Seven-Gene Signature Based on Glycolysis Is Closely Related to the Prognosis and Tumor Immune Infiltration of Patients With Gastric Cancer

Glycolysis-related five-gene signature correlates with prognosis and immune infiltration in gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most common malignancies worldwide. Glycolysis has been demonstrated to be pivotal for the carcinogenesis of GC.

AIM

To develop a glycolysis-based gene signature for prognostic evaluation in GC patients.

METHODS

Differentially expressed genes correlated with glycolysis were identified in stomach adenocarcinoma data (STAD). A risk score was established through a univariate Cox and least absolute shrinkage and selection operator analysis. The model was evaluated using the area under the receiver operating characteristic curves. RNA-sequencing data from high- and low-glycolysis groups of STAD patients were analyzed using Cibersort algorithm and Spearman correlation to analyze the interaction of immune cell infiltration and glycolysis. Multiomics characteristics in different glycolysis status were also analyzed.

RESULTS

A five-gene signature comprising syndecan 2, versican, malic enzyme 1, pyruvate carboxylase and SRY-box transcription factor 9 was constructed. Patients were separated to high- or low-glycolysis groups according to risk scores. Overall survival of patients with high glycolysis was poorer. The sensitivity and specificity of the model in prediction of survival of GC patients were also observed by receiver operating characteristic curves. A nomogram including clinicopathological characteristics and the risk score also showed good prediction for 3- and 5-year overall survival. Gene set variation analysis showed that high-glycolysis patients were related to dysregulation of pancreas beta cells and estrogen late pathways, and low-glycolysis patients were related to Myc targets, oxidative phosphorylation, mechanistic target of rapamycin complex 1 signaling and G2M checkpoint pathways. Tumor-infiltrating immune cells and multiomics analysis suggested that the different glycolysis status was significantly correlated with multiple immune cell infiltration. The patients with high glycolysis had lower tumor mutational burden and neoantigen load, higher incidence of microsatellite instability and lower chemosensitivity. High glycolysis status was often found among patients with grade 2/3 cancer or poor prognosis.

CONCLUSION

The genetic characteristics revealed by glycolysis could predict the prognosis of GC. High glycolysis significantly affects GC phenotype, but the detailed mechanism needs to be further studied.

Predictive significance of glycolysis-associated lncRNA profiles in colorectal cancer progression

BACKGROUND

The Warburg effect is a hallmark characteristic of colorectal cancer (CRC). Despite extensive research, the role of long non-coding RNAs (lncRNAs) in influencing the Warburg effect remains incompletely understood. Our study aims to identify lncRNAs that may modulate the Warburg effect by functioning as competing endogenous RNAs (ceRNAs).

METHODS

Utilizing bioinformatics approaches, we extracted glycolysis-associated gene data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and identified 101 glycolysis-related lncRNAs in CRC. We employed Univariable Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, and Multivariable Cox regression to develop a prognostic model comprising four glycolysis-linked lncRNAs. We then constructed a prognostic nomogram integrating this lncRNA model with other relevant clinical parameters.

RESULTS

The prognostic efficacy of our four-lncRNA signature and its associated nomogram was validated in both training and validation cohorts. Functional assays demonstrated significant glycolysis and hexokinase II (HK2) inhibition following the silencing of RUNDC3A - AS1, a key lncRNA in our prognostic signature, highlighting its regulatory importance in the Warburg effect.

CONCLUSIONS

Our research illuminates the critical role of glycolysis-centric lncRNAs in CRC. The developed prognostic model and nomogram underscore the pivotal prognostic and regulatory significance of the lncRNA RUNDC3A - AS1 in the Warburg effect in colorectal cancer.

A Novel Signature Based on m6A Regulator-Mediated Genes Along Glycolytic Pathway Predicts Prognosis and Immunotherapy Responses of Gastric Cancer Patients

N6-methyladenosine (m6A) modification is involved in many aspects of gastric cancer (GC). Moreover, m6A and glycolysis-related genes (GRGs) play important roles in immunotherapeutic and prognostic implication of GC. However, GRGs involved in m6A regulation have never been analyzed comprehensively in GC. Herein, the study aims to identify and validate a novel signature based on m6A-related GRGs in GC patients. Therefore, a m6A-related GRGs signature is established, which can predict the survival of patients with GC and remain an independent prognostic factor in multivariate analyses. Clinical significance of the model is well validated in internal cohort and independent validation cohort. In addition, the expression levels of risk model-related GRGs in clinical samples are validated. Consistent with the database results, all model genes are up-regulated in expression except DCN. After regrouping the patients based on this risk model, the study can effectively distinguish between them in respect to immune-cell infiltration microenvironment and immunotherapeutic response. Additionally, candidate drugs targeting risk model-related GRGs are confirmed. Finally, a nomogram combining risk scores and clinical parameters is created, and calibration plots show that the nomogram can accurately predict survival. This risk model can serve as a reliable assessment tool for predicting prognosis and immunotherapeutic responses in GC patients.

Transcription factor TEAD4 facilitates glycolysis and proliferation of gastric cancer cells by activating PKMYT1

BACKGROUND

Gastric cancer (GC) ranks third for cancer deaths worldwide, and glycolysis is a hallmark of several cancers, including GC. TEAD4 plays a role in establishing an oncogenic cascade in cancers, including GC. Whether TEAD4 can influence the glycolysis of GC cells remains uncovered. Hence, this study attempted to investigate the impact on glycolysis of GC cells by TEAD4.

METHODS

By using bioinformatics analysis, differentially expressed mRNAs were screened, and downstream regulatory genes were predicted. Expression levels of TEAD4 and PKMYT1 were assessed by qRT-PCR. The binding sites between TEAD4 and PKMYT1 were predicted by the JASPAR database, meanwhile their modulatory relationship was confirmed through dual-luciferase assay and chromatin Immunoprecipitation (ChIP). Cell viability and proliferation were assayed via CCK-8 and colony formation assays. Glycolysis was measured by assaying extracellular acidification rate, oxygen consumption rate, and production of pyruvic acid, lactate, citrate, and malate. Expression levels of proteins (HK-2 and PKM2) related to glycolysis were assessed by Western blot.

RESULTS

TEAD4 was upregulated in GC tissues and cells. TEAD4 knockdown substantially repressed glycolysis and proliferation of GC cells. PKMYT1, the target gene downstream of TEAD4, was identified via bioinformatics prediction, and its expression was elevated in GC. Dual-luciferase and ChIP assay validated the targeted relationship between the promoter region of PKMYT1 and TEAD4. As revealed by rescue experiments, the knockdown of TEAD4 reversed the stimulative effect on GC cell glycolysis and proliferation by forced expression of PKMYT1.

CONCLUSION

TEAD4 activated PKMYT1 to facilitate the proliferation and glycolysis of GC cells. TEAD4 and PKMYT1 may be possible therapeutic targets for GC.

Development of a Comprehensive Gene Signature Linking Hypoxia, Glycolysis, Lactylation, and Metabolomic Insights in Gastric Cancer through the Integration of Bulk and Single-Cell RNA-Seq Data

BACKGROUND

Hypoxia and anaerobic glycolysis are cancer hallmarks and sources of the metabolite lactate. Intriguingly, lactate-induced protein lactylation is considered a novel epigenetic mechanism that predisposes cells toward a malignant state. However, the significance of comprehensive hypoxia-glycolysis-lactylation-related genes (HGLRGs) in cancer is unclear. We aimed to construct a model centered around HGLRGs for predicting survival, metabolic features, drug responsiveness, and immune response in gastric cancer.

METHODS

The integration of bulk and single-cell RNA-Seq data was achieved using data obtained from the TCGA and GEO databases to analyze HGLRG expression patterns. A HGLRG risk-score model was developed based on univariate Cox regression and a LASSO-Cox regression model and subsequently validated. Additionally, the relationships between the identified HGLRG signature and multiple metabolites, drug sensitivity and various cell clusters were explored.

RESULTS

Thirteen genes were identified as constituting the HGLRG signature. Using this signature, we established predictive models, including HGLRG risk scores and nomogram and Cox regression models. The stratification of patients into high- and low-risk groups based on HGLRG risk scores showed a better prognosis in the latter. The high-risk group displayed increased sensitivity to cytotoxic drugs and targeted inhibitors. The expression of the HGLRG BGN displayed a strong correlation with amino acids and lipid metabolites. Notably, a significant difference in immune infiltration, such as that of M1 macrophages and CD8 T cells, was correlated with the HGLRG signature. The abundant DUSP1 within the mesenchymal components was highlighted by single-cell transcriptomics.

CONCLUSION

The innovative HGLRG signature demonstrates efficacy in predicting survival and providing a practical clinical model for gastric cancer. The HGLRG signature reflects the internal metabolism, drug responsiveness, and immune microenvironment components of gastric cancer and is expected to boost patients' response to targeted therapy and immunotherapy.

Gene signature based on glycolysis is closely related to immune infiltration of patients with osteoarthritis

BACKGROUND

Osteoarthritis (OA) is a degenerative arthritis with high levels of clinical heterogeneity. Aberrant metabolism such as shifting from oxidative phosphorylation to glycolysis is a response to changes in the inflammatory microenvironment of OA. Therefore, there is a pressing need to identify novel glycolysis regulators during OA progression.

METHODS

We systematically studied glycolysis patterns mediated by 141 glycolysis regulators in 74 human synovial samples and discussed the characteristics of the immune microenvironment modified by glycolysis. The random forest (RF) method was applied to screen candidate hub glycolysis regulators in OA. RT-qPCR was performed to validate these key regulators. Then distinct glycolysis patterns were identified, and systematic correlation between these glycolysis patterns and immune cell infiltration was analyzed. The glycolysis score was constructed to quantify glycolysis patterns together with immune infiltration of individual OA patient.

RESULTS

56 glycolysis-related differentially expressed genes (DEGs) were identified between OA and non-OA samples. STC1, VEGFA, KDELR3, DDIT4 and PGAM1 were selected as candidate genes to predict the probability of OA. Two glycolysis patterns in OA were identified. Glycolysis cluster A with higher glycolysis score was related to an inflamed phenotype.

CONCLUSIONS

Taken together, our results established a glycolysis-based genetic signature for OA, guided in-depth studies on the metabolic mechanism of OA, and facilitated to explore new clinical treatment strategies.

A novel stratification framework based on anoikis-related genes for predicting the prognosis in patients with osteosarcoma

Background

Anoikis resistance is a prerequisite for the successful development of osteosarcoma (OS) metastases, whether the expression of anoikis-related genes (ARGs) correlates with OS prognosis remains unclear. This study aimed to investigate the feasibility of using ARGs as prognostic tools for the risk stratification of OS.

Methods

The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided transcriptome information relevant to OS. The GeneCards database was used to identify ARGs. Differentially expressed ARGs (DEARGs) were identified by overlapping ARGs with common differentially expressed genes (DEGs) between OS and normal samples from the GSE16088, GSE19276, and GSE99671 datasets. Anoikis-related clusters of patients were obtained by consistent clustering, and gene set variation analysis (GSVA) of the different clusters was completed. Next, a risk model was created using Cox regression analyses. Risk scores and clinical features were assessed for independent prognostic values, and a nomogram model was constructed. Subsequently, a functional enrichment analysis of the high- and low-risk groups was performed. In addition, the immunological characteristics of OS samples were compared between the high- and low-risk groups, and their sensitivity to therapeutic agents was explored.

Results

Seven DEARGs between OS and normal samples were obtained by intersecting 501 ARGs with 68 common DEGs. BNIP3 and CXCL12 were significantly differentially expressed between both clusters (P<0.05) and were identified as prognosis-related genes. The risk model showed that the risk score and tumor metastasis were independent prognostic factors of patients with OS. A nomogram combining risk score and tumor metastasis effectively predicted the prognosis. In addition, patients in the high-risk group had low immune scores and high tumor purity. The levels of immune cell infiltration, expression of human leukocyte antigen (HLA) genes, immune response gene sets, and immune checkpoints were lower in the high-risk group than those in the low-risk group. The low-risk group was sensitive to the immune checkpoint PD-1 inhibitor, and the high-risk group exhibited lower inhibitory concentration values by 50% for 24 drugs, including AG.014699, AMG.706, and AZD6482.

Conclusion

The prognostic stratification framework of patients with OS based on ARGs, such as BNIP3 and CXCL12, may lead to more efficient clinical management.

A glycometabolic gene signature associating with immune infiltration and chemosensitivity and predicting the prognosis of patients with osteosarcoma

Background

Accumulating evidence has suggested that glycometabolism plays an important role in the pathogenesis of tumorigenesis. However, few studies have investigated the prognostic values of glycometabolic genes in patients with osteosarcoma (OS). This study aimed to recognize and establish a glycometabolic gene signature to forecast the prognosis, and provide therapeutic options for patients with OS.

Methods

Univariate and multivariate Cox regression, LASSO Cox regression, overall survival analysis, receiver operating characteristic curve, and nomogram were adopted to develop the glycometabolic gene signature, and further evaluate the prognostic values of this signature. Functional analyses including Gene Ontology (GO), kyoto encyclopedia of genes and genomes analyses (KEGG), gene set enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), and competing endogenous RNA (ceRNA) network, were used to explore the molecular mechanisms of OS and the correlation between immune infiltration and gene signature. Moreover, these prognostic genes were further validated by immunohistochemical staining.

Results

A total of four genes including PRKACB, SEPHS2, GPX7, and PFKFB3 were identified for constructing a glycometabolic gene signature which had a favorable performance in predicting the prognosis of patients with OS. Univariate and multivariate Cox regression analyses revealed that the risk score was an independent prognostic factor. Functional analyses indicated that multiple immune associated biological processes and pathways were enriched in the low-risk group, while 26 immunocytes were down-regulated in the high-risk group. The patients in high-risk group showed elevated sensitivity to doxorubicin. Furthermore, these prognostic genes could directly or indirectly interact with other 50 genes. A ceRNA regulatory network based on these prognostic genes was also constructed. The results of immunohistochemical staining showed that SEPHS2, GPX7, and PFKFB3 were differentially expressed between OS tissues and adjacent normal tissues.

Conclusion

The preset study constructed and validated a novel glycometabolic gene signature which could predict the prognosis of patients with OS, identify the degree of immune infiltration in tumor microenvironment, and provide guidance for the selection of chemotherapeutic drugs. These findings may shed new light on the investigation of molecular mechanisms and comprehensive treatments for OS.

Identification of three metabolic subtypes in gastric cancer and the construction of a metabolic pathway-based risk model that predicts the overall survival of GC patients

Gastric cancer (GC) is highly heterogeneous and GC patients have low overall survival rates. It is also challenging to predict the prognosis of GC patients. This is partly because little is known about the prognosis-related metabolic pathways in this disease. Hence, our objective was to identify GC subtypes and genes related to prognosis, based on changes in the activity of core metabolic pathways in GC tumor samples. Differences in the activity of metabolic pathways in GC patients were analyzed using Gene Set Variation Analysis (GSVA), leading to the identification of three clinical subtypes by non-negative matrix factorization (NMF). Based on our analysis, subtype 1 showed the best prognosis while subtype 3 exhibited the worst prognosis. Interestingly, we observed marked differences in gene expression between the three subtypes, through which we identified a new evolutionary driver gene, CNBD1. Furthermore, we used 11 metabolism-associated genes identified by LASSO and random forest algorithms to construct a prognostic model and verified our results using qRT-PCR (five matched clinical tissues of GC patients). This model was found to be both effective and robust in the GSE84437 and GSE26253 cohorts, and the results from multivariate Cox regression analyses confirmed that the 11-gene signature was an independent prognostic predictor (p < 0.0001, HR = 2.8, 95% CI 2.1-3.7). The signature was found to be relevant to the infiltration of tumor-associated immune cells. In conclusion, our work identified significant GC prognosis-related metabolic pathways in different GC subtypes and provided new insights into GC-subtype prognostic assessment.

A Circular RNA, hsa_circ_0018180 (circPARD3), Triggers Glycolysis and Promotes Malignancy of Head and Neck Squamous Cell Carcinoma Through the miR-5194/ENO1 Axis

Emerging evidence has demonstrated the pivotal roles of circular RNAs (circRNAs) in the modulation of malignancy and pathological progression among multiple human cancers. Glucose metabolism reprogramming is a widely identified characteristic for contributing to facilitate tumorigenesis. Nonetheless, their contributions to head and neck squamous cell carcinoma (HNSCC) cell glycolysis remain to be further elucidated. Herein, we aim to investigate the role of circRNA, hsa_circ_0018180 (also named as circPARD3) in HNSCC. Expression patterns of circPARD3 in HNSCC tissues and different cell lines were determined by qRT-PCR assay, as well as its correlation with the prognosis of survival. CCK-8, EdU incorporation, and transwell assays were carried out to assess the cell viability, proliferation, migration, and invasion, respectively. Glucose uptake and lactate production were evaluated by preforming glycolysis. Mechanistically, the circPARD3/miR-5194/ENO1 axis was verified by RNA immunoprecipitation (RIP) and luciferase reporter assays. Western blot analysis was employed to measure the epithelial-mesenchymal transition (EMT)-associated biomarkers. Upregulated circPARD3 observed in HNSCC tissues and cell lines indicated the poor prognosis of patients. Stable knockdown of circPARD3 dramatically exerted the suppressive effects on cell viability, proliferation, migration, and invasion, as well as glucose uptake and lactate production. Mechanistically, circPARD3 harbored miR-5194, serving as a miRNA sponge, thereby increasing ENO1 expression. Moreover, ENO1 evidently reversed miR-5194-mediated attenuated malignant behaviors. Collectively, our study identified an oncogenic role of circPARD3 in HNSCC through a novel machinery of circPARD3/miR-5194/ENO1 and provided a promising therapeutic target for HNSCC.

A glycolysis-related gene signatures in diffuse large B-Cell lymphoma predicts prognosis and tumor immune microenvironment

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma which that highly aggressive and heterogeneous. Glycolysis has been implicated in the regulation of tumor microenvironment (TME) and development. In this study, we aimed to establish a glycolysis-related prognostic model for the risk stratification, prognosis prediction, and immune landscape evaluation in patients with DLBCL. Methods: Three independent datasets GSE181063, GSE10846, and GSE53786 containing gene expression profiles and clinical data were downloaded from the Gene Expression Omnibus (GEO) database. The glycolysis-related prognostic model was developed with Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression and validated. A nomogram integrating clinical factors and glycolytic risk scores was constructed. The composition of the TME was analyzed with the ESTIMATE algorithm and single-sample gene set enrichment analysis (ssGSEA). Results: A glycolytic risk model containing eight genes was developed. The area under the receiver operating characteristic (ROC) curve (AUC) for the 1-, 3-, and 5-year was 0.718, 0.695, and 0.688, respectively. Patients in the high-risk group had significantly lower immune scores, elevated tumor purity, and poorer survival compared with those in the low-risk group. The nomogram constructed based on glycolytic risk score, age, Eastern Cooperative Oncology Group performance status (ECOG-PS), use of rituximab, and cell of origin (COO) displayed better prediction performance compared with the International Prognostic Index (IPI) in DLBCL. The glycolytic risk score was negatively correlated with the infiltration level of activated CD8 T cells, activated dendritic cells, natural killer cells, and macrophages and immune checkpoint molecules including PD-L2, CTLA4, TIM-3, TIGIT, and B7-H3. Conclusion: These results suggested that the glycolytic risk model could accurately and stably predict the prognosis of patients with DLBCL and might unearth the possible explanation for the glycolysis-related poor prognosis.

Identification of Ferroptosis-Related Biomarkers for Diagnosis and Molecular Classification of Staphylococcus aureus-Induced Osteomyelitis

Objective

Staphylococcus aureus (SA)-induced osteomyelitis (OM) is one of the most common refractory diseases in orthopedics. Early diagnosis is beneficial to improve the prognosis of patients. Ferroptosis plays a key role in inflammation and immune response, while the mechanism of ferroptosis-related genes (FRGs) in SA-induced OM is still unclear. The purpose of this study was to determine the role of ferroptosis-related genes in the diagnosis, molecular classification and immune infiltration of SA-induced OM by bioinformatics.

Methods

Datasets related to SA-induced OM and ferroptosis were collected from the Gene Expression Omnibus (GEO) and ferroptosis databases, respectively. The least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms were combined to screen out differentially expressed-FRGs (DE-FRGs) with diagnostic characteristics, and gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to explore specific biological functions and pathways. Based on these key DE-FRGs, a diagnostic model was established, and molecular subtypes were divided to explore the changes in the immune microenvironment between molecular subtypes.

Results

A total of 41 DE-FRGs were identified. After screening and intersecting with LASSO and SVM-RFE algorithms, 8 key DE-FRGs with diagnostic characteristics were obtained, which may regulate the pathogenesis of OM through the immune response and amino acid metabolism. The ROC curve indicated that the 8 DE-FRGs had excellent diagnostic ability for SA-induced OM (AUC=0.993). Two different molecular subtypes (subtype 1 and subtype 2) were identified by unsupervised cluster analysis. The CIBERSORT analysis revealed that the subtype 1 OM had higher immune cell infiltration rates, mainly in T cells CD4 memory resting, macrophages M0, macrophages M2, dendritic cells resting, and dendritic cells activated.

Conclusion

We developed a diagnostic model related to ferroptosis and molecular subtypes significantly related to immune infiltration, which may provide a novel insight for exploring the pathogenesis and immunotherapy of SA-induced OM.

Increased tumor glycolysis is associated with decreased immune infiltration across human solid tumors

Response to immunotherapy across multiple cancer types is approximately 25%, with some tumor types showing increased response rates compared to others (i.e. response rates in melanoma and non-small cell lung cancer (NSCLC) are typically 30-60%). Patients whose tumors are resistant to immunotherapy often lack high levels of pre-existing inflammation in the tumor microenvironment. Increased tumor glycolysis, acting through glucose deprivation and lactic acid accumulation, has been shown to have pleiotropic immune suppressive effects using in-vitro and in-vivo models of disease. To determine whether the immune suppressive effect of tumor glycolysis is observed across human solid tumors, we analyzed glycolytic and immune gene expression patterns in multiple solid malignancies. We found that increased expression of a glycolytic signature was associated with decreased immune infiltration and a more aggressive disease across multiple tumor types. Radiologic and pathologic analysis of untreated estrogen receptor (ER)-negative breast cancers corroborated these observations, and demonstrated that protein expression of glycolytic enzymes correlates positively with glucose uptake and negatively with infiltration of CD3⁺ and CD8⁺ lymphocytes. This study reveals an inverse relationship between tumor glycolysis and immune infiltration in a large cohort of multiple solid tumor types.

A Novel Glycolysis-Related Long Noncoding RNA Signature for Predicting Overall Survival in Gastric Cancer

Background: The aim of this study was to construct a glycolysis-related long noncoding RNA (lncRNA) signature to predict the prognosis of patients with gastric cancer (GC). Methods: Glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB), lncRNA expression profiles and clinical data of GC patients were obtained from The Cancer Genome Atlas database (TCGA). Furthermore, univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to construct prognostic glycolysis-related lncRNA signature. The specificity and sensitivity of the signature was verified by receiver operating characteristic (ROC) curves. We constructed a nomogram to predict the 1-year, 3-year, and 5-year survival rates of GC patients. Besides, the relationship between immune infiltration and the risk score was analyzed in the high and low risk groups. Multi Experiment Matrix (MEM) was used to analyze glycolysis-related lncRNA target genes. R "limma" package was used to analyze the mRNA expression levels of the glycolysis-related lncRNA target genes in TCGA. Gene set enrichment analysis (GSEA) was employed to further explore the biological pathways in the high-risk group and the glycolysis-related lncRNA target gene. Results: A prognostic signature was conducted based on nine glycolysis-related lncRNAs, which are AL391152.1, AL590705.3, RHOXF1-AS1, CFAP61-AS1, LINC00412, AC005165.1, AC110995.1, AL355574.1 and SCAT1. The area under the ROC curve (AUC) values at 1-year, 3-year, and 5-year were 0.765, 0.828 and 0.707 in the training set, and 0.669, 740 and 0.807 in the testing set, respectively. In addition, the nomogram could efficaciously predict the 1-year, 3-year, and 5-year survival rates of the GC patients. Then, we discovered that GC patients with high-risk scores were more likely to respond to immunotherapy. GSEA revealed that the signature was mainly associated with the calcium signaling pathway, extracellular matrix (ECM) receptor interaction, and focal adhesion in high-risk group, also indicated that SBSPON is related to aminoacyl-tRNA biosynthesis, citrate cycle, fructose and mannose metabolism, pentose phosphate pathway and pyrimidine metabolism. Conclusion: Our study shows that the signature can predict the prognosis of GC and may provide new insights into immunotherapeutic strategies.

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

Background

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

Objective

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

Methods and results

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

Conclusions

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

A nine–consensus–prognostic –gene–based prognostic signature, recognizing the dichotomized subgroups of gastric cancer patients with different clinical outcomes and therapeutic strategies

Background: The increasing prevalence and mortality of gastric cancer (GC) has promoted the urgent need for prognostic signatures to predict the long-term risk and search for therapeutic biomarkers.

Methods and materials: A total of 921 GC patients from three GEO cohorts were enrolled in the current study. The GSE15459 and GSE62254 cohorts were used to select the top prognostic gene via the evaluation of the area under the receiver operating characteristic (ROC) curve (AUC) values. The GSE84437 cohort was used as the external validation cohort. Least absolute shrinkage and selector operation (LASSO) regression analysis was applied to reduce the feature dimension and construct the prognostic signature. Furthermore, a nomogram was constructed by integrating the independent prognostic analysis and validated by calibration plot, decision curve analysis and clinical impact curve. The molecular features and response to chemo-/immunotherapy among risk subgroups were evaluated by the “MOVICS” and “ESTAMATE” R packages and the SubMap algorithm. Lauren classification and ACRG molecular subtype were obtained to compare with the risk model.

Results: Forty-four prognosis-associated genes were identified with a preset cutoff AUC value of 0.65 in both the GSE62254 and GSE15459 cohorts. With the 10-fold cross validation analysis of LASSO, nine genes were selected to construct the nine-consensus-prognostic-gene signature. The signature showed good prognostic value in the GSE62254 (p < 0.001, HR: 3.81, 95% CI: 2.44–5.956) and GSE15459 (p < 0.001, HR: 2.65, 95% CI: 1.892–3.709) cohorts and the external validation GSE84437 cohort (p < 0.001, HR: 2.06, 95% CI: 1.554–2.735). The nomogram constructed based on two independent predictive factors, tumor stage and the signature, predicted events tightly consistent with the actual (Hosmer–Lemeshow p value: 1-year, 0.624; 3-years, 0.795; 5-years, 0.824). For the molecular features, we observed the activation of apical junction, epithelial mesenchymal transition, and immune pathways in the high-risk group, while in the low-risk group, cell cycle associated G2M, E2F and MYC target pathways were activated. Based on the results we obtained, we indicated that gastric patients in the low-risk group are more suitable for 5-fluorouracil therapy, while high-risk group patients are more suitable for anti-CTLA4 immunotherapy, these results need more support in the further studies. After compare with proposed molecular subtypes, we realized that the nine-consensus prognostic gene signature is a powerful addition to identify the gastric patients with poor prognosis.

Conclusion: In summary, we constructed a robust nine-consensus-prognostic-gene signature for the prediction of GC prognosis, which can also predict the personalized treatment of GC patients.

Establishment of a Novel Prognostic Prediction Model for Gastric Cancer Based on Necroptosis-Related Genes

Background: Necroptosis plays a crucial role in the progression of multiple types of cancer. However, the role of necroptosis in gastric cancer (GC) remains unclear. The aim of this study is to establish a necroptosis-related prediction model, which could provide information for treatment monitoring.

Methods: The TCGA-STAD cohort was employed to establish a prognostic prediction signature and the GEO dataset was employed for external validation. The correlation between the risk score and the immune landscape, tumor mutational burden (TMB), microsatellite instability (MSI), as well as therapeutic responses of different therapies were analyzed.

Results: We constructed a prognostic model based on necroptosis-associated genes (NAGs), and its favorable predictive ability was confirmed in an external cohort. The risk score was confirmed as an independent determinant, and a nomogram was further established for prognosis. A high score implies higher tumor immune microenvironment (TIME) scores and more significant TIME cell infiltration. High-risk patients presented with lower TMB, and low-TMB patients had worse overall survival (OS). Meanwhile, Low-risk scores are characterized by MSI-high (MSI-H), lower Tumor Immune Dysfunction and Exclusion (TIDE) score, and higher immunogenicity in immunophenoscore (IPS) analysis.

Conclusion: The developed NAG score provides a novel and effective method for predicting the outcome of GC as well as potential targets for further research.

Circular RNA circPOSTN promotes neovascularization by regulating miR-219a-2-3p/STC1 axis and stimulating the secretion of VEGFA in glioblastoma

Glioblastoma (GBM), the most malignant type of astrocytic tumor, is one of the deadliest cancers prevalent in adults. Along with tumor growth, patients with GBM generally suffer from extensive cerebral edema and apparent symptoms of intracranial hyper-pressure. Accumulating evidence has demonstrated that circRNA plays a critically important role in tumorigenesis and progression. However, the biological function and the underlying mechanism of circRNA in GBM remain elusive. In this study, by conducting gene expression detection based on 15 pairs of GBM clinical specimens and the normal adjunct tissues, we observed that circPOSTN showed abnormally higher expression in GBM. Both loss-of-function and gain-of-function biological experiments demonstrated that circPOSTN scheduled the proliferation, migration, and neovascularization abilities of GBM cells. Further, fluorescence in situ hybridization (FISH) assay, quantitative RT-PCR, and subcellular separation suggested that circPOSTN was predominately localized in the cytoplasm and may serve as a competing endogenous RNA (ceRNA). CircRNA-miRNA interaction prediction based on online analytical processing, AGO2-RIP assay, biotin labeled RNA pulldown assay, and dual-luciferase reporter assay revealed that circPOSTN sponged miR-219a-2-3p, limited its biological function, and ultimately upregulated their common downstream gene STC1. Finally, by carrying out in vitro and in vivo functional assays, we uncovered a new regulatory axis circPOSTN/miR-219a-2-3p/STC1 that promoted GBM neovascularization by increasing vascular endothelial growth factor A (VEGFA) secretion. Our study underscores the critical role of circPOSTN in GBM progression, providing a novel insight into GBM anti-tumor therapy.

Metabolism reprogramming signature associated with stromal cells abundance in tumor microenvironment improve prognostic risk classification for gastric cancer

Background

Stromal cells play an important role in the process of tumor progression, but the relationship between stromal cells and metabolic reprogramming is not very clear in gastric cancer (GC).

Methods

Metabolism-related genes associated with stromal cells were identified in The Cancer Genome Atlas (TCGA) and GSE84437 datasets, and the two datasets with 804 GC patients were integrated into a training cohort to establish the prognostic signature. Univariate Cox regression analysis was used to screen for prognosis-related genes. A risk score was constructed by LASSO regression analysis combined with multivariate Cox regression analysis. The patients were classified into groups with high and low risk according to the median value. Two independent cohorts, GSE62254 (n = 300) and GSE15459 (n = 191), were used to externally verify the risk score performance. The CIBERSORT method was applied to quantify the immune cell infiltration of all included samples.

Results

A risk score consisting of 24 metabolic genes showed good performance in predicting the overall survival (OS) of GC patients in both the training (TCGA and GSE84437) and testing cohorts (GSE62254 and GSE15459). As the risk score increased, the patients’ risk of death increased. The risk score was an independent prognostic indicator in both the training and testing cohorts suggested by the univariate and multivariate Cox regression analyses. The patients were clustered into four subtypes according to the quantification of 22 kinds of immune cell infiltration (ICI). The proportion of ICI Cluster C with the best prognosis in the low-risk group was approximately twice as high as that in the high-risk group, and the risk score of ICI Cluster C was significantly lower than that of the other three subtypes.

Conclusion

Our study proposed the first scheme for prognostic risk classification of GC from the perspective of tumor stromal cells and metabolic reprogramming, which may contribute to the development of therapeutic strategies for GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02451-2.

From Anti-HER-2 to Anti-HER-2-CAR-T Cells: An Evolutionary Immunotherapy Approach for Gastric Cancer

Current Therapeutic modalities provide no survival advantage to gastric cancer (GC) patients. Targeting the human epidermal growth factor receptor-2 (HER-2) is a viable therapeutic strategy against advanced HER-2 positive GC. Antibody-drug conjugates, small-molecule tyrosine kinase inhibitors (TKIs), and bispecific antibodies are emerging as novel drug forms that may abrogate the resistance to HER-2-specific drugs and monoclonal antibodies. Chimeric antigen receptor-modified T cells (CAR-T) targeting HER-2 have shown considerable therapeutic potential in GC and other solid tumors. However, due to the high heterogeneity along with the complex tumor microenvironment (TME) of GC that often leads to immune escape, the immunological treatment of GC still faces many challenges. Here, we reviewed and discussed the current progress in the research of anti-HER-2-CAR-T cell immunotherapy against GC.

Glycolysis-Related LINC02432/Hsa-miR-98–5p/HK2 Axis Inhibits Ferroptosis and Predicts Immune Infiltration, Tumor Mutation Burden, and Drug Sensitivity in Pancreatic Adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is a malignant cancer with high incidence and mortality. Glycometabolic rearrangements (aerobic glycolysis) is a hallmark of PAAD and contributes to tumorigenesis and progression through numerous mechanisms. This study aimed to identify a novel glycolysis-related lncRNA-miRNA-mRNA ceRNA signature in PAAD and explore its potential molecular function. We first calculated the glycolysis score for each PAAD patient by the ssGSEA algorithm and found that patients with higher hallmark glycolysis scores had poorer prognosis. Subsequently, we obtained a novel glycolysis-related LINC02432/hsa-miR-98–5p/HK2 axis from the TCGA and GEO databases using comprehensive bioinformatics analysis and developed a nomogram to predict overall survival. Furthermore, functional characterization analysis revealed that LINC02432/hsa-miR-98–5p/HK2 axis risk score was negatively correlated with ferroptosis. The tumor immune infiltration analysis suggested positive correlations between ceRNA risk score and infiltrated M0 macrophage levels in PAAD. Correlation analysis found that ceRNA risk scores were positively correlated with four chemokines (CXCL3, CXCL5, CXCL8 and CCL20) and one immune checkpoint gene (SIGLEC15). Meanwhile, tumor mutation burden (TMB), an indicator for predicting response to immunotherapy, was positively correlated with ceRNA risk score. Finally, the drug sensitivity analysis showed that the high-risk score patients might be more sensitive to EGFR, MEK and ERK inhibitors than low-risk score patients. In conclusion, our study suggested that LINC02432/hsa-miR-98–5p/HK2 axis may serve as a novel diagnostic, prognostic, and therapeutic target in PAAD treatment.

Low expression of moonlight gene ALAD is correlated with poor prognosis in hepatocellular carcinoma

BACKGROUND

Moonlighting genes may involve in the progression of hepatocellular carcinoma (HCC), and the establishment of a prognostic signature based on moonlighting genes may help predict the prognosis of HCC patients.

METHODS

This study aimed to construct a prognostic signature based on moonlighting genes in HCC and determine whether there is a correlation with tumor microenvironment or immune responses. Then we used HCC cell lines and an HCC cDNA microarray to illuminate the role of moonlighting gene in prognosis of HCC.

RESULTS

We constructed an original prognostic signature based on eight moonlighting genes (ABCB1, S100A9, NCL, PRDX6, ALAD, YBX1, POU2F1, RPL5) with strong prognosis prediction capability. The prognostic signature may demonstrate the immune status of patients with HCC, because high-risk subgroups had significantly higher scores for regulatory T cells, dendritic cells, T follicular helper cells, macrophages, and major histocompatibility complex-I, and different expression levels of immune checkpoint molecules. Importantly, patients in the high-risk subgroup exhibited higher tumor immune dysfunction and exclusion scores, suggesting that they might be less sensitive to immunotherapy. The roles of ABCB1, S100A9, NCL, PRDX6, YBX1, and POU2F1 in HCC have been reported. However, there have been no reports on the association between ALAD and HCC. Then we used bioinformatics to confirm that ALAD expression was lower in HCC and low expression of ALAD was an indicator of poor prognosis. Moreover, we found that ALAD expression was lower in HCC cells than that in normal human hepatocytes or tumor-adjacent tissues, it was negatively correlated with the pathological grade, and low expression of ALAD was related to poor prognosis in patients with HCC.

CONCLUSION

We have successfully established a novel prognostic signature based on moonlighting genes, with a strong predictive capability for prognosis, immune status, and possible response to immunotherapy. Additionally, we have identified ALAD as a prognostic biomarker for HCC.

Development of a Novel Immune-Related Gene Signature to Predict Prognosis and Immunotherapeutic Efficiency in Gastric Cancer

Background: Gastric cancer (GC) is the fifth most common malignancy and the third leading cause of tumor-related deaths globally. Herein, we attempted to build a novel immune-related gene (IRG) signature that could predict the prognosis and immunotherapeutic efficiency for GC patients.

Methods: The mRNA transcription data and corresponding clinical data of GC were downloaded from The Cancer Genome Atlas (TCGA) database as the training group and the GSE84437 data set as the testing cohort, followed by acquisition of IRGs from the InnateDB resource and ImmPort database. Using the univariate Cox regression analysis, an IRG signature was developed. Several immunogenomic analyses were performed to illustrate the associations between the immune risk score and tumor mutational burden, immune cell infiltrations, function of immune infiltration, clinical characteristics, immune subtype, and immunotherapeutic response.

Results: The analysis of 343 GC samples and 30 normal samples from the TCGA database gave rise to 8,713 differentially expressed genes (DEGs) and 513 differentially expressed immune-related genes (DEIRGs) were extracted. The novel IRG signature contained eight DEIRGs (FABP4, PI15, RNASE2, CGB5, INHBE, RLN2, DUSP1, and CD36) and was found to serve as an independent predictive and prognostic factor for GC. Then, the GC patients were separated into the high- and low-risk groups based on the median risk score, wherein the low-risk group presented a better prognosis and was more sensitive to immunotherapy than did the high-risk group. According to the time-dependent ROC curves and AUCs, the immunotherapeutic value of the signature was better than the Tumor Immune Dysfunction and Exclusion (TIDE) and T-cell inflammatory signature (TIS) scores. In addition, the AUCs of the risk score for predicting 1-, 2-, and 3-year OS were 0.675, 0.682, and 0.710, respectively, which indicated that the signature had great predictive power.

Conclusion: This study presents a novel IRG signature based on the tumor immune microenvironment, which could improve the prediction of the prognosis and immunotherapeutic efficiency for GC patients. The powerful signature may serve as novel biomarkers and provide therapeutic targets for precision oncology in clinical practice.

The Immunological Contribution of a Novel Metabolism-Related Signature to the Prognosis and Anti-Tumor Immunity in Cervical Cancer

Cervical cancer is the most frequently diagnosed malignancy in the female reproductive system. Conventional stratification of patients based on clinicopathological characters has gradually been outpaced by a molecular profiling strategy. Our study aimed to identify a reliable metabolism-related predictive signature for the prognosis and anti-tumor immunity in cervical cancer. In this study, we extracted five metabolism-related hub genes, including ALOX12B, CA9, FAR2, F5 and TDO2, for the establishment of the risk score model. The Kaplan-Meier curve suggested that patients with a high-risk score apparently had a worse prognosis in the cervical cancer training cohort (TCGA, n = 304, p < 0.0001), validation cohort (GSE44001, n = 300, p = 0.0059) and pan-cancer cohorts (including nine TCGA tumors). Using a gene set enrichment analysis (GSEA), we observed that the model was correlated with various immune-regulation-related pathways. Furthermore, pan-cancer cohorts and immunohistochemical analysis showed that the infiltration of tumor infiltrating lymphocytes (TILs) was lower in the high-score group. Additionally, the model could also predict the prognosis of patients with cervical cancer based on the expression of immune checkpoints (ICPs) in both the discovery and validation cohorts. Our study established and validated a metabolism-related prognostic model, which might improve the accuracy of predicting the clinical outcome of patients with cervical cancer and provide guidance for personalized treatment.

Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

Background

Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment.

Methods

Gene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC).

Results

Consensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses.

Conclusions

The molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03393-9.

Construction and Verification of a Glycolysis-Associated Gene Signature for the Prediction of Overall Survival in Low Grade Glioma

The overall survival of patients with lower grade glioma (LGG) that might develop into high-grade malignant glioma shows marked heterogeneity. The currently used clinical evaluation index is not sufficient to predict precise prognostic outcomes accurately. To optimize survival risk stratification and the personalized management of patients with LGG, there is an urgent need to develop an accurate risk prediction model. The TCGA-LGG dataset, downloaded from The Cancer Genome Atlas (TCGA) portal, was used as a training cohort, and the Chinese Glioma Genome Atlas (CGGA) dataset and Rembrandt dataset were used as validation cohorts. The levels of various cancer hallmarks were quantified, which identified glycolysis as the primary overall survival-related risk factor in LGGs. Furthermore, using various bioinformatic and statistical methods, we developed a strong glycolysis-related gene signature to predict prognosis. Gene set enrichment analysis showed that in our model, high-risk glioma correlated with the chemoradiotherapy resistance and poor survival. Moreover, based on established risk model and other clinical features, a decision tree and a nomogram were built, which could serve as useful tools in the diagnosis and treatment of LGGs. This study indicates that the glycolysis-related gene signature could distinguish high-risk and low‐risk patients precisely, and thus can be used as an independent clinical feature.

Non-coding RNAs and macrophage interaction in tumor progression

The macrophages are abundantly found in TME and their M2 polarization is in favor of tumor malignancy. On the other hand, non-coding RNAs (ncRNAs) can modulate macrophage polarization in TME to affect cancer progression. The miRNAs can dually induce/suppress M2 polarization of macrophages and by affecting various molecular pathways, they modulate tumor progression and therapy response. The lncRNAs can affect miRNAs via sponging and other molecular pathways to modulate macrophage polarization. A few experiments have also examined role of circRNAs in targeting signaling networks and affecting macrophages. The therapeutic targeting of these ncRNAs can mediate TME remodeling and affect macrophage polarization. Furthermore, exosomal ncRNAs derived from tumor cells or macrophages can modulate polarization and TME remodeling. Suppressing biogenesis and secretion of exosomes can inhibit ncRNA-mediated M2 polarization of macrophages and prevent tumor progression. The ncRNAs, especially exosomal ncRNAs can be considered as non-invasive biomarkers for tumor diagnosis.

Evaluation of the Prognostic Relevance of Differential Claudin Gene Expression Highlights Claudin-4 as Being Suppressed by TGFβ1 Inhibitor in Colorectal Cancer

Background: Claudins (CLDNs) are a family of closely related transmembrane proteins that have been linked to oncogenic transformation and metastasis across a range of cancers, suggesting that they may be valuable diagnostic and/or prognostic biomarkers that can be used to evaluate patient outcomes. However, CLDN expression patterns associated with colorectal cancer (CRC) remain to be defined.

Methods: The mRNA levels of 21 different CLDN family genes were assessed across 20 tumor types using the Oncomine database. Correlations between these genes and patient clinical outcomes, immune cell infiltration, clinicopathological staging, lymph node metastasis, and mutational status were analyzed using the GEPIA, UALCAN, Human Protein Atlas, Tumor Immune Estimation Resource, STRING, Genenetwork, cBioportal, and DAVID databases in an effort to clarify the potential functional roles of different CLDN protein in CRC. Molecular docking analyses were used to probe potential interactions between CLDN4 and TGFβ1. Levels of CLDN4 and CLDN11 mRNA expression in clinical CRC patient samples and in the HT29 and HCT116 cell lines were assessed via qPCR. CLDN4 expression levels in these 2 cell lines were additionally assessed following TGFβ1 inhibitor treatment.

Results: These analyses revealed that COAD and READ tissues exhibited the upregulation of CLDN1, CLDN2, CLDN3, CLDN4, CLDN7, and CLDN12 as well as the downregulation of CLDN5 and CLDN11 relative to control tissues. Higher CLDN11 and CLDN14 expression as well as lower CLDN23 mRNA levels were associated with poorer overall survival (OS) outcomes. Moreover, CLDN2 and CLDN3 or CLDN11 mRNA levels were significantly associated with lymph node metastatic progression in COAD or READ lower in COAD and READ tissues. A positive correlation between the expression of CLDN11 and predicted macrophage, dendritic cell, and CD4+ T cell infiltration was identified in CRC, with CLDN12 expression further being positively correlated with CD4+ T cell infiltration whereas a negative correlation was observed between such infiltration and the expression of CLDN3 and CLDN15. A positive correlation between CLDN1, CLDN16, and neutrophil infiltration was additionally detected, whereas neutrophil levels were negatively correlated with the expression of CLDN3 and CLDN15. Molecular docking suggested that CLDN4 was able to directly bind via hydrogen bond with TGFβ1. Relative to paracancerous tissues, clinical CRC tumor tissue samples exhibited CLDN4 and CLDN11 upregulation and downregulation, respectively. LY364947 was able to suppress the expression of CLDN4 in both the HT29 and HCT116 cell lines.

Conclusion: Together, these results suggest that the expression of different CLDN family genes is closely associated with CRC tumor clinicopathological staging and immune cell infiltration. Moreover, CLDN4 expression is closely associated with TGFβ1 in CRC, suggesting that it and other CLDN family members may represent viable targets for antitumor therapeutic intervention.

Comprehensive Analysis of Immune Implications and Prognostic Value of SPI1 in Gastric Cancer

Background

The transcription factor Spi-1 proto-oncogene (SPI1, also known as PU.1) is a key regulator of signal communication in the immune system and is essential for the development of myeloid cells and lymphocytes. However, the potential role of SPI1 in gastric cancer (GC) and the correlations between SPI1 and immune infiltration remain unclear.

Methods

In the present study, multiple databases including ONCOMINE, TIMER, Kaplan–Meier Plotter, and The Cancer Genome Atlas were used to explore the expression levels and prognostic value of SPI1 in GC. cBioPortal was used to explore the possible reasons for the increased expression of SPI1 in GC. The correlations between SPI1 expression and tumor-infiltrating immune cells (TICs) were analyzed using CIBERSORT and TIMER. Gene set enrichment analysis was used to determine the biological function of SPI1 in the development of GC. In addition, a risk signature based on SPI1-related immunomodulators was constructed to accurately evaluate the prognosis of patients with GC. The upregulation of SPI1 expression in GC was further confirmed through immunohistochemistry, western blotting, and real-time quantitative PCR (RT-qPCR) assay.

Results

The expression of SPI1 was increased significantly in GC according to multiple databases, and high expression of SPI1 was related to poor prognosis and progression of GC. The main factor influencing the high expression of SPI1 mRNA in GC may be diploidy, not DNA methylation. Moreover, immunohistochemistry, western blotting, and RT-qPCR assays also confirmed the upregulated expression of SPI1 in GC. CIBERSORT analysis revealed that SPI1 expression was correlated with seven types of TICs (naive B cells, resting memory CD4 T cells, activated memory CD4 T cells, activated natural killer cells, resting natural killer cells, M2 macrophages, and resting dendritic cells). Gene set enrichment analysis indicated that SPI1 might be related to immune activation in GC and participate in cell cycle regulation. In addition, based on SPI1-related immunomodulators, we developed multiple-gene risk prediction signatures and constructed a nomogram that can independently predict the clinical outcome of GC.

Conclusion

The results of the present study suggest that SPI1 has a critical role in determining the prognosis of GC patients and may be a potential immunotherapeutic target.

Development of an Inflammation-Related lncRNA-miRNA-mRNA Network Based on Competing Endogenous RNA in Breast Cancer at Single-Cell Resolution

The role and mechanism of inflammation in breast cancer is unclear. This study aims to probe the relationship between inflammation and long non-coding RNAs (lncRNAs) and to stablish an inflammation-related competing endogenous RNA (ceRNA) network in breast cancer. Inflammation-related lncRNAs and target genes were screened based on the data from four single-cell RNA sequencing (scRNA-seq) studies and miRNAs were bioinformatically predicted according to ceRNA hypothesis. A series of in silico analyses were performed to construct an inflammation-related ceRNA network in breast cancer. Consequently, a total of seven inflammation-related lncRNAs were selected, after which LRRC75A-AS1 was identified as the most potential lncRNA in view of its expression and prognostic predictive value in breast cancer. Finally, an inflammation-related ceRNA network in breast cancer at the single cell level was established based on lncRNA LRRC75A-AS1, miR-3127-5p, miR-2114-3p, RPL36 and RPL27A mRNAs. Collectively, the lncRNA LRRC75A-AS1 and the LRRC75A-AS1-based on ceRNA network may exert crucial roles in modulating inflammation response during the initiation and progression of breast cancer.

Metabolic Reprogramming in Gastric Cancer: Trojan Horse Effect

Worldwide, gastric cancer (GC) represents the fifth most common cancer for incidence and the third leading cause of death in developed countries. Despite the development of combination chemotherapies, the survival rates of GC patients remain unsatisfactory. The reprogramming of energy metabolism is a hallmark of cancer, especially increased dependence on aerobic glycolysis. In the present review, we summarized current evidence on how metabolic reprogramming in GC targets the tumor microenvironment, modulates metabolic networks and overcomes drug resistance. Preclinical and clinical studies on the combination of metabolic reprogramming targeted agents and conventional chemotherapeutics or molecularly targeted treatments [including vascular endothelial growth factor receptor (VEGFR) and HER2] and the value of biomarkers are examined. This deeper understanding of the molecular mechanisms underlying successful pharmacological combinations is crucial in finding the best-personalized treatment regimens for cancer patients.

EFNA3 Is a Prognostic Biomarker Correlated With Immune Cell Infiltration and Immune Checkpoints in Gastric Cancer

Background: Ephrin A3 (EFNA3), like most genes in the ephrin family, plays a central role in embryonic development and can be dysregulated in a variety of tumors. However, the relationship between EFNA3 and gastric cancer (GC) prognosis and tumor-infiltrating lymphocytes remains unclear.

Methods: Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) were used to analyze the expression of EFNA3. Kaplan-Meier plots and GEPIA2 were used to evaluate the relationship between EFNA3 expression and GC prognosis. Univariable survival and multivariate Cox analyses were used to compare various clinical characteristics with survival. LinkedOmics database was used for gene set enrichment analysis (GSEA). TIMER database and CIBERSORT algorithm were used to examine the relationship between EFNA3 expression and immune infiltration in GC and to explore cumulative survival in GC. The relationship between EFNA3 and immune checkpoints was examined using cBioPortal genomics analysis. Finally, EFNA3 expression in GC cells and tissues was assayed using quantitative real-time polymerase chain reaction.

Results: EFNA3 expression differs in a variety of cancers, and EFNA3 expression was higher in GC tissue than normal gastric tissue. GC patients with high expression of EFNA3 had worse overall survival, disease-free survival, and first progression. Multivariate analysis identified EFNA3 as an independent prognostic factor for GC. GSEA identified ribosome, cell cycle, ribosome biogenesis in eukaryotes, and aminoacyl-tRNA biosynthesis pathways as differentially enriched in patients with high EFNA3 expression. B cells, CD8⁺ T cells, CD4⁺ T cells, macrophages, neutrophils, and dendritic cells were significantly negatively correlated with a variety of immune markers. EFNA3 participates in changes in GC immune checkpoint markers in a collinear manner. EFNA3 expression in HGC-27, AGS, MKN45, and NCI-N87 was cell lines higher than that in GES-1, and patients with high expression of EFNA3 had a worse prognosis.

Conclusion: EFNA3 can be used as a prognostic and immune infiltration and checkpoint marker in GC patients.

Establishment of A Nomogram for Predicting the Prognosis of Soft Tissue Sarcoma Based on Seven Glycolysis-Related Gene Risk Score

Background: Soft tissue sarcoma (STS) is a group of tumors with a low incidence and a complex type. Therefore, it is an arduous task to accurately diagnose and treat them. Glycolysis-related genes are closely related to tumor progression and metastasis. Hence, our study is dedicated to the development of risk characteristics and nomograms based on glycolysis-related genes to assess the survival possibility of patients with STS.

Methods: All data sets used in our research include gene expression data and clinical medical characteristics in the Genomic Data Commons Data Portal (National Cancer Institute) Soft Tissue Sarcoma (TCGA SARC) and GEO database, gene sequence data of corresponding non-diseased human tissues in the Genotype Tissue Expression (GTEx).Next, transcriptome data in TCGA SARC was analyzed as the training set to construct a glycolysis-related gene risk signature and nomogram, which were confirmed in external test set.

Results: We identified and verified the 7 glycolysis-related gene signature that is highly correlated with the overall survival (OS) of STS patients, which performed excellently in the evaluation of the size of AUC, and calibration curve. As well as, the results of the analysis of univariate and multivariate Cox regression demonstrated that this 7 glycolysis-related gene characteristic acts independently as an influence predictor for STS patients. Therefore, a prognostic-related nomogram combing 7 gene signature with clinical influencing features was constructed to predict OS of patients with STS in the training set that demonstrated strong predictive values for survival.

Conclusion: These results demonstrate that both glycolysis-related gene risk signature and nomogram were efficient prognostic indicators for patients with STS. These findings may contribute to make individualize clinical decisions on prognosis and treatment.

Prognosis Implication of a Novel Metabolism-Related Gene Signature in Ewing Sarcoma

Ewing sarcoma (ES) is one of the most common bone cancers in adolescents and children. Growing evidence supports the view that metabolism pathways play critical roles in numerous cancers (He et al. (2020)). However, the correlation between metabolism-associated genes (MTGs) and Ewing sarcoma has not been investigated systematically. Here, based on the univariate Cox regression analysis, we get survival genes from differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) cohort. Multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to establish the MTG signature. Comprehensive survival analyses including receiver operating characteristic (ROC) curves and Kaplan-Meier analysis were applied to estimate the independent prognostic value of the signature. The ICGC cohort served as the validation cohort. A nomogram was constructed based on the risk score of the MTG signature and other independent clinical variables. The CIBERSORT algorithm was applied to estimate immune infiltration. In addition, we explored the correlation between MTG signature and immune checkpoints. Collectively, this work presents a novel MTG signature for prognostic prediction of Ewing sarcoma. It also suggests six genes that are potential prognostic indicators and therapeutic targets for ES.

Identification of crucial genes of pyrimidine metabolism as biomarkers for gastric cancer prognosis

Background

Metabolic reprogramming has been reported in various kinds of cancers and is related to clinical prognosis, but the prognostic role of pyrimidine metabolism in gastric cancer (GC) remains unclear.

Methods

Here, we employed DEG analysis to detect the differentially expressed genes (DEGs) in pyrimidine metabolic signaling pathway and used univariate Cox analysis, Lasso-penalizes Cox regression analysis, Kaplan–Meier survival analysis, univariate and multivariate Cox regression analysis to explore their prognostic roles in GC. The DEGs were experimentally validated in GC cells and clinical samples by quantitative real-time PCR.

Results

Through DEG analysis, we found NT5E, DPYS and UPP1 these three genes are highly expressed in GC. This conclusion has also been verified in GC cells and clinical samples. A prognostic risk model was established according to these three DEGs by Univariate Cox analysis and Lasso-penalizes Cox regression analysis. Kaplan–Meier survival analysis suggested that patient cohorts with high risk score undertook a lower overall survival rate than those with low risk score. Stratified survival analysis, Univariate and multivariate Cox regression analysis of this model confirmed that it is a reliable and independent clinical factor. Therefore, we made nomograms to visually depict the survival rate of GC patients according to some important clinical factors including our risk model.

Conclusion

In a word, our research found that pyrimidine metabolism is dysregulated in GC and established a prognostic model of GC based on genes differentially expressed in pyrimidine metabolism.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02385-x.

Glycolysis-Related Gene Expression Profiling Screen for Prognostic Risk Signature of Pancreatic Ductal Adenocarcinoma

Objective: Pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Although progress has been made in the treatment of PDAC, its prognosis remains unsatisfactory. This study aimed to develop novel prognostic genes related to glycolysis in PDAC and to apply these genes to new risk stratification.

Methods: In this study, based on the Cancer Genome Atlas (TCGA) PAAD cohort, the expression level of glycolysis-related gene at mRNA level in PAAD and its relationship with prognosis were analyzed. Non-negative matrix decomposition (NMF) clustering was used to cluster PDAC patients according to glycolytic genes. Prognostic glycolytic genes, screened by univariate Cox analysis and LASSO regression analysis were established to calculate risk scores. The differentially expressed genes (DEGs) in the high-risk group and the low-risk group were analyzed, and the signal pathway was further enriched to analyze the correlation between glycolysis genes. In addition, based on RNA-seq data, CIBERSORT was used to evaluate the infiltration degree of immune cells in PDAC samples, and ESTIMATE was used to calculate the immune score of the samples.

Results: A total of 319 glycolysis-related genes were retrieved, and all PDAC samples were divided into two clusters by NMF cluster analysis. Survival analysis showed that PDAC patients in cluster 1 had shorter survival time and worse prognosis compared with cluster 2 samples (P < 0.001). A risk prediction model based on 11 glycolysis genes was constructed, according to which patients were divided into two groups, with significantly poorer prognosis in high-risk group than in low-risk group (P < 0.001). Both internal validation and external dataset validation demonstrate good predictive ability of the model (AUC = 0.805, P < 0.001; AUC = 0.763, P < 0.001). Gene aggregation analysis showed that DEGs highly expressed in high-risk group were mainly concentrated in the glycolysis level, immune status, and tumor cell proliferation, etc. In addition, the samples in high-risk group showed immunosuppressed status and infiltrated by relatively more macrophages and less CD8+T cell.

Conclusions: These findings suggested that the gene signature based on glycolysis-related genes had potential diagnostic, therapeutic, and prognostic value for PDAC.

A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

BACKGROUND

Gastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.

METHODS

Weighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.

RESULTS

WGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.

CONCLUSIONS

Our results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

Background

An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated.

Methods

Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300; GSE15459, n = 191; and GSE26901, n = 109). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort (n = 600) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432; GSE84437, n = 431; and TCGA, n = 336). Immune cell infiltration (ICI) was quantified by the CIBERSORT method.

Results

A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort (AUC > 0.7). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC (p < 0.001). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group (p < 0.001), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor (p = 0.011). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score (p = 0.00085). The patients' risk score increased with the progression of the clinicopathological stage.

Conclusion

In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

Identification of a metabolic-related gene signature predicting the overall survival for patients with stomach adenocarcinoma

Background

The reprogramming of energy metabolism and consistently altered metabolic genes are new features of cancer, and their prognostic roles remain to be further studied in stomach adenocarcinoma (STAD).

Methods

Messenger RNA (mRNA) expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and the GSE84437 databases from the Gene Expression Omnibus (GEO) database. A univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression model established a novel metabolic signature based on TCGA. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy.

Results

A novel metabolic-related signature (including acylphosphatase 1, RNA polymerase I subunit A, retinol dehydrogenase 12, 5-oxoprolinase, ATP-hydrolyzing, malic enzyme 1, nicotinamide N-methyltransferase, gamma-glutamyl transferase 5, deoxycytidine kinase, galactosidase alpha, DNA polymerase delta 3, glutathione S-transferase alpha 2, N-acyl sphingosine amidohydrolase 1, and N-acyl sphingosine amidohydrolase 1) was identified. In both TCGA and GSE84437, patients in the high-risk group showed significantly poorersurvival than the patients in the low-risk group. A good predictive value was shown by the AUROC and nomogram. Furthermore, gene set enrichment analyses (GSEAs) revealed several significantly enriched pathways, which may help in explaining the underlying mechanisms.

Conclusions

A novel robust metabolic-related signature for STAD prognosis prediction was conducted. The signature may reflect the dysregulated metabolic microenvironment and can provided potential biomarkers for metabolic therapy in STAD.