RPP25 as a Prognostic-Related Biomarker That Correlates With Tumor Metabolism in Glioblastoma

RPP25, a 25 kDa protein subunit of ribonuclease P (RNase P), is a protein-coding gene. Disorders associated with RPP25 include chromosome 15Q24 deletion syndrome and diffuse scleroderma, while systemic sclerosis can be complicated by malignancy. However, the functional role of RPP25 expression in glioblastoma multiforme (GBM) is unclear. In this study, comprehensive bioinformatics analysis was used to evaluate the impact of RPP25 on GBM occurrence and prognosis. Differential analysis of multiple databases showed that RPP25 was commonly highly expressed in multiple cancers but lowly expressed in GBM. Survival prognostic results showed that RPP25 was prognostically relevant in six tumors (CESC, GBM, LAML, LUAD, SKCM, and UVM), but high RPP25 expression was significantly associated with poor patient prognosis except for CESC. Analysis of RPP25 expression in GBM alone revealed that RPP25 was significantly downregulated in GBM compared with normal tissue. Receiver operating characteristic (ROC) combined with Kaplan-Meier (KM) analysis and Cox regression analysis showed that high RPP25 expression was a prognostic risk factor for GBM and had a predictive value for the 1-year, 2-year, and 3-year survival of GBM patients. In addition, the expression of RPP25 was correlated with the level of immune cell infiltration. The gene set enrichment analysis (GSEA) results showed that RPP25 was mainly associated with signalling pathways related to tumor progression and tumor metabolism.

Current Advancements of Plant-Derived Agents for Triple-Negative Breast Cancer Therapy through Deregulating Cancer Cell Functions and Reprogramming Tumor Microenvironment

Triple-negative breast cancer (TNBC) is defined based on the absence of estrogen, progesterone, and human epidermal growth factor receptor 2 receptors. Currently, chemotherapy is the major therapeutic approach for TNBC patients; however, poor prognosis after a standard chemotherapy regimen is still commonplace due to drug resistance. Abnormal tumor metabolism and infiltrated immune or stromal cells in the tumor microenvironment (TME) may orchestrate mammary tumor growth and metastasis or give rise to new subsets of cancer cells resistant to drug treatment. The immunosuppressive mechanisms established in the TME make cancer cell clones invulnerable to immune recognition and killing, and turn immune cells into tumor-supporting cells, hence allowing cancer growth and dissemination. Phytochemicals with the potential to change the tumor metabolism or reprogram the TME may provide opportunities to suppress cancer metastasis and/or overcome chemoresistance. Furthermore, phytochemical intervention that reprograms the TME away from favoring immunoevasion and instead towards immunosurveillance may prevent TNBC metastasis and help improve the efficacy of combination therapies as phyto-adjuvants to combat drug-resistant TNBC. In this review, we summarize current findings on selected bioactive plant-derived natural products in preclinical mouse models and/or clinical trials with focus on their immunomodulatory mechanisms in the TME and their roles in regulating tumor metabolism for TNBC prevention or therapy.

Recent Metabolomics Analysis in Tumor Metabolism Reprogramming

Metabolic reprogramming has been suggested as a hallmark of cancer progression. Metabolomic analysis of various metabolic profiles represents a powerful and technically feasible method to monitor dynamic changes in tumor metabolism and response to treatment over the course of the disease. To date, numerous original studies have highlighted the application of metabolomics to various aspects of tumor metabolic reprogramming research. In this review, we summarize how metabolomics techniques can help understand the effects that changes in the metabolic profile of the tumor microenvironment on the three major metabolic pathways of tumors. Various non-invasive biofluids are available that produce accurate and useful clinical information on tumor metabolism to identify early biomarkers of tumor development. Similarly, metabolomics can predict individual metabolic differences in response to tumor drugs, assess drug efficacy, and monitor drug resistance. On this basis, we also discuss the application of stable isotope tracer technology as a method for the study of tumor metabolism, which enables the tracking of metabolite activity in the body and deep metabolic pathways. We summarize the multifaceted application of metabolomics in cancer metabolic reprogramming to reveal its important role in cancer development and treatment.

Metabolic characterization and metabolism-score of tumor to predict the prognosis in prostate cancer

Tumor metabolism patterns have been reported to be associated with the prognosis of many cancers. However, the metabolic mechanisms underlying prostate cancer (PCa) remain unknown. This study aimed to explore the metabolic characteristics of PCa. First, we downloaded mRNA expression data and clinical information of PCa samples from multiple databases and quantified the metabolic pathway activity level using single-sample gene set enrichment analysis (ssGSEA). Through unsupervised clustering and principal component analyses, we explored metabolic characteristics and constructed a metabolic score for PCa. Then, we independently validated the prognostic value of our metabolic score and the nomogram based on the metabolic score in multiple databases. Next, we found the metabolic score to be closely related to the tumor microenvironment and DNA mutation using multi-omics data and ssGSEA. Finally, we found different features of drug sensitivity in PCa patients in the high/low metabolic score groups. In total, 1232 samples were analyzed in the present study. Overall, an improved understanding of tumor metabolism through the characterization of metabolic clusters and metabolic score may help clinicians predict prognosis and aid the development of more personalized anti-tumor therapeutic strategies for PCa.

DNA Methylation Modification Regulator-Mediated Molecular Clusters and Tumor Metabolic Characterization in Prostate Cancer

Background. An increasing number of studies have indicated a close link between DNA methylation and tumor metabolism. However, the overall influence of DNA methylation on tumor metabolic characteristics in prostate cancer (PCa) remains unclear. Methods. We first explored the subtypes of DNA methylation modification regulators and tumor metabolic features of 1,205 PCa samples using clustering analysis and gene set variation analysis based on the mRNA levels of DNA methylation modification regulators. A DNA methylation-related score (DMS) was calculated using principal component analysis and the DNA methylation modification-related gene signatures to quantify DNA methylation characteristics. We then performed a meta-analysis to identify the hazard ratio of DMS in the six cohorts. In addition, a nomogram was drawn using univariate and multivariate Cox analyses based on the DMS and clinical variables. Finally, a drug sensitivity analysis of the DMS was performed based on the genomics of drug sensitivity in cancer datasets. Results. Three PCa clusters showing different DNA methylation modification patterns and tumor metabolic features were identified. A DMS system was established to quantify the characteristics of DNA methylation modification. PCa samples showed a differential metabolic landscape between the high and low DMS groups. The prognostic value of the DMS and nomogram was independently validated in multiple cohorts. A high DMS was associated with increases in the tumor mutation burden, copy number variation, and microsatellite instability; high tumor heterogeneity; and poor prognosis. Finally, DMS was closely related to different types of antitumor treatment. Conclusion. Improving the understanding of tumor metabolism by characterizing DNA methylation modification patterns and using the DMS may help clinicians predict prognosis and aid in more personalized antitumor therapy strategies for PCa.

NIMG-16. DEEP LEARNING SUPER-RESOLUTION MR SPECTROSCOPIC IMAGING TO MAP TUMOR METABOLISM IN MUTANT IDH GLIOMA PATIENTS

Metabolic imaging can map spatially abnormal molecular pathways with higher specificity for cancer compared to anatomical imaging. However, acquiring high resolution metabolic maps similar to anatomical MRI is challenging in patients due to low metabolite concentrations, and alternative approaches that increase resolution by post-acquisition image processing can mitigate this limitation. We developed deep learning super-resolution MR spectroscopic imaging (MRSI) to map tumor metabolism in patients with mutant IDH glioma. We used a generative adversarial network (GAN) architecture comprised of a UNet neural network as the generator network and a discriminator network for adversarial training. For initial training we simulated a large data set of 9600 images with realistic quality for acquired MRSI to effectively train the deep learning model to upsample by a factor of four. Two types of training were performed: 1) using only the MRSI data, and 2) using MRSI and prior information from anatomical MRI to further enhance structural details. The performance of super-resolution methods was evaluated by peak SNR (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). After training on simulations, GAN was evaluated on measured MRSI metabolic maps acquired with resolution 5.2×5.2 mm2 and upsampled to 1.3×1.3 mm2. The GAN trained only on MRSI achieved PSNR = 27.94, SSIM = 0.88, FSIM = 0.89. Using prior anatomical MRI improved GAN performance to PSNR = 30.75, SSIM = 0.90, FSIM = 0.92. In the patient measured data, GAN super-resolution metabolic images provided clearer tumor margins and made apparent the tumor metabolic heterogeneity. Compared to conventional image interpolation such as bicubic or total variation, deep learning methods provided sharper edges and less blurring of structural details. Our results indicate that the proposed deep learning method is effective in enhancing the spatial resolution of metabolite maps which may better guide treatment in mutant IDH glioma patients.

Avenues of research in dietary interventions to target tumor metabolism in osteosarcoma

Osteosarcoma (OS) is the most frequent primary bone cancer, affecting mostly children and adolescents. Although much progress has been made throughout the years towards treating primary OS, the 5-year survival rate for metastatic OS has remained at only 20% for the last 30 years. Therefore, more efficient treatments are needed. Recent studies have shown that tumor metabolism displays a unique behavior, and plays important roles in tumor growth and metastasis, making it an attractive potential target for novel therapies. While normal cells typically fuel the oxidative phosphorylation (OXPHOS) pathway with the products of glycolysis, cancer cells acquire a plastic metabolism, uncoupling these two pathways. This allows them to obtain building blocks for proliferation from glycolytic intermediates and ATP from OXPHOS. One way to target the metabolism of cancer cells is through dietary interventions. However, while some diets have shown anticancer effects against certain tumor types in preclinical studies, as of yet none have been tested to treat OS. Here we review the features of tumor metabolism, in general and about OS, and propose avenues of research in dietary intervention, discussing strategies that could potentially be effective to target OS metabolism.

Noncoding RNAs link metabolic reprogramming to immune microenvironment in cancers

Altered metabolic patterns in tumor cells not only meet their own growth requirements but also shape an immunosuppressive microenvironment through multiple mechanisms. Noncoding RNAs constitute approximately 60% of the transcriptional output of human cells and have been shown to regulate numerous cellular processes under developmental and pathological conditions. Given their extensive action mechanisms based on motif recognition patterns, noncoding RNAs may serve as hinges bridging metabolic activity and immune responses. Indeed, recent studies have shown that microRNAs, long noncoding RNAs and circRNAs are widely involved in tumor metabolic rewiring, immune cell infiltration and function. Hence, we summarized existing knowledge of the role of noncoding RNAs in the remodeling of tumor metabolism and the immune microenvironment, and notably, we established the TIMELnc manual, which is a free and public manual for researchers to identify pivotal lncRNAs that are simultaneously correlated with tumor metabolism and immune cell infiltration based on a bioinformatic approach.