A blood-based transcriptomic signature for noninvasive diagnosis of gastric cancer

Advances and applications of multiomics technologies in precision diagnosis and treatment for gastric cancer

Gastric cancer (GC), one of the most prevalent malignancies worldwide, is distinguished by extensive genetic and phenotypic heterogeneity, posing persistent challenges to conventional diagnostic and therapeutic strategies. The significant global burden of GC highlights an urgent need to unravel its complex underlying mechanisms, discover novel diagnostic and prognostic biomarkers, and develop more effective therapeutic interventions. In this context, this review comprehensively examines the transformative roles of cutting-edge technologies, including radiomics, pathomics, genomics, transcriptomics, epigenomics, proteomics, and metabolomics, in advancing precision diagnosis and treatment for GC. Multiomics data analysis not only deepens our understanding of GC pathogenesis and molecular subtypes but also identifies promising biomarkers, facilitating the creation of tailored therapeutic approaches. Additionally, integrating multiomics approaches holds immense potential for elucidating drug resistance mechanisms, predicting patient outcomes, and uncovering novel therapeutic targets, thereby laying a robust foundation for precision medicine in the comprehensive management of GC.

Applications of gene pair methods in clinical research: advancing precision medicine

The rapid evolution of high-throughput sequencing technologies has revolutionized biomedical research, producing vast amounts of gene expression data that hold immense potential for biological discovery and clinical applications. Effectively mining these large-scale, high-dimensional data is crucial for facilitating disease detection, subtype differentiation, and understanding the molecular mechanisms underlying disease progression. However, the conventional paradigm of single-gene profiling, measuring absolute expression levels of individual genes, faces critical limitations in clinical implementation. These include vulnerability to batch effects and platform-dependent normalization requirements. In contrast, emerging approaches analyzing relative expression relationships between gene pairs demonstrate unique advantages. By focusing on binary comparisons of two genes' expression magnitudes, these methods inherently normalize experimental variations while capturing biologically stable interaction patterns. In this review, we systematically evaluate gene pair-based analytical frameworks. We classify eleven computational approaches into two fundamental categories: expression value-based methods quantifying differential expression patterns, and rank-based methods exploiting transcriptional ordering relationships. To bridge methodological development with practical implementation, we establish a reproducible analytical pipeline incorporating feature selection, classifier construction, and model evaluation modules using real-world benchmark datasets from pulmonary tuberculosis studies. These findings position gene pair analysis as a transformative paradigm for mining high-dimensional omics data, with direct implications for precision biomarker discovery and mechanistic studies of disease progression.

Integration of miRNA expression analysis of purified leukocytes and whole blood reveals blood-borne candidate biomarkers for lung cancer

Changes in leukocyte populations may confound the disease-associated miRNA signals in the blood of cancer patients. We aimed to develop a method to detect differentially expressed miRNAs from lung cancer whole blood samples that are not influenced by variations in leukocyte proportions. The Ref-miREO method identifies differential miRNAs unaffected by changes in leukocyte populations by comparing the within-sample relative expression orderings (REOs) of miRNAs from healthy leukocyte subtypes and those from lung cancer blood samples. Over 77% of the differential miRNAs observed between lung cancer and healthy blood samples overlapped with those between myeloid-derived and lymphoid-derived leukocytes, suggesting the potential impact of changes in leukocyte populations on miRNA profile. Ref-miREO identified 16 differential miRNAs that target 19 lung adenocarcinoma-related genes previously linked to leukocytes. These miRNAs showed enrichment in cancer-related pathways and demonstrated high potential as diagnostic biomarkers, with the LASSO regression models effectively distinguishing between healthy and lung cancer blood or serum samples (all AUC > 0.85). Additionally, 12 of these miRNAs exhibited significant prognostic correlations. The Ref-miREO method offers valuable candidates for circulating biomarker detection in cancer that are not affected by changes in leukocyte populations.

ILF2 protein is a promising serum biomarker for early detection of gastric cancer

BACKGROUND

Our previous small-sample study indicated that serum levels of interleukin enhancer binding factor 2 (ILF2) may have the potential for gastric cancer (GC) detection. The present study was conducted to further validate the diagnostic value of serum ILF2 protein for GC.

METHODS

Serum specimens and clinical data were collected from patients with GC (n = 99) or benign gastric disease (BGD) (n = 49) and healthy controls (HC) (n = 51). Serum ILF2 levels were measured using enzyme-linked immunosorbent assay. The diagnostic performance of ILF2 was evaluated using the area under the receiver operating characteristic curve (AUC). The independence and synergy of ILF2 in GC diagnosis were analyzed by modeling with conventional blood indicators.

RESULTS

The median serum ILF2 level was higher in the GC group (227.8ng/mL) than in the BGD group (72.0ng/mL) and the HC group (56.8ng/mL) (p < 0.001), and no significant difference across GC subgroups. The AUCs of ILF2 were 0.915 (95%CI 0.873-0.957) for GC vs. HC, 0.854 (95%CI 0.793-0.915) for GC vs. BGD, 0.885 (95%CI 0.841-0.929) for GC vs. BGD + HC, and 0.888 (95% CI 0.830-0.945) for TNM I stage GC vs. BGD + HC, outperforming conventional blood indicators (corresponding AUCs ranging from 0.641 to 0.782). ILF2 was independent of and synergistic with conventional blood indicators in GC diagnosis, and a simple diagnostic model based on ILF2 and red blood cell count improved the diagnostic performance, with positive rates of approximately 90% in various subgroups of GC.

CONCLUSIONS

Serum ILF2 protein is a novel and potential serum biomarker for the detection of GC, especially for early GC.

A performance evaluation of drug response prediction models for individual drugs

Drug response prediction is important to establish personalized medicine for cancer therapy. Model construction for predicting drug response (i.e., cell viability half-maximal inhibitory concentration [IC₅₀]) of an individual drug by inputting pharmacogenomics in disease models remains critical. Machine learning (ML) has been predominantly applied for prediction, despite the advent of deep learning (DL). Moreover, whether DL or traditional ML models are superior for predicting cell viability IC_50s has to be established. Herein, we constructed ML and DL drug response prediction models for 24 individual drugs and compared the performance of the models by employing gene expression and mutation profiles of cancer cell lines as input. We observed no significant difference in drug response prediction performance between DL and ML models for 24 drugs [root mean squared error (RMSE) ranging from 0.284 to 3.563 for DL and from 0.274 to 2.697 for ML; R² ranging from -7.405 to 0.331 for DL and from -8.113 to 0.470 for ML]. Among the 24 individual drugs, the ridge model of panobinostat exhibited the best performance (R² 0.470 and RMSE 0.623). Thus, we selected the ridge model of panobinostat for further application of explainable artificial intelligence (XAI). Using XAI, we further identified important genomic features for panobinostat response prediction in the ridge model, suggesting the genomic features of 22 genes. Based on our findings, results for an individual drug employing both DL and ML models were comparable. Our study confirms the applicability of drug response prediction models for individual drugs.

Real-World Practice of Gastric Cancer Prevention and Screening Calls for Practical Prediction Models

INTRODUCTION

Some gastric cancer prediction models have been published. Still, the value of these models for application in real-world practice remains unclear. We aim to summarize and appraise modeling studies for gastric cancer risk prediction and identify potential barriers to real-world use.

METHODS

This systematic review included studies that developed or validated gastric cancer prediction models in the general population.

RESULTS

A total of 4,223 studies were screened. We included 18 development studies for diagnostic models, 10 for prognostic models, and 1 external validation study. Diagnostic models commonly included biomarkers, such as Helicobacter pylori infection indicator, pepsinogen, hormone, and microRNA. Age, sex, smoking, body mass index, and family history of gastric cancer were frequently used in prognostic models. Most of the models were not validated. Only 25% of models evaluated the calibration. All studies had a high risk of bias, but over half had acceptable applicability. Besides, most studies failed to clearly report the application scenarios of prediction models.

DISCUSSION

Most gastric cancer prediction models showed common shortcomings in methods, validation, and reports. Model developers should further minimize the risk of bias, improve models' applicability, and report targeting application scenarios to promote real-world use.

Application of Circulating Tumor Cells and Circulating Free DNA from Peripheral Blood in the Prognosis of Advanced Gastric Cancer

Objective

To explore the application value of circulating tumor cells (CTCs) and circulating free DNA (cfDNA) from peripheral blood in the prognosis of advanced gastric cancer (AGC). Here, we measured CTCs and cfDNA quantity for predicting the outcome of patients. Patients and Methods. Forty-five patients with advanced gastric cancer who underwent neoadjuvant chemotherapy and surgical treatment were enrolled in this study. All patients received neoadjuvant chemotherapy with paclitaxel + S-1 + oxaliplatin (PSOX) regimen, and CTCs and cfDNA of the peripheral blood were detected before and after neoadjuvant therapy. Relationships between the number/type of CTC or cfDNA and the efficacy of neoadjuvant chemotherapy were analyzed.

Results

Among 45 patients, 43 (95.6%) were positive, and the positive rate of mesenchymal CTC was increased with the increase in the T stage. The proportion of mesenchymal CTC was positively correlated with the N stage (P < 0.05), and the larger N stage will have the higher proportion of mesenchymal CTC. Patients with a small number of mesenchymal CTC before neoadjuvant chemotherapy were more likely to achieve partial response (PR) with neoadjuvant therapy. Patients with positive CA-199 were more likely to achieve PR with neoadjuvant therapy (P < 0.05). Patients in the PR group were more likely to have decreased/unchanged cfDNA concentration after neoadjuvant therapy (P=0.119). After neoadjuvant therapy (before surgery), the cfDNA concentration was higher and the efficacy of neoadjuvant therapy (SD or PD) was lower (P=0.045).

Conclusions

Peripheral blood CTC, especially interstitial CTC and cfDNA, has a certain value in predicting the efficacy and prognosis of neoadjuvant chemotherapy in advanced gastric cancer.