Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients

A novel adenosine signalling-based prognostic signature in gastric cancer and its association with cancer immune features and immunotherapy response

Reliable prognostic signatures that can reflect the intrinsic characteristics of gastric cancer (GC) are still rare. Here, we developed an adenosine-based prognostic signature and explored its association with the tumour immune in GC patients, aiming at confirming the prognostic value of adenosine-related genes and guiding the GC risk stratification and immunotherapeutic response prediction. We collected adenosine pathway-related genes from STRING websites and manual searching. We enrolled the The Cancer Genome Atlas cohort and four gene expression omnibus cohorts of GC for generating and validating the adenosine pathway-based signature using the Cox regression method. Gene expression in the signature was verified using polymerase chain reaction. We also performed gene set enrichment analysis, immune infiltration assessment and immunotherapy response prediction based on this signature. Our study resulted in a six-gene adenosine signature (GNAS, CXCR4, PPP1R1B, ADCY6, NT5E and NOS3) for risk stratification of GC prognosis, with the highest area under the receiver operating characteristic curve up to 0.767 for predicting 10-year overall survival (OS). In the training cohort, patients with signature-defined high risk had significantly poorer OS than those with low risk (p < .001). Multivariate analysis identified the signature as an independent prognostic factor (hazard ratio 2.863, 95% confidence interval [1.871-4.381], p < .001). These findings were confirmed in four independent cohorts. Expression detection showed that all signature genes were upregulated in both GC tissues and cell lines. Further analysis revealed that the signature-defined high-risk patients were characterised by immunosuppressive states and associated with a poor immunotherapy response. In conclusion, the adenosine pathway-based signature represents a promising risk stratification tool for GC in guiding individualised prognostication and immunotherapy.

A prognostic gene signature for gastric cancer and the immune infiltration-associated mechanism underlying the signature gene, PLG

BACKGROUND

Globally, gastric cancer (GC) is a common and lethal solid malignant tumor. Identifying the molecular signature and its functions can provide mechanistic insights into GC development and new methods for targeted therapy.

METHODS

Differentially expressed genes (DEGs) and prognostic genes (from univariate Cox regression analysis) were overlapped to obtain prognostic DEGs. Subsequently, molecular modules and the functions of these prognostic DEGs were identified by Metascape and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Set Enrichment Analysis (GSEA) enrichment analyses, respectively. Protein-protein interaction (PPI) networks of up- and down-regulated prognostic DEGs in GC were analyzed using the MCC algorithm of the Cytohubba plug-in in Cytoscape. The prognostic gene signature was defined on hub genes of the PPI networks by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. Furthermore, the expressional level of PLG in our clinical GC samples was validated by quantitative PCR (qPCR), western blotting, and immunohistochemistry (IHC). Subsequently, the PLG expression-correlation analysis was performed to assess the role of PLG in GC progression. Immune infiltration analysis was performed by single-sample gene set enrichment analysis (ssGSEA) to assess the inhibitory effect of PLG on immune infiltration.

RESULTS

Firstly, Up- and down-regulated prognostic DEGs and hub genes in protein-protein interaction (PPI) networks in GC were identified. A prognostic five-gene signature (i.e., PLG, SPARC, FGB, SERPINE1, and KLHL41) was identified. Among the five genes, the relationship between plasminogen (PLG) and GC remains largely unclear. Moreover, the functions of PLG-correlated genes in GC, like 'fibrinolysis', 'hemostasis', 'ion channel complex', and 'transporter complex' were identified. In addition, PLG expression correlated negatively with the infiltration of almost all immune cell types. Interestingly, the expression of PLG was significantly and highly correlated with that of CD160, an immune checkpoint inhibitor.

CONCLUSION

Our findings defined a new five-gene signature for predicting GC prognosis, but more validation is required to assess the effects and mechanism of the five genes, especially PLG, for the development of new GC therapies.

Cytohesin-4 Upregulation in Glioma-Associated M2 Macrophages Is Correlated with Pyroptosis and Poor Prognosis

Cytohesin-4 (CYTH4) is a member of the PSCD family. Members of this family appear to mediate the regulation of protein sorting and membrane trafficking. In previous studies, CYTH4 has been linked with multiple brain diseases, but not glioma, the most common type of brain tumor. We utilized multiple glioma single-cell RNA sequencing datasets and bulk data from the TCGA and CGGA and conducted GSEA and KEGG and GO analyses. Biomarker potential was tested via ROC curve analysis. Radar plots were used to study TMB and MSI correlations. Immune cell studies were conducted using CIBERSORT. All statistical analyses were performed in R software and GraphPad Prism 9. CYTH4 was overexpressed in the glioma macrophage population in several single-cell RNA sequencing datasets and was most correlated with M2 macrophages. CYTH4 expression was higher in tumor tissues and was correlated with survival and WHO grade. ROC curves suggested CYTH4 overexpression to be a potential glioma biomarker. GSEA results indicated a relationship between CYTH4 and apoptosis, and PPI analysis supported a pyroptosis correlation. KEGG and GO analysis results linked CYTH4 with antigen processing and presentation and neutrophil activities. In summary, the study identified a CYTH4/pyroptosis/M2 macrophage axis. CYTH4 was upregulated in M2 macrophages in glioma and affected pyroptosis. CYTH4 overexpression is a potential biomarker predicting a poor prognosis.

Apoptotic Induction of Mitochondria-Anchored Aggregation-Induced Emission Luminogens through the Intrinsic Mitochondrial Pathway

Gastric cancer has the third highest mortality rate globally. Chemotherapy is the primary treatment used in advanced gastric cancer. Aggregation-induced emission luminogens (AIEgens) have been exploited as non-toxic and efficient chemotherapy agents for the treatment of cancer. Our previous research demonstrated that tetraphenylethene-substituted pyridinium salt (TPE-Py) is a kind of AIEgen that had the ability to lead to apoptosis in gastric cancer cells. However, it is currently unknown whether TPE-Py induced apoptosis in gastric cancer cells by the mitochondria-mediated pathway. This research confirmed that TPE-Py could target mitochondria and induce apoptotic cell death. In addition, several well-recognized indicators were detected to investigate the functional and morphological changes of mitochondria. We found that TPE-Py could diminish the mitochondrial membrane potential and increase the accumulation of reactive oxygen species and the discharge of cytochrome c, which was related to the mitochondrial apoptotic pathway. Meanwhile, morphological changes in mitochondria were also observed by transmission electron microscopy in gastric cancer cells after incubation with TPE-Py. In conclusion, we provided insights into the mechanism regulating apoptosis in gastric cancer cells and elucidated the mechanism of apoptosis induced by TPE-Py via the intrinsic mitochondrial pathway.

Nomograms for prognostic risk assessment in glioblastoma multiforme: Applications and limitations

Glioblastoma multiforme (GBM) is the most common and aggressive form of brain cancer. Prognosis evaluation is of great significance in guiding individualized treatment and monitoring of GBM. By integrating different prognostic variables, nomograms simplify the statistical risk prediction model into numerical estimates for death or recurrence, and are hence widely applied in prognosis prediction. In the past two decades, the application of high-throughput profiling technology and the establishment of TCGA database and other public data deposits have provided opportunities to identify cancer-related molecules and prognostic biomarkers. As a result, both molecular features and clinical characteristics of cancer have been reported to be the key factors in nomogram model construction. This article comprehensively reviewed 35 studies of GBM nomograms, analyzed the present situation of GBM nomograms, and discussed the role and significance of nomograms in personalized risk assessment and clinical treatment decision-making. To facilitate the application of nomograms in the prognostic prediction of GBM patients, a website has been established for the online access of nomograms based on the studies of this review, which is called Consensus Nomogram Spectrum for Glioblastoma (CNSgbm) and is accessible through https://bioinfo.henu.edu.cn/nom/NomList.jsp.

Pan-Cancer Analysis and Validation Reveals that D-Dimer-Related Genes are Prognostic and Downregulate CD8+ T Cells via TGF-Beta Signaling in Gastric Cancer

Background: Cancer is considered one of the most lethal diseases worldwide. Venous thromboembolism (VTE) is the second leading cause of death in cancer patients. As one of the most reproducible predictors of thromboembolism, the D-dimer level is commonly considered by oncologists. Previous studies have demonstrated that the most correlated genes at the D-dimer level are F3, F5 and FGA.

Methods: Using data from TCGA and multiple webtools, including GEPIA2, UALCAN, TIMER2.0, Kaplan-Meier Plotter and CIBERSORTx, we analyzed the tumor mutation burden (TMB), microsatellite instability (MSI) and functions of D-dimer-related genes in cancer. Validation was conducted via quantitative real-time polymerase chain reaction (qRT-PCR) and independent GEO + GTEx cohort. All statistical analyses were performed in R software and GraphPad Prism 9.

Results: F3, F5 and FGA were expressed differently in multiple cancer types. TMB, MSI and anti-PD1/PDL1 therapy responses were correlated with D-dimer-related gene expression. D-Dimer-related genes expression affect the survival of cancer patients. F3 and F5 functioned in TGF-beta signaling. F3 and F5 were related to immunity and affected the fraction of CD8⁺ T cells by upregulating the TGF-beta signaling pathway, forming an F3, F5/TGF-beta signaling/CD8+ T cell axis.

Conclusion: F3, F5 and FGA serve as satisfactory GC multibiomarkers and potentially influence the immune microenvironment and survival of cancer patients by influencing TGF-beta signaling.

MeCP2 confers 5-fluorouracil resistance in gastric cancer via upregulating the NOX4/PKM2 pathway

Background

Increasing evidence suggests that aberrant methylation is involved in 5-fluorouracil (5-FU) resistance in gastric cancer (GC). Our previous work has identified that Methyl-CpG binding protein 2 (MeCP2) promotes GC progression by binding to the methylation sites of promoter regions of specific genes to affect the downstream signaling pathways. However, the function and molecular mechanisms of MeCP2 in GC 5-FU resistance remain unclear.

Methods

We detected the expression of MeCP2 in 5-FU-resistant GC cells and examined cell behaviors when MeCP2 was silenced. The molecular mechanisms were explored through chromatin immunoprecipitation (ChIP)-qRT-PCR, luciferase reporter assay, clinical tissue samples analysis, and in vivo tumorigenicity assay.

Results

MeCP2 was up-regulated in 5-FU-resistant GC cells. Knockdown of MeCP2 enhanced the sensitivity of the cells to 5-FU. Moreover, MeCP2 promoted NOX4 transcription in the cells by binding to the promoter of NOX4. Silencing NOX4 rescued the inductive effect of MeCP2 overexpression on 5-FU sensitivity of GC cells and reduced the expression of NOX4 and PKM2 in MeCP2 overexpressed 5-FU-resistant GC cells. In addition, our in vivo experiments demonstrated that MeCP2 knockdown enhanced 5-FU sensitivity in tumors.

Conclusion

MeCP2 confers 5-FU resistance in GC cells via upregulating the NOX4/PKM2 pathway, which may lead to a promising therapeutic strategy for GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02489-y.

Combining Algorithms to Find Signatures That Predict Risk in Early-Stage Stomach Cancer

This study applied two mathematical algorithms, lattice up-stream targeting (LUST) and D-basis, to the identification of prognostic signatures from cancer gene expression data. The LUST algorithm looks for metagenes, which are sets of genes that are either overexpressed or underexpressed in the same patients. Whereas LUST runs unsupervised by clinical data, the D-basis algorithm uses implications and association rules to relate gene expression to clinical outcomes. The D-basis selects a small subset of the metagene (a signature) to predict survival. The two algorithms, LUST and D-basis, were combined and applied to mRNA expression and clinical data from The Cancer Genome Atlas (TCGA) for 203 stage 1 and 2 stomach cancer patients. Two small (four-gene) signatures effectively predict survival in early-stage stomach cancer patients. These signatures could be used as a guide for treatment. The first signature (DU4) consists of genes that are underexpressed on the long-survival/low-risk group: FLRT2, KCNB1, MYOC, and TNXB. The second signature consists of genes that are overexpressed on the short-survival/high-risk group: ASB5, SFRP1, SMYD1, and TACR2. Another nine-gene signature (REC9) predicts recurrence: BNC2, CCDC8, DPYSL3, MOXD1, MXRA8, PRELP, SCARF2, TAGLN, and ZNF423. Each patient is assigned a score that is a linear combination of the expression levels for the genes in the signature. Scores below a selected threshold predict low-risk/long survival, whereas high scores indicate a high risk of short survival. The metagenes associate with TCGA cluster C1. Both our signatures and cluster C1 identify tumors that are genomically silent, and have a low mutation load or mutation count. Furthermore, our signatures identify tumors that are predominantly in the WHO classification of poorly cohesive and the Lauren class of diffuse samples, which have a poor prognosis.

Increased gene expression variability in BRCA1-associated and basal-like breast tumours

Purpose

Inherited variants in the cancer susceptibility genes, BRCA1 and BRCA2 account for up to 5% of breast cancers. Multiple gene expression studies have analysed gene expression patterns that maybe associated with BRCA12 pathogenic variant status; however, results from these studies lack consensus. These studies have focused on the differences in population means to identified genes associated with BRCA1/2-carriers with little consideration for gene expression variability, which is also under genetic control and is a feature of cellular function.

Methods

We measured differential gene expression variability in three of the largest familial breast cancer datasets and a 2116 breast cancer meta-cohort. Additionally, we used RNA in situ hybridisation to confirm expression variability of EN1 in an independent cohort of more than 500 breast tumours.

Results

BRCA1-associated breast tumours exhibited a 22.8% (95% CI 22.3–23.2) increase in transcriptome-wide gene expression variability compared to BRCAx tumours. Additionally, 40 genes were associated with BRCA1-related breast cancers that had ChIP-seq data suggestive of enriched EZH2 binding. Of these, two genes (EN1 and IGF2BP3) were significantly variable in both BRCA1-associated and basal-like breast tumours. RNA in situ analysis of EN1 supported a significant (p = 6.3 × 10⁻⁰⁴) increase in expression variability in BRCA1-associated breast tumours.

Conclusion

Our novel results describe a state of increased gene expression variability in BRCA1-related and basal-like breast tumours. Furthermore, genes with increased variability may be driven by changes in DNA occupancy of epigenetic effectors. The variation in gene expression is replicable and led to the identification of novel associations between genes and disease phenotypes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06328-y.

Identification of super enhancer-associated key genes for prognosis of germinal center B-cell type diffuse large B-cell lymphoma by integrated analysis

BACKGROUND

The pathogenesis of germinal center B-cell type diffuse large B-cell lymphoma (GCB-DLBCL) is not fully elucidated. This study aims to explore the regulation of super enhancers (SEs) on GCB-DLBCL by identifying specific SE-target gene.

METHODS

Weighted gene co-expression network analysis (WGCNA) was used to screen modules associated with GCB subtype. Functional analysis was performed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. H3K27ac peaks were used to identify SEs. Overall survival analysis was performed using Kaplan-Meier curve with log-rank and Breslow test. The effect of ADNP, ANKRD28 and RTN4IP1 knockdown on Karpas 422 and SUDHL-4 cells proliferation was analyzed by CCK-8. Karpas 422 and SUDHL-4 cells were treated with bromodomain and extra-terminal domain (BET) inhibitor JQ1, and the expression of ADNP, ANKRD28 and RTN4IP1was measured by qRT-PCR.

RESULTS

A total of 26 modules were screened in DLBCL. Turquoise module was closely related to GCB-DLBCL, and its eigengenes were mainly related to autophagy. There were 971 SEs in Karpas 422 cell and 1088 SEs in SUDHL-4 cell. Function of the nearest genes of overall SEs were related to cancer. Six SE-related genes associated with GCB-DLBCL were identified as prognostic markers. Knockdown of ADNP, ANKRD28 and RTN4IP1 inhibited the proliferation of Karpas 422 and SUDHL-4 cells. JQ1 treatment suppressed ADNP, ANKRD28 and RTN4IP1 expression in Karpas 422 and SUDHL-4 cells.

CONCLUSIONS

A total of 6 SE-related genes associated with GCB-DLBCL overall survival were identified in this study. These results will serve as a theoretical basis for further study of gene regulation and function of GCB-DLBCL.