Integrated Proteomics Based on 2D Gel Electrophoresis and Mass Spectrometry with Validations: Identification of a Biomarker Compendium for Oral Submucous Fibrosis—An Indian Study

Antifibrotic effect of silymarin on arecoline-induced fibrosis in primary human buccal fibroblasts: an in silico and in vitro analysis

BACKGROUND

This study aimed to assess silymarin's anticancer and antifibrotic potential through in silico analysis and investigate its impact on in vitro arecoline-induced fibrosis in primary human buccal fibroblasts (HBF).

METHODS & RESULTS

The study utilized iGEMDOCK for molecular docking, evaluating nine bioflavonoids, and identified silymarin and baicalein as the top two compounds with the highest target affinity, followed by subsequent validation through a 100ns Molecular Dynamic Simulation demonstrating silymarin's stable behavior with Transforming Growth Factor Beta. HBF cell lines were developed from tissue samples obtained from patients undergoing third molar extraction. Arecoline, a known etiological factor in oral submucous fibrosis (OSMF), was employed to induce fibrogenesis in these HBFs. The inhibitory concentration (IC50) of arecoline was determined using the MTT assay, revealing dose-dependent cytotoxicity of HBFs to arecoline, with notable cytotoxicity observed at concentrations exceeding 50µM. Subsequently, the cytotoxicity of silymarin was assessed at 24 and 72 h, spanning concentrations from 5µM to 200µM, and an IC50 value of 143µM was determined. Real-time polymerase chain reaction (qPCR) was used to analyze the significant downregulation of key markers including collagen, epithelial-mesenchymal transition (EMT), stem cell, hypoxia, angiogenesis and stress markers in silymarin-treated arecoline-induced primary buccal fibroblast cells.

CONCLUSION

Silymarin effectively inhibited fibroblast proliferation and downregulated genes associated with cancer progression and EMT pathway, both of which are implicated in malignant transformation. To our knowledge, this study represents the first exploration of silymarin's potential as a novel therapeutic agent in an in vitro model of OSMF.

Clinicopathological Significance of Cancer Stem Cell Markers (OCT-3/4 and SOX-2) in Oral Submucous Fibrosis and Oral Squamous Cell Carcinoma

Oral submucous fibrosis (OSMF) is highly prevalent in South East Asia with higher rates of malignant transformation in Indian subcontinent. Numerous biomarkers are now being studied to predict disease prognosis and detect malignant alterations at an early stage. Patients with clinically and biopsy-proven oral submucous fibrosis and oral squamous cell carcinoma were included in the study as the experimental group, while patients without a tobacco or betel nut habit who had their third molars surgically removed were included as the healthy control group. For the immunohistochemistry (IHC) investigation, 5-μm slices from formalin-fixed, paraffin-embedded tissue blocks (FFPE) were obtained. Fresh tissues (n = 45) from all three groups were collected and gene expression was studied using relative quantitation-based qPCR. The protein expression of octamer-binding transcription factor 3/4 (OCT 3/4) and sex-determining region Y-box 2 (SOX 2) was evaluated in the experimental group and compared with healthy controls. The IHC results showed a significant correlation with the expression of OCT 3/4 (p value = 0.000; χ2 = 20.244) and SOX 2 (p value = 0.006; χ2 = 10.101) among OSCC and OSMF patients in comparison to healthy controls. Both OCT 3/4 and SOX 2 showed overexpression of four-fold and three-fold in OSMF when compared to OSCC and healthy controls, respectively. This study shows the significant importance of cancer stem cell markers OCT 3/4 and SOX 2 to assess the disease prognosis in OSMF.

IN SILICO STUDY OF NOVEL SULFONAMIDE DERIVATIVES BEARING A 1, 2, 4-TRIAZOLE MOIETY ACT AS CARBONIC ANHYDRASE INHIBITORS WITH PROMISING ANTI-CANCER ACTIVITY

OBJECTIVE

Aim: To evaluate the theoretical binding affinities of four synthetic compounds that target the carbonic anhydrase IX enzyme in solid tumors.

PATIENTS AND METHODS

Materials and Methods: To accurately depict the molecular structure, we utilized the Chem Draw Professional 12.0 program. We downloaded the carbonic anhydrase IX enzyme (29.25 KDa) (PDB code: 4YWP) from the Protein Data Bank into the Molecular Operating Environment software. Then, the S-score and rmsd were calculated for the proposed compounds.

RESULTS

Results: The theoretically synthesized compounds demonstrated good binding affinities with the receptor active pockets Sa, Sb, and Sd, with S-scores of -7.6491, -8.3789, and -8.3218, respectively. Substitutions improve compound orientation. The substituted triazoles ring increases flexibility and receptor interaction. In addition, the benzyl chloride derivatives play an important role in the interaction, with varying effects dependent on the groups substituted at position 4 of the benzene ring.

CONCLUSION

Conclusions: The synthesized compounds Sb with para Br substitution (S-score = -8.37) and Sd with para Cl substitution (S-score = -8.32) are considered the best ones as they exhibit a high affinity for the receptor.

Identification of EGFR as a Biomarker in Saliva and Buccal Cells from Oral Submucous Fibrosis Patients—A Baseline Study

Oral Submucous Fibrosis (OSMF) is a chronic debilitating disease more frequently encountered in the South-East Asian population. This disease represents a public health priority as it is grouped within oral potentially malignant disorders, with malignant transformation rates of around 7–19%. Hence, early identification of high-risk OSMF patients is of the utmost importance to prevent malignant transformation. Among various biomarkers, EGFR overexpression has an unfavorable clinical outcome, poor prognosis, and low survival rates in Oral Squamous Cell Carcinoma (OSCC). The current study aimed to evaluate the expression of EGFR in saliva and exfoliated buccal cells of OSMF. Immunoexpression of EGFR was observed in healthy controls (n = 11), OSCC (n = 106), and OPMD with dysplasia (n = 56), which showed significant expression with increasing grades of dysplasia and OSCC. EGFR expression was evaluated in saliva and exfoliated buccal cells of healthy controls (n = 15), OSMF (n = 24), and OSCC (n = 10) patients using ELISA, which revealed significant expression in OSMF and OSCC. Validation studies were also performed using real-time PCR (RT-PCR) to compare gene expression in healthy controls (n = 9), OSMF (n = 9), and OSCC (n = 25), which showed significant 18-fold upregulation in OSCC and three-fold upregulation in OSMF when compared to healthy controls. Hence, saliva and exfoliated buccal cells could be considered as potential non-invasive diagnostic samples for the evaluation of high-risk patients of OSMF using EGFR as a biomarker.

Synthetic Data Generation for the Development of 2D Gel Electrophoresis Protein Spot Models

Two-dimensional electrophoresis gels (2DE, 2DEG) are the result of the procedure of separating, based on two molecular properties, a protein mixture on gel. Separated similar proteins concentrate in groups, and these groups appear as dark spots in the captured gel image. Gel images are analyzed to detect distinct spots and determine their peak intensity, background, integrated intensity, and other attributes of interest. One of the approaches to parameterizing the protein spots is spot modeling. Spot parameters of interest are obtained after the spot is approximated by a mathematical model. The development of the modeling algorithm requires a rich, diverse, representative dataset. The primary goal of this research is to develop a method for generating a synthetic protein spot dataset that can be used to develop 2DEG image analysis algorithms. The secondary objective is to evaluate the usefulness of the created dataset by developing a neural-network-based protein spot reconstruction algorithm that provides parameterization and denoising functionalities. In this research, a spot modeling algorithm based on autoencoders is developed using only the created synthetic dataset. The algorithm is evaluated on real and synthetic data. Evaluation results show that the created synthetic dataset is effective for the development of protein spot models. The developed algorithm outperformed all baseline algorithms in all experimental cases.