Investigation of lipid metabolism dysregulation and the effects on immune microenvironments in pan-cancer using multiple omics data

IGFBP1 promotes the proliferation and migration of lung adenocarcinoma cells through the PPARα pathway

BACKGROUND

The immune status is closely linked to cancer progression, metastasis, and prognosis. Lipid metabolism, crucial for reshaping immune status, plays a key role in regulating the advancement of lung adenocarcinoma (LUAD) and deserves further investigation.

METHODS

This study classifies LUAD patients into different immune subtypes based on lipid metabolism-related genes and compares the clinical characteristics among these subtypes. Single-multi COX analysis screens out key genes related to prognosis, and a risk feature and prognostic model are constructed. Cell cloning, scratch, transwell, western blotting and flow cytometry cell cycle analysis to detect the function of key genes. A subcutaneous tumor animal model is used to investigate the in vivo function and molecular mechanisms of key genes.

RESULTS

LUAD patients are classified into three immune subtypes, among which C3 subtype has lower immune status and higher frequency of gene mutations, and show lower immunoreactivity in immunotherapy. COX analysis identified a prognostic model for four lipid metabolism factors (IGFBP1, NR0B2, PPARA, and POMC). IGFBP1, a core gene in this model, is highly expressed in the C3 subtype. Functionally, knocking down IGFBP1 significantly inhibits tumor cell cloning, scratch, and migration abilities, and downregulates the expression of cell cycle and EMT-related proteins. Knocking down IGFBP1 significantly inhibits tumor burden (P < 0.05). Mechanistically, knocking down IGFBP1 inhibits the activation of PPARα to regulate tumor cell growth.

CONCLUSIONS

This study found that lipid metabolism genes are closely related to LUAD, and IGFBP1 may be a key gene in regulating tumor growth and development.

Fatty Acid Synthase Inhibitor Cerulenin Hinders Liver Cancer Stem Cell Properties through FASN/APP Axis as Novel Therapeutic Strategies

Hepatocellular carcinoma (HCC) poses significant treatment challenges due to high postoperative recurrence rates and the limited effectiveness of targeted medications. Researchers have identified the unique metabolic profiles of cancer stem cells (CSCs) as the primary drivers of cancer recurrence, metastasis, and drug resistance. Therefore, in order to address the therapeutic conundrum, this study focused on rewinding metabolic reprogramming of CSCs as a novel therapeutic strategy. HCC CSCs exhibited elevated fatty acid (FA) metabolism compared with parental cells. In order to specifically target FA metabolism in CSCs, we utilized cerulenin, a fatty acid synthase (FASN) inhibitor. Surprisingly, cerulenin can diminish CSC-like characteristics, including stemness gene expression, spherogenicity, tumorigenicity, and metastatic potential. In addition, sorafenib, a multikinase inhibitor used as targeted therapy for advanced HCC, was employed in combination with cerulenin, demonstrating a great synergistic effect, particularly in CSCs. Importantly, our RNA sequencing analysis disclosed that the amyloid protein precursor (APP) is a crucial downstream effector of FASN in regulating CSC properties. We found that APP plays a crucial role in CSCs' characteristics that can be inhibited by cerulenin. By focusing on FA metabolism, this study identified the FASN/APP axis as a viable target to develop a more potent therapy strategy for advanced HCC.

Cancer associated fibroblasts and metabolic reprogramming: unraveling the intricate crosstalk in tumor evolution

Metabolic reprogramming provides tumors with an energy source and biofuel to support their survival in the malignant microenvironment. Extensive research into the intrinsic oncogenic mechanisms of the tumor microenvironment (TME) has established that cancer-associated fibroblast (CAFs) and metabolic reprogramming regulates tumor progression through numerous biological activities, including tumor immunosuppression, chronic inflammation, and ecological niche remodeling. Specifically, immunosuppressive TME formation is promoted and mediators released via CAFs and multiple immune cells that collectively support chronic inflammation, thereby inducing pre-metastatic ecological niche formation, and ultimately driving a vicious cycle of tumor proliferation and metastasis. This review comprehensively explores the process of CAFs and metabolic regulation of the dynamic evolution of tumor-adapted TME, with particular focus on the mechanisms by which CAFs promote the formation of an immunosuppressive microenvironment and support metastasis. Existing findings confirm that multiple components of the TME act cooperatively to accelerate the progression of tumor events. The potential applications and challenges of targeted therapies based on CAFs in the clinical setting are further discussed in the context of advancing research related to CAFs.

Gut microbiome and plasma lipidome analysis reveals a specific impact of Clostridioides difficile infection on intestinal bacterial communities and sterol metabolism

Clostridioides difficile infection (CDI) causes alterations in the intestinal microbiota, frequently associated with changes in the gut metabolism of bile acids and cholesterol. In addition to the impact on microbiome composition and given the metabolic changes occurring during CDI, our work focuses on the importance to know the effects at the local and systemic levels, both during the infection and its treatment, by paying particular attention to plasma lipid metabolism due to its relationship with CDI pathogenesis. Specific changes, characterized by a loss of microbial richness and diversity and related to a reduction in short-chain acid-producing bacteria and an increase in bile salt hydrolase-producing bacteria, were observed in the gut microbiota of CDI patients, especially in those suffering from recurrent CDI (RCDI). However, gut microbiota showed its ability to restore itself after treatment, resembling healthy individuals, in those patients treated by fecal microbiome transfer (FMT), in contrast with those treated with antibiotics, and displaying increased levels of Eubacterium coprostanoligenes, a cholesterol-reducing anaerobe. Interestingly, changes in plasma lipidome revealed a global depletion in circulating lipids in CDI, with the largest impact on cholesteryl esters. CDI patients also showed a specific and consistent decrease in the levels of lipid species containing linoleic acid-an essential fatty acid-which were only partially recovered after antibiotic treatment. Analysis of the plasma lipidome reflects CDI impact on the gut microbiota and its metabolism, evidencing changes in sterol and fatty acid metabolism that are possibly related to specific alterations observed in gut microbial communities of CDI patients.

IMPORTANCE

There is increasing evidence about the influence the changes in microbiota and its metabolism has on numerous diseases and infections such as Clostridioides difficile infection (CDI). The knowledge of these changes at local and systemic levels can help us manage this infection to avoid recurrences and apply the best therapies, such as fecal microbiota transfer (FMT). This study shows a better restoration of the gut in FMT-treated patients than in antibiotic-treated patients, resembling healthy controls and showing increased levels of cholesterol-reducing bacteria. Furthermore, it evidences the CDI impact on plasma lipidome. We observed in CDI patients a global depletion in circulating lipids, particularly cholesteryl esters, and a specific decrease in linoleic acid-containing lipids, an essential fatty acid. Our observations could impact CDI management because the lipid content was only partially recovered after treatment, suggesting that continued nutritional support, aiming to restore healthy lipid levels, could be essential for a full recovery.

Harnessing lipid metabolism modulation for improved immunotherapy outcomes in lung adenocarcinoma

BACKGROUND

While anti-programmed cell death protein-1 (PD-1) monotherapy has shown effectiveness in treating lung cancer, its response rate is limited to approximately 20%. Recent research suggests that abnormal lipid metabolism in patients with lung adenocarcinoma may hinder the efficacy of anti-PD-1 monotherapy.

METHODS

Here, we delved into the patterns of lipid metabolism in patients with The Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) and their correlation with the immune microenvironment's cellular infiltration characteristics of the tumor. Furthermore, the lipid metabolism score (LMS) system was constructed, and based on the LMS system, we further performed screening for potential agents targeting lipid metabolism. The mechanism of MK1775 was further validated using RNA sequencing, co-culture technology, and in vivo experiments.

RESULTS

We developed an LSM system and identified a potential sensitizing agent, MK1775, which targets lipid metabolism and enhances the effects of anti-PD-1 treatment. Our results demonstrate that MK1775 inhibits tumor progression by influencing lipid crosstalk between tumor cells and tumor-associated macrophages and CD8+T cells, thereby increasing the effectiveness of anti-PD-1 treatment. Further, we found that MK1775 inhibited the phosphatidylinositol 3-kinase(PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling pathway, which on one hand downregulated FASN-mediated synthesis of fatty acids (FAs) to inhibit fatty acid oxidation of tumor-associated macrophages, and on the other hand, promoted IRF-mediated secretion of CXCL10 and CXCL11 to facilitate the infiltration of CD8+ T cells.

CONCLUSIONS

These findings emphasize the important role of lipid metabolism in shaping the complex tumor microenvironment. By manipulating the intricate intricacies of lipid metabolism within the tumor microenvironment, we can uncover and develop promising strategies to sensitize immunotherapy, potentially revolutionizing cancer treatment approaches.

A novel LGALS1-depended and immune-associated fatty acid metabolism risk model in acute myeloid leukemia stem cells

Leukemia stem cells (LSCs) are recognized as the root cause of leukemia initiation, relapse, and drug resistance. Lipid species are highly abundant and essential component of human cells, which often changed in tumor microenvironment. LSCs remodel lipid metabolism to sustain the stemness. However, there is no useful lipid related biomarker has been approved for clinical practice in AML prediction and treatment. Here, we constructed and verified fatty acid metabolism-related risk score (LFMRS) model based on TCGA database via a series of bioinformatics analysis, univariate COX regression analysis, and multivariate COX regression analysis, and found that the LFMRS model could be an independent risk factor and predict the survival time of AML patients combined with age. Moreover, we revealed that Galectin-1 (LGALS1, the key gene of LFMRS) was highly expressed in LSCs and associated with poor prognosis of AML patients, and LGALS1 repression inhibited AML cell and LSC proliferation, enhanced cell apoptosis, and decreased lipid accumulation in vitro. LGALS1 repression curbed AML progression, lipid accumulation, and CD8+ T and NK cell counts in vivo. Our study sheds light on the roles of LFMRS (especially LGALS1) model in AML, and provides information that may help clinicians improve patient prognosis and develop personalized treatment regimens for AML.

Expression characteristics of lipid metabolism-related genes and correlative immune infiltration landscape in acute myocardial infarction

Lipid metabolism is an important part of the heart's energy supply. The expression pattern and molecular mechanism of lipid metabolism-related genes (LMRGs) in acute myocardial infarction (AMI) are still unclear, and the link between lipid metabolism and immunity is far from being elucidated. In this study, 23 Common differentially expressed LMRGs were discovered in the AMI-related mRNA microarray datasets GSE61144 and GSE60993. These genes were mainly related to "leukotriene production involved in inflammatory response", "lipoxygenase pathway", "metabolic pathways", and "regulation of lipolysis in adipocytes" pathways. 12 LMRGs (ACSL1, ADCY4, ALOX5, ALOX5AP, CCL5, CEBPB, CEBPD, CREB5, GAB2, PISD, RARRES3, and ZNF467) were significantly differentially expressed in the validation dataset GSE62646 with their AUC > 0.7 except for ALOX5AP (AUC = 0.699). Immune infiltration analysis and Pearson correlation analysis explored the immune characteristics of AMI, as well as the relationship between these identified LMRGs and immune response. Lastly, the up-regulation of ACSL1, ALOX5AP, CEBPB, and GAB2 was confirmed in the mouse AMI model. Taken together, LMRGs ACSL1, ALOX5AP, CEBPB, and GAB2 are significantly upregulated in AMI patients' blood, peripheral blood of AMI mice, myocardial tissue of AMI mice, and therefore might be new potential biomarkers for AMI.

Longitudinal molecular profiling elucidates immunometabolism dynamics in breast cancer

Although metabolic reprogramming within tumor cells and tumor microenvironment (TME) is well described in breast cancer, little is known about how the interplay of immune state and cancer metabolism evolves during treatment. Here, we characterize the immunometabolic profiles of tumor tissue samples longitudinally collected from individuals with breast cancer before, during and after neoadjuvant chemotherapy (NAC) using proteomics, genomics and histopathology. We show that the pre-, on-treatment and dynamic changes of the immune state, tumor metabolic proteins and tumor cell gene expression profiling-based metabolic phenotype are associated with treatment response. Single-cell/nucleus RNA sequencing revealed distinct tumor and immune cell states in metabolism between cold and hot tumors. Potential drivers of NAC based on above analyses were validated in vitro. In summary, the study shows that the interaction of tumor-intrinsic metabolic states and TME is associated with treatment outcome, supporting the concept of targeting tumor metabolism for immunoregulation.

Inhibition of autophagy-related protein 7 enhances anti-tumor immune response and improves efficacy of immune checkpoint blockade in microsatellite instability colorectal cancer

BACKGROUND

The efficacy of anti-PD-1 therapy is primarily hindered by the limited T-cell immune response rate and immune evasion capacity of tumor cells. Autophagy-related protein 7 (ATG7) plays an important role in autophagy and it has been linked to cancer. However, the role of ATG7 in the effect of immune checkpoint blockade (ICB) treatment on high microsatellite instability (MSI-H)/mismatch repair deficiency (dMMR) CRC is still poorly understood.

METHODS

In this study, patients from the cancer genome altas (TCGA) COAD/READ cohorts were used to investigate the biological mechanism driving ATG7 development. Several assays were conducted including the colony formation, cell viability, qRT-PCR, western blot, immunofluorescence, flow cytometry, ELISA, immunohistochemistry staining and in vivo tumorigenicity tests.

RESULTS

We found that ATG7 plays a crucial role in MSI-H CRC. Its knockdown decreased tumor growth and caused an infiltration of CD8+ T effector cells in vivo. ATG7 inhibition restored surface major histocompatibility complex I (MHC-I) levels, causing improved antigen presentation and anti-tumor T cell response by activating reactive oxygen species (ROS)/NF-κB pathway. Meanwhile, ATG7 inhibition also suppressed cholesterol accumulation and augmentation of anti-tumor immune responses. Combining ATG7 inhibition and statins improved the therapeutic benefit of anti-PD-1 in MSI-H CRC. Importantly, CRC patients with high expression of both ATG7 and recombinant 3-hydroxy-3-methylglutaryl coenzyme A reductase (HMGCR) experienced worse prognosis compared to those with low ATG7 and HMGCR expression.

CONCLUSIONS

Inhibition of ATG7 leads to upregulation of MHC-I expression, augments immune response and suppresses cholesterol accumulation. These findings demonstrate that ATG7 inhibition has therapeutic potential and application of statins can increase the sensitivity to immune checkpoint inhibitors.

Identification of Plasma Lipid Alterations Associated with Melanoma Metastasis

The aim of this study was to apply a state-of-the-art quantitative lipidomic profiling platform to uncover lipid alterations predictive of melanoma progression. Our study included 151 melanoma patients; of these, 83 were without metastasis and 68 with metastases. Plasma samples were analyzed using a targeted Lipidyzer™ platform, covering 13 lipid classes and over 1100 lipid species. Following quality control filters, 802 lipid species were included in the subsequent analyses. Total plasma lipid contents were significantly reduced in patients with metastasis. Specifically, levels of two out of the thirteen lipid classes (free fatty acids (FFAs) and lactosylceramides (LCERs)) were significantly decreased in patients with metastasis. Three lipids (CE(12:0), FFA(24:1), and TAG47:2-FA16:1) were identified as more effective predictors of melanoma metastasis than the well-known markers LDH and S100B. Furthermore, the predictive value substantially improved upon combining the lipid markers. We observed an increase in the cumulative levels of five lysophosphatidylcholines (LPC(16:0); LPC(18:0); LPC(18:1); LPC(18:2); LPC(20:4)), each individually associated with an elevated risk of lymph node metastasis but not cutaneous or distant metastasis. Additionally, seventeen lipid molecules were linked to patient survival, four of which (CE(12:0), CE(14:0), CE(15:0), SM(14:0)) overlapped with the lipid panel predicting metastasis. This study represents the first comprehensive investigation of the plasma lipidome of melanoma patients to date. Our findings suggest that plasma lipid profiles may serve as important biomarkers for predicting clinical outcomes of melanoma patients, including the presence of metastasis, and may also serve as indicators of patient survival.

Tumour cell-expressed PD-L1 reprograms lipid metabolism via EGFR/ITGB4/SREBP1c signalling in liver cancer

Background & Aims

The programmed death-ligand 1 (PD-L1) is a major co-inhibitory checkpoint factor that controls T-cell activities in tumours. PD-L1 is expressed on immune cells and tumour cells. Whether tumour cell-expressed PD-L1 affects tumour cells in an immune cell-independent fashion remains largely elusive. In this study, we investigated the significance of tumour cell-expressed PD-L1 with a focus on downstream signals and changes in lipid metabolism.

Methods

Immune-independent functions of PD-L1 in tumour growth were investigated in vitro and in immuno-deficient mice in vivo. The global influence of PD-L1 in targeted/untargeted lipidomic metabolites was studied by comprehensive mass spectrometry-based metabolomic analysis in liver cancer. Effects on lipid metabolism were confirmed by triglyceride and cholesterol assays as well as by Oil Red O staining in liver, pancreatic, breast, and oesophageal squamous cancer. Underlying mechanisms were investigated by real-time quantitative PCR, Western blot analysis, co-immunoprecipitation, pull-down assays, immunofluorescence staining, and RNA sequencing.

Results

PD-L1 enhanced the accumulation of triglycerides, cholesterol, and lipid droplets in tumours. PD-L1 influenced targeted/untargeted lipidomic metabolites in hepatoma, including lipid metabolism, glucose metabolism, amino acid metabolism, nucleotide metabolism, and energy metabolism, suggesting that PD-L1 globally modulates the metabolic reprogramming of tumours. Mechanistically, PD-L1 activated epidermal growth factor receptor (EGFR) and/or integrin β4 (ITGB4) by forming a complex of PD-L1/EGFR/ITGB4 in the cell membrane, prior to activating PI3K/mTOR/SREBP1c signalling, leading to reprogramming of lipid metabolism in tumours. Functionally, PD-L1-mediated lipid metabolism reprogramming supported the tumour growth in vitro and in vivo through EGFR and/or ITGB4 in an immune cell-independent manner.

Conclusions

Our findings on lipogenesis and EGFR activation by tumour cell-expressed PD-L1 suggest that, in addition to its immunostimulatory effects, anti-PD-L1 may restrict lipid metabolism and EGFR/ITGB4 signalling in liver cancer therapy.

Impact and implications

In this study, we present evidence that PD-L1 drives the reprogramming of lipid metabolism in tumours. PD-L1 forms a complex with epidermal growth factor receptor (EGFR) and ITGB4, activating the PI3K/Akt/mTOR/SREBP1c signalling pathway and thereby contributing to lipid metabolism in cancer progression. Our findings offer novel insights into the mechanisms by which PD-L1 initiates the reprogramming of lipid metabolism in tumours. From a clinical perspective, the anti-PD-L1 antibody may alleviate resistance to the anti-EGFR antibody cetuximab and inhibit the reprogramming of lipid metabolism in tumours.

ACOT7 promotes retinoblastoma resistance to vincristine by regulating fatty acid metabolism reprogramming

The rate of vincristine (VCR) resistance in the treatment of retinoblastoma (RB) is relatively high, and the exact role and mechanism of autophagy and fatty acid (FA) metabolism in RB are still unknown. The aim of this study was to elucidate the molecular mechanism by which acyl-CoA thioesterase 7 (ACOT7) regulates FA metabolism and autophagy, which may lead to potential therapeutic strategies for RB. In the present study, the relationship between FA metabolism and cellular drug sensitivity was evaluated through ACOT7 overexpression or inhibition tests in RB-resistant cells. The lipase inhibitor orlistat and the autophagy inhibitor CQ were used to determine the effects of ACOT7 on FA metabolism, autophagy, and cellular drug sensitivity, as well as the therapeutic value of ACOT7 targeting. The results showed that ACOT7 was upregulated in VCR-resistant RB cells, significantly enhancing cell resistance and indicating that ACOT7 may serve as a biomarker for VCR resistance in RB cells. Knockdown of ACOT7 inhibited FA metabolism and reduced cell viability in VCR-resistant RB cells. The effect of ACOT7 overexpression was opposite to that of ACOT7 knockdown, and ACOT7 overexpression promoted autophagy in VCR-resistant RB cells. After treatment with orlistat or CQ, FA metabolism in VCR-resistant RB cells decreased, cell viability and autophagy were inhibited, EMT was inhibited, and the sensitivity of RB cells to VCR was increased. In conclusion, ACOT7 knockdown can mediate FA metabolism to inhibit autophagy and the migration of RB cells, thereby improving the sensitivity of RB cells to VCR.

Lipid metabolism-related long noncoding RNA RP11-817I4.1 promotes fatty acid synthesis and tumor progression in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common types of tumors. The influence of lipid metabolism disruption on the development of HCC has been demonstrated in published studies.

AIM

To establish an HCC prognostic model for lipid metabolism-related long non-coding RNAs (LMR-lncRNAs) and conduct in-depth research on the specific role of novel LMR-lncRNAs in HCC.

METHODS

Correlation and differential expression analyses of The Cancer Genome Atlas data were used to identify differentially expressed LMR-lncRNAs. Quantitative real-time polymerase chain reaction analysis was used to evaluate the expression of LMR-lncRNAs. Nile red staining was employed to observe intracellular lipid levels. The interaction between RP11-817I4.1, miR-3120-3p, and ATP citrate lyase (ACLY) was validated through the performance of dual-luciferase reporter gene and RIP assays.

RESULTS

Three LMR-lncRNAs (negative regulator of antiviral response, RNA transmembrane and coiled-coil domain family 1 antisense RNA 1, and RP11-817I4.1) were identified as predictive markers for HCC patients and were utilized in the construction of risk models. Additionally, proliferation, migration, and invasion were reduced by RP11-817I4.1 knockdown. An increase in lipid levels in HCC cells was significantly induced by RP11-817I4.1 through the miR-3120-3p/ACLY axis.

CONCLUSION

LMR-lncRNAs have the capacity to predict the clinical characteristics and prognoses of HCC patients, and the discovery of a novel LMR-lncRNAs, RP11-817I4.1, revealed its role in promoting lipid accumulation, thereby accelerating the onset and progression of HCC.

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.

A Novel Eight-Gene Signature for Lipid Metabolism Predicts the Progression of Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

Recently, studies on the mechanisms underlying lipid metabolic reprogramming in cancer have increased. However, its significance in cervical cancer remains unclear. In the present study, a prognostic signature was constructed for patients with cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) using the expression profiles of lipid metabolism-related genes (LMRGs). Furthermore, using various bioinformatics methods, a prognostic gene signature was developed for progression-free survival (PFS). This signature was externally validated using a cervical cancer dataset (GSE44001). The characteristics of the molecular subgroups of LMRGs were analyzed, and target LMRGs were identified via differential gene analysis of the expression profiles and weighted gene correlation network analysis. Thereafter, the identified target genes were used to develop the prognostic gene signature using univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. The performance of the LMRG signature was evaluated using Kaplan-Meier curves, time-dependent receiver operating characteristic curves, decision curve analysis, mutation landscapes, gene set enrichment analysis, and immune score calculation. As a result, a novel eight-LMRG signature comprising ALDH3B2, CERS3, FA2H, GLTP, NR1H3, PLIN3, SLC44A3, and SQLE was constructed. Using this gene signature, patients with CESC and significantly distinguished PFS were divided. This eight-LMRG signature exhibited independent prognostic potential and superior predictive performance compared with a previously developed 12-gene signature. Our findings suggest that our novel eight-LMRG signature contributes to the implementation of precision medicine strategies for managing patients with cervical cancer by facilitating CESC prognosis.

Transcriptomic analysis reveals the lipid metabolism-related gene regulatory characteristics and potential therapeutic agents for myocardial ischemia-reperfusion injury

Background

Impaired energy balance caused by lipid metabolism dysregulation is an essential mechanism of myocardial ischemia-reperfusion injury (MI/RI). This study aims to explore the lipid metabolism-related gene (LMRG) expression patterns in MI/RI and to find potential therapeutic agents.

Methods

Differential expression analysis was performed to screen the differentially expressed genes (DEGs) and LMRGs in the MI/RI-related dataset GSE61592. Enrichment and protein-protein interaction (PPI) analyses were performed to identify the key signaling pathways and genes. The expression trends of key LMRGs were validated by external datasets GSE160516 and GSE4105. The corresponding online databases predicted miRNAs, transcription factors (TFs), and potential therapeutic agents targeting key LMRGs. Finally, the identified LMRGs were confirmed in the H9C2 cell hypoxia-reoxygenation (H/R) model and the mouse MI/RI model.

Results

Enrichment analysis suggested that the "lipid metabolic process" was one of the critical pathways in MI/RI. Further differential expression analysis and PPI analysis identified 120 differentially expressed LMRGs and 15 key LMRGs. 126 miRNAs, 55 TFs, and 51 therapeutic agents were identified targeting these key LMRGs. Lastly, the expression trends of Acadm, Acadvl, and Suclg1 were confirmed by the external datasets, the H/R model and the MI/RI model.

Conclusion

Acadm, Acadvl, and Suclg1 may be the key genes involved in the MI/RI-related lipid metabolism dysregulation; and acting upon these factors may serve as a potential therapeutic strategy.

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.

Analyzing the impact of metabolism on immune cells in tumor microenvironment to promote the development of immunotherapy

Tumor metabolism and tumor immunity are inextricably linked. Targeting the metabolism of tumors is a point worth studying in tumor immunotherapy. Recently, the influence of the metabolism of tumors and immune cells on the occurrence, proliferation, metastasis, and prognosis of tumors has attracted more attention. Tumor tissue forms a specific tumor microenvironment (TME). In addition to tumor cells, there are also immune cells, stromal cells, and other cells in TME. To adapt to the environment, tumor cells go through the metabolism reprogramming of various substances. The metabolism reprogramming of tumor cells may further affect the formation of the tumor microenvironment and the function of a variety of cells, especially immune cells, eventually promoting tumor development. Therefore, it is necessary to study the metabolism of tumor cells and its effects on immune cells to guide tumor immunotherapy. Inhibiting tumor metabolism may restore immune balance and promote the immune response in tumors. This article will describe glucose metabolism, lipid metabolism, amino acid metabolism, and immune cells in tumors. Besides, the impact of metabolism on the immune cells in TME is also discussed for analyzing and exploring tumor immunotherapy.

Lipid exchange at ER-trans-Golgi contact sites governs polarized cargo sorting

Oxysterol binding protein (OSBP) extracts cholesterol from the ER to deliver it to the TGN via counter exchange and subsequent hydrolysis of the phosphoinositide PI(4)P. Here, we show that this pathway is essential in polarized epithelial cells where it contributes not only to the proper subcellular distribution of cholesterol but also to the trans-Golgi sorting and trafficking of numerous plasma membrane cargo proteins with apical or basolateral localization. Reducing the expression of OSBP, blocking its activity, or inhibiting a PI4Kinase that fuels OSBP with PI(4)P abolishes the epithelial phenotype. Waves of cargo enrichment in the TGN in phase with OSBP and PI(4)P dynamics suggest that OSBP promotes the formation of lipid gradients along the TGN, which helps cargo sorting. During their transient passage through the trans-Golgi, polarized plasma membrane proteins get close to OSBP but fail to be sorted when OSBP is silenced. Thus, OSBP lipid exchange activity is decisive for polarized cargo sorting and distribution in epithelial cells.

Bridging the gap between clear cell renal cell carcinoma and cutaneous melanoma: the role of SCARB1 in dysregulated cholesterol metabolism

OBJECTIVE

The metabolism of cholesterol has been found to be closely related to the proliferation, invasion, and metastasis of tumors. The purpose of this study was to investigate the correlation between cholesterol metabolic genes and the prognosis of clear cell renal cell carcinoma (ccRCC).

METHODS

Gene expression profiles and clinical information of individuals diagnosed with prevalent malignant tumors were obtained from the TCGA database. For survival analysis, Kaplan-Meier curves were used. Consensus clustering was utilized to identify distinct molecular clusters. LASSO regression analysis was utilized to construct a novel prognostic signature. Differential analysis was used to analyze the differences in gene expression and various evaluation indicators between different subgroups. RT-qPCR and Immunohistochemistry were performed to examine the gene expression. Small interfering RNA transfection, CCK-8, and clone formation assays were conducted to verify the function of the target gene in ccRCC cell lines.

RESULTS

Based on genes involved in cholesterol metabolism related to survival, two molecular ccRCC subtypes were identified with distinct clinical, immune, and biological features. A molecular signature which would be utilized to evaluate the prognosis and the immune status of the tumor microenvironment of ccRCC patients was also established. The SCARB1-mediated cholesterol-dependent metabolism occurred both in ccRCC and skin cutaneous melanoma.

CONCLUSION

A gene signature related to cholesterol metabolism was developed and validated to forecast the prognosis of ccRCC, demonstrating a correlation with immune infiltration. Cholesterol metabolic genes such as SCARB1, were expected to contribute to the diagnosis and precision treatment of both ccRCC and skin cutaneous melanoma.

Metabolomics of head and neck cancer in biofluids: an integrative systematic review

INTRODUCTION

Head and neck cancer (HNC) is the fifth most common cancer globally. Diagnosis at early stages are critical to reduce mortality and improve functional and esthetic outcomes associated with HNC. Metabolomics is a promising approach for discovery of biomarkers and metabolic pathways for risk assessment and early detection of HNC.

OBJECTIVES

To summarize and consolidate the available evidence on metabolomics and HNC in plasma/serum, saliva, and urine.

METHODS

A systematic search of experimental research was executed using PubMed and Web of Science. Available data on areas under the curve was extracted. Metabolic pathway enrichment analysis were performed to identify metabolic pathways altered in HNC. Fifty-four studies were eligible for data extraction (33 performed in plasma/serum, 15 in saliva and 6 in urine).

RESULTS

Metabolites with high discriminatory performance for detection of HNC included single metabolites and combination panels of several lysoPCs, pyroglutamate, glutamic acid, glucose, tartronic acid, arachidonic acid, norvaline, linoleic acid, propionate, acetone, acetate, choline, glutamate and others. The glucose-alanine cycle and the urea cycle were the most altered pathways in HNC, among other pathways (i.e. gluconeogenesis, glycine and serine metabolism, alanine metabolism, etc.). Specific metabolites that can potentially serve as complementary less- or non-invasive biomarkers, as well as metabolic pathways integrating the data from the available studies, are presented.

CONCLUSION

The present work highlights utility of metabolite-based biomarkers for risk assessment, early detection, and prognostication of HNC, as well as facilitates incorporation of available metabolomics studies into multi-omics data integration and big data analytics for personalized health.

Lipid Metabolic Regulatory Crosstalk Between Cancer Cells and Tumor-Associated Macrophages

In the tumor microenvironment, tumor-associated macrophages (TAMs) are one of the most abundant cell populations, playing key roles in tumorigenesis, chemoresistance, immune evasion, and metastasis. There is an important interaction between TAMs and cancer cells: on the one hand, tumors control the function of infiltrating macrophages, contributing to reprogramming of TAMs, and on the other hand, TAMs affect the growth of cancer cells. This review focuses on lipid metabolism changes in the complex relationship between cancer cells and TAMs. We discuss how lipid metabolism in cancer cells affects macrophage phenotypic and metabolic changes and, subsequently, how altered lipid metabolism of TAMs influences tumor progression. Identifying the metabolic changes that influence the complex interaction between tumor cells and TAMs is also an important step in exploring new therapeutic approaches that target metabolic reprogramming of immune cells to enhance their tumoricidal potential and bypass therapy resistance. Our work may provide new targets for antitumor therapies.

DBLiPro: A Database for Lipids and Proteins in Human Lipid Metabolism

To help researchers in the field of biology, medicine, chemistry, and materials science to use lipidomic data conveniently, there is an urgent need to develop a platform that provides a systematic knowledgebase of human lipid metabolism and lipidome-centric omics analysis tools. DBLiPro is a user-friendly webserver allowing for access to human metabolism-related lipids and proteins knowledge database and an interactive bioinformatics integrative analysis workflow for lipidomics, transcriptomics, and proteomics data. In DBLiPro, there are 3109 lipid-associated proteins (LAPs) and 2098 lipid metabolites in the knowledge base section, which were obtained from Uniprot, Kyoto Encyclopedia of Genes and Genomes (KEGG) and were further annotated by information from other public resources in the knowledge base section, such as RaftProt and PubChem. DBLiPro offers a step-by-step interactive analysis workflow for lipidomics, transcriptomics, proteomics, and their integrating multi-omics analysis focusing on the human lipid metabolism. In summary, DBLiPro is capable of helping users discover key molecules (lipids and proteins) in human lipid metabolism and investigate lipid-protein functions underlying mechanisms based on their own omics data. The DBLiPro is freely available at http://lipid.cloudna.cn/home.

Disturbed Plasma Lipidomic Profiles in Females with Diffuse Large B-Cell Lymphoma: A Pilot Study

Lipidome dysregulation is a hallmark of cancer and inflammation. The global plasma lipidome and sub-lipidome of inflammatory pathways have not been reported in diffuse large B-cell lymphoma (DLBCL). In a pilot study of plasma lipid variation in female DLBCL patients and BMI-matched disease-free controls, we performed targeted lipidomics using LC-MRM to quantify lipid mediators of inflammation and immunity, and those known or hypothesised to be involved in cancer progression: sphingolipids, resolvin D1, arachidonic acid (AA)-derived oxylipins, such as hydroxyeicosatetraenoic acids (HETEs) and dihydroxyeicosatrienoic acids, along with their membrane structural precursors. We report on the role of the eicosanoids in the separation of DLBCL from controls, along with lysophosphatidylinositol LPI 20:4, implying notable changes in lipid metabolic and/or signalling pathways, particularly pertaining to AA lipoxygenase pathway and glycerophospholipid remodelling in the cell membrane. We suggest here the set of S1P, SM 36:1, SM 34:1 and PI 34:1 as DLBCL lipid signatures which could serve as a basis for the prospective validation in larger DLBCL cohorts. Additionally, untargeted lipidomics indicates a substantial change in the overall lipid metabolism in DLBCL. The plasma lipid profiling of DLBCL patients helps to better understand the specific lipid dysregulations and pathways in this cancer.

Insight of a lipid metabolism prognostic model to identify immune landscape and potential target for retroperitoneal liposarcoma

Introduction

The exploration of lipid metabolism dysregulation may provide novel perspectives for retroperitoneal liposarcoma (RPLS). In our study, we aimed to investigate potential targets and facilitate further understanding of immune landscape in RPLS, through lipid metabolism-associated genes (LMAGs) based prognostic model.

Methods

Gene expression profiles and corresponding clinical information of 234 cases were enrolled from two public databases and the largest retroperitoneal tumor research center of East China, including cohort-TCGA (n=58), cohort-GSE30929 (n=92), cohort-FD (n=50), cohort-scRNA-seq (n=4) and cohort-validation (n=30). Consensus clustering analysis was performed to identify lipid metabolism-associated molecular subtypes (LMSs). A prognostic risk model containing 13 LMAGs was established using LASSO algorithm and multivariate Cox analysis in cohort-TCGA. ESTIMATE, CIBERSORT, XCELL and MCP analyses were performed to visualize the immune landscape. WGCNA was used to identify three hub genes among the 13 model LMAGs, and preliminarily validated in both cohort-GSE30929 and cohort-FD. Moreover, TIMER was used to visualize the correlation between antigen-presenting cells and potential targets. Finally, single-cell RNA-sequencing (scRNA-seq) analysis of four RPLS and multiplexed immunohistochemistry (mIHC) were performed in cohort-validation to validate the discoveries of bioinformatics analysis.

Results

LMS1 and LMS2 were characterized as immune-infiltrated and -excluded tumors, with significant differences in molecular features and clinical prognosis, respectively. Elongation of very long chain fatty acids protein 2 (ELOVL2), the enzyme that catalyzed the elongation of long chain fatty acids, involved in the maintenance of lipid metabolism and cellular homeostasis in normal cells, was identified and negatively correlated with antigen-presenting cells and identified as a potential target in RPLS. Furthermore, ELOVL2 was enriched in LMS2 with significantly lower immunoscore and unfavorable prognosis. Finally, a high-resolution dissection through scRNA-seq was performed in four RPLS, revealing the entire tumor ecosystem and validated previous findings.

Discussion

The LMS subgroups and risk model based on LMAGs proposed in our study were both promising prognostic classifications for RPLS. ELOVL2 is a potential target linking lipid metabolism to immune regulations against RPLS, specifically for patients with LMS2 tumors.

The Metabolism and Immune Environment in Diffuse Large B-Cell Lymphoma

Cells utilize different metabolic processes to maintain their growth and differentiation. Tumor cells have made some metabolic changes to protect themselves from malnutrition. These metabolic alterations affect the tumor microenvironment and macroenvironment. Developing drugs targeting these metabolic alterations could be a good direction. In this review, we briefly introduce metabolic changes/regulations of the tumor macroenvironment and microenvironment and summarize potential drugs targeting the metabolism in diffuse large B-cell lymphoma.

Plasma lipid levels are associated with the CD8+ T-cell infiltration and prognosis of patients with pancreatic cancer

BACKGROUND

Recently, researchers have found that the tumour microenvironment plays an important role in tumours. We aimed to investigate the effects of plasma lipids on the prognosis of patients with pancreatic cancer and the infiltration of CD8+ T lymphocytes in tumour tissue.

METHODS

We enrolled patients with pancreatic ductal adenocarcinoma (PDAC) who underwent pancreatic surgery between 2012 and 2021. Clinical pathological data were recorded; total cholesterol (TC) and triglyceride (TG) levels were measured; and tissue samples were collected. A tissue microarray was generated using collected tissue samples, and CD8 staining was performed to determine immunoreactive scores (IRSs). The correlations of TC and TG levels with clinicopathological characteristics and prognosis were analysed. Finally, the correlations of TC and TG levels with CD8+ T-cell infiltration were analysed.

RESULTS

A total of 90 eligible PDAC patients were included. TC levels were significantly correlated with tumour grade and lymph node metastasis, and TG levels were significantly correlated with perineural invasion. The Kaplan-Meier survival analysis results indicated that the prognosis in the high TC group was significantly worse than that in the low TC group, and the prognosis in the high TG group was significantly worse than that in the low TG group. Cox multivariate analysis indicated that TC was an independent indicator of poor prognosis of pancreatic cancer patients after surgery. Spearman correlation analysis indicated that there were significant negative correlations between CD8 IRS and TC and between CD8 IRS and TG.

CONCLUSIONS

TC and TG levels are significantly related to the prognosis of pancreatic cancer patients. They may be associated with tumour progression to higher grades, lymph node metastasis and/or nerve invasion. More importantly, TC and TG may reduce CD8+ T lymphocyte infiltration into pancreatic cancer tissue, affecting antitumour immune functions and immunotherapy efficacy.

The role of metabolic reprogramming of tumor-associated macrophages in shaping the immunosuppressive tumor microenvironment

Macrophages are potent immune effector cells in innate immunity and exert dual-effects in the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) make up a significant portion of TME immune cells. Similar to M1/M2 macrophages, TAMs are also highly plastic, and their functions are regulated by cytokines, chemokines and other factors in the TME. The metabolic changes in TAMs are significantly associated with polarization towards a protumour or antitumour phenotype. The metabolites generated via TAM metabolic reprogramming in turn promote tumor progression and immune tolerance. In this review, we explore the metabolic reprogramming of TAMs in terms of energy, amino acid and fatty acid metabolism and the potential roles of these changes in immune suppression.

Targeting inhibition of prognosis-related lipid metabolism genes including CYP19A1 enhances immunotherapeutic response in colon cancer

BACKGROUND

Lipid metabolic reprogramming in colon cancer shows a potential impact on tumor immune microenvironment and is associated with response to immunotherapy. Therefore, this study aimed to develop a lipid metabolism-related prognostic risk score (LMrisk) to provide new biomarkers and combination therapy strategies for colon cancer immunotherapy.

METHODS

Differentially expressed lipid metabolism-related genes (LMGs) including cytochrome P450 (CYP) 19A1 were screened to construct LMrisk in TCGA colon cancer cohort. The LMrisk was then validated in three GEO datasets. The differences of immune cell infiltration and immunotherapy response between LMrisk subgroups were investigated via bioinformatic analysis. These results were comfirmed by in vitro coculture of colon cancer cells with peripheral blood mononuclear cells, human colon cancer tissue microarray analysis, multiplex immunofluorescence staining and mouse xenograft models of colon cancer.

RESULTS

Six LMGs including CYP19A1, ALOXE3, FABP4, LRP2, SLCO1A2 and PPARGC1A were selected to establish the LMrisk. The LMrisk was positively correlated with the abundance of macrophages, carcinoma-associated fibroblasts (CAFs), endothelial cells and the levels of biomarkers for immunotherapeutic response including programmed cell death ligand 1 (PD-L1) expression, tumor mutation burden and microsatellite instability, but negatively correlated with CD8⁺ T cell infiltration levels. CYP19A1 protein expression was an independent prognostic factor, and positively correlated with PD-L1 expression in human colon cancer tissues. Multiplex immunofluorescence analyses revealed that CYP19A1 protein expression was negatively correlated with CD8⁺ T cell infiltration, but positively correlated with the levels of tumor-associated macrophages, CAFs and endothelial cells. Importantly, CYP19A1 inhibition downregulated PD-L1, IL-6 and TGF-β levels through GPR30-AKT signaling, thereby enhancing CD8⁺ T cell-mediated antitumor immune response in vitro co-culture studies. CYP19A1 inhibition by letrozole or siRNA strengthened the anti-tumor immune response of CD8⁺ T cells, induced normalization of tumor blood vessels, and enhanced the efficacy of anti-PD-1 therapy in orthotopic and subcutaneous mouse colon cancer models.

CONCLUSION

A risk model based on lipid metabolism-related genes may predict prognosis and immunotherapeutic response in colon cancer. CYP19A1-catalyzed estrogen biosynthesis promotes vascular abnormality and inhibits CD8⁺ T cell function through the upregulation of PD-L1, IL-6 and TGF-β via GPR30-AKT signaling. CYP19A1 inhibition combined with PD-1 blockade represents a promising therapeutic strategy for colon cancer immunotherapy.

Blood-based liquid biopsy: insights into early detection, prediction, and treatment monitoring of bladder cancer

Bladder cancer (BC) is a clinical challenge worldwide with late clinical presentation, poor prognosis, and low survival rates. Traditional cystoscopy and tissue biopsy are routine methods for the diagnosis, prognosis, and monitoring of BC. However, due to the heterogeneity and limitations of tumors, such as aggressiveness, high cost, and limited applicability of longitudinal surveillance, the identification of tumor markers has attracted significant attention in BC. Over the past decade, liquid biopsies (e.g., blood) have proven to be highly efficient methods for the discovery of BC biomarkers. This noninvasive sampling method is used to analyze unique tumor components released into the peripheral circulation and allows serial sampling and longitudinal monitoring of tumor progression. Several liquid biopsy biomarkers are being extensively studied and have shown promising results in clinical applications of BC, including early detection, detection of microscopic residual disease, prediction of recurrence, and response to therapy. Therefore, in this review, we aim to provide an update on various novel blood-based liquid biopsy markers and review the advantages and current limitations of liquid biopsy in BC therapy. The role of blood-based circulating tumor cells, circulating tumor DNA, cell-free RNA, exosomes, metabolomics, and proteomics in diagnosis, prognosis, and treatment monitoring, and their applicability to the personalized management of BC, are highlighted.

A lipid metabolism-based prognostic risk model for HBV-related hepatocellular carcinoma

BACKGROUND

Up to 85% of hepatocellular carcinoma (HCC) cases in China can be attributed to infection of hepatitis B virus (HBV). Lipid metabolism performs important function in hepatocarcinogenesis of HBV-related liver carcinoma. However, limited studies have explored the prognostic role of lipid metabolism in HBV-related HCC. This study established a prognostic model to stratify HBV-related HCC based on lipid metabolisms.

METHODS

Based on The Cancer Genome Atlas HBV-related HCC samples, this study selected prognosis-related lipid metabolism genes and established a prognosis risk model by performing uni- and multi-variate Cox regression methods. The final markers used to establish the model were selected through the least absolute shrinkage and selection operator method. Analysis of functional enrichment, immune landscape, and genomic alteration was utilized to investigate the inner molecular mechanism involved in prognosis.

RESULTS

The risk model independently stratified HBV-infected patients with liver cancer into two risk groups. The low-risk groups harbored longer survival times (with P < 0.05, log-rank test). TP53, LRP1B, TTN, and DNAH8 mutations and high genomic instability occurred in high-risk groups. Low-risk groups harbored higher CD8 T cell infiltration and BTLA expression. Lipid-metabolism (including "Fatty acid metabolism") and immune pathways were significantly enriched (P < 0.05) in the low-risk groups.

CONCLUSIONS

This study established a robust model to stratify HBV-related HCC effectively. Analysis results decode in part the heterogeneity of HBV-related liver cancer and highlight perturbation of lipid metabolism in HBV-related HCC. This study's findings could facilitate patients' clinical classification and give hints for treatment selection.

Unravelling the role of obesity and lipids during tumor progression

The dysregulation of the biochemical pathways in cancer promotes oncogenic transformations and metastatic potential. Recent studies have shed light on how obesity and altered lipid metabolism could be the driving force for tumor progression. Here, in this review, we focus on liver cancer and discuss how obesity and lipid-driven metabolic reprogramming affect tumor, immune, and stroma cells in the tumor microenvironment and, in turn, how alterations in these cells synergize to influence and contribute to tumor growth and dissemination. With increasing evidence on how obesity exacerbates inflammation and immune tolerance, we also touch upon the impact of obesity and altered lipid metabolism on tumor immune escape.

The Activation of PPARγ by (2Z,4E,6E)-2-methoxyocta-2,4,6-trienoic Acid Counteracts the Epithelial–Mesenchymal Transition Process in Skin Carcinogenesis

Cutaneous squamous cell carcinoma (cSCC) is the most common UV-induced keratinocyte-derived cancer, and its progression is characterized by the epithelial–mesenchymal transition (EMT) process. We previously demonstrated that PPARγ activation by 2,4,6-octatrienoic acid (Octa) prevents cutaneous UV damage. We investigated the possible role of the PPARγ activators Octa and the new compound (2Z,4E,6E)-2-methoxyocta-2,4,6-trienoic acid (A02) in targeting keratinocyte-derived skin cancer. Like Octa, A02 exerted a protective effect against UVB-induced oxidative stress and DNA damage in NHKs. In the squamous cell carcinoma A431 cells, A02 inhibited cell proliferation and increased differentiation markers’ expression. Moreover, Octa and even more A02 counteracted the TGF-β1-dependent increase in mesenchymal markers, intracellular ROS, the activation of EMT-related signal transduction pathways, and cells’ migratory capacity. Both compounds, especially A02, counterbalanced the TGF-β1-induced cell membrane lipid remodeling and the release of bioactive lipids involved in EMT. In vivo experiments on a murine model useful to study cell proliferation in adult animals showed the reduction of areas characterized by active cell proliferation in response to A02 topical treatment. In conclusion, targeting PPARγ may be useful for the prevention and treatment of keratinocyte-derived skin cancer.

Monoacylglycerol lipase regulates macrophage polarization and cancer progression in uveal melanoma and pan-cancer

Background

Although lipid metabolism has been proven to play a key role in the development of cancer, its significance in uveal melanoma (UM) has not yet been elucidated in the available literature.

Methods

To identify the expression patterns of lipid metabolism in 80 UM patients from the TCGA database, 47 genes involved in lipid metabolism were analyzed. Consensus clustering revealed two distinct molecular groups. ESTIMATE, TIMER, and ssGSEA analyses were done to identify the differences between the two subgroups in tumor microenvironment (TME) and immune state. Using Cox regression and Lasso regression analysis, a risk model based on differentially expressed genes (DEGs) was developed. To validate the expression of monoacylglycerol lipase (MGLL) and immune infiltration in diverse malignancies, a pan-cancer cohort from the UCSC database was utilized. Next, a single-cell sequencing analysis on UM patients from the GEO data was used to characterize the lipid metabolism in TME and the role of MGLL in UM. Finally, in vitro investigations were utilized to study the involvement of MGLL in UM.

Results

Two molecular subgroups of UM patients have considerably varied survival rates. The majority of DEGs between the two subgroups were associated with immune-related pathways. Low immune scores, high tumor purity, a low number of immune infiltrating cells, and a comparatively low immunological state were associated with a more favorable prognosis. An examination of GO and KEGG data demonstrated that the risk model based on genes involved with lipid metabolism can accurately predict survival in patients with UM. It has been demonstrated that MGLL, a crucial gene in this paradigm, promotes the proliferation, invasion, and migration of UM cells. In addition, we discovered that MGLL is strongly expressed in macrophages, specifically M2 macrophages, which may play a function in the M2 polarization of macrophages and M2 macrophage activation in cancer cells.

Conclusion

This study demonstrates that the risk model based on lipid metabolism may be useful for predicting the prognosis of patients with UM. By promoting macrophage M2 polarization, MGLL contributes to the evolution of malignancy in UM, suggesting that it may be a therapeutic target for UM.

Prognostic Role of Long-Chain Acyl-Coenzyme A Synthetase Family Genes in Patients with Clear Cell Renal Cell Carcinoma: A Comprehensive Bioinformatics Analysis Confirmed with External Validation Cohorts

INTRODUCTION

We aimed to determine the prognostic role of long-chain acyl-CoA synthetases (ACSLs) as a disease marker for kidney clear cell carcinoma (KIRC).

PATIENTS AND METHODS

The Cancer Genome Atlas (TCGA) data were accessed via open access LinkedOmics database for KIRC. Provisional datasets were used for analysis as previously described and gene expression quantification data were downloaded. The corresponding clinical information of patients also were obtained from the database. Five ACSL family members, ACSL1, ACSL3, ACSL4, ACSL5, and ACSL6, were investigated in the TCGA-KIRC cohort. Xena browser, cBioPortal and UALCAN, and Cancer Cell Line Encyclopedia (CCLE) databases were also used to confirm the results. External validation was performed using patient cohorts from the Gene Expression Omnibus (GEO-NCBI) database. Finally, the protein-protein interaction (PPI) was constructed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software.

RESULTS

Pathological T3-T4 stage tumors had significantly lower ACSL1 mRNA expression (P = .009). Patients with pathologically confirmed metastasis exhibited significantly lower expression, as well (P = .02). ACSL1 mRNA expression was associated with overall survival (OS) and negatively correlated with OS time. Univariate and multivariate analyses showed that lower ACSL1 mRNA expression level was associated with mortality. Moreover, ACSL1 mRNA expression was exhibited significant difference in some VHL gene region mutations and PBRM1_p.R1010 mutation, and negatively correlated with HIF1-alpha mRNA expression (P < .001). Confirmatory analyses and external validation also revealed similar findings.

CONCLUSION

Lowered ACSL1 mRNA expression is associated with worse tumor histopathology and poor overall survival in KIRC. It may be used for prognostic marker for KIRC.

Targeting lipid metabolism for ferroptotic cancer therapy

It has been 10 years since the concept of ferroptosis was put forward and research focusing on ferroptosis has been increasing continuously. Ferroptosis is driven by iron-dependent lipid peroxidation, which can be antagonized by glutathione peroxidase 4 (GPX4), ferroptosis inhibitory protein 1 (FSP1), dihydroorotate dehydrogenase (DHODH) and Fas-associated factor 1 (FAF1). Various cellular metabolic events, including lipid metabolism, can modulate ferroptosis sensitivity. It is worth noting that the reprogramming of lipid metabolism in cancer cells can promote the occurrence and development of tumors. The metabolic flexibility of cancer cells opens the possibility for the coordinated targeting of multiple lipid metabolic pathways to trigger cancer cells ferroptosis. In addition, cancer cells must obtain immortality, escape from programmed cell death including ferroptosis, to promote cancer progression, which provides new perspectives for improving cancer therapy. Targeting the vulnerability of ferroptosis has received attention as one of the significant possible strategies to treat cancer given its role in regulating tumor cell survival. We review the impact of iron and lipid metabolism on ferroptosis and the potential role of the crosstalk of lipid metabolism reprogramming and ferroptosis in antitumor immunity and sum up agents targeting lipid metabolism and ferroptosis for cancer therapy.

Reprogramming the tumor microenvironment with biotechnology

The tumor microenvironment (TME) is a unique environment that is developed by the tumor and controlled by tumor-induced interactions with host cells during tumor progression. The TME includes immune cells, which can be classified into two types: tumor- antagonizing and tumor-promoting immune cells. Increasing the tumor treatment responses is associated with the tumor immune microenvironment. Targeting the TME has become a popular topic in research, which includes polarizing macrophage phenotype 2 into macrophage phenotype 1 using Toll-like receptor agonists with cytokines, anti-CD47, and anti-SIPRα. Moreover, inhibiting regulatory T cells through blockades and depletion restricts immunosuppressive cells in the TME. Reprogramming T cell infiltration and T cell exhaustion improves tumor infiltrating lymphocytes, such as CD8⁺ or CD4⁺ T cells. Targeting metabolic pathways, including glucose, lipid, and amino acid metabolisms, can suppress tumor growth by restricting the absorption of nutrients and adenosine triphosphate energy into tumor cells. In conclusion, these TME reprogramming strategies exhibit more effective responses using combination treatments, biomaterials, and nanoparticles. This review highlights how biomaterials and immunotherapy can reprogram TME and improve the immune activity.

Construction of a lipid metabolism-related risk model for hepatocellular carcinoma by single cell and machine learning analysis

One of the most common cancers is hepatocellular carcinoma (HCC). Numerous studies have shown the relationship between abnormal lipid metabolism-related genes (LMRGs) and malignancies. In most studies, the single LMRG was studied and has limited clinical application value. This study aims to develop a novel LMRG prognostic model for HCC patients and to study its utility for predictive, preventive, and personalized medicine. We used the single-cell RNA sequencing (scRNA-seq) dataset and TCGA dataset of HCC samples and discovered differentially expressed LMRGs between primary and metastatic HCC patients. By using the least absolute selection and shrinkage operator (LASSO) regression machine learning algorithm, we constructed a risk prognosis model with six LMRGs (AKR1C1, CYP27A1, CYP2C9, GLB1, HMGCS2, and PLPP1). The risk prognosis model was further validated in an external cohort of ICGC. We also constructed a nomogram that could accurately predict overall survival in HCC patients based on cancer status and LMRGs. Further investigation of the association between the LMRG model and somatic tumor mutational burden (TMB), tumor immune infiltration, and biological function was performed. We found that the most frequent somatic mutations in the LMRG high-risk group were CTNNB1, TTN, TP53, ALB, MUC16, and PCLO. Moreover, naïve CD8+ T cells, common myeloid progenitors, endothelial cells, granulocyte-monocyte progenitors, hematopoietic stem cells, M2 macrophages, and plasmacytoid dendritic cells were significantly correlated with the LMRG high-risk group. Finally, gene set enrichment analysis showed that RNA degradation, spliceosome, and lysosome pathways were associated with the LMRG high-risk group. For the first time, we used scRNA-seq and bulk RNA-seq to construct an LMRG-related risk score model, which may provide insights into more effective treatment strategies for predictive, preventive, and personalized medicine of HCC patients.

Acetyl-CoA regulates lipid metabolism and histone acetylation modification in cancer

Acetyl-CoA, as an important molecule, not only participates in multiple intracellular metabolic reactions, but also affects the post-translational modification of proteins, playing a key role in the metabolic activity and epigenetic inheritance of cells. Cancer cells require extensive lipid metabolism to fuel for their growth, while also require histone acetylation modifications to increase the expression of cancer-promoting genes. As a raw material for de novo lipid synthesis and histone acetylation, acetyl-CoA has a major impact on lipid metabolism and histone acetylation in cancer. More importantly, in cancer, acetyl-CoA connects lipid metabolism with histone acetylation, forming a more complex regulatory mechanism that influences cancer growth, proliferation, metastasis.

CD5L-associated gene analyses highlight the dysregulations, prognostic effects, immune associations, and drug-sensitivity predicative potentials of LCAT and CDC20 in hepatocellular carcinoma

BACKGROUND

The dysregulation of CD5L has been reported in hepatocellular carcinoma (HCC). However, its functions in HCC were controversial. In this study, we aimed to identify CD5L-associated pathways and markers and explore their values in HCC diagnosis, prognosis and treatment.

METHODS

HCC datasets with gene expression profiles and clinical data in TCGA and ICGC were downloaded. The immune/stroma cell infiltrations were estimated with xCell. CD5L-associated pathways and CD5L-associated genes (CD5L-AGs) were identified with gene expression comparisons and gene set enrichment analysis (GSEA). Cox regression, Kaplan-Meier survival analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis were performed. The correlations of the key genes with immune/stroma infiltrations, immunoregulators, and anti-cancer drug sensitivities in HCC were investigated. At protein level, the key genes dysregulations, their correlations and prognostic values were validated in clinical proteomic tumor analysis consortium (CPTAC) database. Serum CD5L and LCAT activity in 50 HCC and 30 normal samples were evaluated and compared. The correlations of serum LCAT activity with alpha-fetoprotein (AFP), albumin (ALB) and high-density lipoprotein (HDL) in HCC were also investigated.

RESULTS

Through systemic analyses, 14 CD5L-associated biological pathways, 256 CD5L-AGs and 28 CD5L-associated prognostic and diagnostic genes (CD5L-APDGs) were identified. A risk model consisting of LCAT and CDC20 was constructed for HCC overall survival (OS), which could discriminate HCC OS status effectively in both the training and the validation sets. CD5L, LCAT and CDC20 were shown to be significantly correlated with immune/stroma cell infiltrations, immunoregulators and 31 anti-cancer drug sensitivities in HCC. At protein level, the dysregulations of CD5L, LCAT and CDC20 were confirmed. LCAT and CDC20 were shown to be significantly correlated with proliferation marker MKI67. In serum, no significance of CD5L was shown. However, the lower activity of LCAT in HCC serum was obvious, as well as its significant positive correlations ALB and HDL concentrations.

CONCLUSIONS

CD5L, LCAT and CDC20 were dysregulated in HCC both at mRNA and protein levels. The LCAT-CDC20 signature might be new predicator for HCC OS. The associations of the three genes with HCC microenvironment and anti-cancer drug sensitivities would provide new clues for HCC immunotherapy and chemotherapy.

Identification and validation of a lipid metabolism gene signature for predicting biochemical recurrence of prostate cancer after radical prostatectomy

Background

Pro5state cancer is one of the most commonly diagnosed cancers in men worldwide and biochemical recurrence occurs in approximately 25% of patients after radical prostatectomy. Current decisions regarding biochemical recurrence after radical prostatectomy are largely dependent on clinicopathological parameters, which are less accurate. A growing body of research suggests that lipid metabolism influences tumor development and treatment, and that prostate cancer is not only a malignancy but also a lipid metabolism disease. Therefore, this study aimed to identify the prognostic value of lipid metabolism-related gene signaling disease to better predict biochemical recurrence and contribute to clinical decision-making.

Methods

Expression data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database and the MSKCC database. Candidate modules closely associated with BCR were screened by univariate and LASSOcox regression analyses, and multivariate Cox regression analyses were performed to construct gene signatures. Kaplan-Meier (KM) survival analysis, time-dependent subject operating curves (ROC), independent prognostic analysis, and Nomogram were also used to assess the prognostic value of the signatures. In addition, Gene Ontology Analysis (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore potential biological pathways.

Results

A 6-gene lipid metabolism-related gene signature was successfully constructed and validated to predict biochemical recurrence in prostate cancer patients. In addition, we identified the 6-gene signature as an independent risk factor. Functional analysis showed that lipid metabolism-related genes were closely associated with arachidonic acid metabolism, PPAR transduction signaling pathway, fatty acid metabolism, peroxisome, and glycerophospholipid metabolism. Prognostic models were associated with immune cell infiltration.

Conclusion

We have successfully developed a novel lipid metabolism-related gene signature that is highly effective in predicting BCR in patients with limited prostate cancer after RP and created a prognostic Nomogram. Furthermore, the signature may help clinicians to select high-risk subpopulations, predict patient survival, and facilitate more personalized treatment than traditional clinical factors.

Machine learning-based transcriptome analysis of lipid metabolism biomarkers for the survival prediction in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary malignancy of the liver with a very high fatality rate. Our goal in this study is to find a reliable lipid metabolism-related signature associated with prognostic significance for HCC. In this study, HCC lipid metabolism-related molecular subtype analysis was conducted based on the 243 lipid metabolism genes collected from the Molecular Signatures Database. Several significant disparities in prognosis, clinicopathological characteristics, and immune and ferroptosis-related status were found across the three subtypes, especially between C1 and C3 subgroups. Differential expression analysis yielded 57 differentially expressed genes (DEGs) between C1 and C3 subtypes. GO and KEGG analysis was employed for functional annotation. Three of 21 prognostic DEGs (CXCL8, SLC10A1, and ADH4) were finally selected through machine-learning-based discovery and validation strategy. The risk score = (0.103) × expression value of CXCL8 + (−0.0333) × expression value of SLC10A1 + (−0.0812) × expression value of ADH4. We used these three to construct a HCC prognostic risk model, which stratified the patients of the validation cohort into two risk subtypes with significantly different overall survival. Our work provides possible significance of the lipid metabolism-associated model in stratifying patient prognosis and its feasibility to guide therapeutic selection.

Integration of clinical and transcriptomics reveals programming of the lipid metabolism in gastric cancer

Lipid metabolism has a profound impact on gastric cancer (GC) progression and is a newly targetable vulnerability for cancer therapy. Given the importance of lipids in cancer cellular processes, in this study we employed lipidomic clinical and transcriptomic data to connect the variations of lipid metabolism changes of GC. We constructed a clinical nomogram based on the lipid factors and other clinical items. Then by using multi-omics techniques, we established a lipid-related gene signature for individualized prognosis prediction in patients with GC. Moreover, a total of 1357 GC cases were then applied to evaluate the robustness of this model. WGCNA was used to identify co-expression modules and enriched genes associated with GC lipid metabolism. The role of key genes ACLY in GC was further investigated. The prognostic value of the lipgenesis signature was analyzed using Cox regression model, and clinical nomogram was established. Among them, we observed overexpression of ACLY significantly increased the levels of intracellular free fatty acid and triglyceride, and activated AKT/mTOR pathway to promote cancer development. In conclusion, our findings revealed that GC exhibited a reprogramming of lipid metabolism in association with an altered expression of associated genes. Among them, ACLY significantly promoted GC lipid metabolism and increased cancer cell proliferation, suggesting that this pathway can be targetable as a metabolic vulnerability in future GC therapy.

Discovery of Lipid Metabolism-Related Genes for Predicting Tumor Immune Microenvironment Status and Prognosis in Prostate Cancer

Background. Reprogramming of lipid metabolism is closely associated with tumor development, serving as a common and critical metabolic feature that emerges during tumor evolution. Meanwhile, immune cells in the tumor microenvironment also undergo aberrant lipid metabolism, and altered lipid metabolism also has an impact on the function and status of immune cells, further promoting malignant biological behavior. Consequently, we focused on lipid metabolism-related genes for constructing a novel prognostic marker and evaluating immune status in prostate cancer. Methods. Information about prostate cancer patients was obtained from TCGA and GEO databases. The NMF algorithm was conducted to identify the molecular subtypes. The least absolute shrinkage and selection operator (Lasso) regression analysis was applied to establish a prognostic risk signature. CIBERSORT algorithm was used to calculate immune cell infiltration levels in prostate cancer. External clinical validation data were used to validate the results. Results. Prostate cancer samples were divided into two subtypes according to the NMF algorithm. A six-gene risk signature (PTGS2, SGPP2, ALB, PLA2G2A, SRD5A2, and SLC2A4) was independent of prognosis and showed good stability. There were significant differences between risk groups of patients with respect to the infiltration of immune cells and clinical variables. Response to immunotherapy also differed between different risk groups. Furthermore, the mRNA expression levels of the signature genes were verified in tissue samples by qRT-PCR. Conclusion. We constructed a six-gene signature with lipid metabolism in prostate cancer to effectively predict prognosis and reflect immune microenvironment status.

Lipid Metabolic-Related Signature CYP19A1 is a Potential Biomarker for Prognosis and Immune Cell Infiltration in Gastric Cancer

Background

Altered lipid metabolism is associated with gastric cancer (GC) progression. Comprehensive analysis to identify critical lipid metabolic drivers for predicting overall survival (OS) is not fully elucidated in GC. Our study aim to explore a novel lipid metabolism-related prognostic marker for GC.

Methods

Transcriptional status and clinical features were obtained from the TCGA-STAD database. The differentially expressed lipid metabolic genes and the risk prognostic model were developed by using bioinformatics and Cox regression analyses. ROC and Kaplan-Meier analysis were established to assess the performance of the risk predictive score model. GSE84437 dataset was used for external validation. Immunochemistry (IHC) was used to examine the expression of CYP19A1 in GC patients. Gene Set Enrichment Analysis (GSEA) was conducted to elucidate the underlying enriched mechanisms. TIMER and CIBERSORT analysis were performed to explore the relationship between CYP19A1 and immune microenvironment.

Results

A novel lipid metabolic gene signature (including MTTP, CYP19A1, MYB, SERPINE1), and specifically CYP19A1, might be a promising prognostic factor for GC. Using the validation cohort, ROC curves indicate a good showing of our risk model. Based on the signature yielded a significant difference OS time between the low- and high-risk groups. Cox regression indicates that the signature is an independent prognostic variable. ROC curves present better and reliability predictive accuracy. The IHC data validate that high expression of CYP19A1 was found in GC tissues. GSEA analysis reveals that higher expression of CYP19A1 may significantly up-regulate genes involved in fatty acid metabolism and glycerolipid metabolism. CIBERSORT analysis suggests that CYP19A1 is related to the infiltration of multiple immune cells.

Conclusion

CYP19A1 could be an independent prognostic factor and a novel metabolic-targeted treatment strategy for gastric cancer.

The prognostic value of MicroRNAs associated with fatty acid metabolism in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is the sixth most frequent cancer in humans globally. In addition to smoking and drinking, genetic and epigenetic changes also play a big role in how HNSCC starts and grows. MicroRNAs are short, non-coding RNAs that control cell differentiation and apoptosis by interfering with gene expression. In addition, microRNAs in HNSCC have been shown to affect the clinical behaviors of HNSCC in amazing ways. Moreover, metabolic reprogramming is a key part of cancer and is needed for cancer to turn into a tumor and grow. But it is still not clear what effect microRNAs related to fatty acid metabolism have on the prognosis of HNSCC patients. We downloaded the data of HNSCC patients from the TCGA database and obtained the genes associated with fatty acid metabolism according to the GSEA database. Then, the microRNAs associated with fatty acid metabolism genes were matched. Finally, fatty acid metabolism gene-associated microRNAs for calculating risk scores and then building multifactorial Cox regression models in patients with HNSCC. Heatmap analysis showed that microRNAs involved in fatty acid metabolism were significantly different in HNSCC patients than in healthy controls. A total of 27 microRNAs associated with fatty acid metabolism were screened by univariate Cox analysis (p < 0.05). Using lasso regression, 18 microRNAs substantially linked with the prognosis of HNSCC patients were identified and included in risk scores. The ROC curves demonstrate that risk scores derived from microRNAs involved in fatty acid metabolism can accurately predict the prognosis of HNSCC patients at 1, 3, and 5 years. Moreover, we discovered that 11 microRNAs included in the risk score properly distinguished the prognosis of HNSCC patients. This paper indicated that microRNAs involved with fatty acid metabolism are strongly linked to the prognosis of HNSCC patients. It also indicated that reprogramming of fatty acid metabolism in tumor tissues may play an important role in HNSCC cancer.

Posttranslational control of lipogenesis in the tumor microenvironment

Metabolic reprogramming of cancer cells within the tumor microenvironment typically occurs in response to increased nutritional, translation and proliferative demands. Altered lipid metabolism is a marker of tumor progression that is frequently observed in aggressive tumors with poor prognosis. Underlying these abnormal metabolic behaviors are posttranslational modifications (PTMs) of lipid metabolism-related enzymes and other factors that can impact their activity and/or subcellular localization. This review focuses on the roles of these PTMs and specifically on how they permit the re-wiring of cancer lipid metabolism, particularly within the context of the tumor microenvironment.

The adipocyte microenvironment and cancer

Many epithelial tumors grow in the vicinity of or metastasize to adipose tissue. As tumors develop, crosstalk between adipose tissue and cancer cells leads to changes in adipocyte function and paracrine signaling, promoting a microenvironment that supports tumor growth. Over the last decade, it became clear that tumor cells co-opt adipocytes in the tumor microenvironment, converting them into cancer-associated adipocytes (CAA). As adipocytes and cancer cells engage, a metabolic symbiosis ensues that is driven by bi-directional signaling. Many cancers (colon, breast, prostate, lung, ovarian cancer, and hematologic malignancies) stimulate lipolysis in adipocytes, followed by the uptake of fatty acids (FA) from the surrounding adipose tissue. The FA enters the cancer cell through specific fatty acid receptors and binding proteins (e.g., CD36, FATP1) and are used for membrane synthesis, energy metabolism (β-oxidation), or lipid-derived cell signaling molecules (derivatives of arachidonic and linolenic acid). Therefore, blocking adipocyte-derived lipid uptake or lipid-associated metabolic pathways in cancer cells, either with a single agent or in combination with standard of care chemotherapy, might prove to be an effective strategy against cancers that grow in lipid-rich tumor microenvironments.

The RAGE/multiligand axis: a new actor in tumor biology

The receptor for advanced glycation end-products (RAGE) is a multiligand binding and single-pass transmembrane protein which actively participates in several chronic inflammation-related diseases. RAGE, in addition to AGEs, has a wide repertoire of ligands, including several damage-associated molecular pattern molecules or alarmins such as HMGB1 and members of the S100 family proteins.

Over the last years, a large and compelling body of evidence has revealed the active participation of the RAGE axis in tumor biology based on its active involvement in several crucial mechanisms involved in tumor growth, immune evasion, dissemination, as well as by sculpturing of the tumor microenvironment as a tumor-supportive niche. In the present review, we will detail the consequences of the RAGE axis activation to fuel essential mechanisms to guarantee tumor growth and spreading.

ToPP: Tumor online prognostic analysis platform for prognostic feature selection and clinical patient subgroup selection

Patients with cancer with different molecular characterization and subtypes result in different response to anticancer therapeutics and survival. To identify features that are associated with prognosis is essential to precision medicine by providing clues for target identification, drug discovery. Here, we developed a tumor online prognostic analysis platform (ToPP) which integrated eight multi-omics features and clinical data from 68 cancer projects. It provides multiple approaches for customized prognostic studies, including 1) Prognostic analysis based on multi-omics features and clinical characteristics; 2) Automatic construction of prognostic model; 3) Pancancer prognostic analysis in multi-omics data; 4) Explore the impact of different levels of feature combinations on patient prognosis; 5) More sophisticated prognostic analysis according to regulatory network. ToPP provides a comprehensive source and easy-to-use interface for tumor prognosis research, with one-stop service of multi-omics, subtyping, and online prognostic modeling. The web server is freely available at http://www.biostatistics.online/topp/index.php.

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ToPP platform integrated eight multi-omics and clinical data from 68 cancer projects

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ToPP provides multi-omics combination and subgroup selection for prognostic analysis

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ToPP provides automatic construction of prognostic model for public and custom data

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Users can perform prognostic analysis based on regulatory network or pathways in ToPP