Genome-Wide DNA Methylation and Gene Expression Profiling Characterizes Molecular Subtypes of Esophagus Squamous Cell Carcinoma for Predicting Patient Survival and Immunotherapy Efficacy

Biomarkers and experimental models for cancer immunology investigation

The rapid advancement of tumor immunotherapies poses challenges for the tools used in cancer immunology research, highlighting the need for highly effective biomarkers and reproducible experimental models. Current immunotherapy biomarkers encompass surface protein markers such as PD-L1, genetic features such as microsatellite instability, tumor-infiltrating lymphocytes, and biomarkers in liquid biopsy such as circulating tumor DNAs. Experimental models, ranging from 3D in vitro cultures (spheroids, submerged models, air-liquid interface models, organ-on-a-chips) to advanced 3D bioprinting techniques, have emerged as valuable platforms for cancer immunology investigations and immunotherapy biomarker research. By preserving native immune components or coculturing with exogenous immune cells, these models replicate the tumor microenvironment in vitro. Animal models like syngeneic models, genetically engineered models, and patient-derived xenografts provide opportunities to study in vivo tumor-immune interactions. Humanized animal models further enable the simulation of the human-specific tumor microenvironment. Here, we provide a comprehensive overview of the advantages, limitations, and prospects of different biomarkers and experimental models, specifically focusing on the role of biomarkers in predicting immunotherapy outcomes and the ability of experimental models to replicate the tumor microenvironment. By integrating cutting-edge biomarkers and experimental models, this review serves as a valuable resource for accessing the forefront of cancer immunology investigation.

Application of New Molecular Probes in the Diagnosis and Treatment of Malignant Tumors

Molecular probes, specialized tools or substances meticulously designed to bind to specific molecules or biomarkers within cells, tissues, or biological samples, play a pivotal role in various domains such as biomedical research, diagnostics, and medical treatments [...].

A robust immune-related gene pairs signature for predicting the overall survival of esophageal cancer

BACKGROUND

Identifying reliable biomarkers could effectively predict esophagus carcinoma (EC) patients with poor prognosis. In this work, we constructed an immune-related gene pairs (IRGP) signature to evaluate the prognosis of EC.

RESULTS

The IRGP signature was trained by the TCGA cohort and validated by three GEO datasets, respectively. Cox regression model together with LASSO was applied to construct the overall survival (OS) associated IRGP. 21 IRGPs consisting of 38 immune-related genes were included in our signature, according to which patients were stratified into high- and low-risk groups. The results of Kaplan-Meier survival analyses indicated that high-risk EC patients had worse OS than low-risk group in the training set, meta-validation set and all independent validation datasets. After adjustment in multivariate Cox analyses, our signature continued to be an independent prognostic factor of EC and the signature-based nomogram could effectively predict the prognosis of EC sufferers. Besides, Gene Ontology analysis revealed this signature is related to immunity. 'CIBERSORT' analysis revealed the infiltration levels of plasma cells and activated CD4 memory T cells in two risk groups were significantly different. Ultimately, we validated the expression levels of six selected genes from IRGP index in KYSE-150 and KYSE-450.

CONCLUSIONS

This IRGP signature could be applied to select EC patients with high mortality risk, thereby improving prospects for the treatment of EC.

Nomogram-Based Survival Predictions and Treatment Recommendations for Locally Advanced Esophageal Squamous Cell Carcinoma Treated with Upfront Surgery

Background and purpose: The aim of this study is to develop a prognostic nomogram, quantify survival benefit, and guide risk-dependent adjuvant therapy for locally advanced esophageal squamous cell carcinoma (LA-ESCC) after esophagectomy. Materials and methods: This was a single-center, retrospective study of consecutive LA-ESCCs treated by curative-intent esophagectomy with internal validation and independent external validation in a randomized controlled trial. After factor selection by the least absolute shrinkage and selection operator regression, a nomogram was developed to estimate 5-year overall survival (OS) based on the Cox proportional hazards model. The area under the curve (AUC) and calibration plot were used to determine its discriminative and predictive capacities, respectively. Survival improvement from adjuvant therapy was quantified and plotted corresponding to nomogram score. Results: A total of 1077, 718, and 118 patients were included for model development, internal validation, and external validation, respectively. The nomogram identified eight significant prognostic factors: gender, pathological T and N stages, differentiation, surgical margin, lymphovascular invasion, number of lymph node resection, and adjuvant therapy. The nomogram showed superior discriminative capacity than TNM stage (AUC: 0.76 vs. 0.72, p < 0.01), with significant survival differences among different risk stratifications. The calibration plot illustrated a good agreement between nomogram-predicated and actual 5-year OS. Consistent results were concluded after external validation. At least 10% 5-year OS improvement from adjuvant chemoradiotherapy and chemotherapy was expected in almost all patients (nomogram score 110 to 260) and patients mainly with high-intermediate risk (nomogram score 159 to 207), respectively. Conclusions: The clinicopathological nomogram predicting 5-year OS for LA-ESCC after esophagectomy was developed with high accuracy. The proposed nomogram showed better performance than TNM stage and provided risk-dependent and individualized adjuvant treatment recommendations.