{
  "abstract": "Background\n\nProgrammed death-ligand 1 (PD-L1) expression and tumor mutational burden (TMB) are widely used immunotherapy biomarkers, yet their variability and determinants in lung squamous cell carcinoma (LUSC) remain incompletely characterized in real-world testing settings.Methods\n\nWe analyzed a retrospective real-world LUSC cohort with clinical PD-L1 immunohistochemistry and next-generation sequencing. PD-L1 tumor proportion score (TPS) was modeled as an ordered endpoint using three clinically used categories (< 1%, 1-49%, ≥ 50%) via proportional-odds ordinal regression; TMB was modeled as a continuous outcome after log transformation using multivariable linear regression. To address the practical question of whether imperfect real-world biomarker data can still support hypothesis generation, we additionally applied random forest and XGBoost as exploratory tools for prediction-oriented feature prioritization and evaluated uncertainty using repeated resampling and permutation-based perturbation analyses.Results\n\nRegression models provided an interpretable adjusted effect landscape but yielded limited statistically significant associations after adjustment. In contrast, tree-based models prioritized a small subset of clinicogenomic features with predictive information for PD-L1 TPS and TMB variability. Resampling and permutation analyses provided an internal assessment of uncertainty, rank stability, and sensitivity of these feature-prioritization signals.Conclusions\n\nIn small, heterogeneous real-world LUSC cohorts where conventional regression-based inference may provide limited resolution, machine learning-guided feature prioritization combined with resampling- and permutation-based uncertainty assessment may help identify candidate clinicogenomic signals associated with PD-L1 TPS category and TMB variability. These findings should be interpreted as hypothesis-generating and require validation in larger independent cohorts.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Cardiothoracic Surgery, the People's Liberation Army Joint Logistic, Support Force No 983rd Hospital, Tianjin, 300142, China. shanghongwei2018@126.com.",
    "Department of Cardiothoracic Surgery, the People's Liberation Army Joint Logistic, Support Force No 983rd Hospital, Tianjin, 300142, China.",
    "Department of Cardiothoracic Surgery, Dali Bai Autonomous Prefecture People's Hospital, Yunnan Dali, 671099, China.",
    "Department of Oncology, Huaihai Hospital Affiliated to Xuzhou Medical University, Jiangsu Xuzhou, 221004, China.",
    "University of Victoria, Victoria, Canada.",
    "Innovation Research Institute, Zhejiang Univerity of Technology, ShengZhou, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Hongwei Shang"
    },
    {
      "affs": [
        1
      ],
      "name": "Chao Li"
    },
    {
      "affs": [
        2
      ],
      "name": "Jundong Wang"
    },
    {
      "affs": [
        3
      ],
      "name": "Dongyun Xu"
    },
    {
      "affs": [
        4
      ],
      "name": "Kailun Bai"
    },
    {
      "affs": [
        5
      ],
      "name": "Shuiqing Zhou"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/d3ywp4frpsb51.bib",
  "cited_by": 0,
  "doi": "10.1186/s12890-026-04554-y",
  "fulltext_url": "https://europepmc.org/articles/PMC13721276",
  "id": "d3ywp4frpsb51",
  "issns": [
    "1471-2466"
  ],
  "landing_url": "https://europepmc.org/article/MED/42958183",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Research"
    ],
    [
      "openalex",
      "2730|Cancer Immunotherapy and Biomarkers"
    ]
  ],
  "openalex": "W7171921904",
  "pdf_url": "https://europepmc.org/articles/PMC13721276?pdf=render",
  "pmcid": "PMC13721276",
  "pmid": "42958183",
  "published": "2026-07-31",
  "publisher": "BMC",
  "ris": "https://research.draconitic.com/paper/d3ywp4frpsb51.ris",
  "sources": [
    {
      "first_seen": "2026-11-01T00:20:47Z",
      "native_id": "MED:42958183",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42958183"
    },
    {
      "first_seen": "2026-11-03T12:21:59Z",
      "native_id": "PMC13721276",
      "source": "pmc",
      "url": "https://doi.org/10.1186/s12890-026-04554-y"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2730|Cancer Immunotherapy and Biomarkers",
      "subfield": "oncology"
    }
  ],
  "title": "Machine learning-guided identification of determinants of PD-L1 expression and tumor mutational burden in lung squamous cell carcinoma.",
  "title_text": "Machine learning-guided identification of determinants of PD-L1 expression and tumor mutational burden in lung squamous cell carcinoma.",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:21:59Z",
  "url": "https://research.draconitic.com/paper/d3ywp4frpsb51",
  "venue": "BMC pulmonary medicine",
  "versions": []
}