Plasma Metabolomics Reveals Diagnostic Biomarkers and Risk Factors for Esophageal Squamous Cell Carcinoma

Construction and validation of serum Metabolic Risk Score for early warning of malignancy in esophagus

Using noninvasive biomarkers to identify high-risk individuals prior to endoscopic examination is crucial for optimization of screening strategies for esophageal squamous cell carcinoma (ESCC). We conducted a nested case-control study based on two community-based screening cohorts to evaluate the warning value of serum metabolites for esophageal malignancy. The serum samples were collected at enrollment when the cases had not been diagnosed. We identified 74 differential metabolites and two prominent perturbed metabolic pathways, and constructed Metabolic Risk Score (MRS) based on 22 selected metabolic predictors. The MRS generated an area under the receiver operating characteristics curve (AUC) of 0.815. The model performed well for the within-1-year interval (AUC: 0.868) and 1-to-5-year interval (AUC: 0.845) from blood draw to diagnosis, but showed limited ability in predicting long-term cases (>5 years). In summary, the MRS could serve as a potential early warning and risk stratification tool for establishing a precision strategy of ESCC screening.

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.

Plasma metabolomics reveals risk factors for lung adenocarcinoma

Background

Metabolic reprogramming plays a significant role in the advancement of lung adenocarcinoma (LUAD), yet the precise metabolic changes remain incompletely understood. This study aims to uncover metabolic indicators associated with the progression of LUAD.

Methods

A total of 1083 subjects were recruited, including 670 LUAD, 135 benign lung nodules (BLN) and 278 healthy controls (HC). Gas chromatography-mass spectrometry (GC/MS) was used to identify and quantify plasma metabolites. Odds ratios (ORs) were calculated to determine LUAD risk factors, and machine learning algorithms were utilized to differentiate LUAD from BLN.

Results

High levels of oxalate, glycolate, glycine, glyceric acid, aminomalonic acid, and creatinine were identified as risk factors for LUAD (adjusted ORs>1.2, P<0.03). Remarkably, oxalate emerged as a distinctive metabolic risk factor exhibiting a strong correlation with the progression of LUAD (adjusted OR=5.107, P<0.001; advanced-stage vs. early-stage). The Random Forest (RF) model demonstrated a high degree of efficacy in distinguishing between LUAD and BLN (accuracy = 1.00 and 0.73, F1-score= 1.00 and 0.79, and AUC = 1.00 and 0.76 in the training and validation sets, respectively). TCGA and GTEx gene expression data have shown that lactate dehydrogenase A (LDHA), a crucial enzyme involved in oxalate metabolism, is increasingly expressed in the progression of LUAD. High LDHA expression levels in LUAD patients are also linked to poor prognoses (HR=1.66, 95% CI=1.34-2.07, P<0.001).

Conclusions

This study reveals risk factors associated with LUAD.

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.

Construction and validation of a nomogram model to predict the overall survival rate of esophageal cancer patients receiving neoadjuvant chemotherapy: A population-based study

Introduction

The development of neoadjuvant chemotherapy(nCT) improves the overall survival (OS) of patients with esophageal cancer(EC). The aim of this study was to determine the independent prognostic factors of EC patients receiving nCT, and to construct a nomogram model for predicting OS.

Method

This retrospective analysis was conducted from the National Cancer Institute's Surveillance Epidemiology and End Results, Clinicopathological data of patients with EC who received nCT from 2004 to 2015. The included patients were randomly divided into the training cohort and the validation cohort. Univariate and multivariate Cox proportional hazards models were used to analyze the patients in the training cohort to determine the independent prognostic factors. Based on the independent prognostic variables, nomogram models for 1-year, 2-year and 3-year OS were constructed. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the discriminative ability. The calibration curves, decision curve analysis (DCA) and Kaplan-Meier (K-M) survival analysis were used to evaluate the predictive accuracy and clinical application value.

Results

A total of 2,493 patients were enrolled, with 1,748 patients in the training cohort and 745 patients in the validation cohort. Gender, marital status, tumor pathological grade, T stage, N stage, and M stage were identified as independent prognostic factor (P < 0.05). A novel nomogram model was constructed. ROC curve analysis revealed that the model had moderate predictive performance, which was better than that of the AJCC TNM staging system.The calibration curves showed a high agreement between the actual observed values and the predicted values. The DCA suggested that the newly constructed prediction model had good clinical application value. K-M survival analysis showed that the model was helpful to accurately distinguish the prognosis of patients with different risk levels.

Conclusions

Gender, tumor pathological grade, marital status, T stage, N stage and M stage were identified as independent prognostic factors for overall survival of patients with esophageal cancer who received neoadjuvant chemotherapy. A nomogram prediction model was established, which was helpful to accurately and reliably predict the overall survival rate of patients with esophageal cancer who received neoadjuvant chemotherapy at 1, 2 and 3 years.

Circ_0000705 facilitates proline metabolism of esophageal squamous cell carcinoma cells by targeting miR-621/PYCR1 axis

CircRNAs have been found to play crucial roles in the metabolism and progression of cancers, but their roles and mechanisms in esophageal squamous cell carcinoma (ESCC) have not been fully elucidated. This work is aimed to explore the role and mechanism of hsa_circ_0000705 (circ_0000705) in ESCC. Circ_0000705 expression was up-regulated in ESCC tissues and cell lines, and high circ_0000705 expression was correlated with poor survival. Circ_0000705 facilitated cell proliferation, invasion, migration and proline metabolism of ESCC cells. The inhibitory effects of circ_0000705 knockdown on cell invasion, migration and proline metabolism were partly rescued by miR-621 inhibition or PYCR1 over-expression. Furthermore, circ_0000705 expression is negatively correlated with miR-621 expression, and positively correlated with PYCR1 in ESCC tissues. Mechanistically, circ_0000705 acted as a ceRNA by sponging miR-621, thereby facilitating PYCR1 expression in ESCC cells. In conclusion, circ_0000705 promoted proline metabolism and malignant progression of ESCC by regulating the miR‑621/PYCR1 axis.