Prediction of Two Molecular Subtypes of Gastric Cancer Based on Immune Signature

Molecular subtype of gastric cancer based on apoptosis-related genes reveals differential immune microenvironment and intratumoral microorganisms distribution

BACKGROUND

Gastric cancer (GC) is known for its high heterogeneity, presenting challenges in current clinical treatment strategies. Accurate subtyping and in-depth analysis of the molecular heterogeneity of GC at the molecular level are still not fully understood.

METHODS

This study categorized GC into two subtypes based on apoptosis-related genes (ARGs) and investigated differences in tumor immune microenvironment, intratumoral microorganisms distribution, gene expression, and signaling pathways. Key prognostic genes related to apoptosis in GC were identified through random survival forest analysis, and their specific signaling mechanisms were explored. Expression levels of key genes were validated through PCR in paired GC tissues and cancer cell lines. Moreover, biological functions of these key genes were verified in vitro experiments.

RESULTS

A consistent clustering of GC was conducted using 161 apoptosis-related genes (ARGs), resulting in the identification of two subtypes, C1 and C2. Subsequently, significant differences were found in the tumor immune microenvironment, intratumoral microorganisms, gene expression, signaling pathways, and protein interaction networks between the two subtypes. GPX3, PLAT, and CAV1 were identified as key prognostic genes related to apoptosis in GC, with a focus on their impact on disease progression-related pathways. Furthermore, PCR assays validated that these three key genes exhibited significantly low expression levels in both GC cell lines and tissues. Finally, knocking down key genes expression significantly promoted cell proliferation, colony formation and invasion of GC.

CONCLUSIONS

Our study conducted a comprehensive analysis of the molecular characteristics of ARGs in GC, revealed their association with the tumor immune microenvironment and intratumoral microorganisms. These findings provide new ideas for the molecular classification of GC.

Current development of molecular classifications of gastric cancer based on omics (Review)

Gastric cancer (GC) is a complex and heterogeneous disease with significant phenotypic and genetic variation. Traditional classification systems rely mainly on the evaluation of clinical pathological features and conventional biomarkers and might not capture the diverse clinical processes of individual GCs. The latest discoveries in omics technologies such as next‑generation sequencing, proteomics and metabolomics have provided crucial insights into potential genetic alterations and biological events in GC. Clustering strategies for identifying subtypes of GC might offer new tools for improving GC treatment and clinical trial outcomes by enabling the development of therapies tailored to specific subtypes. However, the feasibility and therapeutic significance of implementing molecular classifications of GC in clinical practice need to addressed. The present review examines the current molecular classifications, delineates the prevailing landscape of clinically relevant molecular features, analyzes their correlations with traditional GC classifications, and discusses potential clinical applications.

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges

Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).

Molecular Classifications in Gastric Cancer: A Call for Interdisciplinary Collaboration

Gastric cancer (GC) is a heterogeneous disease, often diagnosed at advanced stages, with a 5-year survival rate of approximately 20%. Despite notable technological advancements in cancer research over the past decades, their impact on GC management and outcomes has been limited. Numerous molecular alterations have been identified in GC, leading to various molecular classifications, such as those developed by The Cancer Genome Atlas (TCGA) and the Asian Cancer Research Group (ACRG). Other authors have proposed alternative perspectives, including immune, proteomic, or epigenetic-based classifications. However, molecular stratification has not yet transitioned into clinical practice for GC, and little attention has been paid to alternative molecular classifications. In this review, we explore diverse molecular classifications in GC from a practical point of view, emphasizing their relationships with clinicopathological factors, prognosis, and therapeutic approaches. We have focused on classifications beyond those of TCGA and the ACRG, which have been less extensively reviewed previously. Additionally, we discuss the challenges that must be overcome to ensure their impact on patient treatment and prognosis. This review aims to serve as a practical framework to understand the molecular landscape of GC, facilitate the development of consensus molecular categories, and guide the design of innovative molecular studies in the field.

The Roles of Immune Cells in Gastric Cancer: Anti-Cancer or Pro-Cancer?

Simple Summary

Gastric cancer is still one of the leading causes of death caused by cancer in developing countries. The emerging role of immunotherapy in cancer treatment has led to more research to elucidate the roles of essential immune cells in gastric cancer prognosis. We reviewed the roles of immune cells including T cells, B cells, dendritic cells, macrophages and natural killer cells in gastric cancer. Although the studies conducted on the roles of immune cells in gastric cancer pathogenesis produced conflicting results, understanding the roles of immune cells in gastric cancer will help us to harness them for application in immunotherapy for better prognosis and management of gastric cancer patients.

Abstract

Despite the fact that the incidence of gastric cancer has declined over the last decade, it is still the world’s leading cause of cancer-related death. The diagnosis of early gastric cancer is difficult, as symptoms of this cancer only manifest at a late stage of cancer progression. Thus, the prognosis of gastric cancer is poor, and the current treatment for improving patients’ outcomes involves the application of surgery and chemotherapy. Immunotherapy is one of the most recent therapies for gastric cancer, whereby the immune system of the host is programmed to combat cancer cells, and the therapy differs based upon the patient’s immune system. However, an understanding of the role of immune cells, namely the cell-mediated immune response and the humoral immune response, is pertinent for applications of immunotherapy. The roles of immune cells in the prognosis of gastric cancer have yielded conflicting results. This review discusses the roles of immune cells in gastric cancer pathogenesis, specifically, T cells, B cells, macrophages, natural killer cells, and dendritic cells, as well as the evidence presented thus far. Understanding how cancer cells interact with immune cells is of paramount importance in designing treatment options for gastric cancer immunotherapy.

Consensus Molecular Subtypes Efficiently Classify Gastric Adenocarcinomas and Predict the Response to Anti-PD-1 Immunotherapy

Background: Gastric adenocarcinoma (GAC) is highly heterogeneous and closely related to colorectal cancer (CRC) both molecularly and functionally. GAC is currently subtyped using a system developed by TCGA. However, with the emergence of immunotherapies, this system has failed to identify suitable treatment candidates. Methods: Consensus molecular subtypes (CMSs) developed for CRC were used for molecular subtyping in GAC based on public expression cohorts, including TCGA, ACRG, and a cohort of GAC patients treated with the programmed cell death 1 (PD-1) inhibitor pembrolizumab. All aspects of each subtype, including clinical outcome, molecular characteristics, oncogenic pathway activity, and the response to immunotherapy, were fully explored. Results: CMS classification was efficiently applied to GAC. CMS4, characterized by EMT activation, stromal invasion, angiogenesis, and the worst clinical outcomes (median OS 24.2 months), was the predominant subtype (38.8%~44.3%) and an independent prognostic indicator that outperformed classical TCGA subtyping. CMS1 (20.9%~21.5%) displayed hypermutation, low SCNV, immune activation, and best clinical outcomes (median OS > 120 months). CMS3 (17.95%~25.7%) was characterized by overactive metabolism, KRAS mutation, and intermediate outcomes (median OS 85.6 months). CMS2 (14.6%~16.3%) was enriched for WNT and MYC activation, differentiated epithelial characteristics, APC mutation, lack of ARID1A, and intermediate outcomes (median OS 48.7 months). Notably, CMS1 was strongly correlated with immunotherapy biomarkers and favorable for the anti-PD-1 drug pembrolizumab, whereas CMS4 was poorly responsive but became more sensitive after EMT-based stratification. Conclusions: Our study reveals the practical utility of CMS classification for GAC to improve clinical outcomes and identify candidates who will respond to immunotherapy.