Abstract

   Background: Gastric carcinoma (GC) is a molecularly and phenotypically
   highly heterogeneous disease, making the prognostic prediction
   challenging. On the other hand, Inflammation as part of the active
   cross-talk between the tumor and the host in the tumor or its
   microenvironment could affect prognosis.

   Method: We established a prognostic multi lncRNAs signature that could
   better predict the prognosis of GC patients based on
   inflammation-related differentially expressed lncRNAs in GC.

   Results: We identified 10 differently expressed lncRNAs related to
   inflammation associated with GC prognosis. Kaplan-Meier survival
   analysis demonstrated that high-risk inflammation-related lncRNAs
   signature was related to poor prognosis of GC. Moreover, the
   inflammation-related lncRNAs signature had an AUC of 0.788, proving
   their utility in predicting GC prognosis. Indeed, our risk signature is
   more precise in predicting the prognosis of GC patients than
   traditional clinicopathological manifestations. Immune and
   tumor-related pathways for individuals in the low and high-risk groups
   were further revealed by GSEA. Moreover, TCGA based analysis revealed
   significant differences in HLA, MHC class-I, cytolytic activity,
   parainflammation, co-stimulation of APC, type II INF response, and type
   I INF response between the two risk groups. Immune checkpoints revealed
   CD86, TNFSF18, CD200, and LAIR1 were differently expressed between
   lowand high-risk groups.

   Conclusion: A novel inflammation-related lncRNAs ([32]AC015660.1,
   LINC01094, [33]AL512506.1, [34]AC124067.2, [35]AC016737.1,
   [36]AL136115.1, [37]AP000695.1, AC104695.3, LINC00449, [38]AC090772.1)
   signature may provide insight into the new therapies and prognosis
   prediction for GC patients.

   Keywords: gastric carcinoma (GC), inflammation, lncRNAs, long
   non-coding RNAs, prognosis (carcinoma), immune infiltration, signature

Introduction

   Gastric carcinoma (GC) is a highly lethal and aggressive cancer, the
   third most common cause of cancer death globally, a disease with high
   molecular and phenotypic heterogeneity ([39]Smyth et al., 2020), with
   over 1 million estimated new cases annually ([40]Hacker and Lordick,
   2015). Helicobacter pylori (HP) infection, age, high salt intake, and
   diets low in fruit and vegetables are significant risk factors for GC
   progression ([41]Smyth et al., 2020). Although combined treatment,
   including surgery, chemo-radiotherapy, and chemotherapy, have shown
   remarkable improvements, the prognosis of GC remains undesirable
   ([42]Shah and Kelsen, 2010; [43]Cristescu et al., 2015). Moreover, the
   current TNM staging system of the American Joint Committee on Cancer
   (AJCC) or the Joint International Committee on Cancer (UICC) has shown
   valuable but insufficient prognostication for the prognosis and
   estimation of subsets of GC patients ([44]Marano et al., 2015; [45]Kim
   et al., 2016). Therefore, new biomarkers are needed to discriminate
   high-risk patients with GC to improve personalized cancer treatment.

   The study of tumor-associated inflammation has increased rapidly in the
   past few decades, and it has also been described as a hallmark of
   cancer ([46]Hanahan and Weinberg, 2011). Many cancers arise from
   irritation, chronic infection, and Inflammation, and the tumor
   microenvironment is mainly coordinated by inflammatory cells, which are
   indispensable players in fostering proliferation, neoplastic process,
   survival, and migration. Inflammation as an opportunity for anti-cancer
   therapies is on the rise. At the same time, long non-coding RNAs
   (lncRNAs) are a subset of non-coding RNA molecules with about 200 nt,
   which regulates gene expression and participates in various biological
   regulatory processes, including the regulatory process related to tumor
   genesis, progression, and metastasis ([47]Gupta et al., 2010). At
   present, functional lncRNAs are considered critical roles in several
   biological regulatory processes, such as cell growth, development,
   angiogenesis, and inflammation ([48]Ng et al., 2013; [49]Li et al.,
   2014; [50]Michalik et al., 2014; [51]Ghazal et al., 2015). One recent
   study revealed that lincRNA-Cox2 and lncRNA NEAT1 promotes IL-6, an
   inflammatory cytokine, expression via distinct mechanisms. LncRNA NEAT1
   acts further upstream and potentiates IL-6 expression by promoting the
   JNK1/2 and ERK1/2 signaling cascades ([52]Ma et al., 2020). In a
   related study, [53]Zhou et al. (2016) found that lncRNA ANRIL is
   involved in TNF-α-NF-κB signaling to regulate the inflammatory response
   in endothelial cells. Nevertheless, to date, serial studies that
   systematically evaluate inflammation-related lncRNA prognostic
   signature and its correlation with GC patients remain scarce.

   In our study, a prognostic multi lncRNA signature of
   inflammation-related differentially expressed lncRNAs based on the
   Cancer Genome Atlas (TCGA) data was established for the first time.
   Then, we investigated the role of inflammation-related mRNA, immune
   responses, and N6-methylated- adenosine (m6A) mRNA status in GC
   prognosis.

Methods

Data Collection

   RNA-sequence (32 normal and 375 tumor) data of 443 patients were
   extracted from the TCGA-STAD database. Clinical characteristics of the
   GC patients used in this study are shown in [54]Table 1. The
   corresponding inflammation-related genes were downloaded from The
   Molecular Signatures database (MSigDB,
   [55]http://www.gsea-msigdb.org/gsea/login.jsp) ([56]Liberzon et al.,
   2015), a collection of annotated gene sets for use with Gene Set
   Enrichment Analysis (GSEA) software, which provided comprehensive and
   gene sets for inflammatory markers. Overall, we identified 561
   inflammation-related genes ([57]Supplementary Table S1). The
   association between lncRNAs in TCGA-LIHC dataset and
   inflammation-related genes from MSigDB was assessed by Pearson
   correlation analysis. If the correlation coefficient |R ^2| was greater
   than 0.6 and p < 0.001, the correlation is considered significant, and
   then inflammation-related lncRNAs were selected. The
   clinicopathological data of GC patients were collected, including
   grade, age, stage, TMN, gender, survival time, and survival status.
   False discovery rate (FDR) < 0.05 and |log[2]FC| ≥ 1 were set as the
   significant differential expression of lncRNAs related to Inflammation.
   Firstly, we explored the functions of up and downregulated Inflammation
   related differentially expressed genes (DEGs). Then the biological
   processes, cellular components, and molecular functions associated with
   the DEGs were then evaluated by Gene Ontology (GO). Based on data from
   Kyoto Encyclopedia of Genes and Genomes (KEGG), the functions of
   biological pathways by different expressions of Inflammation-related
   lncRNAs were further analyzed in R software.

TABLE 1.

   The clinical characteristics of patients in the TCGA dataset.
   Variable       Number of samples
   Gender
   Male/Female         285/158
   Age at diagnosis
   ≤65/>65/NA         197/241/5
   Grade
   G1/G2/G3/NA      12/159/263/9
   Stage
   I/II/III/IV/NA 59/130/183/44/27
   T
   T1/T2/T3/T4/NA 23/93/198/119/10
   M
   M0/M1/NA           391/30/22
   N
   N0/N1/N2/N3/NA 132/119/85/88/19
   [58]Open in a new tab

Construction of the Inflammation-Related lncRNAs Prognostic Signature

   To construct a robust and stable prognostic, predictive signature, we
   first used univariate Cox regression and lasso regression analysis, and
   finally used multivariate Cox regression analysis to construct an
   inflammation-related lncRNA signature, and stratified them based on the
   risk score (βlncRNA1 × ExpressionlncRNA1 + βlncRNA2 × ExpressionlncRNA2
   + βlncRNA3×ExpressionlncRNA3 +…+ βlncRNAn × ExpressionlncRNAn). The
   relevant risk score of each GC patient was also evaluated. The lncRNAs
   were divided into low-risk (<median number) and high-risk (≥median
   number) groups based on median scores.

The Predictive Nomogram

   To establish a clinically feasible method to predict the survival rate
   of GC patients, we set a prediction model using nomograms and
   considered the clinicopathological manifestations of GC patients. The
   nomogram predicted 1 -, 2 -, and 3-years overall survival (OS) of GC
   patients, and the statistical significance was based on multivariate
   analysis of OS rate. The predictive factors included risk model and
   grade, age, stage, TMN, gender.

Immunity Analysis and Related Gene Expression

   Meanwhile, the relative immune cell infiltration level of individual
   samples was quantified by single-sample gene set enrichment analysis
   (ssGSEA) ([59]Yi et al., 2020) comparing the CIBERSORT ([60]Newman et
   al., 2015; [61]Charoentong et al., 2017), CIBERSORT−ABS ([62]Wang et
   al., 2020), QUANTISEQ ([63]Plattner et al., 2020), MCPCOUNTER ([64]Shi
   et al., 2020a), XCELL ([65]Aran et al., 2017), EPIC ([66]Racle et al.,
   2017) and TIMER ([67]Li et al., 2017a)algorithms to evaluate the
   cellular component or cellular immune response lncRNAs signature
   between the high-risk and low-risk groups based on inflammation
   correlation. Then, the heatmap demonstrates the differences in immune
   responses under different algorithms. In addition, we used the ssGSEA
   method to quantitatively analyze the tumor-infiltrating immune cell
   subsets in the high-risk and low-risk group and evaluate their immune
   functions.

Statistical Analysis

   All the statistical analyses of data were performed in R software
   (version 4.0.3) with Bioconductor packages including “limma,”
   “survival,” “survminer.” Non paired t-test and Wilcoxon test were used
   to analyze the normal distribution and non-normal distribution
   variables. Based on FDR, the different expression of lncRNAs was
   corrected by the Benjamin Hochberg method to control the elevated
   false-positive rate. To analyze the GC DEGs associated immune status in
   each sample in the TCGA cohort, the relative infiltration of 20 immune
   cell types in the tumor microenvironment was calculated via ssGSEA with
   the application of the“GSVA” package in R. The prognostic performance
   of the GC predictive signature compared with other clinicopathological
   features was measured using the time-dependent receiver operating
   characteristic (ROC) and the decision curve analysis (DCA) ([68]Vickers
   et al., 2008). Logistic regression analysis and a heatmap graph were
   used to analyze the association between inflammation-related lncRNAs
   and clinicopathological manifestations. Kaplan Meier survival analysis
   was used to evaluate the OS of GC patients based on
   Inflammation-related lncRNA signature. For each analysis, p < 0.05 in
   the results was considered statistically significant.

Results

Enrichment Analysis of Inflammation-Related Genes

   We uncovered 150 inflammation-related DEGs (114 downregulated and 36
   upregulated; [69]Supplementary Table S2). Biological processes
   participated in the inflammatory response, defense response, and immune
   system process, among others. Molecular functions mainly regulated
   receptor ligand and signaling receptor activator activity, cytokine
   activity, and chemokine activity. Cellular components were regulated
   primarily on the collagen-containing extracellular matrix, external
   side of the plasma membrane, and endoplasmic reticulum lumen.
   Furthermore, KEGG analysis showed that the differentailly expressed
   genes were significantly enriched in Cytokine-cytokine receptor
   interaction, IL-17 signaling pathway, PI3K-AKT signaling pathway, TNF
   signaling pathway, JAK-STAT signaling pathway, Toll-like receptor
   signaling pathway, Chemokine signaling pathway, and NF-kappa B
   signaling pathway ([70]Figure 1).

FIGURE 1.

   [71]FIGURE 1
   [72]Open in a new tab

   Go and KEGG analysis of differentially expressed inflammation-related
   genes. (A) Biological processes, molecular functions, and cellular
   components analysis of GO (B) Pathway enrichment analysis of KEGG.

The Inflammation-Based lncRNAs Prognostic Signature

   In the preliminary screening, 46 inflammation-related lncRNAs were
   obtained to be associated with OS by univariate Cox analysis
   ([73]Figure 2A). Next, inflammation-related lncRNAs with p < 0.05 in
   univariate Cox analysis were penalized by Lasso regression ([74]Figures
   2B,C). Finally, 10 inflammation-related lncRNAs signature were found to
   be independent prognostic factors of GC patients by multivariate Cox
   regression analysis. ([75]Figure 2D). A novel risk score was calculated
   by multiplying the lncRNA expression of each lncRNA and its
   corresponding coefficient, which was obtained by multivariate Cox
   regression analysis. The formula of the risk score was as follows: risk
   score = (Coefficient lncRNA1 × expression of lncRNA1) + (Coefficient
   lncRNA2 × expression of lncRNA2) + + (Coefficient lncRNAn × expression
   lncRNAn). Overall, we evaluated the risk score and identified 10
   differently expressed inflammation-related lncRNAs as independent
   prognosis predictors of GC ([76]Supplementary Table S3).

FIGURE 2.

   [77]FIGURE 2
   [78]Open in a new tab

   Construction of inflammation-related lncRNAs prognostic signature. (A)
   Univariate cox regression illustrated 46 inflammation-related lncRNAs
   associated with prognosis. (B, C) Inflammation-related lncRNAs with p <
   0.05 in univariate Cox analysis were penalized by LASSO regression
   analysis. (D) Multivariate cox regression analysis revealed 10
   inflammation-related lncRNAs prognostic signature of GC.

Survival Results and Multivariate Examination

   Kaplan-Meier analysis revealed that GC patients in the high-risk group
   had poorer survival rates than those in the low-risk group ([79]Figure
   3A). Meanwhile, the signature lncRNAs, with an AUC of 0.788, exhibited
   superior performance in predicting GC prognosis than traditional
   clinicopathological features ([80]Figures 3B,E). We used the patients’
   risk survival status plots and found that the patients’ risk scores
   were inversely related to the GC patients’ survival. The heatmap
   revealed that the novel inflammation-related lncRNAs identified in our
   study were positively correlated with the risk model. ([81]Figure 3C).
   The AUC for ROC analysis was 0.788, 0.847, and 0.847 for the predictive
   value of the 10 inflammation-related lncRNA signatures of GC patients
   for 1 -, 2 -, 3-years survival, respectively, ([82]Figure 3D). Besides,
   the DCA plot indicated a robust and stable prognostic-predictive
   ability of the inflammation-related lncRNAs signature ([83]Figure 3E).
   The heatmap demonstrated the difference in the expression of ten
   inflammation-related lncRNAs in the two risk population groups
   ([84]Figure 3F). Univariate and multivariate Cox analysis revealed that
   the novel lncRNAs signature (HR: 1.05, 95CI: 1.03–1.07) was an
   independent prognosis predictor of GC patients ([85]Figures 4A,B). The
   lncRNA-mRNA relationship was shown in the network ([86]Figure 4C).
   Also, the heatmap analyzed the correlation between the novel prognostic
   signature and clinical-pathological features ([87]Figure 5). The
   nomogram, which combined both the novel prognostic signature and the
   clinicopathological characteristics ([88]Figure 6), was accurate and
   stable and thus applied to the clinical management for GC patients.

FIGURE 3.

   [89]FIGURE 3
   [90]Open in a new tab

   Validation of the Inflammation-related lncRNAs signature. (A) Survival
   curves result, (B). The AUC values for forecasting OS based on risk
   factors, (C). Risk score distribution and survival status of GC
   patients, (D) AUC of time-dependent ROC curves represented of 1, 2,
   3-years survival rates of GC and risk score (E) The DCA plot. (F)
   Expression profile heatmap of the 10 inflammation-related lncRNAs
   signature.

FIGURE 4.

   [91]FIGURE 4
   [92]Open in a new tab

   Estimation of the clinical value of the novel inflammation-related
   lncRNAs prognostic signature in GC patients based on univariate and
   multivariate COX analysis. (A). Univariate Cox analysis, (B).
   Multivariate Cox analysis, (C). The lncRNA-mRNA relationship
   expression.

FIGURE 5.

   [93]FIGURE 5
   [94]Open in a new tab

   Heatmap for clinical pathology manifestations and inflammation-related
   lncRNAs prognostic signature.

FIGURE 6.

   [95]FIGURE 6
   [96]Open in a new tab

   Construction of a hybrid nomogram based on both prognostic
   inflammation-related lncRNAs and prognostic-related clinicopathological
   factors.

Gene Set Enrichment Analyses

   To investigate the underlying pathways and biological processes, GSEA
   was used to reveal the significant pathways by which novel
   inflammation-related lncRNA signatures regulate tumor-associated and
   immune, such as natural killer cell-mediated cytotoxicity, primary
   immunodeficiency, T cell receptor signaling pathway, Toll-like receptor
   signaling pathway, MAPK signaling pathway, Notch signaling pathway,
   JAK-STAT signaling pathway and pathways in cancer ([97]Figure 7;
   [98]Supplementary Table S4).

FIGURE 7.

   [99]FIGURE 7
   [100]Open in a new tab

   GSEA for inflammation-related lncRNAs signature.

Immunological Reaction and Related Gene Expression

   The heatmap showed the immunological responses based on QUANTISEQ,
   CIBERSORT, CIBERSORT-ABS, MCPCOUNTER, XCELL, TIMER, and EPIC
   algorithms. ([101]Figure 8). ssGSEA correlation analysis of immune cell
   subsets with relevant functions based on TCGA-STAD data showed
   cytolytic activity, MHC class-I, HLA, type I INF response, type II INF
   response, parainflammation, and co-stimulation of APC differed
   significantly between the high and low-risk groups ([102]Figure 9A).
   Given the significance of checkpoint inhibitor-based immunotherapy,
   differences in immune checkpoint expression among high-risk and
   low-risk groups were further explored. Among two risk groups,
   significant differences were discovered in gene expression, including
   CD86, TNFSF18, CD200, and LAIR1 ([103]Figure 9B). Comparison of
   m6A-related mRNA expression among high- and low-risk groups revealed
   the significant expression of HNRNPC, RBM15, YTHDC1, YTHDF2, FTO,
   ZC3H13, and METTL3 ([104]Figure 10).

FIGURE 8.

   [105]FIGURE 8
   [106]Open in a new tab

   The immune responses among the high and low-risk group.

FIGURE 9.

   [107]FIGURE 9
   [108]Open in a new tab

   (A). Correlation of immune cell subsets with related functions (B).
   Comparison of immune checkpoints among the two GC risk groups.

FIGURE 10.

   [109]FIGURE 10
   [110]Open in a new tab

   Comparison of m6A-related genes expression among the two GC risk group.

Discussion

   Inflammation is of essentiality in the development of cancer because
   chronic Inflammation has been shown to increase cancer risk by causing
   tumor initiation, promotion, and metastatic progression ([111]So et
   al., 2020). Meanwhile, one key hallmark of cancer is the ability of
   cancer cells to evade immune destruction ([112]Hanahan and Weinberg,
   2011). Thus, it has the potential to become a novel tumor therapy. In
   our study, a novel inflammation-related prognostic lncRNA signature was
   first constructed based on the existing clinical characteristic of GC
   patients from the TCGA dataset. Then, we investigated the potential
   roles of immune infiltrating cells and immune checkpoint inhibitors in
   the tumor microenvironment in GC prognosis. The findings of our study
   revealed potential biomarkers and therapeutic targets in the
   inflammation signaling pathways. Overall, our analyses uncovered 150
   inflammation-related DEGs. KEGG analysis further indicated that these
   genes were mainly involved in the IL-17 signaling pathway, PI3K-Akt
   signaling pathway, Toll-like receptor signaling pathway, TNF signaling
   pathway, and JAK-STAT signaling pathway, and so forth. Some recent
   studies found that Toll-like receptors play a vital role in the innate
   immune system, particularly in the inflammatory response ([113]Takeda
   and Akira, 2004), and Dectin-1 could suppress Toll-like receptor four
   signals to protect against chronic liver disease in hepatic
   inflammatory cells ([114]Seifert et al., 2015). [115]Balkwill (2006)
   reported that one of the vital chemical mediators implicated in
   inflammation-related cancer is TNFα-α; it is involved in the promotion
   and progression of experimental and human cancers, and its pathways
   lead to the activation of NF-κKey to the B, and AP-1 transcription
   factor complex is the intracellular connection.

   Blocking the link of inflammation to cancer might be potentially a new
   way for tumor treatment. Overall, in our study, the ten differently
   expressed inflammation-related lncRNA signature found was an
   independent prognostic predictor of GC. A recent study found LINC01094
   could promote the proliferation, migration, invasion and
   epithelial-mesenchymal transition of ovarian cancer cells by adsorbing
   miR-577 ([116]Xu et al., 2020). Besides, LINC01094 could promote clear
   cell renal cell carcinoma development through the miR-224-5p/CHSY1
   regulatory axis ([117]Jiang et al., 2020). Linc01094 promotes
   proliferation, migration and invasion of glioma cells by adsorbing
   miR-330-3p and upregulating MSI1 expression ([118]Zhu et al., 2020). In
   a related study, overexpression of LINC00449 could inhibit acute
   myeloid leukemia cell invasion and cell proliferation in vitro and in
   vivo, and the LINC00449/miR-150/FOXD3 signaling pathway may be a novel
   prognostic biomarker or therapeutic target for the treatment of acute
   myeloid leukemia ([119]Shi et al., 2020b). Thus, it is of importance to
   establish a novel prognostic multiinflammation-related lncRNAs
   signature for GC patients. Such findings may provide valuable insights
   for cancer control in the future.

   The Inflammation-related lncRNAs with different expressions were
   divided into low-risk and high-risk groups to investigate their
   potential roles in GC. The Inflammation-related lncRNA signature of the
   high-risk group was correlated with poor prognosis of GC patients in
   the study results. Moreover, the inflammation-related lncRNA signature
   had an AUC of 0.788 but performed well in other validations such as
   DCA, demonstrating their utility in predicting GC prognosis and
   indicating that our risk signature also outperforms traditional clinic
   pathology characteristics.

   The direct correlations between lncRNAs and cancer-derived
   Inflammation, epithelial-to-mesenchymal transition, metastasis, and
   other hallmarks of cancer indicate their potential as cancer biomarkers
   and targets ([120]Tamang et al., 2019). LncRNA H19 could be induced by
   helicobacter pylori infection to promote gastric cancer cell growth via
   strengthening NF-κB-induced inflammation ([121]Zhang et al., 2019).
   Besides, CCAT1 lncRNA Promotes Inflammatory bowel disease malignancy by
   destroying Intestinal Barrier via downregulating miR-185-3p ([122]Ma et
   al., 2019). Increasing evidence suggests that lncRNA is critical in
   mediating both inflammation and cancer ([123]Barsyte-Lovejoy et al.,
   2006; [124]Guo et al., 2019; [125]Song et al., 2020; [126]Sun et al.,
   2020).

   Our study, GSEA revealed immune and tumor-related pathways for
   individuals in the low- and high-risk groups. TCGA revealed significant
   differences in HLA, MHC class-I, cytolytic activity, parainflammation,
   co-stimulation of APC, type II INF response, and type I INF response
   between the low and high-risk groups. Moreover, Immune checkpoints such
   as CD86, TNFSF18, CD200, and LAIR1 were differently expressed between
   high and low-risk groups. Li et al. speculated that Inflammation
   triggered by lncRNA CRNDE could regulate tumorigenesis and development
   through the TLR pathway ([127]Li et al., 2017b). In recent years,
   lncRNAs have gained attention as essential regulators of gene
   expression acting through versatile interactions with DNA, RNA, or
   proteins. Intriguingly, lncRNAs play crucial roles in modulating innate
   immune cell development and inflammatory gene expression ([128]Elling
   et al., 2016). Activating inflammatory pathways, particularly the IFN
   response, can sensitize animal cancer models and cancer patients to
   immune checkpoint inhibitors and positively contribute to anti-tumor
   activity ([129]Zemek et al., 2019). At present, few studies have
   explored the relationship among inflammation and immune checkpoint
   inhibitors. Therefore, lncRNAs might be critical players in the
   transformation of inflammation to cancer through the immune
   microenvironment.

   Although we identified a predictive risk model with ten
   inflammation-related lncRNAs and confirmed that the risk model was
   significantly associated with Inflammation, this work has limitations.
   The study conducted with bioinformatics analysis was not robust enough
   and needed to be confirmed via experimental verification. Hence,
   further laboratory experiments, including a multicenter study with
   larger sample sizes, are required. In summary, in this study, we
   developed a robust prognostic risk model with ten inflammation-related
   lncRNAs. Compared to other clinical parameters, the risk score is an
   independent prognostic index. Therefore, this risk model may serve as a
   prognostic signature and provide clues for individualized immunotherapy
   for GC patients.

Conclusion

   Specific Inflammation-related lncRNAs can provide individualized
   prediction of GC patients’ prognosis.

Data Availability Statement

   The original contributions presented in the study are included in the
   article/[130]Supplementary Material, further inquiries can be directed
   to the corresponding authors.

Author Contributions

   SZ designed and analyzed the research study; SZ wrote and revised the
   article, XL collected and analyzed the data and all authors have read
   and approved the article.

Funding

   Discipline construction project of Guangdong Medical University (No.
   4SG21009G).

Conflict of Interest

   The authors declare that the research was conducted in the absence of
   any commercial or financial relationships that could be construed as a
   potential conflict of interest.

Publisher’s Note

   All claims expressed in this article are solely those of the authors
   and do not necessarily represent those of their affiliated
   organizations, or those of the publisher, the editors and the
   reviewers. Any product that may be evaluated in this article, or claim
   that may be made by its manufacturer, is not guaranteed or endorsed by
   the publisher.

Supplementary Material

   The Supplementary Material for this article can be found online at:
   [131]https://www.frontiersin.org/articles/10.3389/fgene.2021.736766/ful
   l#supplementary-material.
   [132]Click here for additional data file.^ (14.1KB, XLS)
   [133]Click here for additional data file.^ (27KB, XLS)
   [134]Click here for additional data file.^ (26.4KB, XLSX)
   [135]Click here for additional data file.^ (47.3KB, XLSX)

Abbreviation

   GC, Gastric carcinoma; OS, overall survival (OS); DEGs, differentially
   expressed genes; TCGA, The Cancer Genome Atlas; GO, Gene ontology;
   KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene set
   enrichment analyses; FDR, false discovery rate; ssGSEA, single-sample
   gene set enrichment analysis (ssGSEA).

References