Noncoding RNAs in the Glycolysis of Ovarian Cancer

Infrared Spectroscopy in Gynecological Oncology: A Comprehensive Review of Diagnostic Potentials and Challenges

The early detection of gynecological cancers, which is critical for improving patient survival rates, is challenging because of the vague early symptoms and the diagnostic limitations of current approaches. This comprehensive review delves into the game-changing potential of infrared (IR) spectroscopy, a noninvasive technology used to transform the landscape of cancer diagnosis in gynecology. By collecting the distinctive vibrational frequencies of chemical bonds inside tissue samples, Fourier-transform infrared (FTIR) spectroscopy provides a 'molecular fingerprint' that outperforms existing diagnostic approaches. We highlight significant advances in this field, particularly the identification of discrete biomarker bands in the mid- and near-IR spectra. Proteins, lipids, carbohydrates, and nucleic acids exhibited different absorption patterns. These spectral signatures not only serve to distinguish between malignant and benign diseases, but also provide additional information regarding the cellular changes associated with cancer. To underscore the practical consequences of these findings, we examined studies in which IR spectroscopy demonstrated exceptional diagnostic accuracy. This review supports the use of IR spectroscopy in normal clinical practice, emphasizing its capacity to detect and comprehend the intricate molecular underpinnings of gynecological cancers.

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

BACKGROUND

Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis.

RESULTS

Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples.

CONCLUSION

A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

CircRNA-regulated glucose metabolism in ovarian cancer: an emerging landscape for therapeutic intervention

Ovarian cancer (OC) has the highest mortality rate among female reproductive system tumours, with limited efficacy of traditional treatments and 5-year survival rates that rarely exceed 40%. Circular RNA (circRNA) is a stable endogenous circular RNA that typically regulates protein expression by binding to downstream miRNA. It has been demonstrated that circRNAs play an important role in the proliferation, migration, and glucose metabolism (such as the Warburg effect) of OC and can regulate the expression of glucose metabolism-related proteins such as GLUT1 and HK2, promoting anaerobic glycolysis of cancer cells, increasing glucose uptake and ATP production, and affecting energy supply and biosynthetic substances to support tumour growth and invasion. This review summarises the formation and characteristics of circRNAs and focuses on their role in regulating glucose metabolism in OC cells and their potential therapeutic value, providing insights for identifying new therapeutic targets.

Insights into the tumor-stromal-immune cell metabolism cross talk in ovarian cancer

The ovarian cancer tumor microenvironment (TME) consists of a constellation of abundant cellular components, extracellular matrix, and soluble factors. Soluble factors, such as cytokines, chemokines, structural proteins, extracellular vesicles, and metabolites, are critical means of noncontact cellular communication acting as messengers to convey pro- or antitumorigenic signals. Vast advancements have been made in our understanding of how cancer cells adapt their metabolism to meet environmental demands and utilize these adaptations to promote survival, metastasis, and therapeutic resistance. The stromal TME contribution to this metabolic rewiring has been relatively underexplored, particularly in ovarian cancer. Thus, metabolic activity alterations in the TME hold promise for further study and potential therapeutic exploitation. In this review, we focus on the cellular components of the TME with emphasis on 1) metabolic signatures of ovarian cancer; 2) understanding the stromal cell network and their metabolic cross talk with tumor cells; and 3) how stromal and tumor cell metabolites alter intratumoral immune cell metabolism and function. Together, these elements provide insight into the metabolic influence of the TME and emphasize the importance of understanding how metabolic performance drives cancer progression.

MiR-19a-3p Promotes Aerobic Glycolysis in Ovarian Cancer Cells via IGFBP3/PI3K/AKT Pathway

Aerobic glycolysis is a prominent feature of cancer. Here, we reported that miR-19a-3p promotes aerobic glycolysis in ovarian cancer cells SKVO3 and ES-2 by increased production of ATP, lactic acid, extracellular acidification (ECAR), and increased expression of PKM2, LDHA, GLUT1 and GLUT3. Further study showed that over-expression of IGFBP3, the target of miR-19a-3p, decreases aerobic glycolysis in ovarian cancer cells, while knockdown of IGFBP3 expression increases aerobic glycolysis. The rescue assay suggested that miR-19a-3p promotes aerobic glycolysis in ovarian cancer cells through targeting IGFBP3. Moreover, over-expression of miR-19a-3p or silencing of IGFBP3 expression promoted activation of AKT, which is important for aerobic glycolysis in cancer cells, indicating that miR-19a-3p promotes aerobic glycolysis in ovarian cancer cells through the IGFBP3/PI3K/AKT pathway. This suggests that miR-19a-3p and IGFBP3 may serve as potential treatment targets of ovarian cancer.

Advances in peptides encoded by non-coding RNAs: A cargo in exosome

The metastasis of malignant tumors determines patient prognosis. This is the main reason for the poor prognosis of patients with cancer and the most challenging aspect of treating malignant tumors. Therefore, it is important to identify early tumor markers and molecules that can predict patient prognosis. However, there are currently no molecular markers with good clinical accuracy and specificity. Many non-coding RNA (ncRNAs)have been identified, which can regulate the process of tumor development at multiple levels. Interestingly, some ncRNAs are translated to produce functional peptides. Exosomes act as signal carriers, are encapsulated in nucleic acids and proteins, and play a messenger role in cell-to-cell communication. Recent studies have identified exosome peptides with potential diagnostic roles. This review aims to provide a theoretical basis for ncRNA-encoded peptides or proteins transported by exosomes and ultimately to provide ideas for further development of new diagnostic and prognostic cancer markers.

Trends in metabolic signaling pathways of tumor drug resistance: A scientometric analysis

Background

Cancer chemotherapy resistance is one of the most critical obstacles in cancer therapy. Since Warburg O first observed alterations in cancer metabolism in the 1950s, people gradually found tumor metabolism pathways play a fundamental role in regulating the response to chemotherapeutic drugs, and the attempts of targeting tumor energetics have shown promising preclinical outcomes in recent years. This study aimed to summarize the knowledge structure and identify emerging trends and potential hotspots in metabolic signaling pathways of tumor drug resistance research.

Methods

Publications related to metabolic signaling pathways of tumor drug resistance published from 1992 to 2022 were retrieved from the Web of Science Core Collection database. The document type was set to articles or reviews with language restriction to English. Two different scientometric software including Citespace and VOS viewer were used to conduct this scientometric analysis.

Results

A total of 2,537 publications including 1,704 articles and 833 reviews were retrieved in the final analysis. The USA made the most contributions to this field. The leading institution was the University of Texas MD Anderson Cancer Center. Avan A was the most productive author, and Hanahan D was the key researcher with the most co-citations, but there is no leader in this field yet. Cancers was the most influential academic journal, and Oncology was the most popular research field. Based on keywords occurrence analysis, these selected keywords could be roughly divided into five main topics: cluster 1 (study of cancer cell apoptosis pathway); cluster 2 (study of resistance mechanisms of different cancer types); cluster 3 (study of cancer stem cells); cluster 4 (study of tumor oxidative stress and inflammation signaling pathways); and cluster 5 (study of autophagy). The keywords burst detection identified several keywords as new research hotspots, including “tumor microenvironment,” “invasion,” and “target”.

Conclusion

Tumor metabolic reprogramming of drug resistance research is advancing rapidly. This study serves as a starting point, providing a thorough overview, the development landscape, and future opportunities in this field.

Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes

Background

The key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs).

Methods

Data of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed.

Results

A total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024).

Conclusions

We established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this prognostic signature is a promising approach for predicting ovarian cancer prognosis and guiding clinical therapy.