Genomics Score Based on Genome-Wide Network Analysis for Prediction of Survival in Gastric Cancer: A Novel Prognostic Signature

Establishment of Golgi apparatus-related genes signature to predict the prognosis and immunotherapy response in gastric cancer patients

The Golgi apparatus plays a crucial role in intracellular protein transportation, processing, and sorting. Dysfunctions of the Golgi apparatus have been implicated in tumorigenesis and drug resistance. This study aimed to investigate the prognostic and treatment response assessment value of Golgi apparatus-related gene (GARGs) features in gastric cancer patients. Transcriptome data and clinical information of gastric cancer patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Cox regression analysis was employed to assess the prognostic significance of GARGs and construct risk features. The immune landscape, drug sensitivity, immune therapy response, gene expression patterns, and somatic mutation characteristics were analyzed between different risk groups. A nomogram model for predicting gastric cancer prognosis was developed and evaluated. Among 1643 GARGs examined, 365 showed significant associations with gastric cancer prognosis. Five independent prognostic GARGs (NGF, ABCG1, CHAC1, GBA2, PCSK7) were selected to construct risk features for gastric cancer patients. These risk features effectively stratified patients into high-risk and low-risk groups, with the former exhibiting worse prognosis than the latter. Patients in the high-risk group displayed higher levels of immune cell infiltration, while the expression levels of NGF, CHAC1, GBA2, PCSK7 were significantly correlated with immune cell infiltration. Notably, the low-risk group exhibited higher sensitivity to epothilone.B, metformin, and tipifarnib compared to the high-risk group. Moreover, patients in the low-risk group demonstrated greater responsiveness to immune therapy than those in the high-risk group. In terms of biological processes and KEGG pathways related to immunity regulation, significant suppression was observed in the high-risk group compared to the low-risk group; meanwhile cell cycle pathways exhibited significant activation in the high-risk group. Furthermore, the low-risk group exhibited a higher tumor mutation burden compared to the high-risk group. The risk features derived from GARGs, in conjunction with age, were identified as independent risk factors for gastric cancer. The nomogram incorporating these factors demonstrated improved performance in predicting gastric cancer prognosis. Our study established risk features derived from GARGs that hold potential clinical utility in prognostic assessment and immune therapy response evaluation of gastric cancer patients.

Relationship between NLRC5 gene polymorphisms and gastric cancer susceptibility

BACKGROUND

The NF-κB signaling pathway exerts a synergistic effect on gastric carcinogenesis. NLRC5 is an upstream regulator of the NF-κB signaling pathway, which is closely related to gastric carcinogenesis. Since NLRC5 expression is different among gastric cancer patients, we speculated that it might be related to NLRC5 gene polymorphisms. We explored the associations between NLRC5 gene polymorphism and gastric carcinogenesis through gene sequencing.

AIM

To investigate the associations of the NLRC5 rs56315364 and rs289726 gene polymorphisms with gastric cancer susceptibility and prognosis.

METHODS

A total of 75 gastric cancer patients and 59 healthy volunteers (age- and sex-matched) were enrolled in the study from September 2014 to October 2016. The NLRC5 rs56315364 and rs289726 genotypes were determined by first-generation sequencing of PCR products. Sequencing products were analyzed using MegAlign and Chromas 2.4.3 software. Differences in NLRC5 gene polymorphisms between patients with gastric cancer and healthy volunteers were identified to investigate the relationship between NLRC5 gene polymorphisms and the prognosis of gastric cancer.

RESULTS

The NLRC5 rs56315364 CC genotype increased the risk of gastric cancer [odds ratio (OR) = 7.06, 95% confidence interval (CI): 2.81-17.72], as did the rs289726 TC and CC genotypes (OR = 11.04, 95%CI: 4.29-28.43; OR = 4.77, 95%CI: 1.57-14.48, respectively). There was no significant difference in the genotype frequency of NLRC5 rs56315364 and rs289726 between the Helicobacter pylori (H. pylori)-negative group and the H. pylori-positive group (P > 0.05). Survival analysis showed that the NLRC5 rs289726 genotype was correlated with the prognosis of gastric cancer (P < 0.05), and the NLRC5 rs289726 CC genotype was associated with the worst prognosis. Multivariate Cox regression analysis showed that age and tumor-node-metastasis (TNM) stage were correlated with the prognosis of gastric cancer patients (P < 0.05)

CONCLUSION

The NLRC5 rs56315364 CC and rs289726 TC genotypes significantly increase the risk of gastric cancer. Older age and higher TNM stage are associated with the worse prognosis of patients with gastric cancer. The prognosis of gastric cancer patients with NLRC5 rs289726 CC genotype is the worst.

Identification of three immune subtypes characterized by distinct tumor immune microenvironment and therapeutic response in stomach adenocarcinoma

BACKGROUND

In primary stomach adenocarcinoma (STAD), the tumor immune microenvironment (TIME) is important for cancer occurrence and progression; however, its clinical significance remains unclear. This study investigated the association between patient survival, TIME, and therapeutic response to STAD.

METHODS

Gene expression profiles of STAD cases were collected from the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus. Molecular subtypes were explored with consistent clustering methods according to 119 immune signatures and the infiltrating scores of 22 immune cells using the Multi-Omics Immuno-Oncology Biological Research algorithm. We determined IFNγ scores and immune cytolytic activity (CYT) scores on the basis of corresponding gene signatures via single-sample Gene Set Enrichment Analysis. Comparisons of survival, TIME, 10 immunity-related oncogenic pathways, immune checkpoint expression, and therapeutic response were conducted among the three subtypes. We further applied linear discriminant analysis to construct a characteristic index to classify the subtypes, and the Pearson correlation coefficient for the relationship between the index and immune checkpoint genes. Weighted Correlation Network Analysis (WGCNA) was used to mine the associated modules and specific genes.

RESULTS

We collected gene expression profiles from 352 STAD cases in the TCGA database, 300 in GSE62254, and 344 in GSE84437. Three STAD subtypes (IS1-IS3) were established according to the TIME signatures. The IS3 subtype had the highest immune score and the best prognosis, as well as markedly increased immune T-cell CYT, Th1/IFNγ scores, and immune checkpoint gene expression, compared to the other two subtypes. It was highly similar to the PD-1 response group in the previous study samples of GSE91061. The established TIME classification index performed well in classifying subtypes and was directly proportional to immune checkpoint-related gene expression levels. WGCNA explored 6 modules and 14 genes, namely DYSF, MAN1C1, HTRA3, EMCN, RFLNB, KANK3, MAGEH1, CD93, PCAT19, FUT11, BMP1, FOSB, DCHS1, and TCF3, which were associated with the established TIME classification index and STAD patient prognosis.

CONCLUSION

TIME phenotypes of STAD patients could be divided into three different molecular subtypes, which displayed different prognoses, immune features, and therapeutic responses. Our results shed new light on predicting patient outcomes and the discovery of new anti-STAD therapeutic strategies according to the TIME.

The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning

Background

Gastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies.

Methods

Based on the abundance of tumor-infiltrating immune cells in GC patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm to identify robust clusters of patients, and assessed their reproducibility in an independent cohort from Gene Expression Omnibus. We further confirmed the feasibility of our immune subtypes in five independent pan-cancer cohorts. Finally, functional enrichment analyses were provided, and a deep learning model studying the pathological images was constructed to identify the immune subtypes.

Results

We identified and validated three reproducible immune subtypes presented with diverse components of tumor-infiltrating immune cells, molecular features, and clinical characteristics. An immune-inflamed subtype 3, with better prognosis and the highest immune score, had the highest abundance of CD8+ T cells, CD4+ T–activated cells, follicular helper T cells, M1 macrophages, and NK cells among three subtypes. By contrast, an immune-excluded subtype 1, with the worst prognosis and the highest stromal score, demonstrated the highest infiltration of CD4+ T resting cells, regulatory T cells, B cells, and dendritic cells, while an immune-desert subtype 2, with an intermediate prognosis and the lowest immune score, demonstrated the highest infiltration of M2 macrophages and mast cells, and the lowest infiltration of M1 macrophages. Besides, higher proportion of EVB and MSI of TCGA molecular subtyping, over expression of CTLA4, PD1, PDL1, and TP53, and low expression of JAK1 were observed in immune subtype 3, which consisted with the results from Gene Set Enrichment Analysis. These subtypes may suggest different immunotherapy strategies. Finally, deep learning can predict the immune subtypes well.

Conclusion

This study offers a conceptual frame to better understand the tumor immune microenvironment of GC. Future work is required to estimate its reference value for the design of immune-related studies and immunotherapy selection.