Establishment of a novel glycolysis-related prognostic gene signature for ovarian cancer and its relationships with immune infiltration of the tumor microenvironment

Glycolysis, the sweet appetite of the tumor microenvironment

Cancer cells display an altered metabolic phenotype, characterised by increased glycolysis and lactate production, even in the presence of sufficient oxygen - a phenomenon known as the Warburg effect. This metabolic reprogramming is a crucial adaptation that enables cancer cells to meet their elevated energy and biosynthetic demands. Importantly, the tumor microenvironment plays a pivotal role in shaping and sustaining this metabolic shift in cancer cells. This review explores the intricate relationship between the tumor microenvironment and the Warburg effect, highlighting how communication within this niche regulates cancer cell metabolism and impacts tumor progression and therapeutic resistance. We discuss the potential of targeting the Warburg effect as a promising therapeutic strategy, with the aim of disrupting the metabolic advantage of cancer cells and enhancing our understanding of this complex interplay within the tumor microenvironment.

TCEB2/HIF1A signaling axis promotes chemoresistance in ovarian cancer cells by enhancing glycolysis and angiogenesis

Ovarian cancer is an extremely malignant gynaecological tumour with a poor patient prognosis and is often associated with chemoresistance. Thus, exploring new therapeutic approaches to improving tumour chemosensitivity is important. The expression of transcription elongation factor B polypeptide 2 (TCEB2) gene is reportedly upregulated in ovarian cancer tumour tissues with acquired resistance, but the specific mechanism involved in tumour resistance remains unclear. In this study, we found that TCEB2 was abnormally highly expressed in cisplatin-resistant tumour tissues and cells. TCEB2 silencing also inhibited the growth and glycolysis of SKOV-3/cisplatin (DDP) and A2780/DDP cells. We further incubated human umbilical vein endothelial cells (HUVECs) with culture supernatants from cisplatin-resistant cells having TCEB2 knockdown. Results revealed that the migration, invasion, and angiogenesis of HUVECs were significantly inhibited. Online bioinformatics analysis revealed that the hypoxia-inducible factor-1A (HIF-1A) protein may bind to TCEB2, and TCEB2 silencing inhibited SKOV-3/DDP cell growth and glycolysis by downregulating HIF1A expression. Similarly, TCEB2 promoted HUVEC migration, invasion, and angiogenesis by upregulating HIF1A expression. In vivo experiments showed that TCEB2 silencing enhanced the sensitivity of ovarian cancer nude mice to cisplatin and that TCEB2 knockdown inhibited the glycolysis and angiogenesis of tumour cells. Our findings can serve as a reference for treating chemoresistant ovarian cancer.

Single-cell transcriptome analysis revealed heterogeneity in glycolysis and identified IGF2 as a therapeutic target for ovarian cancer subtypes

BACKGROUND

As the most malignant tumor of the female reproductive system, ovarian cancer (OC) has garnered increasing attention. The Warburg effect, driven by glycolysis, accounts for tumor cell proliferation under aerobic conditions. However, the metabolic heterogeneity linked to glycolysis in OC remains elusive.

METHODS

We integrated single-cell data with OC to score glycolysis level in tumor cell subclusters. This led to the identification of a subcluster predominantly characterized by glycolysis, with a strong correlation to patient prognosis. Core transcription factors were pinpointed using hdWGCNA and metaVIPER. A specific transcription factor regulatory network was then constructed. A glycolysis-related prognostic model was developed and tested for estimating OC prognosis with a total of 85 machine-learning combinations, focusing on specific upregulated genes of two subtypes. We identified IGF2 as a key within the prognostic model and investigated its impact on OC progression and drug resistance through in vitro experiments, including the transwell assay, lactate production detection, and the CCK-8 assay.

RESULTS

Analysis showed that the Malignant 7 subcluster was primarily related to glycolysis. Two OC molecular subtypes, CS1 and CS2, were identified with distinct clinical, biological, and microenvironmental traits. A prognostic model was built, and IGF2 emerged as a key gene linked to prognosis. Experiments have proven that IGF2 can promote the glycolysis pathway and the malignant biological progression of OC cells.

CONCLUSIONS

We developed two novel OC subtypes based on glycolysis score, established a stable prognostic model, and identified IGF2 as the marker gene. These insights provided a new avenue for exploring OC's molecular mechanisms and personalized treatment approaches.

Establishment of an ovarian cancer exhausted CD8+T cells-related genes model by integrated analysis of scRNA-seq and bulk RNA-seq

Ovarian cancer (OC) was the fifth leading cause of cancer death and the deadliest gynecological cancer in women. This was largely attributed to its late diagnosis, high therapeutic resistance, and a dearth of effective treatments. Clinical and preclinical studies have revealed that tumor-infiltrating CD8+T cells often lost their effector function, the dysfunctional state of CD8+T cells was known as exhaustion. Our objective was to identify genes associated with exhausted CD8+T cells (CD8TEXGs) and their prognostic significance in OC. We downloaded the RNA-seq and clinical data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. CD8TEXGs were initially identified from single-cell RNA-seq (scRNA-seq) datasets, then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression were utilized to calculate risk score and to develop the CD8TEXGs risk signature. Kaplan-Meier analysis, univariate Cox regression, multivariate Cox regression, time-dependent receiver operating characteristics (ROC), nomogram, and calibration were conducted to verify and evaluate the risk signature. Gene set enrichment analyses (GSEA) in the risk groups were used to figure out the closely correlated pathways with the risk group. The role of risk score has been further explored in the homologous recombination repair deficiency (HRD), BRAC1/2 gene mutations and tumor mutation burden (TMB). A risk signature with 4 CD8TEXGs in OC was finally built in the TCGA database and further validated in large GEO cohorts. The signature also demonstrated broad applicability across various types of cancer in the pan-cancer analysis. The high-risk score was significantly associated with a worse prognosis and the risk score was proven to be an independent prognostic biomarker. The 1-, 3-, and 5-years ROC values, nomogram, calibration, and comparison with the previously published models confirmed the excellent prediction power of this model. The low-risk group patients tended to exhibit a higher HRD score, BRCA1/2 gene mutation ratio and TMB. The low-risk group patients were more sensitive to Poly-ADP-ribose polymerase inhibitors (PARPi). Our findings of the prognostic value of CD8TEXGs in prognosis and drug response provided valuable insights into the molecular mechanisms and clinical management of OC.

From monocyte-derived macrophages to resident macrophages-how metabolism leads their way in cancer

Macrophages are innate immune cells that play key roles during both homeostasis and disease. Depending on the microenvironmental cues sensed in different tissues, macrophages are known to acquire specific phenotypes and exhibit unique features that, ultimately, orchestrate tissue homeostasis, defense, and repair. Within the tumor microenvironment, macrophages are referred to as tumor-associated macrophages (TAMs) and constitute a heterogeneous population. Like their tissue resident counterpart, TAMs are plastic and can switch function and phenotype according to the niche-derived stimuli sensed. While changes in TAM phenotype are known to be accompanied by adaptive alterations in their cell metabolism, it is reported that metabolic reprogramming of macrophages can dictate their activation state and function. In line with these observations, recent research efforts have been focused on defining the metabolic traits of TAM subsets in different tumor malignancies and understanding their role in cancer progression and metastasis formation. This knowledge will pave the way to novel therapeutic strategies tailored to cancer subtype-specific metabolic landscapes. This review outlines the metabolic characteristics of distinct TAM subsets and their implications in tumorigenesis across multiple cancer types.

Astragaloside IV inhibits cell viability and glycolysis of hepatocellular carcinoma by regulating KAT2A-mediated succinylation of PGAM1

BACKGROUND

Astragaloside IV (AS-IV) is one of the basic components of Astragali radix, that has been shown to have preventive effects against various diseases, including cancers. This study aimed to explore the role of AS-IV in hepatocellular carcinoma (HCC) and its underlying mechanism.

METHODS

The cell viability, glucose consumption, lactate production, and extracellular acidification rate (ECAR) in SNU-182 and Huh7 cell lines were detected by specific commercial kits. Western blot was performed to analyze the succinylation level in SNU-182 and Huh7 cell lines. The interaction between lysine acetyltransferase (KAT) 2 A and phosphoglycerate mutase 1 (PGAM1) was evaluated by co-immunoprecipitation and immunofluorescence assays. The role of KAT2A in vivo was explored using a xenografted tumor model.

RESULTS

The results indicated that AS-IV treatment downregulated the protein levels of succinylation and KAT2A in SNU-182 and Huh7 cell lines. The cell viability, glucose consumption, lactate production, ECAR, and succinylation levels were decreased in AS-IV-treated SNU-182 and Huh7 cell lines, and the results were reversed after KAT2A overexpression. KAT2A interacted with PGAM1 to promote the succinylation of PGAM1 at K161 site. KAT2A overexpression promoted the viability and glycolysis of SNU-182 and Huh7 cell lines, which were partly blocked following PGAM1 inhibition. In tumor-bearing mice, AS-IV suppressed tumor growth though inhibiting KAT2A-mediated succinylation of PGAM1.

CONCLUSION

AS-IV inhibited cell viability and glycolysis in HCC by regulating KAT2A-mediated succinylation of PGAM1, suggesting that AS-IV might be a potential and suitable therapeutic agent for treating HCC.

The predictive value of PFKFB3 in bladder cancer prognosis

6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-3 (PFKFB3) influences cancer progression via participating in tumor aerobic glycolysis. In this study, we aimed to evaluate the prognostic significance of PFKFB3 in bladder cancer (BLCA) patients by analyzing a combination of publicly available databases, clinical patient data, and bladder tumor samples from our hospital. Single-cell and bulk RNA-seq data of bladder cancer, obtained from ENA, GEO, and TCGA databases, were utilized for our analysis. The results indicated that PFKFB3 mRNA expression was markedly elevated in bladder cancer compared to paired normal tissue. Furthermore, BLCA patients with high PFKFB3 expression exhibited a significantly worse prognosis (P < 0.05). To validate these findings, clinical data and immunohistochemistry staining were performed on specimens obtained from 89 BLCA patients who underwent radical cystectomy at either Qingdao University Affiliated Hospital or Peking Union Medical College Hospital. The findings from this verification process confirmed that high expression of PFKFB3 serves as a biomarker for predicting worse prognosis in BLCA patients (OR: 2.462, 95 % CI: 1.202-5.042, P = 0.012). To facilitate clinical application, we developed a nomogram based on four variables, including PFKFB3 expression, to predict the survival of BLCA patients. Importantly, this nomogram demonstrated a low mean prediction error of 0.03. Taken together, our findings suggest that PFKFB3 has the potential to serve as both a prognostic biomarker and a therapeutic target for BLCA patients.

Pharmacological Mechanism of Mume Fructus in the Treatment of Triple-Negative Breast Cancer Based on Network Pharmacology

Our study aims to find the relevant mechanism of Mume Fructus in the treatment of triple-negative breast cancer (TNBC) by network pharmacology analysis and experimental validation. The effective compounds of Mume Fructus and TNBC-related target genes were imported into Cytoscape to construct a Mume Fructus-effective compounds-disease target network. The common targets of Mume Fructus and TNBC were determined by drawing Venn diagrams. Then, the intersection targets were transferred to the STRING database to construct a protein-protein interaction (PPI) network. To investigate the mechanism of Mume Fructus in treatment of TNBC, breast cancer cell (MDA-MB-231) was treated with Mume Fructus and/or transfected with small interference RNA-PKM2(siPKM2). CCK-8 assay, cell clonal formation assay, transwell, flow cytometry, qRT-PCR, and western blotting were performed. Eight effective compounds and 145 target genes were obtained, and the Mume Fructus- effective compounds-disease target network was constructed. Then through the analysis of the PPI network, we obtained 10 hub genes including JUN, MAPK1, RELA, AKT1, FOS, ESR1, IL6, MAPK8, RXRA, and MYC. KEGG enrichment analysis showed that JUN, MAPK1, RELA, FOS, ESR1, IL6, MAPK8, and RXRA were enriched in the Th17 cell differentiation signaling pathway. Loss of PKM2 and Mume Fructus both inhibited the malignant phenotype of MDA-MB-231 cells. And siPKM2 further aggravated the Mume Fructus inhibition of malignancy of breast cancer cells. Network pharmacology analysis suggests that Mume Fructus has multiple therapeutic targets for TNBC and may play a therapeutic role by modulating the immune microenvironment of breast cancer.

The Prognosis and Immunotherapy Prediction Model of Ovarian Serous Cystadenocarcinoma Patient was Constructed Based on Cuproptosis-Related LncRNA

Cuproptosis can serve as potential prognostic predictors in patients with cancer. However, the role of this relationship in ovarian serous cystadenocarcinoma (OV) remains unclear. 376 OV tumor samples were obtained from the Cancer Genome Atlas (TCGA) database, and long non-coding RNAs (lncRNAs) related to cuproptosis were obtained through correlation analysis. The risk assessment model was further constructed by univariate Cox regression analysis and LASSO Cox regression. Bioinformatics was used to analyze the regulatory effect of relevant risk assessment models on tumor mutational burden (TMB) and immune microenvironment. We obtained 5 lncRNAs (AC025287.2, AC092718.4, AC112721.2, LINC00996, and LINC01639) and incorporated them into the Cox proportional hazards model. Kaplan-Meier (KM) curve analysis of the prognosis found that the high-risk group was associated with a poorer prognosis. The receiver operating characteristic (ROC) curve showed stronger predictive power compared to other clinicopathological features. Immune infiltration analysis showed that high-risk scores were inversely correlated with CD8+ T cells, CD4+ T cells, macrophages, NK cells, and B cells. Functional enrichment analysis found that they may act via the extracellular matrix (ECM)-interacting proteins and other pathways. We successfully constructed a reliable cuproptosis-related lncRNA model for the prognosis of OV.

Machine learning constructs a T cell-related signature for predicting prognosis and drug sensitivity in ovarian cancer

BACKGROUND

The leading cause of death related to gynecologic cancer is ovarian cancer, which typically has a poor prognosis. T cells are referred to as key mediators of immunosurveillance and tumor eradication, and unbalanced regulation or lack of T cells in tumors result in immunotherapy resistance.

METHODS

The identification of T cell related markers depended on single-cell RNA-seq analysis. Using data from multiple datasets, including TCGA, GSE14764, GSE26193, GSE26712, and GSE140082, we constructed a prognostic signature called TRS (T cell-related signature) using 10 different machine learning algorithms. The correlation between TRS and drug sensitivity were analyzed using the data from GSE91061 and IMvigor210 dataset.

RESULTS

PlsRcox method based TRS was as a risk factor for the clinical outcome of ovarian cancer patients. In comparison with stage, grade and many prognostic signatures, the performance of our TRS in evaluating the clinical outcome was better in ovarian cancer. TRS-based risk score showed distinct association with the level of ESTIMATE score, immune-related function score and immune cells. Moreover, TRS could be used to predict the immunotherapy response and chemotherapy response in ovarian cancer.

CONCLUSION

In conclusion, we constructed a powerful TRS in ovarian cancer, which could accurately predict the clinical outcome of patients and be used to predict the immunotherapy response and chemotherapy response.

SMAD4 inhibits glycolysis in ovarian cancer through PI3K/AKT/HK2 signaling pathway by activating ARHGAP10

BACKGROUND

ARHGAP10 is a tumor-suppressor gene related to ovarian cancer (OC) progression; however, its specific mechanism is unclear.

AIMS

To investigate the effect of ARHGAP10 on OC cell migration, invasion, and glycolysis.

METHODS AND RESULTS

Quantitative real-time PCR (qRT-PCR) quantified mRNA and protein expressions of AKT, p-AKT, HK2, and SMAD4 were tested by Western blot. EdU, Wound healing, and Transwell assay were utilized to evaluate OC cell proliferation, migration, and invasion. We used a Seahorse XF24 Extracellular Flux Analyzer to monitor cellular oxygen consumption rates (OCR) and extracellular acidification rates (ECAR). Chromatin immunoprecipitation (ChIP) was used to analyze the transcriptional regulation of ARHGAP10 by SMAD4. ARHGAP10 expression in OC tissues was detected by immunohistochemistry. Our results showed that ARHGAP10 expression was negatively related to lactate levels in human OC tissues. ARHGAP10 overexpression can inhibit the migration, proliferation, and invasion of OC cells, and this function can be blocked by 2-Deoxy-D-glucose. Moreover, we found that ARHGAP10 expression can be rescued with the AKT inhibitor LY294002.

CONCLUSIONS

This study revealed that the antitumor effects of ARHGAP10 in vivo and in vitro possibly suppress oncogenic glycolysis through the PI3K/AKT/HK2-regulated glycolysis metabolism pathway.

Comprehensive analysis based on glycolytic and glutaminolytic pathways signature for predicting prognosis and immunotherapy in ovarian cancer

Background: Our study attempts to develop and identify an aerobic glycolysis and glutamine-related genes (AGGRGs) signature for estimating prognostic effectively of ovarian cancer (OV) patients. Materials & methods: OV related data were extracted from the multiple public databases, including TCGA-OV, GSE26193, GSE63885, and ICGC-OV. A consistent clustering approach was used to characterize the subtypes associated with AGGRGs. LASSO Cox regressions was utilized to construct the prognosis signatures of AGGRGs. In addition, GSE26193, GSE63885 and ICGC-OV served as independent external cohorts to assess the reliability of the model. In vitro and in vivo experiments were conducted to study the role of AAK1 in the malignant progression and glutamine metabolism of OV, and assessed its therapeutic potential for treating OV patients. Results: OV patients could be separated into four subtypes (quiescent, glycolysis, glutaminolytic, and mixed subtypes). The survival outcome of glutaminolytic subtype was notably worse than the glycolytic subtype. Besides, we identified eight AGGRGs (AAK1, GJB6, HMGN5, LPIN3, INTS6L, PPOX, SPAG4, and ZNF316) to establish a prognostic signature for OV patients. Comprehensive analysis revealed that the signature risk score served as an independent prognostic factor for OV. Additionally, high-risk OV patients were less sensitive to platinum and, conversely, were proved to be more responsive to immunotherapy than low-risk score. In cytological experiments, we found that AAK1 could promote cancer progression and glutamine metabolism via activating the Notch3 pathway in OV cells. Furthermore, knockdown of AAK1 significantly inhibited tumor growth and weight, decreased lung metastases, and ultimately extended the survival time of the nude mice. Conclusions: The prognostic signature of AGGRGs constructed could efficiently estimate the prognosis and immunotherapy effectiveness of OV patients. In addition, AAK1 may represent a promising therapeutic target for OV.

Research progress on the mechanism of glycolysis in ovarian cancer

Glycolysis is the preferred energy metabolism pathway in cancer cells even when the oxygen content is sufficient. Through glycolysis, cancer cells convert glucose into pyruvic acid and then lactate to rapidly produce energy and promote cancer progression. Changes in glycolysis activity play a crucial role in the biosynthesis and energy requirements of cancer cells needed to maintain growth and metastasis. This review focuses on ovarian cancer and the significance of key rate-limiting enzymes (hexokinase, phosphofructokinase, and pyruvate kinase, related signaling pathways (PI3K-AKT, Wnt, MAPK, AMPK), transcription regulators (HIF-1a), and non-coding RNA in the glycolytic pathway. Understanding the relationship between glycolysis and these different mechanisms may provide new opportunities for the future treatment of ovarian cancer.

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

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

Hypoxia-induced PPFIA4 accelerates the progression of ovarian cancer through glucose metabolic reprogramming

Dysregulated glycolysis promotes growth and metastasis, which is one of the metabolic characteristics of ovarian cancer. Based on bioinformatics analysis, liprin-alpha-4 (PPFIA4) is a gene associated with hypoxia, and we aimed to investigate the potential mechanism of PPFIA4 during the reprogramming of glucose metabolism in ovarian cancer cells. Currently, the cell viability of ovarian cancer cells under the hypoxia treatment was evaluated by CCK-8 assay, and cell migration and invasion were measured by transwell assay and western blot. The effects of hypoxia treatment on glucose uptake, lactate production, extracellular acidification rate (ECAR), adenosine triphosphate (ATP), reactive oxygen species (ROS), Nicotinamide adenine dinucleotide phosphate (NADPH) and its oxidized form NADP + , and oxygen consumption rate (OCR) in ovarian cancer cells were examined. Then PPFIA4 was identified through bioinformatic analysis, and the regulatory effects of PPFIA4 on glucose metabolic reprogramming. Our data suggested that hypoxia enhanced the migration and invasion ability of ovarian cancer cells in vitro, and promoted the glucose metabolic reprogramming of ovarian cancer cells. Ovarian cancer cell viability, migration, and invasion were inhibited after PPFIA4 knockdown. Inhibition of PPFIA4 inhibited hypoxic-induced glucose metabolic reprogramming in ovarian cancer cells. In addition, PPFIA4 was found to bind to hypoxia-inducible factor 1alpha (HIF1A), and HIF1A prominently induced PPFIA4 expression. Collectively, HIF1A mediated upregulation of PPFIA4 and promoted reprogramming of glucose metabolism in ovarian cancer cells. Therefore, PPFIA4 may be a therapeutic target for ovarian cancer intervention.

A glycometabolic gene signature associating with immune infiltration and chemosensitivity and predicting the prognosis of patients with osteosarcoma

Background

Accumulating evidence has suggested that glycometabolism plays an important role in the pathogenesis of tumorigenesis. However, few studies have investigated the prognostic values of glycometabolic genes in patients with osteosarcoma (OS). This study aimed to recognize and establish a glycometabolic gene signature to forecast the prognosis, and provide therapeutic options for patients with OS.

Methods

Univariate and multivariate Cox regression, LASSO Cox regression, overall survival analysis, receiver operating characteristic curve, and nomogram were adopted to develop the glycometabolic gene signature, and further evaluate the prognostic values of this signature. Functional analyses including Gene Ontology (GO), kyoto encyclopedia of genes and genomes analyses (KEGG), gene set enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), and competing endogenous RNA (ceRNA) network, were used to explore the molecular mechanisms of OS and the correlation between immune infiltration and gene signature. Moreover, these prognostic genes were further validated by immunohistochemical staining.

Results

A total of four genes including PRKACB, SEPHS2, GPX7, and PFKFB3 were identified for constructing a glycometabolic gene signature which had a favorable performance in predicting the prognosis of patients with OS. Univariate and multivariate Cox regression analyses revealed that the risk score was an independent prognostic factor. Functional analyses indicated that multiple immune associated biological processes and pathways were enriched in the low-risk group, while 26 immunocytes were down-regulated in the high-risk group. The patients in high-risk group showed elevated sensitivity to doxorubicin. Furthermore, these prognostic genes could directly or indirectly interact with other 50 genes. A ceRNA regulatory network based on these prognostic genes was also constructed. The results of immunohistochemical staining showed that SEPHS2, GPX7, and PFKFB3 were differentially expressed between OS tissues and adjacent normal tissues.

Conclusion

The preset study constructed and validated a novel glycometabolic gene signature which could predict the prognosis of patients with OS, identify the degree of immune infiltration in tumor microenvironment, and provide guidance for the selection of chemotherapeutic drugs. These findings may shed new light on the investigation of molecular mechanisms and comprehensive treatments for OS.

Identifying TME signatures for cervical cancer prognosis based on GEO and TCGA databases

The mortality rate from cervical cancer (CESC), a malignant tumor that affects women, has increased significantly globally in recent years. The discovery of biomarkers points to a direction for the diagnosis of cervical cancer with the advancement of bioinformatics technology. The goal of this study was to look for potential biomarkers for the diagnosis and prognosis of CESC using the GEO and TCGA databases. Because of the high dimension and small sample size of the omic data, or the use of biomarkers generated from a single omic data, the diagnosis of cervical cancer may be inaccurate and unreliable. The purpose of this study was to search the GEO and TCGA databases for potential biomarkers for the diagnosis and prognosis of CESC. We begin by downloading CESC (GSE30760) DNA methylation data from GEO, then perform differential analysis on the downloaded methylation data and screen out the differential genes. Then, using estimation algorithms, we score immune cells and stromal cells in the tumor microenvironment and perform survival analysis on the gene expression profile data and the most recent clinical data of CESC from TCGA. Then, using the 'limma' package and Venn plot in R language to perform differential analysis of genes and screen out overlapping genes, these overlapping genes were then subjected to GO and KEGG functional enrichment analysis. The differential genes screened by the GEO methylation data and the differential genes screened by the TCGA gene expression data were intersected to screen out the common differential genes. A protein-protein interaction (PPI) network of gene expression data was then created in order to discover important genes. The PPI network's key genes were crossed with previously identified common differential genes to further validate them. The Kaplan-Meier curve was then used to determine the prognostic importance of the key genes. Survival analysis has shown that CD3E and CD80 are important for the identification of cervical cancer and can be considered as potential biomarkers for cervical cancer.

A Novel Glycolysis-Related Signature for Predicting the Prognosis and Immune Infiltration of Uveal Melanoma

INTRODUCTION

As the most common aggressive intraocular cancer in adults, uveal melanoma (UVM) threatens the survival and vision of many people. Glycolysis is a novel hallmark of cancer, but the role of glycolysis-related genes in UVM prognosis remains unknown. The purpose of the study was to establish a glycolysis-related gene signature (GRGS) to predict UVM prognosis.

METHODS

Raw data were obtained from TCGA-UVM and GSE22138 datasets. The GRGS was established by univariate, LASSO, and multivariate Cox regression analyses. Kaplan-Meier survival and time-dependent receiver operating characteristic curves were used to evaluate the predictive ability of the GRGS. The relationships of the GRGS with infiltrating immune cell levels and mutations were analyzed with CIBERSORT and maftools.

RESULTS

A novel GRGS (risk score = 0.690861*ISG20 + 0.070991*MET - 0.227520*SDC2 + 0.690223*FBP1 + 0.048008*CLN6 - 0.128520*SDC3) was developed for predicting UVM prognosis. The GRGS had robust predictive stability in UVM. Enrichment annotation suggested that the high-risk group had stronger adaptive immune responses and that the low-risk group had more innate immune cell infiltration. Moreover, BAP1 mutation was related to high risk, and SF3B1 mutation was related to low risk.

CONCLUSIONS

This study developed and validated a novel GRGS to predict UVM prognosis and immune infiltration. The signature revealed an association between glycolysis-related genes and the tumor microenvironment, providing new insights into the role of glycolysis in UVM.

Metabolic Barriers to Glioblastoma Immunotherapy

Glioblastoma (GBM) is the most common primary brain tumor with a poor prognosis with the current standard of care treatment. To address the need for novel therapeutic options in GBM, immunotherapies which target cancer cells through stimulating an anti-tumoral immune response have been investigated in GBM. However, immunotherapies in GBM have not met with anywhere near the level of success they have encountered in other cancers. The immunosuppressive tumor microenvironment in GBM is thought to contribute significantly to resistance to immunotherapy. Metabolic alterations employed by cancer cells to promote their own growth and proliferation have been shown to impact the distribution and function of immune cells in the tumor microenvironment. More recently, the diminished function of anti-tumoral effector immune cells and promotion of immunosuppressive populations resulting from metabolic alterations have been investigated as contributory to therapeutic resistance. The GBM tumor cell metabolism of four nutrients (glucose, glutamine, tryptophan, and lipids) has recently been described as contributory to an immunosuppressive tumor microenvironment and immunotherapy resistance. Understanding metabolic mechanisms of resistance to immunotherapy in GBM can provide insight into future directions targeting the anti-tumor immune response in combination with tumor metabolism.

Glycolysis Inhibition of Autophagy Drives Malignancy in Ovarian Cancer: Exacerbation by IL-6 and Attenuation by Resveratrol

Cancer cells drive the glycolytic process towards the fermentation of pyruvate into lactate even in the presence of oxygen and functioning mitochondria, a phenomenon known as the "Warburg effect". Although not energetically efficient, glycolysis allows the cancer cell to synthesize the metabolites needed for cell duplication. Autophagy, a macromolecular degradation process, limits cell mass accumulation and opposes to cell proliferation as well as to cell migration. Cancer cells corrupt cancer-associated fibroblasts to release pro-inflammatory cytokines, which in turn promote glycolysis and support the metastatic dissemination of cancer cells. In mimicking in vitro this condition, we show that IL-6 promotes ovarian cancer cell migration only in the presence of glycolysis. The nutraceutical resveratrol (RV) counteracts glucose uptake and metabolism, reduces the production of reactive oxygen species consequent to excessive glycolysis, rescues the mitochondrial functional activity, and stimulates autophagy. Consistently, the lack of glucose as well as its metabolically inert analogue 2-deoxy-D-glucose (2-DG), which inhibits hexokinase 2 (HK2), trigger autophagy through mTOR inhibition, and prevents IL-6-induced cell migration. Of clinical relevance, bioinformatic analysis of The Cancer Genome Atlas dataset revealed that ovarian cancer patients bearing mutated TP53 with low expression of glycolytic markers and IL-6 receptor, together with markers of active autophagy, display a longer overall survival and are more responsive to platinum therapy. Taken together, our findings demonstrate that RV can counteract IL-6-promoted ovarian cancer progression by rescuing glycolysis-mediated inhibition of autophagy and support the view that targeting Warburg metabolism can be an effective strategy to limit the risk for cancer metastasis.

The significance of glycolysis in tumor progression and its relationship with the tumor microenvironment

It is well known that tumor cells rely mainly on aerobic glycolysis for energy production even in the presence of oxygen, and glycolysis is a known modulator of tumorigenesis and tumor development. The tumor microenvironment (TME) is composed of tumor cells, various immune cells, cytokines, and extracellular matrix, among other factors, and is a complex niche supporting the survival and development of tumor cells and through which they interact and co-evolve with other tumor cells. In recent years, there has been a renewed interest in glycolysis and the TME. Many studies have found that glycolysis promotes tumor growth, metastasis, and chemoresistance, as well as inhibiting the apoptosis of tumor cells. In addition, lactic acid, a metabolite of glycolysis, can also accumulate in the TME, leading to reduced extracellular pH and immunosuppression, and affecting the TME. This review discusses the significance of glycolysis in tumor development, its association with the TME, and potential glycolysis-targeted therapies, to provide new ideas for the clinical treatment of tumors.

Exploration of the Immunotyping Landscape and Immune Infiltration-Related Prognostic Markers in Ovarian Cancer Patients

Background

Increasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.

Methods

The ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.

Results

Three immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.

Conclusions

Our study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.

Exploring the Interplay between Metabolism and Tumor Microenvironment Based on Four Major Metabolism Pathways in Colon Adenocarcinoma

Tumor metabolism plays a critical role in tumor progression. However, the interaction between metabolism and tumor microenvironment (TME) has not been comprehensively revealed in colon adenocarcinoma (COAD). We used unsupervised consensus clustering to establish three molecular subtypes (clusters) based on the enrichment score of four major metabolism pathways in TCGA-COAD dataset. GSE17536 was used as a validation dataset. Single-cell RNA sequencing data (GSE161277) was employed to further verify the reliability of subtyping and characterize the correlation between metabolism and TME. Three clusters were identified and they performed distinct prognosis and molecular features. Clust3 had the worst overall survival and the highest enrichment score of glycolysis. 86 differentially expressed genes (DEGs) were identified, in which 11 DEGs were associated with favorable prognosis and 75 DEGs were associated with poor prognosis. Striking correlations were observed between hypoxia and glycolysis, clust3 and hypoxia, and clust3 and angiogenesis (P < 0.001).We constructed a molecular subtyping system which was effective and reliable for predicting COAD prognosis. The 86 identified key DEGs may be greatly involved in COAD progression, and they provide new perspectives and directions for further understanding the mechanism of metabolism in promoting aggressive phenotype by interacting with TME.

PTH2R is related to cell proliferation and migration in ovarian cancer: a multi-omics analysis of bioinformatics and experiments

Background

Ovarian cancer is a common gynecological disease and seriously endangers women's health. Currently, there is still a lack of effective molecular markers for the diagnosis and treatment of ovarian cancer. The present study aimed to investigate the molecular markers associated with ovarian cancer.

Methods

The molecular and gene related to ovarian cancer were extracted from GEO database and TCGA database by bioinformatics, and the related genes and functions were further analyzed. The results were verified by qPCR, WB, CCK-8 and Transwell experiments.

Results

Data analysis showed that PTH2R gene was highly expressed in tumors, and 51 HUB genes were obtained. Finally, experimental verification showed that PTH2R gene was highly expressed in ovarian cancer, and PTH2R gene was involved in the proliferation, invasion and metastasis of ovarian cancer cells.

Conclusions

After experimental verification, we found that knocking down the expression of PTH2R can inhibit the proliferation, invasion and migration of tumor cells.PTH2R is expected to become a new molecular marker for ovarian cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02566-2.

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

Background

Ovarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.

Methods

In this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.

Results

Forty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.

Conclusion

These findings reveal a specific risk model that could serve as effective biomarkers to identify patients’ platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00969-3.