Identification of Immune-Related Risk Signatures for the Prognostic Prediction in Oral Squamous Cell Carcinoma

Multimodal artificial intelligence-based pathogenomics improves survival prediction in oral squamous cell carcinoma

In this study, we aimed to develop a novel prognostic algorithm for oral squamous cell carcinoma (OSCC) using a combination of pathogenomics and AI-based techniques. We collected comprehensive clinical, genomic, and pathology data from a cohort of OSCC patients in the TCGA dataset and used machine learning and deep learning algorithms to identify relevant features that are predictive of survival outcomes. Our analyses included 406 OSCC patients. Initial analyses involved gene expression analyses, principal component analyses, gene enrichment analyses, and feature importance analyses. These insights were foundational for subsequent model development. Furthermore, we applied five machine learning/deep learning algorithms (Random Survival Forest, Gradient Boosting Survival Analysis, Cox PH, Fast Survival SVM, and DeepSurv) for survival prediction. Our initial analyses revealed relevant gene expression variations and biological pathways, laying the groundwork for robust feature selection in model building. The results showed that the multimodal model outperformed the unimodal models across all methods, with c-index values of 0.722 for RSF, 0.633 for GBSA, 0.625 for FastSVM, 0.633 for CoxPH, and 0.515 for DeepSurv. When considering only important features, the multimodal model continued to outperform the unimodal models, with c-index values of 0.834 for RSF, 0.747 for GBSA, 0.718 for FastSVM, 0.742 for CoxPH, and 0.635 for DeepSurv. Our results demonstrate the potential of pathogenomics and AI-based techniques in improving the accuracy of prognostic prediction in OSCC, which may ultimately aid in the development of personalized treatment strategies for patients with this devastating disease.

Oral squamous cell carcinoma gene patterns connected with RNA methylation for prognostic prediction

OBJECTIVES

To determine whether m6A/m1A/m5C/m7G/m6Am/Ψ-related genes influence the prognosis of a patient with oral squamous cell carcinoma.

MATERIALS AND METHODS

We investigated the changes in regulatory genes using publicly available data from The Cancer Genome Atlas. Consensus clustering by RNA methylation-related regulators was used to describe oral squamous cell carcinomas (OSCCs). Then, we developed the prediction model. The tumor microenvironment was investigated using ESTIMATE. Gene set enrichment analysis was used to determine whether pathways or cell types were enriched in different groups. The association between the model and immune-related risk scores was investigated using correlation analysis.

RESULTS

We found 22 gene signatures in this analysis and then developed a predictive model that reveals the genes that are highly connected to the overall survival of OSCC patients. The survival and death rates were substantially different in the two groups (high and low risk) classified by the risk scores. The validation cohort verified the phenotypic diversity and prognostic effects of these genes.

CONCLUSION

Our data reveal that immune cell infiltration, genetic mutation, and survival potential in OSCC patients are linked to m6A/m1A/m5C/m7G/m6Am/Ψ-related genes, and we constructed a dependable prognostic model for OSCC patients.

Unravelling the heterogeneity of oral squamous cell carcinoma by integrative analysis of single-cell and bulk transcriptome data

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy of the head and neck with rising global incidence. Despite advances in treatment modalities, OSCC prognosis remains diverse due to the complex molecular and cellular heterogeneity within tumours, as well as the heterogeneity in tumour microenvironment (TME). In this study, we utilized single-cell RNA sequencing (scRNA-seq) analysis to explore distinct subpopulations of tumour cells in OSCC tissues and their interaction with components in TME. We identified four major tumour cell subpopulations (C0, C1, C2 and C3) with unique molecular characteristics and functional features. Pathway enrichment analysis revealed that C0 primarily expressed genes involved in extracellular matrix interactions and C1 showed higher proliferation levels, suggesting that the two cell subpopulations exhibited tumour aggressiveness. Conversely, C2 and C3 displayed features associated with keratinization and cornified envelope formation. Accordingly, C0 and C1 subpopulations were associated with shorter overall and disease-free survival times, while C2 and C3 were weakly correlated with longer survival. Genomic analysis showed that C1 demonstrated a positive correlation with tumour mutation burden. Furthermore, C0 exhibited resistant to cisplatin treatment, while C1 showed more sensitive to cisplatin treatment, indicating that C0 might exhibit more aggressive compared to C1. Additionally, C0 had a higher level of communication with fibroblasts and endothelial cells in TME via integrin-MAPK signalling, suggesting that the function of C0 was maintained by that pathway. In summary, this study provided critical insights into the molecular and cellular heterogeneity of OSCC, with potential implications for prognosis prediction and personalized therapeutic approaches.

CTSG Suppresses Colorectal Cancer Progression through Negative Regulation of Akt/mTOR/Bcl2 Signaling Pathway

Colorectal cancer (CRC) is the most common gastrointestinal tumor worldwide, which is a severe malignant disease that threatens mankind. Cathepsin G (CTSG) has been reported to be associated with tumorigenesis, whereas its role in CRC is still unclear. This investigation aims to determine the function of CTSG in CRC. Our results indicated that CTSG was inhibited in CRC tissues, and patients with CTSG low expression have poor overall survival. Functional experiments revealed that CTSG overexpression suppressed CRC cell progression in vitro and in vivo, whereas CTSG suppression supports CRC development cells in vitro and in vivo. Mechanistically, CTSG overexpression suppressed Akt/mTOR signaling mechanism and elevated apoptotic-associated markers, and CTSG silencing activated Akt/mTOR signaling mechanisms and inhibited apoptotic-associated markers. Furthermore, the Akt suppression signaling pathway by MK2206 abolishes CTSG-silenced expression-induced cell viability and Bcl2 up-regulation in vitro and in vivo. Altogether, these outcomes demonstrate that CTSG may act as a tumor suppressor gene via Akt/mTOR/Bcl2-mediated anti-apoptotic signaling inactivation, and CTSG represents a potential therapeutic target in CRC.

An oxidative stress-related prognostic signature for indicating the immune status of oral squamous cell carcinoma and guiding clinical treatment

Oral squamous cell carcinoma (OSCC) is the eighth most common cancer worldwide and presents high mortality. Oxidative stress, caused by reactive oxygen species accumulation, plays a crucial role in tumorigenesis, cancer progression, and drug resistance. Nevertheless, the specific prognostic and clinical values of oxidative stress-related genes (OSGs) in OSCC remain unclear. Here, we developed an oxidative stress-related prognostic signature according to mRNA expression data from The Cancer Genome Atlas (TCGA) database and evaluated its connections with the prognosis, clinical features, immune status, immunotherapy, and drug sensitivity of OSCC through a series of bioinformatics analyses. Finally, we filtered out six prognostic OSGs to construct a prognostic signature. On the basis of both TCGA-OSCC and GSE41613 cohorts, the signature was proven to be an independent prognostic factor with high accuracy and was confirmed to be an impactful indicator for predicting the prognosis and immune status of patients with OSCC. Additionally, we found that patients with high-risk scores may obtain greater benefit from immune checkpoint therapy compared to those with low-risk scores, and the risk score presented a close interaction with the tumor microenvironment and chemotherapy sensitivity. The prognostic signature may provide a valid and robust predictive tool that could predict the prognosis and immune status and guide clinicians to develop personalized therapeutic strategies for patients with OSCC.

A Novel Immune-Related Gene Signature to Identify the Tumor Microenvironment and Prognose Disease Among Patients With Oral Squamous Cell Carcinoma Patients Using ssGSEA: A Bioinformatics and Biological Validation Study

Oral squamous cell carcinoma (OSCC) is the most invasive oral malignancy in adults and is associated with a poor prognosis. Accurate prognostic models are urgently needed, however, knowledge of the probable mechanisms behind OSCC tumorigenesis and prognosis remain limited. The clinical importance of the interplay between the immune system and tumor microenvironment has become increasingly evident. This study explored immune-related alterations at the multi-omics level to extract accurate prognostic markers linked to the immune response and presents a more accurate landscape of the immune genomic map during OSCC. The Cancer Genome Atlas (TCGA) OSCC cohort (n = 329) was used to detect the immune infiltration pattern of OSCC and categorize patients into two immunity groups using single-sample gene set enrichment analysis (ssGSEA) and hierarchical clustering analysis. Multiple strategies, including lasso regression (LASSO), Cox proportional hazards regression, and principal component analysis (PCA) were used to screen clinically significant signatures and identify an incorporated prognosis model with robust discriminative power on the survival status of both the training and testing set. We identified two OSCC subtypes based on immunological characteristics: Immunity-high and immunity low, and verified that the categorization was accurate and repeatable. Immunity_ high cluster with a higher immunological and stromal score. 1047 differential genes (DEGs) integrate with immune genes to obtain 319 immue-related DEGs. A robust model with five signatures for OSCC patient prognosis was established. The GEO cohort (n = 97) were used to validate the risk model’s predictive value. The low-risk group had a better overall survival (OS) than the high-risk group. Significant prognostic potential for OSCC patients was found using ROC analysis and immune checkpoint gene expression was lower in the low-risk group. We also investigated at the therapeutic sensitivity of a number of frequently used chemotherapeutic drugs in patients with various risk factors. The underlying biological behavior of the OSCC cell line was preliminarily validated. This study characterizes a reliable marker of OSCC disease progression and provides a new potential target for immunotherapy against this disease.

Immune-related gene signature predicts clinical outcomes and immunotherapy response in acute myeloid leukemia

Background

The immune response in the bone marrow microenvironment has implications for progression and prognosis in acute myeloid leukemia (AML). However, few immune‐related biomarkers for AML prognosis and immunotherapy response have been identified. We aimed to establish a predictive gene signature and to explore the determinants of prognosis in AML.

Methods

Immune‐related genes with clinical significance were screened by a weighted gene co‐expression network analysis. Seven immune‐related genes were used to establish a gene signature by a multivariate Cox regression analysis. Based on the signature, low‐ and high‐risk groups were compared with respect to the immune microenvironment, immune checkpoints, pathway activities, and mutation frequencies. The tumor immune dysfunction and exclusion (TIDE) method was used to predict the response to immune checkpoint blockade (ICB) therapy. The Connectivity Map database was used to explore small‐molecule drugs expected to treat high‐risk populations.

Results

A seven‐gene prognostic signature was used to classify patients into high‐ and low‐risk groups. Prognosis was poorer for patients in the former than in the latter. The high‐risk group displayed higher levels of immune checkpoint molecules (LAG3, PD‐1, CTLA4, PD‐L2, and PD‐L1), immune cell infiltration (dendritic cells, T helper 1, and gamma delta T), and somatic mutations (NPM1 and RUNX1). Moreover, hematopoietic stem cell/leukemia stem cell pathways were enriched in the high‐risk phenotype. Compared with that in the low‐risk group, the lower TIDE score for the high‐risk group implied that this group is more likely to benefit from ICB therapy. Finally, some drugs (FLT3 inhibitors and BCL inhibitors) targeting the expression profiles associated with the high‐risk group were generated using Connectivity Map.

Conclusion

The newly developed immune‐related gene signature is an effective biomarker for predicting prognosis in AML and provides a basis, from an immunological perspective, for the development of comprehensive therapeutic strategies.

Tumor-Infiltrating CD4+ Central Memory T Cells Correlated with Favorable Prognosis in Oral Squamous Cell Carcinoma

OBJECTIVE

Oral squamous cell carcinoma (OSCC) is the most frequent oral malignancy with a poor prognosis, in which tumor-infiltrating immune cells may play a critical role. Therefore, our study aims to screen potential immune cells and immune-related genes for predicting OSCC prognosis.

METHODS

A total of 310 OSCC patients with full transcriptional data and clinical characteristics were extracted from the TCGA database. Then, we obtained their abundance of tumor-infiltrating immune cells on TIMER 2.0 and analyzed them using xCell method. Univariate and multivariate Cox regressions were applied successively to identify the immune cells associated with overall survival of OSCC patients. Furthermore, we screened the prognostic genes that related to the identified immune cells and validated their expressions by immunohistochemistry.

RESULTS

CD4+ central memory T (TCM) cell was recognized as the sole independent immune cell correlated with OSCC prognosis (p = 0.0085). A novel nomogram based on CD4+ TCM cell abundance was established for predicting the prognosis of OSCC patients, with calibration plots showing good performance for 1-, 3-, 5-year overall survival. Thirty-four related prognostic genes were screened according to the differential abundance of CD4+ TCM cell infiltration. In immunohistochemistry analysis, DEFB1 showed a significant positive relationship with the density of CD4+ TCM cells (p = 0.0075).

CONCLUSION

CD4+ central memory T cell was proposed as an independent prognostic biomarker for OSCC patients. DEFB1 might positively regulate the abundance of tumor-infiltrating CD4+ TCM cells, thus improving OSCC prognosis. Our findings may provide a new insight into better prognosis prediction and precise medicine for OSCC.