Deregulation of Lipid Metabolism: The Critical Factors in Ovarian Cancer

Screening of potential targets and small-molecule drugs related to lipid metabolism in ovarian cancer based on bioinformatics

BACKGROUND

about 70 % of ovarian cancer (OC) patients with postoperative chemotherapy relapse within 2-3 years due to drug resistance and metastasis, and the 5-year survival rate is only about 30 %. Lipid metabolism plays an important role in OC. We try to explore the potential targets and drugs related to lipid metabolism to provide clues for the treatment of OC.

METHODS

the gene expression profiles of OC and normal ovarian tissue samples were obtained from the cancer genome atlas (TCGA) and genotype-tissue expression databases (GTEx). The differentially expressed genes (DEGs) were analyzed. Lipid metabolism related genes (LMRGs) were downloaded from MSigDB database. The DEGs related to lipid metabolism in OC was obtained by intersection. And gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) analyses were performed. The protein-protein interaction (PPI) network of lipid metabolism related DEGs was constructed, and seven algorithms were used to screen core potential target genes. Its expression in OC and prognostic ability were analyzed by Univariate Cox. Cmap database mining OC lipid metabolism related potential small-molecular drugs and docking. CCK8, scratch assay, transwell test and free fatty acid (FFA) assay, fluorescence detection of cellular fatty acid uptake, and the reactivity assay of CPT1A were used to detect the biological effects of drugs on OC cell.Rreverse transcription PCR(RT-qPCR) and WesternBlot were performed to measure the expression of core targets.

RESULTS

437 DEGs related to lipid metabolism of OC were screened. GO and KEGG analysis showed that these DEGs were lipid metabolism, fatty acid metabolism, sphingolipid metabolism, PPAR signal pathway and so on. The PPI network based on lipid metabolism DEGs consists of 301 nodes and 1107 interaction pairs, and 6 core target genes were screened. ROC curve analysis showed that all of the 6 genes could predict the prognosis of OC. Three small molecular drugs Cephaeline, AZD8055 and GSK-1059615 were found by cmap and molecular docking showed that they all had good binding ability to target gene. Cephaeline has the strongest inhibitory effect on SKOV3 cells of OC, and could significantly inhibit cell migration and invasion regulate the mRNA and protein expression of some targets, and inhibit lipid metabolism process in ovarian cancer cells.

CONCLUSION

six OC potential genes related to lipid metabolism were identified and verified, which can be used as potential biomarkers and therapeutic targets to evaluate the prognostic risk of OC patients. In addition, three small-molecular drugs that may be effective in the treatment of OC were unearthed, among which Cephaeline has the most potential. We speculate that Cephaeline may target six genes to inhibit progression of OC by affecting lipid metabolism.

Targeting Mitochondrial Cholesterol Efflux via TCF21/ABCA10 Pathway to Enhance Cisplatin Efficacy in Ovarian Cancer

Cisplatin (DDP) resistance is one of the causes of treatment failure for ovarian cancer (OV). Mitochondrial cholesterol level was reported to be associated with OV chemoresistance. We found that ABCA10, a potential cholesterol transport protein, was highly expressed in ovarian tissues and downregulated in OV tissues. Our study aimed to explore TCF21/ABCA10 axis resistance to DDP therapy in ovarian cancer based on regulating mitochondrial cholesterol efflux. Thirty epithelial ovarian cancer tumors and thirty ovarian tissues from non-cancer patients were collected. Western blot and RT-qPCR were used to measure ABCA10 and TCF21 expression levels in these tissues, as well as in a human ovarian epithelial cell line (IOSE-80), OV cells (A2780 and SKOV3), and DDP-resistant OV cell lines (A2780/DDP and SKOV3/DDP). IOSE-80 cells were also infected with ABCA10 knockdown lentivirus to identify the most effective ABCA10 knockdown plasmid. Lentiviral infection was used to create ABCA10 knockdown, ABCA10 overexpression, and TCF21 overexpression anti-DDP OV cell lines. Cell proliferation was detected by CCK-8 and EDU staining, flow cytometry for apoptosis, MTT for metabolic activity, calcium-induced Cytochrome C release, and mitochondrial matrix swelling for mitochondrial function and Oil Red O staining for lipid accumulation. Cholesterol metabolism was evaluated by measuring mitochondrial cholesterol and cholesterol efflux. Protein concentration was determined using the BCA method. A dual-luciferase reporter assay confirmed TCF21's interaction with ABCA10. ChIP also verified this interaction. The mRNA level (P < 0.01) and protein level (P < 0.001) of ABCA10 were downregulated in cancer tissues of OV patients relative to normal ovarian tissues. Relative to human ovarian epithelial cells, ABCA10 expression was significantly downregulated in OV cells (P < 0.01) and even more significantly downregulated in DDP-resistant OV cells (P < 0.001). Compared to the group treated solely with DDP, the overexpression of ABCA10 significantly inhibited the proliferation of DDP-resistant OV cells (P < 0.01), markedly reduced the staining intensity of EDU in these cells (P < 0.05), and substantially accelerated apoptosis in DDP-resistant OV cells (P < 0.01).Overexpression of ABCA10 further accelerated Cytochrome C expression and mitochondrial matrix swelling in DDP-resistant OV cells compared to the DDP-alone group (P < 0.01). The addition of cholesterol reversed the decrease in lipid accumulation, the decrease in mitochondrial cholesterol levels (P < 0.05), and the increase in cholesterol efflux (P < 0.01) in DDP-resistant OV cells caused by overexpression of ABCA10. The transcription factor TCF21 was bound to the promoter of ABCA10. Overexpression of TCF21 significantly increased ABCA10 expression in DDP-resistant OV cells (P < 0.01) and increased cytochrome C expression in A2780/DDP (P < 0.05) and SKOV3/DDP (P < 0.01) cells, with accelerated mitochondrial matrix swelling in A2780/DDP (P < 0.01) and SKOV3/DDP (P < 0.001) cells, while knockdown of ABCA10 reversed these effects. Our study found that TCF21 boosts ABCA10 expression, which in turn reduces DDP resistance in OV cells by enhancing mitochondrial cholesterol efflux. This mechanism increases the sensitivity of DDP-resistant OV cells to DDP. Our findings will provide new therapeutic targets for the treatment of ovarian cancer.

Lipid metabolites abnormally expressed in pelvic fluid as potential biomarkers for ovarian cancer: A case-control study

BACKGROUND

Ovarian cancer is insidious and usually detected in advanced stages of the disease. As the ovaries are pelvic organs, changes in their pelvic fluid metabolites may be associated with ovarian cancer.

METHODS

Metabolomic changes in the pelvic fluid were detected using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in patients with ovarian cancer, ovarian cysts and uterine fibroids. Area under the curve (AUC) analysis was used to assess the diagnostic performance of lipid metabolites and blood tumor indices. The Pearson correlation algorithm was used to analyze the correlation between clinical characteristics and lipid metabolites in ovarian cancer patients.

RESULTS

There were 24 lipid metabolites significantly changed in the pelvic fluid of ovarian cancer patients (p < 0.05). Palmitoylcarnitine, lipoamide, lipid metabolites, and blood tumor indices (CA15-3 and CA125) showed AUC > 0.8, with palmitoylcarnitine reaching a high of 0.942. In addition, we found that some lipid metabolites were significantly associated with the clinical stage, abdominal water volume, lymphatic metastasis, and recurrence (p < 0.05, r > 0.5).

CONCLUSION

Levels of specific lipid metabolites are potential biomarkers of ovarian cancer and may play a key role in the early diagnosis and prognostic assessment of ovarian cancer.

SIGNIFICANCE

Our results showed that pelvic metabolites, especially some lipid metabolites, play an important role in the diagnosis of ovarian cancer. Meanwhile, partial lipid metabolites were closely associated with the clinical presentation and prognosis of patients with ovarian cancer. We believe that our study makes a significant contribution to the literature because it provides a potential approach that is more effective for ovarian cancer detection.

Novel scoring system incorporating lipoproteins to predict outcomes of epithelial ovarian cancer patients

OBJECTIVE

Epithelial ovarian cancer is the most lethal gynecological malignancy worldwide. While common prognostic factors are identified, the impact of serum lipoproteins remains controversial. This retrospective cohort study aims to investigate the association between specific lipoprotein levels and prognosis.

METHODS

Clinical data of 420 participants with epithelial ovarian cancer registered at Women's Hospital, School of Medicine, Zhejiang University, between January 2014 and April 2021 were included. Cox regression analyses and Kaplan-Meier methods were used to assess prognosis, estimated by hazard ratio (HR) with 95% confidence interval (CI). A novel prognostic model incorporating lipoproteins was developed for evaluating the prognosis. Meta-analysis was applied to assess the impact of low density lipoprotein cholesterol (LDL-C) on prognosis.

RESULTS

Among 420 patients, those in advanced stages exhibited higher low density lipoprotein cholesterol (LDL-C) (p=0.008) and lower high density lipoprotein cholesterol (HDL-C) levels (p<0.001), with no significant differences in total cholesterol or triglyceride levels. Elevated LDL-C level was significantly associated with worse overall survival (HR 1.72; 95% CI 1.15 to 2.58; p=0.010) and progression free survival (HR 1.94; 95% CI 1.46 to 2.58; p<0.001), whereas higher HDL-C level was linked to better overall survival (HR 0.56; 95% CI 0.37 to 0.85; p=0.004) and progression free survival (HR 0.61; 95% CI 0.46 to 0.81; p<0.001). A novel prognostic model, low density lipoprotein cholesterol-high density lipoprotein cholesterol-fibrinogen-lactate dehydrogenase-prealbumin-Fe-stage (LH-FLPFS), was established to enhance prognostic predictive efficacy. The meta-analysis further suggested that higher LDL-C level was associated with worse overall survival (HR 1.82; 95% CI 1.39 to 2.38; p<0.001).

CONCLUSIONS

In this study, preoperative LDL-C and HDL-C levels emerged as potential prognostic factors for ovarian cancer. Establishment of a novel prognostic model, LH-FLPFS, holds promise for significantly improving prognostic predictive efficacy.

Mitochondrial activity related genes of mast cells identify poor prognosis and metastasis of ovarian cancer

The pro-tumorigenic or anti-tumorigenic role of tumor infiltrating mast cells (TIMs) in tumors depends not only on the type of cancer and the degree of tumor progression, but also on their location in the tumor bulk. In our investigation, we employed immunohistochemistry to reveal that the mast cells (MCs) in the tumor stroma are positively correlated with metastasis of ovarian cancer (OC), but not in the tumor parenchyma. To delve deeper into the influence of different culture matrix stiffness on MCs' biological functions within the tumor parenchymal and stromal regions, we conducted a transcriptome analysis of the mouse MC line (P815) cultured in two-dimensional (2D) or three-dimensional (3D) culture system. Further research has found that the softer 3D extracellular matrix stiffness could improve the mitochondrial activity of MCs to promote proliferation by increasing the expression levels of mitochondrial activity-related genes, namely Pet100, atp5md, and Cox7a2. Furthermore, employing LASSO regression analysis, we identified that Pet100 and Cox7a2 were closely associated with the prognosis of OC patients. These two genes were subsequently employed to construct a risk score model, which revealed that the high-risk group model as one of the prognostic factors for OC patients. Additionally, the XCell algorithm analysis showed that the high-risk group displayed a broader spectrum of immune cell infiltrations. Our research revealed that TIMs in the tumor stroma could promote the metastasis of OC, and mitochondrial activity-related proteins Pet100/Cox7a2 can serve as biomarkers for prognostic evaluation of OC.

Immunotherapy and the ovarian cancer microenvironment: Exploring potential strategies for enhanced treatment efficacy

Despite progress in cancer immunotherapy, ovarian cancer (OC) prognosis continues to be disappointing. Recent studies have shed light on how not just tumour cells, but also the complex tumour microenvironment, contribute to this unfavourable outcome of OC immunotherapy. The complexities of the immune microenvironment categorize OC as a 'cold tumour'. Nonetheless, understanding the precise mechanisms through which the microenvironment influences the effectiveness of OC immunotherapy remains an ongoing scientific endeavour. This review primarily aims to dissect the inherent characteristics and behaviours of diverse cells within the immune microenvironment, along with an exploration into its reprogramming and metabolic changes. It is expected that these insights will elucidate the operational dynamics of the immune microenvironment in OC and lay a theoretical groundwork for improving the efficacy of immunotherapy in OC management.

Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer

Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of 'omics' data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.

Hepatocellular carcinoma and lipid metabolism: Novel targets and therapeutic strategies

Hepatocellular carcinoma (HCC) is an increasingly prevalent disease that is associated with high and continually rising mortality rates. Lipid metabolism holds a crucial role in the pathogenesis of HCC, in which abnormalities pertaining to the delicate balance of lipid synthesis, breakdown, and storage, predispose for the pathogenesis of the nonalcoholic fatty liver disease (NAFLD), a disease precursor to HCC. If caught early enough, HCC treatment may be curative. In later stages, treatment is only halting the inevitable outcome of death, boldly prompting for novel drug discovery to provide a fighting chance for this patient population. In this review, we begin by providing a summary of current local and systemic treatments against HCC. From such we discuss hepatic lipid metabolism and highlight novel targets that are ripe for anti-cancer drug discovery. Lastly, we provide a targeted summary of current known risk factors for HCC pathogenesis, providing key insights that will be essential for rationalizing future development of anti-HCC therapeutics.

Interplay between altered metabolism and DNA damage and repair in ovarian cancer

Ovarian cancer is the most lethal gynecological malignancy and is often associated with both DNA repair deficiency and extensive metabolic reprogramming. While still emerging, the interplay between these pathways can affect ovarian cancer phenotypes, including therapeutic resistance to the DNA damaging agents that are standard-of-care for this tumor type. In this review, we will discuss what is currently known about cellular metabolic rewiring in ovarian cancer that may impact DNA damage and repair in addition to highlighting how specific DNA repair proteins also promote metabolic changes. We will also discuss relevant data from other cancers that could be used to inform ovarian cancer therapeutic strategies. Changes in the choice of DNA repair mechanism adopted by ovarian cancer are a major factor in promoting therapeutic resistance. Therefore, the impact of metabolic reprogramming on DNA repair mechanisms in ovarian cancer has major clinical implications for targeted combination therapies for the treatment of this devastating disease.

Unveiling the causal link between metabolic factors and ovarian cancer risk using Mendelian randomization analysis

Background

Metabolic abnormalities are closely tied to the development of ovarian cancer (OC), yet the relationship between anthropometric indicators as risk indicators for metabolic abnormalities and OC lacks consistency.

Method

The Mendelian randomization (MR) approach is a widely used methodology for determining causal relationships. Our study employed summary statistics from the genome-wide association studies (GWAS), and we used inverse variance weighting (IVW) together with MR-Egger and weighted median (WM) supplementary analyses to assess causal relationships between exposure and outcome. Furthermore, additional sensitivity studies, such as leave-one-out analyses and MR-PRESSO were used to assess the stability of the associations.

Result

The IVW findings demonstrated a causal associations between 10 metabolic factors and an increased risk of OC. Including "Basal metabolic rate" (OR= 1.24, P= 6.86×10-4); "Body fat percentage" (OR= 1.22, P= 8.20×10-3); "Hip circumference" (OR= 1.20, P= 5.92×10-4); "Trunk fat mass" (OR= 1.15, P= 1.03×10-2); "Trunk fat percentage" (OR= 1.25, P= 8.55×10-4); "Waist circumference" (OR= 1.23, P= 3.28×10-3); "Weight" (OR= 1.21, P= 9.82×10-4); "Whole body fat mass" (OR= 1.21, P= 4.90×10-4); "Whole body fat-free mass" (OR= 1.19, P= 4.11×10-3) and "Whole body water mass" (OR= 1.21, P= 1.85×10-3).

Conclusion

Several metabolic markers linked to altered fat accumulation and distribution are significantly associated with an increased risk of OC.

Targeting LTA4H facilitates the reshaping of the immune microenvironment mediated by CCL5 and sensitizes ovarian cancer to Cisplatin

Ovarian cancer is the most lethal and aggressive gynecological cancer with a high recurrence rate and is often diagnosed late. In ovarian cancer, multiple metabolic enzymes of lipid metabolism are abnormally expressed, resulting in metabolism disorder. As a characteristic pathway in polyunsaturated fatty acid (PUFA) metabolism, arachidonic acid (AA) metabolism is disturbed in ovarian cancer. Therefore, we established a 10-gene signature model to evaluate the prognostic risk of PUFA-related genes. This 10-gene signature has strong robustness and can play a stable predictive role in datasets of various platforms (TCGA, ICGC, and GSE17260). The high association between the risk subgroups and clinical characteristics indicated a good performance of the model. Our data further indicated that the high expression of LTA4H was positively correlated with poor prognosis in ovarian cancer. Deficiency of LTA4H enhanced sensitivity to Cisplatin and modified the characteristics of immune cell infiltration in ovarian cancer. Additionally, our results indicate that CCL5 was involved in the aberrant metabolism of the AA/LTA4H axis, which contributes to the reduction of tumor-infiltrating CD8+ T cells and immune escape in ovarian cancer. These findings provide new insights into the prognosis and potential target of LTA4H/CCL5 in treating ovarian cancer.

Construction and validation of a hypoxia-related gene signature to predict the prognosis of breast cancer

BACKGROUND

Among the most common forms of cancer worldwide, breast cancer posed a serious threat to women. Recent research revealed a lack of oxygen, known as hypoxia, was crucial in forming breast cancer. This research aimed to create a robust signature with hypoxia-related genes to predict the prognosis of breast cancer patients. The function of hypoxia genes was further studied through cell line experiments.

MATERIALS AND METHODS

In the bioinformatic part, transcriptome and clinical information of breast cancer were obtained from The Cancer Genome Atlas(TCGA). Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. The TCGA filtered data was evenly split, ensuring a 1:1 distribution between the training and testing sets. Prognostic-related DEHRGs were identified through Cox regression. The signature was established through the training set. Then, it was validated using the test set and external validation set GSE131769 from Gene Expression Omnibus (GEO). The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristiccurve. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments.

RESULTS

In the bioinformatic part, 141 up-regulated and 157 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis than higher-risk ones (P < 0.001). The signature was double-validated in the test set and GSE131769 (P = 0.006 and P = 0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancer cells, while the down-regulation of KCNJ11 did not have any functions.

CONCLUSION

Based on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast cancer patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast cancer by hypoxia-related genes.

Targeting endogenous fatty acid synthesis stimulates the migration of ovarian cancer cells to adipocytes and promotes the transport of fatty acids from adipocytes to cancer cells

Despite significant advances in oncology, 1 of 108 female patients succumb to ovarian cancer (OC) each year. Improved novel treatments against this aggressive disease would be a major improvement. The growth of OC cells has been demonstrated to be highly dependent on lipids. OC cells are abundantly present in the abdominal cavity and omentum, the main sites of OC expansion. Accordingly, it has been attempted not only to block the hyperactive synthesis of fatty acids (FAs) in cancer cells, but also to disrupt lipid supply. While either strategy has yielded promising results as monotherapy, the induction of resistance pathways diminishing the anticancer effects is yet conceivable. The endogenous regulation of lipid biosynthesis in OC has been extensively studied. However, the role of stromal cells in the modulation of the effects of anti‑lipogenic drugs has not yet been well documented. The present study thus examined the interaction between OC cells and associated stromal cells, when de novo FA synthesis was blocked. It has recently been revealed by the authors that when FA are provided to OC cells in monoculture, the lipid deficiency induced by pharmacological inhibition of FA synthase (FASN), the key enzyme of endogenous FA synthesis, cannot be compensated through an increased FA uptake by OC cells. In the present study, OC cells were co‑cultured with adipocytes preloaded with fluorescent FA and the effects of FASN‑inhibition on OC homing to adipocytes and the transcellular delivery of fluorescent FA from adipocytes to OC cells were examined. The FASN inhibitors, G28UCM and Fasnall, stimulated the spontaneous migration of A2780 OC cells in a concentration‑dependent manner and stimulated the transfer of FA from adipocytes to OC cells. Similar effects were observed with all types of adipocytes tested. The models applied in the present study demonstrated that co‑cultured cancer‑associated adipocytes may attenuate the anticancer effects of FASN inhibitors by attracting tumor cells and by supplying the cells with FA. This lipid‑mediated dependency may provide a rationale for the design of new treatment approaches for the treatment of OC.

LncRNA RPARP-AS1 promotes the progression of osteosarcoma cells through regulating lipid metabolism

Osteosarcoma (OS) is a highly malignant tumor, and its dysregulated lipid metabolism is associated with tumorigenesis and unfavorable prognosis. Interestingly, long noncoding RNAs (lncRNAs) have emerged as pivotal regulators of lipid metabolism, exerting notable impacts on tumor proliferation. Nevertheless, the involvement of RPARP-AS1, a novel lipid metabolism-associated lncRNA, remains unexplored in the context of OS. This study aims to identify functionally relevant lncRNAs impacting OS proliferation and lipid metabolism and seeks to shed light on the upstream regulatory mechanisms governing lipogenic enzyme activity. Based on comprehensive bioinformatic analysis and the establishment of a risk model, we identified seven lncRNAs significantly associated with clinical characteristics and lipid metabolism-related genes in patients with OS. Among these, RPARP-AS1 was selected for in-depth investigation regarding its roles in OS proliferation and lipid metabolism. Experimental techniques including RT-qPCR, Western blot, cell viability assay, assessment, and quantification of free fatty acids (FFAs) and triglycerides (TGs) were utilized to elucidate the functional significance of RPARP-AS1 in OS cells and validate its effects on lipid metabolism. Manipulation of RPARP-AS1 expression via ectopic expression or siRNA-mediated knockdown led to alterations in epithelial-mesenchymal transition (EMT) and expression of apoptosis-associated proteins, thereby influencing OS cell proliferation and apoptosis. Mechanistically, RPARP-AS1 was found to augment the expression of key lipogenic enzymes (FABP4, MAGL, and SCD1) and potentially modulate the Akt/mTOR pathway, thereby contributing to lipid metabolism (involving alterations in FFA and TG levels) in OS cells. Collectively, our findings establish RPARP-AS1 as a novel oncogene in OS cells and suggest its role in fostering tumor growth through the enhancement of lipid metabolism.

Inside anticancer therapy resistance and metastasis. Focus on CD36

Despite recent advances in targeted cancer therapies, drug resistance remains an important setback in tumor control. Understanding the complex mechanisms involved in both innate and acquired drug resistance represents the first step in discovering novel therapeutic agents. Because of its importance in tumorigenesis, progression, and metastasis, lipid metabolism is increasingly garnering attention. CD36 is a membrane receptor at the top of the signaling cascade that transports lipids. Its expression has been repeatedly presented as an unfavorable prognostic factor for various tumor types, raising the question: could CD36 be a critical factor in cancer treatment resistance? In our review, we set out to explore the most prominent studies on the implication of CD36 in resistance to platinum-based drugs and other adjuvant cancer therapies in solid and haematological neoplasia. Moreover, we provide an overview of the latest anti-CD36 cancer therapies, thus opening new perspectives for future personalized medicine.

Exploring the causal role of multiple metabolites on ovarian cancer: a two sample Mendelian randomization study

BACKGROUND

The mechanisms and risk factors underlying ovarian cancer (OC) remain under investigation, making the identification of new prognostic biomarkers and improved predictive factors critically important. Recently, circulating metabolites have shown potential in predicting survival outcomes and may be associated with the pathogenesis of OC. However, research into their genetic determinants is limited, and there are some inadequacies in understanding the distinct subtypes of OC. In this context, we conducted a Mendelian randomization study aiming to provide evidence for the relationship between genetically determined metabolites (GDMs) and the risk of OC and its subtypes.

METHODS

In this study, we consolidated genetic statistical data of GDMs with OC and its subtypes through a genome-wide association study (GWAS) and conducted a two-sample Mendelian randomization (MR) analysis. The inverse variance weighted (IVW) method served as the primary approach, with MR-Egger and weighted median methods employed for cross-validation to determine whether a causal relationship exists between the metabolites and OC risk. Moreover, a range of sensitivity analyses were conducted to validate the robustness of the results. MR-Egger intercept, and Cochran's Q statistical analysis were used to evaluate possible heterogeneity and pleiotropy. False discovery rate (FDR) correction was applied to validate the findings. We also conducted a reverse MR analysis to validate whether the observed blood metabolite levels were influenced by OC risk. Additionally, metabolic pathway analysis was carried out using the MetaboAnalyst 5.0 software.

RESULTS

In MR analysis, we discovered 18 suggestive causal associations involving 14 known metabolites, 8 metabolites as potential risk factors, and 6 as potential cancer risk reducers. In addition, three significant pathways, "caffeine metabolism," "arginine biosynthesis," and "citrate cycle (TCA cycle)" were associated with the development of mucinous ovarian cancer (MOC). The pathways "caffeine metabolism" and "alpha-linolenic acid metabolism" were associated with the onset of endometrioid ovarian cancer (OCED).

CONCLUSIONS

Our MR analysis revealed both protective and risk-associated metabolites, providing insights into the potential causal relationships between GDMs and the metabolic pathways related to OC and its subtypes. The metabolites that drive OC could be potential candidates for biomarkers.

Lipid alterations play a role in the integration of PD-1/PD-L1 inhibitors and anlotinib for the treatment of advanced non-small-cell lung cancer

BACKGROUND

Studies have shown that integrating anlotinib with programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors enhances survival rates among progressive non-small-cell lung cancer (NSCLC) patients lacking driver mutations. However, not all individuals experience clinical benefits from this therapy. As a result, it is critical to investigate the factors that contribute to the inconsistent response of patients. Recent investigations have emphasized the importance of lipid metabolic reprogramming in the development and progression of NSCLC.

METHODS

The objective of this investigation was to examine the correlation between lipid variations and observed treatment outcomes in advanced NSCLC patients who were administered PD-1/PD-L1 inhibitors alongside anlotinib. A cohort composed of 30 individuals diagnosed with advanced NSCLC without any driver mutations was divided into three distinct groups based on the clinical response to the combination treatment, namely, a group exhibiting partial responses, a group manifesting progressive disease, and a group demonstrating stable disease. The lipid composition of patients in these groups was assessed both before and after treatment.

RESULTS

Significant differences in lipid composition among the three groups were observed. Further analysis revealed 19 differential lipids, including 2 phosphatidylglycerols and 17 phosphoinositides.

CONCLUSION

This preliminary study aimed to explore the specific impact of anlotinib in combination with PD-1/PD-L1 inhibitors on lipid metabolism in patients with advanced NSCLC. By investigating the effects of using both anlotinib and PD-1/PD-L1 inhibitors, this study enhances our understanding of lipid metabolism in lung cancer treatment. The findings from this research provide valuable insights into potential therapeutic approaches and the identification of new therapeutic biomarkers.

Integrated transcriptomic and metabolomic investigation of the genes and metabolites involved in swine follicular cyst formation

Follicular cysts are a common reproductive disorder in mammals that is usually caused by stress. However, the pathogenesis of follicular cysts in sows remains unclear. To provide new insights into the mechanisms of follicular cyst formation in pigs, we conducted a combined transcriptomic and metabolomic analysis on theca interna and mural granulosa cells of follicular cysts and mature follicles. We identified 2,533 up-regulated and 1,355 down-regulated genes in follicular cysts, compared with mature follicles. These differentially expressed genes were mainly found in signaling pathways related to tumor formation and cortisol synthesis and secretion as shown by Ingenuity Pathway Analysis, which predicted 4,362 upstream regulatory factors. The combined gene expression and pathway analysis identified the following genes as potential biomarkers for porcine follicular cysts: cytochrome P450 family 2 subfamily C polypeptide 18, L-lactate dehydrogenase, carbamoyl-phosphate synthase, fibroblast growth factor 7, integrin binding sialoprotein, interleukin 23 receptor, prolactin receptor, epiregulin, interleukin 1 receptor type II, arginine vasopressin receptor 1A, fibroblast growth factor 10, claudin 7, G Protein Subunit Gamma 3, cholecystokinin B receptor and cytosolic phospholipase A2. Metabolomics analysis found significant differences in 87 metabolites, which were enriched in unsaturated fatty acid biosynthesis, and sphingolipid signaling pathways. These results provide valuable information on the molecular mechanisms of follicular cyst formation, which may facilitate the development of new therapeutics to prevent and treat follicular cysts.

Essential Trace Elements in Patients with Dyslipidemia: A Meta-analysis

BACKGROUND

Lipid metabolism is a complex process that includes lipid uptake, transport, synthesis, and degradation. Trace elements are vital in maintaining normal lipid metabolism in the human body. This study explores the relationship between serum trace elements and lipid metabolism.

METHODS

In this study, we reviewed articles on the relationship between alterations in somatic levels of zinc, iron, calcium, copper, chrome, manganese, selenium, and lipid metabolism. In this systematic review and mate-analysis, databases such as PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI), Wanfang was searched for articles on the relationship published between January 1, 1900, and July 12, 2022. The meta-analysis was performed using Review Manager5.3 (Cochrane Collaboration).

RESULTS

No significant association was found between serum zinc and dyslipidemia, while other serum trace elements (iron, selenium, copper, chromium, and manganese) were associated with hyperlipidemia.

CONCLUSION

The present study suggested that the human body's zinc, copper, and calcium content may be related to lipid metabolism. However, findings on lipid metabolism and Iron, Manganese have not been conclusive. In addition, the relationship between lipid metabolism disorders and selenium levels still needs to be further studied. Further research is needed on treating lipid metabolism diseases by changing trace elements.

Metabolomics profiling and chemoresistance mechanisms in ovarian cancer cell lines: Implications for targeting glutathione pathway

Ovarian cancer presents a significant challenge due to its high rate of chemoresistance, which complicates the effectiveness of drug-response therapy. This study provides a comprehensive metabolomic analysis of ovarian cancer cell lines OVCAR-3 and SK-OV-3, characterizing their distinct metabolic landscapes. Metabolomics coupled with chemometric analysis enabled us to discriminate between the metabolic profiles of these two cell lines. The OVCAR-3 cells, which are sensitive to doxorubicin (DOX), exhibited a preference for biosynthetic pathways associated with cell proliferation. Conversely, DOX-resistant SK-OV-3 cells favored fatty acid oxidation for energy maintenance. Notably, a marked difference in glutathione (GSH) metabolism was observed between these cell lines. Our investigations further revealed that GSH depletion led to a profound change in drug sensitivity, inducing a shift from a cytostatic to a cytotoxic response. The results derived from this comprehensive metabolomic analysis offer potential targets for novel therapeutic strategies to overcome drug resistance. Our study suggests that targeting the GSH pathway could potentially enhance chemotherapy's efficacy in treating ovarian cancer.

LRP1B-a prognostic marker in tubo-ovarian high-grade serous carcinoma

Low-density lipoprotein (LDL) receptor-related protein 1B (LRP1B) is a member of the LDL receptor family and has often been discussed as a tumor suppressor gene, as its down-regulation is correlated with a poor prognosis in multiple carcinoma entities. Due to the high metastasis rate into the fatty peritoneal cavity and current research findings showing a dysregulation of lipid metabolism in tubo-ovarian high-grade serous carcinoma (HGSC), we questioned the prognostic impact of the LRP1B protein expression. We examined a well-characterized large cohort of 571 patients with primary HGSC and analyzed the LRP1B protein expression via immunohistochemical staining (both in tumor and stroma cells separately), performed precise bioimage analysis with QuPath, and calculated the prognostic impact using SPSS. Our results demonstrate that LRP1B functions as a significant prognostic marker for overall survival (OS) and progression-free survival (PFS) in HGSC on the protein level. High cytoplasmic expression of LRP1B in tumor, stroma, and combined tumor and stroma cells has a significantly positive association with a mean prolongation of the OS by 42 months (P = .005), 29 months (P = .005), and 25 months (P = .001), respectively. Additionally, the mean PFS was 18 months longer in tumor (P = .002), 19 months in stroma (P = .004), and 19 months in both cell types combined (P = .01). Our results remained significant in multivariate analysis. We envision LRP1B as a potential prognostic tool that could help us understand the functional role of lipid metabolism in advanced HGSC, especially regarding liposomal medications.

Comprehensive analyses of fatty acid metabolism-related lncRNA for ovarian cancer patients

Ovarian cancer (OC) is a disease with difficult early diagnosis and treatment and poor prognosis. OC data profiles were downloaded from The Cancer Genome Atlas. Eight key fatty acid metabolism-related long non-coding RNAs (lncRNAs) were finally screened for building a risk scoring model by univariate/ multifactor and least absolute shrinkage and selection operator (LASSO) Cox regression. To make this risk scoring model more applicable to clinical work, we established a nomogram containing the clinical characteristics of OC patients after confirming that the model has good reliability and validity and the ability to distinguish patient prognosis. To further explore how these key lncRNAs are involved in OC progression, we explored their relationship with LUAD immune signatures and tumor drug resistance. The structure shows that the risk scoring model established based on these 8 fatty acid metabolism-related lncRNAs has good reliability and validity and can better predict the prognosis of patients with different risks of OC, and LINC00861in these key RNAs may be a hub gene that affects the progression of OC and closely related to the sensitivity of current OC chemotherapy drugs. In addition, combined with immune signature analysis, we found that patients in the high-risk group are in a state of immunosuppression, and Tfh cells may play an important role in it. We innovatively established a prognostic prediction model with excellent reliability and validity from the perspective of OC fatty acid metabolism reprogramming and lncRNA regulation and found new molecular/cellular targets for future OC treatment.

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.

Lymphatic vasculature in ovarian cancer

Ovarian cancer (OVCA) is the second most common gynecological cancer and one of the leading causes of cancer related mortality among women. Recent studies suggest that among ovarian cancer patients at least 70% of the cases experience the involvement of lymph nodes and metastases through lymphatic vascular network. However, the impact of lymphatic system in the growth, spread and the evolution of ovarian cancer, its contribution towards the landscape of ovarian tissue resident immune cells and their metabolic responses is still a major knowledge gap. In this review first we present the epidemiological aspect of the OVCA, the lymphatic architecture of the ovary, we discuss the role of lymphatic circulation in regulation of ovarian tumor microenvironment, metabolic basis of the upregulation of lymphangiogenesis which is often observed during progression of ovarian metastasis and ascites development. Further we describe the implication of several mediators which influence both lymphatic vasculature as well as ovarian tumor microenvironment and conclude with several therapeutic strategies for targeting lymphatic vasculature in ovarian cancer progression in present day.

MMDAE-HGSOC: A novel method for high-grade serous ovarian cancer molecular subtypes classification based on multi-modal deep autoencoder

High-grade serous ovarian cancer (HGSOC) is a type of ovarian cancer developed from serous tubal intraepithelial carcinoma. The intrinsic differences among molecular subtypes are closely associated with prognosis and pathological characteristics. At present, multi-omics data integration methods include early integration and late integration. Most existing HGSOC molecular subtypes classification methods are based on the early integration of multi-omics data. The mutual interference among multi-omics data is ignored, which affects the effectiveness of feature learning. High-dimensional multi-omics data contains genes unassociated with HGSOC molecular subtypes, resulting in redundant information, which is not conducive to model training. In this paper, we propose a multi-modal deep autoencoder learning method, MMDAE-HGSOC. MiRNA expression, DNA methylation, and copy number variation (CNV) are integrated with mRNA expression data to construct a multi-omics feature space. The multi-modal deep autoencoder network is used to learn the high-level feature representation of multi-omics data. The superposition LASSO (S-LASSO) regression algorithm is proposed to fully obtain the associated genes of HGSOC molecular subtypes. The experimental results show that MMDAE-HGSOC is superior to the existing classification methods. Finally, we analyze the enrichment gene ontology (GO) terms and biological pathways of these significant genes, which are discovered during the gene selection process.

UPLC-QE-Orbitrap-Based Cell Metabolomics and Network Pharmacology to Reveal the Mechanism of N-Benzylhexadecanamide Isolated from Maca (Lepidium meyenii Walp.) against Testicular Dysfunction

Testicular dysfunction (TDF) is characterized by testosterone deficiency and is caused by oxidative stress injury in Leydig cells. A natural fatty amide named N-benzylhexadecanamide (NBH), derived from cruciferous maca, has been shown to promote testosterone production. Our study aims to reveal the anti-TDF effect of NBH and explore its potential mechanism in vitro. This study examined the effects of H₂O₂ on cell viability and testosterone levels in mouse Leydig cells (TM3) under oxidative stress. In addition, cell metabolomics analysis based on UPLC-Q-Exactive-MS/MS showed that NBH was mainly involved in arginine biosynthesis, aminoacyl-tRNA biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, the TCA cycle and other metabolic pathways by affecting 23 differential metabolites, including arginine and phenylalanine. Furthermore, we also performed network pharmacological analysis to observe the key protein targets in NBH treatment. The results showed that its role was to up-regulate ALOX5, down-regulate CYP1A2, and play a role in promoting testicular activity by participating in the steroid hormone biosynthesis pathway. In summary, our study not only provides new insights into the biochemical mechanisms of natural compounds in the treatment of TDF, but also provides a research strategy that integrates cell metabolomics and network pharmacology in order to promote the screening of new drugs for the treatment of TDF.

Identification of metabolic biomarkers for diagnosis of epithelial ovarian cancer using internal extraction electrospray ionization mass spectrometry (iEESI-MS)

BACKGROUND

Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic malignancies. The poor prognosis of EOC is mainly due to its asymptomatic early stage, lack of effective screening methods, and a late diagnosis in the advanced stages of the disease.

OBJECTIVE

This study investigated metabolomic abnormalities in epithelial ovarian cancers.

METHODS

Our study developed a novel strategy to rapidly identify the metabolic biomarkers in the plasma of the EOC patients using Internal Extraction Electrospray Ionization Mass Spectrometry (IEESI-MS) and Liquid Chromatography-mass Spectrometry (HPLC-MS), which could distinguish the differential metabolites in between plasma samples collected from 98 patients with epithelial ovarian cancer, including 78 cases with original (P), and 20 cases with self-configuration (ZP), as well as 60 healthy subjects, including 30 cases in the original sample (H), 30 cases in self-configuration (ZH), and 6 cases in a blind sample (B).

RESULTS

Our study detected 880 metabolites based on criteria variable importance in projection (VIP) > 1, among which 26 metabolites were selected for further identification. They are mainly metabolism-related lipids, amino acids, nucleic acids, and others. The metabolic pathways associated with the differential metabolites were explored by the KEGG analysis, a comprehensive database that integrates genome, chemistry, and system function information. The abnormal metabolites of EOC patients identified by IEESI-MS and HPLC-MS included Lysophosphatidylcholine (16:0) [Lyso PC (16:0)], L-Phenylalanine, L-Leucine, Phenylpyruvic acid, L-Tryptophan, and L-Histidine.

CONCLUSIONS

Identifying the abnormal metabolites of EOC patients through metabolomics analyses could provide a new strategy to identify valuable potential biomarkers for the screening and early diagnosis of EOC.

Rs15285, a functional polymorphism located in lipoprotein lipase, predicts the risk and prognosis of gastric cancer

Lipoprotein lipase (LPL), a crucial gene in lipid metabolism, has a significant role in the progression of malignant tumors. The purpose of this research was to investigate the impact of rs15285 found in the LPL gene's 3'UTR region on the risk, biological behavior, and gastric cancer (GC) prognosis as well as to examine its potential function. Genotyping of rs15285 in 888 GC cases and 874 controls was conducted by SNaPshot technology. We used bioinformatics analysis and in vitro experiments to study the role of rs15285. First, this study revealed for the first time that polymorphism rs15285 increases the risk of GC (OR = 1.48, 95%CI = 1.16-1.89, P = 0.002). Although no relationship was found between rs12585 and the pathological features of GC, the prognosis of individuals with the rs12585 TT genotype was poorer than that of patients with the CC or CC+CT genotype (HR = 2.39 for TT vs. CC, P = 0.025; HR = 2.38 for TT vs. CC+CT, P = 0.025). In addition, bioinformatics analysis showed rs12585 may affect the binding of miRNAs to LPL, resulting in an increase of LPL expression to promote cancer progression. Ultimately, in vitro tests revealed that the rs15285 T allele increased LPL expression on the mRNA as well as the protein levels, promoting GC cell proliferation, invasion, and metastasis. The LPL rs12528 TT genotype increased the risk of GC and predicted a poor prognosis. Mechanistically, the rs15285 T allele could improve the expression of LPL, and thus promotes the malignant phenotype of GC. Therefore, our study may provide new biological predictors and a theoretical basis for the prognosis and customized therapy of stomach cancer patients. KEY POINTS: • Rs15285 polymorphism is a risk factor for GC. • Rs12585 TT genotype predicts a bad outcome in GC individuals. • Rs15285 T allele enhances GC cells malignant biological behavior.

How do serum lipid levels change and influence progression-free survival in epithelial ovarian cancer patients receiving bevacizumab treatment?

Background

This study aimed to investigate how serum lipid levels affect epithelial ovarian cancer (EOC) patients receiving bevacizumab treatment and to develop a model for predicting the patients’ prognosis.

Methods

A total of 139 EOC patients receiving bevacizumab treatment were involved in this study. Statistical analysis was used to compare the median and average values of serum lipid level variables between the baseline and final follow-up. Additionally, a method based on machine learning was proposed to identify independent risk factors for estimating progression-free survival (PFS) in EOC patients receiving bevacizumab treatment. A PFS nomogram dividing the patients into low- and high-risk categories was created based on these independent prognostic variables. Finally, Kaplan–Meier curves and log-rank tests were utilized to perform survival analysis.

Results

Among EOC patients involved in this study, statistical analysis of serum lipid level variables revealed a substantial increase in total cholesterol, triglycerides, apolipoprotein A1, and free fatty acids, and a significant decrease in apolipoprotein B from baseline to final follow-up. Our method identified FIGO stage, combined chemotherapy regimen, activated partial thromboplastin time, globulin, direct bilirubin, free fatty acids, blood urea nitrogen, high-density lipoprotein cholesterol, and triglycerides as risk factors. These risk factors were then included in our nomogram as independent predictors for EOC patients. PFS was substantially different between the low-risk group (total score &lt; 298) and the high-risk group (total score ≥ 298) according to Kaplan–Meier curves (P &lt; 0.05).

Conclusion

Serum lipid levels changed variously in EOC patients receiving bevacizumab treatment. A prediction model for PFS of EOC patients receiving bevacizumab treatment was constructed, and it can be beneficial in determining the prognosis, selecting a treatment plan, and monitoring these patients’ long-term care.

Prostate Cancer: Advances in Genetic Testing and Clinical Implications

The demand for genetic testing (GT) for prostate cancer (PCa) is expanding, but there is limited knowledge about the genetic counseling (GC) needs of men. A strong-to-moderate inherited genetic predisposition causes approximately 5–20% of prostate cancer (PCa). In men with prostate cancer, germline testing may benefit the patient by informing treatment options, and if a mutation is noticed, it may also guide screening for other cancers and have family implications for cascade genetic testing (testing of close relatives for the same germline mutation). Relatives with the same germline mutations may be eligible for early cancer detection strategies and preventive measures. Cascade family testing can be favorable for family members, but it is currently unutilized, and strategies to overcome obstacles like knowledge deficiency, family communication, lack of access to genetic services, and testing expenses are needed. In this review, we will look at the genetic factors that have been linked to prostate cancer, as well as the role of genetic counseling and testing in the early detection of advanced prostate cancer.

Plasmonic Hydroxyl Radical-Driven Epoxidation of Fatty Acid Double Bonds in Nanoseconds for On-Tissue Mass-Spectrometric Analysis and Bioimaging

Lipids represent a large family of compounds with highly diverse structures that are involved in complex biological processes. A photocatalytic technique of on-tissue epoxidation of C=C double bonds has been developed for in situ mass spectrometric identification and spatial imaging of positional isomers of lipids. It is based on the plasmonic hot-electron transfer from irradiated gold nanowires to redox-active organic matrix compounds that undergo bond cleavages and generate hydroxyl radicals in nanoseconds. Intermediate radical anions and negative fragment ions have been unambiguously identified. Under the irradiation of a pulsed laser of the third harmonic of Nd³⁺:YAG (355 nm), the hydroxyl radical-driven epoxidation of unsaturated lipids with different numbers of C=C bonds can be completed in nanoseconds with high yields of up to 95%. Locations of C=C bonds were recognized with diagnostic fragment ions that were produced by either collision with an inert gas or auto-fragmentation resulting from the impact of energetic hot electrons and vibrational excitation. This technique has been applied to the analysis of breast cancer tissues of mice models without extensive sample processes. It was experimentally demonstrated that C=C bonds may be formed at different positions of not only regular mono- or poly-unsaturated fatty acids but also other odd-numbered long-chain fatty acids.

miR-33b in human cancer: Mechanistic and clinical perspectives

The microRNAs (miRNAs), an extensive class of small noncoding RNAs (∼22 nucleotides), have been shown to have critical functions in various biological processes during development. miR-33b (or hsa-miR-33b) is down-regulated in cancer of multiple systems. Notably, at least 27 protein-coding genes can be targeted by miR-33b. miR-33b regulates the cell cycle, cell proliferation, various metabolism pathways, epithelial-mesenchymal transition (EMT), cancer cell invasion and migration, etc. In prostate cancer, Cullin 4B (CUL4B) can be recruited to the promoter to inhibit the expression of miR-33b. In gastric cancer, the hypermethylation of the CpG island regulated the expression of miR-33b. Besides, miR-33b could be negatively regulated by 7 competing-endogenous RNAs (ceRNAs), which are all long non-coding RNAs (lncRNAs). There are at least 4 signaling pathways, including NF-κB, MAP8, Notch1, and Wnt/β-catenin signaling pathways, which could be regulated partially by miR-33b. Additionally, low expression of miR-33b was associated with clinicopathology and prognosis in cancer patients. In addition, the aberrant expression of miR-33b was connected with the resistance of cancer cells to 5 anticancer drugs (cisplatin, docetaxel, bortezomib, paclitaxel, and daunorubicin). Importantly, our work systematically summarizes the aberrant expression of miR-33b in various neoplastic diseases and the effect of its downregulation on the biological behavior of cancer cells. Furthermore, this review focuses on recent advances in understanding the molecular regulation mechanisms of miR-33b. Moreso, the relationship between the miR-33b expression levels and the clinicopathological data and prognosis of tumor patients was summarized for the first time. Overall, we suggest that the current studies of miR-33b are insufficient but provide potential hints and direction for future miR-33b-related research.

A Novel Cuproptosis-Associated Gene Signature to Predict Prognosis in Patients with Pancreatic Cancer

Background

Pancreatic cancer (PAAD) is a malignant tumor with a poor prognosis and lacks sensitive biomarkers for diagnosis and targeted therapy. Cuproptosis, a recently proposed form of cell death based on cellular copper ion concentration, plays a key role in cancer biology. This study is aimed at constructing a risk model for predicting the prognosis of PAAD patients based on cuproptosis-related genes.

Methods

Pancreatic-related data from UCSC-TCGA and UCSC-GTEx databases were extracted for analysis, and TCGA-PAAD samples were randomly divided into the training and validation groups. Pearson correlation analysis was used to obtain cuproptosis-related genes coexpressed with 19 copper death genes. Univariate Cox and Lasso regression analyses were used to obtain cuproptosis-related prognostic genes. Multivariate Cox regression analysis was used to construct the final prognostic risk model. The risk score curve, Kaplan-Meier survival curves, and ROC curve were used to evaluate the predictive ability of the Cox risk model. Finally, the functional annotation of the risk model was obtained through enrichment analysis.

Results

The Cox risk model has an eight prognostic cuproptosis-related gene signature. Kaplan-Meier survival curves demonstrated that the high-risk group had a shorter survival time. The ROC curve of the risk score was well created to predict one-, three-, and five-year survival rates, and AUC of the risk score was higher than other clinical characteristics. Cox regression analysis revealed that the risk score has an independent prognostic value for PAAD. GSEA reveals specific tumor pathways associated with the risk model (Myc targets v1, mTORC1 signaling, and E2F targets).

Conclusions

We constructed a prognostic model containing eight cuproptosis-related genes (AKR1B10, KLHL29, PROM2, PIP5K1C, KIF18B, AMIGO2, MRPL3, and PI4KB) that can accurately predict the prognosis of PAAD patients. The results will provide new perspectives for individualized outcome prediction and new therapy development for PAAD patients.

Total Polyunsaturated Fatty Acid Level in Abdominal Adipose Tissue as an Independent Predictor of Recurrence-Free Survival in Women with Ovarian Cancer

Prognostic factors for epithelial ovarian cancers (EOCs) are in particular clinical factors such as pathology staging at diagnosis (FIGO stages), genetic mutation, or histological phenotypes. In the present study, FIGO stage, tumor residue after surgery, and body mass index were clinical predictors of recurrence-free survival (RFS). Nonetheless, a number of studies support a lipid metabolism disorder in ovarian cancer patients. The objective of this pilot study was to explore whether fatty acid composition of adipose reflecting the qualitative dietary intake and fatty acids metabolism may be associated with RFS. Forty-six women with EOCs and six with borderline ovarian tumors between March 2017 and January 2020 were included in this prospective study at Tours university teaching hospital (central France). The patients involved in the present study are part of the METERMUS trial (clinicaltrials.gov NCT03027479). Adipose tissue specimens from four abdominal locations (superficial and deep subcutaneous, visceral (pericolic), and omental) were collected during surgery or exploratory laparoscopy. A fatty acid profile of adipose tissue triglycerides was established by gas chromatography. Fatty acids composition was compared among the four locations using nonparametric Friedman’s ANOVA test for repeated measures. Median follow-up of EOC patients was 15 months and patients’ RFS was analyzed using Kaplan−Meier survival curves and log-rank test by separating patients into two groups according to median fatty acid levels. The content of long-chain saturated fatty acids (SFAs) was increased and that of long-chain polyunsaturated fatty acids (PUFAs) decreased in deep versus superficial subcutaneous adipose tissue in EOC patients. Nevertheless, the content of total SFAs was ~28%, monounsaturated fatty acids (MUFAs) ~55%, PUFAs n-6 ~11.5%, and PUFAs n-3 about 1.3%, whatever the adipose tissue. When EOC patients were separated into two groups by median fatty acid content, total PUFAs (n-6+n-3) levels, whatever the adipose tissue, were positively and independently associated with RFS. RFS was about two times longer in EOC patients with high versus low total PUFA content (median survival: 12 vs. 27 months, p = 0.01 to <0.0001 according to the tissue). Content of total PUFAs (n-6+n-3) in abdominal adipose tissue (visceral and subcutaneous) are new prognostic factors in EOC.

Pharmacological Inhibition of Lipid Import and Transport Proteins in Ovarian Cancer

Ovarian cancer (OC) is the most lethal gynecological malignancy with a 5-year survival rate of 49%. This is caused by late diagnosis when cells have already metastasized into the peritoneal cavity and to the omentum. OC progression is dependent on the availability of high-energy lipids/fatty acids (FA) provided by endogenous de novo biosynthesis and/or through import from the microenvironment. The blockade of these processes may thus represent powerful strategies against OC. While this has already been shown for inhibition of FA/lipid biosynthesis, evidence of the role of FA/lipid import/transport is still sparse. Therefore, we treated A2780 and SKOV3 OC cells with inhibitors of the lipid uptake proteins fatty acid translocase/cluster of differentiation 36 (FAT/CD36) and low-density lipoprotein (LDL) receptor (LDLR), as well as intracellular lipid transporters of the fatty acid-binding protein (FABP) family, fatty acid transport protein-2 (FATP2/SLC27A2), and ADP-ribosylation factor 6 (ARF6), which are overexpressed in OC. Proliferation was determined by formazan dye labeling/photometry and cell counting. Cell cycle analysis was performed by propidium iodide (PI) staining, and apoptosis was examined by annexin V/PI and active caspase 3 labeling and flow cytometry. RNA-seq data revealed altered stress and metabolism pathways. Overall, the small molecule inhibitors of lipid handling proteins BMS309403, HTS01037, NAV2729, SB-FI-26, and sulfosuccinimidyl oleate (SSO) caused a drug-specific, dose-/time-dependent inhibition of FA/LDL uptake, associated with reduced proliferation, cell cycle arrest, and apoptosis. Our findings indicate that OC cells are very sensitive to lipid deficiency. This dependency should be exploited for development of novel strategies against OC.

Metabolic reprogramming of the tumor immune microenvironment in ovarian cancer: A novel orientation for immunotherapy

Ovarian cancer is the most lethal gynecologic tumor, with the highest mortality rate. Numerous studies have been conducted on the treatment of ovarian cancer in the hopes of improving therapeutic outcomes. Immune cells have been revealed to play a dual function in the development of ovarian cancer, acting as both tumor promoters and tumor suppressors. Increasingly, the tumor immune microenvironment (TIME) has been proposed and confirmed to play a unique role in tumor development and treatment by altering immunosuppressive and cytotoxic responses in the vicinity of tumor cells through metabolic reprogramming. Furthermore, studies of immunometabolism have provided new insights into the understanding of the TIME. Targeting or activating metabolic processes of the TIME has the potential to be an antitumor therapy modality. In this review, we summarize the composition of the TIME of ovarian cancer and its metabolic reprogramming, its relationship with drug resistance in ovarian cancer, and recent research advances in immunotherapy.

Identification of fatty acid signature to predict prognosis and guide clinical therapy in patients with ovarian cancer

High-grade serous ovarian cancer (HGSOC) is a heterogeneous cancer characterized by high relapse rate. Approximately 80% of women are diagnosed with late-stage disease, and 15–25% of patients experience primary treatment resistance. Ovarian cancer brings tremendous suffering and is the most malignant type in all gynecologic malignancies. Metabolic reprogramming in tumor microenvironment (TME), especially fatty acid metabolism, has been identified to play a crucial role in cancer prognosis. Yet, the underlying mechanism of fatty acid metabolism on ovarian cancer progression is severely understudied. Recently, studies have demonstrated the role of fatty acid metabolism reprogramming in immune cells, but their roles on cancer cell metastasis and cancer immunotherapy response are poorly characterized. Here, we reported that the fatty acid–related genes are aberrantly varied between ovarian cancer and normal samples. Using samples in publicly databases and bio-informatic analyses with fatty acid–related genes, we disentangled that cancer cases can be classified into high- and low-risk groups related with prognosis. Furthermore, the nomogram model was constructed to predict the overall survival. Additionally, we reported that different immune cells infiltration was presented between groups, and immunotherapy response differed in two groups. Results showed that our signature may have good prediction value on immunotherapy efficacy, especially for anti–PD-1 and anti–CTLA-4. Our study systematically marked the critical association between cancer immunity in TME and fatty acid metabolism, and bridged immune phenotype and metabolism programming in tumors, thereby constructed the metabolic-related prognostic model and help to understand the underlying mechanism of immunotherapy response.

Integration of Transcriptome and Epigenome to Identify and Develop Prognostic Markers for Ovarian Cancer

DNA methylation is a widely researched epigenetic modification. It is associated with the occurrence and development of cancer and has helped evaluate patients' prognoses. However, most existing DNA methylation prognosis models have not simultaneously considered the changes of the downstream transcriptome. Methods. The RNA-Sequencing data and DNA methylation omics data of ovarian cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database. The Consensus Cluster Plus algorithm was used to construct the methylated molecular subtypes of the ovary. Lasso regression was employed to build a multi-gene signature. An independent data set was applied to verify the prognostic value of the signature. The Gene Set Variation Analysis (GSVA) was used to carry out the enrichment analysis of the pathways linked to the gene signature. The IMvigor 210 cohort was used to explore the predictive efficacy of the gene signature for immunotherapy response. Results. We distinguished ovarian cancer samples into two subtypes with different prognosis, based on the omics data of DNA methylation. Differentially expressed genes and enrichment analysis among subtypes indicated that DNA methylation was related to fatty acid metabolism and the extracellular matrix (ECM)-receptor. Furthermore, we constructed an 8-gene signature, which proved to be efficient and stable in predicting prognostics in ovarian cancer patients with different data sets and distinctive pathological characteristics. Finally, the 8-gene signature could predict patients' responses to immunotherapy. The polymerase chain reaction experiment was further used to verify the expression of 8 genes. Conclusion. We analyzed the prognostic value of the related genes of methylation in ovarian cancer. The 8-gene signature predicted the prognosis and immunotherapy response of ovarian cancer patients well and is expected to be valuable in clinical application.

FABP4 in obesity-associated carcinogenesis: Novel insights into mechanisms and therapeutic implications

The increasing prevalence of obesity worldwide is associated with an increased risk of various diseases, including multiple metabolic diseases, cardiovascular diseases, and malignant tumors. Fatty acid binding proteins (FABPs) are members of the adipokine family of multifunctional proteins that are related to fatty acid metabolism and are divided into 12 types according to their tissue origin. FABP4 is mainly secreted by adipocytes and macrophages. Under obesity, the synthesis of FABP4 increases, and the FABP4 content is higher not only in tissues but also in the blood, which promotes the occurrence and development of various cancers. Here, we comprehensively investigated obesity epidemiology and the biological mechanisms associated with the functions of FABP4 that may explain this effect. In this review, we explore the molecular mechanisms by which FABP4 promotes carcinoma development and the interaction between fat and cancer cells in obese circumstances here. This review leads us to understand how FABP4 signaling is involved in obesity-associated tumors, which could increase the potential for advancing novel therapeutic strategies and molecular targets for the systematic treatment of malignant tumors.

The Knockout of the ASIP Gene Altered the Lipid Composition in Bovine Mammary Epithelial Cells via the Expression of Genes in the Lipid Metabolism Pathway

Agouti signalling protein (ASIP) is a coat colour-related protein and also is a protein-related to lipid metabolism, which had first been found in agoutis. According to our previous study, ASIP is a candidate gene that affects the lipid metabolism in bovine adipocytes. However, its effect on milk lipid has not been reported yet. This study focused on the effect of the ASIP gene on the lipid metabolism of mammary epithelial cells in cattle. The ASIP gene was knocked out in bMECs by using CRISPR/Cas9 technology. The result of transcriptome sequencing showed that the differentially expressed genes associated with lipid metabolism were mainly enriched in the fatty acids metabolism pathways. Furthermore, the contents of intracellular triglycerides were significantly increased (p < 0.05), and cholesterol tended to rise (p > 0.05) in bMECs with the knockout of the ASIP gene. Fatty acid assays showed a significant alteration in medium and long-chain fatty acid content. Saturated and polyunsaturated fatty acids were significantly up-regulated (p < 0.05), and monounsaturated fatty acids were significantly decreased in the ASIP knockout bMECs (p < 0.05). The Q-PCR analysis showed that knockout of ASIP resulted in a significant reduction of gene expressions like PPARγ, FASN, SCD, and a significant up-regulation of genes like FABP4, ELOVL6, ACSL1, HACD4 prompted increased mid-to long-chain fatty acid synthesis. Overall, ASIP plays a pivotal role in regulating lipid metabolism in bMECs, which could further influence the component of lipid in milk.

Targeting lipid metabolism in the treatment of ovarian cancer

Cancer cells undergo alterations in lipid metabolism to support their high energy needs, tumorigenesis and evade an anti-tumor immune response. Alterations in fatty acid production are controlled by multiple enzymes, chiefly Acetyl CoA Carboxylase, ATP-Citrate Lyase, Fatty Acid Synthase, and Stearoyl CoA Desaturase 1. Ovarian cancer (OC) is a common gynecological malignancy with a high rate of aggressive carcinoma progression and drug resistance. The accumulation of unsaturated fatty acids in ovarian cancer supports cell growth, increased cancer cell migration, and worse patient outcomes. Ovarian cancer cells also expand their lipid stores via increased uptake of lipids using fatty acid translocases, fatty acid-binding proteins, and low-density lipoprotein receptors. Furthermore, increased lipogenesis and lipid uptake promote chemotherapy resistance and dampen the adaptive immune response needed to eliminate tumors. In this review, we discuss the role of lipid synthesis and metabolism in driving tumorigenesis and drug resistance in ovarian cancer conferring poor prognosis and outcomes in patients. We also cover some aspects of how lipids fuel ovarian cancer stem cells, and how these metabolic alterations in intracellular lipid content could potentially serve as biomarkers of ovarian cancer.

Recent advances on discovery of enzyme inhibitors from natural products using bioactivity screening

The essence of enzymes is to keep the homeostasis and balance of human by catalyzing metabolic responses and modulating cell. Suppression of enzyme slows the progress of some diseases, making it a therapeutic target. Therefore, it is important to develop enzyme inhibitors by proper bioactivity screening strategies for the future treatment of some major diseases. In this review, we summarized the recent (2015-2020) applications of several screening strategies (electrophoretically mediated microanalysis, enzyme immobilization, affinity chromatography, and affinity ultrafiltration) in finding enzyme inhibitors from certain species of bioactive natural compounds of plant origin (flavonoids, alkaloids, phenolic acids, saponins, anthraquinones, coumarins). At the same time, the advantages and disadvantages of each strategy were also discussed, and the future possible development direction in enzyme inhibitor screening has prospected. To sum up, it is expected to help readers select suitable screening strategies for enzyme inhibitors and provide useful information for the study of the biological of specific kinds of natural products. This article is protected by copyright. All rights reserved.

MS4A15 acts as an oncogene in ovarian cancer through reprogramming energy metabolism

Membrane-spanning 4-domains subfamily A 15 (MS4A15) belongs to transmembrane proteins and has been recognized as a regulator of various biological events including cell metabolism. Dysregulation of cell metabolism is a component of malignant transformation in numerous types of tumors, including ovarian cancer (OC). Nevertheless, whether MS4A15 is involved in OC progression remains obscure, as well as the underlying mechanisms. In the present study, we found that MS4A15 expression was significantly up-regulated in tumor tissues from OC patients compared with the matched normal adjacent samples. Higher MS4A15 expression predicted poorer overall survival rate in patients with OC. Our in vitro studies subsequently showed that MS4A15 knockdown markedly reduced the proliferation of OC cells, while its over-expression accelerated the proliferative capacity of OC cells through mediating the progression of G0/G1 cell cycle. Consistently, stable MS4A15 knockdown strongly inhibited the tumor growth in the established xenograft mouse models, along with evidently decreased expression of KI-67 positive staining. However, xenograft mouse models with MS4A15 over-expression exerted significantly accelerated tumor growth rates. We then found that MS4A15 reprogrammed energy metabolism to enhance OC progression. Under normal status, MS4A15 enhanced de novo lipid synthesis in OC cells. Upon glucose starvation, MS4A15 elevated oxidative phosphorylation (OXPHOS) to protect OC cells from starvation-induced cell death. Taken together, our findings demonstrated that MS4A15 may play an essential role in promoting OC growth mainly via reprogramming energy metabolism, and thus could be considered as a novel therapeutic target for OC treatment.

Fatty Acid Metabolism in Ovarian Cancer: Therapeutic Implications

Ovarian cancer is the most malignant gynecological tumor. Previous studies have reported that metabolic alterations resulting from deregulated lipid metabolism promote ovarian cancer aggressiveness. Lipid metabolism involves the oxidation of fatty acids, which leads to energy generation or new lipid metabolite synthesis. The upregulation of fatty acid synthesis and related signaling promote tumor cell proliferation and migration, and, consequently, lead to poor prognosis. Fatty acid-mediated lipid metabolism in the tumor microenvironment (TME) modulates tumor cell immunity by regulating immune cells, including T cells, B cells, macrophages, and natural killer cells, which play essential roles in ovarian cancer cell survival. Here, the types and sources of fatty acids and their interactions with the TME of ovarian cancer have been reviewed. Additionally, this review focuses on the role of fatty acid metabolism in tumor immunity and suggests that fatty acid and related lipid metabolic pathways are potential therapeutic targets for ovarian cancer.

Prognostic Impact of CD36 Immunohistochemical Expression in Patients with Muscle-Invasive Bladder Cancer Treated with Cystectomy and Adjuvant Chemotherapy

Neoadjuvant chemotherapy followed by a cystectomy is the standard treatment in muscle-invasive bladder cancer (MIBC). However, the role of chemotherapy in the adjuvant setting remains controversial, and therefore new prognostic and predictive biomarkers are needed to improve the selection of MIBC patients. While lipid metabolism has been related to several biological processes in many tumours, including bladder cancer, no metabolic biomarkers have been identified as prognostic in routine clinical practice. In this multicentre, retrospective study of 198 patients treated with cystectomy followed by platinum-based adjuvant chemotherapy, we analysed the immunohistochemical expression of CD36 and correlated our findings with clinicopathological characteristics and survival. CD36 immunostaining was positive in 30 patients (15%) and associated with more advanced pathologic stages (pT3b-T4; p = 0.015). Moreover, a trend toward lymph node involvement in CD36-positive tumours, especially in earlier disease stages (pT1-T3; p = 0.101), was also observed. Among patients with tumour progression during the first 12 months after cystectomy, disease-free survival was shorter in CD36-positive tumours than in those CD36-negative (6.51 months (95% CI 5.05–7.96) vs. 8.74 months (95% CI 8.16–9.32); p = 0.049). Our results suggest an association between CD36 immunopositivity and more aggressive features of MIBC and lead us to suggest that CD36 could well be a useful prognostic marker in MIBC.

Unraveling Autocrine Signaling Pathways through Metabolic Fingerprinting in Serous Ovarian Cancer Cells

Focusing on defining metabolite-based inter-tumoral heterogeneity in ovarian cancer, we investigated the metabolic diversity of a panel of high-grade serous ovarian carcinoma (HGSOC) cell-lines using a metabolomics platform that interrogate 731 compounds. Metabolic fingerprinting followed by 2-dimensional and 3-dimensional principal component analysis established the heterogeneity of the HGSOC cells by clustering them into five distinct metabolic groups compared to the fallopian tube epithelial cell line control. An overall increase in the metabolites associated with aerobic glycolysis and phospholipid metabolism were observed in the majority of the cancer cells. A preponderant increase in the levels of metabolites involved in trans-sulphuration and glutathione synthesis was also observed. More significantly, subsets of HGSOC cells showed an increase in the levels of 5-Hydroxytryptamine, γ-aminobutyrate, or glutamate. Additionally, 5-hydroxytryptamin synthesis inhibitor as well as antagonists of γ-aminobutyrate and glutamate receptors prohibited the proliferation of HGSOC cells, pointing to their potential roles as oncometabolites and ligands for receptor-mediated autocrine signaling in cancer cells. Consistent with this role, 5-Hydroxytryptamine synthesis inhibitor as well as receptor antagonists of γ-aminobutyrate and Glutamate-receptors inhibited the proliferation of HGSOC cells. These antagonists also inhibited the three-dimensional spheroid growth of TYKNU cells, a representative HGSOC cell-line. These results identify 5-HT, GABA, and Glutamate as putative oncometabolites in ovarian cancer metabolic sub-type and point to them as therapeutic targets in a metabolomic fingerprinting-based therapeutic strategy.

A carrier free delivery system of a monoacylglycerol lipase hydrophobic inhibitor

Monoacylglycerol lipase (MAGL) is an emerging therapeutic target for cancer. It is involved in lipid metabolism and its inhibition impairs many hallmarks of cancer including cell proliferation, migration/invasion and tumor growth. For these reasons, our group has recently developed a potent reversible MAGL inhibitor (MAGL23), which showed promising anticancer activities. Here in, to improve its pharmacological properties, a nanoformulation based on nanocrystals coated with albumin was prepared for therapeutic applications. MAGL23 was solubilized by a nanocrystallization method with Pluronic F-127 as surfactant into an organic solvent and was recovered as nanocrystals in water after solvent evaporation. Finally, the solubilized nanocrystals were stabilized by human serum albumin to create a smart delivery carrier. An in-silico prediction (lipophilicity, structure at different pH and solubility in water), as well as experimental studies (solubility), have been performed to check the chemical properties of the inhibitor and nanocrystals. The solubility in water increases from less than 0.01 mg/mL (0.0008 mg/mL, predicted) up to 0.82 mg/mL in water. The formulated inhibitor maintained its potency in ovarian and colon cancer cell lines as the free drug. Furthermore, the system was thoroughly observed at each step of the solubilization process till the final formulation stage by different spectroscopic techniques and a comparative study was performed to check the effects of Pluronic F-127 and CTAB as surfactants. The formulated system is favorable to release the drug at physiological pH conditions (at pH 7.4, after 24 h, less than 20% of compound is released). In vivo studies have shown that albumin-complexed nanocrystals increase the therapeutic window of MAGL23 along with a favorable biodistribution. As per our knowledge, we are reporting the first ever nanoformulation of a MAGL inhibitor, which is promising as a therapeutic system where the MAGL enzyme is involved, especially for cancer therapeutic applications.

Aberrant Cholesterol Metabolism in Ovarian Cancer: Identification of Novel Therapeutic Targets

Cholesterol is an essential substance in mammalian cells, and cholesterol metabolism plays crucial roles in multiple biological functions. Dysregulated cholesterol metabolism is a metabolic hallmark in several cancers, beyond the Warburg effect. Reprogrammed cholesterol metabolism has been reported to enhance tumorigenesis, metastasis and chemoresistance in multiple cancer types, including ovarian cancer. Ovarian cancer is one of the most aggressive malignancies worldwide. Alterations in metabolic pathways are characteristic features of ovarian cancer; however, the specific role of cholesterol metabolism remains to be established. In this report, we provide an overview of the key proteins involved in cholesterol metabolism in ovarian cancer, including the rate-limiting enzymes in cholesterol biosynthesis, and the proteins involved in cholesterol uptake, storage and trafficking. Also, we review the roles of cholesterol and its derivatives in ovarian cancer and the tumor microenvironment, and discuss promising related therapeutic targets for ovarian cancer.

SPME-LC/MS-based serum metabolomic phenotyping for distinguishing ovarian cancer histologic subtypes: a pilot study

Epithelial ovarian cancer (EOC) is the most common cause of death from gynecological cancer. The outcomes of EOC are complicated, as it is often diagnosed late and comprises several heterogenous subtypes. As such, upfront treatment can be highly challenging. Although many significant advances in EOC management have been made over the past several decades, further work must be done to develop early detection tools capable of distinguishing between the various EOC subtypes. In this paper, we present a sophisticated analytical pipeline based on solid-phase microextraction (SPME) and three orthogonal LC/MS acquisition modes that facilitates the comprehensive mapping of a wide range of analytes in serum samples from patients with EOC. PLS-DA multivariate analysis of the metabolomic data was able to provide clear discrimination between all four main EOC subtypes: serous, endometrioid, clear cell, and mucinous carcinomas. The prognostic performance of discriminative metabolites and lipids was confirmed via multivariate receiver operating characteristic (ROC) analysis (AUC value > 88% with 20 features). Further pathway analysis using the top 57 dysregulated metabolic features showed distinct differences in amino acid, lipid, and steroids metabolism among the four EOC subtypes. Thus, metabolomic profiling can serve as a powerful tool for complementing histology in classifying EOC subtypes.