Dataset Information

A novel multiplex detection array revealed systemic complement activation in oral squamous cell carcinoma.

ABSTRACT: Oral squamous cell carcinoma (OSCC) is one of the most common tumors within the oral cavity. Early diagnosis and prognosis tools are urgently needed. This study aimed to investigate the activation of the complement system in OSCC patients as potential biomarker. Therefore, an innovative complement activation array was developed. Characterized antibodies detecting the complement activation specific epitopes C3a, C5a and sC5b-9 along with control antibodies were implemented into a suspension bead array. Human serum from a healthy (n = 46) and OSCC patient (n = 57) cohort were used to investigate the role of complement activation in oral tumor progression. The novel multiplex assay detected C3a, C5a and sC5b-9 from a minimal sample volume of human tears, aqueous humor and blood samples. Limits of detection were 0.04 ng/mL for C3a, 0.03 ng/mL for C5a and 18.9 ng/mL for sC5b-9, respectively. Biological cut-off levels guaranteed specific detections from serum. The mean serum concentration of a healthy control cohort was 680 ng/mL C3a, 70 ng/mL C5a and 2247 ng/mL sC5b-9, respectively. The assay showed an intra-assay precision of 2.9-6.4% and an inter-assay precision of 9.2-18.2%. Increased systemic C5a (p < 0.0001) and sC5b-9 (p = 0.01) concentrations in OSCC patients were determined using the validated multiplex complement assay. Higher C5a concentrations correlated with tumor differentiation and OSCC extension state. Systemic sC5b-9 determination provided a novel biomarker for infiltrating tumor growth and C3a levels were associated with local tumor spreading. Our study suggests that systemic complement activation levels in OSCC patients may be useful to assess disease progression.

SUBMITTER: Gallenkamp J

PROVIDER: S-EPMC5790441 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

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A novel multiplex detection array revealed systemic complement activation in oral squamous cell carcinoma.

Gallenkamp Juliane J Spanier Gerrit G Wörle Elisabeth E Englbrecht Markus M Kirschfink Michael M Greslechner Roman R Braun Regine R Schäfer Nicole N Bauer Richard J RJ Pauly Diana D

Oncotarget 20171206 3

Oral squamous cell carcinoma (OSCC) is one of the most common tumors within the oral cavity. Early diagnosis and prognosis tools are urgently needed. This study aimed to investigate the activation of the complement system in OSCC patients as potential biomarker. Therefore, an innovative complement activation array was developed. Characterized antibodies detecting the complement activation specific epitopes C3a, C5a and sC5b-9 along with control antibodies were implemented into a suspension bead ...[more]

PMID: 29423024

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Project description:We designed a study to investigate genetic relationships between primary tumors of oral squamous cell carcinoma (OSCC) and their lymph node metastases, and to identify genomic copy number aberrations (CNAs) related to lymph node metastasis. For this purpose, we collected a total of 42 tumor samples from 25 patients and analyzed their genomic profiles by array-based comparative genomic hybridization. We then compared the genetic profiles of metastatic primary tumors (MPTs) with their paired lymph node metastases (LNMs), and also those of LNMs with non-metastatic primary tumors (NMPTs). Firstly, we found that although there were some distinctive differences in the patterns of genomic profiles between MPTs and their paired LNMs, the paired samples shared similar genomic aberration patterns in each case. Unsupervised hierarchical clustering analysis grouped together 12 of the 15 MPT-LNM pairs. Furthermore, similarity scores between paired samples were significantly higher than those between non-paired samples. These results suggested that MPTs and their paired LNMs are composed predominantly of genetically clonal tumor cells, while minor populations with different CNAs may also exist in metastatic OSCCs. Secondly, to identify CNAs related to lymph node metastasis, we compared CNAs between grouped samples of MPTs and LNMs, but were unable to find any CNAs that were more common in LNMs. Finally, we hypothesized that subpopulations carrying metastasis-related CNAs might be present in both the MPT and LNM. Accordingly, we compared CNAs between NMPTs and LNMs, and found that gains of 7p, 8q and 17q were more common in the latter than in the former, suggesting that these CNAs may be involved in lymph node metastasis of OSCC. In conclusion, our data suggest that in OSCCs showing metastasis, the primary and metastatic tumors share similar genomic profiles, and that cells in the primary tumor may tend to metastasize after acquiring metastasis-associated CNAs.

Project description:IntroductionOral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model's objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization.MethodsThe research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies.ResultsThe customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model's efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC.DiscussionThis research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model's ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC.

Dataset Information

A novel multiplex detection array revealed systemic complement activation in oral squamous cell carcinoma.

Publications

A novel multiplex detection array revealed systemic complement activation in oral squamous cell carcinoma.

Similar Datasets

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