Single-cell RNA sequencing and traditional RNA sequencing reveals the role of cancer-associated fibroblasts in oral squamous cell carcinoma cohort

Abstract

Chronic inflammation of the alveolar bones and connective tissues supporting teeth causes periodontal disease, one of the most prevalent infectious diseases in humans. It was previously reported that oral cancer was the sixth most common cancer in the world, followed by squamous cell carcinoma. Periodontal disease has been linked to an increased risk for oral cancer in some studies, and these studies have found a positive relationship between oral cancer and periodontal disease. In this work, we aimed to explore the potential correlation between oral squamous cell carcinoma (OSCC) and Periodontal disease. The single-cell RNA sequence analysis was applied to explore the genes that were closely associated with cancer-associated fibroblasts (CAFs). the head and neck squamous cell carcinoma. The Single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was applied to explore the scores of CAFs. Subsequently, the differentially expressed analysis was applied to explore the CAFs-related genes that play a key role in the OSCC cohort. The LASSO regression analysis and the COX regression analysis were applied to construct the CAFs-based periodontal disease-related risk model. In addition, the correlation analysis was used to explore the correlation between the risk model and clinical features, immune-related cells, and immune-related genes. By using the single-cell RNA sequence analysis, we successfully obtained the biomarkers for the CAFs. Finally, we successfully obtained a six-CAFs-related genes risk model. The ROC curve and survival analysis revealed that the risk model showed good predictive value in OSCC patients. Our analysis successfully provided a new direction for the treatment and prognosis of OSCC patients.

Analysis of Bladder Cancer Staging Prediction Using Deep Residual Neural Network, Radiomics, and RNA-Seq from High-Definition CT Images

“…AdaBoost is renowned for its capacity to convert a series of weak classifiers into a strong classifier, making it particularly suitable for our dataset where the predictive power of individual features might be modest [ 24 ]. Through iterative processes, this algorithm rectifies errors made by weak classifiers and modifies the weights of misclassified instances, thereby enhancing the model's capacity to generalize beyond the training data to new data [ 25 ].…”

Identification and Validation of a Prognostic Signature Derived from the Cancer Stem Cells for Oral Squamous Cell Carcinoma

“…For instance, Wang et al [4] obtained gene expression profiles from the GEO database and identified the prognostic signature of six genes in OSCC through bioinformatics analysis and RT-qPCR validation. Wu et al [29] constructed an OSCC-related risk model based on cancer-associated fibroblasts by scRNA-seq analysis and explored the potential correlation between OSCC and periodontal disease. However, current research lacks the investigation of potential mechanisms underlying the occurrence and progression of OSCC through the establishment of prognostic signatures based on CSCMGs.…”