Immune Cell Landscape in Gastric Cancer

Biological Clock Genes are Crucial and Promising Biomarkers for the Therapeutic Targets and Prognostic Assessment in Gastric Cancer

BACKGROUND

Gastric cancer is one of the major public health problems worldwide. Circadian rhythm disturbances driven by circadian clock genes play a role in the development of cancer. However, whether circadian clock genes can serve as potential therapeutic targets and prognostic biomarkers for gastric cancer remains elusive.

METHODS

In this study, we comprehensively analyzed the potential relationship between circadian clock genes and gastric cancer using online bioinformatics databases such as GEPIA, cBioPortal, STRING, GeneMANIA, Metascape, TIMER, TRRUST, and GEDS.

RESULTS

Biological clock genes are expressed differently in human tumors. Compared with normal tissues, only PER1, CLOCK, and TIMELESS expression differences were statistically significant in gastric cancer (p < 0.05). PER1 (p = 0.0169) and CLOCK (p = 0.0414) were associated with gastric cancer pathological stage (p < 0.05). Gastric cancer patients with high expression of PER1 (p = 0.0028) and NR1D1 (p = 0.016) had longer overall survival, while those with high expression of PER1 (p = 0.042) and NR1D1 (p = 0.016) had longer disease-free survival. The main function of the biological clock gene is related to the circadian rhythms and melatonin metabolism and effects. CLOCK, NPAS2, and KAT2B were key transcription factors for circadian clock genes. In addition, we also found important correlations between circadian clock genes and various immune cells in the gastric cancer microenvironment.

CONCLUSIONS

This study may establish a new gastric cancer prognostic indicator based on the biological clock gene and develop new drugs for the treatment of gastric cancer using biological clock gene targets.

Decoding the regulatory landscape of lncRNAs as potential diagnostic and prognostic biomarkers for gastric and colorectal cancers

Colorectal cancer (CRC) and gastric cancer (GC) are major contributors to cancer-related mortality worldwide. Despite advancements in understanding molecular mechanisms and improved drug treatments, the overall survival rate for patients remains unsatisfactory. Metastasis and drug resistance are major challenges contributing to the high mortality rate in both CRC and GC. Recent research has shed light on the role of long noncoding RNAs (lncRNAs) in the development and progression of these cancers. LncRNAs regulate gene expression through various mechanisms, including epigenetic modifications and interactions with microRNAs (miRNAs) and proteins. They can serve as miRNA precursors or pseudogenes, modulating gene expression at transcriptional and post-transcriptional levels. Additionally, circulating lncRNAs have emerged as non-invasive biomarkers for the diagnosis, prognosis, and prediction of drug therapy response in CRC and GC. This review explores the intricate relationship between lncRNAs and CRC/GC, encompassing their roles in cancer development, progression, and chemoresistance. Furthermore, it discusses the potential of lncRNAs as therapeutic targets in these malignancies. The interplay between lncRNAs, miRNAs, and tumor microenvironment is also highlighted, emphasizing their impact on the complexity of cancer biology. Understanding the regulatory landscape and molecular mechanisms governed by lncRNAs in CRC and GC is crucial for the development of effective diagnostic and prognostic biomarkers, as well as novel therapeutic strategies. This review provides a comprehensive overview of the current knowledge and paves the way for further exploration of lncRNAs as key players in the management of CRC and GC.

Identification of Immune-Related Genes as Biomarkers for Uremia

Purpose

Uremia, which is characterized by immunodeficiency, is associated with the deterioration of kidney function. Immune-related genes (IRGs) are crucial for uremia progression.

Methods

The co-expression network was constructed to identify key modular genes associated with uremia. IRGs were intersected with differentially expressed genes (DEGs) between uremia and control groups and key modular genes to obtain differentially expressed IRGs (DEIRGs). DEIRGs were subjected to functional enrichment analysis. The protein-protein interaction (PPI) network was constructed. The candidate genes were identified using the cytoHubba tool. The biomarkers were identified using various machine learning algorithms. The diagnostic value of the biomarkers was evaluated using receiver operating characteristic (ROC) analysis. The immune infiltration analysis was implemented. The biological pathways of biomarkers were identified using gene set enrichment analysis and ingenuity pathway analysis. The mRNA expression of biomarkers was validated using blood samples of patients with uremia and healthy subjects with quantitative real-time polymerase chain reaction (qRT-PCR).

Results

In total, four biomarkers (PDCD1, NGF, PDGFRB, and ZAP70) were identified by machine learning methods. ROC analysis demonstrated that the area under the curve values of individual biomarkers were > 0.9, indicating good diagnostic power. The nomogram model of biomarkers exhibited good predictive power. The proportions of six immune cells significantly varied between the uremia and control groups. ZAP70 expression was positively correlated with the proportions of resting natural killer (NK) cells, naïve B cells, and regulatory T cells. Functional enrichment analysis revealed that the biomarkers were mainly associated with translational function and neuroactive ligand-receptor interaction. ZAP70 regulated NK cell signaling. The PDCD1 and NGF expression levels determined using qRT-PCR were consistent with those determined using bioinformatics analysis.

Conclusion

PDCD1, NGF, PDGFRB, and ZAP70 were identified as biomarkers for uremia, providing a theoretical foundation for uremia diagnosis.

Effect of IL-10-producing B cells in peripheral blood and tumor tissue on gastric cancer

BACKGROUND

Interleukin (IL)-10-producing B (B10) cells are generated in response to signals from the tumor microenvironment and promote tumor growth by interacting with B10 cells. We investigated the distributions of immune cells in peripheral blood and tumor tissue samples from patients with gastric cancer (GC).

METHODS

Patients with GC who underwent radical gastrectomy in Seoul St. Mary's Hospital between August 2020 and May 2021 were enrolled in this study. Forty-two samples of peripheral blood were collected, and a pair of gastric mucosal samples (normal and cancerous mucosa; did not influence tumor diagnosis or staging) was collected from each patient after surgery. B10 cells in peripheral blood and cancer mucosa samples were investigated by flow cytometry and immunofluorescence. AGS cells, gastric cancer cell line, were cultured with IL-10 and measured cell death and cytokine secretion. Also, AGS cells were co-cultured with CD19 + B cells and measured cytokine secretion.

RESULTS

The population of B10 cells was significantly larger in the blood of patients with GC compared with controls. In confocal images of gastric mucosal tissues, cancerous mucosa contained more B10 cells than normal mucosa. The population of B10 cells in cancerous mucosa increased with cancer stage. When AGS cells were cultured under cell-death conditions, cellular necrosis was significantly decreased, and proliferation was increased, for 1 day after IL-10 stimulation. Tumor necrosis factor (TNF)-α, IL-8, IL-1β, and vascular endothelial growth factor secretion by cancer cells was significantly increased by coculture of AGS cells with GC-derived CD19+ B cells.

CONCLUSIONS

B cells may be one of the populations that promote carcinogenesis by inducing the production of inflammatory mediators, such as IL-10, in GC. Targeting B10 cells activity could improve the outcomes of antitumor immunotherapy. Video Abstract.

Expression patterns and immunological characterization of PANoptosis -related genes in gastric cancer

Background

Accumulative studies have demonstrated the close relationship between tumor immunity and pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis in gastric cancer (GC) is yet to be fully understood.

Methods

This research attempted to identify the expression patterns of PANoptosis regulators and the immune landscape in GC by integrating the GSE54129 and GSE65801 datasets. We analyzed GC specimens and established molecular clusters associated with PANoptosis-related genes (PRGs) and corresponding immune characteristics. The differentially expressed genes were determined with the WGCNA method. Afterward, we employed four machine learning algorithms (Random Forest, Support Vector Machine, Generalized linear Model, and eXtreme Gradient Boosting) to select the optimal model, which was validated using nomogram, calibration curve, decision curve analysis (DCA), and two validation cohorts. Additionally, this study discussed the relationship between infiltrating immune cells and variables in the selected model.

Results

This study identified dysregulated PRGs and differential immune activities between GC and normal samples, and further identified two PANoptosis-related molecular clusters in GC. These clusters demonstrated remarkable immunological heterogeneity, with Cluster1 exhibiting abundant immune infiltration. The Support Vector Machine signature was found to have the best discriminative ability, and a 5-gene-based SVM signature was established. This model showed excellent performance in the external validation cohorts, and the nomogram, calibration curve, and DCA indicated its reliability in predicting GC patterns. Further analysis confirmed that the 5 selected variables were remarkably related to infiltrating immune cells and immune-related pathways.

Conclusion

Taken together, this work demonstrates that the PANoptosis pattern has the potential as a stratification tool for patient risk assessment and a reflection of the immune microenvironment in GC.

Gastric Cancer and the Immune System: The Key to Improving Outcomes?

Gastric adenocarcinoma is by far the most common form of gastric cancer (GC) and is a highly lethal form of cancer arising from the gastric epithelium. GC is an important area of focus of the medical community, given its often late-stage of diagnosis and associated high mortality rate. While surgery and chemotherapy remain the primary treatments, attention has been drawn to the use of immunologic therapies, which have shown promise in the treatment of other malignancies. The role for immune-based therapies has become clearer as we obtain a greater understanding of the role of the immune system in gastric cancer formation and growth. A variety treatment to augment the immune system are under evaluation in clinical trials, and these include immune checkpoint inhibitors, antibody-drug conjugates, and immune cell-based therapies. Here, we review the immune landscape and immune-based therapies for GC.

A signature based on five immune-related genes to predict the survival and immune characteristics of neuroblastoma

BACKGROUND

MYCN amplification (MNA) has been proved to be related to poor prognosis in neuroblastoma (NBL), but the MYCN-related immune signatures and genes remain unclear.

METHODS

Enrichment analysis was used to identify the significant enrichment pathways of differentially expressed immune-related genes (DEIRGs). Weight gene coexpression network analysis (WGCNA) was applied to reveal the correlation between these DEIRGs and MYCN status. Univariate and multivariate Cox analyses were used to construct risk model. The relevant fractions of immune cells were evaluated by CIBERSORT and single-sample gene set enrichment analysis (ssGSEA).

RESULTS

Five genes, including CHGA, PTGER1, SHC3, PLXNC1, and TRIM55 were enrolled into the risk model. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve showed that our model performed well in predicting the outcomes of NBL (3-years AUC = 0.720, 5-year AUC = 0.775, 10-years AUC = 0.782), which has been validated in the GSE49711 dataset and the E-MTAB-8248 dataset. By comparing with the tumor immune dysfunction and exclusion (TIDE) and tumor inflammation signature (TIS), we further proved that our model is reliable. Univariate and multivariate Cox regression analyses indicated that the risk score, age, and MYCN can serve as independent prognostic factors in the E-MATB-8248. Functional enrichment analysis showed the DEIRGs were enriched in leukocyte adhesion-related signaling pathways. Gene set enrichment analysis (GSEA) revealed the significantly enriched pathways of the five MYCN-related DEIRGs. The risk score was negatively correlated with the immune checkpoint CD274 (PD-L1) but no significant difference with the TMB. We also confirmed the prognostic value of our model in predicting immunotherapeutics.

CONCLUSION

We constructed and verified a signature based on DEIRG that related to MNA and predicted the survival of NBL based on relevant immune signatures. These findings could provide help for predicting prognosis and developing immunotherapy in NBL.

H19 may regulate the immune cell infiltration in carcinogenesis of gastric cancer through miR-378a-5p/SERPINH1 signaling

Background

Increasing studies have indicated that noncoding RNA (ncRNA)-mediated competing endogenous RNA (ceRNA) network serves as a significant role in cancer progression, but the underlying regulatory mechanisms of which in gastric cancer (GC) remain largely unclear.

Methods

Based on Gene Expression Omnibus and The Cancer Genome Atlas datasets, potential biomarkers for GC were screened and validated by machine learning. Then, upstream regulatory ncRNA of potential biomarkers was identified to construct a novel ceRNA network in GC through means of stepwise reverse prediction and validation. Ultimately, tumor immune cell infiltration analysis was performed based on the EPIC algorithm.

Results

A total of 188 differentially expressed genes (DEGs) were screened, and three candidate diagnostic biomarkers (FAP, PSAPL1, and SERPINH1) for GC were identified and validated. Subsequently, H19 and miR-378a-5p were identified as upstream regulatory ncRNAs that could potentially bind SERPINH1 in GC. Moreover, Immune infiltration analysis revealed that each component in the ceRNA network (H19/miR-378a-5p/SERPINH1) was significantly correlated with the infiltration abundances of diverse tumor-infiltrating immune cells.

Conclusions

H19 may regulate the immune cell infiltration in carcinogenesis of GC through miR-378a-5p/SERPINH1 signaling.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02760-6.

Prevalence of Epstein-Barr Virus Infection and Mismatch Repair Protein Deficiency and the Correlation of Immune Markers in Tibetan Patients with Gastric Cancer

Background

Gastric cancer (GC) is a major cause of cancer-related death in China. Immunotherapies based on PD-1/PD-L1 inhibitors have improved the survival of some patients with GC. Epstein–Barr virus (EBV) infection, mismatch repair (MMR) deficiency, and tumor immune microenvironment (TIME) markers (such as CD3, CD8, and PD-L1) may help to identify specific patients who will respond to PD-1/PD-L1 inhibitors. Considering racial heterogeneity, the pattern of TIME markers in Tibetan patients with GC is still unclear. We aimed to identify the prevalence of EBV infection and the MMR status and their association with immune markers in Tibetan GC to aid in patient selection for immunotherapy.

Materials and Methods

From 2001 to 2015, we retrospectively collected 120 tissue samples from consecutive Tibetan GC patients and constructed tissue microarrays. EBV infection was assessed by Epstein–Barr-encoded RNA (EBER) in situ hybridization, and MMR protein levels were measured. Immune markers (including CD3 and CD8) in intraepithelial, stromal, and total areas were detected by immunohistochemistry (IHC). PD-L1 expression was assessed by the combined positive score (CPS). We also analyzed the relationships of EBV infection and MMR status with immune markers.

Results

Of the 120 samples, 11 (9.17%) were EBV positive (+), and 6 (5%) were MMR deficient (dMMR). PD-L1 CPS ≥1% was found in 32.5% (39/120) of Tibetan GC patients. EBV infection was associated with higher numbers of CD3+ T cells (P < 0.05) and CD8+ T cells (P < 0.05) and higher PD-L1 expression (P < 0.05). For the limited number of dMMR patients, no significant relationship was observed between dMMR and TIME markers (P > 0.05).

Conclusions

In Tibetan GC patients, the rates of EBV infection, dMMR, and positive PD-L1 expression were 9.17%, 5%, and 32.5%, respectively. EBV infection was associated with the numbers of CD3+ T cells and CD8+ T cells and PD-L1 expression within the tumor. These markers may guide the selection of Tibetan GC patients for immunotherapy.

Predicting the Survival and Immune Landscape of Colorectal Cancer Patients Using an Immune-Related lncRNA Pair Model

Background

Accumulating evidence has demonstrated that immune-related long non-coding ribonucleic acids (irlncRNAs) can be used as prognostic indicators of overall survival (OS) in patients with colorectal cancer (CRC). Our aim in this research, therefore, was to construct a risk model using irlncRNA pairs with no requirement for a specific expression level, in hope of reliably predicting the prognosis and immune landscape of CRC patients.

Methods

Clinical and transcriptome profiling data of CRC patients downloaded from the Cancer Genome Atlas (TCGA) database were analyzed to identify differentially expressed (DE) irlncRNAs. The irlncRNA pairs significantly correlated with the prognosis of patients were screened out by univariable Cox regression analysis and a prognostic model was constructed by Lasso and multivariate Cox regression analyses. A receiver operating characteristic (ROC) curve was then plotted, with the area under the curve calculated to confirm the reliability of the model. Based on the optimal cutoff value, CRC patients in the high- or low-risk groups were distinguished, laying the ground for evaluating the risk model from the following perspectives: survival, clinicopathological traits, tumor-infiltrating immune cells (TIICs), antitumor drug efficacy, kinase inhibitor efficacy, and molecules related to immune checkpoints.

Results

A prognostic model consisting of 15 irlncRNA pairs was constructed, which was found to have a high correlation with patient prognosis in a cohort from the TCGA (p < 0.001, HR = 1.089, 95% CI [1.067-1.112]). According to both univariate and multivariate Cox analyses, this model could be used as an independent prognostic indicator in the TCGA cohort (p < 0.001). Effective differentiation between high- and low-risk patients was also accomplished, on the basis of aggressive clinicopathological characteristics, sensitivity to antitumor drugs, and kinase inhibitors, the tumor immune infiltration status, and the expression levels of specific molecules related to immune checkpoints.

Conclusion

The prognostic model established with irlncRNA pairs is a promising indicator for prognosis prediction in CRC patients.