis was applied to search for genes with significant association with overall survival and external database was employed for further validation.\nRESULTS: The immune-related scores based on ESTIMATE algorithm was closely associated with other categories of scores, the HPV infection status, prognosis and the mutation levels of multiple CSCC-related genes (HLA and TP53). Eighteen new representative immune microenvironment-related genes were finally screened closely associated with patient prognosis and were further validated by the independent dataset GSE44001.\nCONCLUSION: Our present study suggested that the immune-related scores based on ESTIMATE algorithm can help to screen out novel immune-related diagnostic indicators, therapeutic targets and prognostic predictors in CSCC.", "claims": [{"id": "claim-method", "statement": "Spearman’s correlation coefficient was used to calculate the correlations among these scoring systems (shown in Fig. 2).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32904067", "locator": "sections[9] \"The immune-related scores based on ESTIMATE algorithm is the most suitable immune scoring method for CSCC\" paragraph 2", "quote": "Spearman’s correlation coefficient was used to calculate the correlations among these scoring systems (shown in Fig. 2)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The following information was supplied regarding data availability:\nThe raw measurements are available as Supplemental Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32904067", "locator": "sections[25] \"Data Availability Statement\" paragraphs 1-3", "quote": "The following information was supplied regarding data availability:\nThe raw measurements are available as Supplemental Files."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:32904067"], "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:GSE44001"], "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:GSE44001"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:32904067", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.9627", "sha256": 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