Sphingolipid metabolism and drug resistance in ovarian cancer

Unravelling the Mechanism of Cisplatin Resistance in Triple-Negative Breast Cancer: Insights from Metabolomic Profiling via Mass Spectrometry Analysis

Triple-negative breast cancer (TNBC) poses a significant challenge due to its aggressive nature and limited treatment options, with cisplatin often used in treatment. However, the mechanism underlying the cisplatin resistance in TNBC is poorly understood. This study aimed to develop a cisplatin-resistant (cisR) TNBC cell line and understand its metabolic alterations. Characterization of cisR and cisplatin-sensitive (cisS) cell lines involved cytotoxicity, wound healing, and morphological studies. This study further employed untargeted and targeted mass spectrometry analyses for a deep metabolome comparison between cisR and cisS TNBC cell lines to elucidate the molecular mechanisms driving cisplatin resistance. Metabolomics profiling of cisR and cisS cell lines resulted in the identification of significantly altered metabolites, such as N8-acetylspermidine, d-pantothenic acid, sphingosine, sphinganine 1-phosphate (S1P), nicotinamide, choline, and certain amino acids. This global and targeted metabolomics study also revealed the downregulation of N8-acetylspermidine and d-pantothenic acid, indicating that their dysregulation is associated with cisplatin resistance in TNBC cells. Furthermore, this study unravels the dysregulation of sphingolipid metabolism, particularly the downregulation of ceramide, sphingosine, and S1P, and glycerophospholipid metabolism (choline, LysoPC) as a potential contributor to cisplatin resistance in TNBC cells. Similarly, upregulation of nicotinamide metabolism key players nicotinate and 1-methylnicotinamide emerges as a contributor to cisplatin resistance. Aminoacyl t-RNA biosynthesis and ABC transporter metabolic pathways involving proline, valine, threonine, glutamic acid, and phenylalanine amino acids are also implicated in developing TNBC-resistant cells. This comprehensive metabolomics study identifies distinct metabolic signatures and key dysregulated pathways associated with cisplatin resistance in TNBC, offering potential candidate marker and therapeutic targets.

Metabolism-associated marker gene-based predictive model for prognosis, targeted therapy, and immune landscape in ovarian cancer: an integrative analysis of single-cell and bulk RNA sequencing with spatial transcriptomics

BACKGROUND

Ovarian cancer (OC) is a formidable gynecological tumor marked with the highest mortality rate. The lack of effective biomarkers and treatment drugs places a substantial proportion of patients with OC at significant risk of mortality, primarily due to metastasis. Glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism are closely intertwined with the occurrence and progression of OC. Thus, it is of utmost significance to identify potent prognostic biomarkers and delve into the exploration of novel therapeutic drugs and targets, in pursuit of advancing the treatment of OC.

METHODS

Single-cell RNA sequencing (scRNA-seq) data related to OC were analyzed using AUCell scores to identify subpopulations at the single-cell level. The "AddModuleScore" function of the "Seurat" package was adopted to score and select marker genes from four gene sets: glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism. A prognostic model for metabolism-related genes (MRGs) was constructed and validated using OC-related marker genes selected from bulk RNAseq data. The MRG-based prognostic model was further utilized for functional analysis of the model gene set, pan-cancer analysis of genomic variations, spatial transcriptomics analysis, as well as GO and KEGG enrichment analysis. CIBERSORT and ESTIMATE algorithms were utilized for assessing the immune microenvironment of TCGA-ovarian serous cystadenocarcinoma (OV) samples. Furthermore, the Tracking Tumor Immunophenotype (TIP) database was employed to examine the anti-cancer immune response in patients with OC. To gain a more in-depth understanding of the process, the frequency of somatic mutations and different types of mutated genes were visualized through the somatic mutation profile of the TCGA database. Moreover, the benefits of immune checkpoint inhibitor (ICI) therapy in individuals with OC were predicted in the TIDE database. In addition, the CMap database was used to predict small-molecule drugs for the treatment of OC. Furthermore, immunohistochemistry, RT-qPCR, CCK-8, Transwell assay, and in vivo tumor xenograft experiments were conducted to validate the prognostic ability of the MRG Triggering Receptor Expressed on Myeloid Cells-1 (TREM1) in OC.

RESULTS

Monocytes were selected using AUCell scoring, and two subpopulations of monocytes, marked by the expression of C1QC+ tumor-associated macrophages (TAMs) and FCN1+ resident tissue macrophages (RTMs), were identified as marker genes for OC. Subsequently, a prognostic model consisting of 12 MRGs was constructed and validated. Genomic exploration of the prognostic model unveiled an array of biological functions linked with metabolism. Furthermore, copy number variation (CNV), mRNA expression, single nucleotide variation (SNV), and methylation were significantly different across diverse tumors. Analysis of the TIP database demonstrated that the low-risk group, as determined by the MRG-based prognostic model, exhibited significantly higher anti-cancer immune activity relative to the high-risk group. Furthermore, predictions from the TIDE database revealed that individuals in the high-risk group were more prone to immune evasion when treated with ICIs. The resulting data identified candesartan and PD-123319 as potential therapeutic drugs for OC, possibly acting on the target ATGR2. In vitro and in vivo experiments elucidated that the targeted downregulation of TREM1 effectively inhibited the proliferation and migration of OC cells.

CONCLUSION

The MRG-based prognostic model constructed through the combined analysis of glycolysis metabolism, lipid metabolism, choline metabolism, and sphingolipid metabolism is potentially effective as a prognostic biomarker. Furthermore, candesartan and PD-123319 may be potential therapeutic drugs for OC, possibly acting on the target ATGR2.

Drug Resistance: The Role of Sphingolipid Metabolism

A significant challenge in cancer treatment is the rising problem of drug resistance that reduces the effectiveness of therapeutic strategies. Current knowledge shows that multiple mechanisms play a role in cancer drug resistance. Another mechanism that has gained attention is the alteration in sphingolipid trafficking and the dysregulation of its metabolism, which was reported to cause cancer-associated drug resistance. Sphingolipids are lipids containing sphingosine and have multiple roles, ranging from lipid raft formation, apoptosis, and cell signaling to immune cell trafficking. Recent studies show that in developing cancer cells, altered or dysregulated sphingolipids are associated with drug efflux and promote the survival of cancer cells by bypassing apoptosis. Upregulated levels of the glucosylceramide synthase (GCS), an enzyme that functions in sphingolipid metabolism, lead to the upregulated ABCB1 gene that induces drug efflux from the cancer cells. These bypass mechanisms make drugs that induce apoptosis in tumor cells ineffective. By highlighting the current findings, this review aims to provide a mechanism of drug resistance caused by the dysregulation of glucosylceramide synthase, sphingosine kinase, and acid ceramidase enzymes as possible therapeutic targets to enhance the effectiveness of the currently used chemotherapeutic agents.

Synergistic effect of the sphingosine kinase inhibitor safingol in combination with 2'-nitroflavone in breast cancer

Sphingosine kinase-1 (SPHK1), the enzyme that catalyzes the synthesis of the pro-oncogenic molecule sphingosine-1-phosphate, is commonly upregulated in breast cancer cells and has been linked with poor prognosis and progression by promoting cell transformation, proliferation, angiogenesis, and metastasis. Therefore, SPHK1-targeting drugs have been proposed for breast cancer treatment, with better antitumor results when they are combined with chemotherapy. Previously, we demonstrated that the synthetic flavonoid 2'-nitroflavone (2'NF) exerted a potent and selective antiproliferative effect in murine HER2-positive LM3 mammary tumor cells. As we found that these cells overexpress SPHK1, we decided to explore the antitumor action of the combination of SPHK inhibitors (safingol or SKI-II) with 2'NF. In vitro assays showed that the combination induced a synergistic antiproliferative effect in LM3 cells. Similar results were obtained when human HER2-positive MDA-MB-453 breast cancer cells were treated with the combination of 2'NF/safingol. We also found that safingol potentiated the 2'NF apoptotic effect in both cell lines. The synergistic antitumor effect was confirmed in vivo in an LM3 syngeneic breast cancer model. Moreover, western blot analysis of tumor lysates revealed that combined treatment increased PARP cleavage and Bax protein levels and decreased anti-apoptotic Bcl-xL and Bcl-2 protein levels. Additionally, mice treated with both compounds showed no histopathological effects on different organ tissues. In summary, these findings suggest that the combination safingol/2'NF can be proposed as a potential therapeutic strategy for HER2-positive breast cancer treatment. KEY MESSAGES: The combination safingol/2'-nitroflavone exerts a synergic antitumor action in vitro. Safingol potentiates 2'-nitroflavone apoptotic effect in breast cancer cells. Safingol enhances the 2'-nitroflavone antitumor activity in vivo in breast cancer.

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.

Ultrahigh resolution lipid mass spectrometry imaging of high-grade serous ovarian cancer mouse models

No effective screening tools for ovarian cancer (OC) exist, making it one of the deadliest cancers among women. Considering that little is known about the detailed progression and metastasis mechanism of OC at a molecular level, it is crucial to gain more insights into how metabolic and signaling alterations accompany its development. Herein, we present a comprehensive study using ultra-high-resolution Fourier transform ion cyclotron resonance matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) to investigate the spatial distribution and alterations of lipids in ovarian tissues collected from double knockout (n = 4) and triple mutant mouse models (n = 4) of high-grade serous ovarian cancer (HGSOC). Lipids belonging to a total of 15 different classes were annotated and their abundance changes were compared to those in healthy mouse reproductive tissue (n = 4), mapping onto major lipid pathways involved in OC progression. From intermediate-stage OC to advanced HGSC, we provide direct visualization of lipid distributions and their biological links to inflammatory response, cellular stress, cell proliferation, and other processes. We also show the ability to distinguish tumors at different stages from healthy tissues via a number of highly specific lipid biomarkers, providing targets for future panels that could be useful in diagnosis.

The Contribution of Lipidomics in Ovarian Cancer Management: A Systematic Review

Lipidomics is a comprehensive study of all lipid components in living cells, serum, plasma, or tissues, with the aim of discovering diagnostic, prognostic, and predictive biomarkers for diseases such as malignant tumors. This systematic review evaluates studies, applying lipidomics to the diagnosis, prognosis, prediction, and differentiation of malignant and benign ovarian tumors. A literature search was performed in PubMed, Science Direct, and SciFinder. Only publications written in English after 2012 were included. Relevant citations were identified from the reference lists of primary included studies and were also included in our list. All studies included referred to the application of lipidomics in serum/plasma samples from human cases of OC, some of which also included tumor tissue samples. In some of the included studies, metabolome analysis was also performed, in which other metabolites were identified in addition to lipids. Qualitative data were assessed, and the risk of bias was determined using the ROBINS-I tool. A total of twenty-nine studies were included, fifteen of which applied non-targeted lipidomics, seven applied targeted lipidomics, and seven were reviews relevant to our objectives. Most studies focused on the potential application of lipidomics in the diagnosis of OC and showed that phospholipids and sphingolipids change most significantly during disease development. In conclusion, this systematic review highlights the potential contribution of lipids as biomarkers in OC management.

Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma

BACKGROUND

Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults and is highly metastatic, resulting in a poor patient prognosis. Sphingolipid metabolism plays an important role in tumor development, diagnosis, and prognosis. This study aimed to establish a reliable signature based on sphingolipid metabolism genes (SMGs), thus providing a new perspective for assessing immunotherapy response and prognosis in patients with UVM.

METHODS

In this study, SMGs were used to classify UVM from the TCGA-UVM and GEO cohorts. Genes significantly associated with prognosis in UVM patients were screened using univariate cox regression analysis. The most significantly characterized genes were obtained by machine learning, and 4-SMGs prognosis signature was constructed by stepwise multifactorial cox. External validation was performed in the GSE84976 cohort. The level of immune infiltration of 4-SMGs in high- and low-risk patients was analyzed by platforms such as CIBERSORT. The prediction of 4-SMGs on immunotherapy and immune checkpoint blockade (ICB) response in UVM patients was assessed by ImmuCellAI and TIP portals.

RESULTS

4-SMGs were considered to be strongly associated with the prognosis of UVM and were good predictors of UVM prognosis. Multivariate analysis found that the model was an independent predictor of UVM, with patients in the low-risk group having higher overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores had good prognostic power. The high-risk group showed better results when receiving immunotherapy.

CONCLUSIONS

4-SMGs signature and nomogram showed excellent predictive performance and provided a new perspective for assessing pre-immune efficacy, which will facilitate future precision immuno-oncology studies.

Clinical relevance of CERK and SPHK1 in breast cancer and their association with metastasis and drug resistance

Despite numerous reports on the altered sphingolipids metabolism in human cancers, their clinical significance in breast cancer remains obscure. Previously, we identified the high levels of sphingolipids, ceramide phosphates and sphingosine phosphates, and the genes involved in their synthesis, CERK and SPHK1, in breast cancer patients. The present study aimed to determine the correlations of CERK and SPHK1 with clinical outcomes as well as metastasis and drug resistance markers. Both local and TCGA cohorts were analysed. High-confidence regulatory interaction network was constructed to find association of target genes with metastasis and drug resistance. Furthermore, correlations of CERK and SPHK1 with selected metastasis and drug resistance markers were validated in both cohorts. Overexpression of CERK and SPHK1 was associated with nodal metastasis, late tumor stage and high proliferation potency. In addition, increased CERK expression was also indicative of poor patient survival. Computational network analysis revealed the association of CERK and SPHK1 with known metastasis markers MMP-2 and MMP-9 and drug resistance markers ABCC1 and ABCG2. Correlation analysis confirmed the associations of target genes with these markers in both local as well as TCGA cohort. The above findings suggest clinical utility of CERK and SPHK1 as potential biomarkers in breast cancer patients and thus could provide novel leads in the development of therapeutics.

Inhibition of Ceramide Glycosylation Enhances Cisplatin Sensitivity in Cholangiocarcinoma by Limiting the Activation of the ERK Signaling Pathway

Cholangiocarcinoma (CCA) is an aggressive tumor of the biliary epithelium with poor survival that shows limited response to conventional chemotherapy. Increased expression of glucosylceramide synthase (GCS) contributes to drug resistance and the progression of various cancers; the expression profiles of GCS (UGCG) and the genes for glucocerebrosidases 1, 2, and 3 (GBA1, GBA2, and GBA3) were therefore studied in CCA. The biological functions of GCS for cell proliferation and cisplatin sensitivity in CCA were explored. GCS expression was higher in CCA tumor tissues than that of GBA1, GBA2, and GBA3. Verification of GCS expression in 29 paired frozen CCA tissues showed that 8 of 29 cases (27.6%) had high GCS expression. The expression of GCS and GBA2 was induced in CCA cell lines following low-dose cisplatin treatment. Suppression of GCS by either palmitoylamino-3-morpholino-1-propanol (PPMP), GCS knockdown or a combination of the two resulted in reduced cell proliferation. These treatments enhanced the effect of cisplatin-induced CCA cell death, increased the expression of apoptotic proteins and reduced phosphorylation of ERK upon cisplatin treatment. Taken together, inhibition of the GCS increased cisplatin-induced CCA apoptosis via the inhibition of the ERK signaling pathway. Thus, targeting GCS might be a strategy for CCA treatment.

Identification and validation of a prognostic risk-scoring model based on sphingolipid metabolism-associated cluster in colon adenocarcinoma

Colon adenocarcinoma (COAD) is the primary factor responsible for cancer-related mortalities in western countries, and its development and progression are affected by altered sphingolipid metabolism. The current study aimed at investigating the effects of sphingolipid metabolism-related (SLP) genes on multiple human cancers, especially on COAD. We obtained 1287 SLP genes from the GeneCard and MsigDb databases along with the public transcriptome data and the related clinical information. The univariate Cox regression analysis suggested that 26 SLP genes were substantially related to the prognosis of COAD, and a majority of SLP genes served as the risk genes for the tumor, insinuating a potential pathogenic effect of SLP in COAD development. Pan-cancer characterization of SLP genes summarized their expression traits, mutation traits, and methylation levels. Subsequently, we focused on the thorough research of COAD. With the help of unsupervised clustering, 1008 COAD patients were successfully divided into two distinct subtypes (C1 and C2). C1 subtype is characterized by a poor prognosis, activation of SLP pathways, high expression of SLP genes, disordered carcinogenic pathways, and immune microenvironment. Based on the clusters of SLP, we developed and validated a novel prognostic model, consisting of ANO1, C2CD4A, EEF1A2, GRP, HEYL, IGF1, LAMA2, LSAMP, RBP1, and TCEAL2, to quantitatively evaluate the clinical outcomes of COAD. The Kaplain-Meier survival curves and ROC curves highlighted the accuracy of our SLP model in both internal and external cohorts. Compared to normal colon tissues, expression of C2CD4A was detected to be significantly higher in COAD; whereas, expression levels of EEF1A2, IGF1, and TCEAL2 were detected to be significantly lower in COAD. Overall, our research emphasized the pathogenic role of SLP in COAD and found that targeting SLP might help improve the clinical outcomes of COAD. The risk model based on SLP metabolism provided a new horizon for prognosis assessment and customized patient intervention.

Comparative sphingolipidomic analysis reveals significant differences between doxorubicin-sensitive and -resistance MCF-7 cells

Drug resistance is responsible for the failure of many available anticancer drugs. Several studies have demonstrated the association between the alteration in sphingolipids (SPLs) and the development of drug resistance. To investigate the association between SPLs metabolism and doxorubicin (dox)-resistance in MCF-7 cells, a comparative sphingolipidomics analysis between dox-sensitive (parental) and -resistant MCF-7 cell lines along with validation by gene expression analysis were conducted. A total of 31 SPLs representing 5 subcategories were identified. The data obtained revealed that SPLs were clustered into two groups differentiating parental from dox-resistant cells. Eight SPLs were significantly altered in response to dox-resistance including SM (d18:1/16), SM (d18:1/24:2), SM (d18:1/24:0), SM (d18:1/20:0), SM (d18:1/23:1), HexCer (d18:1/24:0), SM (d18:1/15:0), DHSM (d18:0/20:0). The current study is the first to conclusively ascertain the potential involvement of dysregulated SPLs in dox-resistance in MCF-7 cells. SPLs metabolism in dox-resistant MCF-7 cells is oriented toward the downregulation of ceramides (Cer) and the concomitant increase in sphingomyelin (SM). Gene expression analysis has revealed that dox-resistant cells tend to escape from the Cer-related apoptosis by the activation of SM-Cer and GluCer-LacCer-ganglioside pathways. The enzymes that were correlated to the alteration in SPLs metabolism of dox-resistant MCF-7 cells and significantly altered in gene expression can represent potential targets that can represent a winning strategy for the future development of promising anticancer drugs.

Sphingomyelin synthase 2 but not sphingomyelin synthase 1 is upregulated in ovarian cancer and involved in migration, growth and survival via different mechanisms

Sphingomyelin synthase 1 (SMS1) and 2 (SMS2) are two enzymes required for sphingomyelin de novo synthesis, and their roles in tumor transformation and development have been recently recognized. In this work, we systematically evaluated the expression patterns of SMS1 and 2 in ovarian cancer patient samples and cell lines. Furthermore, we analyzed the functions of SMS2 and its underlying mechanisms. We observed a specific increase in SMS2 expression in ovarian cancer tissues compared to the adjacent normal ovary tissues in majority of patients' samples. This is regardless of their clinico-pathological characteristics. SMS1 expression was similar between ovarian cancer and its normal counterpart in 30 patients tested. The upregulation of SMS2 but not SMS1 was also reproducible in a panel of ovarian cancer cell lines. Functional analysis indicated that SMS2 plays a predominant role in promoting migration rather than proliferation in ovarian cancer. SMS2 depletion suppressed migration, growth and survival, and furthermore this was dependent on SMS2 baseline level in ovarian cancer cells. SMS2 inhibition significantly augmented cisplatin's efficacy. We further found that migration inhibition induced by SMS2 depletion was largely due to the suppression of RhoA/ROCK/LIMK/cofilin and RhoA/ROCK/FAK/paxillin pathways. In addition, lipid metabolism disruption, oxidative stress and damage, and impaired mitochondrial function contributed to the inhibitory effects of SMS2 depletion in ovarian cancer growth and survival. Our work demonstrates that SMS2 but not SMS1 is upregulated in ovarian cancer and involved in migration, growth and survival via different mechanisms. Our findings highlight the therapeutic value of SMS2 inhibition in the treatment of ovarian cancer.

Convergence of Plasma Metabolomics and Proteomics Analysis to Discover Signatures of High-Grade Serous Ovarian Cancer

Simple Summary

In-time diagnosing ovarian cancer, intractable cancer that has no symptoms can increase the survival of women. The aim of this study was to discover biomarkers from liquid biopsy samples using multi-omics approach, metabolomics and proteomics for the diagnosis of ovarian cancer. To verify our biomarker candidates, we conducted comparative analysis with other previous published studies. Despite the limitations of non-invasive samples, our findings are able to discover emerging properties through the interplay between metabolites and proteins and mechanism-based biomarkers through integrated protein and metabolite analysis.

Abstract

The 5-year survival rate in the early and late stages of ovarian cancer differs by 63%. In addition, a liquid biopsy is necessary because there are no symptoms in the early stage and tissue collection is difficult without using invasive methods. Therefore, there is a need for biomarkers to achieve this goal. In this study, we found blood-based metabolite or protein biomarker candidates for the diagnosis of ovarian cancer in the 20 clinical samples (10 ovarian cancer patients and 10 healthy control subjects). Plasma metabolites and proteins were measured and quantified using mass spectrometry in ovarian cancer patients and control groups. We identified the differential abundant biomolecules (34 metabolites and 197 proteins) and statistically integrated molecules of different dimensions to better understand ovarian cancer signal transduction and to identify novel biological mechanisms. In addition, the biomarker reliability was verified through comparison with existing research results. Integrated analysis of metabolome and proteome identified emerging properties difficult to grasp with the single omics approach, more reliably interpreted the cancer signaling pathway, and explored new drug targets. Especially, through this analysis, proteins (PPCS, PMP2, and TUBB) and metabolites (L-carnitine and PC-O (30:0)) related to the carnitine system involved in cancer plasticity were identified.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor-host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

Lipid Transfer Proteins and Membrane Contact Sites in Human Cancer

Lipid-transfer proteins (LTPs) were initially discovered as cytosolic factors that facilitate lipid transport between membrane bilayers in vitro. Since then, many LTPs have been isolated from bacteria, plants, yeast, and mammals, and extensively studied in cell-free systems and intact cells. A major advance in the LTP field was associated with the discovery of intracellular membrane contact sites (MCSs), small cytosolic gaps between the endoplasmic reticulum (ER) and other cellular membranes, which accelerate lipid transfer by LTPs. As LTPs modulate the distribution of lipids within cellular membranes, and many lipid species function as second messengers in key signaling pathways that control cell survival, proliferation, and migration, LTPs have been implicated in cancer-associated signal transduction cascades. Increasing evidence suggests that LTPs play an important role in cancer progression and metastasis. This review describes how different LTPs as well as MCSs can contribute to cell transformation and malignant phenotype, and discusses how “aberrant” MCSs are associated with tumorigenesis in human.

New insights into the roles and regulation of SphK2 as a therapeutic target in cancer chemoresistance

Chemoresistance is a complicated process developed by most cancers and accounts for the majority of relapse and metastasis in cancer. The main mechanisms of chemoresistance phenotype include increased expression and/or activated drug efflux pumps, altered DNA repair, altered metabolism of therapeutics as well as impaired apoptotic signaling pathways. Aberrant sphingolipid signaling has also recently received considerable attention in chemoresistance. Sphingolipid metabolites regulate main biological processes such as apoptosis, cell survival, proliferation, and differentiation. Two sphingosine kinases, SphK1 and SphK2, convert sphingosine to sphingosine-1-phosphate, an antiapoptotic bioactive lipid mediator. Numerous evidence has revealed the involvement of activated SphK1 in tumorigenesis and resistance, however, contradictory results have been found for the role of SphK2 in these functions. In some studies, overexpression of SphK2 suppressed cell growth and induced apoptosis. In contrast, some others have shown cell proliferation and tumor promotion effect for SphK2. Our understanding of the role of SphK2 in cancer does not have a sufficient integrity. The main focus of this review will be on the re-evaluation of the role of SphK2 in cell death and chemoresistance in light of our new understanding of molecular targeted therapy. We will also highlight the connections between SphK2 and the DNA damage response. Finally, we will provide our insight into the regulatory mechanisms of SphKs by two main categories, micro and long, noncoding RNAs as the novel players of cancer chemoresistance.

Iron granules in plasma cells

Resistance of cancer cells to chemotherapy is the first cause of cancer-associated death. Thus, new strategies to deal with the evasion of drug response and to improve clinical outcomes are needed. Genetic and epigenetic mechanisms associated with uncontrolled cell growth result in metabolism reprogramming. Cancer cells enhance anabolic pathways and acquire the ability to use different carbon sources besides glucose. An oxygen and nutrient-poor tumor microenvironment determines metabolic interactions among normal cells, cancer cells and the immune system giving rise to metabolically heterogeneous tumors which will partially respond to metabolic therapy. Here we go into the best-known cancer metabolic profiles and discuss several studies that reported tumors sensitization to chemotherapy by modulating metabolic pathways. Uncovering metabolic dependencies across different chemotherapy treatments could help to rationalize the use of metabolic modulators to overcome therapy resistance.