Browse
Submit Data
Databases
API
Help

Dataset Information

8 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Prediction of oral squamous cell carcinoma based on machine learning of breath samples: a prospective controlled study.

ABSTRACT:

Background

The aim of this study was to evaluate the possibility of breath testing as a method of cancer detection in patients with oral squamous cell carcinoma (OSCC).

Methods

Breath analysis was performed in 35 OSCC patients prior to surgery. In 22 patients, a subsequent breath test was carried out after surgery. Fifty healthy subjects were evaluated in the control group. Breath sampling was standardized regarding location and patient preparation. All analyses were performed using gas chromatography coupled with ion mobility spectrometry and machine learning.

Results

Differences in imaging as well as in pre- and postoperative findings of OSCC patients and healthy participants were observed. Specific volatile organic compound signatures were found in OSCC patients. Samples from patients and healthy individuals could be correctly assigned using machine learning with an average accuracy of 86-90%.

Conclusions

Breath analysis to determine OSCC in patients is promising, and the identification of patterns and the implementation of machine learning require further assessment and optimization. Larger prospective studies are required to use the full potential of machine learning to identify disease signatures in breath volatiles.

SUBMITTER: Mentel S

PROVIDER: S-EPMC8496028 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.

Project description:BackgroundRecently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets.MethodsRNA sequencing data from SCC tissues from various sites, including oral, non-oral head and neck, oesophageal, and cervical regions, were downloaded from The Cancer Genome Atlas (TCGA). The feature genes were extracted through a convolutional neural network (CNN) and machine learning, and the performance of each analysis was compared.ResultsThe ability of the machine learning analysis to classify OSCC tumours was excellent. However, the tool exhibited poorer performance in discriminating histopathologically dissimilar cancers derived from the same type of tissue than in differentiating cancers of the same histopathologic type with different tissue origins, revealing that the differential gene expression pattern is a more important factor than the histopathologic features for differentiating cancer types.ConclusionThe CNN-based diagnostic model and the visualisation methods using RNA sequencing data were useful for correctly categorising OSCC. The analysis showed differentially expressed genes in multiwise comparisons of various types of SCCs, such as KCNA10, FOSL2, and PRDM16, and extracted leader genes from pairwise comparisons were FGF20, DLC1, and ZNF705D.

| S-EPMC8164276 | biostudies-literature

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma

Project description:Background Tongue squamous cell carcinoma (TSCC) is the most common subtype of oral cavity squamous cell carcinoma (OCSCC), and it also has the worst prognosis. It is crucial to find an effective way to solve the challenges in diagnosis and prognosis prediction for TSCC. Machine learning (ML) has been widely used in medical research and has shown good performance. It can be used for feature extraction, feature selection, model construction, etc. Radiomics and deep learning (DL), the new components of ML, have also been utilized to explore the relationship between image features and diseases. The current study aimed to highlight the importance of ML as a potential method for addressing the challenges in diagnosis and prognosis prediction of TSCC by reviewing studies on ML in TSCC. Methods The studies on ML in TSCC in PubMed, Scopus, Web of Science, and China National Knowledge Infrastructure published between the dates of inception of these databases and April 30, 2022, were reviewed. Key Content and Findings ML (including radiomics and DL) which was used in diagnosis and prognosis prediction for TSCC, has shown promising performance. Conclusions Despite its limitations, ML is still a potential approach that can help to deal with the challenges in diagnosis and prognosis prediction for TSCC. Nevertheless, more efforts are needed to enhance the usefulness of ML in this field.

| S-EPMC9834582 | biostudies-literature

The Assessment of Serum Cytokines in Oral Squamous Cell Carcinoma Patients: An Observational Prospective Controlled Study.

Project description:BackgroundThe oral squamous cell carcinoma (OSCC) tumor microenvironment (TME) is a complex interweb of cells and mediators balancing carcinogenesis, inflammation, and the immune response. However, cytokines are not only secreted within the TME but also released by a variety of other cells that do not comprise the TME; therefore, a thorough assessment of humoral changes in OSCC should include the measurement of serum cytokines.MethodsWe assessed the role of various serum cytokines in the evolution of OSCC, before and after treatment, versus a control group. We measured the serum concentrations of MIP-1α, IL-1β, IL-4, IL-6, IL-8, IL-10, and TNF-α.ResultsSignificantly higher values (p < 0.01) were noted for IL-1β, IL-6, IL-8, IL-10, and TNF-α in the OSCC group before treatment (n = 13) compared with the control group (n = 14), and the increased concentrations persisted after treatment (n = 11). Furthermore, the variations in the values of MIP-1α, IL-1β, IL-10, and TNF-α are correlated both before and after treatment (p < 0.01). In the pretherapeutic group, IL-6 and IL-8 concentrations also correlate with IL-1β and IL-10 serum levels (p < 0.01), while in the posttherapeutic group, IL-4 varies with MIP-1α and TNF-α (p < 0.01).ConclusionIn OSCC patients, serum cytokine levels are significantly higher compared with control, but they are not significantly altered by treatment, therefore implying that they are also influenced by systemic factors. The interactions between all involved cytokines and the various pathways they regulate warrant further studies to clarify their definitive roles.

| S-EPMC9503270 | biostudies-literature

Study of morphological and textural features for classification of oral squamous cell carcinoma by traditional machine learning techniques.

Project description:BackgroundOral squamous cell carcinoma (OSCC) is the most prevalent form of oral cancer. Very few researches have been carried out for the automatic diagnosis of OSCC using artificial intelligence techniques. Though biopsy is the ultimate test for cancer diagnosis, analyzing a biopsy report is a very much challenging task. To develop computer-assisted software that will diagnose cancerous cells automatically is very important and also a major need of the hour.AimTo identify OSCC based on morphological and textural features of hand-cropped cell nuclei by traditional machine learning methods.MethodsIn this study, a structure for semi-automated detection and classification of oral cancer from microscopic biopsy images of OSCC, using clinically significant and biologically interpretable morphological and textural features, are examined and proposed. Forty biopsy slides were used for the study from which a total of 452 hand-cropped cell nuclei has been considered for morphological and textural feature extraction and further analysis. After making a comparative analysis of commonly used methods in the segmentation technique, a combined technique is proposed. Our proposed methodology achieves the best segmentation of the nuclei. Henceforth the features extracted were fed into five classifiers, support vector machine, logistic regression, linear discriminant, k-nearest neighbors and decision tree classifier. Classifiers were also analyzed by training time. Another contribution of the study is a large indigenous cell level dataset of OSCC biopsy images.ResultsWe achieved 99.78% accuracy applying decision tree classifier in classifying OSCC using morphological and textural features.ConclusionIt is found that both morphological and textural features play a very important role in OSCC diagnosis. It is hoped that this type of framework will help the clinicians/pathologists in OSCC diagnosis.

| S-EPMC7941561 | biostudies-literature

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

2013-01-01 | GSE29210 | GEO

Prediction of biomarkers of oral squamous cell carcinoma using microarray technology.

Project description:Microarray data is used to screen the genes of oral squamous cell carcinoma (OSCC). Microarray data of OSCC and normal tissues were downloaded from GEO database and analyzed with Benjamini-Hochberg (BH) method. Differentially expressed genes (DEGs) were then uploaded on DAVID database to process enrichment analysis. Target genes were finally chosen for verification experiment in vitro and in vivo. 78 DEGs were selected from 54676 genes, including 46 up- and 32 down- regulation. GO term showed that these genes were related to epidermal growth (biological processes), extracellular region (cellular components) and cytokines activity (molecular function). Protein network interaction demonstrated that OSCC was closely allied to the five key genes including CXCL10, IFI6, IFI27, ADAMTS2 and COL5A1, which was consistent with the RT-PCR data. High-expressed gene CXCL10 was chosen for further cell experiment, and the results indicated that CXCL10 can promote the proliferation, migration and invasion of normal cells and inhibited the cancer cells after si-RNA transfection. Moreover, it has been proven that CXCL10 was possibly related to the occurrence and development of OSCC. Understanding the regulation of OSCC expression will shed light on the screening of cancer biomarker.

| S-EPMC5296717 | biostudies-literature

Affymetrix SNP array data for oral squamous cell carcinoma and oral dysplasia samples

Project description:The development of oral squamous cell carcinoma (OSCC) is a multistep process requiring the accumulation of genetic alterations. Oral carcinogenesis is a multifactorial process involving numerous genetic changes that affect the activity of oncogenes, tumor suppressor genes and other classes of disease-related genes.Therefore, to identify the responsive genes for progression of oral dysplasia or OSCC, we here performed CGH analysis to DNA from oral dysplasia and OSCC by microdissection

2013-10-22 | GSE51265 | GEO

Affymetrix SNP array data for oral squamous cell carcinoma samples

Project description:The development of oral squamous cell carcinoma (OSCC) is a multistep process requiring the accumulation of genetic alterations. To identify genes responsible for OSCC development, we performed high-density single-nucleotide polymorphism array analysis and genome-wide gene expression profiling on OSCC tumors. These analyses identified "absent in melanoma (AIM2)" and "interferon-inducible gene 16 (IFI16)," mapped to the hematopoietic IFN-inducible nuclear proteins with 200-amino acid repeat (HIN-200) gene cluster in the amplified region of chromosome 1q23, with overexpression in OSCCs. AIM2 and IFI16 are cytoplasmic double-stranded DNA sensors for innate immunity and act as tumor suppressors in several human cancers. Knockdown of AIM2 or IFI16 in OSCC cells resulted in the suppression of cell growth and the induction of apoptosis, accompanied by the downregulation of NF-κB activation. Because all of the OSCC cell lines had impairment of p53 activity, wild-type p53 was introduced in p53-deficient OSCC cells, and as a result, the expression of wild-type p53 suppressed cell growth and induced apoptosis via suppression of NF-κB activity. Finally, the coexpression of AIM2 and IFI16 significantly enhanced cell growth in p53-deficient cells; in contrast, the expression of AIM2 and/or IFI16 in cells bearing wild-type p53 suppressed cell growth. Moreover, AIM2 and IFI16 synergistically enhanced NF-κB signaling in p53-deficient cells. Thus, expression of AIM2 and IFI16 may have oncogenic functions in OSCC cells inactivating p53 system.

2011-12-30 | GSE34507 | GEO

Novel DNA methylation targets in oral rinse samples predict survival of patients with oral squamous cell carcinoma.

Project description:The objective of this study was to identify novel survival-associated biomarkers in oral rinse samples collected from patients with oral squamous cell carcinoma (OSCC).We screened for putative survival-associated markers using publicly available methylation array data from 88 OSCC tumors. Cox models were then fit to methylation array data restricted to these putative loci in oral rinse samples of 82 OSCC patients from greater Boston. Pyrosequencing assays were designed for each locus that replicated in the oral rinse samples and applied to a validation set of oral rinse samples from another 61 OSCC patients.We identified 7 survival-associated methylation markers in oral rinse samples from OSCC patients, and have validated one, located in the body of GABBR1, by pyrosequencing.The 7 CpG loci identified through this study represent novel prognostic biomarkers for patients with OSCC that can be detected using a non-invasive oral rinse collection technique.

| S-EPMC4254027 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data