Identification and characterization of a novel molecular classification incorporating oxidative stress and metabolism-related genes for stomach adenocarcinoma in the framework of predictive, preventive, and personalized medicine

Oxidative stress controls lncRNA-mediated sow granulosa cell functions in a FoxO1-dependent manner

BACKGROUND

Oxidative stress (OS) is involved in low female fertility by altering multi-omics such as the transcriptome, miRome, and lncRNome in follicular cells and follicular fluid. However, the mechanism by which OS affects multi-omics dynamics remains largely unknown. Here, we report that OS induces lncRNome dynamics in sow granulosa cells (sGCs), which is partially dependent on the transcription factor activity of its effector, FoxO1.

RESULTS

A total of 2,283 putative FoxO recognition elements (FREs) were identified in the promoters of 394 lncRNAs, accounting for 91.20% (394/432) of the lncRNAs regulated by OS. ChIP and reporter assays showed that the effector FoxO1 mediated OS regulation of lncRNA transcription in a transcription factor activity-dependent manner. In sGCs, OS induces the transcription and function (e.g., apoptosis) of NORSF (non-coding RNA involved in sow fertility), a nuclear lncRNA involved in sGC function via FoxO1. Furthermore, FoxO1 has been identified as a transcriptional activator of NORSF in sGCs that interacts with the FRE motif of its promoter. Meanwhile, OS downregulates the transcription of CYP19A1, which encodes an essential enzyme for estrogen synthesis and 17β-estradiol (E2) release by sGCs via the FoxO1 and NORSF axis. Phenotypically, dysregulation of NORSF transcription caused by 2 novel adjacent transitions in the promoter leads to decreased sow fertility.

CONCLUSION

These results suggest a model of OS-stimulated lncRNome dynamics in sGCs and a new signaling pathway of OS that influences sGC function and sow fertility.

Cancer metabolic reprogramming and precision medicine-current perspective

Despite the advanced technologies and global attention on cancer treatment strategies, cancer continues to claim lives and adversely affects socio-economic development. Although combination therapies were anticipated to eradicate this disease, the resilient and restorative nature of cancers allows them to proliferate at the expense of host immune cells energetically. This proliferation is driven by metabolic profiles specific to the cancer type and the patient. An emerging field is exploring the metabolic reprogramming (MR) of cancers to predict effective treatments. This mini-review discusses the recent advancements in cancer MR that have contributed to predictive, preventive, and precision medicine. Current perspectives on the mechanisms of various cancer types and prospects for MR and personalized cancer medicine are essential for optimizing metabolic outputs necessary for personalized treatments.

Research advances in the molecular classification of gastric cancer

Gastric cancer (GC) is a malignant tumor with one of the lowest five-year survival rates. Traditional first-line treatment regimens, such as platinum drugs, have limited therapeutic efficacy in treating advanced GC and significant side effects, greatly reducing patient quality of life. In contrast, trastuzumab and other immune checkpoint inhibitors, such as nivolumab and pembrolizumab, have demonstrated consistent and reliable efficacy in treating GC. Here, we discuss the intrinsic characteristics of GC from a molecular perspective and provide a comprehensive review of classification and treatment advances in the disease. Finally, we suggest several strategies based on the intrinsic molecular characteristics of GC to aid in overcoming clinical challenges in the development of precision medicine and improve patient prognosis.

Unraveling the role of ADAMs in clinical heterogeneity and the immune microenvironment of hepatocellular carcinoma: insights from single-cell, spatial transcriptomics, and bulk RNA sequencing

Background

Hepatocellular carcinoma (HCC) is a prevalent and heterogeneous tumor with limited treatment options and unfavorable prognosis. The crucial role of a disintegrin and metalloprotease (ADAM) gene family in the tumor microenvironment of HCC remains unclear.

Methods

This study employed a novel multi-omics integration strategy to investigate the potential roles of ADAM family signals in HCC. A series of single-cell and spatial omics algorithms were utilized to uncover the molecular characteristics of ADAM family genes within HCC. The GSVA package was utilized to compute the scores for ADAM family signals, subsequently stratified into three categories: high, medium, and low ADAM signal levels through unsupervised clustering. Furthermore, we developed and rigorously validated an innovative and robust clinical prognosis assessment model by employing 99 mainstream machine learning algorithms in conjunction with co-expression feature spectra of ADAM family genes. To validate our findings, we conducted PCR and IHC experiments to confirm differential expression patterns within the ADAM family genes.

Results

Gene signals from the ADAM family were notably abundant in endothelial cells, liver cells, and monocyte macrophages. Single-cell sequencing and spatial transcriptomics analyses have both revealed the molecular heterogeneity of the ADAM gene family, further emphasizing its significant impact on the development and progression of HCC. In HCC tissues, the expression levels of ADAM9, ADAM10, ADAM15, and ADAM17 were markedly elevated. Elevated ADAM family signal scores were linked to adverse clinical outcomes and disruptions in the immune microenvironment and metabolic reprogramming. An ADAM prognosis signal, developed through the utilization of 99 machine learning algorithms, could accurately forecast the survival duration of HCC, achieving an AUC value of approximately 0.9.

Conclusions

This study represented the inaugural report on the deleterious impact and prognostic significance of ADAM family signals within the tumor microenvironment of HCC.

Bioinformatics analyses and experimental validation of the role of phagocytosis in low-grade glioma

BACKGROUND

Phagocytosis is of vital importance in tumor immune response. The alteration of phagocytosis in low-grade glioma (LGG) has not been investigated.

METHODS

The mRNA, copy number variation, single nucleotide variation, and methylation levels of phagocytosis-related genes were summarized in pan-cancer. Non-negative matrix factorization clustering was utilized to identify two LGG subtypes. LASSO regression analysis was performed to construct a phagocytosis-related prognostic signature (PRPS). Immune characteristics, immunotherapy response, and targeted-drug sensitivity were further explored. The phagocytosis activity in glioma was evaluated using scRNA-seq data. Multiplex immunohistochemical (m-IHC) technology was performed to identify the tumor-infiltrating immune cells in LGG.

RESULTS

The phagocytosis-related genes altered obviously in pan-cancer compared with corresponding normal tissues. Two LGG subtypes were obtained and the subtype with poor prognosis was combined with lower tumor purity, more active immune-related pathways, increasing infiltration of CD4+ T cells, CD8+ T cells, and natural killer (NK) cells, decreasing infiltration of macrophages, mast cells, and neutrophils, distinct pathway activity and cell death status, greater response to immunotherapy, and higher sensitivity to cyclophosphamide, erlotinib, gefitinib, lapatinib, and sorafenib. In addition, a PRPS involving 10 genes (i.e., SLC11A1, CAMK1D, PLA2G5, STAP1, ALOX15, PLCG2, SFTPD, AZU1, RAB27A, and LAMTOR2) was constructed to estimate the risk level of each LGG sample and high risk LGG patients had poor prognosis, upregulated infiltration of neutrophil, macrophage, Treg, and myeloid dendritic cell, down regulated infiltration of monocyte and NK cell, and increasing expression of large number of immune checkpoint genes. The phagocytosis activity is notably active in monocyte/macrophage. The m-IHC results confirmed increased infiltration of macrophages and neutrophils in LGG samples with high SLC11A1 expression.

CONCLUSION

The molecular characteristics of phagocytosis were revealed and the PRPS laid the foundation for personalized therapy in LGG.

Network pharmacology analysis and experimental validation to explore the mechanism of kaempferol in the treatment of osteoporosis

Osteoporosis (OP) is a prevalent global disease characterized by bone mass loss and microstructural destruction, resulting in increased bone fragility and fracture susceptibility. Our study aims to investigate the potential of kaempferol in preventing and treating OP through a combination of network pharmacology and molecular experiments. Kaempferol and OP-related targets were retrieved from the public database. A protein-protein interaction (PPI) network of common targets was constructed using the STRING database and visualized with Cytoscape 3.9.1 software. Enrichment analyses for GO and KEGG of potential therapeutic targets were conducted using the Hiplot platform. Molecular docking was performed using Molecular operating environment (MOE) software, and cell experiments were conducted to validate the mechanism of kaempferol in treating OP. Network pharmacology analysis identified 54 overlapping targets between kaempferol and OP, with 10 core targets identified. The primarily enriched pathways included atherosclerosis-related signaling pathways, the AGE/RAGE signaling pathway, and the TNF signaling pathway. Molecular docking results indicated stable binding of kaempferol and two target proteins, AKT1 and MMP9. In vitro cell experiments demonstrated significant upregulation of AKT1 expression in MC3T3-E1 cells (p < 0.001) with kaempferol treatment, along with downregulation of MMP9 expression (p < 0.05) compared to the control group. This study predicted the core targets and pathways of kaempferol in OP treatment using network pharmacology, and validated these findings through in vitro experiments, suggesting a promising avenue for future clinical treatment of OP.

Single-cell data revealed CD14-type and FCGR3A-type macrophages and relevant prognostic factors for predicting immunotherapy and prognosis in stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD) exhibits profound tumor heterogeneity and represents a great therapeutic challenge. Single-cell sequencing technology is a powerful tool to identify characteristic cell types.

Methods

Single-cell sequencing data (scRNA-seq) GSE167297 and bulk RNA-seq data from TCGA, GTEx, GSE26901 and GSE15459 database were included in this study. By downscaling and annotating the cellular data in scRNA-seq, critical cell types in tumor progression were identified by AUCell score. Relevant gene modules were then identified by weighted gene co-expression network analysis (WGCNA). A prognostic scoring system was constructed by identifying prognostic factors in STAD by Least absolute shrinkage and selection operator (LASSO) COX model. The prognosis and model performance in the RiskScore groups were measured by Kaplan-Meier (K-M) curves and Receiver operating characteristic (ROC) curves. Nomogram was drawn based on RiskScore and prognosis-related clinical factors. In addition, we evaluated patient's feedback on immunotherapy in the RiskScore groups by TIMER, ESTIMATE and TIDE analysis. Finally, the expression levels of prognostic factors were verified in gastric cancer cell lines (MKN7 and MKN28) and human normal gastric mucosal epithelial cells (GES-1), and the effects of prognostic factors on the viability of gastric cancer cells were examined by the CCK8 assay and cell cycle.

Results

scRNA-seq analysis revealed that 11 cell types were identified, and macrophages exhibited relatively higher AUCell scores and specifically expressed CD14 and FCGR3A. High macrophage scores worsened the prognosis of STAD patients. We intersected the specifically expressed genes in macrophages subgroups (670) and macrophage module genes (2,360) obtained from WGCNA analysis. Among 86 common genes, seven prognostic factors (RGS2, GNAI2, ANXA5, MARCKS, CD36, NRP1 and PDE4A) were identified and composed a RiskScore model. Patients in low Risk group showed a better survival advantage. Nomogram also provided a favorable prediction for survival at 1, 3 and 5 years in STAD patients. Besides, we found positive feedback to immunotherapy in patients with low RiskScore. The expression tendency of the seven prognostic factors in MKN7 and MKN28 was consistent with that in the RNA-seq data in addition to comparison of protein expression levels in the public HPA (The Human Protein Atlas) database. Further functional exploration disclosed that MARCKS was an important prognostic factor in regulating cell viability in STAD.

Conclusion

This study preliminary uncovered a single cell atlas for STAD patients, and Macrophages relevant gene signature and nomogram displayed favorable immunotherapy and prognostic prediction ability. Collectively, our work provides a new insight into the molecular mechanisms and therapeutic approach for LUAD patients.

Combination of bulk RNA sequencing and scRNA sequencing uncover the molecular characteristics of MAPK signaling in kidney renal clear cell carcinoma

The MAPK signaling pathway significantly impacts cancer progression and resistance; however, its functions remain incompletely assessed across various cancers, particularly in kidney renal clear cell carcinoma (KIRC). Therefore, there is an urgent need for comprehensive pan-cancer investigations of MAPK signaling, particularly within the context of KIRC. In this research, we obtained TCGA pan-cancer multi-omics data and conducted a comprehensive analysis of the genomic and transcriptomic characteristics of the MAPK signaling pathway. For in-depth investigation in KIRC, status of MAPK pathway was quantitatively estimated by ssGSEA and Ward algorithm was utilized for cluster analysis. Molecular characteristics and clinical prognoses of KIRC patients with distinct MAPK activities were comprehensively explored using a series of bioinformatics algorithms. Subsequently, a combination of LASSO and COX regression analyses were utilized sequentially to construct a MAPK-related signature to help identify the risk level of each sample. Patients in the C1 subtype exhibited relatively higher levels of MAPK signaling activity, which were associated with abundant immune cell infiltration and favorable clinical outcomes. Single-cell RNA sequencing (scRNA-seq) analysis of KIRC samples identified seven distinct cell types, and endothelial cells in tumor tissues had obviously higher MAPK scores than normal tissues. The immunohistochemistry results indicated the reduced expression levels of PAPSS1, MAP3K11, and SPRED1 in KIRC samples. In conclusion, our study represents the first integration of bulk RNA sequencing and single-cell RNA sequencing to elucidate the molecular characteristics of MAPK signaling in KIRC, providing a solid foundation for precision oncology.

Cancer Therapeutic Potential and Prognostic Value of the SLC25 Mitochondrial Carrier Family: A Review

Transporters of the solute carrier family 25 (SLC25) regulate the intracellular distribution and concentration of nucleotides, amino acids, dicarboxylates, and vitamins within the mitochondrial and cytoplasmic matrices. This mechanism involves changes in mitochondrial function, regulation of cellular metabolism, and the ability to provide energy. In this review, important members of the SLC25 family and their pathways affecting tumorigenesis and progression are elucidated, highlighting the diversity and complexity of these pathways. Furthermore, the significant potential of the members of SLC25 as both cancer therapeutic targets and biomarkers will be emphasized.

Neutrophil-To-Lymphocyte Ratio as a Prognostic Indicator in Thyroid Cancer

OBJECTIVE

In this study, we aimed to perform a comprehensive analysis to assess the prognostic value of neutrophil-to-lymphocyte ratio (NLR) levels in patients diagnosed with thyroid cancer.

METHODS

We systematically searched multiple databases, including PubMed, the Cochrane Library, EMBASE, and Google Scholar, up until March 30, 2023, to identify relevant articles. The clinical outcomes evaluated in this study included overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), and cause-specific survival (CSS).

RESULTS

This analysis includes 21 articles with 5187 patients in total. The pooled results revealed that patients with high NLR levels had significantly poorer OS (HR: 2.578, 95% CI: 2.050-3.242, P < 0.001), PFS (HR: 2.143, 95% CI: 1.616-2.843, P < 0.001), DFS (HR: 1.377, 95% CI: 1.045-1.816, P = 0.023), and CSS (HR: 2.842, 95% CI: 1.334-6.053, P = 0.007). The subgroup analyses were performed based on different study regions, treatment modalities, cancer types, and NLR cut-off values, and the above conclusion remained consistent in the majority of subgroup analyses. The stability and reliability of the aforementioned results were supported by the sensitivity analysis and publication bias test.

CONCLUSION

The baseline NLR levels were useful predictors of outcomes in patients with thyroid cancer.

Low-density lipoprotein receptor promotes crosstalk between cell stemness and tumor immune microenvironment in breast cancer: a large data-based multi-omics study

BACKGROUND

Tumor cells with stemness in breast cancer might facilitate the immune microenvironment's suppression process and led to anti-tumor immune effects. The primary objective of this study was to identify potential targets to disrupt the communication between cancer cell stemness and the immune microenvironment.

METHODS

In this study, we initially isolated tumor cells with varying degrees of stemness using a spheroid formation assay. Subsequently, we employed RNA-seq and proteomic analyses to identify genes associated with stemness through gene trend analysis. These stemness-related genes were then subjected to pan-cancer analysis to elucidate their functional roles in a broader spectrum of cancer types. RNA-seq data of 3132 patients with breast cancer with clinical data were obtained from public databases. Using the identified stemness genes, we constructed two distinct stemness subtypes, denoted as C1 and C2. We subsequently conducted a comprehensive analysis of the differences between these subtypes using pathway enrichment methodology and immune infiltration algorithms. Furthermore, we identified key immune-related stemness genes by employing lasso regression analysis and a Cox survival regression model. We conducted in vitro experiments to ascertain the regulatory impact of the key gene on cell stemness. Additionally, we utilized immune infiltration analysis and pan-cancer analysis to delineate the functions attributed to this key gene. Lastly, single-cell RNA sequencing (scRNA-seq) was employed to conduct a more comprehensive examination of the key gene's role within the microenvironment.

RESULTS

In our study, we initially identified a set of 65 stemness-related genes in breast cancer cells displaying varying stemness capabilities. Subsequently, through survival analysis, we pinpointed 41 of these stemness genes that held prognostic significance. We observed that the C2 subtype exhibited a higher stemness capacity compared to the C1 subtype and displayed a more aggressive malignancy profile. Further analysis using Lasso-Cox algorithm identified LDLR as a pivotal immune-related stemness gene. It became evident that LDLR played a crucial role in shaping the immune microenvironment. In vitro experiments demonstrated that LDLR regulated the cell stemness of breast cancer. Immune infiltration analysis and pan-cancer analysis determined that LDLR inhibited the proliferation of immune cells and might promote tumor cell progression. Lastly, in our scRNA-seq analysis, we discovered that LDLR exhibited associations with stemness marker genes within breast cancer tissues. Moreover, LDLR demonstrated higher expression levels in tumor cells compared to immune cells, further emphasizing its relevance in the context of breast cancer.

CONCLUSION

LDLR is an important immune stemness gene that regulates cell stemness and enhances the crosstalk between breast cancer cancer cell stemness and tumor immune microenvironment.

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient's quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients. Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach. Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis. Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC.

The core role of macrophages in hepatocellular carcinoma: the definition of molecular subtypes and the prognostic risk system

Background: In patients with hepatocellular carcinoma (HCC), the tumor microenvironment (TME) is resistant to immunotherapy because of its specificity. It is meaningful to explore the role of macrophage, which is one of the most abundant immune cells in the TME, in cellular communication and its effect on the prognosis and immunotherapy of HCC. Methods: Dimensionality reduction and clustering of the single-cell RNA-seq data from the GSE149614 dataset were carried out to identify the cellular composition of HCC. CellChat was used to analyze the communication between different cells. The specifically highly expressed genes of macrophages were extracted for univariate Cox regression analysis to obtain prognostic genes for HCC cluster analysis, and the risk system of macrophage-specifically highly expressed genes was developed by random forest analysis and multivariate Cox regression analysis. Prognosis, TME infiltration, potential responses to immunotherapy, and antineoplastic drugs were compared among molecular subtypes and between risk groups. Results: We found that HCC included nine identifiable cell types, of which macrophages had the highest communication intensity with each of the other eight cell types. Of the 179 specifically highly expressed genes of macrophage, 56 were significantly correlated with the prognosis of HCC, which classified HCC into three subtypes, which were reproducible and produced different survival outcomes, TME infiltration, and immunotherapy responses among the subtypes. In the integration of four macrophage-specifically highly expressed genes for the development of a risk system, the risk score was significantly involved in higher immune cell infiltration, poor prognosis, immunotherapy response rate, and sensitivity of six drugs. Conclusion: In this study, through single-cell RNA-seq data, we identified nine cell types, among which macrophage had the highest communication intensity with the rest of the cell types. Based on specifically highly expressed genes of macrophage, we successfully divided HCC patients into three clusters with distinct prognosis, TME, and therapeutic response. Additionally, a risk system was constructed, which provided a potential reference index for the prognostic target and preclinical individualized treatment of HCC.

Multi-omics analyses based on genes associated with oxidative stress and phospholipid metabolism revealed the intrinsic molecular characteristics of pancreatic cancer

Oxidative stress (OS), which impacts lipid metabolic reprogramming, can affect the biological activities of cancer cells. How oxidative stress and phospholipid metabolism (OSPM) influence the prognosis of pancreatic cancer (PC) needs to be elucidated. The metabolic data of 35 pancreatic tumor samples, 34 para-carcinoma samples, and 31 normal pancreatic tissues were obtained from the previously published literature. Pan-cancer samples were obtained from The Cancer Genome Atlas (TCGA). And the Gene Expression Omnibus (GEO), International Cancer Genome Consortium (ICGC), ArrayExpress, and the Genotype-Tissue Expression (GTEx) databases were searched for more PC and normal pancreatic samples. The metabolites in PC were compared with normal and para-carcinoma tissues. The characteristics of the key OSPM genes were summarized in pan-cancer. The random survival forest analysis and multivariate Cox regression analysis were utilized to construct an OSPM-related signature. Based on this signature, PC samples were divided into high- and low-risk subgroups. The dysregulations of the tumor immune microenvironment were further investigated. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was conducted to investigate the expression of genes in the signature in PC and normal tissues. The protein levels of these genes were further demonstrated. In PC, metabolomic studies revealed the alteration of PM, while transcriptomic studies showed different expressions of OSPM-related genes. Then 930 PC samples were divided into three subtypes with different prognoses, and an OSPM-related signature including eight OSPM-related genes (i.e., SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, ECT2, SLC22A3, and FGD6) was developed. High- and low-risk subgroups divided by the signature showed different prognoses, expression levels of immune checkpoint genes, immune cell infiltration, and tumor microenvironment. The risk score was negatively correlated with the proportion of TIL, pDC, Mast cell, and T cell co-stimulation. The expression levels of genes in the signature were verified in PC and normal samples. The protein levels of SLC2A1, MMP14, TOP2A, MBOAT2, ANLN, and SLC22A3 showed up-regulation in PC samples compared with normal tissues. After integrating metabolomics and transcriptomics data, the alterations in OSPM in PC were investigated, and an OSPM-related signature was developed, which was helpful for the prognostic assessment and individualized treatment for PC.

Neutrophil elastase as a potential biomarker related to the prognosis of gastric cancer and immune cell infiltration in the tumor immune microenvironment

Exploring biomarkers interrelated the tumor immune microenvironment (TIME) provides novel ideas for predicting the prognosis of gastric cancer (GC) and developing new treatment strategies. We analyzed the differential gene expression levels between the high and low StromalScore and ImmuneScore groups. Neutrophil elastase (ELANE) was evaluated as a potential biomarker by conducting intersection analysis of the protein-protein interaction network and univariate Cox regression analysis. The expression of ELANE was evaluated by immunohistochemistry. Its prognostic value was evaluated using Kaplan-Meier (K-M) survival curves and multivariate Cox regression analysis and its potential biological molecular mechanism was examined by gene set enrichment analysis (GSEA). We applied the CIBERSORT computing method to analyze the relationship between ELANE and tumor immune-infiltrating cells (TIICs). K-M survival curve showed that higher ELANE expression was closely related to shorter overall survival. The Cox regression analysis indicated that the high expression of ELANE was an independent prognostic risk factor in patients with GC. The GSEA revealed that genes in the ELANE high-expression group were involved in the signaling pathways regulating immune response; genes in the ELANE low-expression group were involved in the signaling pathways that regulate metabolism. ELANE might be participate in the change of TIME from immunodominant to metabolically dominant and its expression was closely related to tumor mutation burden and multiple TIICs. ELANE is a potential biomarker for predicting the GC patients' survival and prognosis. It influences the tumor immune cell infiltration in the TIME, and affects the TIME to maintain their immune status.