The T-Cell-Inflammation Status Can Predict Outcomes of Adjuvant Chemotherapy in Patients with Gastric Cancer

Adipocytes impact on gastric cancer progression: Prognostic insights and molecular features

BACKGROUND

Adipocytes, especially adipocytes within tumor tissue known as cancer-associated adipocytes, have been increasingly recognized for their pivotal role in the tumor microenvironment of gastric cancer (GC). Their influence on tumor progression and patient prognosis has sparked significant interest in recent research. The main objectives of this study were to investigate adipocyte infiltration, assess its correlation with clinical pathological features, develop a prognostic prediction model based on independent prognostic factors, evaluate the impact of adipocytes on immune cell infiltration and tumor invasiveness in GC, and identify and validate genes associated with high adipocyte expression, exploring their potential diagnostic and prognostic value.

AIM

To explore the relationship between increased adipocytes within tumor tissue and prognosis in GC patients as well as the associated mechanisms and potential biomarkers, using public databases and clinical data.

METHODS

Using mRNA microarray datasets from the Gene Expression Omnibus database and clinical samples from Jiangsu Provincial Hospital, survival and regression analyses were conducted to determine the relevant prognostic factors in GC. Feature gene selection was performed using least absolute shrinkage and selection operator and support vector machine recursive feature elimination algorithms, followed by differential gene expression analysis, gene ontology, pathway analysis, and Gene Set Enrichment Analysis. Immune cell infiltration was analyzed using the CIBERSORT algorithm.

RESULTS

Tumor adipocyte infiltration correlated with poor prognosis in GC, leading to the development of a highly accurate and discriminative prognostic prediction model. Key genes, ADH1B, SFRP1, PLAC9, and FABP4, were identified as associated with high adipocyte expression in GC. The diagnostic and prognostic potential of these identified genes was validated using independent datasets. Downregulation of immune cells was observed in GC with high adipocyte expression.

CONCLUSION

GC with high intratumoral adipocyte expression demonstrated aggressive tumor biology and a poorer prognosis. The genes ADH1B, SFRP1, PLAC9, and FABP4 have been identified as holding diagnostic and prognostic significance in GC. These findings strongly support the use of adipocyte expression as a valuable indicator of tumor invasiveness and anticipated patient outcomes in GC.

Classification of stomach adenocarcinoma based on fatty acid metabolism-related genes frofiling

Background: Fatty acid metabolism (FAM)-related genes play a key role in the development of stomach adenocarcinoma (STAD). Although immunotherapy has led to a paradigm shift in STAD treatment, the overall response rate of immunotherapy for STAD is low due to heterogeneity of the tumor immune microenvironment (TIME). How FAM-related genes affect TIME in STAD remains unclear.

Methods: The univariate Cox regression analysis was performed to screen prognostic FAM-related genes using transcriptomic profiles of the Cancer Genome Atlas (TCGA)-STAD cohort. Next, the consensus clustering analysis was performed to divide the STAD cohort into two groups based on the 13 identified prognostic genes. Then, gene set enrichment analysis (GSEA) was carried out to identify enriched pathways in the two groups. Furthermore, we developed a prognostic signature model based on 7 selected prognostic genes, which was validated to be capable in predicting the overall survival (OS) of STAD patients using the univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. Finally, the “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” (ESTIMATE) algorithm was used to evaluate the stromal, immune, and ESTIMATE scores, and tumor purity of each STAD sample.

Results: A total of 13 FAM-related genes were identified to be significantly associated with OS in STAD patients. Two molecular subtypes, which we named Group 1 and Group 2, were identified based on these FAM-related prognostic genes using the consensus clustering analysis. We showed that Group 2 was significantly correlated with poor prognosis and displayed higher programmed cell death ligand 1 (PD-L1) expressions and distinct immune cell infiltration patterns. Furthermore, using GSEA, we showed that apoptosis and HCM signaling pathways were significantly enriched in Group 2. We constructed a prognostic signature model using 7 selected FAM-related prognostic genes, which was proven to be effective for prediction of STAD (HR = 1.717, 95% CI = 1.105–1.240, p < 0.001). After classifying the patients into the high- and low-risk groups based on our model, we found that patients in the high-risk group tend to have more advanced T stages and higher tumor grades, as well as higher immune scores. We also found that the risk scores were positively correlated with the infiltration of certain immune cells, including resting dendritic cells (DCs), and M2 macrophages. We also demonstrated that elevated expression of gamma-glutamyltransferase 5 (GGT5) is significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and increased immune cell infiltration, suggesting that STAD patients with high GGT5 expression in the tumor tissues might have a better response to immunotherapy.

Conclusion: FAM-related genes play critical roles in STAD prognosis by shaping the TIME. These genes can regulate the infiltration of various immune cells and thus are potential therapeutic targets worthy of further investigation. Furthermore, GGT5 was a promising marker for predicting immunotherapeutic response in STAD patients.

Integrative analysis-based identification and validation of a prognostic immune cell infiltration-based model for patients with advanced gastric cancer

BACKGROUNDS

Advanced gastric cancer (GC) remains difficult to conduct individualized prognostic evaluations owing to the highly heterogeneous nature and the low level of immune cell infiltration (ICI) within GC tumors. This study thus sought to develop a model capable of classifying GC patients according to the degree of tumor ICI and gauging prognosis.

METHODS

The degree of ICI in GC patients from the GSE15459, GSE57303, and GSE62254 datasets were estimated, and these values were used to group patients via an unsupervised clustering approach, after which ICI cluster-related genes were identified the association with prognosis through Cox and LASSO regression analyses. The primary risk genes were then verified by immunohistochemical staining of GC tumor tissue samples.

RESULTS

570 patients were clustered into three clusters and 289 ICI cluster-related genes were identified. A prognostic model based on the expression of six crucial ICI risk genes (CXCL11, RBPMS2, LOC400043, JCHAIN, CT83, and ORM1) wa constructed. Patients identified as being high risk based upon the model have poorer clinical features and survival outcomes compared to the other patients. Adjuvant intervention was found to be more beneficial for patients expressing high levels of RBPMS2, JCHAIN, or ORM1. Furthermore, patients expressing low levels of JCHAIN or CT83 in GC tumor tissues were verified to exhibit a significantly better prognosis in a CMU cohort.

CONCLUSION

Advanced GC patients were successfully grouped into clusters based on the degree of intratumoral ICI, and a prognostic evaluation model based on 6 ICI risk genes was developed and validated.

N6-Methyladenosine Related Long Non-Coding RNAs and Immune Cell Infiltration in the Tumor Microenvironment of Gastric Cancer

Aim

To illustrate the influence of N6-methyladenosine long non-coding RNAs and immune cell infiltration in gastric cancer.

Methods

We downloaded workflow-type data and clinical data from The Cancer Genome Atlas project. The relationship of lncRNA and m6A was identified. Kyoto Encyclopedia of Genes and Genomes gene expression enrichment analysis was performed. Lasso regression was utilized to construct a prognostic model. Survival analysis to explore the relationship between m6A lncRNA and clinical survival data. Differential analysis of the tumor microenvironment and immune correlation analysis to determine immune cell infiltration levels and their correlation with clinical prognosis.

Results

Co-expression analysis indicated that lncRNA expression was associated closely with m6A. m6A-lncRNAs were partially highly expressed in tumor tissue and could be used in a prognostic model to predict GC prognosis, independent of other clinical characteristics. “ADIPPOCYTOKINE SIGNALING PATHWAY” was most significantly enriched according to GSEA. ACBD3-AS1 was overexpressed in tumor tissue. Naïve B cell, Plasma cells, resting CD4 memory T cell were highly infiltrated tissues in cluster 2, while Macrophages M2, resting Mast cells, Monocytes, regulates T cells were lowly in cluster 1. All related scores were higher in cluster 2, indicating a lower purity of tumor cells and higher density of immune-related cells in the tumor microenvironment.

Conclusion

m6A lncRNA is closely related to the occurrence and progression of GC. The corresponding prognostic model can be utilized to evaluate the prognosis of GC. m6A lncRNA and related immune cell infiltration in the tumor microenvironment can provide novel therapeutic targets for further research.