ASMT Regulates Tumor Metastasis Through the Circadian Clock System in Triple-Negative Breast Cancer

RatDEGdb: a knowledge base of differentially expressed genes in the rat as a model object in biomedical research

The animal models used in biomedical research cover virtually every human disease. RatDEGdb, a knowledge base of the differentially expressed genes (DEGs) of the rat as a model object in biomedical research is a collection of published data on gene expression in rat strains simulating arterial hypertension, age-related diseases, psychopathological conditions and other human afflictions. The current release contains information on 25,101 DEGs representing 14,320 unique rat genes that change transcription levels in 21 tissues of 10 genetic rat strains used as models of 11 human diseases based on 45 original scientific papers. RatDEGdb is novel in that, unlike any other biomedical database, it offers the manually curated annotations of DEGs in model rats with the use of independent clinical data on equal changes in the expression of homologous genes revealed in people with pathologies. The rat DEGs put in RatDEGdb were annotated with equal changes in the expression of their human homologs in affected people. In its current release, RatDEGdb contains 94,873 such annotations for 321 human genes in 836 diseases based on 959 original scientific papers found in the current PubMed. RatDEGdb may be interesting first of all to human geneticists, molecular biologists, clinical physicians, genetic advisors as well as experts in biopharmaceutics, bioinformatics and personalized genomics. RatDEGdb is publicly available at https://www.sysbio.ru/RatDEGdb.

Prognostic risk factors of serous ovarian carcinoma based on mesenchymal stem cell phenotype and guidance for therapeutic efficacy

BACKGROUND

Epithelial ovarian cancer is the leading cause of death from gynecologic cancer, in which serous ovarian carcinoma (SOC) is the most common histological subtype. Although PARP inhibitors (PARPi) and antiangiogenics have been accepted as maintenance treatment in SOC, response to immunotherapy of SOC patients is limited.

METHODS

The source of transcriptomic data of SOC was from the Cancer Genome Atlas database and Gene Expression Omnibus. The abundance scores of mesenchymal stem cells (MSC scores) were estimated for each sample by xCell. Weighted correlation network analysis is correlated the significant genes with MSC scores. Based on prognostic risk model construction with Cox regression analysis, patients with SOC were divided into low- and high-risk groups. And distribution of immune cells, immunosuppressors and pro-angiogenic factors in different risk groups was achieved by single-sample gene set enrichment analysis. The risk model of MSC scores was further validated in datasets of immune checkpoint blockade and antiangiogenic therapy. In the experiment, the mRNA expression of prognostic genes related to MSC scores was detected by real-time polymerase chain reaction, while the protein level was evaluated by immunohistochemistry.

RESULTS

Three prognostic genes (PER1, AKAP12 and MMP17) were the constituents of risk model. Patients classified as high-risk exhibited worse prognosis, presented with an immunosuppressive phenotype, and demonstrated high micro-vessel density. Additionally, these patients were insensitive to immunotherapy and would achieve a longer overall survival with antiangiogenesis treatment. The validation experiments showed that the mRNA of PER1, AKAP12, and MMP17 was highly expressed in normal ovarian epithelial cells compared to SOC cell lines and there was a positive correlation between protein levels of PER1, AKAP12 and MMP17 and metastasis in human ovarian serous tumors.

CONCLUSION

This prognostic model established on MSC scores can predict prognosis of patients and provide the guidance for patients receiving immunotherapy and molecular targeted therapy. Because the number of prognostic genes was fewer than other signatures of SOC, it will be easily accessible on clinic.

Major roles of the circadian clock in cancer

Circadian rhythms are natural rhythms that widely exist in all creatures, and regulate the processes and physiological functions of various biochemical reactions. The circadian clock is critical for cancer occurrence and progression. Its function is regulated by metabolic activities, and the expression and transcription of various genes. This review summarizes the composition of the circadian clock; the biological basis for its function; its relationship with, and mechanisms in, cancer; its various functions in different cancers; the effects of anti-tumor treatment; and potential therapeutic targets. Research in this area is expected to advance understanding of circadian locomotor output cycles kaput (CLOCK) and brain and muscle ARNT-like protein 1 (BMAL1) in tumor diseases, and contribute to the development of new anti-tumor treatment strategies.

Identification of tryptophan metabolic gene-related subtypes, development of prognostic models, and characterization of tumor microenvironment infiltration in gliomas

BACKGROUND

Epigenetic regulation and immunotherapy of tumor microenvironment (TME) is a hot topic in recent years. However, the potential value of tryptophan metabolism genes in regulating TME and immunotherapy is still unclear.

MATERIALS AND METHODS

A comprehensive study of glioma patients was carried out based on 40 tryptophan metabolic genes. Subsequently, these prognostic tryptophan metabolic genes are systematically associated with immunological characteristics and immunotherapy. A risk score model was constructed and verified in the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) cohorts to provide guidance for prognosis prediction and immunotherapy of glioma patients.

RESULTS

We described the changes of tryptophan metabolism genes in 966 glioma samples from genetic and transcriptional fields and evaluated their expression patterns from two independent data sets. We identified two different molecular subtypes and found that two subtypes were associated with clinicopathological features, prognosis, TME cell infiltration, and immune checkpoint blockers (ICBs). Then, four genes (IL4I1, CYP1A1, OGDHL, and ASMT) were screened out by univariate and multivariate cox regression analysis of tryptophan metabolism genes, and a risk score model for predicting the overall survival (OS) of glioma patients was constructed. And its predictive ability is verified using the CGGA database. At the same time, we verified the expression of IL4I1, CYP1A1, OGDHL, and ASMT four genes in glioma specimens and cell lines in GES4260 and GES15824. Therefore, we constructed a nomogram to improve the clinical applicability of the risk assessment model. The high risk score group, characterized by increased TMB and immune cell infiltration, was also sensitive to temozolomide immunotherapy. Our comprehensive analysis of tryptophan metabolic genes in gliomas shows that they play a potential role in tumor immune stromal microenvironment, clinicopathological features, and prognosis.

CONCLUSION

Tryptophan metabolism genes play an indispensable role in the complexity, diversity, and prognosis of TME. This risk score model based on tryptophan metabolism gene is a new predictor of clinical prognosis and immunotherapy response of glioma, and guides a more appropriate immunotherapy strategy for glioma patients.

Circadian Clock Genes Act as Diagnostic and Prognostic Biomarkers of Glioma: Clinic Implications for Chronotherapy

Gliomas are the most common primary intracranial tumors and closely related to circadian clock. Due to the high mortality and morbidity of gliomas, exploring novel diagnostic and early prognostic markers is necessary. Circadian clock genes (CCGs) play important roles in regulating the daily oscillation of biological processes and the development of tumor. Therefore, we explored the influences that the oscillations of circadian clock genes (CCGs) on diagnosis and prognosis of gliomas using bioinformatics. In this work, we systematically analyzed the rhythmic expression of CCGs in brain and found that some CCGs had strong rhythmic expression; the expression levels were significantly different between day and night. Four CCGs (ARNTL, NPAS2, CRY2, and DBP) with rhythmic expression were not only identified as differentially expressed genes but also had significant independent prognostic ability in the overall survival of glioma patients and were highly correlated with glioma prognosis in COX analysis. Besides, we found that CCG-based predictive model demonstrated higher predictive accuracy than that of the traditional grade-based model; this new prediction model can greatly improve the accuracy of glioma prognosis. Importantly, based on the four CCGs’ circadian oscillations, we revealed that patients sampled at night had higher predictive ability. This may help detect glioma as early as possible, leading to early cancer intervention. In addition, we explored the mechanism of CCGs affecting the prognosis of glioma. CCGs regulated the cell cycle, DNA damage, Wnt, mTOR, and MAPK signaling pathways. In addition, it also affects prognosis through gene coexpression and immune infiltration. Importantly, ARNTL can rhythmically modulated the cellular sensitivity to clinic drugs, temozolomide. The optimal point of temozolomide administration should be when ARNTL expression is highest, that is, the effect is better at night. In summary, our study provided a basis for optimizing clinical dosing regimens and chronotherapy for glioma. The four key CCGs can serve as potential diagnostic and prognostic biomarkers for glioma patients, and ARNTL also has obvious advantages in the direction of glioma chronotherapy.