Abstract

Background

   Gastric cancer (GC) is the fifth most common cancer and the second
   leading cause of cancer-related deaths worldwide. Due to the lack of
   specific markers, the early diagnosis of gastric cancer is very low,
   and most patients with gastric cancer are diagnosed at advanced stages.
   The aim of this study was to identify key biomarkers of GC and to
   elucidate GC-associated immune cell infiltration and related pathways.

Methods

   Gene microarray data associated with GC were downloaded from the Gene
   Expression Omnibus (GEO). Differentially expressed genes (DEGs) were
   analyzed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia,
   Gene Set Enrichment Analysis (GSEA) and Protein−Protein Interaction
   (PPI) networks. Weighted gene coexpression network analysis (WGCNA) and
   the least absolute shrinkage and selection operator (LASSO) algorithm
   were used to identify pivotal genes for GC and to assess the diagnostic
   accuracy of GC hub markers using the subjects’ working characteristic
   curves. In addition, the infiltration levels of 28 immune cells in GC
   and their interrelationship with hub markers were analyzed using
   ssGSEA. And further validated by RT-qPCR.

Results

   A total of 133 DEGs were identified. The biological functions and
   signaling pathways closely associated with GC were inflammatory and
   immune processes. Nine expression modules were obtained by WGCNA, with
   the pink module having the highest correlation with GC; 13 crossover
   genes were obtained by combining DEGs. Subsequently, the LASSO
   algorithm and validation set verification analysis were used to finally
   identify three hub genes as potential biomarkers of GC. In the immune
   cell infiltration analysis, infiltration of activated CD4 T cell,
   macrophages, regulatory T cells and plasmacytoid dendritic cells was
   more significant in GC. The validation part demonstrated that three hub
   genes were expressed at lower levels in the gastric cancer cells.

Conclusion

   The use of WGCNA combined with the LASSO algorithm to identify hub
   biomarkers closely related to GC can help to elucidate the molecular
   mechanism of GC development and is important for finding new
   immunotherapeutic targets and disease prevention.

   Keywords: gastric cancer (GC), hub markers, immune cell infiltration,
   WGCNA, LASSO

1. Introduction

   GC is one of the most common malignancies in the human digestive tract.
   According to Global Cancer Statistics, GC has become the fifth most
   frequently diagnosed cancer and the third leading cause of cancer
   deaths, making it a major global health crisis ([31]1). In China, the
   total number of new cases of GC in 2020 was 478,000, ranking 2nd in the
   number of incidences of malignant tumors and 373,000 deaths, ranking
   3rd in the number of deaths from malignant tumors ([32]2). The above
   figures are sufficient to show that GC is highly malignant, has a low
   survival rate and poor prognosis and is a serious threat to human
   health and life.

   GC is a malignant disease caused by a combination of factors, such as
   Helicobacter pylori infection, unhealthy lifestyle, genetics and immune
   cell imbalance. The pathogenesis of GC is still not fully understood,
   but the activation of proto-oncogenes caused by the abovementioned
   oncogenic factors is an important molecular mechanism. The molecular
   mechanisms involved in the pathogenesis of the disease still need to be
   further elucidated. Clinical treatments for GC based on surgical
   resection, chemotherapy, radiotherapy or a combination of targeted
   therapies have difficulty completely removing the tumor lesions, and
   the tumor is prone to progression or recurrence with high toxic side
   effects, with a 5-year survival rate of patients as low as 10% to 15%
   ([33]3–[34]5). It is important to emphasize that GC is usually
   asymptomatic in the early stages, and some patients are already at an
   advanced stage when diagnosed, with a survival rate of only 24%
   ([35]6). Therefore, it is important to develop effective biomarkers for
   the prognosis of gastric cancer and for targeted therapy.

   The tumor microenvironment (TME), due to its key role in cancer
   progression and drug resistance, has emerged as a potential
   immunotherapeutic target for a variety of malignancies, including GC.
   The TME consists of different cell types, including immune and
   inflammatory cells (lymphocytes and macrophages), stromal cells
   (fibroblasts, adipocytes and pericytes), small cell organelles, RNA,
   blood vessels and lymphatic vessels, extracellular matrix (ECM) and
   secreted proteins. The cells involved in the GC immune microenvironment
   are called tumor infiltrating immune cells (TIICs) ([36]7).
   Immunotherapy in the treatment of advanced GC improves survival and is
   associated with good survival in GC patients, according to the results
   of the CheckMate 649 case study presented at the European Society for
   Medical Oncology (ESMO) 2020 virtual meeting ([37]8, [38]9). However,
   recent studies have found that abnormal activation of the immune system
   may also be a key factor in the development of GC ([39]10). In short,
   tapping into immune cell-related targets is an effective pathway to
   optimize tumor immunotherapy.

   Due to advances in genomic technology, bioinformatics analysis of gene
   expression profiles has become increasingly popular in molecular
   mechanistic studies and is playing an increasingly important role in
   the discovery of disease-specific biomarkers. Weighted gene
   coexpression network analysis was proposed by Zhang & Horvath in 2005
   as a systematic algorithm widely used for bioinformatics data, avoiding
   the drawbacks of traditional differential gene screening methods, which
   tend to miss core molecules in the regulatory process and make it
   difficult to explore the whole biological system, and has been widely
   used to screen molecular diagnostic markers or therapeutic targets for
   complex diseases ([40]11, [41]12). This provides a new way to predict
   the function of coexpressed genes and to find genes that play a key
   role in human disease. LASSO is a regression method that allows the
   calculation of correlation coefficients between variables and more
   accurate screening of variables ([42]13). There have been a host of
   studies on screening GC biomarkers based on bioinformatics methods both
   domestically and internationally, but there are problems with a small
   sample size and a single data analysis method as well as lack of
   further experimental verification ([43]14–[44]16). Thus, this article
   comprehensively utilizes various bioinformatics methods to integrate
   and analyze gene datasets from multiple platforms, and expand sample
   size and validated by in vitro cellular experiments, for improving the
   scientific nature of bioinformatics analysis, and in order to more
   accurately explore the pathogenesis and therapeutic targets of GC, and
   provide molecular biology basis and new research ideas and directions
   for subsequent experimental research.

   Based on the above, this study used the [45]GSE54129 and [46]GSE65801
   datasets to construct a gene weighted coexpression network by the WGCNA
   algorithm to screen out pivotal modules that are highly relevant to the
   development of GC, analyze the biological functions of the pivotal
   modules and use the LASSO regression model to screen key genes and
   validate them with the [47]GSE118916 dataset, and then further identify
   important prognostic molecular markers and assess the extent of
   associated immune cell infiltration, with a view to providing new
   references for studying the development of GC, potential molecular