Impairing energy metabolism in solid tumors through agents targeting oncogenic signaling pathways

A mini-review-cancer energy reprogramming on drug resistance and immune response

With the growing interest to harness cancer metabolism and energy reprogramming, this mini review aimed to explain the metabolic programming revealing the mechanisms regarding the treatment resistance. This mini review summarized the prominent cancer metabolic reprogramming on macromolecules. In addition, metabolic reprogramming explaining immune response and treatment resistance as well as energy reprogramming mechanisms are briefly discussed. Finally, some prospects in MR for reversing cancer drug resistance are highlighted.

Targeting metabolic adaptive responses induced by glucose starvation inhibits cell proliferation and enhances cell death in osimertinib-resistant non-small cell lung cancer (NSCLC) cell lines

Osimertinib, a tyrosine kinase inhibitor targeting mutant EGFR, has received approval for initial treatment in patients with Non-Small Cell Lung Cancer (NSCLC). While effective in both first- and second-line treatments, patients eventually develop acquired resistance. Metabolic reprogramming represents a strategy through which cancer cells may resist and adapt to the selective pressure exerted by the drug. In the current study, we investigated the metabolic adaptations associated with osimertinib-resistance in NSCLC cells under low glucose culture conditions. We demonstrated that, unlike osimertinib-sensitive cells, osimertinib-resistant cells were able to survive under low glucose conditions by increasing the rate of glucose and glutamine uptake and by shifting towards mitochondrial metabolism. Inhibiting glucose/pyruvate contribution to mitochondrial respiration, glutamine deamination to glutamate, and oxidative phosphorylation decreased the proliferation and survival abilities of osimertinib-resistant cells to glucose starvation. Our findings underscore the remarkable adaptability of osimertinib-resistant NSCLC cells in a low glucose environment and highlight the pivotal role of mitochondrial metabolism in mediating this adaptation. Targeting the metabolic adaptive responses triggered by glucose shortage emerges as a promising strategy, effectively inhibiting cell proliferation and promoting cell death in osimertinib-resistant cells.

Cancer energy reprogramming and the immune responses

Cancer as an uncontrolled growth of cells due to existing mutation in host cells that may proliferate, induce angiogenesis and sometimes metastasize due to the favorable tumor microenvironment (TME). Since it kills more than any disease, biomedical science does not relent in studying the exact pathogenesis. It was believed to be a problem that lies in the nucleus of the host cells; however, recent oncology findings are shifting attention to the mitochondria as an adjuvant to cancer pathogenesis. The changes in the gene are strongly related to cellular metabolism and metabolic reprogramming. It is now understood that reprogramming the TME will have a direct effect on the immune cells' metabolism. Although there are a number of studies on immune cells' response towards tumor energy reprogramming and cancer progression, there is still no existence with the updated collation of these immune cells' response to distinct energy reprogramming in cancer studies. To this end, this mini review shed some light on cancer energy reprogramming mechanisms and enzyme degradation pathways, the cancer pathogenicity activity series involved with reduced lactate production, the specific immune cell responses due to the energy reprogramming. This study highlighted some prospects and future experiments in harnessing the host immune response towards the altered energy metabolism due to cancer.

Genetic loss of Nrf1 and Nrf2 leads to distinct metabolism reprogramming of HepG2 cells by opposing regulation of the PI3K-AKT-mTOR signalling pathway

As a vital hallmarker of cancer, the metabolic reprogramming has been shown to play a pivotal role in tumour occurrence, metastasis and drug resistance. Amongst a vast variety of signalling molecules and metabolic enzymes involved in the regulation of cancer metabolism, two key transcription factors Nrf1 and Nrf2 are required for redox signal transduction and metabolic homeostasis. However, the regulatory effects of Nrf1 and Nrf2 (both encoded by Nfe2l1 and Nfe2l2, respectively) on the metabolic reprogramming of hepatocellular carcinoma cells have been not well understood to date. Here, we found that the genetic deletion of Nrf1 and Nrf2 from HepG2 cells resulted in distinct metabolic reprogramming. Loss of Nrf1α led to enhanced glycolysis, reduced mitochondrial oxygen consumption, enhanced gluconeogenesis and activation of the pentose phosphate pathway in the hepatocellular carcinoma cells. By striking contrast, loss of Nrf2 attenuated the glycolysis and gluconeogenesis pathways, but with not any significant effects on the pentose phosphate pathway. Moreover, knockout of Nrf1α also caused fat deposition and increased amino acid synthesis and transport, especially serine synthesis, whilst Nrf2 deficiency did not cause fat deposition, but attenuated amino acid synthesis and transport. Further experiments revealed that such distinctive metabolic programming of between Nrf1α-/- and Nrf2-/- resulted from substantial activation of the PI3K-AKT-mTOR signalling pathway upon the loss of Nrf1, leading to increased expression of critical genes for the glucose uptake, glycolysis, the pentose phosphate pathway, and the de novo lipid synthesis, whereas deficiency of Nrf2 resulted in the opposite phenomenon by inhibiting the PI3K-AKT-mTOR pathway. Altogether, these provide a novel insight into the cancer metabolic reprogramming and guide the exploration of a new strategy for targeted cancer therapy.

Targeting glucosylceramide synthase induces antiproliferative and proapoptotic effects in osimertinib-resistant NSCLC cell models

The EGFR tyrosine kinase inhibitor osimertinib has been approved for the first-line treatment of EGFR-mutated Non-Small Cell Lung Cancer (NSCLC) patients. Despite its efficacy, patients develop resistance. Mechanisms of resistance are heterogeneous and not fully understood, and their characterization is essential to find new strategies to overcome resistance. Ceramides are well-known regulators of apoptosis and are converted into glucosylceramides (GlcCer) by glucosylceramide synthase (GCS). A higher content of GlcCers was observed in lung pleural effusions from NSCLC patients and their role in osimertinib-resistance has not been documented. The aim of this study was to determine the therapeutic potential of inhibiting GCS in NSCLC EGFR-mutant models resistant to osimertinib in vitro and in vivo. Lipidomic analysis showed a significant increase in the intracellular levels of glycosylceramides, including GlcCers in osimertinib resistant clones compared to sensitive cells. In resistant cells, the GCS inhibitor PDMP caused cell cycle arrest, inhibition of 2D and 3D cell proliferation, colony formation and migration capability, and apoptosis induction. The intratumoral injection of PDMP completely suppressed the growth of OR xenograft models. This study demonstrated that dysregulation of ceramide metabolism is involved in osimertinib-resistance and targeting GCS may be a promising therapeutic strategy for patients progressed to osimertinib.

Comprehensive analysis of the clinical and biological significances of cholesterol metabolism in lower-grade gliomas

BACKGROUND

As a component of membrane lipids and the precursor of oxysterols and steroid hormones, reprogrammed cholesterol metabolism contributes to the initiation and progression of multiple cancers. Thus, we aim to further investigate the significances of cholesterol metabolism in lower-grade gliomas (LGGs).

METHODS

The present study included 413 LGG samples from TCGA RNA-seq dataset (training cohort) and 172 LGG samples from CGGA RNA-seq dataset (validation cohort). The cholesterol metabolism-related signature was identified by the LASSO regression model. Bioinformatics analyses were performed to explore the functional roles of this signature in LGGs. Kaplan-Meier and Cox regression analyses were enrolled to estimate prognostic value of the risk signature.

RESULTS

Our findings suggested that cholesterol metabolism was tightly associated clinicopathologic features and genomic alterations of LGGs. Bioinformatics analyses revealed that cholesterol metabolism played a key role in immunosuppression of LGGs, mainly by promoting macrophages polarization and T cell exhaustion. Kaplan-Meier curve and Cox regression analysis showed that cholesterol metabolism was an independent prognostic indicator for LGG patients. To improve the clinical application value of the risk signature, we also constructed a nomogram model to predict the 1-, 3- and 5-year survival of LGG patients.

CONCLUSION

The cholesterol metabolism was powerful prognostic indicator and could serve as a promising target to enhance personalized treatment of LGGs.

Establishment of a risk score model for bladder urothelial carcinoma based on energy metabolism-related genes and their relationships with immune infiltration

Bladder urothelial carcinoma (BLCA) is a common malignant tumor of the human urinary system, and a large proportion of BLCA patients have a poor prognosis. Therefore, there is an urgent need to find more efficient and sensitive biomarkers for the prognosis of BLCA patients in clinical practice. RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas, and 584 energy metabolism-related genes (EMRGs) were obtained from the Reactome pathway database. Cox regression analysis and least absolute shrinkage and selection operator analysis were applied to assess prognostic genes and build a risk score model. The estimate and cibersort algorithms were used to explore the immune microenvironment, immune infiltration, and checkpoints in BLCA patients. Furthermore, we used the Human Protein Atlas database and our single-cell RNA-seq datasets of BLCA patients to verify the expression of 13 EMRGs at the protein and single-cell levels. We constructed a risk score model; the area under the curve of the model at 5 years was 0.792. The risk score was significantly correlated with the immune markers M0 macrophages, M2 macrophages, CD8 T cells, follicular helper T cells, regulatory T cells, and dendritic activating cells. Furthermore, eight immune checkpoint genes were significantly upregulated in the high-risk group. The risk score model can accurately predict the prognosis of BLCA patients and has clinical application value. In addition, according to the differences in immune infiltration and checkpoints, BLCA patients with the most significant benefit can be selected for immune checkpoint inhibitor therapy.

Metabolism-Based Molecular Subtyping Endows Effective Ketogenic Therapy in p53-Mutant Colon Cancer

Although targeting cancer metabolism is a promising therapeutic strategy, clinical success depends on accurate molecular and metabolic subtyping. Here, this study reports two metabolism-based molecular subtypes associated with the ketogenic treatment of colon cancer: glycolytic (glycolysis+ /ketolysis- ) and ketolytic (glycolysis+ /ketolysis+ ), which are manifested by distinct profiles of metabolic enzymes and mitochondrial dysfunction, and by different responses to ketone-containing interventions in vitro and in vivo. Notably, the glycolytic subtype is able to be transformed into the ketolytic subtype in p53-mutated tumors upon glucose limitation, rendering resistance to ketogenic therapy associated with upregulation of ketolytic enzymes, such as OXCT1 by mutant p53. The allosteric activator of mutant p53 effectively blocks the rewired molecular expression and the reprogrammed metabolism, leading to the suppression of tumor growth. The findings highlight the utility of metabolic subtyping to guide ketogenic therapy in colon cancer and identify mutant p53 as a synthetic lethality target for ketogenic treatment.

Prognostic signature construction of energy metabolism-related genes in pancreatic cancer

Pancreatic cancer is the 7th leading cause of cancer death worldwide, and its incidence and mortality rate have been on the rise in recent years in Western developed countries. The specificity of the disease and the lack of appropriate treatments have resulted in a 5-year overall survival rate of only 9%. In this study, we conducted a study based on the TCGA database and GEO database and analyzed using the energy metabolism gene set to establish a prognostic model with the least absolute shrinkage and selection operator to identify 7-genes prognostic signature, and the gene expression was verified by Real-time PCR. The model was validated using a risk score calculation, and the OS rates of the 7 genes were analyzed using one-way Cox regression. The prognostic relationship between vesicle-associated membrane protein 2 (VAMP2) and pancreatic cancer patients was analyzed by OS and progression-free survival, and the prognosis was found to be significantly worse in the high-expression group. A Nomogram showed that VAMP2 was an independent prognostic factor in pancreatic cancer. Gene set enrichment analysis showed that VAMP2 upregulation was enriched in pathways associated with immune response and that VAMP2 downregulation was enriched in metabolism-related pathways. The association of VAMP2 with immune cell infiltration was analyzed for the enrichment results, and VAMP2 was found to be positively associated with all 6 immune cells. The results of this study suggest that VAMP2 is an independent prognostic factor associated with energy metabolism in pancreatic cancer and may be involved in the immune response.

Metabolic Rewiring in Glioblastoma Cancer: EGFR, IDH and Beyond

Glioblastoma multiforme (GBM), a highly invasive and incurable tumor, is the humans’ foremost, commonest, and deadliest brain cancer. As in other cancers, distinct combinations of genetic alterations (GA) in GBM induce a diversity of metabolic phenotypes resulting in enhanced malignancy and altered sensitivity to current therapies. Furthermore, GA as a hallmark of cancer, dysregulated cell metabolism in GBM has been recently linked to the acquired GA. Indeed, Numerous point mutations and copy number variations have been shown to drive glioma cells’ metabolic state, affecting tumor growth and patient outcomes. Among the most common, IDH mutations, EGFR amplification, mutation, PTEN loss, and MGMT promoter mutation have emerged as key patterns associated with upregulated glycolysis and OXPHOS glutamine addiction and altered lipid metabolism in GBM. Therefore, current Advances in cancer genetic and metabolic profiling have yielded mechanistic insights into the metabolism rewiring of GBM and provided potential avenues for improved therapeutic modalities. Accordingly, actionable metabolic dependencies are currently used to design new treatments for patients with glioblastoma. Herein, we capture the current knowledge of genetic alterations in GBM, provide a detailed understanding of the alterations in metabolic pathways, and discuss their relevance in GBM therapy.

Prognostic Implication of Energy Metabolism-Related Gene Signatures in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the major non-small-cell lung cancer pathological subtype with poor prognosis worldwide. Herein, we aimed to build an energy metabolism-associated prognostic gene signature to predict patient survival.

Methods

The gene expression profiles of patients with LUAD were downloaded from the TCGA and GEO databases, and energy metabolism (EM)-related genes were downloaded from the GeneCards database. Univariate Cox and LASSO analyses were performed to identify the prognostic EM-associated gene signatures. Kaplan–Meier and receiver operating characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signatures. A CIBERSORT analysis was used to evaluate the correlation between the risk model and immune cells. A nomogram was used to predict the survival probability of LUAD based on a risk model.

Results

We constructed a prognostic signature comprising 13 EM-related genes (AGER, AHSG, ALDH2, CIDEC, CYP17A1, FBP1, GNB3, GZMB, IGFBP1, SORD, SOX2, TRH and TYMS). The Kaplan–Meier curves validated the good predictive ability of the prognostic signature in TCGA AND two GEO datasets (p<0.0001, p=0.00021, and p=0.0034, respectively). The area under the curve (AUC) of the ROC curves also validated the predictive accuracy of the risk model. We built a nomogram to predict the survival probability of LUAD, and the calibration curves showed good predictive ability. Finally, a functional analysis also unveiled the different immune statuses between the two different risk groups.

Conclusion

Our study constructed and verified a novel EM-related prognostic gene signature that could improve the individualized prediction of survival probability in LUAD.

Integrated Analysis of Energy Metabolism Signature-Identified Distinct Subtypes of Bladder Urothelial Carcinoma

Background: Bladder urothelial carcinoma (BLCA) is the most common type of bladder cancer. In this study, the correlation between the metabolic status and the outcome of patients with BLCA was evaluated using data from the Cancer Genome Atlas and Gene Expression Omnibus datasets.

Methods: The clinical and transcriptomic data of patients with BLCA were downloaded from the Cancer Genome Atlas and cBioPortal datasets, and energy metabolism-related gene sets were obtained from the Molecular Signature Database. A consensus clustering algorithm was then conducted to classify the patients into two clusters. Tumor prognosis, clinicopathological features, mutations, functional analysis, ferroptosis status analysis, immune infiltration, immune checkpoint-related gene expression level, chemotherapy resistance, and tumor stem cells were analyzed between clusters. An energy metabolism-related signature was further developed and verified using data from cBioPortal datasets.

Results: Two clusters (C1 and C2) were identified using a consensus clustering algorithm based on an energy metabolism-related signature. The patients with subtype C1 had more metabolism-related pathways, different ferroptosis status, higher cancer stem cell scores, higher chemotherapy resistance, and better prognosis. Subtype C2 was characterized by an increased number of advanced BLCA cases and immune-related pathways. Higher immune and stromal scores were also observed for the C2 subtype. A signature containing 16 energy metabolism-related genes was then identified, which can accurately predict the prognosis of patients with BLCA.

Conclusion: We found that the energy metabolism-associated subtypes of BLCA are closely related to the immune microenvironment, immune checkpoint-related gene expression, ferroptosis status, CSCs, chemotherapy resistance, prognosis, and progression of BLCA patients. The established energy metabolism-related gene signature was able to predict survival in patients with BLCA.

Reprogramming of Lipid Metabolism in Lung Cancer: An Overview with Focus on EGFR-Mutated Non-Small Cell Lung Cancer

Lung cancer is the leading cause of cancer deaths worldwide. Most of lung cancer cases are classified as non-small cell lung cancers (NSCLC). EGFR has become an important therapeutic target for the treatment of NSCLC patients, and inhibitors targeting the kinase domain of EGFR are currently used in clinical settings. Recently, an increasing interest has emerged toward understanding the mechanisms and biological consequences associated with lipid reprogramming in cancer. Increased uptake, synthesis, oxidation, or storage of lipids has been demonstrated to contribute to the growth of many types of cancer, including lung cancer. In this review, we provide an overview of metabolism in cancer and then explore in more detail the role of lipid metabolic reprogramming in lung cancer development and progression and in resistance to therapies, emphasizing its connection with EGFR signaling. In addition, we summarize the potential therapeutic approaches targeting lipid metabolism for lung cancer treatment.

Impact of cancer metabolism on therapy resistance - Clinical implications

Despite an increasing arsenal of anticancer therapies, many patients continue to have poor outcomes due to the therapeutic failures and tumor relapses. Indeed, the clinical efficacy of anticancer therapies is markedly limited by intrinsic and/or acquired resistance mechanisms that can occur in any tumor type and with any treatment. Thus, there is an urgent clinical need to implement fundamental changes in the tumor treatment paradigm by the development of new experimental strategies that can help to predict the occurrence of clinical drug resistance and to identify alternative therapeutic options. Apart from mutation-driven resistance mechanisms, tumor microenvironment (TME) conditions generate an intratumoral phenotypic heterogeneity that supports disease progression and dismal outcomes. Tumor cell metabolism is a prototypical example of dynamic, heterogeneous, and adaptive phenotypic trait, resulting from the combination of intrinsic [(epi)genetic changes, tissue of origin and differentiation dependency] and extrinsic (oxygen and nutrient availability, metabolic interactions within the TME) factors, enabling cancer cells to survive, metastasize and develop resistance to anticancer therapies. In this review, we summarize the current knowledge regarding metabolism-based mechanisms conferring adaptive resistance to chemo-, radio-and immunotherapies as well as targeted therapies. Furthermore, we report the role of TME-mediated intratumoral metabolic heterogeneity in therapy resistance and how adaptations in amino acid, glucose, and lipid metabolism support the growth of therapy-resistant cancers and/or cellular subpopulations. We also report the intricate interplay between tumor signaling and metabolic pathways in cancer cells and discuss how manipulating key metabolic enzymes and/or providing dietary changes may help to eradicate relapse-sustaining cancer cells. Finally, in the current era of personalized medicine, we describe the strategies that may be applied to implement metabolic profiling for tumor imaging, biomarker identification, selection of tailored treatments and monitoring therapy response during the clinical management of cancer patients.

The mitochondrial landscape of ovarian cancer: emerging insights

Ovarian cancer (OC) is known to be the most lethal cancer in women worldwide, and its etiology is poorly understood. Recent studies show that mitochondrial DNA (mtDNA) content as well as mtDNA and nuclear genes encoding mitochondrial proteins influence OC risk. This review presents an overview of role of mitochondrial genetics in influencing OC development and discusses the contribution of mitochondrial proteome in OC development, progression and therapy. A role of mitochondrial genetics in racial disparity is also highlighted. In-depth understanding of role of mitochondria in OC will help develop strategies toward prevention and treatment and improving overall survival in women with OC.

Metabolism and Distribution of Novel Tumor Targeting Drugs In Vivo

BACKGROUND

As a new tumor therapy, targeted therapy is becoming a hot topic due to its high efficiency and low toxicity. Drug effects of targeted tumor drugs are closely related to pharmacokinetics, so it is important to understand their distribution and metabolism in vivo.

METHODS

A systematic review of the literature on the metabolism and distribution of targeted drugs over the past 20 years was conducted, and the pharmacokinetic parameters of approved targeted drugs were summarized in combination with the FDA's drug instructions. Targeting drugs are divided into two categories: small molecule inhibitors and monoclonal antibodies. Novel targeting drugs and their mechanisms of action, which have been developed in recent years, are summarized. The distribution and metabolic processes of each drug in the human body are reviewed.

RESULTS

In this review, we found that the distribution and metabolism of small molecule kinase inhibitors (TKI) and monoclonal antibodies (mAb) showed different characteristics based on the differences of action mechanism and molecular characteristics. TKI absorbed rapidly (Tmax ≈ 1-4 h) and distributed in large amounts (Vd > 100 L). It was mainly oxidized and reduced by cytochrome P450 CYP3A4. However, due to the large molecular diameter, mAb was distributed to tissues slowly, and the volume of distribution was usually very low (Vd < 10 L). It was mainly hydrolyzed and metabolized into peptides and amino acids by protease hydrolysis. In addition, some of the latest drugs are still in clinical trials, and the in vivo process still needs further study.

CONCLUSION

According to the summary of the research progress of the existing targeting drugs, it is found that they have high specificity, but there are still deficiencies in drug resistance and safety. Therefore, the development of safer and more effective targeted drugs is the future research direction. Meanwhile, this study also provides a theoretical basis for clinical accurate drug delivery.

Identification of a prognosis‑associated signature associated with energy metabolism in triple‑negative breast cancer

At present, a large number of exciting results have been found regarding energy metabolism within the triple-negative breast cancer (TNBC) field. Apart from aerobic glycolysis, a number of other catabolic pathways have also been demonstrated to participate in energy generation. However, the prognostic value of energy metabolism for TNBC currently remains unclear. In the present study, the association between gene expression profiles of energy metabolism and outcomes in patients with TNBC was examined using datasets obtained from the Gene Expression Omnibus and The Cancer Genome Atlas. In total, four robust TNBC subtypes were identified on the basis of negative matrix factorization clustering and gene expression patterns, which exhibited distinct immunological, molecular and prognostic (disease-free survival) features. The differentially expressed genes were subsequently identified from the subgroup that demonstrated the poorest prognosis compared with the remaining 3 subgroups, where their biological functions were assessed further by means of gene ontology enrichment analysis. Any signatures found to be associated with energy metabolism were then established using the Cox proportional hazards model to assess patient prognosis. According to results of Kaplan-Meier analysis, the constructed signature consisting of eight genes that were associated with energy metabolism distinguished patient outcomes into low- and high-risk groups. In addition, this signature, which was found to be markedly associated with the clinical characteristics of the patients, served as an independent factor in predicting TNBC patient prognosis. According to gene set enrichment analysis, the gene sets related to the high-risk group participated in the MAPK signal transduction pathway, focal adhesion and extracellular matrix receptor interaction, whilst those related to the low-risk group were revealed to be mainly associated with mismatch repair and propanoate metabolism. Findings from the present study shed new light on the role of energy metabolism within TNBC, where the eight-gene signature associated with energy metabolism constructed can be utilized as a new prognostic marker for predicting survival in patients with TNBC.

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Changes in energy metabolism may be potential biomarkers and therapeutic targets for cancer as they frequently occur within cancer cells. However, basic cancer research has failed to reach a consistent conclusion on the function(s) of mitochondria in energy metabolism. The significance of energy metabolism in the prognosis of ovarian cancer remains unclear; thus, there remains an urgent need to systematically analyze the characteristics and clinical value of energy metabolism in ovarian cancer. Based on gene expression patterns, the present study aimed to analyze energy metabolism‑associated characteristics to evaluate the prognosis of patients with ovarian cancer. A total of 39 energy metabolism‑related genes significantly associated with prognosis were obtained, and three molecular subtypes were identified by nonnegative matrix factorization clustering, among which the C1 subtype was associated with poor clinical outcomes of ovarian cancer. The immune response was enhanced in the tumor microenvironment. A total of 888 differentially expressed genes were identified in C1 compared with the other subtypes, and the results of the pathway enrichment analysis demonstrated that they were enriched in the 'PI3K‑Akt signaling pathway', 'cAMP signaling pathway', 'ECM‑receptor interaction' and other pathways associated with the development and progression of tumors. Finally, eight characteristic genes (tolloid‑like 1 gene, type XVI collagen, prostaglandin F2α, cartilage intermediate layer protein 2, kinesin family member 26b, interferon inducible protein 27, growth arrest‑specific gene 1 and chemokine receptor 7) were obtained through LASSO feature selection; and a number of them have been demonstrated to be associated with ovarian cancer progression. In addition, Cox regression analysis was performed to establish an 8‑gene signature, which was determined to be an independent prognostic factor for patients with ovarian cancer and could stratify sample risk in the training, test and external validation datasets (P<0.01; AUC >0.8). Gene Set Enrichment Analysis results revealed that the 8‑gene signature was involved in important biological processes and pathways of ovarian cancer. In conclusion, the present study established an 8‑gene signature associated with metabolic genes, which may provide new insights into the effects of energy metabolism on ovarian cancer. The 8‑gene signature may serve as an independent prognostic factor for ovarian cancer patients.

Identification of an ATP metabolism-related signature associated with prognosis and immune microenvironment in gliomas

As the core element of material and energy metabolism pathways, the biological functions and prognostic significance of ATP metabolism in diffuse gliomas have so far remained unclear. Based on comprehensive analysis of ATP metabolism‐related gene expression profiles, we constructed an ATP metabolism‐related risk signature to determine the role of ATP metabolism. We found that this ATP metabolism‐related gene expression profile could divide patients into 2 robust groups with distinct clinical characteristics and prognosis. Patients in the high‐risk group tended to be predicted as malignant entities, indicating that the activation of ATP metabolism may promote the malignant progress of diffuse gliomas. Cox regression and Kaplan‐Meier analyses suggested that this risk signature was an independent predictor for prognosis. Furthermore, we constructed an individualized prognosis prediction model through nomogram and time‐dependent receiver operating characteristic (ROC) curve analyses. Functional analysis suggested that, in addition to material and energy metabolism, ATP metabolism also played an essential role in the regulation of the tumor immune microenvironment. In brief, the ATP metabolism‐related signature was tightly associated with regulation of the tumor immune microenvironment and could serve as an independent prognostic biomarker in diffuse gliomas.

Identification of an energy metabolism-related signature associated with clinical prognosis in diffuse glioma

Now, numerous exciting findings have been yielded in the field of energy metabolism within glioma cells. In addition to aerobic glycolysis, multiple catabolic pathways are employed for energy production. However, the prognostic significance of energy metabolism in glioma remains obscure. Here, we explored the relationship between energy metabolism gene profile and outcome of diffuse glioma patients using The Cancer Genome Altas (TCGA) and Chinese Glioma Genome Altas (CGGA) datasets. Based on the gene expression profile, consensus clustering identified two robust clusters of glioma patients with distinguished prognostic and molecular features. With the Cox proportional hazards model with elastic net penalty, an energy metabolism-related signature was built to evaluate patients’ prognosis. Kaplan-Meier analysis found that the acquired signature could differentiate the outcome of low and high-risk groups of patients in both cohorts. Moreover, the signature, significantly associated with the clinical and molecular features, could serve as an independent prognostic factor for glioma patients. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) showed that gene sets correlated with high-risk group were involved in immune and inflammatory response, with the low-risk group were mainly related to glutamate receptor signaling pathway. Our results provided new insight into energy metabolism role in diffuse glioma.

Multiple effects of CDK4/6 inhibition in cancer: From cell cycle arrest to immunomodulation

Dysregulation of the cell cycle is a hallmark of cancer that leads to aberrant cellular proliferation. CDK4/6 are cyclin-dependent kinases activated in response to proliferative signaling, which induce RB hyper-phosphorylation and hence activation of E2F transcription factors, thus promoting cell cycle progression through the S phase. Pharmacologic inhibition of CDK4/6 by palbociclib, ribociclib, or abemaciclib has been showing promising activity in multiple cancers with the best results achieved in combination with other agents. Indeed, CDK4/6 inhibitors are currently approved in combination with endocrine therapy for the treatment of estrogen receptor-positive, human epidermal growth factor receptor 2-negative advanced or metastatic breast cancer. Moreover, a number of clinical trials are currently underway to test the efficacy of combining CDK4/6 inhibitors with different drugs not only in breast but also in other types of cancer. Beyond the inhibition of cell proliferation, CDK4/6 inhibitors have recently revealed new effects on cancer cells and on tumor microenvironment. In particular, it has been reported that these agents induce a senescent-like phenotype, impact on cell metabolism and exert both immunomodulatory and immunogenic effects. Here we describe recent data on the anti-tumor effects of CDK4/6 inhibitors as single agents or in combined therapies, focusing in particular on their metabolic and immunomodulatory activities.

Pre-treatment with the CDK4/6 inhibitor palbociclib improves the efficacy of paclitaxel in TNBC cells

Triple Negative Breast Cancer (TNBC) is a challenging disease due to the lack of druggable targets; therefore, chemotherapy remains the standard of care and the identification of new targets is a high clinical priority. Alterations in the components of the cell cycle machinery have been frequently reported in cancer; given the success obtained with the CDK4/6 inhibitor palbocicib in ER-positive BC, we explored the potential of combining this drug with chemotherapy in Rb-positive TNBC cell models. The simultaneous combination of palbociclib with paclitaxel exerted an antagonistic effect; by contrast, the sequential treatment inhibited cell proliferation and increased cell death more efficaciously than single treatments. By down-regulating the E2F target c-myc, palbociclib reduced HIF-1α and GLUT-1 expression, and hence glucose uptake and consumption both under normoxic and hypoxic conditions. Importantly, these inhibitory effects on glucose metabolism were enhanced by palbociclib/paclitaxel sequential combination; the superior efficacy of such combination was ascribed to the ability of paclitaxel to inhibit palbociclib-mediated induction of AKT and to further down-regulate the Rb/E2F/c-myc signaling. Our results suggest that the efficacy of standard chemotherapy can be significantly improved by a pre-treatment with palbociclib, thus offering a better therapeutic option for Rb-proficient TNBC.