Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer
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Objectives. The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medi-cine in patients with head and neck squamous cell carcinoma (HNSCC) cannot be overstated. Recently, pathomics,which relies on quantitative analysis of medical imaging, has come to the forefront. CXCL8, an essential inflammatorycytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8mRNA expression and pathomics features and aimed to explore the biological underpinnings of CXCL8. Methods. Clinical information and transcripts per million mRNA sequencing data were obtained from The Cancer GenomeAtlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survivalrates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGAdatabase was conducted. Pathomics features were extracted from hematoxylin and eosin–stained images, and thenthe minimum redundancy maximum relevance, with recursive feature elimination (mRMR-RFE) method was applied,followed by screening with the logistic regression algorithm. Results. Kaplan-Meier curves indicated that high expression of CXCL8 was significantly associated with decreased OS. Thelogistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in thetraining set and demonstrated strong performance in the testing set. Calibration plots showed that the probability ofhigh gene expression predicted by the pathomics model was in good agreement with actual observations, suggestingthe model’s high clinical applicability. Conclusion. The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patientswith HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNAdamage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.
Abstract
Objectives. The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medi-cine in patients with head and neck squamous cell carcinoma (HNSCC) cannot be overstated. Recently, pathomics,which relies on quantitative analysis of medical imaging, has come to the forefront. CXCL8, an essential inflammatorycytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8mRNA expression and pathomics features and aimed to explore the biological underpinnings of CXCL8. Methods. Clinical information and transcripts per million mRNA sequencing data were obtained from The Cancer GenomeAtlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survivalrates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGAdatabase was conducted. Pathomics features were extracted from hematoxylin and eosin–stained images, and thenthe minimum redundancy maximum relevance, with recursive feature elimination (mRMR-RFE) method was applied,followed by screening with the logistic regression algorithm. Results. Kaplan-Meier curves indicated that high expression of CXCL8 was significantly associated with decreased OS. Thelogistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in thetraining set and demonstrated strong performance in the testing set. Calibration plots showed that the probability ofhigh gene expression predicted by the pathomics model was in good agreement with actual observations, suggestingthe model’s high clinical applicability. Conclusion. The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patientswith HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNAdamage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.
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