Comparison of global gene expression of gastric cardia and noncardia cancers from a high-risk population in china

Quercetin Inhibits Gastric Cancer Progression via FAM198B/MAPK Pathway Modulation

Background

The family with the sequence similarity 198 member B (FAM198B) has been found to contribute to the progression of gastric cancer (GC). However, the role and molecular mechanism of FAM198B in GC remains poorly understood. This work found a link between FAM198B and quercetin, and the regulatory effect of FAM198B on the MAPK pathway of GC.

Methods

FAM198B expression was identified through multiple public data sets and verified in clinical tissue samples. The associations between FAM198B and the prognosis of patients with GC were analyzed via the Kaplan‒Meier plotter and Cox regression analysis. Gene set enrichment analysis, coexpressed genes, and RNA sequencing were used to explore the related functions and signaling pathways of FAM198B in GC. In vitro assays assessed the effects of FAM198B knockdown on GC cells. FAM198B was found as a quercetin target by the HERB database and in vitro assays.

Results

FAM198B was highly expressed in tissues from GC patients (p<0.001) and was positively associated with poor prognosis (p<0.001) and immune cell infiltration in GC patients. FAM198B knockdown inhibited the proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) of GC cells (all p<0.05). In addition, FAM198B knockdown decreased the phosphorylation of p-Erk1/2 and p-p38 in GC cells (all p<0.01). Quercetin inhibited FAM198B expression and the phosphorylation of p-Erk1/2 and p-p38 in GC cells (all p<0.05).

Conclusion

Quercetin inhibits the proliferation, migration, invasion, and EMT of GC cells by inhibiting the FAM198B/MAPK signaling pathway. These discoveries lay the groundwork for developing the treatment of GC by quercetin and targeting FAM198B. In the future, more preclinical and clinical studies are needed to confirm the efficacy and safety of quercetin and target FAM198B in GC.

Multi-omics analysis identifies diagnostic circulating biomarkers and potential therapeutic targets, revealing IQGAP1 as an oncogene in gastric cancer

This study employed a multi-omics integration approach to identify circulating biomarkers for gastric cancer (GC). We analyzed plasma and tumor tissue single-cell RNA sequencing data, along with gene and protein quantitative trait loci analyses. Leveraging data from UK Biobank and FinnGen, we investigated genetic associations with GC. Through colocalization, Mendelian Randomization, and various filtering analyses, we identified four genes (IQGAP1, KRTCAP2, PARP1, MLF2) and four proteins (EGFL9 [DLK2], ECM1, PDIA5, TIMP4) as potential GC biomarkers. These were selected based on significant genetic colocation probabilities and significant associations with GC. Seven of these biomarkers demonstrated predictive capability for GC occurrence, with AUC ranging from 0.61 to 0.99. Drug prediction analysis identified seven protein biomarkers as potential targets for immunotherapy, targeted therapies, and tumor chemotherapy. Further scRNA-seq analysis revealed significant expression differences between gastric tumor and normal tissues, particularly the upregulation of IQGAP1, which highlights its role in tumor growth.

Machine learning reveals glycolytic key gene in gastric cancer prognosis

Glycolysis is recognized as a central metabolic pathway in the neoplastic evolution of gastric cancer, exerting profound effects on the tumor microenvironment and the neoplastic growth trajectory. However, the identification of key glycolytic genes that significantly affect gastric cancer prognosis remains underexplored. In this work, five machine-learning algorithms were used to elucidate the intimate association between the glycolysis-associated gene phosphofructokinase fructose-bisphosphate 3 (PFKFB3) and the prognosis of gastric cancer patients. Validation across multiple independent datasets confirmed the prognostic significance of PFKFB3. Further, we delved into the functional implications of PFKFB3 in modulating immune responses and biological processes within gastric cancer patients, as well as its broader relevance across multiple cancer types. Results underscore the potential of PFKFB3 as a prognostic biomarker and therapeutic target in gastric cancer. Our project can be found at https://github.com/PiPiNam/ML-GCP .

IGFBP7 regulates cell proliferation and migration through JAK/STAT pathway in gastric cancer and is regulated by DNA and RNA methylation

New biomarkers for early diagnosis of gastric cancer (GC), the second leading cause of cancer-related death, are urgently needed. IGFBP7, known to play various roles in multiple tumours, is complexly regulated across diverse cancer types, as evidenced by our pancancer analysis. Bioinformatics analysis revealed that IGFBP7 expression was related to patient prognosis, tumour clinicopathological characteristics, tumour stemness, microsatellite instability and immune cell infiltration, as well as the expression of oncogenes and immune checkpoints. GSEA links IGFBP7 to several cancer-related pathways. IGFBP7 deficiency inhibited GC cell proliferation and migration in vitro. Furthermore, an in vivo nude mouse model revealed that IGFBP7 downregulation suppressed the tumorigenesis of GC cells. Western blotting analysis showed that the JAK1/2-specific inhibitor ruxolitinib could rescue alterations induced by IGFBP7 overexpression in GC cells. Additionally, our bioinformatics analysis and in vitro assays suggested that IGFBP7 is regulated by DNA methylation at the genetic level and that the RNA m6A demethylase FTO modulates it at the posttranscriptional level. This study emphasizes the clinical relevance of IGFBP7 in GC and its influence on cell proliferation and migration via the JAK/STAT signalling pathway. This study also highlights the regulation of IGFBP7 in GC by DNA and m6A RNA methylation.

Developing a prognostic signature: identifying differentially expressed genes in cardia and non-cardia gastric cancer for immunity and therapeutic sensitivity analysis

Background

Gastric cancer (GC) can be anatomically categorized into two subtypes; that is, cardia gastric cancer (CGC) and non-cardia gastric cancer (NCGC), which have distinct molecular mechanisms and prognoses. At present, the majority of pharmacological interventions for GC adhere to non-specific treatment regimens. The stratification of GC based on molecular disparities between CGC and NCGC has important clinical guidance value and could help in the development of precision therapies tailored to individual patient needs. Nevertheless, research in this specialized field remains notably limited. This study aims to investigate the molecular differences between CGC and NCGC and to leverage these differences to develop a prognostic risk scoring model (PRSM).

Methods

We used patient data from The Cancer Genome Atlas (TCGA) and performed a differentially expressed gene (DEG) analysis between CGC and NCGC. A PRSM was developed from the prognosis-associated DEGs identified through Cox regression analyses and was well validated using Gene Expression Omnibus (GEO) data.

Results

A total of 339 DEGs were identified between CGC and NCGC, and four prognosis-associated genes were used to construct the PRSM. Using the risk coefficients and expression levels of signature genes, a median risk score (RS) was calculated to classify patients into high- and low-risk groups. The high-risk group had a significantly worse prognosis than the low-risk group. An in-depth analysis revealed that TP53 mutations were more prevalent in the high-risk group, and MUC16 mutations were more prevalent in the low-risk group. A gene set enrichment analysis (GSEA) and the CIBERSORT algorithm were used to assess the differences in the significantly enriched pathways and immune microenvironment in the high- and low-risk groups, respectively. The inhibitory concentration (IC50) values of the chemotherapy drugs for GC also varied between the two groups.

Conclusions

This study elucidated the unique molecular characteristics of GC subtypes based on the anatomical site and provided a preliminary contribution for the development of precision medicine for GC.

FASTKD1 as a diagnostic and prognostic biomarker for STAD: Insights into m6A modification and immune infiltration

Fas-activated serine/threonine kinase domain 1 (FASTKD1), a known modulator of mitochondrial-mediated cell death and survival processes, has garnered attention for its potential role in various biological contexts. However, its involvement in gastric cancer remains unclear. Thus, the present study aimed to investigate the relationship between FASTKD1 expression and key factors, including clinicopathological characteristics, immune infiltration and m6A modification in stomach adenocarcinoma (STAD). The expression of FASTKD1 was analyzed in STAD and normal adjacent tissues to assess its association with clinicopathological characteristics and survival prognosis. Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used in this study. Additionally, the findings were validated through immunohistochemical staining. Co-expression analysis of FASTKD1 was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment analysis, Gene Set Enrichment Analysis (GSEA) and LinkedOmics database analysis. An in-depth analysis was conducted using databases, such as Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), GEO and TCGA to explore the potential correlation between FASTKD1 expression and immune infiltration and m6A modification in STAD. The results revealed that FASTKD1 was significantly upregulated across different tumor types, including STAD. Notably, FASTKD1 was able to distinguish between tumor and normal tissue samples with accuracy. Furthermore, the expression levels of FASTKD1 were significantly associated with clinical stage and survival. Through GO/KEGG enrichment analysis and GSEA, it was revealed that the genes co-expressed with FASTKD1 were active in a variety of biological processes. Within the TIMER, GEPIA and TCGA databases, a notable inverse correlation was observed between FASTKD1 expression and the abundance of immune cell subsets. Notably, significant correlations were established between FASTKD1 and m6A modification genes, YTHDF1 and LRPPRC, in both TCGA and GEO datasets. In conclusion, FASTKD1 may serve a significant role in m6A modification and immune infiltration processes, making it a potentially valuable diagnostic and prognostic biomarker in STAD.

Unique Genomic Alterations and Microbial Profiles Identified in Patients With Gastric Cancer of African, European, and Asian Ancestry: A Novel Path for Precision Oncology

OBJECTIVE

Here, we characterize differences in the genetic and microbial profiles of GC in patients of African (AFR), European, and Asian ancestry.

BACKGROUND

Gastric cancer (GC) is a heterogeneous disease with clinicopathologic variations due to a complex interplay of environmental and biological factors, which may affect disparities in oncologic outcomes..

METHODS

We identified 1042 patients with GC with next-generation sequencing data from an institutional Integrated Mutation Profiling of Actionable Cancer Targets assay and the Cancer Genomic Atlas group. Genetic ancestry was inferred from markers captured by the Integrated Mutation Profiling of Actionable Cancer Targets and the Cancer Genomic Atlas whole exome sequencing panels. Tumor microbial profiles were inferred from sequencing data using a validated microbiome bioinformatics pipeline. Genomic alterations and microbial profiles were compared among patients with GC of different ancestries.

RESULTS

We assessed 8023 genomic alterations. The most frequently altered genes were TP53 , ARID1A , KRAS , ERBB2 , and CDH1 . Patients of AFR ancestry had a significantly higher rate of CCNE1 alterations and a lower rate of KRAS alterations ( P < 0.05), and patients of East Asian ancestry had a significantly lower rate of PI3K pathway alterations ( P < 0.05) compared with other ancestries. Microbial diversity and enrichment did not differ significantly across ancestry groups ( P > 0.05).

CONCLUSIONS

Distinct patterns of genomic alterations and variations in microbial profiles were identified in patients with GC of AFR, European, and Asian ancestry. Our findings of variation in the prevalence of clinically actionable tumor alterations among ancestry groups suggest that precision medicine can mitigate oncologic disparities.

Subsite-specific metastatic organotropism and risk in gastric cancer: A population-based cohort study of the US SEER database and a Chinese single-institutional registry

BACKGROUND

Studies exploring whether metastatic organotropism and risk in gastric cancer (GC) differ by primary anatomical site are scarce.

METHODS

This study included 15,260 and 1623 patients diagnosed with GC from the Surveillance, Epidemiology, and End Results (SEER) registry database and the Nanfang Hospital in China, respectively. Patients were stratified according to primary site of GC, and the incidence of metastasis to different organs was used to determine the metastatic organotropism for each GC subsite. Finally, the metastatic organotropism and risk were compared among the different subsite groups.

RESULTS

Liver metastasis was the most common metastasis site in cardia GC, whereas other-site metastases were more common in the body, antrum, overlapping lesions, and unspecified GCs. Liver and other-site metastases were also frequently observed in the fundus, pylorus, lesser curvature, and greater curvature GCs. Patients with GC with definite primary tumor sites in the SEER and validation Nanfang hospital cohorts were compared by grouping as proximal and distal GCs for further analysis. In the SEER cohort, the top three metastatic sites of proximal GC were liver (21.4%), distant lymph node (LN) (14.6%), and other-site (mainly peritoneum, 11.9%), whereas those of distal GC were other-site (mainly peritoneum, 19.5%), liver (11.8%), and distant LN (9.5%). The incidence of metastasis to the liver, distant LN, lung, and brain was significantly higher in patients with proximal GC than in those with distal GC in both the SEER and Nanfang cohorts (p < 0.05). However, metastasis to other-site/peritoneum was significantly lower in patients with proximal GC compared to those with distal GC in the Nanfang Hospital and SEER cohorts, respectively (p < 0.05).

CONCLUSION

Liver and distant LN are the preferred metastatic sites for proximal GC, whereas peritoneal metastasis is more common in distal GC. Proximal GC has a higher risk of lymphatic and hematogenous metastases, and a lower risk of transcoelomic metastasis than distal GC. Our findings highlight the need to stratify GC by its primary subsite to aid in planning and decision-making related to metastatic management in clinical practice.

Clinically conserved genomic subtypes of gastric adenocarcinoma

Gastric adenocarcinoma (GAC) is a lethal disease characterized by genomic and clinical heterogeneity. By integrating 8 previously established genomic signatures for GAC subtypes, we identified 6 clinically and molecularly distinct genomic consensus subtypes (CGSs). CGS1 have the poorest prognosis, very high stem cell characteristics, and high IGF1 expression, but low genomic alterations. CGS2 is enriched with canonical epithelial gene expression. CGS3 and CGS4 have high copy number alterations and low immune reactivity. However, CGS3 and CGS4 differ in that CGS3 has high HER2 activation, while CGS4 has high SALL4 and KRAS activation. CGS5 has the high mutation burden and moderately high immune reactivity that are characteristic of microsatellite instable tumors. Most CGS6 tumors are positive for Epstein Barr virus and show extremely high levels of methylation and high immune reactivity. In a systematic analysis of genomic and proteomic data, we estimated the potential response rate of each consensus subtype to standard and experimental treatments such as radiation therapy, targeted therapy, and immunotherapy. Interestingly, CGS3 was significantly associated with a benefit from chemoradiation therapy owing to its high basal level of ferroptosis. In addition, we also identified potential therapeutic targets for each consensus subtype. Thus, the consensus subtypes produced a robust classification and provide for additional characterizations for subtype-based customized interventions.

A Novel Gene List Identifies Tumors with a Stromal-Mesenchymal Phenotype and Worse Prognosis in Gastric Cancer

BACKGROUND

Molecular biomarkers that predict disease progression can help identify tumor subtypes and shape treatment plans. In this study, we aimed to identify robust biomarkers of prognosis in gastric cancer based on transcriptomic data obtained from primary gastric tumors.

METHODS

Microarray, RNA sequencing, and single-cell RNA sequencing-based gene expression data from gastric tumors were obtained from public databases. Freshly frozen gastric tumors (n = 42) and matched FFPE (formalin-fixed, paraffin-embedded) (n = 40) tissues from a Turkish gastric cancer cohort were used for quantitative real-time PCR and immunohistochemistry-based assessments of gene expression, respectively.

RESULTS

A novel list of 20 prognostic genes was identified and used for the classification of gastric tumors into two major tumor subgroups with differential stromal gene expression ("Stromal-UP" (SU) and "Stromal-DOWN" (SD)). The SU group had a more mesenchymal profile with an enrichment of extracellular matrix-related gene sets and a poor prognosis compared to the SD group. Expression of the genes within the signature correlated with the expression of mesenchymal markers ex vivo. A higher stromal content in FFPE tissues was associated with shorter overall survival.

CONCLUSIONS

A stroma-rich, mesenchymal subgroup among gastric tumors identifies an unfavorable clinical outcome in all cohorts tested.

Bulk and single-cell transcriptome profiling reveal the metabolic heterogeneity in gastric cancer

Metabolic reprogramming has been defined as a key hall mark of human tumors. However, metabolic heterogeneity in gastric cancer has not been elucidated. Here we separated the TCGA-STAD dataset into two metabolic subtypes. The differences between subtypes were elaborated in terms of transcriptomics, genomics, tumor-infiltrating cells, and single-cell resolution. We found that metabolic subtype 1 is predominantly characterized by low metabolism, high immune cell infiltration. Subtype 2 is mainly characterized by high metabolism and low immune cell infiltration. From single-cell resolution, we found that the high metabolism of subtype 2 is dominated by epithelial cells. Not only epithelial cells, but also various immune cells and stromal cells showed high metabolism in subtype 2 and low metabolism in subtype 1. Our study established a classification of gastric cancer metabolic subtypes and explored the differences between subtypes from multiple dimensions, especially the single-cell resolution.

LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications

OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.

Current Trends in Volume and Surgical Outcomes in Gastric Cancer

Gastric cancer is ranked as the fifth most frequently diagnosed type of cancer. Complete resection with adequate lymphadenectomy represents the goal of treatment with curative intent. Quality assurance is a crucial factor in the evaluation of oncological surgical care, and centralization of healthcare in referral hospitals has been proposed in several countries. However, an international agreement about the setting of "high-volume hospitals" as well as "minimum volume standards" has not yet been clearly established. Despite the clear postoperative mortality benefits that have been described for gastric cancer surgery conducted by high-volume surgeons in high-volume hospitals, many authors have highlighted the limitations of a non-composite variable to define the ideal postoperative period. The textbook outcome represents a multidimensional measure assessing the quality of care for cancer patients. Transparent and easily available hospital data will increase patients' awareness, providing suitable elements for a more informed hospital choice.

The Comprehensive Analysis of N6-Methyadenosine Writer METTL3 and METTL14 in Gastric Cancer

Methyltransferase-like 3 (METTL3) and methyltransferase-like 14 (METTL14) were two core components of the N6-methyadenosine (m6A) methyltransferase complex (MTC) and played a basic role in maintaining an appropriate m6A level of target genes. In gastric cancer (GC), previous researches on the expression and role of METTL3 and METTL14 were not consistent, and their specific function and mechanism have remained elusive. In this study, the expression of METTL3 and METTL14 was evaluated based on the TCGA database, 9 paired GEO datasets, and our 33 GC patient samples, and METTL3 was highly expressed and acted as a poor prognostic factor, whereas METTL14 showed no significant difference. Moreover, GO and GSEA analyses were performed, and the results pointed out that METTL3 and METTL14 were jointly involved in multiple biological processes, while they could also take part in different oncogenic pathways independently. And BCLAF1 was predicted and identified as a novel shared target of METTL3 and METTL14 in GC. In total, we conducted a comprehensive analysis of METTL3 and METTL14 in GC including their expression, function, and role, which could provide a novel insight into the research of m6A modification in GC.

Identification of significant genes associated with prognosis of gastric cancer by bioinformatics analysis

BACKGROUND

Gastric cancer (GC) ranks second in mortality among all malignant diseases worldwide. However, the cause and molecular mechanism underlying gastric cancer are not clear. Here, we used integrated bioinformatics to identify possible key genes and reveal the pathogenesis and prognosis of gastric cancer.

METHODS

The gene expression profiles of GSE118916, GSE79973, and GSE29272 were available from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between GC and normal gastric tissues were screened by R software and Venn diagram software. GO and KEGG pathway enrichment of DEGs was performed using the DAVID database. A protein-protein interaction (PPI) network was established by STRING and visualized using Cytoscape software. Then the influence of hub genes on expression and survival was assessed using TCGA database.

RESULTS

A total of 83 DEGs were found in the three datasets, including 41 up-regulated genes and 42 down-regulated genes. These DEGs were mainly enriched in extracellular matrix organization and cell adhesion. The enriched pathways obtained in the KEGG pathway analysis were extracellular matrix (ECM)-receptor interaction and focal adhesion. A PPI network of DEGs was analyzed using the Molecular Complex Detection (MCODE) app of Cytoscape. Four genes were considered hub genes, including COL5A1, FBN1, SPARC, and LUM. Among them, LUM was found to have a significantly worse prognosis based on TCGA database.

CONCLUSIONS

We screened DEGs associated with GC by integrated bioinformatics analysis and found one potential biomarker that may be involved in the progress of GC. This hub gene may serve as a guide for further molecular biological experiments.

An antigen processing and presentation signature for prognostic evaluation and immunotherapy selection in advanced gastric cancer

Objective

The aim of the study was to propose a signature based on genes associated with antigen processing and presentation (APscore) to predict prognosis and response to immune checkpoint inhibitors (ICIs) in advanced gastric cancer (aGC).

Background

How antigen presentation-related genes affected the immunotherapy response and whether they could predict the clinical outcomes of the immune checkpoint inhibitor (ICI) in aGC remain largely unknown.

Methods

In this study, an aGC cohort (Kim cohort, RNAseq, N=45) treated by ICIs, and 467 aGC patients from seven cohorts were conducted to investigate the value of the APscore predicting the prognosis and response to ICIs. Subsequently, the associations of the APscore with the tumor microenvironment (TME), molecular characteristics, clinical features, and somatic mutation variants in aGC were assessed. The area under the receiver operating characteristic curve (AUROC) of the APscore was analyzed to estimate response to ICIs. Cox regression or Log-rank test was used to estimate the prognosis of aGC patients.

Results

The APscore constructed by principal component analysis algorithms was an effective predictive biomarker of the response to ICIs in the Kim cohort and 467 aGC patients (Kim: AUC =0.85, 95% CI: 0.69–1.00; 467 aGC: AUC =0.69, 95% CI: 0.63–0.74). The APscore also was a prognostic biomarker in 467 aGC patients (HR=1.73, 95% CI: 1.21−2.46). Inhibitory immunity, decreased TMB and low stromal scores were observed in the high APscore group, while activation of immunity, increased TMB, and high stromal scores were observed in the low APscore group. Next, we evaluated the value of several central genes in predicting the prognosis and response to ICIs in aGC patients, and verified them using immunogenic, transcriptomic, genomic, and multi-omics methods. Lastly, a predictive model built successfully discriminated patients with vs. without immunotherapy response and predicted the survival of aGC patients.

Conclusions

The APscore was a new biomarker for identifying high-risk aGC patients and patients with responses to ICIs. Exploration of the APscore and hub genes in multi-omics GC data may guide treatment decisions.

Apoptosis-Related Gene-Mediated Cell Death Pattern Induces Immunosuppression and Immunotherapy Resistance in Gastric Cancer

Background: Apoptosis is a type of cell death, which can produce abundant mediators to modify the tumor microenvironment. However, relationships between apoptosis, immunosuppression, and immunotherapy resistance of gastric cancer (GC) remain unclear.

Methods: Gene expression data and matching clinical information were extracted from TCGA-STAD, GSE84437, GSE34942, GSE15459, GSE57303, ACRG/GSE62254, GSE29272, GSE26253, and IMvigor210 datasets. A consensus clustering analysis based on six apoptosis-related genes (ARGs) was performed to determine the molecular subtypes, and then an apoptosisScore was constructed based on differentially expressed and prognostic genes between molecular subtypes. Estimate R package was utilized to calculate the tumor microenvironment condition. Kaplan-Meier analysis and ROC curves were performed to further confirm the apoptosisScore efficacy.

Results: Based on six ARGs, two molecular subgroups with significantly distinct survival and immune cell infiltration were identified. Then, an apoptosisScore was built to quantify the apoptosis index of each GC patient. Next, we investigated the correlations between the clinical characteristics and apoptosisScore using logistic regression. Multivariate Cox analysis shows that low apoptosisScore was an independent predictor of poor overall survival in TCGA and ACRG datasets, and was associated with the higher pathological stage. Meanwhile, low apoptosisScore was associated with higher immune cell, higher ESTIMATEScore, higher immuneScore, higher stromalScore, higher immune checkpoint, and lower tumorpurity, which was consistent with the “immunity tidal model theory”. Importantly, low apoptosisScore was sensitive to immunotherapy. In addition, GSEA indicated that several gene ontology and Kyoto Encyclopedia of Genes and Genomes items associated with apoptosis, several immune-related pathways, and JAK–STAT signal pathway were considerably enriched in the low apoptosisScore phenotype pathway.

Conclusion: Our findings propose that low apoptosisScore is a prognostic biomarker, correlated with immune infiltrates, and sensitivity to immunotherapy in GC.

Tumor Microenvironment Characterization for Assessment of Recurrence and Survival Outcome in Gastric Cancer to Predict Chemotherapy and Immunotherapy Response

Background

The tumor microenvironment (TME) is crucial for tumor recurrence, prognosis, and therapeutic responses. We comprehensively investigated the TME characterization associated with relapse and survival outcomes of gastric cancer (GC) to predict chemotherapy and immunotherapy response.

Methods

A total of 2,456 GC patients with complete gene-expression data and clinical annotations from twelve cohorts were included. The TME characteristics were evaluated using three proposed computational algorithms. We then developed a TME-classifier, a TME-cluster, and a TME-based risk score for the assessment of tumor recurrence and prognosis in patients with GC to predict chemotherapy and immunotherapy response.

Results

Patients with tumor recurrence presented with inactive immunogenicity, namely, high infiltration of tumor-associated stromal cells, low infiltration of tumor-associated immunoactivated lymphocytes, high stromal score, and low immune score. The TME-classifier of 4 subtypes with distinct clinicopathology, genomic, and molecular characteristics was significantly associated with tumor recurrence (P = 0.002), disease-free survival (DFS, P <0.001), and overall survival (OS, P <0.001) adjusted by confounding variables in 1,193 stage I–III GC patients who underwent potential radical surgery. The TME cluster and TME-based risk score can also predict DFS (P <0.001) and OS (P <0.001). More importantly, we found that patients in the TMEclassifier-A, TMEclassifier-C, and TMEclassifier-D groups benefited from adjuvant chemotherapy, and patients in the TMEclassifier-B group without chemotherapy benefit responded best to pembrolizumab treatment (PD-1 inhibitor), followed by patients in the TMEclassifier-A, while patients in the C and D groups of the TMEclassifier responded poorly to immunotherapy.

Conclusion

We determined that TME characterization is significantly associated with tumor recurrence and prognosis. The TME-classifier we proposed can guide individualized chemotherapy and immunotherapy decision-making.

Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches

OMIC datasets have high dimensions, and the connection among OMIC features is very complicated. It is difficult to establish linkages among these features and certain biological traits of significance. The proposed ensemble swarm intelligence-based approaches can identify key biomarkers and reduce feature dimension efficiently. It is an end-to-end method that only relies on the rules of the algorithm itself, without presets such as the number of filtering features. Additionally, this method achieves good classification accuracy without excessive consumption of computing resources.

Identification of GGT5 as a Novel Prognostic Biomarker for Gastric Cancer and its Correlation With Immune Cell Infiltration

Gastric cancer (GC) is a common malignant tumor of the digestive system. Recent studies revealed that high gamma-glutamyl-transferase 5 (GGT5) expression was associated with a poor prognosis of gastric cancer patients. In the present study, we aimed to confirm the expression and prognostic value of GGT5 and its correlation with immune cell infiltration in gastric cancer. First, we compared the differential expression of GGT5 between gastric cancer tissues and normal gastric mucosa in the cancer genome atlas (TCGA) and GEO NCBI databases using the most widely available data. Then, the Kaplan-Meier method, Cox regression, and univariate logistic regression were applied to explore the relationships between GGT5 and clinical characteristics. We also investigated the correlation of GGT5 with immune cell infiltration, immune-related genes, and immune checkpoint genes. Finally, we estimated enrichment of gene ontologies categories and relevant signaling pathways using GO annotations, KEGG, and GSEA pathway data. The results showed that GGT5 was upregulated in gastric cancer tissues compared to normal tissues. High GGT5 expression was significantly associated with T stage, histological type, and histologic grade (p < 0.05). Moreover, gastric cancer patients with high GGT5 expression showed worse 10-years overall survival (p = 0.008) and progression-free intervals (p = 0.006) than those with low GGT5 expression. Multivariate analysis suggested that high expression of GGT5 was an independent risk factor related to the worse overall survival of gastric cancer patients. A nomogram model for predicting the overall survival of GC was constructed and computationally validated. GGT5 expression was positively correlated with the infiltration of natural killer cells, macrophages, and dendritic cells but negatively correlated with Th17 infiltration. Additionally, we found that GGT5 was positively co-expressed with immune-related genes and immune checkpoint genes. Functional analysis revealed that differentially expressed genes relative to GGT5 were mainly involved in the biological processes of immune and inflammatory responses. In conclusion, GGT5 may serve as a promising prognostic biomarker and a potential immunological therapeutic target for GC, since it is associated with immune cell infiltration in the tumor microenvironment.

Expression Patterns of Glycosylation Regulators Define Tumor Microenvironment and Immunotherapy in Gastric Cancer

Glycosylation (Glyc) is prevalently related to gastric cancer (GC) pathophysiology. However, studies on the relationship between glycosylation regulators and tumor microenvironment (TME) and immunotherapy of GC remain scarce. We extracted expression data of 1,956 patients with GC from eight cohorts and systematically characterized the glycosylation patterns of six marker genes into phenotype clusters using the unsupervised clustering method. Next, we constructed a Glyc. score to quantify the glycosylation index of each patient with GC. Finally, we analyzed the relationship between Glyc. score and clinical traits including molecular subtype, TME, and immunotherapy of GC. On the basis of prognostic glycosylation-related differentially expressed genes, we constructed the Glyc. score and divided the samples into the high– and low–Glyc. score groups. The high–Glyc. score group showed a poor prognosis and was validated in multiple cohorts. Functional enrichment analysis revealed that the high–Glyc. score group was enriched in metabolism-related pathways. Furthermore, the high–Glyc. score group was associated with the infiltration of immune cells. Importantly, the established Glyc. score would contribute to predicting the response to anti–PD-1/L1 immunotherapy. In conclusion, the Glyc. score is a potentially useful tool to predict the prognosis of GC. Comprehensive analysis of glycosylation may provide novel insights into the epigenetics of GC and improve treatment strategies.

Zoo: Selecting Transcriptomic and Methylomic Biomarkers by Ensembling Animal-Inspired Swarm Intelligence Feature Selection Algorithms

Biological omics data such as transcriptomes and methylomes have the inherent “large p small n” paradigm, i.e., the number of features is much larger than that of the samples. A feature selection (FS) algorithm selects a subset of the transcriptomic or methylomic biomarkers in order to build a better prediction model. The hidden patterns in the FS solution space make it challenging to achieve a feature subset with satisfying prediction performances. Swarm intelligence (SI) algorithms mimic the target searching behaviors of various animals and have demonstrated promising capabilities in selecting features with good machine learning performances. Our study revealed that different SI-based feature selection algorithms contributed complementary searching capabilities in the FS solution space, and their collaboration generated a better feature subset than the individual SI feature selection algorithms. Nine SI-based feature selection algorithms were integrated to vote for the selected features, which were further refined by the dynamic recursive feature elimination framework. In most cases, the proposed Zoo algorithm outperformed the existing feature selection algorithms on transcriptomics and methylomics datasets.

Construction and Validation of a Novel Prognostic Signature for Intestinal Type of Gastric Cancer

Background

Intestinal type of gastric cancer (IGC) is the largest subtype of gastric cancer (GC) by Lauren classification. The purpose of this present study was to construct a prognostic signature for IGC patients, based on the high-grade dysplasia (HGD) and IGC tissues, to improve and enhance the prognostic accuracy.

Methods

The microarray datasets and associated clinical characteristics of HGD and IGC were obtained from the Gene Expression Omnibus (GEO) database. Based on the differential expression analysis between HGD and IGC, the prognostic-related differential expression genes (DEGs) were identified in a training set by univariate COX regression analysis. The least absolute shrinkage and selection operator (LASSO) regression was used to construct an optimal prognostic signature. The enrichment analysis was performed by using Gene Set Enrichment Analysis (GSEA). The performance of the nomogram was assessed by the calibration curve and concordance index (C-index). The results were validated by using a testing set.

Results

We identified 35 prognostic-related DGEs in the training set. The nine-gene signature was established by LASSO analysis. The nine-gene signature was an independent risk factor in both the training and testing sets. The areas under the curve (AUC) values of receiver operating characteristic (ROC) analysis were 0.733 and 0.700 for the training and testing sets, respectively. In GSEA analysis, the gene expression in high-risk group was enriched in hedgehog signaling, epithelial mesenchymal transition, and angiogenesis. The nomogram for IGC showed good performance with C-index of 0.81 (95% CI: 0.76-0.86) and 0.70 (95% CI: 0.63-0.77) in the training and testing sets, respectively.

Conclusion

We identified and verified a nine-gene signature for the prognostic prediction of IGC patients, which might identify subgroups of IGC patients and select more suitable therapeutic options.

Two Similar Signatures for Predicting the Prognosis and Immunotherapy Efficacy of Stomach Adenocarcinoma Patients

Background

Globally, stomach adenocarcinoma (STAD)’s high morbidity and mortality should arouse our urgent attention. How long can STAD patients survive after surgery and whether novel immunotherapy is effective are questions that our clinicians cannot escape.

Methods

Various R packages, GSEA software, Metascape, STRING, Cytoscape, Venn diagram, TIMER2.0 website, TCGA, and GEO databases were used in our study.

Results

In the TCGA and GEO, macrophage abundance of STAD tissues was significantly higher than that of adjacent tissues and was an independent prognostic factor, significantly related to the overall survival (OS) of STAD patients. Between the high- and low- macrophage abundance, we conducted differential expression, univariate and multivariate Cox analysis, and obtained 12 candidate genes, and finally constructed a 3-gene signature. Both low macrophage abundance group and group D had higher TMB and PD-L1 expression. Furthermore, top 5 common gene-mutated STAD tissues had lower macrophage abundance. Macrophage abundance and 3 key genes expression were also lower in the Epstein-Barr Virus (EBV) and HM-indel STAD subtypes and significantly correlated with the tumor microenvironment score. The functional enrichment and ssGSEA revealed 2 signatures were similar and closely related to BOQUEST_STEM_CELL_UP, including genes up-regulated in proliferative stromal stem cells. Hsa-miR-335-5p simultaneously regulated 3 key genes and significantly related to the expression of PD-L1, CD8A and PDCD1.

Conclusion

macrophage abundance and 3-gene signature could simultaneously predict the OS and immunotherapy efficacy, and both 2 signatures had remarkable similarities. Hsa-miR-335-5p and BOQUEST_STEM_CELL_UP might be novel immunotherapy targets.

Low CBS expression can identify patients who benefit from adjuvant chemotherapy in gastric cancer

Aim: To explore the clinical significance of Cystathionine beta-synthase (CBS) expression in gastric cancer (GC).Research design and methods: CBS expression and clinicopathological/follow-up information of patients with gastric cancer undergoing operation were collected from The Cancer Genome Atlas (TCGA) database. The association of CBS expression with patients' overall survival (OS) was determined in the entire cohort and different subgroups. Validation was performed in two external cohorts from NCBI Gene Expression Omnibus (GEO) database. The estimated drug response of the tumors with different CBS expressions was characterized. The potential CBS-related cellular pathways in chemoresistance were explored.Results: High CBS was associated with poor OS in patients receiving adjuvant chemotherapy (ACT) but not those without ACT. And ACT was associated with favorable OS in patients with low CBS expression but not those with high CBS expression. The results were verified in two external cohorts. Drug response prediction suggested that patients with low CBS expression showed high sensitivity to 5-Fluorouracil. Gene Set Enrichment Analysis (GSEA) suggested that CBS might contribute to GC chemoresistance via modulating many cellular pathways, including down-regulating apoptosis and P53 pathways while up-regulating DNA repair pathway.Conclusion: Low CBS expression can predict the benefit from ACT in GC.

The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis

To tackle the challenges in genomic data analysis caused by their tens of thousands of dimensions while having a small number of examples and unbalanced examples between classes, the technique of unsupervised feature selection based on standard deviation and cosine similarity is proposed in this paper. We refer to this idea as SCFS (Standard deviation and Cosine similarity based Feature Selection). It defines the discernibility and independence of a feature to value its distinguishable capability between classes and its redundancy to other features, respectively. A 2-dimensional space is constructed using discernibility as x-axis and independence as y-axis to represent all features where the upper right corner features have both comparatively high discernibility and independence. The importance of a feature is defined as the product of its discernibility and its independence (i.e., the area of the rectangular enclosed by the feature’s coordinate lines and axes). The upper right corner features are by far the most important, comprising the optimal feature subset. Based on different definitions of independence using cosine similarity, there are three feature selection algorithms derived from SCFS. These are SCEFS (Standard deviation and Exponent Cosine similarity based Feature Selection), SCRFS (Standard deviation and Reciprocal Cosine similarity based Feature Selection) and SCAFS (Standard deviation and Anti-Cosine similarity based Feature Selection), respectively. The KNN and SVM classifiers are built based on the optimal feature subsets detected by these feature selection algorithms, respectively. The experimental results on 18 genomic datasets of cancers demonstrate that the proposed unsupervised feature selection algorithms SCEFS, SCRFS and SCAFS can detect the stable biomarkers with strong classification capability. This shows that the idea proposed in this paper is powerful. The functional analysis of these biomarkers show that the occurrence of the cancer is closely related to the biomarker gene regulation level. This fact will benefit cancer pathology research, drug development, early diagnosis, treatment and prevention.

Intratumoral Foxp3+RORγt+ T cell infiltration determines poor prognosis and immunoevasive contexture in gastric cancer patients

BACKGROUND

Foxp3⁺RORγt⁺ T cells possess both characteristics of regulatory T cells and T helper 17 cells and show significant immunoregulatory functions in autoimmune diseases. However, the role and clinical significance of Foxp3⁺RORγt⁺ T cells in gastric cancer remains unclear.

METHODS

We enrolled 452 gastric cancer tissue microarray samples and 60 fresh tumor tissue samples from Zhongshan Hospital. The infiltration of Foxp3⁺RORγt⁺ T cells and immune contexture were examined by immunohistochemistry and flow cytometry. Survival analyses of patient subgroups were conducted by Kaplan-Meier curves, log-rank test and Cox proportional model.

RESULTS

High infiltration of Foxp3⁺RORγt⁺ T cells predicted poor overall survival (P = 0.0222 and 0.0110) and inferior therapeutic response (P = 0.003 for interaction) in gastric cancer. Foxp3⁺RORγt⁺ T cells were associated with impaired effective function of CD8⁺ T cells featured by decreased interferon-γ, granzyme B and CD107a expression. Co-evaluation of Foxp3⁺RORγt⁺ T cells and CD8⁺ T cells could predict survival outcomes and chemotherapeutic responsiveness more precisely.

CONCLUSIONS

We found that Foxp3⁺RORγt⁺ T cells could potentially attenuate effective functions of CD8⁺ T cells and led to adverse survival outcomes and inferior chemotherapeutic responsiveness. Moreover, the novel co-evaluation system might be useful for prognosis prediction for appropriate treatment in gastric cancer.

NOVELTY AND IMPACT STATEMENTS

Clinical significance of Foxp3⁺RORγts⁺ T cells has not been studied in gastric cancer. Herein, we investigated the prognostic value of Foxp3+RORγt+ T cells in 452 patients. We demonstrated that intratumoral Foxp3⁺RORγt⁺ T cell infiltration was a prognostic biomarker for overall survival and the identification of patients might benefit from post-gastrectomy 5-fluorouracil. These findings allow a more precise stratification upon the co-evaluation with CD8⁺ T cells to better clinical management for patients who would benefit from 5-fluorouracil.

Identification of key methylation differentially expressed genes in posterior fossa ependymoma based on epigenomic and transcriptome analysis

Background

Posterior fossa ependymoma (EPN-PF) can be classified into Group A posterior fossa ependymoma (EPN-PFA) and Group B posterior fossa ependymoma (EPN-PFB) according to DNA CpG island methylation profile status and gene expression. EPN-PFA usually occurs in children younger than 5 years and has a poor prognosis.

Methods

Using epigenome and transcriptome microarray data, a multi-component weighted gene co-expression network analysis (WGCNA) was used to systematically identify the hub genes of EPN-PF. We downloaded two microarray datasets (GSE66354 and GSE114523) from the Gene Expression Omnibus (GEO) database. The Limma R package was used to identify differentially expressed genes (DEGs), and ChAMP R was used to analyze the differential methylation genes (DMGs) between EPN-PFA and EPN-PFB. GO and KEGG enrichment analyses were performed using the Metascape database.

Results

GO analysis showed that enriched genes were significantly enriched in the extracellular matrix organization, adaptive immune response, membrane raft, focal adhesion, NF-kappa B pathway, and axon guidance, as suggested by KEGG analysis. Through WGCNA, we found that MEblue had a significant correlation with EPN-PF (R = 0.69, P = 1 × 10^–08) and selected the 180 hub genes in the blue module. By comparing the DEGs, DMGs, and hub genes in the co-expression network, we identified five hypermethylated, lower expressed genes in EPN-PFA (ATP4B, CCDC151, DMKN, SCN4B, and TUBA4B), and three of them were confirmed by IHC.

Conclusion

ssGSEA and GSVA analysis indicated that these five hub genes could lead to poor prognosis by inducing hypoxia, PI3K-Akt-mTOR, and TNFα-NFKB pathways. Further study of these dysmethylated hub genes in EPN-PF and the pathways they participate in may provides new ideas for EPN-PF treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02834-1.

A Novel Biomarker Identification Approach for Gastric Cancer Using Gene Expression and DNA Methylation Dataset

As one type of complex disease, gastric cancer has high mortality rate, and there are few effective treatments for patients in advanced stage. With the development of biological technology, a large amount of multiple-omics data of gastric cancer are generated, which enables computational method to discover potential biomarkers of gastric cancer. That will be very important to detect gastric cancer at earlier stages and thus assist in providing timely treatment. However, most of biological data have the characteristics of high dimension and low sample size. It is hard to process directly without feature selection. Besides, only using some omic data, such as gene expression data, provides limited evidence to investigate gastric cancer associated biomarkers. In this research, gene expression data and DNA methylation data are integrated to analyze gastric cancer, and a feature selection approach is proposed to identify the possible biomarkers of gastric cancer. After the original data are pre-processed, the mutual information (MI) is applied to select some top genes. Then, fold change (FC) and T-test are adopted to identify differentially expressed genes (DEG). In particular, false discover rate (FDR) is introduced to revise p_value to further screen genes. For chosen genes, a deep neural network (DNN) model is utilized as the classifier to measure the quality of classification. The experimental results show that the approach can achieve superior performance in terms of accuracy and other metrics. Biological analysis for chosen genes further validates the effectiveness of the approach.

MGRFE: Multilayer Recursive Feature Elimination Based on an Embedded Genetic Algorithm for Cancer Classification

Microarray gene expression data have become a topic of great interest for cancer classification and for further research in the field of bioinformatics. Nonetheless, due to the "large p, small n" paradigm of limited biosamples and high-dimensional data, gene selection is becoming a demanding task, which is aimed at selecting a minimal number of discriminatory genes associated closely with a phenotype. Feature or gene selection is still a challenging problem owing to its nondeterministic polynomial time complexity and thus most of the existing feature selection algorithms utilize heuristic rules. A multilayer recursive feature elimination method based on an embedded integer-coded genetic algorithm, MGRFE, is proposed here, which is aimed at selecting the gene combination with minimal size and maximal information. On the basis of 19 benchmark microarray datasets including multiclass and imbalanced datasets, MGRFE outperforms state-of-the-art feature selection algorithms with better cancer classification accuracy and a smaller selected gene number. MGRFE could be regarded as a promising feature selection method for high-dimensional datasets especially gene expression data. Moreover, the genes selected by MGRFE have close biological relevance to cancer phenotypes. The source code of our proposed algorithm and all the 19 datasets used in this paper are available at https://github.com/Pengeace/MGRFE-GaRFE.

Tumor suppressor ATP4B serve as a promising biomarker for worsening of gastric atrophy and poor differentiation

BACKGROUND

Hydrogen/potassium ATPase β (ATP4B) is a proton pump acting an essential role in gastric acid secretion. This study aimed to investigate the diagnostic performance of ATP4B and its biological role in tumor progression in gastric cancer.

METHODS

The correlations between ATP4B expression level and clinicopathologic parameters, as well as the relevance of ATP4B expression with overall survival were assessed. The functional roles of ATP4B in gastric cancer were verified by gain- and loss-of-function cell models and tumor xenograft models. The possible downstream effects of ATP4B were analyzed by iTRAQ-based quantitative proteomics analysis.

RESULTS

A dramatic decrease in ATP4B was associated with malignant transformation in gastric mucosa lesions and correlated with poor differentiation. Restoration of ATP4B expression in gastric cancer cells significantly suppressed cell proliferation, cell viability, migration, invasion, tumorigenicity and induced apoptosis, whereas ATP4B silencing exerted the opposite effects. Mechanistically, we found a quality control on mitochondrial metabolism and functions in ATP4B-overexpression GC cells.

CONCLUSIONS

Our data suggest that decreasing ATP4B is an indicator for gastric mucosa malignant transformation and GC aggressive phenotype and it plays an inhibitory role in gastric cancer as a tumor suppressor via regulating mitochondrial metabolism and apoptosis pathway.

An improved algorithm for the maximal information coefficient and its application

The maximal information coefficient (MIC) captures both linear and nonlinear correlations between variable pairs. In this paper, we proposed the BackMIC algorithm for MIC estimation. The BackMIC algorithm adds a searching back process on the equipartitioned axis to obtain a better grid partition than the original implementation algorithm ApproxMaxMI. And similar to the ChiMIC algorithm, it terminates the grid search process by the χ ²-test instead of the maximum number of bins B(n, α). Results on simulated data show that the BackMIC algorithm maintains the generality of MIC, and gives more reasonable grid partition and MIC values for independent and dependent variable pairs under comparable running times. Moreover, it is robust under different α in B(n, α). MIC calculated by the BackMIC algorithm reveals an improvement in statistical power and equitability. We applied (1-MIC) as the distance measurement in the K-means algorithm to perform a clustering of the cancer/normal samples. The results on four cancer datasets demonstrated that the MIC values calculated by the BackMIC algorithm can obtain better clustering results, indicating the correlations between samples measured by the BackMIC algorithm were more credible than those measured by other algorithms.

A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers

MOTIVATION

A feature selection algorithm may select the subset of features with the best associations with the class labels. The recursive feature elimination (RFE) is a heuristic feature screening framework and has been widely used to select the biological OMIC biomarkers. This study proposed a dynamic recursive feature elimination (dRFE) framework with more flexible feature elimination operations. The proposed dRFE was comprehensively compared with 11 existing feature selection algorithms and five classifiers on the eight difficult transcriptome datasets from a previous study, the ten newly collected transcriptome datasets and the five methylome datasets.

RESULTS

The experimental data suggested that the regular RFE framework did not perform well, and dRFE outperformed the existing feature selection algorithms in most cases. The dRFE-detected features achieved Acc=1.0000 for the two methylome datasets GSE53045 and GSE66695. The best prediction accuracies of the dRFE-detected features were 0.9259, 0.9424, and 0.8601 for the other three methylome datasets GSE74845, GSE103186, and GSE80970, respectively. Four transcriptome datasets received Acc=1.0000 using the dRFE-detected features, and the prediction accuracies for the other six newly collected transcriptome datasets were between 0.6301 and 0.9917.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identification and Analysis of Key Genes Driving Gastric Cancer Through Bioinformatics

Objective: The aim of this study was to use bioinformatic analyses to identify key genes and pathways driving gastric cancer (GC). Materials and Methods: The gene expression profiles, from human gastric tissue samples were downloaded from the Gene Expression Omnibus (GSE)29272 dataset. These data revealed 284 differentially expressed genes (DEGs) that included a group upregulated in cancer tissues (n = 142) and another group that were downregulated in cancer tissues. (n = 142). These DEGs were identified using the GEO2R. We used multiple online analysis tools, including, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction networks, gene expression profiling interactive analysis (GEPIA), and the cBio Cancer Genomics Portal (cBioportal) database. Next, we identified the most significant DEGs using the Kaplan-Meier plotter (KM-plotter) database. Multiple bioinformatic platforms were used to identify candidate prognostic marker genes. We then analyzed freshly frozen GC tissues for the expression of these marker genes to validate the informatic findings. Results: We identified three DEGs related to overall survival from our analyses of the GEO data. Next, we analyzed these three DEGs in GEPIA and the cBioportal database and found that the biglycan (BGN) gene was related to invasion and metastases of GCs. This finding of differential gene expression was confirmed in a separate laboratory analysis of normal and GC tissues. In this analysis we found that high levels of BGN expression were correlated with GC clinicopathological characteristics, including microvascular tumor thrombus (p = 0.018), lymph node metastases (p = 0.013), and vessel invasion (p = 0.004). Conclusions: BGN expression levels appear to be an independent prognostic factor for predicting the survival times of GC patients.

High Expression of Interleukin-2 Receptor Subunit Gamma Reveals Poor Prognosis in Human Gastric Cancer

Precision medicine for gastric cancer (GC) is still an unsolved issue, because most available target drugs are not specifically designed for GC. Exploring novel signaling molecules with target value for GC is in urgent need. This study aimed to reveal that interleukin-2 receptor subunit gamma (IL2RG) is such a key molecule in human GC progression. GC tissues and paracancerous gastric tissues were collected from 7 patients (5 males and 2 females) during tumor radical excision surgery. These tissues were used to identify the differentially expressed genes (DEGs) with RNA-seq and serial bioinformatics analyses including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, gene expression profiling interactive analysis (GEPIA), and survival analysis. RT-qPCR and western blotting were performed to compare the mRNA and protein expression levels of IL2RG between GC tissues and adjacent normal gastric tissues as well as between GC cell line SGC-7901 and normal gastric epithelial cell line GES-1. Results showed striking elevations of IL2RG both in the mRNA and protein levels in GC tissues and human gastric cancer SGC-7901 cell line compared, respectively, with the adjacent normal gastric tissues and normal GES-1 cells, and higher IL2RG expression was associated with lower survival. Analyses on the GSE29272 and GSE15459 datasets from Gene Expression Omnibus verified that IL2RG was highly expressed in GC patients and was associated with poor overall survival. In addition, molecular docking showed that a small molecule, resatorvid (TAK 242), might be an inhibitor of IL2RG. We conclude that IL2RG is overexpressed in advanced GC and is associated with low survival. IL2RG may serve as a biomarker of GC progression and poor prognosis.

Optimal tumor coverage with different beam energies by IMRT, VMAT and TOMO: Effects on patients with proximal gastric cancer

To compare the effects of different photon energies on radiation planning by intensity-modulated radiotherapy (IMRT), volumetric-modulated arc therapy (VMAT) and helical tomotherapy (TOMO) for proximal gastric cancer (PGC). Network analysis with microarray procession and gene ontology were used to identify the effect of radiotherapy (RT) on PGC. Then, we retrospectively analyzed 8 PGC patients after receiving irradiation with a prescribed dose of 50.4 Gy. The Pinnacle treatment planning system (TPS, V9.8) was used to generate IMRT and VMAT plans by using 6 or 10 MV. TOMO plans were calculated on the Tomotherapy Planning Station Hi-Art Version 4.2.3 workstation (Tomotherapy Incorporated, Madison, WI, USA). PGC is associated with high DNA repair ability. TOMO plan results in higher tumor coverage and a better conformity index than IMRT and VMAT. 10-MV VMAT yields better dosimetric quality of the gradient index than 6-MV VMAT (P = .012). TOMO was associated with a lower irradiation dose in the mean dose to the right kidney (P = .049), left kidney and heart than 6-MV IMRT and 6-MV VMAT. 6-MV IMRT plan presented a higher dose of lung Dmean (P = .017) than 10-MV IMRT. Additionally, VMAT, using a planning energy of 6 MV, was associated with a significantly higher left kidney Dmean (P = .018) and V10 (P = .036) than a planning energy of 10 MV. TOMO is a better RT plan not only for tumor coverage but also for sparing organs at risk. IMRT and VMAT plans with 10 MV beams are more suitable than 6 MV beams for PGC treatment.

Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

The role of an extracellular matrix- (ECM-) receptor interaction signature has not been fully clarified in gastric cancer. This study performed comprehensive analyses on the differentially expressed ECM-related genes, clinicopathologic features, and prognostic application in gastric cancer. The differentially expressed genes between tumorous and matched normal tissues in The Cancer Genome Atlas (TCGA) and validation cohorts were identified by a paired t-test. Consensus clusters were built to find the correlation between clinicopathologic features and subclusters. Then, the least absolute shrinkage and selection operator (lasso) method was used to construct a risk score model. Correlation analyses were made to reveal the relation between risk score-stratified subgroups and clinicopathologic features or significant signatures. In TCGA (26 pairs) and validation cohort (134 pairs), 25 ECM-related genes were significantly highly expressed and 11 genes were downexpressed in gastric cancer. ECM-based subclusters were slightly related to clinicopathologic features. We constructed a risk score model = 0.081∗log₂ (CD36) + 0.043∗log₂ (COL5A2) + 0.001∗log₂ (ITGB5) + 0.039∗log₂ (SDC2) + 0.135∗log₂ (SV2B) + 0.012∗log₂ (THBS1) + 0.068∗log₂ (VTN) + 0.023∗log₂ (VWF). The risk score model could well predict the outcome of patients with gastric cancer in both training (n = 351, HR: 1.807, 95% CI: 1.292-2.528, P = 0.00046) and validation (n = 300, HR: 1.866, 95% CI: 1.347-2.584, P = 0.00014) cohorts. Besides, risk score-based subgroups were associated with angiogenesis, cell adhesion molecules, complement and coagulation cascades, TGF-beta signaling, and mismatch repair-relevant signatures (P < 0.0001). By univariate (1.845, 95% CI: 1.382-2.462, P < 0.001) and multivariate (1.756, 95% CI: 1.284-2.402, P < 0.001) analyses, we regarded the risk score as an independent risk factor in gastric cancer. Our findings revealed that ECM compositions became accomplices in the tumorigenesis, progression, and poor survival of gastric cancer.

Comprehensive Analysis of the Expression, Relationship to Immune Infiltration and Prognosis of TIM-1 in Cancer

TIM-1 is a critical gene that regulates T-helper cell development. However, little research has revealed the distribution, prognosis, and immune infiltration of TIM-1 in cancers. TCGA, GEO, Oncomine, TIMER, Kaplan-Meier, PrognoScan, GEPIA, TISIDB, and HPA databases were used to analyze TIM-1 in cancers. High TIM-1 expression was observed in bladder, cholangio, head and neck, colorectal, gastric, kidney, liver, lung adenocarcinoma, skin, uterine corpus endometrial, and pancreatic cancers compared to the normal tissues, and immunofluorescence shows that TIM-1 is mainly localized in vesicles. Simultaneously, high TIM-1 expression was closely related with poorer overall survival in gastric, lung adenocarcinoma, and poorer disease-specific survival in gastric cancer in the TCGA cohort, and was validated in the GEO cohort. Moreover, high expression of TIM-1, correlated with clinical relevance of gastric cancer and lung adenocarcinoma, was associated with tumor-infiltrating lymphocytes in lung adenocarcinoma and gastric cancer. Finally, immunohistochemistry showed TIM-1 expression was higher in lung adenocarcinoma and gastric cancer compared to the normal tissues. In summary, we applied integrated bioinformatics approaches to suggest that TIM-1 can be used as a prognostic biomarker in gastric and lung adenocarcinoma, which might provide a novel direction to explore the pathogenesis of gastric and lung adenocarcinoma.

A novel ceRNA axis involves in regulating immune infiltrates and macrophage polarization in gastric cancer

BACKGROUND

Increasing evidence suggests that the lncRNA-miRNA-mRNA regulatory network is highly correlated with gastric cancer (GC) development. However, a prognosis-associated lncRNA-miRNA-mRNA network remains to be identified in GC.

METHODS

Differentially expressed genes (DEGs) were screened by integrating 6 microarray datasets using the RRA method. Hub genes were identified by analysing their degrees in a PPI (protein-protein interaction) network. Upstream miRNAs and lncRNAs of hub genes were predicted by miRTarBase and miRNet, respectively. Key genes, miRNAs and lncRNAs were identified by evaluating their expression and prognosis in GEPIA and Kaplan-Meier plotter, respectively. A key lncRNA-miRNA-mRNA network was constructed in Cytoscape, and the correlations were analysed in the ENCORI database. We also evaluated the mRNA expression of ceRNA axes in the TIMER and Oncomine databases and their correlation with prognosis in GC patients with different clinical features using Kaplan-Meier plotter. In addition, correlations between mRNA and immune infiltrating cells in GC were investigated by the TIMER database. Finally, several experiments were conducted to verify our analyses.

RESULTS

Forty-two upregulated and 86 downregulated DEGs were obtained from the "RRA" integrated analysis. Eight of the 20 hub genes were identified as key genes by analysing their expression and prognosis. Seventeen miRNAs were predicted to target key genes, and low expression of 4 miRNAs suggested poor outcome in GC. Furthermore, 155 lncRNAs were predicted to target 4 key miRNAs, and only 5 lncRNAs were highly expressed, suggesting poor outcomes in patients with GC. Then, the H19-miR-29a-3p-COL1A2 axis was constructed by correlation analysis. In addition, COL1A2 was positively correlated with lymphatic metastasis, immune infiltrating cell levels, markers of monocytes, tumour-associated macrophages (TAMs), and M2 macrophages but not M1 macrophages in GC. The experimental results revealed that the H19-miR-29a-3p-COL1A2 axis may promote macrophage polarization from M1 to M2 in GC.

CONCLUSIONS

A novel lncRNA-miRNA-mRNA axis was identified and may be involved in regulating immune cell infiltration and macrophage polarization, which may provide new treatment strategies for GC.

Upregulation of ECT2 is associated with transcriptional program of cancer stem cells and predicts poor clinical outcome in gastric cancer

Gastric cancer remains the third leading cause of cancer-associated mortality worldwide. The identification of prognostic indicators that are associated with clinical characteristics is urgently required. The aim of the present study was to determine the involvement of epithelial cell transforming 2 (ECT2) in gastric cancer. The results of the present study demonstrated that ECT2 expression was upregulated in human gastric cancer samples. Furthermore, high ECT2 expression was associated with advanced Tumor-Node-Metastasis stage and deeper tumor invasion. ECT2 upregulation was further confirmed in several independent publicly available clinical cohorts from the Gene Expression Omnibus database. In addition, patients with gastric cancer, with high ECT2 expression exhibited a significantly shorter overall survival time than those with low ECT2 expression, and Cox regression analysis demonstrated that ECT2 expression was an independent prognostic marker for overall survival time. Characterization of the transcriptome profiles of ECT2 upregulated gastric tumors indicated that ECT2 upregulation may be associated with transcriptional features of cancer stem cells (CSCs). Additionally, BUB1 mitotic checkpoint serine/threonine kinase and E2F transcription factor 7, two genes previously reported to account for the functionality of CSCs, were strongly enriched in ECT2^High gastric cancer samples. Taken together, the results of the present study suggest that ECT2 may serve as a novel marker for CSCs and may be a potential prognostic indicator in gastric cancer.

Key Genes Associated with Prognosis and Tumor Infiltrating Immune Cells in Gastric Cancer Patients Identified by Cross-Database Analysis

Background: The molecular mechanisms underlying gastric cancer (GC) progression are unclear. The authors examined key genes associated with the prognosis and tumor-infiltrating immune cells in patients with GC. Materials and Methods: Gene expression omnibus (GEO) was used to filter and obtain GC-related differentially expressed genes (DEGs). The molecular functions, biological processes, and cellular components of the DEGs were subjected to enrichment analysis. Protein-protein interaction networks of proteins encoded by the DEGs were analyzed using STRING. The authors also identified hub genes of GC, as well as their expression levels in GC and their relationship with patient prognosis. The relationship between hub genes and tumor-infiltrating immune cells was analyzed by Tumor IMmune Estimation Resource. Results: Six GEO datasets were included in this study, and 265 DEGs were identified. These DEGs were enriched in different signaling pathways and had different biological functions. Six hub genes were potentially significantly related to the molecular mechanisms of GC (TOP2A, FN1, SPARC, COL3A1, COL1A1, and TIMP1). These genes are potential markers of prognosis. Five hub genes were significantly positively correlated with the number of macrophages, neutrophils, and dendritic cells. Conclusions: The authors provide a theoretical basis for exploring the molecular regulation mechanism underlying GC and identifying therapeutic targets.

MUC1 is associated with TFF2 methylation in gastric cancer

Background

Emerging evidence has shown that MUC1 and TFF2 play crucial roles in the H. pylori-infected pathogenesis of gastric cancer (GC). A recent study revealed that H. pylori infection induced obviously increased Tff2 methylation levels in Muc1^−/− mice compared with controls. However, little is known of the molecular mechanism on MUC1 regulating the expression of TFF2.

Methods

We conducted a correlation analysis of MUC1 and TFF2 in public databases and our adjacent GC tissues. Besides, MUC1 overexpression vector or small interfering RNA (siRNA) was transfected into GC cells to assess the change in TFF2 expression. Furthermore, the methylation status of TFF2 was measured by bisulfite sequencing PCR (BSP).

Results

The expression of MUC1 was significantly lower in non-cardia and cardia tumor tissues than that in normal tissues. Downregulation of TFF2 expression was also observed in GC tissues. In addition, we found that MUC1 expression was positively associated with TFF2 expression in GC tissues, especially among GC patients with H. pylori infection. Overexpression of MUC1 in BGC-823 and SGC-7901 cell lines substantially increased the TFF2 expression, whereas knockdown of MUC1 reverted this effect. Moreover, MUC1 was negatively related to the methylation of TFF2 in the co-expression analysis. The results of BSP experiments showed that compared with negative vector group, the methylation level of TFF2 was decreased in GC cells transfected with MUC1 overexpression vector. Additionally, survival analysis indicated that GC patients with lower level of MUC1 or TFF2 had a worse outcome.

Conclusion

Our results indicated that MUC1 was associated with the methylation of TFF2, which may have implications for TFF2 expression in GC. These findings warrant further research toward the underlying mechanism of MUC1 influenced the TFF2 methylation.

MFAP2 is overexpressed in gastric cancer and promotes motility via the MFAP2/integrin α5β1/FAK/ERK pathway

Gastric cancer (GC) is one of the most common malignancies and its prognosis is extremely poor. This study identifies a novel oncogene, microfibrillar-associated protein 2 (MFAP2) in GC. With integrative reanalysis of transcriptomic data, we found MFAP2 as a GC prognosis-related gene. And the aberrant expression of MFAP2 was explored in GC samples. Subsequent experiments indicated that silencing and exogenous MFAP2 could affect motility of cancer cells. The inhibition of silencing MFAP2 could be rescued by another FAK activator, fibronectin. This process is probably through affecting the activation of focal adhesion process via modulating ITGB1 and ITGA5. MFAP2 regulated integrin expression through ERK1/2 activation. Silencing MFAP2 by shRNA inhibited tumorigenicity and metastasis in nude mice. We also revealed that MFAP2 is a novel target of microRNA-29, and miR-29/MFAP2/integrin α5β1/FAK/ERK1/2 could be an important oncogenic pathway in GC progression. In conclusion, our data identified MFAP2 as a novel oncogene in GC and revealed that miR-29/MFAP2/integrin α5β1/FAK/ERK1/2 could be an important oncogenic pathway in GC progression.

Constraining classifiers in molecular analysis: invariance and robustness

Analysing molecular profiles requires the selection of classification models that can cope with the high dimensionality and variability of these data. Also, improper reference point choice and scaling pose additional challenges. Often model selection is somewhat guided by ad hoc simulations rather than by sophisticated considerations on the properties of a categorization model. Here, we derive and report four linked linear concept classes/models with distinct invariance properties for high-dimensional molecular classification. We can further show that these concept classes also form a half-order of complexity classes in terms of Vapnik-Chervonenkis dimensions, which also implies increased generalization abilities. We implemented support vector machines with these properties. Surprisingly, we were able to attain comparable or even superior generalization abilities to the standard linear one on the 27 investigated RNA-Seq and microarray datasets. Our results indicate that a priori chosen invariant models can replace ad hoc robustness analysis by interpretable and theoretically guaranteed properties in molecular categorization.

Phytochemicals for the Prevention and Treatment of Gastric Cancer: Effects and Mechanisms

Gastric cancer is the fifth most common cancer, and the third most prevalent cause of cancer-related deaths in the world. Voluminous evidence has demonstrated that phytochemicals play a critical role in the prevention and management of gastric cancer. Most epidemiological investigations indicate that the increased intake of phytochemicals could reduce the risk of gastric cancer. Experimental studies have elucidated the mechanisms of action, including inhibiting cancer cell proliferation, inducing apoptosis and autophagy, and suppressing angiogenesis as well as cancer cell metastasis. These mechanisms have also been related to the inhibition of Helicobacter pylori and the modulation of gut microbiota. In addition, the intake of phytochemicals could enhance the efficacy of anticancer chemotherapeutics. Moreover, clinical studies have illustrated that phytochemicals have the potential for the prevention and the management of gastric cancer in humans. To provide an updated understanding of relationships between phytochemicals and gastric cancer, this review summarizes the effects of phytochemicals on gastric cancer, highlighting the underlying mechanisms. This review could be helpful for guiding the public in preventing gastric cancer through phytochemicals, as well as in developing functional food and drugs for the prevention and treatment of gastric cancer.

Distinct expression and prognostic values of the replication protein A family in gastric cancer

The replication protein A (RPA)1-4 family are single-stranded DNA-binding proteins that are essential components of DNA replication, repair and recombination, and cell cycle regulation. The present study aimed to evaluate the prognostic value of the RPA family members in patients with gastric cancer (GC), using datasets retrieved from the Oncomine public database. Datasets were retrieved for the purpose of comparing the RPA expression levels between GC and normal tissues. Additionally, Kaplan-Meier analysis was used to compare the overall survival (OS) times of GC patients that expressed different levels of RPA proteins. RPA1, 2, and 3 expression levels were all significantly upregulated in gastric intestinal-type, diffuse gastric, and gastric mixed adenocarcinomas, compared with those in normal mucosal tissues. Moreover, high mRNA expression levels of RPA3 and 4 predicted poorer OS times in all GCs, as well as patients with human epidermal growth factor receptor 2-negative and -positive GC. The high-risk group, separated by RPA signature, showed a poorer outcome than the low-risk group. RPA3 was the most strongly correlated with CD4+ T-cell levels. In conclusion, RPAs are novel prognostic indicators in GC, and can also predict the features of immunological diseases. Future experimental investigation into the roles of RPAs concerning the pathogenesis and development of GC may provide a novel biomarker or therapeutic target, improving the prognosis of patients with GC.

SE translocation gene but not zinc finger or X-linked factor is down-regulated in gastric cancer

AIM

The current study aimed to identify the expression levels of SE Translocation (SET), Zinc Finger, and X-Linked Factor (ZFX) in gastric cancer tissues and their corresponding adjacent non-cancerous tissues (ANCTs).

BACKGROUND

SET has been first identified as a component of a fusion protein produced by chromosomal rearrangement in a patient with acute undifferentiated leukemia. Subsequently, multiple functions have been attributed to this gene in different disorders such as cancer and Alzheimer's disease. The expression of SET is regulated by ZFX, a transcription factor which has a potential role in gastric cancer.

METHODS

In this case-control study, we evaluated the expression of SET and ZFX in gastric cancer tissues (n=28) and their corresponding ANCTs (n=28) via quantitative real-time PCR.

RESULTS

SET1 gene was down-regulated in tumoral tissues compared with ANCTs (expression ratio=0.25, P=0.015). However, the expression of ZFX was similar between tumoral tissues and ANCTs (expression ratio=0.97, P=0.945). We detected a significant association between the site of primary tumor and SET1 relative expression in tumoral tissues versus ANCTs, where this gene was down-regulated in all tumors originating from cardia. Based on the area under the receiver operating characteristic curve, the diagnostic power of transcription levels of SET1 in gastric cancer was 0.68. Finally, we observed remarkable correlations between expression levels of SET1 and ZFX both in tumoral tissues (R²=0.38, P<0.05) and in ANCTs (R²=0.23, P<0.05).

CONCLUSION

Overall, our results imply the role of SET1 in gastric cancer and potentially functional associations between this gene and ZFX in gastric tissues.

High expression of fibronectin 1 indicates poor prognosis in gastric cancer

Fibronectin 1 (FN1) is involved in the occurrence and development of various tumors and is upregulated in multiple cancer types. FN1 has been demonstrated to promote cell proliferation and migration in gastric cancer cell lines. However, the relationship between the expression of FN1 and clinicopathological factors and prognosis is not clear in gastric cancer (GC). The aim of the present study was to investigate the association between FN1 expression and clinicopathology and prognosis of gastric cancer. In this study, 17 publicly available GC cohorts (n=2,376) with gene expression data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Oncomine databases were tested. In addition, FN1 protein expression was validated by immunohistochemistry in a separate cohort (n=190). The meta-analysis results demonstrated an increase in FN1 expression at the protein and mRNA level in GC tissues, and the FN1 gene was highly expressed at the mRNA level in the advanced T stage (T2 + T3 + T4) group compared with that in the early T stage (T1) group. In addition, the expression of epithelial FN1 at the protein level was positively correlated with tumor size. FN1 expression at the protein and mRNA level was a predictor of poor prognosis following radical resection of GC. In conclusion, the expression of FN1 in GC tissues is upregulated compared with adjacent normal tissues, and it is a potential biomarker of poor prognosis in patients with GC.

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.