Identification and Characterization of Robust Hepatocellular Carcinoma Prognostic Subtypes Based on an Integrative Metabolite-Protein Interaction Network

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.

A Novel Amino Acid-Related Gene Signature Predicts Overall Survival in Patients With Hepatocellular Carcinoma

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is an extremely harmful malignant tumor in the world. Since the energy metabolism and biosynthesis of HCC cells are closely related to amino acids, it is necessary to further explore the relationship between amino acid-related genes and the prognosis of HCC to achieve individualized treatment. We herein aimed to develop a prognostic model for HCC based on amino acid genes.

METHODS

In this study, RNA-sequencing data of HCC patients were downloaded from the TCGA-LIHC cohort as the training cohort and the GSE14520 cohort as the validation cohort. Amino acid-related genes were derived from the Molecular Signatures Database. Univariate Cox and Lasso regression analysis were used to construct an amino acid-related signature (AARS). The predictive value of this risk score was evaluated by Kaplan-Meier (K-M) curve, receiver operating characteristic (ROC) curve, univariate and multivariate Cox regression analysis. Gene set variation analysis (GSVA) and immune characteristics evaluation were used to explore the underlying mechanisms. Finally, a nomogram was established to help the personalized prognosis assessment of patients with HCC.

RESULTS

The AARS comprises 14 amino acid-related genes to predict overall survival (OS) in HCC patients. HCC patients were divided into AARS-high group and AARS-low group according to the AARS scores. The K-M curve, ROC curve, and univariate and multivariate Cox regression analysis verified the good prediction efficiency of the risk score. Using GSVA, we found that AARS variants were concentrated in four pathways, including cholesterol metabolism, delayed estrogen response, fatty acid metabolism, and myogenesis metabolism.

CONCLUSION

Our results suggest that the AARS as a prognostic model based on amino acid-related genes is of great value in the prediction of survival of HCC, and can help improve the individualized treatment of patients with HCC.

Crosstalk of non-apoptotic RCD panel in hepatocellular carcinoma reveals the prognostic and therapeutic optimization

Non-apoptotic regulated cell death (RCD) of tumor cells profoundly affects tumor progression and plays critical roles in determining response to immune checkpoint inhibitors (ICIs). Prognosis-distinctive HCC subtypes were identified by consensus cluster analysis based on the expressions of 507 non-apoptotic RCD genes obtained from databases and literature. Meanwhile, a set of bioinformatic tools was integrated to analyze the differences of the tumor immune microenvironment infiltration, genetic mutation, copy number variation, and epigenetics alternations within two subtypes. Finally, a non-apoptotic RCDRS signature was constructed and its reliability was evaluated in HCC patients' tissues. The high-RCDRS HCC subgroup showed a significantly lower overall survival and less sensitivity to ICIs compared to low-RCDRS subgroup, but higher sensitivity to cisplatin, paclitaxel, and sorafenib. Overall, we established an RCDRS panel consisting of four non-apoptotic RCD genes, which might be a promising predictor for evaluating HCC prognosis, guiding therapeutic decision-making, and ultimately improving patient outcomes.

TMErisk score: A tumor microenvironment-based model for predicting prognosis and immunotherapy in patients with head and neck squamous cell carcinoma

Tumor microenvironment (TME) is closely associated with the progression and prognosis of head and neck squamous cell carcinoma (HNSCC). To investigate potential biomarkers for predicting therapeutic outcomes in HNSCC, we analyzed the immune and stromal status of HNSCC based on the genes associated with TME using the ESTIMATE algorithm. Immune and stromal genes were identified with differential gene expression and weighted gene co-expression network analysis (WGCNA). From these genes, 118 were initially selected through Cox univariate regression and then further input into least absolute shrinkage and selection operator (LASSO) regression analysis. As a result, 11 genes were screened out for the TME-related risk (TMErisk) score model which presented promising overall survival predictive potential. The TMErisk score was negatively associated with immune and stromal scores but positively associated with tumor purity. Individuals with high TMErisk scores exhibited decreased expression of most immune checkpoints and all human leukocyte antigen family genes, and reduced abundance of infiltrating immune cells. Divergent genes were mutated in HNSCC. In both high and low TMErisk score groups, the tumor protein P53 exhibited the highest mutation frequency. A higher TMErisk score was found to be associated with reduced overall survival probability and worse outcomes of immunotherapy. Therefore, the TMErisk score could serve as a valuable model for the outcome prediction of HNSCC in clinic.

Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through a comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas [TCGA], Cancer Cell Line Encyclopedia [CCLE], and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium [CPTAC] proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathway-based subgroups (MPSs). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup had a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic metagene signature based on metabolic molecules. Thus, a 13-hub gene-based classifier was constructed to predict patients' MPS subtypes, offering a more accessible and practical approach. This metabolism-based classification system can enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.

Metabolic self-feeding in HBV-associated hepatocarcinoma centered on feedback between circulation lipids and the cellular MAPK/mTOR axis

INTRODUCTION

Hepatitis B Virus (HBV) is widely recognized as a "metabolic virus" that disrupts hepatic metabolic homeostasis, rendering it one of the foremost risk factors for hepatocellular carcinoma (HCC). Except for antiviral therapy, the fundamental principles underlying HBV- and HBV+ HCC have remained unchanged, limiting HCC treatment options.

OBJECTIVES

In this study, we aim to identify the distinctive metabolic profile of HBV-associated HCC, with the promise of identifying novel metabolic targets that confer survival advantages and ultimately impede cancer progression.

METHODS

We employed a comprehensive methodology to evaluate metabolic alterations systematically. Initially, we analyzed transcriptomic and proteomic data obtained from a public database, subsequently validating these findings within our test cohort at both the proteomic and transcriptomic levels. Additionally, we conducted a comprehensive analysis of tissue metabolomics profiles, lipidomics, and the activity of the MAPK and AKT signaling pathway to corroborate the abovementioned changes.

RESULTS

Our multi-omics approach revealed distinct metabolic dysfunctions associated with HBV-associated HCC. Specifically, we observed upregulated steroid hormone biosynthesis, primary bile acid metabolism, and sphingolipid metabolism in HBV-associated HCC patients' serum. Notably, metabolites involved in primary bile acid and sphingolipids can activate the MAPK/mTOR pathway. Tissue metabolomics and lipidomics analyses further validated the serum metabolic alterations, particularly alterations in lipid composition and accumulation of unsaturated fatty acids.

CONCLUSION

Our findings emphasize the pivotal role of HBV in HCC metabolism, elucidating the activation of a unique MAPK/mTOR signaling axis by primary bile acids and sphingolipids. Moreover, the hyperactive MAPK/mTOR signaling axis transduction leads to significant reprogramming in lipid metabolism within HCC cells, further triggering the activation of the MAPK/mTOR pathway in turn, thereby establishing a self-feeding circle driven by primary bile acids and sphingolipids.

Predicting effect of anti-PD-1/PD-L1 inhibitors therapy for hepatocellular carcinoma by detecting plasma metabolite based on UHPLC-MS

Introduction

Anti-PD-1/PD-L1 inhibitors therapy has become a promising treatment for hepatocellular carcinoma (HCC), while the therapeutic efficacy varies significantly among effects for individual patients are significant difference. Unfortunately, specific predictive biomarkers indicating the degree of benefit for patients and thus guiding the selection of suitable candidates for immune therapy remain elusive.no specific predictive biomarkers are available indicating the degree of benefit for patients and thus screening the preferred population suitable for the immune therapy.

Methods

Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) considered is an important method for analyzing biological samples, since it has the advantages of high rapid, high sensitivity, and high specificity. Ultra-high-pressure liquid chromatography-mass spectrometry (UHPLC-MS) has emerged as a pivotal method for analyzing biological samples due to its inherent advantages of rapidity, sensitivity, and specificity. In this study, potential metabolite biomarkers that can predict the therapeutic effect of HCC patients receiving immune therapy were identified by UHPLC-MS.

Results

A partial least-squares discriminant analysis (PLS-DA) model was established using 14 glycerophospholipid metabolites mentioned above, and good prediction parameters (R2 = 0.823, Q2 = 0.615, prediction accuracy = 0.880 and p < 0.001) were obtained. The relative abundance of glycerophospholipid metabolite ions is closely related to the survival benefit of HCC patients who received immune therapy.

Discussion

This study reveals that glycerophospholipid metabolites play a crucial role in predicting the efficacy of immune therapy for HCC.

Calcification-associated molecular traits and therapeutic strategies in hormone receptor-positive HER2-negative breast cancer

OBJECTIVE

Mammographic calcifications are a common feature of breast cancer, but their molecular characteristics and treatment implications in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer remain unclear.

METHODS

We retrospectively collected mammography records of an HR+/HER2- breast cancer cohort (n = 316) with matched clinicopathological, genomic, transcriptomic, and metabolomic data. On the basis of mammographic images, we grouped tumors by calcification status into calcification-negative tumors, tumors with probably benign calcifications, tumors with calcification of low-moderate suspicion for maligancy and tumors with calcification of high suspicion for maligancy. We then explored the molecular characteristics associated with each calcification status across multiple dimensions.

RESULTS

Among the different statuses, tumors with probably benign calcifications exhibited elevated hormone receptor immunohistochemical staining scores, estrogen receptor (ER) pathway activation, lipid metabolism, and sensitivity to endocrine therapy. Tumors with calcifications of high suspicion for malignancy had relatively larger tumor sizes, elevated lymph node metastasis incidence, Ki-67 staining scores, genomic instability, cell cycle pathway activation, and may benefit from cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors.

CONCLUSIONS

Our research established links between tumor calcifications and molecular features, thus proposing potential precision treatment strategies for HR+/HER2- breast cancer.

Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities

Molecular profiling guides precision treatment of breast cancer; however, Asian patients are underrepresented in publicly available large-scale studies. We established a comprehensive multiomics cohort of 773 Chinese patients with breast cancer and systematically analyzed their genomic, transcriptomic, proteomic, metabolomic, radiomic and digital pathology characteristics. Here we show that compared to breast cancers in white individuals, Asian individuals had more targetable AKT1 mutations. Integrated analysis revealed a higher proportion of HER2-enriched subtype and correspondingly more frequent ERBB2 amplification and higher HER2 protein abundance in the Chinese HR+HER2+ cohort, stressing anti-HER2 therapy for these individuals. Furthermore, comprehensive metabolomic and proteomic analyses revealed ferroptosis as a potential therapeutic target for basal-like tumors. The integration of clinical, transcriptomic, metabolomic, radiomic and pathological features allowed for efficient stratification of patients into groups with varying recurrence risks. Our study provides a public resource and new insights into the biology and ancestry specificity of breast cancer in the Asian population, offering potential for further precision treatment approaches.

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.

Metabolism-related biomarkers, molecular classification, and immune infiltration in diabetic ulcers with validation

Diabetes mellitus (DM) can lead to diabetic ulcers (DUs), which are the most severe complications. Due to the need for more accurate patient classifications and diagnostic models, treatment and management strategies for DU patients still need improvement. The difficulty of diabetic wound healing is caused closely related to biological metabolism and immune chemotaxis reaction dysfunction. Therefore, the purpose of our study is to identify metabolic biomarkers in patients with DU and construct a molecular subtype-specific prognostic model that is highly accurate and robust. RNA-sequencing data for DU samples were obtained from the Gene Expression Omnibus (GEO) database. DU patients and normal individuals were compared regarding the expression of metabolism-related genes (MRGs). Then, a novel diagnostic model based on MRGs was constructed with the random forest algorithm, and classification performance was evaluated utilizing receiver operating characteristic (ROC) analysis. The biological functions of MRGs-based subtypes were investigated using consensus clustering analysis. A principal component analysis (PCA) was conducted to determine whether MRGs could distinguish between subtypes. We also examined the correlation between MRGs and immune infiltration. Lastly, qRT-PCR was utilized to validate the expression of the hub MRGs with clinical validations and animal experimentations. Firstly, 8 metabolism-related hub genes were obtained by random forest algorithm, which could distinguish the DUs from normal samples validated by the ROC curves. Secondly, DU samples could be consensus clustered into three molecular classifications by MRGs, verified by PCA analysis. Thirdly, associations between MRGs and immune infiltration were confirmed, with LYN and Type 1 helper cell significantly positively correlated; RHOH and TGF-β family remarkably negatively correlated. Finally, clinical validations and animal experiments of DU skin tissue samples showed that the expressions of metabolic hub genes in the DU groups were considerably upregulated, including GLDC, GALNT6, RHOH, XDH, MMP12, KLK6, LYN, and CFB. The current study proposed an auxiliary MRGs-based DUs model while proposing MRGs-based molecular clustering and confirmed the association with immune infiltration, facilitating the diagnosis and management of DU patients and designing individualized treatment plans.

Deep neural network for discovering metabolism-related biomarkers for lung adenocarcinoma

Introduction

Lung cancer is a major cause of illness and death worldwide. Lung adenocarcinoma (LUAD) is its most common subtype. Metabolite-mRNA interactions play a crucial role in cancer metabolism. Thus, metabolism-related mRNAs are potential targets for cancer therapy.

Methods

This study constructed a network of metabolite-mRNA interactions (MMIs) using four databases. We retrieved mRNAs from the Tumor Genome Atlas (TCGA)-LUAD cohort showing significant expressional changes between tumor and non-tumor tissues and identified metabolism-related differential expression (DE) mRNAs among the MMIs. Candidate mRNAs showing significant contributions to the deep neural network (DNN) model were mined. Using MMIs and the results of function analysis, we created a subnetwork comprising candidate mRNAs and metabolites.

Results

Finally, 10 biomarkers were obtained after survival analysis and validation. Their good prognostic value in LUAD was validated in independent datasets. Their effectiveness was confirmed in the TCGA and an independent Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset by comparison with traditional machine-learning models.

Conclusion

To summarize, 10 metabolism-related biomarkers were identified, and their prognostic value was confirmed successfully through the MMI network and the DNN model. Our strategy bears implications to pave the way for investigating metabolic biomarkers in other cancers.

Exploring the Prognosis-Related Genetic Variation in Gastric Cancer Based on mGWAS

The use of metabolome genome-wide association studies (mGWAS) has been shown to be effective in identifying functional genes in complex diseases. While mGWAS has been applied to biomedical and pharmaceutical studies, its potential in predicting gastric cancer prognosis has yet to be explored. This study aims to address this gap and provide insights into the genetic basis of GC survival, as well as identify vital regulatory pathways in GC cell progression. Genome-wide association analysis of plasma metabolites related to gastric cancer prognosis was performed based on the Generalized Linear Model (GLM). We used a log-rank test, LASSO regression, multivariate Cox regression, GO enrichment analysis, and the Cytoscape software to visualize the complex regulatory network of genes and metabolites and explored in-depth genetic variation in gastric cancer prognosis based on mGWAS. We found 32 genetic variation loci significantly associated with GC survival-related metabolites, corresponding to seven genes, VENTX, PCDH 7, JAKMIP1, MIR202HG, MIR378D1, LINC02472, and LINC02310. Furthermore, this study identified 722 Single nucleotide polymorphism (SNP) sites, suggesting an association with GC prognosis-related metabolites, corresponding to 206 genes. These 206 possible functional genes for gastric cancer prognosis were mainly involved in cellular signaling molecules related to cellular components, which are mainly involved in the growth and development of the body and neurological regulatory functions related to the body. The expression of 23 of these genes was shown to be associated with survival outcome in gastric cancer patients in The Cancer Genome Atlas (TCGA) database. Based on the genome-wide association analysis of prognosis-related metabolites in gastric cancer, we suggest that gastric cancer survival-related genes may influence the proliferation and infiltration of gastric cancer cells, which provides a new idea to resolve the complex regulatory network of gastric cancer prognosis.

Emerging therapies in cancer metabolism

Metabolic reprogramming in cancer is not only a biological hallmark but also reveals treatment vulnerabilities. Numerous metabolic molecules have shown promise as treatment targets to impede tumor progression in preclinical studies, with some advancing to clinical trials. However, the intricacy and adaptability of metabolic networks hinder the effectiveness of metabolic therapies. This review summarizes the metabolic targets for cancer treatment and provides an overview of the current status of clinical trials targeting cancer metabolism. Additionally, we decipher crucial factors that limit the efficacy of metabolism-based therapies and propose future directions. With advances in integrating multi-omics, single-cell, and spatial technologies, as well as the ability to track metabolic adaptation more precisely and dynamically, clinicians can personalize metabolic therapies for improved cancer treatment.

Clinical prognosis and related molecular features of hepatitis B-associated adolescent and young adult hepatocellular carcinoma

BACKGROUND

Inattention has been given to the pathogenesis of adolescent and young adult (AYA) hepatocellular carcinoma (HCC). Due to the more advanced tumor progression and poorer prognosis of AYA-HCC, together with a better tolerance ability, noncirrhotic background, and a stronger willingness to treat AYA-HCC, clinical and molecular biology studies are urgent and necessary, especially for those with hepatitis B infection.

METHODS

For clinical aspects, the overall survival, the recurrence-free survival, and the Cox analyses were performed. Then, functional analysis, gene clustering, metabolic-related analysis, immune infiltration and competing endogenous RNA (ceRNA) construction were carried out using whole transcriptome sequencing technique.

RESULTS

Based on the clinical information of our HCC cohort, the overall survival and recurrence-free survival rates were worse in the AYA group than in the elderly group as previously described. According to our whole transcriptome sequencing results, functional analysis revealed that metabolism-related pathways as well as protein translation and endoplasmic reticulum processing were enriched. Then the hub metabolism-related genes were screened by metabolite-protein interactions (MPIs) and protein-protein interactions (PPIs). Fatty acid metabolism is a crucial component of metabolic pathways, abnormalities of which may be the reason for the worse prognosis of HBV-AYA HCC. Finally, the relationship of disrupted expression of metabolism-related genes with immune infiltration was also analyzed, and the lncRNA‒miRNA‒mRNA-related ceRNA network for HBV-AYA HCC was constructed, which may provide new cues for HBV-AHA HCC prevention.

CONCLUSION

The worse prognosis and recurrence rate of HBV-AYA HCC may be related to abnormalities in metabolism-related pathways, especially disorders of fatty acid metabolism.

Bulk and single-cell transcriptome profiling reveal the metabolic heterogeneity in gastric cancer

Metabolic reprogramming has been defined as a key hall mark of human tumors. However, metabolic heterogeneity in gastric cancer has not been elucidated. Here we separated the TCGA-STAD dataset into two metabolic subtypes. The differences between subtypes were elaborated in terms of transcriptomics, genomics, tumor-infiltrating cells, and single-cell resolution. We found that metabolic subtype 1 is predominantly characterized by low metabolism, high immune cell infiltration. Subtype 2 is mainly characterized by high metabolism and low immune cell infiltration. From single-cell resolution, we found that the high metabolism of subtype 2 is dominated by epithelial cells. Not only epithelial cells, but also various immune cells and stromal cells showed high metabolism in subtype 2 and low metabolism in subtype 1. Our study established a classification of gastric cancer metabolic subtypes and explored the differences between subtypes from multiple dimensions, especially the single-cell resolution.

Systematic analysis of the aberrances and functional implications of cuproptosis in cancer

Cuproptosis is a novel form of cell death driven by a copper-dependent proteotoxic stress response whose comprehensive landscape in tumors remains unclear. Here, we comprehensively characterized cuproptosis-related genes (CRGs) across 33 cancers using multi-omic data from The Cancer Genome Atlas (TCGA), showing complicated and diverse results in different cancers. We also explored the relationships between CRGs and cancer metabolic patterns, pathway activity, and tumor microenvironment (TME), suggesting that they played critical roles in tumor progression and TME cell infiltration. We further established the cuproptosis potential index (CPI) to reveal the functional roles of cuproptosis, and characterized multi-omic molecular features associated with cuproptosis. In clinical applications, we performed a combined analysis of the sensitivity of CRGs and CPI to drug response and immunotherapy. This study provides a rich resource for understanding cuproptosis, offering a broad molecular perspective for future functional and therapeutic studies of multiple cancer pathways mediated by cuproptosis.

Machine learning-based construction of immunogenic cell death-related score for improving prognosis and response to immunotherapy in melanoma

BACKGROUND

Immunogenic cell death (ICD) is a form of regulated cell death (RCD) which could drive the activation of the innate and adaptive immune responses. In this work, we aimed to develop an ICD-related signature to facilitate the assessment of prognosis and immunotherapy response for melanoma patients.

METHODS

A set of machine learning methods, including consensus clustering, non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithm were used to evaluate the infiltration of immune cells. The 'pRRophetic' package in R and 6 cohorts of melanoma patients receiving immunotherapy were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures were also compared.

RESULTS

The ICDscore could predict prognosis and immunotherapy response in multiple cohorts, and displayed superior performance than other forms of cell death-related signatures or 52 published signatures. The melanoma patients with low ICDscore were marked with high infiltration of immune cells, high expression of immune checkpoint inhibitor-related genes, and increased tumor mutation burden.

CONCLUSIONS

In conclusion, we constructed a stable and robust ICD-related signature for evaluating the prognosis and benefits of immunotherapy, and it could serve as a promising tool to guide decision-making and surveillance for individual melanoma patients.

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabolic-protein interaction (MPI) can provide meaningful insights into cancer heterogeneity. Moreover, the potential of lipids and lactate for identifying prognostic subtypes of glioma remains relatively unexplored. Therefore, we proposed a method to construct an MPI relationship matrix (MPIRM) based on a triple-layer network (Tri-MPN) combined with mRNA expression, and processed the MPIRM by deep learning to identify glioma prognostic subtypes. These Subtypes with significant differences in prognosis were detected in glioma (p-value < 2e-16, 95% CI). These subtypes had a strong correlation in immune infiltration, mutational signatures and pathway signatures. This study demonstrated the effectiveness of node interaction from MPI networks in understanding the heterogeneity of glioma prognosis.

Investigation of Hippo pathway-related prognostic lncRNAs and molecular subtypes in liver hepatocellular carcinoma

This study aimed to investigate Hippo pathway-related prognostic long noncoding RNAs (lncRNAs) and their prognostic value in liver hepatocellular carcinoma (LIHC). Expression and clinical data regarding LIHC were acquired from The Cancer Genome Atlas and European Bioinformatics Institute array databases. Hippo pathway-related lncRNAs and their prognostic value were revealed, followed by molecular subtype investigations. Differences in survival, clinical characteristics, immune cell infiltration, and checkpoint expression between the subtypes were explored. LASSO regression was used to determine the most valuable prognostic lncRNAs, followed by the establishment of a prognostic model. Survival and differential expression analyses were conducted between two groups (high- and low-risk). A total of 313 Hippo pathway-related lncRNAs were identified from LIHC, of which 88 were associated with prognosis, and two molecular subtypes were identified based on their expression patterns. These two subtypes showed significant differences in overall survival, pathological stage and grade, vascular invasion, infiltration abundance of seven immune cells, and expression of several checkpoints, such as CTLA-4 and PD-1/L1 (P < 0.05). LASSO regression identified the six most valuable independent prognostic lncRNAs for establishing a prognosis risk model. Risk scores calculated by the risk model assigned patients into two risk groups with an AUC of 0.913 and 0.731, respectively, indicating that the high-risk group had poor survival. The risk score had an independent prognostic value with an HR of 2.198. In total, 3007 genes were dysregulated between the two risk groups, and the expression of most genes was elevated in the high-risk group, involving the cell cycle and pathways in cancers. Hippo pathway-related lncRNAs could stratify patients for personalized treatment and predict the prognosis of patients with LIHC.

A Novel Phenazine Analog, CPUL1, Suppresses Autophagic Flux and Proliferation in Hepatocellular Carcinoma: Insight from Integrated Transcriptomic and Metabolomic Analysis

BACKGROUND

CPUL1, a phenazine analog, has demonstrated potent antitumor properties against hepatocellular carcinoma (HCC) and indicates a promising prospect in pharmaceutical development. However, the underlying mechanisms remain largely obscure.

METHODS

Multiple HCC cell lines were used to investigate the in vitro effects of CPUL1. The antineoplastic properties of CPUL1 were assessed in vivo by establishing a xenograft nude mice model. After that, metabolomics, transcriptomics, and bioinformatics were integrated to elucidate the mechanisms underlying the therapeutic efficacy of CPUL1, highlighting an unanticipated involvement of autophagy dysregulation.

RESULTS

CPUL1 suppressed HCC cell proliferation in vitro and in vivo, thereby endorsing the potential as a leading agent for HCC therapy. Integrative omics characterized a deteriorating scenario of metabolic debilitation with CPUL1, presenting an issue in the autophagy contribution of autophagy. Subsequent observations indicated that CPUL1 treatment could impede autophagic flow by suppressing autophagosome degradation rather than its formation, which supposedly exacerbated cellular damage triggered by metabolic impairment. Moreover, the observed late autophagosome degradation may be attributed to lysosome dysfunction, which is essential for the final stage of autophagy and cargo disposal.

CONCLUSIONS

Our study comprehensively profiled the anti-hepatoma characteristics and molecular mechanisms of CPUL1, highlighting the implications of progressive metabolic failure. This could partially be ascribed to autophagy blockage, which supposedly conveyed nutritional deprivation and intensified cellular vulnerability to stress.

An immunometabolism subtyping system identifies S100A9+ macrophage as an immune therapeutic target in colorectal cancer based on multiomics analysis

Immunometabolism in the tumor microenvironment (TME) and its influence on the immunotherapy response remain uncertain in colorectal cancer (CRC). We perform immunometabolism subtyping (IMS) on CRC patients in the training and validation cohorts. Three IMS subtypes of CRC, namely, C1, C2, and C3, are identified with distinct immune phenotypes and metabolic properties. The C3 subtype exhibits the poorest prognosis in both the training cohort and the in-house validation cohort. The single-cell transcriptome reveals that a S100A9⁺ macrophage population contributes to the immunosuppressive TME in C3. The dysfunctional immunotherapy response in the C3 subtype can be reversed by combination treatment with PD-1 blockade and an S100A9 inhibitor tasquinimod. Taken together, we develop an IMS system and identify an immune tolerant C3 subtype that exhibits the poorest prognosis. A multiomics-guided combination strategy by PD-1 blockade and tasquinimod improves responses to immunotherapy by depleting S100A9⁺ macrophages in vivo.

Abnormal expression of HADH, an enzyme of fatty acid oxidation, affects tumor development and prognosis (Review)

Tumor occurrence and progression are closely associated with abnormal energy metabolism and energy metabolism associated with glucose, proteins and lipids. The reprogramming of energy metabolism is one of the hallmarks of cancer. As a form of energy metabolism, fatty acid metabolism includes fatty acid uptake, de novo synthesis and β‑oxidation. In recent years, the role of abnormal fatty acid β‑oxidation in tumors has gradually been recognized. Mitochondrial trifunctional protein (MTP) serves an important role in fatty acid β‑oxidation and HADH (two subtypes: α subunit, HADHA and β subunit, HADHB) are important subunits of MTP. HADH participates in the steps of 2, 3 and 4 fatty acid β‑oxidation. However, there is no review summarizing the specific role of HADH in tumors. Therefore, the present study focused on HADH as the main indicator to explore the changes in fatty acid β‑oxidation in several types of tumors. The present review summarized the changes in HADH in 11 organs (cerebrum, oral cavity, esophagus, liver, pancreas, stomach, colorectum, lymph, lung, breast, kidney), the effect of up‑ and downregulation and the relationship of HADH with prognosis. In summary, HADH can be either a suppressor or a promoter depending on where the tumor is located, which is closely associated with prognostic assessment. HADHA and HADHB have similar prognostic roles in known and comparable tumors.

Intratumoral microbiome and gastrointestinal cancers

Emerging studies have revealed the role of microbiota in regulating tumorigenesis, development, and response to antitumor treatment. However, most studies have focused on gut microbiota, and little is known about the intratumoral microbiome. To date, the latest research has indicated that the intratumoral microbiome is a key component of the tumor microenvironment (TME), and can promote a heterogeneous immune microenvironment, reprogram tumor metabolism to affect tumor invasion and metastasis. In this review, we will summarize existing studies on the intratumoral microbiome of gastrointestinal cancers and reveal their crosstalk. This will provide a better understanding of this emerging field and help to explore new therapeutic approaches for cancer patients by targeting the intratumoral microbiome.

A comprehensive pan-cancer analysis of necroptosis molecules in four gynecologic cancers

Background

In recent years, it has been proved that necroptosis plays an important role in the occurrence, development, invasion, metastasis and drug resistance of malignant tumors. Hence, further evaluation and targeting of necroptosis may be of clinical benefit for gynecologic cancers (GCs).

Methods

To compare consistency and difference, we explored the expression pattern and prognostic value of necroptosis-related genes (NRGs) in pan-GC analysis through Linear regression and Empirical Bayesian, Univariate Cox analysis, and public databases from TCGA and Genotype-Tissue Expression (GTEx), including CESC, OV, UCEC, and UCS. We explored the copy number variation (CNV), methylation level and enrichment pathways of NRGs in the four GCs. Based on LASSO Cox regression analysis or principal component analysis, we established the prognostic NRG-signature or necroptosis-score for the four GCs. In addition, we predicted and compared functional pathways, tumor mutational burden (TMB), somatic mutation features, immunity status, immunotherapy, chemotherapeutic drug sensitivity of the NRG-signature based on NRGs. We also examined the expression level of several NRGs in OV samples that we collected using Quantitative Real-time PCR.

Results

We confirmed the presence of NRGs in expression, prognosis, CNV, and methylation for four GCs, thus comparing the consistency and difference among the four GCs. The prognosis and independent prognostic value of the risk signatures based on NRGs were determined. Through the results of subclass mapping, we found that GC patients with lower risk score may be more sensitive to PDL1 response and more sensitive to immune checkpoint blockade therapy. Drug susceptibility analysis showed that, 51, 45, 64, and 29 drugs with differences between risk groups were yielded in CESC, OV, UCEC, and UCS respectively. For OV, the expression differences of several NRGs in the tissues we collected were similar to that in TCGA.

Conclusion

Our comprehensive analysis of NRGs and NRG-signature demonstrated their similarity and difference, as well as their potential roles in prognosis and could guide therapeutic strategies, thus improving the outcome of GC patients.

Gene interaction perturbation network deciphers a high-resolution taxonomy in colorectal cancer

Molecular subtypes of colorectal cancer (CRC) are currently identified via the snapshot transcriptional profiles, largely ignoring the dynamic changes of gene expressions. Conversely, biological networks remain relatively stable irrespective of time and condition. Here, we introduce an individual-specific gene interaction perturbation network-based (GIN) approach and identify six GIN subtypes (GINS1-6) with distinguishing features: (i) GINS1 (proliferative, 24%~34%), elevated proliferative activity, high tumor purity, immune-desert, PIK3CA mutations, and immunotherapeutic resistance; (ii) GINS2 (stromal-rich, 14%~22%), abundant fibroblasts, immune-suppressed, stem-cell-like, SMAD4 mutations, unfavorable prognosis, high potential of recurrence and metastasis, immunotherapeutic resistance, and sensitive to fluorouracil-based chemotherapy; (iii) GINS3 (KRAS-inactivated, 13%~20%), high tumor purity, immune-desert, activation of EGFR and ephrin receptors, chromosomal instability (CIN), fewer KRAS mutations, SMOC1 methylation, immunotherapeutic resistance, and sensitive to cetuximab and bevacizumab; (iv) GINS4 (mixed, 10%~19%), moderate level of stromal and immune activities, transit-amplifying-like, and TMEM106A methylation; (v) GINS5 (immune-activated, 12%~24%), stronger immune activation, plentiful tumor mutation and neoantigen burden, microsatellite instability and high CpG island methylator phenotype, BRAF mutations, favorable prognosis, and sensitive to immunotherapy and PARP inhibitors; (vi) GINS6, (metabolic, 5%~8%), accumulated fatty acids, enterocyte-like, and BMP activity. Overall, the novel high-resolution taxonomy derived from an interactome perspective could facilitate more effective management of CRC patients.

Metabolomic and Transcriptomic Profiling Identified Significant Genes in Thymic Epithelial Tumor

Thymomas and thymic carcinomas are malignant thymic epithelial tumors (TETs) with poor outcomes if non-resectable. However, the tumorigenesis, especially the metabolic mechanisms involved, is poorly studied. Untargeted metabolomics analysis was utilized to screen for differential metabolic profiles between thymic cancerous tissues and adjunct noncancerous tissues. Combined with transcriptomic data, we comprehensively evaluated the metabolic patterns of TETs. Metabolic scores were constructed to quantify the metabolic patterns of individual tumors. Subsequent investigation of distinct clinical outcomes and the immune landscape associated with the metabolic scores was conducted. Two distinct metabolic patterns and differential metabolic scores were identified between TETs, which were enriched in a variety of biological pathways and correlated with clinical outcomes. In particular, a high metabolic score was highly associated with poorer survival outcomes and immunosuppressive status. More importantly, the expression of two prognostic genes (ASNS and BLVRA) identified from differential metabolism-related genes was significantly associated with patient survival and may play a key role in the tumorigenesis of TETs. Our findings suggest that differential metabolic patterns in TETs are relevant to tumorigenesis and clinical outcome. Specific transcriptomic alterations in differential metabolism-related genes may serve as predictive biomarkers of survival outcomes and potential targets for the treatment of patients with TETs.