CGB5 expression is independently associated with poor overall survival and recurrence-free survival in patients with advanced gastric cancer

Glutamine metabolism genes prognostic signature for stomach adenocarcinoma and immune infiltration: potential biomarkers for predicting overall survival

Background

Stomach adenocarcinoma (STAD), caused by mutations in stomach cells, is characterized by poor overall survival. Chemotherapy is commonly administered for stomach cancer patients following surgical resection. An imbalance in tumor metabolic pathways is connected to tumor genesis and growth. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. Metabolic reprogramming is associated with clinical prognosis in various cancers. However, the role of glutamine metabolism genes (GlnMgs) in the fight against STAD remains poorly understood.

Methods

GlnMgs were determined in STAD samples from the TCGA and GEO datasets. The TCGA and GEO databases provide information on stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and clinical characteristics. Lasso regression was performed to build the prediction model. The relationship between gene expression and Gln metabolism was investigated using co-expression analysis.

Results

GlnMgs, found to be overexpressed in the high-risk group even in the absence of any symptomatology, demonstrated strong predictive potential for STAD outcomes. GSEA highlighted immunological and tumor-related pathways in the high-risk group. Immune function and m6a gene expression differed significantly between the low- and high-risk groups. AFP, CST6, CGB5, and ELANE may be linked to the oncology process in STAD patients. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and medication sensitivity all revealed a strong link to the gene.

Conclusion

GlnMgs are connected to the genesis and development of STAD. These corresponding prognostic models aid in predicting the prognosis of STAD GlnMgs and immune cell infiltration in the tumor microenvironment (TME) may be possible therapeutic targets in STAD. Furthermore, the glutamine metabolism gene signature presents a credible alternative for predicting STAD outcomes, suggesting that these GlnMgs could open a new field of study for STAD-focused therapy Additional trials are needed to validate the results of the current study.

Development and validation of a tobacco smoking-related index for predicting overall survival and immunotherapy response in bladder cancer

Bladder cancer is one of the top five most prevalent cancers in the United States and a major cause of cancer-related mortality worldwide. Meanwhile, tobacco smoking is a well-established modifiable risk factor for bladder cancer, with a population-attributable risk of approximately 50%. But the relationship between the prognosis of bladder cancer and tobacco smoking remains unclear. To further explore the potential relationship between tobacco smoking and bladder cancer prognosis, the bladder cancer dataset from The Cancer Genome Atlas Program was used to build a tobacco smoking-related signature known as the "smoker index" for prognosis prediction. Additionally, we validated the efficacy of the signature with some external datasets. Finally, we preliminarily verified the role of CGB5, the hub gene in the smoker index, through pan-cancer analysis and in vitro assays. The study digs into the underlying connection between tobacco smoking and the prognosis of bladder cancer from a multi-omics perspective.

CGB5, INHBA and TRAJ19 Hold Prognostic Potential as Immune Genes for Patients with Gastric Cancer

BACKGROUND

Gastric cancer (GC) seriously threatens people's health and life quality worldwide.

AIM

The current study sought to explore prognostic immune genes and their regulatory network in GC.

METHODS

First, expression data in GC and normal samples were analyzed based on bioinformatics analysis. Immune-related genes were identified and confirmed with univariate/multivariate Cox analysis and receiver-operating characteristic curve. The upstream transcription factors of immune genes were subsequently predicted, and their regulatory network was constructed. GC and adjacent normal tissues were obtained from 76 patients with GC to determine the expression patterns of immune genes and their correlation with overall prognosis. CD8⁺ T-cell infiltration of patients with high or low risk was detected by means of immunohistochemistry.

RESULTS

Bioinformatics analysis highlighted 3689 differentially expressed genes in GC, including 87 immune genes, 8 of which were significantly associated with patient survival. CGB5 and INHBA were high-risk genes, while TRAJ19 was identified as a low-risk gene, all of which were found to be regulated by 11 different transcription factors. Furthermore, CGB5 and INHBA exhibited negative correlation with the prognosis of GC patients; however, TRAJ19 was positively correlated with GC patient prognosis. The incidence of lymph node metastasis was higher, the pathological stage was advanced and the infiltrated CD8⁺ T cells were fewer in the high-risk GC group.

CONCLUSIONS

Overall, our findings identified the key roles of CGB5, INHBA and TRAJ19 in prognosis GC patients, serving as an important gene set for prognostic prediction.

An immune-related prognostic signature associated with immune landscape and therapeutic responses in gastric cancer

Immune-related genes (IRGs) have attracted attention in recent years as therapeutic targets in various tumors. However, the role of IRGs in gastric cancer (GC) has not been clearly elucidated. This study presents a comprehensive analysis exploring the clinical, molecular, immune, and drug response features characterizing the IRGs in GC. Data were acquired from the TCGA and GEO databases. The Cox regression analyses were performed to develop a prognostic risk signature. The genetic variants, immune infiltration, and drug responses associated with the risk signature were explored using bioinformatics methods. Lastly, the expression of the IRS was verified by qRT-PCR in cell lines. In this manner, an immune-related signature (IRS) was established based on 8 IRGs. According to the IRS, patients were divided into the low-risk group (LRG) and high-risk group (HRG). Compared with the HRG, the LRG was characterized by a better prognosis, high genomic instability, more CD8+ T cell infiltration, greater sensitivity to chemotherapeutic drugs, and greater likelihood of benefiting from the immunotherapy. Moreover, the expression result showed good consistency between the qRT-PCR and TCGA cohort. Our findings provide insights into the specific clinical and immune features underlying the IRS, which may be important for patient treatment.

A novel necroptosis-related gene index for predicting prognosis and a cold tumor immune microenvironment in stomach adenocarcinoma

Background

Gastric cancer (GC) represents a major global clinical problem with very limited therapeutic options and poor prognosis. Necroptosis, a recently discovered inflammatory form of cell death, has been implicated in carcinogenesis and inducing necroptosis has also been considered as a therapeutic strategy.

Objective

We aim to evaluate the role of this pathway in gastric cancer development, prognosis and immune aspects of its tumor microenvironment.

Methods and results

In this study, we evaluated the gene expression of 55 necroptosis-related genes (NRGs) that were identified via carrying out a comprehensive review of the medical literature. Necroptosis pathway was deregulated in gastric cancer samples (n=375) as compared to adjacent normal tissues (n=32) obtained from the “The Cancer Genome Atlas (TCGA)”. Based on the expression of these NRGs, two molecular subtypes were obtained through consensus clustering that also showed significant prognostic difference. Differentially expressed genes between these two clusters were retrieved and subjected to prognostic evaluation via univariate cox regression analysis and LASSO cox regression analysis. A 13-gene risk signature, termed as necroptosis-related genes prognostic index (NRGPI), was constructed that comprehensively differentiated the gastric cancer patients into high- and low-risk subgroups. The prognostic significance of NRGPI was validated in the GEO cohort (GSE84437: n=408). The NRGPI-high subgroup was characterized by upregulation of 10 genes (CYTL1, PLCL1, CGB5, CNTN1, GRP, APOD, CST6, GPX3, FCN1, SERPINE1) and downregulation of 3 genes (EFNA3, E2F2, SOX14). Further dissection of these two risk groups by differential gene expression analysis indicated involvement of signaling pathways associated with cancer cell progression and immune suppression such as WNT and TGF-β signaling pathway. Para-inflammation and type-II interferon pathways were activated in NRGPI-high patients with an increased infiltration of Tregs and M2 macrophage indicating an exhausted immune phenotype of the tumor microenvironment. These molecular characteristics were mainly driven by the eight NRGPI oncogenes (CYTL1, PLCL1, CNTN1, GRP, APOD, GPX3, FCN1, SERPINE1) as validated in the gastric cancer cell lines and clinical samples. NRGPI-high patients showed sensitivity to a number of targeted agents, in particular, the tyrosine kinase inhibitors.

Conclusions

Necroptosis appears to play a critical role in the development of gastric cancer, prognosis and shaping of its tumor immune microenvironment. NRGPI can be used as a promising prognostic biomarker to identify gastric cancer patients with a cold tumor immune microenvironment and poor prognosis who may response to selected molecular targeted therapy.

A novel prognostic index of stomach adenocarcinoma based on immunogenomic landscape analysis and immunotherapy options

Stomach adenocarcinoma (STAD) is one of the most common malignant tumors worldwide. In this study, we attempted to construct a valid immune-associated gene prognostic index risk model that can predict the survival of patients with STAD and the efficacy of immune checkpoint inhibitors (ICIs) treatment. Transcriptome, clinical, and gene mutational data were obtained from the TCGA database. Immune-related genes were downloaded from the ImmPort and InnateDB databases. A total of 493 immune-related genes were identified to be enriched in functions associated with immune response, as well as in immune and tumor-related pathways. Further, 36 candidate genes related to the overall survival (OS) of STAD were obtained by weighted gene co-expression network analysis (WGCNA). Next, based on a Cox regression analysis, we constructed an immune-associated gene prognostic index (IAGPI) risk model based on eight genes, which was verified using the GEO STAD cohort. The patients were divided into two subsets according to their risk score. Patients in the low-risk group had better OS than those in the high-risk group. In the low-risk group, there were more CD8, activated memory CD4, and follicular helper T cells, and M1 macrophages, whereas monocytes, M2 macrophages, eosinophils, and neutrophils were more abundant in the high-risk group. The patients in the low-risk group were more sensitive to ICIs therapy. The IAGPI risk model can precisely predict the prognosis, reflect the tumor immune microenvironment, and predict the efficacy of ICIs therapy in patients with STAD.

A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

Background

Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC.

Methods

RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.

Results

A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment.

Conclusions

Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

Identification of a novel immune prognostic model in gastric cancer

PURPOSE

The tumor immune microenvironment (TIME) is now considered as an important factor during gastric cancer (GC) development. This study identified a novel immune-related risk model for predicting prognosis and assessing the immune status of GC patients.

METHODS

Transcriptomic data were obtained from the TCGA database. The differential expressed immune-related genes (IRGs) were identified through the ImmPort portal. Enrichment analysis was performed to explore the potential molecular mechanism of these IRGs. By the Cox regression analysis, we constructed the immune prognostic model. Then, the association between the model and the immune microenvironment was estimated. The model was validated in the GSE84433 dataset.

RESULTS

Totally, we identified 222 differentially expressed IRGs. These IRGs were closely correlated with immune response and immune signaling pathways. Through the Cox regression analysis, we developed the immune prognostic model based on the expression of seven IRGs (CXCL3, NOX4, PROC, FAM19A4, RNASE2, IGHD2-15, CGB5). Patients were stratified into two groups according to immune-related risk scores. Survival analysis indicated that the prognosis of high-risk patients was poorer than low-risk patients. Moreover, the immune-related risk score was an independent prognostic biomarker. More importantly, we found that the infiltration level of immunosuppressive cells and the expression of inhibitory immune checkpoints were higher in high-risk patients. The immune microenvironment tended to be a suppressive status in patients with high-risk scores.

CONCLUSION

This study demonstrated that our model had predictive value for prognosis and the TIME in GC. It might be a robust tool to improve personalized patient management.

Identification of an immune-related gene-based signature to predict prognosis of patients with gastric cancer

BACKGROUND

Gastric cancer (GC) is the most commonly diagnosed malignancy worldwide. Increasing evidence suggests that it is necessary to further explore genetic and immunological characteristics of GC.

AIM

To construct an immune-related gene (IRG) signature for accurately predicting the prognosis of patients with GC.

METHODS

Differentially expressed genes (DEGs) between 375 gastric cancer tissues and 32 normal adjacent tissues were obtained from The Cancer Genome Atlas (TCGA) GDC data portal. Then, differentially expressed IRGs from the ImmPort database were identified for GC. Cox univariate survival analysis was used to screen survival-related IRGs. Differentially expressed survival-related IRGs were considered as hub IRGs. Genetic mutations of hub IRGs were analyzed. Then, hub IRGs were selected to conduct a prognostic signature. Receiver operating characteristic (ROC) curve analysis was used to evaluate the prognostic performance of the signature. The correlation of the signature with clinical features and tumor-infiltrating immune cells was analyzed.

RESULTS

Among all DEGs, 70 hub IRGs were obtained for GC. The deletions and amplifications were the two most common types of genetic mutations of hub IRGs. A prognostic signature was identified, consisting of ten hub IRGs (including S100A12, DEFB126, KAL1, APOH, CGB5, GRP, GLP2R, LGR6, PTGER3, and CTLA4). This prognostic signature could accurately distinguish patients into high- and low- risk groups, and overall survival analysis showed that high risk patients had shortened survival time than low risk patients (P < 0.0001). The area under curve of the ROC of the signature was 0.761, suggesting that the prognostic signature had a high sensitivity and accuracy. Multivariate regression analysis demonstrated that the prognostic signature could become an independent prognostic predictor for GC after adjustment for other clinical features. Furthermore, we found that the prognostic signature was significantly correlated with macrophage infiltration.

CONCLUSION

Our study proposed an immune-related prognostic signature for GC, which could help develop treatment strategies for patients with GC in the future.

Identification of hub genes correlated with the pathogenesis and prognosis of gastric cancer via bioinformatics methods

BACKGROUND

Gastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world and 5-year overall survival (OS) rate is less than 10%. So, it is urgent to identified novel diagnostic and prognostic biomarkers.

METHODS

Twelve GEO (gene expression omnibus) datasets were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between GC and normal tissues were screened and integrated using limma and RobustRankAggreg (RRA) packages in R software. Protein-protein interaction (PPI) network, GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses for DEGs were conducted via STRING and DAVID, respectively. Moreover, Cox regression model was used to construct a gene prognosis signature.

RESULTS

Ten genes (COL1A1, CXCL8, COL3A1, SPP1, COL1A2, TIMP1, CXCL1, BGN, MMP3 and SERPINE1) were identified and might be highly related to GC. Further analysis showed high expression of CXCL8, COL3A1, CXCL1, MMP3 and SERPINE1, were significantly associated with late stage of GC. Lastly, we build a seven-gene prognosis signature (CYP19A1, SERPINE1, CGB5, CALCR, ASGR2, CYTL1 and ABCB5), which can give a good prediction of OS.

CONCLUSIONS

Our article screened out key genes highly associating with GC's developments and prognosis, and it is useful for researcher to further understand GC's molecular basis and direct the synthesis medicine of GC.

β-hCG promotes epithelial ovarian cancer metastasis through ERK/MMP2 signaling pathway

Epithelial ovarian cancer (EOC) is the most lethal gynecologic malignancy, with typically extensive intraperitoneal implantation leading to poor prognosis. Our previous study preliminarily demonstrated β-hCG can promote tumorigenesis in immortalized nontumorigenic ovarian epithelial cells. In this study, the roles and mechanisms of β-hCG in regulating EOC proliferation and metastasis were thoroughly explored. First, histologically, β-hCG was aberrantly overexpressed in human EOC metastatic tissues, and significantly correlated with FIGO stage, tumor size, differentiation, histologic grade and high grade serous ovarian carcinoma (HGSOC) (P < 0.05). However, serologically, β-hCG expression showed no significant difference between EOC and nonmalignant ovarian patients. Second, β-hCG was confirmed to have no significant effects on EOC proliferation in vitro and in vivo, while β-hCG upregulation was proven to promote migration and invasion ability in ES-2 and OVCAR-3 cells in vitro (P < 0.05), and β-hCG downregulation in SKOV3 cells had the opposite effect. Moreover, more invadopodia protrusions, mitochondria accumulations and cytoskeletal rearrangements were observed in β-hCG-overexpressing ES-2 cells, while β-hCG-depleted SKOV3 cells produced the opposite effect. Furthermore, β-hCG was confirmed to clearly facilitate intraperitoneal metastasis in nude mouse orthotopic ovarian xenograft models. Importantly, these effects of β-hCG were mediated by activation of the ERK/MMP2 signaling pathway, independently of luteinizing hormone/chorionic gonadotropin receptor (LHCGR) presence, and inhibition the pathway with the p-ERK1/2 inhibitor SCH772984 significantly impaired the tumor-promoting effects induced by β-hCG. Collectively, these data provide new insight into the roles and mechanisms of β-hCG in regulating EOC metastasis through ERK/MMP2 signaling pathway and may become a new target for therapeutic intervention.