Metabolomics of Esophageal Squamous Cell Carcinoma Tissues: Potential Biomarkers for Diagnosis and Promising Targets for Therapy

Clinical Correlation of Transcription Factor SOX3 in Cancer: Unveiling Its Role in Tumorigenesis

Members of the SOX (SRY-related HMG box) family of transcription factors are crucial for embryonic development and cell fate determination. This review investigates the role of SOX3 in cancer, as aberrations in SOX3 expression have been implicated in several cancers, including osteosarcoma, breast, esophageal, endometrial, ovarian, gastric, hepatocellular carcinomas, glioblastoma, and leukemia. These dysregulations modulate key cancer outcomes such as apoptosis, epithelial-mesenchymal transition (EMT), invasion, migration, cell cycle, and proliferation, contributing to cancer development. SOX3 exhibits varied expression patterns correlated with clinicopathological parameters in diverse tumor types. This review aims to elucidate the nuanced role of SOX3 in tumorigenesis, correlating its expression with clinical and pathological characteristics in cancer patients and cellular modelsBy providing a comprehensive exploration of SOX3 involvement in cancer, this review underscores the multifaceted role of SOX3 across distinct tumor types. The complexity uncovered in SOX3 function emphasizes the need for further research to unravel its full potential in cancer therapeutics.

Novel milestones for early esophageal carcinoma: From bench to bed

Esophageal cancer (EC) is the seventh most common cancer worldwide, and esophageal squamous cell carcinoma (ESCC) accounts for the majority of cases of EC. To effectively diagnose and treat ESCC and improve patient prognosis, timely diagnosis in the initial phase of the illness is necessary. This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs. Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quantitative polymerase chain reaction (qPCR), high-throughput sequencing technology (next-generation sequencing), and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis, treatment, and prognosis of diseases. The investigation of the microbiome is a swiftly progressing area in human cancer research, and microorganisms with complex functions are potential components of the tumor microenvironment. The intratumoral microbiota was also found to be connected to tumor progression. The application of endoscopy as a crucial technique for the early identification of ESCC has been essential, and with ongoing advancements in technology, endoscopy has continuously improved. With the advancement of artificial intelligence (AI) technology, the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent. The implementation of AI can effectively resolve the discrepancies among observers, improve the detection rate, assist in predicting the depth of invasion and differentiation status, guide the pericancerous margins, and aid in a more accurate diagnosis of ESCC.

Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer

BACKGROUND

Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC).

METHODS

A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC.

RESULTS

We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816-0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716-0.805).

CONCLUSIONS

These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC.

High-performance liquid chromatography-tandem mass spectrometry method for the analysis of N-oleoyl glycine and N-oleoyl alanine in brain and plasma

Interest has increased in the role of N-acyl amino acids in a variety of disease states and as potential pharmacotherapies. Recently, N-oleoyl glycine and N-oleoyl alanine have shown promise in reducing the rewarding effects of drugs of abuse and alleviating withdrawal signs in rodent models. Previously published methods for the quantitation of these analytes by high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) in tissue were part of extensive lipidomic panels which may result in limited sensitivity and selectivity and also reported low recovery. Presented is a method for the extraction and HPLC-MS/MS analysis of N-oleoyl glycine and N-oleoyl alanine. The bias and precision of the assay were determined to be within ± 20%. The method was shown to be reliable and robust, with over 90% recovery for the low-level analytes. Increasing concentrations of N-oleoyl glycine and N-oleoyl alanine were quantitated in mouse brain and plasma following exogenous administration. This method was developed to serve to support studies investigating the pharmacokinetics and involvement of N-oleoyl glycine and N-oleoyl alanine in drug dependence and other diseases.