CAD/POLD2 gene expression is associated with poor overall survival and chemoresistance in bladder urothelial carcinoma

The emerging roles of glutamine amidotransferases in metabolism and immune defense

Glutamine amidotransferases (GATs) catalyze the synthesis of nucleotides, amino acids, glycoproteins and an enzyme cofactor, thus serving as key metabolic enzymes for cell proliferation. Carbamoyl-phosphate synthetase, Aspartate transcarbamoylase, and Dihydroorotase (CAD) is a multifunctional enzyme of the GAT family and catalyzes the first three steps of the de novo pyrimidine synthesis. Following our findings that cellular GATs are involved in immune evasion during herpesvirus infection, we discovered that CAD reprograms cellular metabolism to fuel aerobic glycolysis and nucleotide synthesis via deamidating RelA. Deamidated RelA activates the expression of key glycolytic enzymes, rather than that of the inflammatory NF-κB-responsive genes. As such, cancer cells prime RelA for deamidation via up-regulating CAD activity or accumulating RelA mutations. Interestingly, the recently emerged SARS-CoV-2 also activates CAD to couple evasion of inflammatory response to activated nucleotide synthesis. A small molecule inhibitor of CAD depletes nucleotide supply and boosts antiviral inflammatory response, thus greatly reducing SARS-CoV-2 replication. Additionally, we also found that CTP synthase 1 (CTPS1) deamidates interferon (IFN) regulatory factor 3 (IRF3) to mute IFN induction. Our previous studies have implicated phosphoribosyl formylglycinamidine synthase (PFAS) and phosphoribosyl pyrophosphate amidotransferase (PPAT) in deamidating retinoic acid-inducible gene I (RIG-I) and evading dsRNA-induced innate immune defense in herpesvirus infection. Overall, these studies have uncovered an unconventional enzymatic activity of cellular GATs in metabolism and immune defense, offering a molecular link intimately coupling these fundamental biological processes.

Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics

BACKGROUND

Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome.

METHODS

This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database.

RESULTS

The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan-Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making.

CONCLUSION

In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.

POLD2 is activated by E2F1 to promote triple-negative breast cancer proliferation

Triple-negative breast cancer (TNBC) is a highly malignant breast cancer subtype with a poor prognosis. Improved insight into the molecular biology basis of TNBC progression is urgently needed. Herein, we reported that POLD2 was highly expressed in TNBC and patients with high POLD2 expression in their tumors had poor clinical outcomes. In functional studies, knockdown of POLD2 inhibited the proliferation of TNBC. Mechanistically, we revealed that transcription factor E2F1 directly bound to the promoter of POLD2 and regulated its expression in TNBC cells, which in turn contributed to the proliferation of TNBC. Additionally, rescue experiments validated that E2F1-mediated cell proliferation in TNBC was dependent on POLD2. Taken together, our results elucidated a novel mechanism of the E2F1-POLD2 axis in TNBC proliferation, and POLD2 may be a potential therapeutic target for TNBC treatment.

An integrative analysis revealing POLD2 as a tumor suppressive immune protein and prognostic biomarker in pan-cancer

Introduction: POLD2 is an indispensable subunit of DNA polymerase δ, which is responsible for the synthesis of the backward accompanying strand in eukaryotic organisms. Current studies have found an association between POLD2 and the development of a variety of cancers. However, its value in cancer immunotherapy has not been fully established.

Methods: POLD2 expression was analyzed using RNA expression and clinical data from TCGA and GTEx databases. The prognostic impact of POLD2 on tumor patients was analyzed using clinical survival data from TCGA. Gene enrichment analysis was performed using the R package “cluster analyzer” to explore the role of POLD2. We used the TIMER2 database to analyze the relationship between immune cell infiltration and POLD2 expression in TCGA. We downloaded relevant data from TCGA and analyzed the relationship between POLD2 and immune checkpoints, immunosuppressive genes, immune activating genes, chemokines and chemokine receptors.

Results: POLD2 was significantly overexpressed in most tumors compared to normal tissue. High POLD2 expression was significantly associated with advanced tumor stage, significantly shorter overall survival and progression-free survival. Also, we found that POLD2 expression correlated strongly with immunomodulatory genes, and significantly negatively with most immune checkpoints (PD-L1, CTLA4, TIM3, and CD28). Pathway enrichment analysis suggests that low expression of POLD2 promotes immune regulation-related pathways and high expression promotes metabolic and DNA repair-related pathways. Furthermore, tumor microenvironment analysis suggests that high POLD2 expression inhibits infiltration of CD8⁺ T cells and CD4⁺ memory T cells.

Discussion: In conclusion, POLD2 may be a molecular biomarker for pan-cancer prognosis and immunotherapy. It may serve as a potential target for new insights in human tumor prognosis prediction and immunotherapy assessment.

A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers

The main hallmarks of cancer include sustaining proliferative signaling and resisting cell death. We analyzed the genes of the WNT pathway and seven cross-linked pathways that may explain the differences in aggressiveness among cancer types. We divided six cancer types (liver, lung, stomach, kidney, prostate, and thyroid) into classes of high (H) and low (L) aggressiveness considering the TCGA data, and their correlations between Shannon entropy and 5-year overall survival (OS). Then, we used principal component analysis (PCA), a random forest classifier (RFC), and protein-protein interactions (PPI) to find the genes that correlated with aggressiveness. Using PCA, we found GRB2, CTNNB1, SKP1, CSNK2A1, PRKDC, HDAC1, YWHAZ, YWHAB, and PSMD2. Except for PSMD2, the RFC analysis showed a different list, which was CAD, PSMD14, APH1A, PSMD2, SHC1, TMEFF2, PSMD11, H2AFZ, PSMB5, and NOTCH1. Both methods use different algorithmic approaches and have different purposes, which explains the discrepancy between the two gene lists. The key genes of aggressiveness found by PCA were those that maximized the separation of H and L classes according to its third component, which represented 19% of the total variance. By contrast, RFC classified whether the RNA-seq of a tumor sample was of the H or L type. Interestingly, PPIs showed that the genes of PCA and RFC lists were connected neighbors in the PPI signaling network of WNT and cross-linked pathways.

ShRNA-based POLD2 expression knockdown sensitizes glioblastoma to DNA-Damaging therapeutics

Glioblastoma (GBM) has limited therapeutic options. DNA repair mechanisms contribute GBM cells to escape therapies and re-establish tumor growth. Multiple studies have shown that POLD2 plays a critical role in DNA replication, DNA repair and genomic stability. We demonstrate for the first time that POLD2 is highly expressed in human glioma specimens and that expression correlates with poor patient survival. siRNA or shRNA POLD2 inhibited GBM cell proliferation, cell cycle progression, invasiveness, sensitized GBM cells to chemo/radiation-induced cell death and reversed the cytoprotective effects of EGFR signaling. Conversely, forced POLD2 expression was found to induce GBM cell proliferation, colony formation, invasiveness and chemo/radiation resistance. POLD2 expression associated with stem-like cell subsets (CD133⁺ and SSEA-1⁺ cells) and positively correlated with Sox2 expression in clinical specimens. Its expression was induced by Sox2 and inhibited by the forced differentiation of GBM neurospheres. shRNA-POLD2 modestly inhibited GBM neurosphere-derived orthotopic xenografts growth, when combined with radiation, dramatically inhibited xenograft growth in a cooperative fashion. These novel findings identify POLD2 as a new potential therapeutic target for enhancing GBM response to current standard of care therapeutics.

Precision medicine for locally advanced breast cancer: frontiers and challenges in Latin America

Advances in high-throughput technologies and their involvement in the 'omics' of cancer have made possible the identification of hundreds of biomarkers and the development of predictive and prognostic platforms that model the management of cancer from evidence-based medicine to precision medicine. Latin America (LATAM) is a region characterised by fragmented healthcare, high rates of poverty and disparities to access to a basic standard of care not only for cancer but also for other complex diseases. Patients from the public setting cannot afford targeted therapy, the facilities offering genomic platforms are scarce and the use of high-precision radiotherapy is limited to few facilities. Despite the fact that LATAM oncologists are well-trained in the use of genomic platforms and constantly participate in genomic projects, a medical practice based in precision oncology is a great challenge and frequently limited to private practice. In breast cancer, we are waiting for the results of large basket trials to incorporate the detection of actionable mutations to select targeted treatments, in a similar way to the management of lung cancer. On the other hand and paradoxically, in the 'one fit is not for all' era, clinical and genomic studies continue grouping our patients under the single label 'Latin American' or 'Hispanic' despite the different ancestries and genomic backgrounds seen in the region. More regional cancer genomic initiatives and public availability of this data are needed in order to develop more precise oncology in locally advanced breast cancer.

Revisiting the role of dihydroorotate dehydrogenase as a therapeutic target for cancer

Identified as a hallmark of cancer, metabolic reprogramming allows cancer cells to rapidly proliferate, resist chemotherapies, invade, metastasize, and survive a nutrient-deprived microenvironment. Rapidly growing cells depend on sufficient concentrations of nucleotides to sustain proliferation. One enzyme essential for the de novo biosynthesis of pyrimidine-based nucleotides is dihydroorotate dehydrogenase (DHODH), a known therapeutic target for multiple diseases. Brequinar, leflunomide, and teriflunomide, all of which are potent DHODH inhibitors, have been clinically evaluated but failed to receive FDA approval for the treatment of cancer. Inhibition of DHODH depletes intracellular pyrimidine nucleotide pools and results in cell cycle arrest in S-phase, sensitization to current chemotherapies, and differentiation in neural crest cells and acute myeloid leukemia (AML). Furthermore, DHODH is a synthetic lethal susceptibility in several oncogenic backgrounds. Therefore, DHODH-targeted therapy has potential value as part of a combination therapy for the treatment of cancer. In this review, we focus on the de novo pyrimidine biosynthesis pathway as a target for cancer therapy, and in particular, DHODH. In the first part, we provide a comprehensive overview of this pathway and its regulation in cancer. We further describe the relevance of DHODH as a target for cancer therapy using bioinformatic analyses. We then explore the preclinical and clinical results of pharmacological strategies to target the de novo pyrimidine biosynthesis pathway, with an emphasis on DHODH. Finally, we discuss potential strategies to harness DHODH as a target for the treatment of cancer.

Downregulation of miR-638 promotes progression of breast cancer and is associated with prognosis of breast cancer patients

Background

Breast cancer is the most common tumor among women. miR-638 has been demonstrated to play an important role in various cancers.

Purpose

In this study, we aimed to investigate the function and prognostic value of miR-638 in breast cancer.

Methods

Quantitative real-time polymerase chain reaction analysis was used to evaluate the expression of miR-638 in breast cancer tissues and cell lines. The correlation of miR-638 with clinicopathological features was analyzed using the chi-squared test. Kaplan–Meier survival analysis and Cox regression assay were performed to investigate the prognostic value of miR-638 in breast cancer patients. The effects of miR-638 on the biological behavior of breast cancer cells were evaluated using functional assays.

Results

The expression of miR-638 was downregulated in breast cancer tissues and cell lines (all P<0.05). Decreased expression of miR-638 was significantly correlated with lymph node metastasis (P=0.015) and TNM stage (P=0.021). Patients with low miR-638 expression had shorter overall survival compared with those with high levels (Log-rank P=0.025). The miR-638 could be considered as an independent prognostic factor for the patients (HR =0.321, 95% CI =0.117–0.882, P=0.027). Downregulation of miR-638 was capable of promoting cell proliferation, migration, and invasion in MDA-MB-231 and MCF-7 cells.

Conclusion

All the results indicate that miR-638 is a tumor suppressor in breast cancer and is involved in the progression of breast cancer. Thus, it may serve as a prognostic biomarker for breast cancer.