Metabolic reprogramming, including enhanced biosynthesis of macromolecules, altered energy metabolism, and maintenance of redox homeostasis, is considered a hallmark of cancer, sustaining cancer cell growth. Multiple signaling pathways, transcription factors and metabolic enzymes participate in the modulation of cancer metabolism and thus, metabolic reprogramming is a highly complex process. Recent studies have observed that ubiquitination and deubiquitination are involved in the regulation of metabolic reprogramming in cancer cells. As one of the most important type of post-translational modifications, ubiquitination is a multistep enzymatic process, involved in diverse cellular biological activities. Dysregulation of ubiquitination and deubiquitination contributes to various disease, including cancer. Here, we discuss the role of ubiquitination and deubiquitination in the regulation of cancer metabolism, which is aimed at highlighting the importance of this post-translational modification in metabolic reprogramming and supporting the development of new therapeutic approaches for cancer treatment.

Multiple studies have shown that cancer‐associated fibroblasts (CAFs) play an important role in tumour progression, including carcinogenesis, invasion, metastasis and the chemoresistance of cancer cells. Immune cells, including macrophages, natural killer cells, dendritic cells and T cells, play a dual role in the tumour microenvironment. Although increasing research has focused on studying interactions between distinct cells in the tumour microenvironment, the complex relationships between CAFs and immune cells remain unclear and need further study. Here, we summarize our current understanding of crosstalk between CAFs and immune cells, which may help clarify their diagnostic and therapeutic value in tumour progression.

The importance of autophagy in polycystic ovary syndrome (PCOS)-related metabolic disorders is increasingly being recognized, but few studies have investigated the role of autophagy in PCOS. Here, transmission electron microscopy demonstrated that autophagy was enhanced in the ovarian tissue from both humans and rats with PCOS. Consistent with this, ovarian granulosa cells from PCOS rats showed increases in the autophagy marker protein light chain 3B (LC3B), whereas levels of the autophagy substrate SQSTM1/p62 were decreased. In addition, the ratio of LC3-II/LC3-I was markedly elevated in human PCOS ovarian tissue compared with normal ovarian tissue. Real-time PCR arrays indicated that 7 and 34 autophagy-related genes were down- and up-regulated in human PCOS , Signal-Net, and regression analysis suggested that there are a wide range of interactions among these 41 genes, and a potential network based on EGFR, ERBB2, FOXO1, MAPK1, NFKB1, IGF1, TP53 and MAPK9 may be responsible for autophagy activation in PCOS. Systematic functional analysis of 41 differential autophagy-related genes indicated that these genes are highly involved in specific cellular processes such as response to stress and stimulus, and are linked to four significant pathways, including the insulin, ERBB, mTOR signaling pathways and protein processing in the endoplasmic reticulum. This study provides evidence for a potential role of autophagy disorders in PCOS in which autophagy may be an important molecular event in the pathogenesis of PCOS.

Background: Ovarian cancer (OC) is the most malignant tumor in the female reproductive system. About 75% of OC in complete remission of clinical symptoms still develop a recurrence. Therefore, searching for new treatment methods plays an important role in improving the prognosis of OC. Methods: We downloaded the MAF files, RNA-seq data and clinical information from the TCGA database. The “maftools” package in R software was used to visualize the OC mutation data. We calculated the tumor mutation burden (TMB) of OC and analyzed its correlation with clinicopathological parameters and prognostic value. Tumor mutation burden related signature model was constructed to predict the overall survival (OS) of OC. Results: The results revealed that there was a statistical correlation between TMB and FIGO stage, grade and tumor residual size of ovarian cancer patients. The Kaplan–Meier curve indicated that a high TMB is associated with better clinical outcomes of OC. The difference analysis indicated 24 upregulated genes and 619 downregulated genes in the high-TMB group compared with the low-TMB group. Besides, the TMBRS model based on five hub genes (RBMS3, PLA2G5, CDH2, AMHR2 and ADAMTS8) was constructed to predict the OS of OC. The ROC curve and validation data sets all revealed that the TMBRS model was reliable in predicting recurrence risk. Immune microenvironment analysis indicated the correlations between TMB and infiltrating immune cells. Conclusions: Our results suggest that TMB plays an important role in the prognosis and guiding immunotherapy of OC. By detecting the TMB of OC, clinicians can more accurately treat patients with immunotherapy, thereby improving their survival rate.