Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers

An energy metabolism-related signature relevant to the tumor immune microenvironment in HNSCC

The importance of energy metabolism in cancer was explored by accumulating studies. Energy metabolism can affect the cellular activities of tumors. However, there is few research exploring the role of energy metabolism in tumor immune microenvironment. In this context, we constructed a novel energy metabolism-related prognostic signature containing 8 genes. The risk score calculated by the signature was analyzed to be an independent value of head and neck squamous cell carcinoma (HNSCC). We further validated the effectiveness and accuracy of our signature in The Cancer Genome Atlas Program (TCGA) cohort and Gene Expression Omnibus (GEO) cohort. Moreover, we also revealed a negative correlation between the risk score and the activity of the immune processes. Finally, we validated the function of Desmoglein 2 protein (DSG2), a risk gene in the signature, in tumor progression and found that knockdown of DSG2 remarkably suppressed the proliferation and migration of HNSCC cells, which further validated our analysis. In conclusion, the energy metabolism-related gene signature we built is a prospective biomarker of HNSCC, which can offer valuable clues for the research and development of immunotherapeutic drugs in HNSCC.

Novel metabolic subtypes in IDH-mutant gliomas: implications for prognosis and therapy

BACKGROUND

Although IDH-mutant glioma generally has a better prognosis than their IDH-wildtype counterparts, considerable prognostic heterogeneity persists among patients with the same IDH mutation. Current study has primarily focused on the different IDH statuses or grades, while the metabolic heterogeneity within IDH-mutant gliomas remains insufficiently characterized. This study aims to identify transcriptomic metabolic subtypes and associated immune microenvironment differences to better understand survival variability and potential therapeutic targets in IDH-mutant glioma.

METHODS

Patients with IDH-mutant gliomas were included from four public datasets (TCGA, n = 373; CGGA325, n = 167; CGGA693, n = 333; GLASS, n = 100), supplemented by 22 cases from Beijing Tiantan Hospital as an independent cohort. Consensus clustering was used to define novel metabolic subtypes. Clinical features were assessed using chi-square tests and Kaplan-Meier analysis. Metabolic profiles were characterized through enrichment analysis and GSVA; immune infiltration was analyzed using CIBERSORTx and ESTIMATE. Tumor samples from the independent cohort underwent untargeted metabolomics for validation. LASSO regression was applied to select metabolic signatures, and the CGP2014 drug library was used for drug screening.

RESULTS

Three metabolic subtypes (C1-C3) with distinct prognoses (p < 0.05) were identified. C1 exhibited enhanced carbohydrate and nucleotide metabolism; C2 displayed upregulated amino acid and lipid metabolism; and C3 demonstrated elevated lipid, nucleotide, and vitamin metabolism. These patterns were validated in the independent cohort. Subtypes were also correlated with immune infiltration. A 13-gene metabolic signature was established to stratify prognostic risk and suggest subtype-specific drug sensitivities.

CONCLUSIONS

Our study provided a novel metabolic subtype for IDH-mutant glioma and highlighted these patients' metabolic heterogeneity and potential therapeutic strategies.

iMetAct: An integrated systematic inference of metabolic activity for dissecting tumor metabolic preference and tumor-immune microenvironment

Metabolic enzymes play a central role in cancer metabolic reprogramming, and their dysregulation creates vulnerabilities that can be exploited for therapy. However, accurately measuring metabolic enzyme activity in a high-throughput manner remains challenging due to the complex, multi-layered regulatory mechanisms involved. Here, we present iMetAct, a framework that integrates metabolic-transcription networks with an information propagation strategy to infer enzyme activity from gene expression data. iMetAct outperforms expression-based methods in predicting metabolite conversion rates by accounting for the effects of post-translational modifications. With iMetAct, we identify clinically significant subtypes of hepatocellular carcinoma with distinct metabolic preferences driven by dysregulated enzymes and metabolic regulators acting at both the transcriptional and non-transcriptional levels. Moreover, applying iMetAct to single-cell RNA sequencing data allows for the exploration of cancer cell metabolism and its interplay with immune regulation in the tumor microenvironment. An accompanying online platform further facilitates tumor metabolic analysis, patient stratification, and immune microenvironment characterization.

Single-cell transcriptomic analyses reveal heterogeneity and key subsets associated with survival and response to PD-1 blockade in cervical squamous cell carcinoma

BACKGROUND

Understanding the intricate tumor microenvironment (TME) is crucial for elucidating the mechanisms underlying the progression of cervical squamous cell carcinoma (CSCC) and its response to anti-PD-1 therapy.

METHODS

In this study, we characterized 50,649 cells obtained from the CSCC for single-cell RNA sequencing and integrated bulk sequencing data from The Cancer Genome Atlas (TCGA) and clinical samples to explore their cell composition, metabolic processes, signaling pathways, specific transcription factors, lineage tracking and response to immunotherapy. In vivo experiments were performed to validate the function of key cell subsets.

RESULTS

We identified ten major cell type and 35 subsets of stromal and immune cells in TME and observed distinct patterns in the metabolic processes and signaling pathways of these cells between tumor and normal tissues. Furthermore, PCNA clamp-associated factor (PCLAF)+ tumor-associated epithelial cell (TAEpis) was negatively correlated with the number of C-X-C motif chemokine ligand 13 (CXCL13)+ CD8+ T cells, overall survival, and response to anti-programmed cell death-1(PD-1) therapy in patients with CSCC. Both in vivo and in vitro experiments demonstrated that PCLAF+ TAEpis promotes the apoptosis of CD8+ T and tumor growth, while also inhibiting T cell infiltration and function.

CONCLUSION

Our findings illuminate the heterogeneity of the complex TME in CSCC and offer evidence supporting PCLAF+ TAEpis as a promising therapeutic target.

Metabolic expression profiling analysis reveals pyruvate-mediated EPHB2 upregulation promotes lymphatic metastasis in head and neck squamous cell carcinomas

Lymphatic metastasis is a well-known factor for initiating distant metastasis of head and neck squamous cell carcinoma (HNSCC), which caused major death in most patients with cancer. Meanwhile, metabolic reprogramming to support metastasis is regarded as a prominent hallmark of cancers. However, how metabolic disorders drive in HNSCC remains unclear. We firstly established a new classification of HNSCC patients based on metabolism gene expression profiles from the TCGA and GEO database, and identified an enriched carbohydrate metabolism subgroup which was significantly associated with lymphatic metastasis and worse clinical outcome. Moreover, we found that highly activated pyruvate metabolism endowed tumors with EPHB2 upregulation and promoted tumor lymphangiogenesis independently of VEGF-C/VEGFR3 signaling pathway. Mechanically, high nuclear acetyl-CoA production from pyruvate metabolism promoted histone acetylation, which in turn transcriptionally upregulated EPHB2 expression and secretion in tumor cells. EPHB2 bound with EFNB1 in lymphatic endothelial cells promoted YAP/TAZ cytoplasmic retention, which alleviated YAP/TAZ-mediated prospero homeobox protein 1 (PROX1) transcriptional repression, and then triggered tumor lymphangiogenesis. Importantly, combined treatment with EFNB1-Fc and VEGFR3 inhibitor synergistic abrogated lymphangiogenesis in vitro and in vivo, suggesting that targeting EPHB2 might be a potential strategy to patients with no or slight response to VEGFR3 inhibitor. These findings uncover the mechanism by which pyruvate metabolism is linked to lymphatic metastasis of tumor and provides a promising therapeutic strategy for the prevention of HNSCC metastasis.

Tumor metabolism as a factor affecting diversity in cancer cachexia

Cancer cachexia is a multifaceted metabolic syndrome characterized by muscle wasting, fat redistribution, and metabolic dysregulation, commonly associated with advanced cancer but sometimes also evident in early-stage disease. More subtle body composition changes have also been reported in association with cancer, including sarcopenia, myosteatosis, and increased fat radiodensity. Emerging evidence reveals that body composition changes including sarcopenia, myosteatosis, and increased fat radiodensity, arise from distinct biological mechanisms and significantly impact survival outcomes. Importantly, these features often occur independently, with their combined presence exacerbating poor prognoses. Tumor plays a pivotal role in driving these host changes, either by acting as a metabolic parasite or by releasing mediators that disrupt normal tissue function. This review explores the diversity of tumor metabolism. It highlights the potential for tumor-specific metabolic phenotypes to influence systemic effects, including fat redistribution and sarcopenia. Addressing this tumor-host metabolic interplay requires personalized approaches that disrupt tumor metabolism while preserving host health. Promising strategies include targeted pharmacological interventions and anticachexia agents like growth differentiation factor 15 (GDF-15) inhibitors. Nutritional modifications such as ketogenic diets and omega-3 fatty acid supplementation also merit further investigation. In addition to preserving muscle, these therapies will need to be evaluated for their capability to improve survival and quality of life. This review underscores the need for further research into tumor-driven metabolic effects on the host and the development of integrative treatment strategies to address the interconnected challenges of cancer progression and cachexia.

Single-cell and spatial-resolved profiling reveals cancer-associated fibroblast heterogeneity in colorectal cancer metabolic subtypes

BACKGROUND

Colorectal cancer (CRC) presents significant treatment challenges due to its high heterogeneity and complex intercellular interactions. Further exploration of CRC subtypes and interactions among tumor-specific clusters will facilitate the development of personalized treatment strategies.

METHODS

Single-cell RNA sequencing and bulk RNA sequencing datasets were integrated to determine CRC metabolic subtypes by hierarchical clustering. The analysis was further extended to cellchat, pseudotime, immune infiltration, and clinicopathological relevance to explore the characteristics of secreted frizzled related protein 2 (SFRP2) + cancer-associated fibroblast (CAF) clusters, and validated by spatial transcriptomics (ST), in vivo experiments, and multiple immunohistochemistry (mIHC).

RESULTS

CRC samples were stably classified into three heterogeneous metabolic subtypes, each exhibiting different microenvironment and CAF heterogeneity, particularly in the distribution of SFRP2 + CAF, which was aligned with metabolic activity. SFRP2 + CAF exhibits high extracellular matrix (ECM) activity and is closely involved in cellular communication, not only promoting the malignant progression of cancer cells but also inducing the differentiation of Tregs. Compared to responders of chemotherapy, the proportion of SFRP2 + CAFs is significantly increased in non-responders. Importantly, mIHC and ST analyses confirm that cancer cells with low expression of agmatinase (AGMAT) can recruit SFRP2 + CAFs, and Treg infiltration surrounding SFRP2 + CAFs was observed. AGMAT combined with oxaliplatin showed the best efficacy in vivo, which may be associated with the inhibition of SFRP2 + CAF infiltration.

CONCLUSIONS

Our study identified and described the potential protumor biological properties of SFRP2 + CAFs, and AGMAT may be a valuable target for disrupting their properties.

Characterization of tumor prognosis and sensitive chemotherapy drugs based on cuproptosis-related gene signature in ovarian cancer

BACKGROUND

Cuproptosis is a novel form of cell death, acting on the tricarboxylic acid cycle in mitochondrial respiration and mediated by protein lipoylation. Other cancer cell death processes, such as necroptosis, pyroptosis, and ferroptosis, have been shown to play crucial roles in the therapy and prognosis of ovarian cancer. However, the role of cuproptosis in ovarian cancer remains unclear.

METHODS

The expression profiles of 10 cuproptosis-related genes were extracted from GSE140082. Kaplan-Meier survival and Cox proportional hazards regression were used to identify prognostic genes for constructing risk models. Following this, Least Absolute Shrinkage and Selection Operator regression was employed to construct a risk score model. Next, a nomogram was constructed to predict overall survival in ovarian cancer. Ultimately, our analysis compared the two groups across various dimensions, including clinical characteristics, tumor progression, metabolism-related pathways, immune landscape, and drug sensitivity.

RESULTS

MTF1 and LIAS were identified as protective factors in ovarian cancer, with patients in the higher risk group being significantly associated with poorer survival. Furthermore, integrating the risk score with clinical characteristics in the nomogram demonstrated high specificity and sensitivity in predicting survival. A higher propotion of M2 macrophages, follicular helper T cells, and resting mast cells was observed in the high-risk group. Additionally, the IC50 values of Dasatinib, Bortezomib, Parthenolide, and Imatinib were significantly lower in the high-risk group.

CONCLUSIONS

The study highlights the prognostic significance of cuproptosis-related genes and provides new insights into developing pharmacological therapeutic strategies targeting cuproptosis for the prevention and treatment of ovarian cancer.

A risk signature constructed by Tregs-related genes predict the clinical outcomes and immune therapeutic response in kidney cancer

Regulatory T cells (Tregs) have been found to be related to immune therapeutic resistance in kidney cancer. However, the potential Tregs-related genes still need to be explored. Our study found that patients with high Tregs activity show poor prognosis. Through co-expression and differential expression analysis, we screened several Tregs-related genes (KTRGs) in kidney renal clear cell carcinoma. We further conducted the univariate Cox regression analysis and determined the prognosis-related KTRGs. Through the machine learning algorithm-Boruta, the potentially important KTRGs were screened further and submitted to construct a risk model. The risk model could predict the prognosis of RCC patients well, high risk patients show a poorer outcomes than low risk patients. Multivariate Cox regression analysis reveals that risk score is an independent prognostic factor. Then, the nomogram model based on KTRG risk score and other clinical variables was further established, which shows a high predicted accuracy and clinical benefit based on model validation methods. In addition, we found EMT, JAK/STAT3, and immune-related pathways highly enriched in high risk groups, while metabolism-related pathways show a low enrichment. Through analyzing two other external immune therapeutic datasets, we found that the risk score could predict the patient's immune therapeutic response. High-risk groups represent a worse therapeutic response than low-risk groups. In summary, we identified several Tregs-related genes and constructed a risk model to predict prognosis and immune therapeutic response. We hope these organized data can provide a theoretical basis for exploring potential Tregs' targets to synergize the immune therapy for RCC patients.

Nicotinamide mononucleotide restores impaired metabolism, endothelial cell proliferation and angiogenesis in old sedentary male mice

Aging is accompanied by a decline in neovascularization potential and increased susceptibility to ischemic injury. Here, we confirm the age-related impaired neovascularization following ischemic leg injury and impaired angiogenesis. The age-related deficits in angiogenesis arose primarily from diminished EC proliferation capacity, but not migration or VEGF sensitivity. Aged EC harvested from the mouse skeletal muscle displayed a pro-angiogenic gene expression phenotype, along with considerable changes in metabolic genes. Metabolomics analysis and 13C glucose tracing revealed impaired ATP production and blockade in glycolysis and TCA cycle in late passage HUVECs, which occurred at nicotinamide adenine dinucleotide (NAD⁺)-dependent steps, along with NAD+ depletion. Supplementation with nicotinamide mononucleotide (NMN), a precursor of NAD⁺, enhances late-passage EC proliferation and sprouting angiogenesis from aged mice aortas. Taken together, our study illustrates the importance of NAD+-dependent metabolism in the maintenance of EC proliferation capacity with age, and the therapeutic potential of NAD precursors.

Impact of metabolism-related markers on outcomes in ovarian cancer patients: Findings of the MITO16A/MaNGO-OV2 trial

INTRODUCTION

In ovarian cancer, expression of metabolism-related markers has been investigated in several studies focusing on individual markers; however, a parallel quantitative evaluation of markers mapping to distinct metabolic processes and their prognostic value in large patient cohorts is still lacking.

METHODS

Here, by using immunohistochemistry followed by digital pathology, we investigated the expression of several markers related to glycolysis including monocarboxylate transporter 1 and 4 (MCT1, MCT4), glutamine metabolism (glutaminase, GLS) and hypoxia/acidosis (carbonic anhydrase 9, CA IX) in tissue microarrays of > 300 patients recruited in the MITO16A clinical trial, which involved treatment of ovarian cancer patients with carboplatin/taxol plus bevacizumab.

RESULTS

Regarding the prognostic impact of these markers, results indicate that GLS expression correlated with progression-free survival, but this effect disappeared when data were corrected for multiple testing. All other markers showed no correlation with clinical outcome.

CONCLUSION

These results indicate marked heterogeneity of expression of metabolism-associated markers in ovarian cancer; however, there was a lack of association with clinical benefit after chemotherapy/anti-vascular endothelial growth factor treatment. Notwithstanding the lack of prognostic value, knowledge of the pattern of expression of these biomarkers in tumors can be useful for patient stratification purposes when new drugs targeting these metabolic pathways will be tested.

Inhibition of LDHB suppresses the metastatic potential of lung cancer by reducing mitochondrial GSH catabolism

Metastasis, the leading cause of cancer death, is closely linked to lactate metabolism. Our study aimed to investigate the role of lactate dehydrogenase B (LDHB), which mainly catalyzes the conversion of lactate to pyruvate, in the metastatic potential of lung cancer. We found that LDHB silencing reduced the invasion and migration ability of lung cancer cells in vitro. On the molecular level, LDHB silencing decreased the total intracellular levels of the antioxidant glutathione (GSH). Surprisingly, LDHB silencing did not increase cellular or mitochondrial reactive oxygen species (ROS) levels. Furthermore, supplementation with GSH monoethyl ester (GSH-mee), a cell-permeable derivative of GSH, partially restored the reduced in vitro colony formation capacity, the oxygen consumption rate, and the invasion and migration capacity of lung cancer cells after LDHB silencing. Using metabolic inhibitors, we showed that the rescue of colony formation after silencing LDHB by GSH-mee was due to enhanced GSH catabolism by γ-L-Glutamyl transpeptidase (GGT), which was mainly present in the mitochondrial fraction of lung cancer cells. Furthermore, we observed that high GGT expression was a prerequisite for the rescue of migratory capacity by GSH-mee after LDHB silencing. Finally, our in vivo experiments demonstrated that targeting LDHB reduced the metastasis of human and mouse lung cancer cells in immunodeficient and immunocompetent mouse models, respectively. In conclusion, LDHB silencing decreases GSH catabolism mediated by GGT, which is primarily located in the mitochondria of cancer cells. Therefore, targeting LDHB is a promising therapeutic approach for the prevention and treatment of metastatic lung cancer.

Gene clusters-based pathway enrichment analysis identifies four pan-cancer subtypes with distinct molecular and clinical features

Pathways-based clustering methods have been proposed to explore tumor heterogeneity. However, such methods are currently disadvantageous in that specific pathways need to be explicitly claimed. We developed the PathClustNet algorithm, a pathway-based clustering method designed to identify cancer subtypes. This method first detects gene clusters and identifies overrepresented pathways associated with them. Based on the pathway enrichment scores, it reveals cancer subtypes by clustering analysis. We applied the method to TCGA pan-cancer data and identified four pan-cancer subtypes, termed C1, C2, C3 and C4. C1 exhibited high metabolic activity, favorable survival, and the lowest TP53 mutation rate. C2 had high immune, developmental, and stromal pathway activities, the lowest tumor purity, and intratumor heterogeneity. C3, which overexpressed cell cycle and DNA repair pathways, was the most genomically unstable and had the highest TP53 mutation rate. C4 overrepresented neuronal pathways, with the lowest response rate to chemotherapy, but the highest tumor purity and genomic stability. Furthermore, age showed positive correlations with most pathways but a negative correlation with neuronal pathways. Smoking, viral infections, and alcohol use were found to affect the activities of neuron, cell cycle, immune, stromal, developmental, and metabolic pathway in varying degrees. The PathClustNet algorithm unveils a novel classification of pan-cancer based on metabolic, immune, stromal, developmental, cell cycle, and neuronal pathways. These subtypes display different molecular and clinical features to warrant the investigation of precision oncology.

AI-Powered cellular morphometric biomarkers discovered in needle biopsy of prostatic cancer predict neoadjuvant androgen deprivation therapy response and prognosis: an international multicenter retrospective study

It is imperative to identify patients with prostate cancer (PCa) who will benefit from androgen receptor signaling inhibitors that can impact quality of life upon prolonged use. Using our extensively-validated artificial-intelligence technique: cellular morphometric biomarker via machine learning (CMB-ML), we identified 13 CMBs from whole slide images of needle biopsies from the trial specimens ( NCT02430480 , n=37) that accurately predicted response to neoadjuvant androgen deprivation therapy (NADT) (AUC: 0.980). Notably, 13-CMB model stratified PCa patients into responder and non-responder groups after NADT treatment in an independent hospital cohort (n=122) that significantly associated with pathologic complete response (p=0.0005), biochemical-recurrence-free survival (p=0.024) and mTOR signaling pathway (p=0.03), suggesting potentially more clinical benefit from mTOR inhibitors in non-responder group. Additionally, genetic and genomic analysis revealed interplay between genetic variants and CMBs on NADT resistance, and provided molecular annotations for CMBs. Overall, prospective clinical implementation of 13-CMB model could assist precision care of PCa patients.

Significance

We describe a highly accurate CMB model to predict the therapeutic benefit in prostate cancer patients and uncover the complex interplay between genetic variants and CMBs on NADT resistance. Our model relies only on widely available needle biopsy specimens and provides a robust and cost-effective solution for clinical implementation.

Normal tissue transcriptional signatures for tumor-type-agnostic phenotype prediction

Cancer transcriptional patterns reflect both unique features and shared hallmarks across diverse cancer types, but whether differences in these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that these shared transcriptomic signatures reflect repurposed versions of functional tasks performed by normal tissues. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

Single cell atlas reveals multilayered metabolic heterogeneity across tumour types

BACKGROUND

Metabolic reprogramming plays a pivotal role in cancer progression, contributing to substantial intratumour heterogeneity and influencing tumour behaviour. However, a systematic characterization of metabolic heterogeneity across multiple cancer types at the single-cell level remains limited.

METHODS

We integrated 296 tumour and normal samples spanning six common cancer types to construct a single-cell compendium of metabolic gene expression profiles and identify cell type-specific metabolic properties and reprogramming patterns. A computational approach based on non-negative matrix factorization (NMF) was utilised to identify metabolic meta-programs (MMPs) showing intratumour heterogeneity. In-vitro cell experiments were conducted to confirm the associations between MMPs and chemotherapy resistance, as well as the function of key metabolic regulators. Survival analyses were performed to assess clinical relevance of cellular metabolic properties.

FINDINGS

Our analysis revealed shared glycolysis upregulation and divergent regulation of citric acid cycle across different cell types. In malignant cells, we identified a colorectal cancer-specific MMP associated with resistance to the cuproptosis inducer elesclomol, validated through in-vitro cell experiments. Furthermore, our findings enabled the stratification of patients into distinct prognostic subtypes based on metabolic properties of specific cell types, such as myeloid cells.

INTERPRETATION

This study presents a nuanced understanding of multilayered metabolic heterogeneity, offering valuable insights into potential personalized therapies targeting tumour metabolism.

FUNDING

National Key Research and Development Program of China (2021YFA1300601). National Natural Science Foundation of China (key grants 82030081 and 81874235). The Shenzhen High-level Hospital Construction Fund and Shenzhen Basic Research Key Project (JCYJ20220818102811024). The Lam Chung Nin Foundation for Systems Biomedicine.

Identification of molecular subtypes based on bile acid metabolism in cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma is a highly heterogeneous tumor with bile acid metabolism involving in its development. The aim of this study was to characterize bile acid metabolism and identify specific subtypes to better stratify cholangiocarcinoma patients for individualized treatment and prognostic assessment.

METHODS

A total of 30 bile acids were quantified using the ultra-performance liquid chromatography tandem mass spectrometry. Using Consensus clustering, the molecular subtypes related to bile acid metabolism were identified. The prognosis, clinicopathologic characteristics, immune landscape, and therapeutic response were compared between these subtypes. The single-cell RNA sequencing (scRNA-seq) analysis and preliminary cell experiment were also conducted to verify our findings.

RESULTS

The altered bile acid profile and genetic variation of bile acid metabolism-related genes in cholangiocarcinoma were demonstrated. The cholangiocarcinoma was categorized into bile acid metabolism-active and -inactive subtypes with different prognoses, clinicopathologic characteristics, tumor microenvironments (TME) and therapeutic responses. This categorization was reproducible and predictable. Specifically, the bile acid metabolism-active subtype showed a poor prognosis with an immunosuppressive microenvironment and an inactive response to immunotherapy, while the bile acid metabolism-inactive subtype showed the opposite characteristics. Moreover, the scRNA-seq revealed that immunotherapy altered bile acid metabolism in TME of cholangiocarcinoma. Finally, a prognostic signature related to bile acid metabolism was developed, which exhibited strong power for prognostic assessment of cholangiocarcinoma. Consistently, these results were verified by immunohistochemistry, cell proliferation, migration, and apoptosis assays.

CONCLUSION

In conclusion, a novel cholangiocarcinoma classification based on bile acid metabolism was established. This classification was significant for the estimation of TME and prognosis.

Cuproptosis-Associated lncRNA Gene Signature Establishes New Prognostic Profile and Predicts Immunotherapy Response in Endometrial Carcinoma

Uterine corpus endometrial carcinoma (UCEC), a prevalent kind of cancerous tumor in female reproductive system that has a dismal prognosis in women worldwide. Given the very limited studies of cuproptosis-related lncRNAs (CRLs) in UCEC. Our purpose was to construct a prognostic profile based on CRLs and explore its assess prognostic value in UCEC victims and its correlation with the immunological microenvironment.

METHODS

554 UCEC tumor samples and 23 normal samples' RNA-seq statistics and clinical details were compiled from data in the TCGA database. CRLs were obtained using Pearson correlation analysis. Using LASSO Cox regression, multivariate Cox regression, and univariate Cox regression analysis, six CRLs are confirmed to develop a risk prediction model at last.We identified two main molecular subtypes and observed that multilayer CRLs modifications were related to patient clinicopathological features, prognosis, and tumor microenvironment (TME) cell infiltration characteristics, and then we verified the prognostic hallmark of UCEC and examined its immunological landscape.Finally, using qRT-PCR, model hub genes' expression patterns were confirmed.

RESULTS

A unique CRL signature was established by the combination of six differently expressed CRLs that were highly linked with the prognosis of UCEC patients. According to their CRLs signatures, the patients were divided into two groups: the low-risk and the high-risk groups. Compared to individuals at high risk, patients at low risk had higher survival rates (p < 0.001). Additionally, Cox regression reveals that the profiles of lncRNAs linked to cuproptosis may independently predict prognosis in UCEC patients. The 1-, 3-, and 5-year risks' respective receiver operating characteristics (ROC) exhibited AUC values of 0.778, 0.810, and 0.854. Likewise, the signature could predict survival in different groups based on factors like stage, age, and grade, among others. Further investigation revealed differences between the different risk score groups in terms of drug sensitivity,immune cell infiltration,tumor mutation burden (TMB) score and microsatellite instability (MSI) score. Compared to the group of high risk, the low-risk group had greater rates of TMB and MSI. Results from qRT-PCR revealed that in UCEC vs normal tissues, AC026202.2, NRAV, AC079466.2, and AC090617.5 were upregulated,while LINC01545 and AL450384.1 were downregulated.

CONCLUSIONS

Our research clarified the relationship between CRLs signature and the immunological profile and prognosis of UCEC.This signature will establish the framework for future investigations into the endometrial cancer CRLs mechanism as well as the exploitation of new diagnostic tools and new therapeutic.

Metabolic reprogramming-related gene classifier distinguishes malignant from the benign pulmonary nodules

The current existing classifiers for distinguishing malignant from benign pulmonary nodules is limited by effectiveness or clinical practicality. In our study, we aimed to develop and validate a gene classifier for lung cancer diagnosis. To identify the genes involved in this process, we used the weighted gene co-expression network analysis to analyze gene expression datasets from Gene Expression Omnibus (GEO). We identified the three most relevant modules associated with malignant nodules and performed functional enrichment analysis on them. The results indicated significant involvement in metabolic, immune-related, cell cycle, and viral-related processes. All three modules showed enrichment in metabolic reprogramming pathways. Based on these genes, we intersected genes from the three modules with metabolic reprogramming-related genes and further intersected with differentially expressed genes to get 78 genes. After machine learning algorithms and manual selection, we finally got a nine-gene classifier consisting of SEC24D, RPSA, PSME3, PSMD8, PSMB7, NCOA1, MED12, LPCAT1, and AKR1C3. Our developed and validated classifier-based model demonstrated good discrimination, with an area under the curve (AUC) of 0.763 in the development cohort, 0.744 in the internal validation cohort, and 0.718 in the external validation cohort, and outperformed previous clinical models. Moreover, the addition of nodule size improved the predictive capability of the classifier. We further verify the expression of the gene in the classifier using TCGA lung cancer samples and found eight of the genes showed significant differential expression in lung adenocarcinoma while all nine genes showed significant differential expression in lung squamous carcinoma. Our findings underscore the significance of metabolic reprogramming pathways in patients with malignant pulmonary nodules, and our gene classifier can assist clinicians in differentiating benign from malignant pulmonary nodules in clinical settings.

Identification of two heterogeneous subtypes of hepatocellular carcinoma with distinct pathway activities and clinical outcomes based on gene set variation analysis

Background

High heterogeneity is an essential feature of malignant tumors. This study aims to reveal the drivers of hepatocellular carcinoma heterogeneity for prognostic stratification and to guide individualized treatment.

Methods

Omics data and clinical data for two HCC cohorts were derived from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Atlas (ICGC), respectively. CNV data and methylation data were downloaded from the GSCA database. GSVA was used to estimate the transcriptional activity of KEGG pathways, and consensus clustering was used to categorize the HCC samples. The pRRophetic package was used to predict the sensitivity of samples to anticancer drugs. TIMER, MCPcounter, quanTIseq, and TIDE algorithms were used to assess the components of TME. LASSO and COX analyses were used to establish a prognostic gene signature. The biological role played by genes in HCC cells was confirmed by in vitro experiments.

Results

We classified HCC tissues into two categories based on the activity of prognostic pathways. Among them, the transcriptional profile of cluster A HCC is similar to that of normal tissue, dominated by cancer-suppressive metabolic pathways, and has a better prognosis. In contrast, cluster B HCC is dominated by high proliferative activity and has significant genetic heterogeneity. Meanwhile, cluster B HCC is often poorly differentiated, has a high rate of serum AFP positivity, is prone to microvascular invasion, and has shorter overall survival. In addition, we found that mutations, copy number variations, and aberrant methylation were also crucial drivers of the differences in heterogeneity between the two HCC subtypes. Meanwhile, the TME of the two HCC subtypes is also significantly different, which offers the possibility of precision immunotherapy for HCC patients. Finally, based on the prognostic value of molecular subtypes, we developed a gene signature that could accurately predict patients' OS. The riskscore quantified by the signature could evaluate the heterogeneity of HCC and guide clinical treatment. Finally, we confirmed through in vitro experiments that RFPL4B could promote the progression of Huh7 cells.

Conclusion

The molecular subtypes we identified effectively exposed the heterogeneity of HCC, which is important for discovering new effective therapeutic targets.

VIRMA promotes the progression of head and neck squamous cell carcinoma by regulating UBR5 mRNA and m6A levels

Head and neck squamous cell carcinoma (HNSCC) is a globally prevalent and lethal cancer form which precise mechanisms remain incompletely understood. Increasing evidence suggests that N6-methyladenosine (m6A) plays a crucial role in cancer progression. This study aimed to explore the biological function of m6A modification and vir-like m6A methyltransferase associated (VIRMA) in HNSCC. We conducted an analysis of VIRMA expression in HNSCC cells using The Cancer Genome Atlas (TCGA) database and employed reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blotting to assess its expression levels in HNSCC cell lines. Additionally, m6A levels in HNSCC cells were quantified, and the correlation between VIRMA expression levels and the clinical and pathological features of other genes was analyzed. Upon knocking down VIRMA levels, we assessed HNSCC cell proliferation, migration, and invasion and validated downstream genes using RT-qPCR and western blot. Our findings suggested that VIRMA, as an m6A-related regulator, may significantly influence HNSCC progression by regulating ubiquitin protein ligase E3 component N-recognin 5 (UBR5) through m6A modification. Therefore, VIRMA may serve as a prognostic biomarker.

Multistate Gene Cluster Switches Determine the Adaptive Mitochondrial and Metabolic Landscape of Breast Cancer

Adaptive metabolic switches are proposed to underlie conversions between cellular states during normal development as well as in cancer evolution. Metabolic adaptations represent important therapeutic targets in tumors, highlighting the need to characterize the full spectrum, characteristics, and regulation of the metabolic switches. To investigate the hypothesis that metabolic switches associated with specific metabolic states can be recognized by locating large alternating gene expression patterns, we developed a method to identify interspersed gene sets by massive correlated biclustering and to predict their metabolic wiring. Testing the method on breast cancer transcriptome datasets revealed a series of gene sets with switch-like behavior that could be used to predict mitochondrial content, metabolic activity, and central carbon flux in tumors. The predictions were experimentally validated by bioenergetic profiling and metabolic flux analysis of 13C-labeled substrates. The metabolic switch positions also distinguished between cellular states, correlating with tumor pathology, prognosis, and chemosensitivity. The method is applicable to any large and heterogeneous transcriptome dataset to discover metabolic and associated pathophysiological states. Significance: A method for identifying the transcriptomic signatures of metabolic switches underlying divergent routes of cellular transformation stratifies breast cancer into metabolic subtypes, predicting their biology, architecture, and clinical outcome.

Characterization of Expression-Based Gene Clusters Gives Insights into Variation in Patient Response to Cancer Therapies

Background

Transcriptomics can reveal much about cellular activity, and cancer transcriptomics have been useful in investigating tumor cell behaviors. Patterns in transcriptome-wide gene expression can be used to investigate biological mechanisms and pathways that can explain the variability in patient response to cancer therapies.

Methods

We identified gene expression patterns related to patient drug response by clustering tumor gene expression data and selecting from the resulting gene clusters those where expression of cluster genes was related to patient survival on specific drugs. We then investigated these gene clusters for biological meaning using several approaches, including identifying common genomic locations and transcription factors whose targets were enriched in these clusters and performing survival analyses to support these candidate transcription factor-drug relationships.

Results

We identified gene clusters related to drug-specific survival, and through these, we were able to associate observed variations in patient drug response to specific known biological phenomena. Specifically, our analysis implicated 2 stem cell-related transcription factors, HOXB4 and SALL4, in poor response to temozolomide in brain cancers. In addition, expression of SNRNP70 and its targets were implicated in cetuximab response by 3 different analyses, although the mechanism remains unclear. We also found evidence that 2 cancer-related chromosomal structural changes may impact drug efficacy.

Conclusion

In this study, we present the gene clusters identified and the results of our systematic analysis linking drug efficacy to specific transcription factors, which are rich sources of potential mechanistic relationships impacting patient outcomes. We also highlight the most promising of these results, which were supported by multiple analyses and by previous research. We report these findings as promising avenues for independent validation and further research into cancer treatments and patient response.

Endometriotic lesions exhibit distinct metabolic signature compared to paired eutopic endometrium at the single-cell level

Current therapeutics of endometriosis focus on hormonal disruption of endometriotic lesions (ectopic endometrium, EcE). Recent findings show higher glycolysis utilization in EcE, suggesting non-hormonal strategy for disease treatment that addresses cellular metabolism. Identifying metabolically altered cell types in EcE is important for targeted metabolic drug therapy without affecting eutopic endometrium (EuE). Here, using single-cell RNA-sequencing, we examine twelve metabolic pathways in paired samples of EuE and EcE from women with confirmed endometriosis. We detect nine major cell types in both EuE and EcE. Metabolic pathways are most differentially regulated in perivascular, stromal, and endothelial cells, with the highest changes in AMPK signaling, HIF-1 signaling, glutathione metabolism, oxidative phosphorylation, and glycolysis. We identify transcriptomic co-activation of glycolytic and oxidative metabolism in perivascular and stromal cells of EcE, indicating a critical role of metabolic reprogramming in maintaining endometriotic lesion growth. Perivascular cells, involved in endometrial stroma repair and angiogenesis, may be potential targets for non-hormonal treatment of endometriosis.

Lecithin-cholesterol acyltransferase is a potential tumor suppressor and predictive marker for hepatocellular carcinoma metastasis

BACKGROUND

Hepatocellular carcinoma (HCC) is a major cause of cancer mortality worldwide, and metastasis is the main cause of early recurrence and poor prognosis. However, the mechanism of metastasis remains poorly understood.

AIM

To determine the possible mechanism affecting HCC metastasis and provide a possible theoretical basis for HCC treatment.

METHODS

The candidate molecule lecithin-cholesterol acyltransferase (LCAT) was screened by gene microarray and bioinformatics analysis. The expression levels of LCAT in clinical cohort samples was detected by quantitative real-time polymerase chain reaction and western blotting. The proliferation, migration, invasion and tumor-forming ability were measured by Cell Counting Kit-8, Transwell cell migration, invasion, and clonal formation assays, respectively. Tumor formation was detected in nude mice after LCAT gene knockdown or overexpression. The immunohistochemistry for Ki67, E-cadherin, N-cadherin, matrix metalloproteinase 9 and vascular endothelial growth factor were performed in liver tissues to assess the effect of LCAT on HCC. Gene set enrichment analysis (GSEA) on various gene signatures were analyzed with GSEA version 3.0. Three machine-learning algorithms (random forest, support vector machine, and logistic regression) were applied to predict HCC metastasis in The Cancer Genome Atlas and GEO databases.

RESULTS

LCAT was identified as a novel gene relating to HCC metastasis by using gene microarray in HCC tissues. LCAT was significantly downregulated in HCC tissues, which is correlated with recurrence, metastasis and poor outcome of HCC patients. Functional analysis indicated that LCAT inhibited HCC cell proliferation, migration and invasion both in vitro and in vivo. Clinicopathological data showed that LCAT was negatively associated with HCC size and metastasis (HCC size ≤ 3 cm vs 3-9 cm, P < 0.001; 3-9 cm vs > 9 cm, P < 0.01; metastatic-free HCC vs extrahepatic metastatic HCC, P < 0.05). LCAT suppressed the growth, migration and invasion of HCC cell lines via PI3K/AKT/mTOR signaling. Our results indicated that the logistic regression model based on LCAT, TNM stage and the serum level of α-fetoprotein in HCC patients could effectively predict high metastatic risk HCC patients.

CONCLUSION

LCAT is downregulated at translational and protein levels in HCC and might inhibit tumor metastasis via attenuating PI3K/AKT/mTOR signaling. LCAT is a prognostic marker and potential therapeutic target for HCC.

Molecular characterization and clinical relevance of metabolic signature subtypes in gastric cancer

Metabolic reprogramming dictates tumor molecular attributes and therapeutic potentials. However, the comprehensive metabolic characteristics in gastric cancer (GC) remain obscure. Here, metabolic signature-based clustering analysis identifies three subtypes with distinct molecular and clinical features: MSC1 showed better prognosis and upregulation of the tricarboxylic acid (TCA) cycle and lipid metabolism, combined with frequent TP53 and RHOA mutation; MSC2 had moderate prognosis and elevated nucleotide and amino acid metabolism, enriched by intestinal histology and mismatch repair deficient (dMMR); and MSC3 exhibited poor prognosis and enhanced glycan and energy metabolism, accompanied by diffuse histology and frequent CDH1 mutation. The Shandong Provincial Hospital (SDPH) in-house dataset with matched transcriptomic, metabolomic, and spatial-metabolomic analysis also validated these findings. Further, we constructed the metabolic subtype-related prognosis gene (MSPG) scoring model to quantify the activity of individual tumors and found a positive correlation with cuproptosis signaling. In conclusion, comprehensive recognition of the metabolite signature can enhance the understanding of diversity and heterogeneity in GC.

Mechanisms of drug resistance in nutrient-depleted colorectal cancer cells: insights into lysosomal and mitochondrial drug sequestration

This Review delves into the mechanisms behind drug resistance in colorectal cancer (CRC), particularly examining the role of nutrient depletion and its contribution to multidrug resistance (MDR). The study highlights metabolic adaptations of cancer cells as well as metabolic adaptations of cancer cells under low nutrient availability, including shifts in glycolysis and lipid metabolism. It emphasizes the significance of MDR1 and its encoded efflux transporter, P-glycoprotein (P-gp/B1), in mediating drug resistance and how pathways such as HIF1α, AKT, and mTOR influence the expression of P-gp/B1 under limited nutrient availability. Additionally, the Review explores the dual roles of autophagy in drug sensitivity and resistance under nutrient limited conditions. It further investigates the involvement of lysosomes and mitochondria, focusing on their roles in drug sequestration and the challenges posed by lysosomal entrapment facilitated by non-enzymatic processes and ABC transporters like P-gp/B1. Finally, the Review underscores the importance of understanding the interplay between drug sequestration, lysosomal functions, nutrient depletion, and MDR1 gene modulation. It suggests innovative strategies, including structural modifications and nanotechnology, as promising approaches to overcoming drug resistance in cancer therapy.

Nomogram for preoperative estimation of microvascular invasion risk in hepatocellular carcinoma

Microvascular invasion (MVI) is an adverse prognostic indicator of tumor recurrence after surgery for hepatocellular carcinoma (HCC). Therefore, developing a nomogram for estimating the presence of MVI before liver resection is necessary. We retrospectively included 260 patients with pathologically confirmed HCC at the Fifth Medical Center of Chinese PLA General Hospital between January 2021 and April 2024. The patients were randomly divided into a training cohort (n = 182) for nomogram development, and a validation cohort (n = 78) to confirm the performance of the model (7:3 ratio). Significant clinical variables associated with MVI were then incorporated into the predictive nomogram using both univariate and multivariate logistic analyses. The predictive performance of the nomogram was assessed based on its discrimination, calibration, and clinical utility. Serum carnosine dipeptidase 1 ([CNDP1] OR 2.973; 95 % CI 1.167-7.575; p = 0.022), cirrhosis (OR 8.911; 95 % CI 1.922-41.318; p = 0.005), multiple tumors (OR 4.095; 95 % CI 1.374-12.205; p = 0.011), and tumor diameter ≥3 cm (OR 4.408; 95 % CI 1.780-10.919; p = 0.001) were independent predictors of MVI. Performance of the nomogram based on serum CNDP1, cirrhosis, number of tumors and tumor diameter was achieved with a concordance index of 0.833 (95 % CI 0.771-0.894) and 0.821 (95 % CI 0.720-0.922) in the training and validation cohorts, respectively. It fitted well in the calibration curves, and the decision curve analysis further confirmed its clinical usefulness. The nomogram, incorporating significant clinical variables and imaging features, successfully predicted the personalized risk of MVI in HCC preoperatively.

Environmental monobutyl phthalate exposure promotes liver cancer via reprogrammed cholesterol metabolism and activation of the IRE1α-XBP1s pathway

Humans are widely exposed to phthalates, a major chemical plasticizer that accumulates in the liver. However, little is known about the impact of chronic phthalate exposure on liver cancer development. In this study, we applied a long-term cell culture model by treating the liver cancer cell HepG2 and normal hepatocyte L02 to environmental dosage of monobutyl phthalate (MBP), the main metabolite of phthalates. Interestingly, we found that long-term MBP exposure significantly accelerated the growth of HepG2 cells in vitro and in vivo, but barely altered the function of L02 cells. MBP exposure triggered reprogramming of lipid metabolism in HepG2 cells, where cholesterol accumulation subsequently activated the IRE1α-XBP1s axis of the unfolded protein response. As a result, the XBP1s-regulated gene sets and pathways contributed to the increased aggressiveness of HepG2 cells. In addition, we also showed that MBP-induced cholesterol accumulation fostered an immunosuppressive microenvironment by promoting tumor-associated macrophage polarization toward the M2 type. Together, these results suggest that environmental phthalates exposure may facilitate liver cancer progression, and alerts phthalates exposure to patients who already harbor liver tumors.

Acetyl-CoA metabolic accumulation promotes hepatocellular carcinoma metastasis via enhancing CXCL1-dependent infiltration of tumor-associated neutrophils

High levels of acetyl-CoA are considered a key metabolic feature of metastatic cancers. However, the impacts of acetyl-CoA metabolic accumulation on cancer microenvironment remodeling are poorly understood. In this study, using human hepatocellular carcinoma (HCC) tissues and orthotopic xenograft models, we found a close association between high acetyl-CoA levels in HCCs, increased infiltration of tumor-associated neutrophils (TANs) in the cancer microenvironment and HCC metastasis. Cytokine microarray and enzyme-linked immunosorbent assays (ELISA) revealed the crucial role of the chemokine (C-X-C motif) ligand 1(CXCL1). Mechanistically, acetyl-CoA accumulation induces H3 acetylation-dependent upregulation of CXCL1 gene expression. CXCL1 recruits TANs, leads to neutrophil extracellular traps (NETs) formation and promotes HCC metastasis. Collectively, our work linked the accumulation of acetyl-CoA in HCC cells and TANs infiltration, and revealed that the CXCL1-CXC receptor 2 (CXCR2)-TANs-NETs axis is a potential target for HCCs with high acetyl-CoA levels.

Surgical Tumor Resection Deregulates Hallmarks of Cancer in Resected Tissue and the Surrounding Microenvironment

Surgery exposes tumor tissue to severe hypoxia and mechanical stress leading to rapid gene expression changes in the tumor and its microenvironment, which remain poorly characterized. We biopsied tumor and adjacent normal tissues from patients with breast (n = 81) and head/neck squamous cancers (HNSC; n = 10) at the beginning (A), during (B), and end of surgery (C). Tumor/normal RNA from 46/81 patients with breast cancer was subjected to mRNA-Seq using Illumina short-read technology, and from nine patients with HNSC to whole-transcriptome microarray with Illumina BeadArray. Pathways and genes involved in 7 of 10 known cancer hallmarks, namely, tumor-promoting inflammation (TNF-A, NFK-B, IL18 pathways), activation of invasion and migration (various extracellular matrix-related pathways, cell migration), sustained proliferative signaling (K-Ras Signaling), evasion of growth suppressors (P53 signaling, regulation of cell death), deregulating cellular energetics (response to lipid, secreted factors, and adipogenesis), inducing angiogenesis (hypoxia signaling, myogenesis), and avoiding immune destruction (CTLA4 and PDL1) were significantly deregulated during surgical resection (time points A vs. B vs. C). These findings were validated using NanoString assays in independent pre/intra/post-operative breast cancer samples from 48 patients. In a comparison of gene expression data from biopsy (analogous to time point A) with surgical resection samples (analogous to time point C) from The Cancer Genome Atlas study, the top deregulated genes were the same as identified in our analysis, in five of the seven studied cancer types. This study suggests that surgical extirpation deregulates the hallmarks of cancer in primary tumors and adjacent normal tissue across different cancers.

IMPLICATIONS

Surgery deregulates hallmarks of cancer in human tissue.

Metabolic reprogramming-driven homologous recombination and TCA cycle dysregulation contribute to poor prognoses in lung adenocarcinoma

Increasing evidence has shown that homologous recombination (HR) and metabolic reprogramming are essential for cellular homeostasis. These two processes are independent as well as closely intertwined. Nevertheless, they have rarely been reported in lung adenocarcinoma (LUAD). We analysed the genomic, immune microenvironment and metabolic microenvironment features under different HR activity states. Using cell cycle, EDU and cell invasion assays, we determined the impacts of si-SHFM1 on the LUAD cell cycle, proliferation and invasion. The levels of isocitrate dehydrogenase (IDH) and α-ketoglutarate dehydrogenase (α-KGDH) were determined by ELISA in the NC and si-SHFM1 groups of A549 cells. Finally, cell samples were used to extract metabolites for HPIC-MS/MS to analyse central carbon metabolism. We found that high HR activity was associated with a poor prognosis in LUAD, and HR was an independent prognostic factor for TCGA-LUAD patients. Moreover, LUAD samples with a high HR activity presented low immune infiltration levels, a high degree of genomic instability, a good response status to immune checkpoint blockade therapy and a high degree of drug sensitivity. The si-SHFM1 group presented a significantly higher proportion of cells in the G0/G1 phase, lower levels of DNA replication, and significantly lower levels of cell migration and both TCA enzymes. Our current results indicated that there is a strong correlation between HR and the TCA cycle in LUAD. The TCA cycle can promote SHFM1-mediated HR in LUAD, raising their activities, which can finally result in a poor prognosis and impair immunotherapeutic efficacy.

Unraveling metabolic characteristics and clinical implications in gastric cancer through single-cell resolution analysis

Background: Gastric cancer is a highly prevalent malignant neoplasm. Metabolic reprogramming is intricately linked to both tumorigenesis and cancer immune evasion. The advent of single-cell RNA sequencing technology provides a novel perspective for evaluating cellular metabolism. This study aims to comprehensively investigate the metabolic pathways of various cell types in tumor and normal samples at high resolution and delve into the intricate regulatory mechanisms governing the metabolic activity of malignant cells in gastric cancer. Methods: Utilizing single-cell RNA sequencing data from gastric cancer, we constructed metabolic landscape maps for different cell types in tumor and normal samples. Employing unsupervised clustering, we categorized malignant cells in tumor samples into high and low metabolic subclusters and further explored the characteristics of these subclusters. Results: Our research findings indicate that epithelial cells in tumor samples exhibit significantly higher activity in most KEGG metabolic pathways compared to other cell types. Unsupervised clustering, based on the scores of metabolic pathways, classified malignant cells into high and low metabolic subclusters. In the high metabolic subcluster, it demonstrated the potential to induce a stronger immune response, correlating with a relatively favorable prognosis. In the low metabolic subcluster, a subset of cells resembling cancer stem cells (CSCs) was identified, and its prognosis was less favorable. Furthermore, a set of risk genes associated with this subcluster was discovered. Conclusion: This study reveals the intricate regulatory mechanisms governing the metabolic activity of malignant cells in gastric cancer, offering new perspectives for improving prognosis and treatment strategies.

Deciphering metabolic heterogeneity in retinoblastoma unravels the role of monocarboxylate transporter 1 in tumor progression

BACKGROUND

Tumors exhibit metabolic heterogeneity, influencing cancer progression. However, understanding metabolic diversity in retinoblastoma (RB), the primary intraocular malignancy in children, remains limited.

METHODS

The metabolic landscape of RB was constructed based on single-cell transcriptomic sequencing from 11 RB and 5 retina samples. Various analyses were conducted, including assessing overall metabolic activity, metabolic heterogeneity, and the correlation between hypoxia and metabolic pathways. Additionally, the expression pattern of the monocarboxylate transporter (MCT) family in different cell clusters was examined. Validation assays of MCT1 expression and function in RB cell lines were performed. The therapeutic potential of targeting MCT1 was evaluated using an orthotopic xenograft model. A cohort of 47 RB patients was analyzed to evaluate the relationship between MCT1 expression and tumor invasion.

RESULTS

Distinct metabolic patterns in RB cells, notably increased glycolysis, were identified. This metabolic heterogeneity correlated closely with hypoxia. MCT1 emerged as the primary monocarboxylate transporter in RB cells. Disrupting MCT1 altered cell viability and energy metabolism. In vivo studies using the MCT1 inhibitor AZD3965 effectively suppressed RB tumor growth. Additionally, a correlation between MCT1 expression and optic nerve invasion in RB samples suggested prognostic implications.

CONCLUSIONS

This study enhances our understanding of RB metabolic characteristics at the single-cell level, highlighting the significance of MCT1 in RB pathogenesis. Targeting MCT1 holds promise as a therapeutic strategy for combating RB, with potential prognostic implications.

Epithelial-mesenchymal transition-related signaling pathways in gastric Cancer cells distinctively respond to long-term experimental ketosis

BACKGROUND

The interrelationship between cellular metabolism and the epithelial-to-mesenchymal transition (EMT) process has made it an interesting topic to investigate the adjuvant effect of therapeutic diets in the treatment of cancers. However, the findings are controversial. In this study, the effects of glucose limitation along and with the addition of beta-hydroxybutyrate (bHB) were examined on the expression of specific genes and proteins of EMT, Wnt, Hedgehog, and Hippo signaling pathways, and also on cellular behavior of gastric cancer stem-like (MKN-45) and non-stem-like (KATO III) cells.

METHODS AND RESULTS

The expression levels of chosen genes and proteins studied in cancer cells gradually adopted a low-glucose condition of one-fourth, along and with the addition of bHB, and compared to the unconditioned control cells. The long-term switching of the metabolic fuels successfully altered the expression profiles and behaviors of both gastric cancer cells. However, the results for some changes were the opposite. Glucose limitation along and with the addition of bHB reduced the CD44+ population in MKN-45 cells. In KATO III cells, glucose restriction increased the CD44+ population. Glucose deprivation alleviated EMT-related signaling pathways in MKN-45 cells but stimulated EMT in KATO III cells. Interestingly, bHB enrichment reduced the beneficial effect of glucose starvation in MKN-45 cells, but also alleviated the adverse effects of glucose restriction in KATO III cells.

CONCLUSIONS

The findings of this research clearly showed that some controversial results in clinical trials for ketogenic diet in cancer patients stemmed from the different signaling responses of various cells to the metabolic changes in a heterogeneous cancer mass.

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.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Molecular subtype construction and prognosis model for stomach adenocarcinoma characterized by metabolism-related genes

Background

Metabolic reprogramming is implicated in cancer progression. However, the impact of metabolism-associated genes in stomach adenocarcinomas (STAD) has not been thoroughly reviewed. Herein, we characterized metabolic transcription-correlated STAD subtypes and evaluated a metabolic RiskScore for evaluation survival.

Method

Genes related to metabolism were gathered from previous study and metabolic subtypes were screened using ConsensusClusterPlus in TCGA-STAD and GSE66229 dataset. The ssGSEA, MCP-Count, ESTIMATE and CIBERSORT determined the immune infiltration. A RiskScore model was established using the WGCNA and LASSO Cox regression in the TCGA-STAD queue and verified in the GSE66229 datasets. RT-qPCR was employed to measure the mRNA expressions of genes in the model.

Result

Two metabolism-related subtypes (C1 and C2) of STAD were constructed on account of the expression profiles of 113 prognostic metabolism genes with different immune outcomes and apparently distinct metabolic characteristic. The overall survival (OS) of C2 subtype was shorter than that of C1 subtype. Four metabolism-associated genes in turquoise model, which closely associated with C2 subtype, were employed to build the RiskScore (MATN3, OSBPL1A, SERPINE1, CPNE8) in TCGA-train dataset. Patients developed a poorer prognosis if they had a high RiskScore than having a low RiskScore. The promising effect of RiskScore was verified in the TCGA-test, TCGA-STAD and GSE66229 datasets. The prediction reliability of the RiskScore was validated by time-dependent receiver operating characteristic curve (ROC) and nomogram. Moreover, samples with high RiskScore had an enhanced immune status and TIDE score. Moreover, MATN3, OSBPL1A, SERPINE1 and CPNE8 mRNA levels were all elevated in SGC7901 cells. Inhibition of OSBPL1A decreased SGC7901 cells invasion numbers.

Conclusion

This work provided a new perspective into heterogeneity in metabolism and its association with immune escape in STAD. RiskScore was considered to be a strong prognostic label that could help individualize the treatment of STAD patients.

NOX4 promotes tumor progression through the MAPK-MEK1/2-ERK1/2 axis in colorectal cancer

BACKGROUND

Metabolic reprogramming plays a key role in cancer progression and clinical outcomes; however, the patterns and primary regulators of metabolic reprogramming in colorectal cancer (CRC) are not well understood.

AIM

To explore the role of nicotinamide adenine dinucleotide phosphate oxidase 4 (NOX4) in promoting progression of CRC.

METHODS

We evaluated the expression and function of dysregulated and survival-related metabolic genes using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Consensus clustering was used to cluster CRC based on dysregulated metabolic genes. A prediction model was constructed based on survival-related metabolic genes. Sphere formation, migration, invasion, proliferation, apoptosis and clone formation was used to evaluate the biological function of NOX4 in CRC. mRNA sequencing was utilized to explore the alterations of gene expression NOX4 over-expression tumor cells. In vivo subcutaneous and lung metastasis mouse tumor model was used to explore the effect of NOX4 on tumor growth.

RESULTS

We comprehensively analyzed 3341 metabolic genes in CRC and identified three clusters based on dysregulated metabolic genes. Among these genes, NOX4 was highly expressed in tumor tissues and correlated with worse survival. In vitro, NOX4 overexpression induced clone formation, migration, invasion, and stemness in CRC cells. Furthermore, RNA-sequencing analysis revealed that NOX4 overexpression activated the mitogen-activated protein kinase-MEK1/2-ERK1/2 signaling pathway. Trametinib, a MEK1/2 inhibitor, abolished the NOX4-mediated tumor progression. In vivo, NOX4 overexpression promoted subcutaneous tumor growth and lung metastasis, whereas trametinib treatment can reversed the metastasis.

CONCLUSION

Our study comprehensively analyzed metabolic gene expression and highlighted the importance of NOX4 in promoting CRC metastasis, suggesting that trametinib could be a potential therapeutic drugs of CRC clinical therapy targeting NOX4.

Identifying cancer subtypes based on embryonic and hematopoietic stem cell signatures in pan-cancer

PURPOSE

Cancer cells with stem cell-like properties may contribute to cancer development and therapy resistance. The advancement of multi-omics technology has sparked interest in exploring cancer stemness from a multi-omics perspective. However, there is a limited number of studies that have attempted to subtype cancer by combining different types of stem cell signatures.

METHODS

In this study, 10,323 cancer specimens from 33 TCGA cancer types were clustered based on the enrichment scores of six stemness gene sets, representing two types of stem cell backgrounds: embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs).

RESULTS

We identified four subtypes of pan-cancer, termed StC1, StC2, StC3 and StC4, which displayed distinct molecular and clinical features, including stemness, genome integrity, intratumor heterogeneity, methylation levels, tumor microenvironment, tumor progression, responses to chemotherapy and immunotherapy, and survival prognosis. Importantly, this subtyping method for pan-cancer is reproducible at the protein level.

CONCLUSION

Our findings indicate that the ESC signature is an adverse prognostic factor in cancer, while the HSC signature and ratio of HSC/ESC signatures are positive prognostic factors. The subtyping of cancer based on ESC and HSC signatures may provide insights into cancer biology and clinical implications of cancer.

Genetic and molecular characterization of metabolic pathway-based clusters in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive types of squamous cell carcinoma and represents a significant proportion of esophageal cancer. Metabolic reprogramming plays a key role in the occurrence and development of ESCC. Unsupervised clustering analysis was employed to stratify ESCC samples into three clusters: MPC1-lipid type, MPC2-amino acid type, and MPC3-energy type, based on the enrichment scores of metabolic pathways extracted from the Reactome database. The MPC3 cluster exhibited characteristics of energy metabolism, with heightened glycolysis, cofactors, and nucleotide metabolism, showing a trend toward increased aggressiveness and poorer survival rates. On the other hand, MPC1 and MPC2 primarily involved lipid and amino acid metabolism, respectively. In addition, liquid chromatography‒mass spectrometry-based metabolite profiles and potential therapeutic agents were explored and compared among ESCC cell lines with different MPCs. MPC3 amplified energy metabolism markers, especially carnitines. In contrast, MPC1 and MPC2 predominantly had elevated levels of lipids (primarily triacylglycerol) and amino acids, respectively. Furthermore, MPC3 demonstrated a suboptimal clinical response to PD-L1 immunotherapy but showed increased sensitivity to the doramapimod chemotherapy regimen, as evident from drug sensitivity evaluations. These insights pave the way for a more personalized therapeutic approach, potentially enhancing treatment precision for ESCC patients.

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

Cancer transcriptional patterns exhibit both shared and unique features across diverse cancer types, but whether these patterns are sufficient to characterize the full breadth of tumor phenotype heterogeneity remains an open question. We hypothesized that cancer transcriptional diversity mirrors patterns in normal tissues optimized for distinct functional tasks. Starting with normal tissue transcriptomic profiles, we use non-negative matrix factorization to derive six distinct transcriptomic phenotypes, called archetypes, which combine to describe both normal tissue patterns and variations across a broad spectrum of malignancies. We show that differential enrichment of these signatures correlates with key tumor characteristics, including overall patient survival and drug sensitivity, independent of clinically actionable DNA alterations. Additionally, we show that in HR+/HER2- breast cancers, metastatic tumors adopt transcriptomic signatures consistent with the invaded tissue. Broadly, our findings suggest that cancer often arrogates normal tissue transcriptomic characteristics as a component of both malignant progression and drug response. This quantitative framework provides a strategy for connecting the diversity of cancer phenotypes and could potentially help manage individual patients.

Heterogeneity of Lipid Metabolism and its Clinical and Immune Correlation in Lung Adenocarcinoma

INTRODUCTION

The role of lipid metabolism in lung adenocarcinoma (LUAD) is not completely researched. Lipid metabolism reprogramming is a characteristic of malignancies and contributes to carcinogenesis and progression. The transcriptome and scRNA- seq data and clinical information were downloaded from the public databases.

METHODS

Lipid metabolism pathways were collected from the MSigDB database, and molecular subtypes were classified based on lipid metabolism features via consensus clustering. The bidirectional crosstalk between immune cells and malignant cells was analyzed. Differences in lipid metabolism at the single-cell level and their correlation with the tumor microenvironment (TME) were also studied. LUAD patients were classified into two subtypes, showing distinct mutation and lipid metabolism features based on lipid metabolism characteristics. Meanwhile, significant differences in the overall survival, clinical characteristics, and immune landscape were observed between the two subtypes. We also found that clust1 had higher oxidative stress status. There were 116 differentially expressed genes between the two subtypes, which were significantly associated with cell cycle progression. We identified 4001 immune cells, including 483 malignant cells and 3518 normal cells, and found active intercellular communication and significant differences in lipid metabolism characteristics between the malignant cells and normal cells. Furthermore, several lipid metabolism pathways were found to be associated with TME factors, including hypoxia and angiogenesis.

RESULT

The current findings indicated that lipid metabolism was involved in the development and cellular heterogeneity of LUAD and revealed widespread reprogramming across multiple cellular elements in the TME of LUAD.

CONCLUSION

This characterization improved the current understanding of tumor biology and enabled the identification of novel targets for immunotherapy.

Modification patterns and metabolic characteristics of m6A regulators in digestive tract tumors

M6A is essential for tumor occurrence and progression. The expression patterns of m6A regulators differ in various kinds of tumors. Transcriptomic expression statistics together with clinical data from a database were analyzed to distinguish patients with digestive tract tumors. Based on the expression patterns of diverse m6A regulators, patients were divided into several clusters. Survival analysis suggested significant differences in patient prognosis among the m6A clusters. The results showed overlapping of m6A expression patterns with energy metabolism and nucleotide metabolism. Functional analyses imply that m6A modifications in tumor cells probably drive metabolic reprogramming to sustain rapid proliferation of cancer cells. Our analysis highlights the m6A risk characterizes various kinds of metabolic features and predicts chemotherapy sensitivity in digestive tract tumors, providing evidence for m6A regulators as markers to predict patient outcomes.

Exploring the relationship between metabolism and immune microenvironment in osteosarcoma based on metabolic pathways

BACKGROUND

Metabolic remodeling and changes in tumor immune microenvironment (TIME) in osteosarcoma are important factors affecting prognosis and treatment. However, the relationship between metabolism and TIME needs to be further explored.

METHODS

RNA-Seq data and clinical information of 84 patients with osteosarcoma from the TARGET database and an independent cohort from the GEO database were included in this study. The activity of seven metabolic super-pathways and immune infiltration levels were inferred in osteosarcoma patients. Metabolism-related genes (MRGs) were identified and different metabolic clusters and MRG-related gene clusters were identified using unsupervised clustering. Then the TIME differences between the different clusters were compared. In addition, an MRGs-based risk model was constructed and the role of a key risk gene, ST3GAL4, in osteosarcoma cells was explored using molecular biological experiments.

RESULTS

This study revealed four key metabolic pathways in osteosarcoma, with vitamin and cofactor metabolism being the most relevant to prognosis and to TIME. Two metabolic pathway-related clusters (C1 and C2) were identified, with some differences in immune activating cell infiltration between the two clusters, and C2 was more likely to respond to two chemotherapeutic agents than C1. Three MRG-related gene clusters (GC1-3) were also identified, with significant differences in prognosis among the three clusters. GC2 and GC3 had higher immune cell infiltration than GC1. GC3 is most likely to respond to immune checkpoint blockade and to three commonly used clinical drugs. A metabolism-related risk model was developed and validated. The risk model has strong prognostic predictive power and the low-risk group has a higher level of immune infiltration than the high-risk group. Knockdown of ST3GAL4 significantly inhibited proliferation, migration, invasion and glycolysis of osteosarcoma cells and inhibited the M2 polarization of macrophages.

CONCLUSION

The metabolism of vitamins and cofactors is an important prognostic regulator of TIME in osteosarcoma, MRG-related gene clusters can well reflect changes in osteosarcoma TIME and predict chemotherapy and immunotherapy response. The metabolism-related risk model may serve as a useful prognostic predictor. ST3GAL4 plays a critical role in the progression, glycolysis, and TIME of osteosarcoma cells.

EMPathways2: Estimation of Enzyme Expression and Metabolic Pathway Activity Using RNA-Seq Reads

In this chapter, we outline an approach to analyzing metatranscriptomic data, focusing on the assessment of differential enzyme expression and metabolic pathway activities using a novel bioinformatics software tool, EMPathways2. The analysis pipeline commences with raw data originating from a sequencer and concludes with an output of enzyme expressions and an estimate of metabolic pathway activities. The initial step involves aligning specific transcriptomes assembled from RNA-Seq data using Bowtie2 and acquiring gene expression data with IsoEM2. Subsequently, the pipeline proceeds to quality assessment and preprocessing of the input data, ensuring accurate estimates of enzymes and their differential regulation. Upon completion of the preprocessing stage, EMPathways2 is employed to decipher the intricate relationships between genes, enzymes, and pathways. An online repository containing sample data has been made available, alongside custom Python scripts designed to modify the output of the programs within the pipeline for diverse downstream analyses. This chapter highlights the technical aspects and practical applications of using EMPathways2, which facilitates the advancement of transcriptome data analysis and contributes to a deeper understanding of the complex regulatory mechanisms underlying living systems.

Supramolecular nanoparticles based on elastin-like peptides modified capsid protein as drug delivery platform with enhanced cancer chemotherapy efficacy

Cancer, a prevalent disease posing significant threats to human health and longevity, necessitates effective therapeutic interventions. Chemotherapy has emerged as a primary strategy following surgical procedures for combating most malignancies. Despite the considerable efficacy of conventional chemotherapeutic agents against cancer cells, their utility is hindered by profound challenges such as multidrug resistance and deleterious toxic side effects, thereby limiting their systemic application. To tackle these challenges, we have devised a promising nanomedicine platform based on a plant virus. Specifically, we have selected the cowpea melanoma mottled virus (CCMV) as our nano-delivery system owing to its monodisperse and homogeneous size, as well as its intrinsic ability for controlled self-assembly. Leveraging the potential of this platform, we have engineered CCMV-based nanoparticles functionalized with elastin-like peptides (ELPs) at their N-terminal region. The target protein, CP-ELP, was expressed via E.coli, enabling encapsulation of the model drug DOX upon structural domain modification of the protein. The resulting nanoparticles exhibit uniform size distribution, facilitating efficient internalization by tumor cells and subsequent intracellular drug release, leading to enhanced antitumor efficacy. In addition, EVLP@DOX nanoparticles were found to activate immune response of tumor microenvironment in vivo, which further inhibiting tumor growth. Our designed nanoparticles have also demonstrated remarkable therapeutic effectiveness and favorable biological safety profiles in both murine melanoma and colorectal cancer models.

Low CDKN1B Expression Associated with Reduced CD8+ T Lymphocytes Predicts Poor Outcome in Breast Cancer in a Machine Learning Analysis

The cyclin-dependent kinase inhibitor 1B (CDKN1B) gene, which encodes the p27Kip1 protein, is important in regulating the cell cycle process and cell proliferation. Its role in breast cancer prognosis is controversial. We evaluated the significance and predictive role of CDKN1B expression in breast cancer prognosis. We investigated the clinicopathologic factors, survival rates, immune cells, gene sets, and prognostic models according to CDKN1B expression in 3794 breast cancer patients. We performed gene set enrichment analysis (GSEA), in silico cytometry, pathway network analyses, gradient boosting machine (GBM) learning, and in vitro drug screening. High CDKN1B expression levels in breast cancer correlated with high lymphocyte infiltration signature scores and increased CD8+ T cells, both of which were associated with improved prognosis in breast cancer. which were associated with a better prognosis. CDKN1B expression was associated with gene sets for the upregulation of T-cell receptor signaling pathways and downregulation of CD8+ T cells. Pathway network analysis revealed a direct link between CDKN1B and the pathway involved in the positive regulation of the protein catabolic process pathway. In addition, an indirect link was identified between CDKN1B and the T-cell receptor signaling pathway. In in vitro drug screening, BMS-345541 demonstrated efficacy as a therapeutic targeting of CDKN1B, effectively impeding the growth of breast cancer cells characterized by low CDKN1B expression. The inclusion of CDKN1B expression in GBM models increased the accuracy of survival predictions. CDKN1B expression plays a significant role in breast cancer progression, implying that targeting CDKN1B might be a promising strategy for treating breast cancer.

Identification of efferocytosis-related subtypes in gliomas and elucidating their characteristics and clinical significance

Introduction: Gliomas, the most prevalent tumors of the central nervous system, are known for their aggressive nature and poor prognosis. The heterogeneity among gliomas leads to varying responses to the same treatments, even among similar glioma types. In our study, we efferocytosis-related subtypes and explored their characteristics in terms of immune landscape, intercellular communication, and metabolic processes, ultimately elucidating their potential clinical implications. Methods and Results: We first identified efferocytosis-related subtypes in Bulk RNA-seq using the NMF algorithm. We then preliminarily demonstrated the correlation of these subtypes with efferocytosis by examining enrichment scores of cell death pathways, macrophage infiltration, and the expression of immune ligands. Our analysis of single-cell RNA-seq data further supported the association of these subtypes with efferocytosis. Through enrichment analysis, we found that efferocytosis-related subtypes differ from other types of gliomas in terms of immune landscape, intercellular communication, and substance metabolism. Moreover, we found that the efferocytosis-related classification is a prognostic factor with robust predictive performance by calculating the AUC values. We also found that efferocytosis-related subtypes, when compared with other gliomas in drug sensitivity, survival, and TIDE scores, show a clear link to the effectiveness of chemotherapy, radiotherapy, and immunotherapy in glioma patients. Discussion: We identified efferocytosis-related subtypes in gliomas by analyzing the expression of 137 efferocytosis-associated genes, exploring their characteristics in immune landscape, intercellular communication, metabolic processes, and genomic variations. Moreover, we discovered that the classification of efferocytosis-related subtypes has a strong prognostic predictive power and holds potential significance in guiding clinical treatment.

MMP3C: an in-silico framework to depict cancer metabolic plasticity using gene expression profiles

Metabolic plasticity enables cancer cells to meet divergent demands for tumorigenesis, metastasis and drug resistance. Landscape analysis of tumor metabolic plasticity spanning different cancer types, in particular, metabolic crosstalk within cell subpopulations, remains scarce. Therefore, we proposed a new in-silico framework, termed as MMP3C (Modeling Metabolic Plasticity by Pathway Pairwise Comparison), to depict tumor metabolic plasticity based on transcriptome data. Next, we performed an extensive metabo-plastic analysis of over 6000 tumors comprising 13 cancer types. The metabolic plasticity within distinct cell subpopulations, particularly interplay with tumor microenvironment, were explored at single-cell resolution. Ultimately, the metabo-plastic events were screened out for multiple clinical applications via machine learning methods. The pilot research indicated that 6 out of 13 cancer types exhibited signs of the Warburg effect, implying its high reliability and robustness. Across 13 cancer types, high metabolic organized heterogeneity was found, and four metabo-plastic subtypes were determined, which link to distinct immune and metabolism patterns impacting prognosis. Moreover, MMP3C analysis of approximately 60 000 single cells of eight breast cancer patients unveiled several metabo-plastic events correlated to tumorigenesis, metastasis and immunosuppression. Notably, the metabolic features screened out by MMP3C are potential biomarkers for diagnosis, tumor classification and prognosis. MMP3C is a practical cross-platform tool to capture tumor metabolic plasticity, and our study unveiled a core set of metabo-plastic pairs among diverse cancer types, which provides bases toward improving response and overcoming resistance in cancer therapy.