Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer

Overview

Journal Clin Exp Otorhinolaryngol

Date 2024 Jan 21

PMID 38246983

Authors

Weihua Wang

Suyu Ruan

Yuhang Xie

Shengjian Fang

Junxian Yang

Xueyan Li

Yu Zhang

Affiliations

Soon will be listed here.

Abstract

Objectives: The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medicine 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 inflammatory cytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8 mRNA 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 Genome Atlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survival rates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGA database was conducted. Pathomics features were extracted from hematoxylin and eosin-stained images, and then the 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. The logistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in the training set and demonstrated strong performance in the testing set. Calibration plots showed that the probability of high gene expression predicted by the pathomics model was in good agreement with actual observations, suggesting the model's high clinical applicability.

Conclusion: The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patients with HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNA damage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.

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References

Qi W, Zhang Q, Wang J . CXCL8 is a potential biomarker for predicting disease progression in gastric carcinoma. Transl Cancer Res. 2022; 9(2):1053-1062. PMC: 8797801. DOI: 10.21037/tcr.2019.12.52. View

Yuen K, Liu L, Gupta V, Madireddi S, Keerthivasan S, Li C . High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade. Nat Med. 2020; 26(5):693-698. PMC: 8286544. DOI: 10.1038/s41591-020-0860-1. View

Ma Y, Wang B, He P, Qi W, Xiang L, Maswikiti E . Coagulation- and fibrinolysis-related genes for predicting survival and immunotherapy efficacy in colorectal cancer. Front Immunol. 2022; 13:1023908. PMC: 9748552. DOI: 10.3389/fimmu.2022.1023908. View

Wang F, Zhang W, Chai Y, Wang H, Liu Z, He Y . Constrast-enhanced computed tomography radiomics predicts CD27 expression and clinical prognosis in head and neck squamous cell carcinoma. Front Immunol. 2022; 13:1015436. PMC: 9705340. DOI: 10.3389/fimmu.2022.1015436. View

Qu W, Tian M, Lu H, Zhou Y, Liu W, Tang Z . Development of a deep pathomics score for predicting hepatocellular carcinoma recurrence after liver transplantation. Hepatol Int. 2023; 17(4):927-941. PMC: 10386986. DOI: 10.1007/s12072-023-10511-2. View

Saednia K, Lagree A, Alera M, Fleshner L, Shiner A, Law E . Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment tumor biopsies. Sci Rep. 2022; 12(1):9690. PMC: 9188550. DOI: 10.1038/s41598-022-13917-4. View

Zeng H, Chen L, Zhang M, Luo Y, Ma X . Integration of histopathological images and multi-dimensional omics analyses predicts molecular features and prognosis in high-grade serous ovarian cancer. Gynecol Oncol. 2021; 163(1):171-180. DOI: 10.1016/j.ygyno.2021.07.015. View

Fousek K, Horn L, Palena C . Interleukin-8: A chemokine at the intersection of cancer plasticity, angiogenesis, and immune suppression. Pharmacol Ther. 2020; 219:107692. PMC: 8344087. DOI: 10.1016/j.pharmthera.2020.107692. View

Han Z, Li Y, Yang L, Cheng H, Liu X, Chen H . Roles of the CXCL8-CXCR1/2 Axis in the Tumor Microenvironment and Immunotherapy. Molecules. 2022; 27(1). PMC: 8746913. DOI: 10.3390/molecules27010137. View

10.

Sanmamed M, Perez-Gracia J, Schalper K, Fusco J, Gonzalez A, Rodriguez-Ruiz M . Changes in serum interleukin-8 (IL-8) levels reflect and predict response to anti-PD-1 treatment in melanoma and non-small-cell lung cancer patients. Ann Oncol. 2017; 28(8):1988-1995. PMC: 5834104. DOI: 10.1093/annonc/mdx190. View

11.

Liu Q, Li A, Tian Y, Wu J, Liu Y, Li T . The CXCL8-CXCR1/2 pathways in cancer. Cytokine Growth Factor Rev. 2016; 31:61-71. PMC: 6142815. DOI: 10.1016/j.cytogfr.2016.08.002. View

12.

Chen D, Fu M, Chi L, Lin L, Cheng J, Xue W . Prognostic and predictive value of a pathomics signature in gastric cancer. Nat Commun. 2022; 13(1):6903. PMC: 9653436. DOI: 10.1038/s41467-022-34703-w. View

13.

Xiao R, An Y, Ye W, Derakhshan A, Cheng H, Yang X . Dual Antagonist of cIAP/XIAP ASTX660 Sensitizes HPV and HPV Head and Neck Cancers to TNFα, TRAIL, and Radiation Therapy. Clin Cancer Res. 2019; 25(21):6463-6474. PMC: 6825532. DOI: 10.1158/1078-0432.CCR-18-3802. View

14.

Lukaszewicz-Zajac M, Paczek S, Muszynski P, Kozlowski M, Mroczko B . Comparison between clinical significance of serum CXCL-8 and classical tumor markers in oesophageal cancer (OC) patients. Clin Exp Med. 2019; 19(2):191-199. PMC: 6469833. DOI: 10.1007/s10238-019-00548-9. View

15.

Chen E, Qin X, Peng K, Xu X, Li W, Cheng X . Identification of Potential Therapeutic Targets Among CXC Chemokines in Breast Tumor Microenvironment Using Integrative Bioinformatics Analysis. Cell Physiol Biochem. 2018; 45(5):1731-1746. DOI: 10.1159/000487782. View

16.

Chen X, Lei H, Cheng Y, Fang S, Sun W, Zhang X . CXCL8, MMP12, and MMP13 are common biomarkers of periodontitis and oral squamous cell carcinoma. Oral Dis. 2022; 30(2):390-407. DOI: 10.1111/odi.14419. View

17.

David J, Dominguez C, Hamilton D, Palena C . The IL-8/IL-8R Axis: A Double Agent in Tumor Immune Resistance. Vaccines (Basel). 2016; 4(3). PMC: 5041016. DOI: 10.3390/vaccines4030022. View

18.

Classe M, Lerousseau M, Scoazec J, Deutsch E . Perspectives in pathomics in head and neck cancer. Curr Opin Oncol. 2021; 33(3):175-183. DOI: 10.1097/CCO.0000000000000731. View

19.

Huang Y, Wei L, Hu Y, Shao N, Lin Y, He S . Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer. Front Oncol. 2021; 11:706733. PMC: 8416497. DOI: 10.3389/fonc.2021.706733. View

20.

Wang R, Dai W, Gong J, Huang M, Hu T, Li H . Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients. J Hematol Oncol. 2022; 15(1):11. PMC: 8785554. DOI: 10.1186/s13045-022-01225-3. View