Identification And Characterization Of HER2-Associated Metabolic Heterogeneity In Gastric Cancer Based On The Multi-Omics Integration Strategy

Background: Gastric cancer(GC),one of the most lethal digestive tract cancers,is the third leading cause of cancer-related death globally.GC is a metabolic illness in addition to a consumptive disease.The metabolic heterogeneity of GC is determined by metabolic reprogramming in tumor microenvironment,which influences the treatment strategy and clinical outcomes of patients.In recent years,with the optimization of the comprehensive treatment strategy of multidisciplinary team cooperation,the morbidity and mortality of GC show a downward trend,but the overall prognosis is still not optimistic.Human epidermal growth factor receptor 2(HER2),commonly known as ERBB2,is the therapeutic target of trastuzumab,which is often used with chemotherapy or immunotherapy to improve patients’ prognoses.Primary and acquired drug resistances are the primary causes of its diminishing effectiveness.Consequently,a comprehensive investigation of HER2-associated metabolic heterogeneity may provide novel insights for the advancement of precision cancer therapy.Aim: We aimed to investigate the significant role of HER2/ERBB2 in the incidence and progression of tumors through bioinformatics analysis.The integrated multi-omics integration strategy emphasized the significant regulatory impact of HER2 on GC metabolic reprogramming and the immunological microenvironment,and summarized the GC metabolic profile.We intended to identify the classical metabolic pathways associated with HER2,precisely characterize the metabolic heterogeneity of GC,and forecast the clinical prognosis and therapeutic methods for GC patients with distinct metabolic features.Materials and methods: From June to December 2021,blood samples of 112 patients with GC and 112 healthy volunteers from First Affiliated Hospital of Dalian Medical University were obtained for non-target metabolomics detection.The TCGA and TCPA databases were queried for pan-cancer’s transcriptome data,GC’s proteome data,and relevant clinical information(including prognostic information,tumor stage and grade).The comprehensive characterization of ERBB2 in pan-cancer was carried out using bioinformatics methods.The differentially expressed metabolites between GC and healthy volunteers were compared.Weighted metabolite correlation network analysis was implemented to identify HER2 co-expressed metabolites.Metabo Analyst5.0 and MBROLE2.0 platforms were employed to perform the enrichment analysis of differentially expressed metabolites and co-expressed metabolites,respectively.The GC samples were hierarchically clustered using the "Consensus Cluster Plus" R package,and the metabolic classifier of GC was created based on the intrinsic metabolic heterogeneity in order to achieve precise classification.We further investigated the clinical outcomes,molecular characteristics and treatment strategies of GC patients with different metabolic subtypes.Finally,we depicted the panoramic overview of HER2 co-expressed metabolic pathways,therefore laying the groundwork for subsequent research on various kinds of malignancies.Results:1.Pan-cancer analysis of ERBB2 highlighted its close association with the immune microenvironment and metabolic remodelling of multiple human cancers:(1)The expression level of ERBB2 was detected to be significantly different between cancerous and para-cancerous tissues,which was closely associated with tumor stage,grade,and prognosis.(2)Transcriptomic and proteomic analyses revealed that ERBB2/HER2 played an intricate regulatory role in tumor immune microenvironment and metabolic reprogramming.2.Characterization of metabolic landscape of GC and identification of HER2co-expressed metabolic pathways:(1)Many amino acid metabolites were considerably elevated in the blood of GC patients compared to healthy controls,but lipid metabolite levels reduced dramatically.These differentially expressed metabolites were mainly enriched in alanine,aspartate and glutamate(AAG)metabolism.(2)34 metabolites were identified to be co-expressed with HER2 level,which were mainly enriched in AAG and glycolysis/gluconeogenesis(GG)metabolism.(3)The expression of L-aspartic acid in the peripheral blood of GC patients was highly up-regulated,had a co-expression relationship with HER2,and was strongly associated with the tumor’s stage,grade,and subtype,making it a viable therapeutic target.3.Identification of metabolic heterogeneity and individualized therapeutic options for GC patients:(1)GC patients were successfully divided into four metabolic subtypes(i.e.quiescent,GG,AAG and mixed subtypes)based on the consensus clustering algorithm.(2)The GG subtype was characterized by a lower level of ERBB2 expression,a higher proportion of the inflammatory phenotype,higher infiltration abundance of immunocytes,higher expression levels of immune checkpoints,and the worst prognosis.However,contradictory features were found in the mixed subtype with the best prognosis.(3)The GG and mixed subtypes were found to be highly sensitive to chemotherapy,whereas the quiescent and AAG subtypes were more likely to benefit from immunotherapy.4.Investigation of the panoramic overview of GG and AAG metabolism in pan-cancer:(1)Significant difference in the expression levels and methylation levels of numerous GG and AAG metabolism-related genes were observed between cancer tissues and adjacent non-cancerous tissues.Many GG and AAG metabolism-related genes were closely associated with the clinical outcomes of patients with GC.(2)There were apparent single nucleotide variants and copy number variations of numerous GG and AAG metabolism-related genes in pan-cancer.(3)The expression levels of numerous GG and AAG metabolism-related genes were significantly associated with a variety of typical oncogenic pathways.Conclusions:1.Transcriptomics and proteomics analyses highlighted that HER2/ERBB2 was significantly associated with the occurrence,progression,immune microenvironment and metabolic reprogramming of GC patients.2.Metabolomics analysis revealed the co-expressed relationship between alanine,aspartate and glutamate and glycolysis/gluconeogenesis metabolisms and HER2 level in GC.Aberrant expressed L-aspartic acid served as a potential therapeutic target for GC patients.3.Based on the metabolic pathways co-expressed by HER2,we successfully created a metabolic classifier of GC and properly recognized its metabolic heterogeneity,giving a novel perspective for the development of precision medicine.4.The pan-cancer characterization of the GG and AAG metabolism emphasized the central role of metabolic reprogramming in the pathophysiological process of tumors and established the groundwork for future research on the tumor mechanism.