Clinical and genomic landscape of gastric cancer with a mesenchymal phenotype

GCclassifier: An R package for the prediction of molecular subtypes of gastric cancer

Gastric cancer (GC) is one of the most commonly diagnosed malignancies, threatening millions of lives worldwide each year. Importantly, GC is a heterogeneous disease, posing a significant challenge to the selection of patients for more optimized therapy. Over the last decades, extensive community effort has been spent on dissecting the heterogeneity of GC, leading to the identification of distinct molecular subtypes that are clinically relevant. However, so far, no tool is publicly available for GC subtype prediction, hindering the research into GC subtype-specific biological mechanisms, the design of novel targeted agents, and potential clinical applications. To address the unmet need, we developed an R package GCclassifier for predicting GC molecular subtypes based on gene expression profiles. To facilitate the use by non-bioinformaticians, we also provide an interactive, user-friendly web server implementing the major functionalities of GCclassifier. The predictive performance of GCclassifier was demonstrated using case studies on multiple independent datasets.

Inhibition of glycolysis enhances the efficacy of immunotherapy via PDK-mediated upregulation of PD-L1

BACKGROUND

Immunotherapy for gastric cancer remains a challenge due to its limited efficacy. Metabolic reprogramming toward glycolysis has emerged as a promising avenue for enhancing the sensitivity of tumors to immunotherapy. Pyruvate dehydrogenase kinases (PDKs) play pivotal roles in regulating glycolysis. The importance of PDKs in the context of gastric cancer immunotherapy and their potential as therapeutic targets have not been fully explored.

METHODS

PDK and PD-L1 expression was analyzed using data from the GSE66229 and The Cancer Genome Atlas (TCGA) cohorts. Additionally, the Immune Checkpoint Blockade Therapy Atlas (ICBatlas) database was utilized to assess PDK expression in an immune checkpoint blockade (ICB) therapy group. Subsequently, the upregulation of PD-L1 and the enhancement of anticancer effects achieved by targeting PDK were validated through in vivo and in vitro assays. The impact of PDK on histone acetylation was investigated using ChIP‒qPCR to detect changes in histone acetylation levels.

RESULTS

Our analysis revealed a notable negative correlation between PD-L1 and PDK expression. Downregulation of PDK led to a significant increase in PD-L1 expression. PDK inhibition increased histone acetylation levels by promoting acetyl-CoA generation. The augmentation of acetyl-CoA production and concurrent inhibition of histone deacetylation were found to upregulate PD-L1 expression in gastric cancer cells. Additionally, we observed a significant increase in the anticancer effect of PD-L1 antibodies following treatment with a PDK inhibitor.

CONCLUSIONS

Downregulation of PDK in gastric cancer cells leads to an increase in PD-L1 expression levels, thus potentially improving the efficacy of PD-L1 immune checkpoint blockade therapy.

CPT1C-positive cancer-associated fibroblast facilitates immunosuppression through promoting IL-6-induced M2-like phenotype of macrophage

Cancer-associated fibroblasts (CAFs) exhibit remarkable phenotypic heterogeneity, with specific subsets implicated in immunosuppression in various malignancies. However, whether and how they attenuate anti-tumor immunity in gastric cancer (GC) remains elusive. CPT1C, a unique isoform of carnitine palmitoyltransferase pivotal in regulating fatty acid oxidation, is briefly indicated as a protumoral metabolic mediator in the tumor microenvironment (TME) of GC. In the present study, we initially identified specific subsets of fibroblasts exclusively overexpressing CPT1C, hereby termed them as CPT1C+CAFs. Subsequent findings indicated that CPT1C+CAFs fostered a stroma-enriched and immunosuppressive TME as they correlated with extracellular matrix-related molecular features and enrichment of both immunosuppressive subsets, especially M2-like macrophages, and multiple immune-related pathways. Next, we identified that CPT1C+CAFs promoted the M2-like phenotype of macrophage in vitro. Bioinformatic analyses unveiled the robust IL-6 signaling between CPT1C+CAFs and M2-like phenotype of macrophage and identified CPT1C+CAFs as the primary source of IL-6. Meanwhile, suppressing CPT1C expression in CAFs significantly decreased IL-6 secretion in vitro. Lastly, we demonstrated the association of CPT1C+CAFs with therapeutic resistance. Notably, GC patients with high CPT1C+CAFs infiltration responded poorly to immunotherapy in clinical cohort. Collectively, our data not only present the novel identification of CPT1C+CAFs as immunosuppressive subsets in TME of GC, but also reveal the underlying mechanism that CPT1C+CAFs impair tumor immunity by secreting IL-6 to induce the immunosuppressive M2-like phenotype of macrophage in GC.

E-cadherin loss drives diffuse-type gastric tumorigenesis via EZH2-mediated reprogramming

Diffuse-type gastric adenocarcinoma (DGAC) is a deadly cancer often diagnosed late and resistant to treatment. While hereditary DGAC is linked to CDH1 mutations, the role of CDH1/E-cadherin inactivation in sporadic DGAC tumorigenesis remains elusive. We discovered CDH1 inactivation in a subset of DGAC patient tumors. Analyzing single-cell transcriptomes in malignant ascites, we identified two DGAC subtypes: DGAC1 (CDH1 loss) and DGAC2 (lacking immune response). DGAC1 displayed distinct molecular signatures, activated DGAC-related pathways, and an abundance of exhausted T cells in ascites. Genetically engineered murine gastric organoids showed that Cdh1 knock-out (KO), KrasG12D, Trp53 KO (EKP) accelerates tumorigenesis with immune evasion compared with KrasG12D, Trp53 KO (KP). We also identified EZH2 as a key mediator promoting CDH1 loss-associated DGAC tumorigenesis. These findings highlight DGAC's molecular diversity and potential for personalized treatment in CDH1-inactivated patients.

A prognostic signature of fatty acid metabolism-related genes for predicting survival of gastric cancer patients

To analyze the expression profile of fatty acid metabolism (FAM)-related genes, identify a prognostic signature, and evaluate its clinical value for gastric cancer (GC) patients. The mRNA expression profiles of 493 FAM-related genes were obtained from TCGA database. Differentially expressed genes (DEGs) between cancer and non-cancer samples were identified, and their relationships with overall survival (OS) of GC patients were evaluated. A prognostic signature of FAM-related genes was identified by the LASSO regression model, and its predictive performance was tested by an independent external cohort. Ninety-three DEGs were identified, of which 44 were downregulated and 49 were upregulated. After optimizing risk characteristics, a prognostic signature of four FAM-related genes (ACBD5, AVPR1A, ELOVL4, and FAAH) were developed. All patients were divided into high-risk (>1.020) and low-risk groups (≤1.020) on the basis of the median risk score. Survival analysis indicated that high-risk patients had a shorter OS than low-risk patients (5-year OS rate, 26.3% vs. 45.0%, p < 0.001). The AUC values for the prediction of 3-year and 5-year OS were 0.664 and 0.624, respectively. In the GSE62254 data set, the 5-year OS rate of high-risk and low-risk patients were 44.7% versus 61.5%, respectively (p = 0.003). The AUC values were 0.632 and 0.627 at 3-year and 5-year prediction. The prognostic signature of FAM-related genes was an independent predictor of OS (hanzard ratio [HR] for TCGA cohort: 1.851, 95% confidence interval [CI]: 1.394-2.458, p < 0.001; HR for GSE62254: 1.549, 95% CI: 1.098-2.185, p = 0.013). The risk signature of four FAM-related genes was a valuable prognostic tool, and it might be helpful for clinical management and therapeutic decision of gastric cancer patients.

FGFR-targeted therapeutics: clinical activity, mechanisms of resistance and new directions

Fibroblast growth factor (FGF) signalling via FGF receptors (FGFR1-4) orchestrates fetal development and contributes to tissue and whole-body homeostasis, but can also promote tumorigenesis. Various agents, including pan-FGFR inhibitors (erdafitinib and futibatinib), FGFR1/2/3 inhibitors (infigratinib and pemigatinib), as well as a range of more-specific agents, have been developed and several have entered clinical use. Erdafitinib is approved for patients with urothelial carcinoma harbouring FGFR2/3 alterations, and futibatinib and pemigatinib are approved for patients with cholangiocarcinoma harbouring FGFR2 fusions and/or rearrangements. Clinical benefit from these agents is in part limited by hyperphosphataemia owing to off-target inhibition of FGFR1 as well as the emergence of resistance mutations in FGFR genes, activation of bypass signalling pathways, concurrent TP53 alterations and possibly epithelial-mesenchymal transition-related isoform switching. The next generation of small-molecule inhibitors, such as lirafugratinib and LOXO-435, and the FGFR2-specific antibody bemarituzumab are expected to have a reduced risk of hyperphosphataemia and the ability to overcome certain resistance mutations. In this Review, we describe the development and current clinical role of FGFR inhibitors and provide perspective on future research directions including expansion of the therapeutic indications for use of FGFR inhibitors, combination of these agents with immune-checkpoint inhibitors and the application of novel technologies, such as artificial intelligence.

Feature-specific quantile normalization and feature-specific mean-variance normalization deliver robust bi-directional classification and feature selection performance between microarray and RNAseq data

BACKGROUND

Cross-platform normalization seeks to minimize technological bias between microarray and RNAseq whole-transcriptome data. Incorporating multiple gene expression platforms permits external validation of experimental findings, and augments training sets for machine learning models. Here, we compare the performance of Feature Specific Quantile Normalization (FSQN) to a previously used but unvalidated and uncharacterized method we label as Feature Specific Mean Variance Normalization (FSMVN). We evaluate the performance of these methods for bidirectional normalization in the context of nested feature selection.

RESULTS

FSQN and FSMVN provided clinically equivalent bidirectional model performance with and without feature selection for colon CMS and breast PAM50 classification. Using principal component analysis, we determine that these methods eliminate batch effects related to technological platforms. Without feature selection, no statistical difference was identified between the performance of FSQN and FSMVN of cross-platform data compared to within-platform distributions. Under optimal feature selection conditions, balanced accuracy was FSQN and FSMVN were statistically equivalent to the within-platform distribution performance in multivariable linear regression analysis. FSQN and FSMVN also provided similar performance to within-platform distributions as the number of selected genes used to create models decreases.

CONCLUSIONS

In the context of generating supervised machine learning classifiers for molecular subtypes, FSQN and FSMVN are equally effective. Under optimal modeling conditions, FSQN and FSMVN provide equivalent model accuracy performance on cross-platform normalization data compared to within-platform data. Using cross-platform data should still be approached with caution as subtle performance differences may exist depending on the classification problem, training, and testing distributions.

The development of a prediction model based on deep learning for prognosis prediction of gastrointestinal stromal tumor: a SEER-based study

Accurately predicting the prognosis of Gastrointestinal stromal tumor (GIST) patients is an important task. The goal of this study was to create and assess models for GIST patients' survival patients using the Surveillance, Epidemiology, and End Results Program (SEER) database based on the three different deep learning models. Four thousand five hundred thirty-eight patients were enrolled in this study and divided into training and test cohorts with a 7:3 ratio; the training cohort was used to develop three different models, including Cox regression, RSF, and DeepSurv model. Test cohort was used to evaluate model performance using c-index, Brier scores, calibration, and the area under the curve (AUC). The net benefits at risk score stratification of GIST patients based on the optimal model was compared with the traditional AJCC staging system using decision curve analysis (DCA). The clinical usefulness of risk score stratification compared to AJCC tumor staging was further assessed using the Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI). The DeepSurv model predicted cancer-specific survival (CSS) in GIST patients showed a higher c-index (0.825), lower Brier scores (0.142), and greater AUC of receiver operating characteristic (ROC) analysis (1-year ROC:0.898; 3-year:0.853, and 5-year ROC: 0.856). The calibration plots demonstrated good agreement between the DeepSurv model's forecast and actual results. The NRI values ( training cohort: 0.425 for 1-year, 0.329 for 3-year and 0.264 for 5-year CSS prediction; test cohort:0.552 for 1-year,0.309 for 3-year and 0.255 for 5-year CSS prediction) and IDI (training cohort: 0.130 for 1-year,0.141 for 5-year and 0.155 for 10-year CSS prediction; test cohort: 0.154 for 1-year,0.159 for 3-year and 0.159 for 5-year CSS prediction) indicated that the risk score stratification performed significantly better than the AJCC staging alone (P < 0.001). DCA demonstrated the risk score stratification as more clinically beneficial and discriminatory than AJCC staging. Finally, an interactive native web-based prediction tool was constructed for the survival prediction of GIST patients. This study established a high-performance prediction model for projecting GIST patients based on deep learning, which has advantages in predicting each person's prognosis and risk stratification.

Nortriptyline hydrochloride, a potential candidate for drug repurposing, inhibits gastric cancer by inducing oxidative stress by triggering the Keap1-Nrf2 pathway

Effective drugs for the treatment of gastric cancer (GC) are still lacking. Nortriptyline Hydrochloride (NTP), a commonly used antidepressant medication, has been demonstrated by numerous studies to have antitumor effects. This study first validated the ability of NTP to inhibit GC and preliminarily explored its underlying mechanism. To begin with, NTP inhibits the activity of AGS and HGC27 cells (Human-derived GC cells) in a dose-dependent manner, as well as proliferation, cell cycle, and migration. Moreover, NTP induces cell apoptosis by upregulating BAX, BAD, and c-PARP and downregulating PARP and Bcl-2 expression. Furthermore, the mechanism of cell death caused by NTP is closely related to oxidative stress. NTP increases intracellular reactive oxygen species (ROS) and malondialdehyde (MDA) levels, decreasing the mitochondrial membrane potential (MMP) and inducing glucose (GSH) consumption. While the death of GC cells can be partially rescued by ROS inhibitor N-acetylcysteine (NAC). Mechanistically, NTP activates the Kelch-like ECH-associated protein (Keap1)-NF-E2-related factor 2 (Nrf2) pathway, which is an important pathway involved in oxidative stress. RNA sequencing and proteomics analysis further revealed molecular changes at the mRNA and protein levels and provided potential targets and pathways through differential gene expression analysis. In addition, NTP can inhibited tumor growth in nude mouse subcutaneous tumor models constructed respectively using AGS and MFC (mouse-derived GC cells), providing preliminary evidence of its effectiveness in vivo. In conclusion, our study demonstrated that NTP exhibits significant anti-GC activity and is anticipated to be a candidate for drug repurposing.

Immunosuppressive MFAP2+ cancer associated fibroblasts conferred unfavorable prognosis and therapeutic resistance in gastric cancer

PURPOSE

To explore the predictive merit of MFAP2+ cancer associated fibroblasts (CAFs) infiltration for clinical outcomes and adjuvant chemotherapy or immunotherapy responsiveness in gastric cancer (GC).

METHODS

In this study, several independent cohorts were included respectively to dissect the relationship of clinical outcomes, therapeutic responses and tumor microenvironment with different MFAP2+ CAFs infiltration. Drug sensitivity analysis was conducted to predict the relationship between MFAP2+ CAFs infiltration and targeted drug response. Kaplan-Meier curves and the log-rank test were used to compare clinical outcomes of patients with different MFAP2+ CAFs infiltration.

RESULTS

High MFAP2+ CAFs infiltration yielded inferior prognosis in terms of overall survival, progress free survival and recurrence free survival in GC. Patients with low MFAP2+ CAFs infiltration were more likely to gain benefit from adjuvant therapy. Moreover, low MFAP2+ CAFs infiltration could predict a promising response to immunotherapy in GC patients. MFAP2+ CAFs with immunosuppressive features were highly relevant to immune evasive contexture characterized by the dysfunction of CD8+ T cells. We found that MFAP2+ CAFs communicated with T cells, B cells and Macrophages through releasing macrophage migration inhibitor factor (MIF), which further suggested that MFAP2+ CAFs might promote therapeutic resistance through regulating T cells dysfunction and M2 macrophages polarization.

CONCLUSION

Immunosuppressive MFAP2+ CAFs constructed an immune evasive tumor microenvironment characterized by incapacitated immune effector cells, consequently predicting inferior clinical outcomes and response on adjuvant therapy and immunotherapy in patients with GC. The potential of immunosuppressive MFAP2+ CAFs as a therapeutic target for GC deserved thoroughly exploration.

Macrophages reprogramming improves immunotherapy of IL-33 in peritoneal metastasis of gastric cancer

Peritoneal metastasis (PM) has a suppressive tumor immune microenvironment (TIME) that limits the effects of immunotherapy. This study aimed to investigate the immunomodulatory effects of intraperitoneal administration of IL-33, a cytokine that is reported to potentiate antitumor immunity and inhibit metastasis. We found survival was significantly prolonged in patients with high IL-33 mRNA expression. In immunocompetent mice, intraperitoneal administration of IL-33 could induce a celiac inflammatory environment, activate immunologic effector cells, and reverse the immunosuppressive tumor microenvironment, which effectively delayed tumor progression and PM of gastric cancer. Mechanistically, IL-33 could induce M2 polarization by activating p38-GATA-binding protein 3 signaling. IL-33 combined with anti-CSF1R or p38 inhibitor to regulate tumor-associated macrophages (TAMs) had a synergistic antitumor effect. Inducing a local inflammatory milieu by IL-33 administration provided a novel approach for treating peritoneal metastasis, which, when combined with TAM reprogramming to reshape TIME, can achieve better treatment efficacy.

Neoadjuvant chemotherapy is linked to an amended anti-tumorigenic microenvironment in gastric cancer

BACKGROUND

Neoadjuvant chemotherapy (NAC) is a frequently intervention for patients with locally advanced gastric cancer (GC). Nevertheless, its impact on the tumor immune microenvironment remains unclear.

METHODS

We used immunohistochemistry to identify T-cell subpopulations, tumor-associated neutrophils (TANs), and tumor-associated macrophages (TAMs) in the GC microenvironment (GCME) among paired samples (pre-chemotherapy and post-chemotherapy) from 48 NAC-treated patients. Multiplex immunofluorescence (mIF) was performed to assess immune biomarkers, including CK, CD4, CD8, FOXP3, PD1, PD-L1, CD163, CD86, myeloperoxidase and Arginase-1 in paired samples from 6 GC patients whose response to NAC were rigorously defined.

RESULTS

NAC was intricately linked to enhanced CD8+:CD4+ ratio, reduced CD163+ M2-like macrophages, augmented CD86+ M1: CD163+ M2-like macrophage ratio, and diminished FOXP3+ regulatory T cells (T-regs) and TANs density. Based on mIF, PD1+CD8+T-cells, FOXP3+T-regs, PD-L1+ TANs, and CD163+ M2-like macrophages exhibited marked reduction and greater co-localization with tumor cells following NAC. The pre-NAC FOXP3+ T-regs and CD163+ M2-like macrophages content was substantially elevated in the response cohort, whereas, the post-NAC CD8+:CD4+ and CD86+ M1: CD163+ M2-like macrophage ratios were intricately linked to the tumor pathologic response. We observed greater CD163+ M2-like macrophages and tumor cells co-localization following NAC, which was correlated with tumor pathologic response. Lastly, multivariate analysis revealed that post-NAC CD8+:CD4+ and CD86+ M1: CD163+ M2-like macrophage ratios were stand-alone indicators of positive patient prognosis.

CONCLUSIONS

NAC converts the GCME to an anti-tumorigenic state that is conducive to enhanced patient outcome. These finding can significantly benefit the future planning of highly efficacious and personalized GC immunotherapy.

ANKRD2 expression combined with TNFRSF19 expression for evaluating the prognosis of oral squamous cell carcinoma patients

Objective

As an important chemotherapy drug, cisplatin has been widely used in the treatment of many cancers. However, many patients, including oral squamous cell carcinoma (OSCC) patients, experience unacceptable outcomes from cisplatin treatment. Thus, we devised a risk model for predicting the sensitivity of OSCC patients to cisplatin treatment, to provide a reference for clinical practice.

Methods

CAL-27 and SCC-9 cell lines treated or not with cisplatin and data from The Cancer Genome Atlas (TCGA) were screened for simultaneously and significantly differentially expressed genes. Next, we built a risk model for predicting cisplatin sensitivity in OSCC patients. Reverse transcription-polymerase chain reaction (RT-PCR), pathological samples and clinical data were used to examine the reliability of the model.

Results

ANKRD2 and TNFRSF19 were differentially expressed between the OSCC metastasis cell line HSC-3 treated and not treated with cisplatin, as well as between the OSCC cell line SCC-25 and the cell line SCC25-DDP, which has cisplatin chemoresistance. We found that the expression of ANKRD2 and TNFRSF19 had a significant influence on the prognosis of OSCC patients. The risk model that combined ANKRD2 and TNFRSF19 to predict sensitivity to cisplatin in OSCC patients was confirmed by analysing the pathological samples and follow-up information of clinical patients.

Conclusions

The expression of ANKRD2 and TNFRSF19 is associated with cisplatin sensitivity and prognosis in patients with OSCC. The survival outcome of patients with oral squamous cell carcinoma (OSCC) was found to be significantly worse in those with high expression of ANKRD2 combined with low expression of TNFRSF19. ANKRD2 and TNFRSF19 may be targets for cisplatin sensitivity prediction in OSCC patients. These findings may provide novel strategies for overcoming cisplatin resistance.

Identification of immune-related genes and integrated analysis of immune-cell infiltration in melanoma

OBJECTIVE

This study was conducted to screen out immune-related genes in connection with the prognosis of melanoma, construct a prognosis model and explore the relevant mechanisms.

METHODS AND MATERIALS

1973 genes associated with immune system were derived from the Immport database, and RNA-seq data of melanoma and information of patients were searched from the Xena database. Cox univariate analysis, Lasso analysis and Cox multivariate analysis were used to screen out six genes to construct the model. Then the risk scores were estimated for patients based on our constructed prognosis model. Estimate was used to affirm that the model was about immune infiltration, and CIBERSORT was used to screen out immune cells associated with prognosis. TIDE was applied to predict the efficacy of immunotherapy. Finally, GSE65904 and GSE19234 were used to confirm the effectiveness of the model.

RESULTS

ADCYAP1R1, GPI, NTS might cause poor prognosis while IFITM1, KIR2DL4, LIF were more likely conductive to prognosis of melanoma patients and a model of prognosis was constructed on the basis of these six genes. The effectiveness of the model has been proven by the ROC curve, and the miRNAs targeting the screened genes were found out, suggesting that the immune system might impact on the prognosis of melanoma by T cell CD8+, T cell CD4+ memory and NK cells.

CONCLUSIONS

In this study, the screened six genes were associated with the prognosis of melanoma, which was conductive to clinical prognostic prediction and individualized treatment strategy.

Stem cell landscape aids in tumor microenvironment identification and selection of therapeutic agents in gastric cancer

Gastric cancer stem cells (GCSCs) are strongly associated with the refractory characteristics of gastric cancer, including drug resistance, recurrence, and metastasis. The prognosis for advanced gastric cancer patients treated with multimodal therapy after surgery remains discouraging. GCSCs hold promise as therapeutic targets for GC patients. We obtained 26 sets of stem cell-related genes from the StemChecker database. The Consensus clustering algorithm was employed to discern three distinct stemness subtypes. Prognostic outcomes, components of the tumor microenvironment (TME), and responses to therapies were compared among these subtypes. Following this, a stemness-risk model was formulated using weighted gene correlation network analysis (WGCNA), alongside Cox regression and random survival forest analyses. The C2 subtype predominantly showed enrichment in negative prognostic CSC gene sets and demonstrated an immunosuppressive TME. This specific subtype exhibited minimal responsiveness to immunotherapies and demonstrated reduced sensitivity to drugs. Four pivotal genes were integrated into the construction of the stemness model. Gastric cancer patients with higher stemness-risk scores demonstrated poorer prognoses, a greater presence of immunosuppressive components in TME, and lower rates of treatment response. Subset analysis indicated that only the low-stemness risk subtype derives benefit from 5-fluorouracil-based adjuvant chemotherapy. The model's effectiveness in immunotherapeutic prediction was further validated in the PRJEB25780 cohort. Our study categorized gastric cancer patients into three stemness subtypes, each demonstrating distinct prognoses, components of TME infiltration, and varying sensitivity or resistance to standard chemotherapy or targeted therapy. We propose that the stemness risk model may help the development of well-grounded treatment recommendations and prognostic assessments.

A methylation-related signature for predicting prognosis and sensitivity to first-line therapies in gastric cancer

Background

Methylation modification patterns play a crucial role in human cancer progression, especially in gastrointestinal cancers. We aimed to use methylation regulators to classify patients with gastric adenocarcinoma and build a model to predict prognosis, promoting the application of precision medicine.

Methods

We obtained RNA sequencing data and clinical data from The Cancer Genome Atlas (TCGA) database (n=335) and Gene Expression Omnibus (GEO) database (n=865). Unsupervised consensus clustering was used to identify subtypes of gastric adenocarcinoma. We performed functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular feature analysis to determine the clinical application for different subtypes. The univariate Cox regression analysis and the LASSO regression analysis were subsequently used to identify prognosis-related methylation regulators and construct a risk model.

Results

Through unsupervised consensus clustering, patients were divided into two subtypes (cluster A and cluster B) with different clinical outcomes. Cluster B included patients with a better prognosis outcome and who were more likely to respond to immunotherapy. We then successfully built a predictive model and found five methylation-related genes (CHAF1A, CPNE8, PHLDA3, SPARC, and EHF) potentially significant to the prognosis of patients. The 1-, 3-, and 5-year areas under the curve of the risk model were 0.712, 0.696, and 0.759, respectively. The risk score was an independent prognostic factor and had the highest concordance index among common clinical indicators. Meanwhile, the tumor microenvironment, sensitivity of chemotherapeutic drugs, molecular features, and oncogenic dedifferentiation differed significantly across the risk groups and subtypes.

Conclusions

We classified patients with gastric adenocarcinoma based on methylation regulators, which has positive implications for first-line clinical treatment. The prognostic model could predict the prognosis of patients and help to promote the development of precision medicine.

High Expression of THY1 in Intestinal Gastric Cancer as a Key Factor in Tumor Biology: A Poor Prognosis-Independent Marker Related to the Epithelial-Mesenchymal Transition Profile

Gastric cancer (GC) is an important cancer-related death worldwide. Among its histological subtypes, intestinal gastric cancer (IGC) is the most common. A previous work showed that increased expression of the THY1 gene was associated with poor overall survival in IGC. Furthermore, it was shown that IGC tumor cells with high expression of THY1 have a greater capacity for tumorigenesis and metastasis in vitro. This study aimed to identify molecular differences between IGC with high and low expression of THY1. Using a feature selection method, a group of 35 genes were found to be the most informative gene set for THY1high IGC tumors. Through a classification model, these genes differentiate THY1high from THY1low tumors with 100% of accuracy both in the test subset and the independent test set. Additionally, this group of 35 genes correctly clustered 100% of the samples. An extensive validation of this potential molecular signature in multiple cohorts successfully segregated between THY1high and THY1low IGC tumors (>95%), proving to be independent of the gene expression quantification methodology. These genes are involved in central processes to tumor biology, such as the epithelial-mesenchymal transition (EMT) and remodeling of the tumor tissue composition. Moreover, patients with THY1high IGC demonstrated poor survival and a more advanced clinicopathological staging. Our findings revealed a molecular signature for IGC with high THY1 expression. This signature showed EMT and remodeling of the tumor tissue composition potentially related to the biology of IGC. Altogether, our results indicate that THY1high IGC tumors are a particular subset of tumors with a specific molecular and prognosis profile.

Long non-coding RNA signature for predicting gastric cancer survival based on genomic instability

BACKGROUND

Gastric cancer is a prevalent type of tumor with a poor prognosis. Given the high occurrence of genomic instability in gastric cancer, it is essential to investigate the prognostic significance of genes associated with genomic instability in this disease.

METHODS

We identified genomic instability-related lncRNAs (GInLncRNAs) by analyzing somatic mutation and transcriptome profiles. We evaluated co-expression and enrichment using various analyses, including univariate COX analysis and LASSO regression. Based on these findings, we established an lncRNA signature associated with genomic instability, which we subsequently assessed for prognostic value, immune cell and checkpoint analysis, drug sensitivity, and external validation. Finally, PCR assay was used to verify the expression of key lncRNAs.

RESULTS

Our study resulted in the establishment of a seven-lncRNA prognostic signature, including PTENP1-AS, LINC00163, RP11-169F17.1, C8ORF87, RP11-389G6.3, LINCO1210, and RP11-115H13.1. This signature exhibited independent prognostic value and was associated with specific immune cells and checkpoints in gastric cancer. Additionally, the model was correlated with somatic mutation and several chemotherapeutic drugs. We further confirmed the prognostic value of LINC00163, which was included in our model, in an independent dataset. Our model demonstrated superior performance compared to other models. PCR showed that LINC00163 was significantly up-regulated in 4 adjacent normal tissues compared with the GC tissues.

CONCLUSIONS

Our study resulted in the establishment of a seven-lncRNA signature associated with genomic instability, which demonstrated robust prognostic value in predicting the prognosis of gastric cancer. The signature also identified potential chemotherapeutic drugs, making it a valuable tool for clinical decision-making and medication use.

RHOAL57V drives the development of diffuse gastric cancer through IGF1R-PAK1-YAP1 signaling

Cancer-associated mutations in the guanosine triphosphatase (GTPase) RHOA are found at different locations from the mutational hotspots in the structurally and biochemically related RAS. Tyr42-to-Cys (Y42C) and Leu57-to-Val (L57V) substitutions are the two most prevalent RHOA mutations in diffuse gastric cancer (DGC). RHOAY42C exhibits a gain-of-function phenotype and is an oncogenic driver in DGC. Here, we determined how RHOAL57V promotes DGC growth. In mouse gastric organoids with deletion of Cdh1, which encodes the cell adhesion protein E-cadherin, the expression of RHOAL57V, but not of wild-type RHOA, induced an abnormal morphology similar to that of patient-derived DGC organoids. RHOAL57V also exhibited a gain-of-function phenotype and promoted F-actin stress fiber formation and cell migration. RHOAL57V retained interaction with effectors but exhibited impaired RHOA-intrinsic and GAP-catalyzed GTP hydrolysis, which favored formation of the active GTP-bound state. Introduction of missense mutations at KRAS residues analogous to Tyr42 and Leu57 in RHOA did not activate KRAS oncogenic potential, indicating distinct functional effects in otherwise highly related GTPases. Both RHOA mutants stimulated the transcriptional co-activator YAP1 through actin dynamics to promote DGC progression; however, RHOAL57V additionally did so by activating the kinases IGF1R and PAK1, distinct from the FAK-mediated mechanism induced by RHOAY42C. Our results reveal that RHOAL57V and RHOAY42C drive the development of DGC through distinct biochemical and signaling mechanisms.

Tumor microenvironment remodeling plus immunotherapy could be used in mesenchymal-like tumor with high tumor residual and drug resistant rate

Epithelial-mesenchymal transition (EMT) is a common process during tumor progression and is always related to residual tumor, drug resistance and immune suppression. However, considering the heterogeneity in EMT process, there is still a need to establish robust EMT classification system with reasonable molecular, biological and clinical implications to investigate whether these unfavorable survival factors are common or unique in different individuals. In our work, we classify tumors with four EMT status, that is, EMTlow, EMTmid, EMThigh-NOS (Not Otherwise Specified), and EMThigh-AKT (AKT pathway overactivation) subtypes. We find that EMThigh-NOS subtype is driven by intrinsic somatic alterations. While, EMThigh-AKT subtype is maintained by extrinsic cellular interplay between tumor cells and macrophages in an AKT-dependent manner. EMThigh-AKT subtype is both unresectable and drug resistant while EMThigh-NOS subtype can be treated with cell cycle related drugs. Importantly, AKT activation in EMThigh-AKT not only enhances EMT process, but also contributes to the immunosuppressive microenvironment. By remodeling tumor immune-microenvironment by AKT inhibition, EMThigh-AKT can be treated by immune checkpoint blockade therapies. Meanwhile, we develop TumorMT website ( http://tumormt.neuroscience.org.cn/ ) to apply this EMT classification and provide reasonable therapeutic guidance.

NNMT enriches for AQP5+ cancer stem cells to drive malignant progression in early gastric cardia adenocarcinoma

OBJECTIVE

Early gastric cardia adenocarcinoma (EGCA) is a highly heterogeneous cancer, and the understanding of its classification and malignant progression is limited. This study explored the cellular and molecular heterogeneity in EGCA using single-cell RNA sequencing (scRNA-seq).

DESIGN

scRNA-seq was conducted on 95 551 cells from endoscopic biopsies of low-grade intraepithelial neoplasia, well/moderately/poorly differentiated EGCA and their paired adjacent nonmalignant biopsy samples. Large-scale clinical samples and functional experiments were employed.

RESULTS

Integrative analysis of epithelial cells revealed that chief cells, parietal cells and enteroendocrine cells were rarely detected in the malignant epithelial subpopulation, whereas gland and pit mucous cells and AQP5+ stem cells were predominant during malignant progression. Pseudotime and functional enrichment analyses showed that the WNT and NF-κB signalling pathways were activated during the transition. Cluster analysis of heterogeneous malignant cells revealed that NNMT-mediated nicotinamide metabolism was enriched in gastric mucin phenotype cell population, which was associated with tumour initiation and inflammation-induced angiogenesis. Furthermore, the expression level of NNMT was gradually increased during the malignant progression and associated with poor prognosis in cardia adenocarcinoma. Mechanistically, NNMT catalysed the conversion of nicotinamide to 1-methyl nicotinamide via depleting S-adenosyl methionine, which led to a reduction in H3K27 trimethylation (H3K27me3) and then activated the WNT signalling pathway to maintain the stemness of AQP5+ stem cells during EGCA malignant progression.

CONCLUSION

Our study extends the understanding of the heterogeneity of EGCA and identifies a functional NNMT+/AQP5+ population that may drive malignant progression in EGCA and could be used for early diagnosis and therapy.

CDK5 promotes apoptosis and attenuates chemoresistance in gastric cancer via E2F1 signaling

BACKGROUND

Chemoresistance is a major clinical challenge that leads to tumor metastasis and poor clinical outcome. The mechanisms underlying gastric cancer resistance to chemotherapy are still unclear.

METHODS

We conducted bioinformatics analyses of publicly available patient datasets to establish an apoptotic phenotype and determine the key pathways and clinical significance. In vitro cell models, in vivo mouse models, and numerous molecular assays, including western blotting, qRT-PCR, immunohistochemical staining, and coimmunoprecipitation assays were used to clarify the role of factors related to apoptosis in gastric cancer in this study. Differences between datasets were analyzed using the Student's t-test and two-way ANOVA; survival rates were estimated based on Kaplan-Meier analysis; and univariate and multivariate Cox proportional hazards models were used to evaluate prognostic factors.

RESULTS

Bulk transcriptomic analysis of gastric cancer samples established an apoptotic phenotype. Proapoptotic tumors were enriched for DNA repair and immune inflammatory signaling and associated with improved prognosis and chemotherapeutic benefits. Functionally, cyclin-dependent kinase 5 (CDK5) promoted apoptosis of gastric cancer cells and sensitized cells and mice to oxaliplatin. Mechanistically, we demonstrate that CDK5 stabilizes DP1 through direct binding to DP1 and subsequent activation of E2F1 signaling. Clinicopathological analysis indicated that CDK5 depletion correlated with poor prognosis and chemoresistance in human gastric tumors.

CONCLUSION

Our findings reveal that CDK5 promotes cell apoptosis by stabilizing DP1 and activating E2F1 signaling, suggesting its potential role in the prognosis and therapeutic decisions for patients with gastric cancer.

The diagnostic or prognostic values of FADD in cancers based on pan‑cancer analysis

Previous studies have determined that aberrant expression of the fas-associated death domain (FADD) contributes to the development of cancer. However, no pan-cancer analysis has been reported to explore the relationship between FADD and various cancers. Multiple databases were screened to identify cancer datasets for the present study and to validate the expression of FADD in various tumors. The association of FADD alteration with cancer prognosis, clinical features and tumor immunity was also evaluated. Reverse transcription-quantitative PCR (RT-qPCR) was utilized to confirm the expression of FADD in breast, colon, liver and gastric cancer cells. Analysis of Gene Expression Omnibus database and The Cancer Genome Atlas database indicated that FADD was highly expressed in breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma, cholangiocarcinoma, colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD) and prostate adenocarcinoma, whereas RT-qPCR results revealed that FADD was highly expressed in breast cancer and colon cancer. Further analyses demonstrated that FADD expression was significantly altered in ESCA, head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma and BRCA. FADD expression was observed to be a risk factor of the overall survival in patients with HNSC, LIHC and LUAD as demonstrated by Kaplan-Meier and Cox regression analyses. The results of the present study demonstrated that FADD is highly expressed in numerous malignancies and can be utilized as a biomarker for the diagnosis of BRCA, COAD, LIHC and stomach adenocarcinoma. Moreover, FADD expression is a predictive risk factor for the development of HNSC, LIHC and LUAD and can potentially be used as a prognostic marker for these cancers.

Comprehensive analysis of anoikis-related genes in prognosis and immune infiltration of gastric cancer based on bulk and single-cell RNA sequencing data

BACKGROUND

Accumulating evidence suggests that anoikis resistance is a key process in cancer cell metastasis, making it an attractive therapeutic target. Therefore, anoikis may become a new treatment for gastric cancer.

METHODS

We used the univariate Cox regression method to screen gastric cancer-related anoikis genes, and a prognostic risk model was established. We analyzed differences between high- and low-risk groups in terms of tumor infiltrating immune cells, gene mutation signatures, and treatment of gastric cancer. Analysis of model associated genes at single-cell resolution was performed.

RESULTS

We filtered to 12 anoikis-related genes and built a prognostic risk model using seven of them, which performed well in multiple datasets. Patients with CCDC178 mutations had a worse prognosis. We also found that patients at low risk were more likely to benefit from chemotherapy and immunotherapy. ERBB2 was found to be highly expressed in epithelial cells and fibroblasts. Our analysis also indicated that gastric cancer samples with high infiltration of iCAFs had a worse prognosis.

CONCLUSION

Seven anoikis-related genes were selected to establish a risk model. The model can be used to predict the prognosis of patients and guide the drug treatment, which provides a new idea for the evaluation and treatment of gastric cancer patients.

Microdissection of cancer-associated fibroblast infiltration subtypes unveils the secreted SERPINE2 contributing to immunosuppressive microenvironment and immuotherapeutic resistance in gastric cancer: A large-scale study integrating bulk and single-cell transcriptome profiling

In the era of immunotherapy, the suboptimal response rate and the development of acquired resistance among the initial beneficiaries continue to present significant challenges across multiple malignancies, including gastric cancer (GC). Considering that the interactions of tumor stroma, especially the cancer-associated fibroblasts (CAFs), with immune and tumor cells, play indispensable roles in tumor progression, tumor microenvironment remodeling and therapeutic responsiveness, in-depth exploration on the roles of CAFs and pivotal mediators of their functions may provide novel clues to increase the effectiveness of current immunotherapeutic drugs and further achieve synergistic antitumor response. Herein, through the consensus clustering of canonical biomarkers, three GC subclasses with different abundance of CAFs were virtually microdissected in four integrated bulk cohorts encompassing 2148 GC patients from 11 independent datasets. An extensive immunogenomic analysis revealed that tumors with high CAFs infiltration were characterized with unfavorable outcomes, aggressive phenotypes, decreased tumor immunogenicity, high risk of immune evasion and thus immunotherapeutic resistance. By leveraging large-scale single-cell transcriptomic profiling, a series of CAF-secreted proteins were identified, among which the SERPINE2 was confirmed to be restrictively enriched in stromal fibroblasts of GC tissues and contribute to promoting a protumor milieu and fostering an immunosuppressive microenvironment via bioinformatics computations and tissue microarray analysis. Moreover, pan-cancer investigations generalized the immunological roles of SERPINE2, especially in pan-gastrointestinal malignancies, with multiple real-world immunotherapy cohorts further confirming its implications on predicting immunotherapeutic efficacy. In conclusion, these findings suggest that the CAF-derived SERPINE2 is a promising immune-oncology target with therapeutic implications to further synergize the immunotherapeutic combinations.

Machine learning and single-cell sequencing reveal the potential regulatory factors of mitochondrial autophagy in the progression of gastric cancer

BACKGROUND

As an important regulatory mechanism to remove damaged mitochondria and maintain the balance between internal and external cells, mitochondrial autophagy plays a key role in the progression and treatment of cancer Onishi (EMBO J 40(3): e104705, 2021). The purpose of this study is to comprehensively analyze the role of mitochondrial autophagy-related genes in the progression of gastric cancer (GC) by RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq).

METHODS

GSE26942, GSE54129,GSE66229,GSE183904 and other data sets were obtained by GEO databases. Using support vector machine recursive feature elimination (SVM-RVF) algorithm and random forest algorithm, the mitochondrial autophagy-related genes related to gastric cancer were obtained, respectively. After that, the model was constructed and the inflammatory factors, immune score and immune cell infiltration were analyzed. Furthermore, according to the scRNA-seq data of 28,836 cells from 13 GC samples, 18 cell clusters and 7 cell types were identified by scRNA-seq analysis. The expression level and signal pathway of related genes were verified by cell communication analysis. Finally, the regulatory network of cells was analyzed by SCENIC.

RESULTS

MAP1LC3B, PGAW5, PINK1, TOMM40 and UBC are identified as key genes through machine learning algorithms. CXCL12-CXCR4, LGALS9-CD44, LGALS9-CD45 and MIF (CD74 + CD44) pathways may play an important role in endothelial cells with high score scores of T cells and monocytes in tumor environment. CEBPB, ETS1, GATA2, MATB, SPl1 and XBP1 were identified as candidate TF with specific regulatory expression in the GC cell cluster.

CONCLUSION

The results of this study will provide implications for the study of the mechanism, diagnosis and treatment of mitochondrial autophagy in GC.

Immune perturbation network identifies an EMT subtype with chromosomal instability and tumor immune-desert microenvironment

Most gastric cancer (GC) subtypes are identified through transcriptional profiling overlooking dynamic changes and interactions in gene expression. Based on the background network of global immune genes, we constructed sample-specific edge-perturbation matrices and identified four molecular network subtypes of GC (MNG). MNG-1 displayed the best prognosis and vigorous cell cycle activity. MNG-2 was enriched by immune-hot phenotype with the potential for immunotherapy response. MNG-3 and MNG-4 were identified with epithelial-mesenchymal transition (EMT) peculiarity and worse prognosis, termed EMT subtypes. MNG-3 was characterized by low mutational burden and stromal cells and considered a replica of previous subtypes associated with poor prognosis. Notably, MNG-4 was considered a previously undefined subtype with a dismal prognosis, characterized by chromosomal instability and immune-desert microenvironment. This subtype tended to metastasize and was resistant to respond to immunotherapy. Pharmacogenomics analysis showed three therapeutic agents (NVP-BEZ235, LY2606368, and rutin) were potential interventions for MNG-4.

CDH1 loss promotes diffuse-type gastric cancer tumorigenesis via epigenetic reprogramming and immune evasion

Diffuse-type gastric adenocarcinoma (DGAC) is a deadly cancer often diagnosed late and resistant to treatment. While hereditary DGAC is linked to CDH1 gene mutations, causing E-Cadherin loss, its role in sporadic DGAC is unclear. We discovered CDH1 inactivation in a subset of DGAC patient tumors. Analyzing single-cell transcriptomes in malignant ascites, we identified two DGAC subtypes: DGAC1 (CDH1 loss) and DGAC2 (lacking immune response). DGAC1 displayed distinct molecular signatures, activated DGAC-related pathways, and an abundance of exhausted T cells in ascites. Genetically engineered murine gastric organoids showed that Cdh1 knock-out (KO), KrasG12D, Trp53 KO (EKP) accelerates tumorigenesis with immune evasion compared to KrasG12D, Trp53 KO (KP). We also identified EZH2 as a key mediator promoting CDH1 loss-associated DGAC tumorigenesis. These findings highlight DGAC's molecular diversity and potential for personalized treatment in CDH1-inactivated patients.

Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox β regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-β pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

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.

Construction and validation of a signature for T cell-positive regulators related to tumor microenvironment and heterogeneity of gastric cancer

Background

Positive regulators of T cell function play a vital role in the proliferation and differentiation of T cells. However, their functions in gastric cancer have not been explored so far.

Methods

The TCGA-STAD dataset was utilized to perform consensus clustering in order to identify subtypes related to T cell-positive regulators. The prognostic differentially expressed genes of these subtypes were identified using the least absolute shrinkage and selection operator (LASSO) regression analysis. To validate the robustness of the identified signature, verification analyses were conducted across the TCGA-train, TCGA-test, and GEO datasets. Additionally, a nomogram was constructed to enhance the clinical efficacy of this predictive tool. Transwell migration, colony formation, and T cell co-culture assays were used to confirm the function of the signature gene in gastric cancer and its influence on T cell activation.

Results

Two distinct clusters of gastric cancer, related to T cell-positive regulation, were discovered through the analysis of gene expression. These clusters exhibited notable disparities in terms of survival rates (P = 0.028), immune cell infiltration (P< 0.05), and response to immunotherapy (P< 0.05). Furthermore, a 14-gene signature was developed to classify gastric cancer into low- and high-risk groups, revealing significant differences in survival rates, tumor microenvironment, tumor mutation burden, and drug sensitivity (P< 0.05). Lastly, a comprehensive nomogram model was constructed, incorporating risk factors and various clinical characteristics, to provide an optimal predictive tool. Additionally, an assessment was conducted on the purported molecular functionalities of low- and high-risk gastric cancers. Suppression of DNAAF3 has been observed to diminish the migratory and proliferative capabilities of gastric cancer, as well as attenuate the activation of T cells induced by gastric cancer within the tumor microenvironment.

Conclusion

We identified an ideal prognostic signature based on the positive regulators of T cell function in this study.

Protection of c-Fos from autophagic degradation by PRMT1-mediated methylation fosters gastric tumorigenesis

Both AP-1 and PRMT1 are vital molecules in variety of cellular progresssion, but the interaction between these proteins in the context of cellular functions is less clear. Gastric cancer (GC) is one of the pernicious diseases worldwide. An in-depth understanding of the molecular mode of action underlying gastric tumorigenesis is still elusive. In this study, we found that PRMT1 directly interacts with c-Fos and enhances AP-1 activation. PRMT1-mediated arginine methylation (mono- and dimethylation) of c-Fos synergistically enhances c-Fos-mediated AP-1 liveliness and consequently increases c-Fos protein stabilization. Consistent with this finding, PRMT1 knockdown decreases the protein level of c-Fos. We discovered that the c-Fos protein undergoes autophagic degradation and found that PRMT1-mediated methylation at R287 protects c-Fos from autophagosomal degradation and is linked to clinicopathologic variables as well as prognosis in stomach tumor. Together, our data demonstrate that PRMT1-mediated c-Fos protein stabilization promotes gastric tumorigenesis. We contend that targeting this modification could constitute a new therapeutic strategy in gastric cancer.

Identification of a six-gene signature to predict survival and immunotherapy effectiveness of gastric cancer

Background

Gastric cancer (GC) ranks as the fifth most prevalent malignancy and the second leading cause of oncologic mortality globally. Despite staging guidelines and standard treatment protocols, significant heterogeneity exists in patient survival and response to therapy for GC. Thus, an increasing number of research have examined prognostic models recently for screening high-risk GC patients.

Methods

We studied DEGs between GC tissues and adjacent non-tumor tissues in GEO and TCGA datasets. Then the candidate DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of DEGs. We used the ROC curve, Kaplan-Meier curve, and risk score plot to evaluate the signature's performance and prognostic power. ESTIMATE, xCell, and TIDE algorithm were used to explore the relationship between the risk score and immune landscape relationship. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.

Results

There were 3211 DEGs in TCGA, 2371 DEGs in GSE54129, 627 DEGs in GSE66229, and 329 DEGs in GSE64951 selected as candidate genes and intersected with to obtain DEGs. In total, the 208 DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of 6 DEGs. External validation showed favorable predictive efficacy. We studied interaction between risk models, immunoscores, and immune cell infiltrate based on six-gene signature. The high-risk group exhibited significantly elevated ESTIMATE score, immunescore, and stromal score relative to low-risk group. The proportions of CD4⁺ memory T cells, CD8⁺ naive T cells, common lymphoid progenitor, plasmacytoid dentritic cell, gamma delta T cell, and B cell plasma were significantly enriched in low-risk group. According to TIDE, the TIDE scores, exclusion scores and dysfunction scores for low-risk group were lower than those for high-risk group. As a final step, nomogram was developed in this study, utilizing both clinical characteristics and a prognostic model.

Conclusion

In conclusion, we discovered a 6 gene signature to forecast GC patients' OS. This risk signature proves to be a valuable clinical predictive tool for guiding clinical practice.

Identification of novel prognostic circRNA biomarkers in circRNA-miRNA-mRNA regulatory network in gastric cancer and immune infiltration analysis

BACKGROUND

Gastric cancer (GC) carries significant morbidity and mortality globally. An increasing number of studies have confirmed that circular RNA (circRNA) is tightly associated with the carcinogenesis and development of GC, especially acting as a competing endogenous RNA for miRNAs.

OBJECTIVE

Our study aimed to construct the circRNA-miRNA-mRNA regulatory network and analyze the function and prognostic significance of the network using bioinformatics tools.

METHODS

We first downloaded the GC expression profile from the Gene Expression Omnibus database and identified differentially expressed genes and differentially expressed circRNAs. Then, we predicted the miRNA-mRNA interaction pairs and constructed the circRNA-miRNA-mRNA regulatory network. Next, we established a protein-protein interaction network and analyzed the function of these networks. Finally, we primarily validated our results by comparison with The Cancer Genome Atlas cohort and by performing qRT-PCR.

RESULTS

We screened the top 15 hub genes and 3 core modules. Functional analysis showed that in the upregulated circRNA network, 15 hub genes were correlated with extracellular matrix organization and interaction. The function of downregulated circRNAs converged on physiological functions, such as protein processing, energy metabolism and gastric acid secretion. We ascertained 3 prognostic and immune infiltration-related genes, COL12A1, COL5A2, and THBS1, and built a nomogram for clinical application. We validated the expression level and diagnostic performance of key prognostic differentially expressed genes.

CONCLUSIONS

In conclusion, we constructed two circRNA-miRNA-mRNA regulatory networks and identified 3 prognostic and screening biomarkers, COL12A1, COL5A2, and THBS1. The ceRNA network and these genes could play important roles in GC development, diagnosis and prognosis.

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.

Comprehensive transcriptomic analyses identify KDM genes-related subtypes with different TME infiltrates in gastric cancer

Histone lysine demethylases (KDMs) have been reported in various malignances, which affect transcriptional regulation of tumor suppressor or oncogenes. However, the relationship between KDMs and formation of tumor microenvironment (TME) in gastric cancer (GC) remain unclear and need to be comprehensively analyzed.In the present study, 24 KDMs were obtained and consensus molecular subtyping was performed using the "NMF" method to stratify TCGA-STAD into three clusters. The ssGSEA and CIBERSORT algorithms were employed to assess the relative infiltration levels of various cell types in the TME. The KDM_score was devised to predict patient survival outcomes and responses to both immunotherapy and chemotherapy.Three KDM genes-related molecular subtypes were Figured out in GC with distinctive clinicopathological and prognostic features. Based on the robust KDM genes-related risk_score and nomogram, established in our work, GC patients' clinical outcome can be well predicted. Furthermore, low KDM genes-related risk_score exhibited the more effective response to immunotherapy and chemotherapy.This study characterized three KDM genes-related TME pattern with unique immune infiltration and prognosis by comprehensively analyses of transcriptomic profiling. Risk_score was also built to help clinicians decide personalized anticancer treatment for GC patients, including in prediction of immunotherapy and chemotherapy response for patients.

Clinical molecular subtyping reveals intrinsic mesenchymal reprogramming in gastric cancer cells

The mesenchymal cancer phenotype is known to be clinically related to treatment resistance and a poor prognosis. We identified gene signature-based molecular subtypes of gastric cancer (GC, n = 547) based on transcriptome data and validated their prognostic and predictive utility in multiple external cohorts. We subsequently examined their associations with tumor microenvironment (TME) features by employing cellular deconvolution methods and sequencing isolated GC populations. We further performed spatial transcriptomics analysis and immunohistochemistry, demonstrating the presence of GC cells in a partial epithelial-mesenchymal transition state. We performed network and pharmacogenomic database analyses to identify TGF-β signaling as a driver pathway and, thus, a therapeutic target. We further validated its expression in tumor cells in preclinical models and a single-cell dataset. Finally, we demonstrated that inhibition of TGF-β signaling negated mesenchymal/stem-like behavior and therapy resistance in GC cell lines and mouse xenograft models. In summary, we show that the mesenchymal GC phenotype could be driven by epithelial cancer cell-intrinsic TGF-β signaling and propose therapeutic strategies based on targeting the tumor-intrinsic mesenchymal reprogramming of medically intractable GC.

Construction of an RNA modification-related gene predictive model associated with prognosis and immunity in gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most common causes of cancer-related fatalities worldwide, and its progression is associated with RNA modifications. Here, using RNA modification-related genes (RNAMRGs), we aimed to construct a prognostic model for patients with GC.

METHODS

Based on RNAMRGs, RNA modification scores (RNAMSs) were obtained for GC samples from The Cancer Genome Atlas and were divided into high- and low-RNAMS groups. Differential analysis and weighted correlation network analysis were performed for the differential expressed genes (DEGs) to obtain the key genes. Next, univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were performed to obtain the model. According to the model risk score, samples were divided into high- and low-risk groups. Enrichment analysis and immunoassays were performed for the DEGs in these groups. Four external datasets from Gene Expression Omnibus data base were used to test the accuracy of the predictive model.

RESULTS

We identified SELP and CST2 as key DEGs, which were used to generate the predictive model. The high-risk group had a worse prognosis compared to the low-risk group (p < 0.05). Enrichment analysis and immunoassays revealed that 144 DEGs related to immune cell infiltration were associated with the Wnt signaling pathway and included hub genes such as ELN. Overall mutation levels, tumor mutation burden, and microsatellite instability were lower, but tumor immune dysfunction and exclusion scores were greater (p < 0.05) in the high-risk group than in the low-risk group. The validation results showed that the prediction model score can accurately predict the prognosis of GC patients. Finally, a nomogram was constructed using the risk score combined with the clinicopathological characteristics of patients with GC.

CONCLUSION

This risk score from the prediction model related to the tumor microenvironment and immunotherapy could accurately predict the overall survival of GC patients.

Systematic analysis of the cuprotosis in tumor microenvironment and prognosis of gastric cancer

Cuprotosis is a new programmed cell death related to cancer. However, the characteristics of cuprotosis in gastric cancer (GC) remain unknown. Ten cuprotosis molecules from 1544 GC patients were used to identify three GC molecular genotypes. Cluster A was characterized by the best clinical outcome and was significantly enriched in metabolic signaling pathways. Cluster B exhibited elevated immune activation, high immune stroma scores and was significantly enriched in tumor immune signaling pathways. Cluster C was characterized by severe immunosuppression and poor response to immunotherapy. Notably, the citrate cycle, cell cycle, and p53 signaling pathways were enriched in the differentially expressed genes among the three subtypes, which were critical signaling pathways for cell death. We also developed a cuprotosis signature risk score that could accurately predict the survival, immunity, and subtype of GC. This study presents a systematic analysis of cuprotosis molecules and provides new immunotherapeutic targets for GC patients.

Artificial intelligence-guided discovery of gastric cancer continuum

BACKGROUND

Detailed understanding of pre-, early and late neoplastic states in gastric cancer helps develop better models of risk of progression to gastric cancers (GCs) and medical treatment to intercept such progression.

METHODS

We built a Boolean implication network of gastric cancer and deployed machine learning algorithms to develop predictive models of known pre-neoplastic states, e.g., atrophic gastritis, intestinal metaplasia (IM) and low- to high-grade intestinal neoplasia (L/HGIN), and GC. Our approach exploits the presence of asymmetric Boolean implication relationships that are likely to be invariant across almost all gastric cancer datasets. Invariant asymmetric Boolean implication relationships can decipher fundamental time-series underlying the biological data. Pursuing this method, we developed a healthy mucosa → GC continuum model based on this approach.

RESULTS

Our model performed better against publicly available models for distinguishing healthy versus GC samples. Although not trained on IM and L/HGIN datasets, the model could identify the risk of progression to GC via the metaplasia → dysplasia → neoplasia cascade in patient samples. The model could rank all publicly available mouse models for their ability to best recapitulate the gene expression patterns during human GC initiation and progression.

CONCLUSIONS

A Boolean implication network enabled the identification of hitherto undefined continuum states during GC initiation. The developed model could now serve as a starting point for rationalizing candidate therapeutic targets to intercept GC progression.

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.

Cytoplasmic localization of IRF5 induces Wnt5a/E-cadherin degradation and promotes gastric cancer cells metastasis

IRF5, a nucleoplasm shuttling protein, is a pivotal transcription factor regulating immune system activity. It's well known that immunosuppression is involved in the development of gastric cancer. However, no data exist for the expression and function of IRF5 in gastric cancer. This study demonstrated that IRF5 was cytoplasm-enriched in gastric cancer cells. IRF5 promoted gastric cancer cell migration, which involved the inhibition of Wnt5a and E-cadherin proteins expression. IRF5 (LA) localized in nucleus had no significant effect on Wnt5a and E-cadherin expressions, while mutation of IRF5 (ΔNLS), which prevents IRF5 nuclear translocation, had more impact on these inhibitory effects. In addition, degradation rates of both Wnt5a and E-cadherin were enhanced by resiquimod, an IRF5 agonist. Further in vivo experiments indicated that IRF5 knockout of gastric cancer cells repressed their pulmonary metastasis in nude mice. Finally, the expression and clinical significance of IRF5 were analyzed using gastric cancer tissue microarrays, which suggested that the expression of IRF5 varied procedurally in different progressive stages of gastric cancer. Our data revealed that IRF5 cytoplasmic localization were associated with Wnt5a and E-cadherin degradation and gastric cancer cell metastasis. Inhibiting IRF5 expression and/or its cytoplasmic localization may provide a novel target for gastric cancer therapy.

Targeting the E3 ligase NEDD4 as a novel therapeutic strategy for IGF1 signal pathway-driven gastric cancer

The IGF1 signal pathway is highly activated in some subtype of gastric cancer(GC) that exhibits poor survival and chemotherapy resistance. Although the results of clinical trials of anti-IGF1R monoclonal antibodies and IGF-1R inhibitors have been mostly disappointing in unselected cancer patients, some patients benefit from anti-IGF1R therapy in these failed studies. Therefore, it is necessary to characterize the complex IGF signaling in GC and help refine the strategies targeting the IGF1 pathway. We found that GC cell lines exhibit differential responses to the specific IGF1R inhibitor OSI906. According to the phosphorylation status of Akt upon the OSI906 treatment, we divided the GC cell lines into IGF1R-dependent and IGF1R-independent cells. Both in vitro and in vivo experiments indicate that Dox-induced knockdown of NEDD4 significantly suppresses tumor growth of IGF1R-dependent GC cells and NEDD4 overexpression promotes tumor growth of IGF1R-dependent GC cells. In contrast, the proliferation of IGF1R-independent GC cells is not affected by NEDD4 silencing and overexpression. The rescue experiments show that a PTEN-IRS1 axis is required for NEDD4-mediated regulation of Akt activation and tumor growth in GC cells. Clinically, NEDD4 is expressed higher in IGF1-high GC tissues compared with IGF1-low GC tissues and normal tissues, and the co-high expression of NEDD4 and IGF1 predicts a worse prognosis in GC patients. Taken together, our study demonstrated that NEDD4 specifically promotes proliferation of GC cells dependent on IGF1/IGF1R signaling by antagonizing the protein phosphatase activity of PTEN to IRS1, and targeting NEDD4 may be a promising therapeutic strategy for IGF1 signal pathway-driven gastric cancer.

Escape from cell-cell and cell-matrix adhesion dependence underscores disease progression in gastric cancer organoid models

OBJECTIVE

Cell-cell (CC) and cell-matrix (CM) adhesions are essential for epithelial cell survival, yet dissociation-induced apoptosis is frequently circumvented in malignant cells.

DESIGN

We explored CC and CM dependence in 58 gastric cancer (GC) organoids by withdrawing either ROCK inhibitor, matrix or both to evaluate their tumorigenic potential in terms of apoptosis resistance, correlation with oncogenic driver mutations and clinical behaviour. We performed mechanistic studies to determine the role of diffuse-type GC drivers: ARHGAP fusions, RHOA and CDH1, in modulating CC (CCi) or CM (CMi) adhesion independence.

RESULTS

97% of the tumour organoids were CMi, 66% were CCi and 52% were resistant to double withdrawal (CCi/CMi), while normal organoids were neither CMi nor CCi. Clinically, the CCi/CMi phenotype was associated with an infiltrative tumour edge and advanced tumour stage. Moreover, the CCi/CMi transcriptome signature was associated with poor patient survival when applied to three public GC datasets. CCi/CMi and CCi phenotypes were enriched in diffuse-type GC organoids, especially in those with oncogenic driver perturbation of RHO signalling via RHOA mutation or ARHGAP fusions. Inducible knockout of ARHGAP fusions in CCi/CMi tumour organoids led to resensitisation to CC/CM dissociation-induced apoptosis, upregulation of focal adhesion and tight junction genes, partial reversion to a more normal cystic phenotype and inhibited xenograft formation. Normal gastric organoids engineered with CDH1 or RHOA mutations became CMi or CCi, respectively.

CONCLUSIONS

The CCi/CMi phenotype has a critical role in malignant transformation and tumour progression, offering new mechanistic information on RHO-ROCK pathway inhibition that contributes to GC pathogenicity.

USP51/ZEB1/ACTA2 axis promotes mesenchymal phenotype in gastric cancer and is associated with low cohesion characteristics

poorly cohesive (PC) gastric cancer (GC) (PC-GC) is a distinct histological subtype of GC and is defined as a tumor consisting of isolated or small clusters of tumor cells with poorly differentiated and metastatic characteristics. According to multiple studies, PC-GC is intrinsically heterogeneous, with mesenchymal variants being the most aggressive. However, to date, the molecular mechanisms associated with PC-GC are still not fully understood. This study investigated the role of the USP51/ZEB1/ACTA2 axis in promoting GC metastasis. Single-cell sequencing revealed that E-box binding homeobox 1 (ZEB1) expression was significantly increased in a subpopulation of low-adherent cells and was an independent prognostic factor in GC patients. Furthermore, the bulk transcriptome analysis revealed a significant positive correlation between Ubiquitin Specific Peptidase 51 (USP51), ZEB1, and Actin Alpha 2 (ACTA2), and our data further confirmed that all three were highly co-localized in PC-GC tissues. According to the findings of in vitro and in vivo experiments, USP51 was able to maintain ZEB1 expression to promote ACTA2 transcription, thereby activating the mesenchymal phenotype of GC cells and promoting tumor metastasis. Moreover, USP51 could recruit and activate stromal cells, including M2-like macrophages and fibroblasts, through cancer cells. Clinical data suggested that overexpression of USP51 predicts that patients have difficulty benefiting from immunotherapy and is associated with immune-exclusion tumor characteristics. Collectively, the findings of this study shed light on a key mechanism by which elevated USP51 expression induces Epithelial-mesenchymal transition (EMT) in GC cells, hence facilitating GC cell proliferation, survival, and dissemination. In this view, USP51/ZEB1/ACTA2 may serve as a candidate therapeutic target against GC metastasis.

Regulatory enhancer profiling of mesenchymal-type gastric cancer reveals subtype-specific epigenomic landscapes and targetable vulnerabilities

OBJECTIVE

Gastric cancer (GC) comprises multiple molecular subtypes. Recent studies have highlighted mesenchymal-subtype GC (Mes-GC) as a clinically aggressive subtype with few treatment options. Combining multiple studies, we derived and applied a consensus Mes-GC classifier to define the Mes-GC enhancer landscape revealing disease vulnerabilities.

DESIGN

Transcriptomic profiles of ~1000 primary GCs and cell lines were analysed to derive a consensus Mes-GC classifier. Clinical and genomic associations were performed across >1200 patients with GC. Genome-wide epigenomic profiles (H3K27ac, H3K4me1 and assay for transposase-accessible chromatin with sequencing (ATAC-seq)) of 49 primary GCs and GC cell lines were generated to identify Mes-GC-specific enhancer landscapes. Upstream regulators and downstream targets of Mes-GC enhancers were interrogated using chromatin immunoprecipitation followed by sequencing (ChIP-seq), RNA sequencing, CRISPR/Cas9 editing, functional assays and pharmacological inhibition.

RESULTS

We identified and validated a 993-gene cancer-cell intrinsic Mes-GC classifier applicable to retrospective cohorts or prospective single samples. Multicohort analysis of Mes-GCs confirmed associations with poor patient survival, therapy resistance and few targetable genomic alterations. Analysis of enhancer profiles revealed a distinctive Mes-GC epigenomic landscape, with TEAD1 as a master regulator of Mes-GC enhancers and Mes-GCs exhibiting preferential sensitivity to TEAD1 pharmacological inhibition. Analysis of Mes-GC super-enhancers also highlighted NUAK1 kinase as a downstream target, with synergistic effects observed between NUAK1 inhibition and cisplatin treatment.

CONCLUSION

Our results establish a consensus Mes-GC classifier applicable to multiple transcriptomic scenarios. Mes-GCs exhibit a distinct epigenomic landscape, and TEAD1 inhibition and combinatorial NUAK1 inhibition/cisplatin may represent potential targetable options.

Personalized immune subtypes based on machine learning predict response to checkpoint blockade in gastric cancer

Immune checkpoint inhibitors (ICI) show high efficiency in a small fraction of advanced gastric cancer (GC). However, personalized immune subtypes have not been developed for the prediction of ICI efficiency in GC. Herein, we identified Pan-Immune Activation Module (PIAM), a curated gene expression profile (GEP) representing the co-infiltration of multiple immune cell types in tumor microenvironment of GC, which was associated with high expression of immunosuppressive molecules such as PD-1 and CTLA-4. We also identified Pan-Immune Dysfunction Genes (PIDG), a conservative PIAM-derivated GEP indicating the dysfunction of immune cell cooperation, which was associated with upregulation of metastatic programs (extracellular matrix receptor interaction, TGF-β signaling, epithelial-mesenchymal transition and calcium signaling) but downregulation of proliferative signalings (MYC targets, E2F targets, mTORC1 signaling, and DNA replication and repair). Moreover, we developed 'GSClassifier', an ensemble toolkit based on top scoring pairs and extreme gradient boosting, for population-based modeling and personalized identification of GEP subtypes. With PIAM and PIDG, we developed four Pan-immune Activation and Dysfunction (PAD) subtypes and a GSClassifier model 'PAD for individual' with high accuracy in predicting response to pembrolizumab (anti-PD-1) in advance GC (AUC = 0.833). Intriguingly, PAD-II (PIAMhighPIDGlow) displayed the highest objective response rate (60.0%) compared with other subtypes (PAD-I, PIAMhighPIDGhigh, 0%; PAD-III, PIAMlowPIDGhigh, 0%; PAD-IV, PIAMlowPIDGlow, 17.6%; P = 0.003), which was further validated in the metastatic urothelial cancer cohort treated with atezolizumab (anti-PD-L1) (P = 0.018). In all, we provided 'GSClassifier' as a refined computational framework for GEP-based stratification and PAD subtypes as a promising strategy for exploring ICI responders in GC. Metastatic pathways could be potential targets for GC patients with high immune infiltration but resistance to ICI therapy.

High expression of TMEM200A is associated with a poor prognosis and immune infiltration in gastric cancer

Background: Gastric cancer (GC) is one of the global malignant tumors with high incidence and poor prognosis. Exploring new GC molecular markers is important to improve GC prognosis. Transmembrane protein 200A (TMEM200A) is a member of the family of transmembrane proteins (TMEM). This study is the first to investigate the potential function of TMEM200A and its relationship with immune infiltration in GC. Methods: The differential expression of TMEM200A was determined through the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The receiver operating characteristic (ROC) curve was drawn to assess the diagnostic value of TMEM200A for GC. The relationship between TMEM200A and the clinical characteristics of patients with GC was investigated using the Wilcoxon test and the Kruskal-Wallis test. The effect of TMEM200A on overall survival (OS) was identified using the Kaplan-Meier method, the Log-rank test, the univariate/multivariate Cox regression analysis, and the nomogram prediction model. The co-expressed genes and gene set enrichment analysis (GSEA) were used to explore the potential biological functions of TMEM200A. We used the Tumor Immune Estimation Resource (TIMER) database and the ssGSEA algorithm to estimate the relationship between TMEM200A and immune cell infiltration. Furthermore, we investigated the correlation of TMEM200A with immune checkpoint/immune cell surface markers using the TCGA-STAD data set. Finally, we identified prognosis-related methylation sites in TMEM200A using MethSurv. Results: TMEM200A was highly expressed in GC tissues. TMEM200A had a good diagnostic value for GC. High expression of TMEM200A may shorten the OS of GC patients and may be an independent risk factor for OS in GC patients. TMEM200A participates in the construction of a predictive model with a good predictive effect on the survival rate of GC patients at 1, 3, and 5 years. Co-expressed genes and GSEA indicated that TMEM200A may be an adhesion molecule closely associated with tumor invasion and metastasis. In addition, TMEM200A may be significantly associated with immune cell infiltration and immune checkpoint expression. We also found that TMEM200A contains three methylation sites associated with a poor prognosis. Conclusion: Upregulated TMEM200A may be a promising prognostic marker for GC and is closely associated with the tumor microenvironment (TME).

Toxicogenomics scoring system: TGSS, a novel integrated risk assessment model for chemical carcinogenicity prediction

BACKGROUND

Given the increasing exposure of humans to environmental chemicals and the limitations of conventional toxicity test, there is an urgent need to develop next-generation risk assessment methods.

OBJECTIVES

This study aims to establish a novel computational system named Toxicogenomics Scoring System (TGSS) to predict the carcinogenicity of chemicals coupling chemical-gene interactions with multiple cancer transcriptomic datasets.

METHODS

Chemical-related gene signatures were derived from chemical-gene interaction data from the Comparative Toxicogenomics Database (CTD). For each cancer type in TCGA, genes were ranked by their effects on tumorigenesis, which is based on the differential expression between tumor and normal samples. Next, we developed carcinogenicity scores (C-scores) using pre-ranked GSEA to quantify the correlation between chemical-related gene signatures and ranked gene lists. Then we established TGSS by systematically evaluating the C-scores in multiple chemical-tumor pairs. Furthermore, we examined the performance of our approach by ROC curves or prognostic analyses in TCGA and multiple independent cancer cohorts.

RESULTS

Forty-six environmental chemicals were finally included in the study. C-score was calculated for each chemical-tumor pair. The C-scores of IARC Group 3 chemicals were significantly lower than those of chemicals in Group 1 (P-value = 0.02) and Group 2 (P-values = 7.49 ×10^-5). ROC curves analysis indicated that C-score could distinguish "high-risk chemicals" from the other compounds (AUC = 0.67) with a specificity and sensitivity of 0.86 and 0.57. The results of survival analysis were also in line with the assessed carcinogenicity in TGSS for the chemicals in Group 1. Finally, consistent results were further validated in independent cancer cohorts.

CONCLUSION

TGSS highlighted the great potential of integrating chemical-gene interactions with gene-cancer relationships to predict the carcinogenic risk of chemicals, which would be valuable for systems toxicology.