Identification and Validation of a Hypoxia-Immune-Based Prognostic mRNA Signature for Oral Squamous Cell Carcinoma

RNA methylation-related genes INHBB and SOWAHA are associated with MSI status in colorectal cancer patients and may serve as prognostic markers for predicting immunotherapy efficacy

The role of RNA methylation is vital in the advancement and spread of tumors. However, its exact role in microsatellite instability in colorectal cancer (CRC) is still not fully understood. To address this gap in knowledge, this study investigated the impact of genes associated with RNA methylation on the prognosis and response to immunotherapy in individuals diagnosed with low microsatellite instability (MSI-L) or microsatellite stable (MSS) CRC. The differentially expressed genes (DEGs) in two groups of patients: those with high microsatellite instability (MSI-H) and those with MSI-L/MSS was thoroughly investigated and compared with aims of exploring the association between them and the 60 RNA methylation regulators. We employed these genes and developed an MSI-RMscore to establish a risk signature capable of forecasting patient outcomes. Furthermore, an investigation of the immunophenotypic traits was conducted encompassing patients categorized as high-risk and low-risk. By combining the MSI-RMscore and clinicopathological features, a predictive nomogram was developed, which was subsequently validated using the GEO database. Furthermore, immunohistochemistry was employed to establish the correlation between INHBB and SOWAHA and the MSI status, as well as patient prognosis. Our findings indicated that the high-risk subgroup exhibited unfavorable overall survival rates, reduced responsiveness to immune checkpoint blockers, elevated estimate scores, and increased infiltration of macrophages and fibroblasts. We also confirmed that INHBB and SOWAHA were associated with CRC patient prognosis and MSI status, as well as immunotherapy response. These findings suggest that targeting INHBB and SOWAHA could be a promising strategy to enhance patient responsiveness to immunotherapy.

MMP14 expression levels accurately predict the presence of extranodal extensions in oral squamous cell carcinoma: a retrospective cohort study

BACKGROUND

Extranodal extension (ENE) is an adverse prognostic factor for oral squamous cell carcinoma (OSCC), and patients with OSCC along with ENE require neck dissection. In this study, we developed a novel ENE histology-based pathological predictor using MMP14 expression patterns in small biopsy specimens.

METHODS

A total of 71 surgically resected tissue, 64 dissected lymph node (LN), and 46 biopsy specimens were collected from 71 patients with OSCC. Immunohistochemical analyses of total MMP14 expression in the tumour nest and cancer-associated fibroblasts (CAFs) were performed using the MMP14 co-scoring system (high- or low-risk). The association analysis of MMP14 expression in metastatic LNs was performed with respect to the presence and absence of ENE. Clinicopathological analyses and multivariate examinations were performed to assess the risks of metastasis and ENE presence. The predictive value of ENE and the impact of ENE and MMP14 expression on 5-year overall survival were examined.

RESULTS

High-risk MMP14 expression was detected in metastatic LN specimens with ENE. MMP14 expression in tumour nests and CAFs and its overexpression at the tumour-stromal interface significantly correlated with the presence of ENE. The MMP14 co-scoring system was an independent risk predictor for ENE, with sensitivity, specificity, and accuracy of over 80% in biopsy samples; patients with a high risk in the MMP14 co-scoring system had significantly worse prognoses in both resections and biopsies.

CONCLUSION

The MMP14 co-scoring system accurately predicted ENE presence and poor prognosis via immunohistochemical evaluation of small biopsies. This system is a simple, accurate, and inexpensive immunohistochemical approach that can be used in routine pathological diagnosis for effective treatment planning.

Artificial intelligence for prediction of response to cancer immunotherapy

Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-care systems in multiple areas such as diagnostic confirmation, risk stratification, analysis, prognosis prediction, treatment surveillance, and virtual health support, which has considerable potential to revolutionize and reshape medicine. In terms of immunotherapy, AI has been applied to unlock underlying immune signatures to associate with responses to immunotherapy indirectly as well as predict responses to immunotherapy responses directly. The AI-based analysis of high-throughput sequences and medical images can provide useful information for management of cancer immunotherapy considering the excellent abilities in selecting appropriate subjects, improving therapeutic regimens, and predicting individualized prognosis. In present review, we aim to evaluate a broad framework about AI-based computational approaches for prediction of response to cancer immunotherapy on both indirect and direct manners. Furthermore, we summarize our perspectives about challenges and opportunities of further AI applications on cancer immunotherapy relating to clinical practicability.

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.