Integrated Bioinformatics Analysis Reveals Diagnostic Biomarkers and Immune Cell Infiltration Characteristics of Solar Lentigines.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38230305", "kind": "science-record", "revision": 4, "content_hash": "sha256:b63a04f3f19a4c5783010817f7d7c33b5e7445cf8ac6ebf1792c44c7b8ea4f7d", "created_at": "2026-11-01T19:20:08Z", "updated_at": "2026-11-01T22:53:28Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated Bioinformatics Analysis Reveals Diagnostic Biomarkers and Immune Cell Infiltration Characteristics of Solar Lentigines.", "doi": "10.2147/CCID.S439655", "pmid": "38230305", "authors": ["Xin Yang", "Zhikuan Xia", "Yunlong Fan", "Yitong Xie", "Ge Ge", "Dexiu Lang", "Junhong Ao", "Danxia Yue", "Jiamin Wu", "Tong Chen", "Yuekun Zou", "Mingwang Zhang", "Rongya Yang"], "journal": "Clinical, cosmetic and investigational dermatology", "year": 2024, "abstract": "Background: Solar lentigines (SLs), serving as a prevalent characteristic of skin photoaging, present as cutaneous aberrant pigmentation. However, the underlying pathogenesis remains unclear and there is a dearth of reliable diagnostic biomarkers.\nObjective: The aim of this study was to identify diagnostic biomarkers for SLs and reveal its immunological features.\nMethods: In this study, gene expression profiling datasets (GSE192564 and GSE192565) of SLs were obtained from the GEO database. The GSE192564 was used as the training group for screening of differentially expressed genes (DEGs) and subsequent depth analysis. Gene set enrichment analysis (GSEA) was employed to explore the biological states associated with SLs. The weighted gene co-expression network analysis (WGCNA) was employed to identify the significant modules and hub genes. Then, the feature genes were further screened by the overlapping of hub genes and up-regulated differential genes. Subsequently, an artificial neural network was constructed for identifying SLs samples. The GSE192565 was used as the test group for validation of feature genes expression level and the model's classification performance. Furthermore, we conducted immune cell infiltration analysis to reveal the immune infiltration landscape of SLs.\nResults: The 9 feature genes were identified as diagnostic biomarkers for SLs in this study. And an artificial neural network based on diagnostic biomarkers was successfully constructed for identification of SLs. GSEA highlighted potential role of immune system in pathogenesis of SLs. SLs samples had a higher proportion of several immune cells, including activated CD8 T cell, dendritic cell, myeloid-derived suppressor cell and so on. And diagnostic biomarkers exhibited a strong relationship with the infiltration of most immune cells.\nConclusion: Our study identified diagnostic biomarkers for SLs and explored its immunological features, enhancing the comprehension of its pathogenesis.", "claims": [{"id": "claim-method", "statement": "The R package “limma” was employed to identify the DEGs between the control group and the SLs group.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38230305", "locator": "sections[4] \"Identification of the Differentially Expressed Genes (DEGs)\" paragraph 2", "quote": "The R package “limma” was employed to identify the DEGs between the control group and the SLs group."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data generated or analyzed during this study are included in this article and its Supplementary Material Files. Further enquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38230305", "locator": "sections[17] \"Data Sharing Statement\" paragraph 2", "quote": "All data generated or analyzed during this study are included in this article and its Supplementary Material Files. Further enquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 200 DEGs were identified in the training group, including 86 up-regulated genes and 114 down-regulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38230305", "locator": "sections[10] \"Identification of DEGs\" paragraph 2", "quote": "A total of 200 DEGs were identified in the training group, including 86 up-regulated genes and 114 down-regulated genes (shown in Figure 2; seen in Supplementary Table 1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A total of 200 DEGs were identified in the training group, including 86 up-regulated genes and 114 down-regulated genes.", "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:38230305"], "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:GSE192564"], "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:GSE192564"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38230305", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/CCID.S439655", "sha256": "sha256:386823e691b7efb39d6f1d602d3bca8848362d6a849fc56d5d485628b4081b45", "bytes": 21951, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/CCID.S439655", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10790640", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE192564", "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=GSE192564", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE192564", "version": null, "note": "All data generated or analyzed during this study are included in this article and its Supplementary Material Files. Further enquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data generated or analyzed during this study are included in this article and its Supplementary Material Files. 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Further enquiries can be directed to the corresponding author.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "All data generated or analyzed during this study are included in this article and its Supplementary Material Files. 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