RSPO2-associated mitochondrial metabolism defines molecular subtypes with distinct clinical and immune features in esophageal cancer.

BACKGROUND: Esophageal cancer is a highly aggressive malignancy with limited treatment options and poor prognosis. The identification of novel molecular subtypes and therapeutic targets is crucial for improving clinical outcomes. METHOD: In this study, we investigated the role of R-spondin 2 (RSPO2) in esophageal cancer and its association with mitochondrial metabolism. Using bioinformatics analysis of publicly available datasets, we identified a panel of RSPO2-related mitochondrial metabolism genes and their expression patterns in esophageal cancer. Based on these genes, we stratified esophageal cancer patients into distinct molecular subtypes with different survival rates, immune cell infiltration profiles, and drug sensitivities. RESULTS: Our findings suggest that RSPO2-related mitochondrial metabolism genes may serve as potential therapeutic targets and prognostic markers for esophageal cancer. These genes play an important role in the prognosis, immune cell infiltration and drug sensitivity of esophageal cancer. CONCLUSION: The identified molecular subtypes provide valuable insights into the underlying molecular mechanisms of esophageal cancer and could guide personalized treatment strategies in the future.

A novel DNA methylation signature associated with lymph node metastasis status in early gastric cancer.

BACKGROUND: Lymph node metastasis (LNM) is an important factor for both treatment and prognosis of early gastric cancer (EGC). Current methods are insufficient to evaluate LNM in EGC due to suboptimal accuracy. Herein, we aim to identify methylation signatures for LNM of EGC, facilitate precision diagnosis, and guide treatment modalities. METHODS: For marker discovery, genome-wide methylation sequencing was performed in a cohort (marker discovery) using 47 fresh frozen (FF) tissue samples. The identified signatures were subsequently characterized for model development using formalin-fixed paraffin-embedded (FFPE) samples by qPCR assay in a second cohort (model development cohort, n = 302, training set: n = 151, test set: n = 151). The performance of the established model was further validated using FFPE samples in a third cohorts (validation cohort, n = 130) and compared with image-based diagnostics, conventional clinicopathology-based model (conventional model), and current standard workups. RESULTS: Fifty LNM-specific methylation signatures were identified de novo and technically validated. A derived 3-marker methylation model for LNM diagnosis was established that achieved an AUC of 0.87 and 0.88, corresponding to the specificity of 80.9% and 85.7%, sensitivity of 80.6% and 78.1%, and accuracy of 80.8% and 83.8% in the test set of model development cohort and validation cohort, respectively. Notably, this methylation model outperformed computed tomography (CT)-based imaging with a superior AUC (0.88 vs. 0.57, p < 0.0001) and individual clinicopathological features in the validation cohort. The model integrated with clinicopathological features demonstrated further enhanced AUCs of 0.89 in the same cohort. The 3-marker methylation model and integrated model reduced 39.4% and 41.5% overtreatment as compared to standard workups, respectively. CONCLUSIONS: A novel 3-marker methylation model was established and validated that shows diagnostic potential to identify LNM in EGC patients and thus reduce unnecessary gastrectomy in EGC.

Identifying potential DNA methylation markers in early-stage colorectal Cancer.

Colorectal cancer (CRC) is the second leading malignancy worldwide. Accurate screening is pivotal to early CRC detection, yet current screening modality involves invasive colonoscopy while non-invasive FIT tests have limited sensitivity. We applied a DNA methylation assay to identify biomarkers for early-stage CRC detection, risk stratification and precancerous lesion screening at tissue level. A model of biomarkers SFMBT2, ITGA4, THBD and ZNF304 showed 96.1% sensitivity and 87.0% specificity in CRC detection, with 100.0% sensitivity for advanced precancerous lesion and stage I CRC. Performances were further validated with TCGA data set, which showed a consistent AUC of 0.99 and exhibited specificity against other cancer types. KCNJ12, VAV3-AS1 and EVC were further identified for stage stratification (stage 0-I versus stage II-IV), with AUC of 0.87, 83.0% sensitivity and 71.2% specificity. Additionally, dual markers of NEUROD1 and FAM72C showed 83.2% sensitivity and 77.4% specificity in differing non-advanced precancerous lesions from inflammatory bowel diseases.