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

Identification and validation of a metabolism-related gene signature for predicting the prognosis of paediatric medulloblastoma

Medulloblastoma (MB) is a malignant brain tumour that is highly common in children and has a tendency to spread to the brain and spinal cord. MB is thought to be a metabolically driven brain tumour. Understanding tumour cell metabolic patterns and characteristics can provide a promising foundation for understanding MB pathogenesis and developing treatments. Here, by analysing RNA-seq data of MB samples from the Gene Expression Omnibus (GEO) database, 12 differentially expressed metabolic-related genes (DE-MRGs) were chosen for the construction of a predictive risk score model for MB. This model demonstrated outstanding accuracy in predicting the outcomes of MB patients and served as a standalone predictor. An evaluation of functional enrichment revealed that the risk score showed enrichment in pathways related to cancer promotion and the immune response. In addition, a high risk score was an independent poor prognostic factor for MB in patients with different ages, sexes, metastasis stages and subgroups (SHH and Group 4). Consistently, the metabolic enzyme ornithine decarboxylase (ODC1) was upregulated in MB patients with poor survival time. Inhibition of ODC1 in primary and metastatic MB cell lines decreased cell proliferation, migration and invasion but increased immune infiltration. This study could aid in identifying metabolic targets for MB as well as optimizing risk stratification systems and individual treatment plans for MB patients via the use of a metabolism-related gene prognostic risk score signature.

Comprehensive multi-omics analysis of tryptophan metabolism-related gene expression signature to predict prognosis in gastric cancer

Introduction: The 5-year survival of gastric cancer (GC) patients with advanced stage remains poor. Some evidence has indicated that tryptophan metabolism may induce cancer progression through immunosuppressive responses and promote the malignancy of cancer cells. The role of tryptophan and its metabolism should be explored for an in-depth understanding of molecular mechanisms during GC development. Material and methods: We utilized the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset to screen tryptophan metabolism-associated genes via single sample gene set enrichment analysis (ssGSEA) and correlation analysis. Consensus clustering analysis was employed to construct different molecular subtypes. Most common differentially expressed genes (DEGs) were determined from the molecular subtypes. Univariate cox analysis as well as lasso were performed to establish a tryptophan metabolism-associated gene signature. Gene Set Enrichment Analysis (GSEA) was utilized to evaluate signaling pathways. ESTIMATE, ssGSEA, and TIDE were used for the evaluation of the gastric tumor microenvironment. Results: Two tryptophan metabolism-associated gene molecular subtypes were constructed. Compared to the C2 subtype, the C1 subtype showed better prognosis with increased CD4 positive memory T cells as well as activated dendritic cells (DCs) infiltration and suppressed M2-phenotype macrophages inside the tumor microenvironment. The immune checkpoint was downregulated in the C1 subtype. A total of eight key genes, EFNA3, GPX3, RGS2, CXCR4, SGCE, ADH4, CST2, and GPC3, were screened for the establishment of a prognostic risk model. Conclusion: This study concluded that the tryptophan metabolism-associated genes can be applied in GC prognostic prediction. The risk model established in the current study was highly accurate in GC survival prediction.

Constructing a novel mitochondrial-related gene signature for evaluating the tumor immune microenvironment and predicting survival in stomach adenocarcinoma

BACKGROUND

The incidence and mortality of gastric cancer ranks fifth and fourth worldwide among all malignancies, respectively. Accumulating evidences have revealed the close relationship between mitochondrial dysfunction and the initiation and progression of stomach cancer. However, rare prognostic models for mitochondrial-related gene risk have been built up in stomach cancer.

METHODS

In current study, the expression and prognostic value of mitochondrial-related genes in stomach adenocarcinoma (STAD) patients were systematically analyzed to establish a mitochondrial-related risk model based on available TCGA and GEO databases. The tumor microenvironment (TME), immune cell infiltration, tumor mutation burden, and drug sensitivity of gastric adenocarcinoma patients were also investigated using R language, GraphPad Prism 8 and online databases.

RESULTS

We established a mitochondrial-related risk prognostic model including NOX4, ALDH3A2, FKBP10 and MAOA and validated its predictive power. This risk model indicated that the immune cell infiltration in high-risk group was significantly different from that in the low-risk group. Besides, the risk score was closely related to TME signature genes and immune checkpoint molecules, suggesting that the immunosuppressive tumor microenvironment might lead to poor prognosis in high-risk groups. Moreover, TIDE analysis demonstrated that combined analysis of risk score and immune score, or stromal score, or microsatellite status could more effectively predict the benefit of immunotherapy in STAD patients with different stratifications. Finally, rapamycin, PD-0325901 and dasatinib were found to be more effective for patients in the high-risk group, whereas AZD7762, CEP-701 and methotrexate were predicted to be more effective for patients in the low-risk group.

CONCLUSIONS

Our results suggest that the mitochondrial-related risk model could be a reliable prognostic biomarker for personalized treatment of STAD patients.

A prognostic 15-gene model based on differentially expressed genes among metabolic subtypes in diffuse large B-cell lymphoma

The outcomes of patients with diffuse large B-cell lymphoma (DLBCL) vary widely, and about 40% of them could not be cured by the standard first-line treatment, R-CHOP, which could be due to the high heterogeneity of DLBCL. Here, we aim to construct a prognostic model based on the genetic signature of metabolic heterogeneity of DLBCL to explore therapeutic strategies for DLBCL patients. Clinical and transcriptomic data of one training and four validation cohorts of DLBCL were obtained from the GEO database. Metabolic subtypes were identified by PAM clustering of 1,916 metabolic genes in the 7 major metabolic pathways in the training cohort. DEGs among the metabolic clusters were then analyzed. In total, 108 prognosis-related DEGs were identified. Through univariable Cox and LASSO regression analyses, 15 DEGs were used to construct a risk score model. The overall survival (OS) and progression-free survival (PFS) of patients with high risk were significantly worse than those with low risk (OS: HR 2.86, 95%CI 2.04-4.01, p < 0.001; PFS: HR 2.42, 95% CI 1.77-3.31, p < 0.001). This model was also associated with OS in the four independent validation datasets (GSE10846: HR 1.65, p = 0.002; GSE53786: HR 2.05, p = 0.02; GSE87371: HR 1.85, p = 0.027; GSE23051: HR 6.16, p = 0.007) and PFS in the two validation datasets (GSE87371: HR 1.67, p = 0.033; GSE23051: HR 2.74, p = 0.049). Multivariable Cox analysis showed that in all datasets, the risk model could predict OS independent of clinical prognosis factors (p < 0.05). Compared with the high-risk group, patients in the low-risk group predictively respond to R-CHOP (p = 0.0042), PI3K inhibitor (p < 0.05), and proteasome inhibitor (p < 0.05). Therefore, in this study, we developed a signature model of 15 DEGs among 3 metabolic subtypes, which could predict survival and drug sensitivity in DLBCL patients.

Identifying a Novel Endoplasmic Reticulum-Related Prognostic Model for Hepatocellular Carcinomas

From the standpoint of the ER (endoplasmic reticulum), we were interested in identifying hub genes that impact clinical prognosis for HCC (hepatocellular carcinoma) patients and developing an ER-related prognostic model. Using TCGA-LIHC (The Cancer Genome Atlas-Liver Hepatocellular Carcinoma) and GSE14520 datasets, we conducted a series of analyses, which included differential gene screening, clinical prognostic analysis, Lasso regression, nomogram prediction, tumour clustering, gene functional enrichment, and tumour infiltration of immune cells. Following our screening for ER-related genes ( $n=1975$ ), we conducted a Lasso regression model to obtain five hub genes, KPNA2, FMO3, SPP1, KIF2C, and LPCAT1, using TCGA-LIHC as a training set. According to risk scores, HCC samples within either the TCGG-LIHC or GSE14520 cohort were categorized into high- and low-risk groups. Compared to the high-risk group of HCC patients, patients in the low-risk group had a better prognosis of OS (overall survival) or RFS (relapse-free survival). For TCGA-LIHC training set, with the factors of risk score, stage, age, and sex, we plotted a nomogram for 1-, 3-, and 5-year survival predictions. Our model demonstrated better clinical validity in both TCGA-LIHC and GSE14520 cohorts. Additionally, events related to biological enzyme activity, biological metabolic processes, or the cell cycle were associated with the prognostic risk of ER. Furthermore, two HCC prognosis-associated tumour clusters were identified by ER hub gene-based consensus clustering. Our findings indicated a link between ER prognostic signature-related high/low risk and tumour infiltration levels of several immune cells, such as “macrophages M2/M0” and “regulatory T cells (Tregs).” Overall, we developed a novel ER-related clinical prognostic model for HCC patients.

Identification of survival-related metabolic genes and a novel gene signature predicting the overall survival for patients with uveal melanoma

INTRODUCTION

Uveal melanoma (UM) is the most common primary intraocular malignancy among adults. Altered metabolism has been shown to contributes to the development of cancer closely, but the prognostic role of metabolism in UM remains to be explored. This study aimed to construct a metabolic-related signature for UM.

METHOD

We collected the mRNA sequencing data and corresponding clinical information from The Cancer Genome Atlas and Gene Expression Omnibus databases. A univariate Cox regression analysis, the Lasso-penalized Cox regression analysis and multivariate Cox regression analyzes were used to construct a metabolic signature based on TCGA. The time-dependent ROC and Kaplan-Meier survival curves were calculated to validate the prognostic ability of the signature. The immune-related features and mutation profile were characterized by CIBERSORT and maftools between high- and low risk groups. Result： A novel metabolic-related signature (risk score= -0.246*SLC25A38-0.50186*ABCA12 +0.032*CA12 +0.086*SYNJ2) was constructed to predict the prognosis of UM patients. In TCGA and GSE22138, the signature had high sensitivity and specificity in predicting the prognosis of UM patients (survival probability; P＜0.0001,P=0.012）. GO pathway enrichment analysis and GSEA were used to discriminate several significantly enriched metabolism-related pathways, including channel activity and passive transmembrane transporter activity, which may reveal the underlying mechanisms. The high-risk group had more immune cell infiltration and greater distribution of BAP1 mutations. Conclusion： Our study developed a robust metabolic-gene signature based on TCGA to predict the prognosis of UM patients. The signature indicates a dysregulated metabolic microenvironment and provides new metabolic biomarkers and therapeutic targets for UM patients.

Complex roles of nicotinamide N-methyltransferase in cancer progression

Nicotinamide N-methyltransferase (NNMT) is an intracellular methyltransferase, catalyzing the N-methylation of nicotinamide (NAM) to form 1-methylnicotinamide (1-MNAM), in which S-adenosyl-l-methionine (SAM) is the methyl donor. High expression of NNMT can alter cellular NAM and SAM levels, which in turn, affects nicotinamide adenine dinucleotide (NAD⁺)-dependent redox reactions and signaling pathways, and remodels cellular epigenetic states. Studies have revealed that NNMT plays critical roles in the occurrence and development of various cancers, and analysis of NNMT expression levels in different cancers from The Cancer Genome Atlas (TCGA) dataset indicated that NNMT might be a potential biomarker and therapeutic target for tumor diagnosis and treatment. This review provides a comprehensive understanding of recent advances on NNMT functions in different tumors and deciphers the complex roles of NNMT in cancer progression.

Comprehensive Analysis of the Prognostic Value and Immune Function of Immune Checkpoints in Stomach Adenocarcinoma

Background

Stomach adenocarcinoma (STAD) is one of the most prevalent malignances and ranks fifth in incidence and third in the cancer-related deaths among all malignances. The prognosis of STAD is poor. Immunotherapy based on immune checkpoint blockade is ever-increasingly suggested as the most promising therapy strategy for STAD. However, the prognosis and therapy value of immune checkpoints in STAD is far from clarified.

Methods

In our study, bioinformatics methods were performed to explore the expression and prognosis value of immune checkpoints in STAD and their association with immune infiltration. qRT-PCR was performed to verify our result.

Results

Most of the immune checkpoints were upregulated in STAD. There were lots of genetic mutations among immune checkpoints in STAD, including missense_mutation, frame_shift_del et al. Interestingly, most of immune checkpoints were associated with drug sensitivity and drug resistance. Moreover, CD274, PVR, LGALS9, ICOSLG and CD70 were associated with the overall survival, post progression survival and first progression in STAD. The univariate and multivariate analysis revealed that CD70, ICOSLG, age, pTNM stage, and radiation therapy were independent factors affecting the prognosis of STAD patients. The expression of ICOSLG and CD70 was correlated with immune cells as well as immune biomarkers, including CD8+ T cells, CD4+ T cells, macrophage, neutrophils and dendritic cells.

Conclusion

All in all, our study performed a comprehensive analysis of the prognostic value and immune function of immune checkpoints in STAD, and our result suggested that immune checkpoint ICOSLG and CD70 serve as prognostic biomarkers and associate with immune infiltration in STAD.

The circadian clock is associated with prognosis and immune infiltration in stomach adenocarcinoma

Background: Stomach adenocarcinoma (STAD) is one of the most prevalent malignances and ranks fifth in incidence and third in cancer-related death among all malignances. The prognosis of STAD is poor. The circadian clock is regulated by interlocked transcriptional-translational feedback loops that orchestrate circadian rhythms in some biological processes, including the immune response and metabolism. However, the association between core circadian clock genes and STAD patient prognosis is unclear.

Materials and Methods: In our study, bioinformatics methods were performed to explore the expression and prognostic value of core circadian clock genes in STAD and their association with immune infiltration.

Results: The mRNA levels of CLOCK, CRY1 and NR1D1 were upregulated, while the mRNA levels of CRY2, PER1, PER3 and RORA were downregulated in STAD tissues compared with normal tissues. Core circadian clock genes exert promoting or inhibiting effects on certain cancer-related hallmark pathways, including the DNA damage response, cell cycle, apoptosis and RAS/MAPK pathways. Moreover, core circadian clock genes were linked to drug sensitivity or drug resistance. Prognosis analysis revealed that high expression of PER1 and NR1D1 was associated with poor overall survival, progression-free survival, and disease-free survival rates in STAD patients. Validation analysis further confirmed our result. Immune infiltration analysis demonstrated that the expression of ICOSLG and CD70 was significantly correlated with immune cells, immune biomarkers, chemokines and their receptors.

Conclusions: Our results suggest that NR1D1 and PER1 are prognostic biomarkers and are associated with immune infiltration in STAD.

A preclinical report of a cobimetinib-inspired novel anticancer small-molecule scaffold of isoflavones, NSC777213, for targeting PI3K/AKT/mTOR/MEK in multiple cancers

The phosphatidylinositol 3-kinase (PI3K)/protein kinase B/mammalian target of rapamycin (mTOR) and mitogen-activated protein kinase kinase/extracellular signal-regulated kinase (MEK/ERK) signaling pathways are critical for normal human physiology, and any alteration in their regulation leads to several human cancers. These pathways are well interconnected and share a survival mechanism for escaping the depressant effect of antagonists. Therefore, novel small molecules capable of targeting both pathways with minimal or no toxicity are better alternatives to current drugs, which are disadvantaged by their accompanying resistance and toxicity. In this study, we demonstrate that the PI3K/AKT/mTOR/MEK is a crucial oncoimmune signature in multiple cancers. Moreover, we describe NSC777213, a novel isoflavone core and cobimetinib-inspired small molecule, which exhibit both antiproliferative activities against all panels of NCI60 human tumor cell lines (except COLO205 and HT29) and a selective cytotoxic preference for melanoma, non-small-cell lung cancer (NSCLC), brain, renal, and ovarian cancer cell lines. Notably, for NSC777213 treatment, chemoresistant ovarian cancer cell lines, including SK-OV-3, OVCAR-3, OVCAR-4, and NCI/ADR-RES, exhibited a higher antiproliferative sensitivity (total growth inhibition (TGI) = 7.62-31.50 µM) than did the parental cell lines OVCAR-8 and IGROV1 (TGI > 100 µM). NSC777213 had a mechanistic correlation with clinical inhibitors of PI3K/AKT/mTOR/MEK. NSC777213 demonstrates robust binding interactions and higher affinities for AKT and mTOR than did isoflavone, and also demonstrate a higher affinity for human MEK-1 kinase than some MEK inhibitors under clinical developments. In addition, treatment of U251 and U87MG cells with NSC777213 significantly downregulated the expression levels of the total and phosphorylated forms of PI3K/AKT/mTOR/MEK. Our study suggests that NSC777213 is a promising PI3K/AKT/mTOR/MEK inhibitor for further preclinical and clinical evaluation as a chemotherapeutic agent, particularly for the treatment of NSCLC, melanoma, and brain, renal, and ovarian cancers.