Uncovering potential molecular biomarkers for cancer-associated secondary lymphedema through integrated analyses of RNA-sequencing, machine learning, and clinical data
Dong H., Miao J., Liu Z., Sun Y., Li P., Xia S.
Prospective Study with a reported sample of 40, published in Front Oncol (2026) — summary generated from the PubMed abstract.
Early human evidence such as case series or small samples is exploring possible benefits.
- Level A · Stronger Clinical Evidence
- Level B · Emerging clinical evidence with positive signals
- Level C · Early human research exploring benefits
- Level D · Scientific groundwork from lab and animal studies
- Emerging · Emerging topic under active research
This page is generated from the PubMed record. The Thai description is an automated summary of bibliographic fields and the abstract, not a full translation, and is not medical advice.
- Study type
- Prospective Study
- Journal
- Front Oncol (2026)
- Reported sample size
- 40
- Source database
- Europe PMC
- PMID
- 41768256
- PMCID
- PMC12945810
- DOI
- 10.3389/fonc.2026.1760040
Abstract (original English)
Background Cancer-associated secondary lymphedema (CASL) commonly occurs after tumor-related lymph node dissection and radiotherapy. Nevertheless, the mechanisms of CASL remain unclear, and there are no specific molecular markers for its diagnosis and treatment. Methods In this study, RNA sequencing was performed on adipose tissues from 10 normal controls and 40 patients with CASL. Differentially expressed genes were screened using two machine learning algorithms to identify potential molecular markers for CASL. Subsequently, seven machine learning algorithms were employed to develop predictive models based on the identified markers. The contribution of each feature to the predictive outcomes was interpreted using Shapley additive explanation (SHAP). Immune cell infiltration was profiled through CIBERSORT and MCP-counter algorithms, and single-cell RNA sequencing (scRNA-seq) data were integrated to explore interactions between characteristic genes and immune cell subpopulations. Furthermore, associations between characteristic genes and clinical parameters were also assessed. Results IL2RG, HOXD10, and TSPAN1 were identified as potential biomarkers of CASL. Diagnostic models built on these three genes showed excellent performance. Functional enrichment analysis suggested that the dysregulation of cytokine-cytokine receptor interactions and immune pathways underlies the patholog
What this study does not prove
- • This study does not prove SVF is an approved treatment or a replacement for standard care.
Evidence level
Early human evidence such as case series or small samples is exploring possible benefits.
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