Dataset Information

Detection and comparison of microsatellite marker D9S1747 with clinical stages and grades of oral squamous cell carcinoma.

ABSTRACT: Background:One of the main characteristics of oral squamous cell carcinoma (OSCC) is genetic alteration in specific target regions. Allelic imbalance in tumor suppressor genes is the key event in OSCC which is associated with loss of heterozygosity mostly on chromosome 9p21 locus which includes p16 marker. p16 (D9S1747) is a microsatellite marker which detects early changes in OSCC. To redefine more clearly the role of D9S1747 (p16 microsatellite marker) and its expression in OSCC, the study was designed with the aim to check the detection of D9S1747 in OSCC and to compare the same with histopathological grades and tumor node metastasis staging. Materials and Methods:Forty cases of paraffin-embedded tissue section which was histologically confirmed as OSCC and 10 cases of normal tissues were retrieved from the archives. DNA was extracted from the tissue sections and subjected for polymerase chain reaction to detect p16 microsatellite marker D9S1747. Data were analyzed using Chi-square test and Fisher's exact test. Results:Twenty-seven cases (67.5%) showed p16 microsatellite marker positivity for OSCC. It was observed that 44.4%, 51.9% and 3.7% p16 microsatellite markers were positive in Stage 1, Stage 2 and Stage 4 OSCC cases, respectively. p16 microsatellite marker positivity was found in 77.8%, 22.2% and 0% for well-differentiated, moderately differentiated and poorly differentiated OSCC cases, respectively. Conclusion:The observations of the present study revealed D9S1747 marker as an early event in OSCC, and this can be used as a prognostic marker.

SUBMITTER: Babji D

PROVIDER: S-EPMC6503782 | biostudies-literature | 2019 Jan-Apr

REPOSITORIES: biostudies-literature

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Detection and comparison of microsatellite marker D9S1747 with clinical stages and grades of oral squamous cell carcinoma.

Babji Deepa D Nayak Ramakant R Bhat Kishore K Kotrashetti Vijayalakshmi V Babaji Harsha H Kugaji Manohar S MS

Journal of oral and maxillofacial pathology : JOMFP 20190101 1

<h4>Background</h4>One of the main characteristics of oral squamous cell carcinoma (OSCC) is genetic alteration in specific target regions. Allelic imbalance in tumor suppressor genes is the key event in OSCC which is associated with loss of heterozygosity mostly on chromosome 9p21 locus which includes p16 marker. p16 (D9S1747) is a microsatellite marker which detects early changes in OSCC. To redefine more clearly the role of D9S1747 (p16 microsatellite marker) and its expression in OSCC, the s ...[more]

PMID: 31110442

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Project description:Oral squamous cell carcinoma (OSCC) is most common oral cancer with multifactorial etiology. Surgical therapy is treatment of choice but known to have recurrence. The main reason for recurrence is associated with surgical margins which need to be tumor free. Changes at genetic level cannot be ascertained only through routine light microscopy in surgical margins, even though they are tumor free. Detection of early marker like p16 can help in predicting the risk of recurrence. Hence study aimed to detect p16 microsatellite marker (D9s1747) in surgical margins of oral squamous cell carcinoma and compare the same with p16 marker through immunohistochemistry. Total of 40 paraffin embedded tissue samples diagnosed and surgically treated cases of OSCC were included. From each sample one tumor proper and one surgical margin was obtained. From paraffin embedded tissue sample 2 sections of 4 µm thick was obtained from tumor proper and tumor margin. One section was stained with hematoxylin and eosin and other section was stained immunohistochemically using p16 antibody. DNA extraction was done for tumor proper and surgical margin tissue and PCR analysis was carried for p16 microsatellite marker (D91747). Out of 40 cases 37 cases showed positivity in tumor proper for p16 with IHC. Out of 37 cases 23 cases showed positivity for both tumor proper and surgical margin. There were 3 cases negative for tumor proper. Out of these 3 cases, 1 (33.3%) case was positive for surgical margin. Out of 40 cases 27 cases showed positivity for tumor proper with p16 microsatellite marker. Out of 27 cases 16 cases showed positivity for both tumor proper and surgical margin. There were 13 cases negative for tumor proper. However there were 8 (61.5%) cases negative which were in tumor proper but showed positivity for surgical margin. Other 5 cases were negative in both tumor proper and surgical margin. Our study reveals that surgical margins of OSCC exhibit alteration in p16 markers both by IHC and PCR techniques. p16 and p16 microsatellite marker detection in margins indicates field change. Further studies with larger sample size comparing expression with clinical and histological parameter and follow up has to be done to substantiate our findings.

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

Detection and comparison of microsatellite marker D9S1747 with clinical stages and grades of oral squamous cell carcinoma.

Publications

Detection and comparison of microsatellite marker D9S1747 with clinical stages and grades of oral squamous cell carcinoma.

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