Bioinformatic Profiling Identifies a Fatty Acid Metabolism-Related Gene Risk Signature for Malignancy, Prognosis, and Immune Phenotype of Glioma

Delineation of fatty acid metabolism in gastric cancer: Therapeutic implications

BACKGROUND

The prognosis of gastric cancer is extremely poor. Metabolic reprogramming involving lipids has been associated with cancer occurrence and progression.

AIM

To illustrate fatty acid metabolic mechanisms in gastric cancer, detect core genes, develop a prognostic model, and provide treatment options.

METHODS

Raw data from The Cancer Genome Atlas and Gene Expression Omnibus databases were collected and analyzed. Differentially expressed fatty acid metabolism genes were identified and incorporated into a risk model based on least absolute shrinkage and selection operator regression analysis. Then, patients from The Cancer Genome Atlas were assigned to high- and low-risk cohorts according to the mean value of the risk score as the threshold, which was verified in the Gene Expression Omnibus database. Relationships between chemotherapeutic sensitivity and tumor microenvironment features were assessed.

RESULTS

An integrated evaluation was performed in this study. Fatty acid metabolism-related genes were used to construct the risk model. Patients classified into the high-risk cohort were considered to be resistant to chemotherapy based on results of the "pRRophetic" R package. Patients in the high-risk cohort were associated with type I/II interferon activation, increased inflammation level, immune cell infiltration, and tumor immune dysfunction based on the exclusion algorithm, indicating the potential benefit of immunotherapy in these patients.

CONCLUSION

We constructed a fatty acid-related risk score model to assess the comprehensive fatty acid features in gastric cancer and validated its vital role in prognosis, chemotherapy sensitivity, and immunotherapy.

Delineation of fatty acid metabolism in gastric cancer: Therapeutic implications

BACKGROUND

The prognosis of gastric cancer is extremely poor. Metabolic reprogramming involving lipids has been associated with cancer occurrence and progression.

AIM

To illustrate fatty acid metabolic mechanisms in gastric cancer, detect core genes, develop a prognostic model, and provide treatment options.

METHODS

Raw data from The Cancer Genome Atlas and Gene Expression Omnibus databases were collected and analyzed. Differentially expressed fatty acid metabolism genes were identified and incorporated into a risk model based on least absolute shrinkage and selection operator regression analysis. Then, patients from The Cancer Genome Atlas were assigned to high- and low-risk cohorts according to the mean value of the risk score as the threshold, which was verified in the Gene Expression Omnibus database. Relationships between chemotherapeutic sensitivity and tumor microenvironment features were assessed.

RESULTS

An integrated evaluation was performed in this study. Fatty acid metabolism-related genes were used to construct the risk model. Patients classified into the high-risk cohort were considered to be resistant to chemotherapy based on results of the “pRRophetic” R package. Patients in the high-risk cohort were associated with type I/II interferon activation, increased inflammation level, immune cell infiltration, and tumor immune dysfunction based on the exclusion algorithm, indicating the potential benefit of immunotherapy in these patients.

CONCLUSION

We constructed a fatty acid-related risk score model to assess the comprehensive fatty acid features in gastric cancer and validated its vital role in prognosis, chemotherapy sensitivity, and immunotherapy.

Identification of two novel lipid metabolism-related long non-coding RNAs (SNHG17 and LINC00837) as potential signatures for osteosarcoma prognosis and precise treatment

OBJECTIVE

Dysregulated lipid metabolism enhances the development and advancement of many cancers, including osteosarcoma (OS); however, the underlying mechanisms are still largely unknown. Therefore, this investigation aimed to elucidate novel potential lipid metabolism-related long non-coding RNAs (lncRNAs) that regulate OS development and provide novel signatures for its prognosis and precise treatment.

MATERIALS AND METHODS

The GEO datasets (GSE12865 and GSE16091) were downloaded and analyzed using R software packages. Immunohistochemistry (IHC) was used to evaluate protein levels in OS tissues while real-time qPCR was used to measure lncRNA levels, and MTT assays were used to assess OS cell viability.

RESULTS

Two lipid metabolism-associated lncRNAs (LM-lncRNAs), small nucleolar RNA host gene 17 (SNHG17) and LINC00837, were identified as efficient and independent prognostic indicators for OS. In addition, further experiments confirmed that SNHG17 and LINC00837 were significantly elevated in OS tissues and cells than para-cancerous counterparts. Knockdown of SNHG17 and LINC00837 synergistically suppressed the viability of OS cells, whereas overexpression of the two lncRNAs promoted OS cell proliferation. Moreover, bioinformatics analysis was conducted to construct six novel SNHG17-microRNA-mRNA competing endogenous RNA (ceRNA) networks, and three lipid metabolism-associated genes (MIF, VDAC2, and CSNK2A2) were found to be abnormally upregulated in OS tissues, suggesting that they were potential effector genes of SNHG17.

CONCLUSION

In summary, SNHG17 and LINC00837 were found to promote OS cell malignancy, suggesting their use as ideal biomarkers for OS prognosis and treatment.

Construction and validation of a fatty acid metabolism-related gene signature for predicting prognosis and therapeutic response in patients with prostate cancer

Background

Reprogramming of fatty acid metabolism is a newly-identified hallmark of malignancy. However, no studies have systematically investigated the fatty acid metabolism related-gene set in prostate cancer (PCa).

Methods

A cohort of 381 patients with gene expression and clinical data from The Cancer Genome Atlas was used as the training set, while another cohort of 90 patients with PCa from GEO (GSE70769) was used as the validation set. Differentially expressed fatty acid metabolism-related genes were subjected to least absolute shrinkage and selection operator (LASSO)-Cox regression to establish a fatty acid metabolism-related risk score. Associations between the risk score and clinical characteristics, immune cell infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE) score, and response to chemotherapy were analyzed. Finally, the expression level of genes included in the model was validated using real-time PCR.

Results

A prognostic risk model based on five fatty acid metabolism related genes (ALDH1A1, CPT1B, CA2, CROT, and NUDT19) were constructed. Tumors with higher risk score were associated with larger tumor size, lymph node involvement, higher Gleason score, and poorer biochemical recurrence (BCR)-free survival. Furthermore, the high- and low-risk tumors exhibited distinct immune cell infiltration features and immune-related pathway activation. High-risk tumors were associated with favorable response to immunotherapy as indicated by high TMB and low TIDE score, but poor response to bicalutamide and docetaxel chemotherapy.

Conclusion

This study established a fatty acid metabolism-related gene signature which was predictive of BCR and response to chemotherapy and immunotherapy, providing a novel therapeutic biomarker for PCa.

Comprehensive analysis of fatty acid metabolism-related gene signatures for predicting prognosis in patients with prostate cancer

Fatty acid metabolism (FAM) is an important factor in tumorigenesis and development. However, whether fatty acid metabolism (FAM)-related genes are associated with prostate cancer (PCa) prognosis is not known. Therefore, we established a novel prognostic model based on FAM-related genes to predict biochemical recurrence in PCa patients. First, PCa sequencing data were acquired from TCGA as the training cohort and GSE21032 as the validation cohort. Second, a prostate cancer prognostic model containing 10 FAM-related genes was constructed using univariate Cox and LASSO. Principal component analysis and t-distributed stochastic neighbour embedding analysis showed that the model was highly effective. Third, PCa patients were divided into high- and low-risk groups according to the model risk score. Survival analysis, ROC curve analysis, and independent prognostic analysis showed that the high-risk group had short recurrence-free survival (RFS), and the risk score was an independent diagnostic factor with diagnostic value in PCa patients. External validation using GSE21032 also showed that the prognostic model had high reliability. A nomogram based on a prognostic model was constructed for clinical use. Fourth, tumor immune correlation analyses, such as the ESTIMATE, CIBERSORT algorithm, and ssGSEA, showed that the high-risk group had higher immune cell infiltration, lower tumour purity, and worse RFS. Various immune checkpoints were expressed at higher levels in high-risk patients. In summary, this prognostic model is a promising prognostic biomarker for PCa that should improve the prognosis of PCa patients. These data provide new ideas for antitumour immunotherapy and have good potential value for the development of targeted drugs.

Identification of fatty acid metabolism-related molecular subtype biomarkers and their correlation with immune checkpoints in cutaneous melanoma

PURPOSE

Fatty acid metabolism (FAM) affects the immune phenotype in a metabolically dynamic tumor microenvironment (TME), but the use of FAM-related genes (FAMGs) to predict the prognosis and immunotherapy response of cutaneous melanoma (CM) patients has not been investigated. In this study, we aimed to construct FAM molecular subtypes and identify key prognostic biomarkers in CM.

METHODS

We used a CM dataset in The Cancer Genome Atlas (TCGA) to construct FAM molecular subtypes. We performed Kaplan-Meier (K-M) analysis, gene set enrichment analysis (GSEA), and TME analysis to assess differences in the prognosis and immune phenotype between subtypes. We used weighted gene co-expression network analysis (WGCNA) to identify key biomarkers that regulate tumor metabolism and immunity between the subtypes. We compared overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) between CM patients with high or low biomarker expression. We applied univariable and multivariable Cox analyses to verify the independent prognostic value of the FAM biomarkers. We used GSEA and TME analysis to investigate the immune-related regulation mechanism of the FAM subtype biomarker. We evaluated the immune checkpoint inhibition (ICI) response and chemotherapy sensitivity between CM patients with high or low biomarker expression. We performed real-time fluorescent quantitative PCR (qRT-PCR) and semi-quantitative analysis of the immunohistochemical (IHC) data from the Human Protein Atlas to evaluate the mRNA and protein expression levels of the FAM biomarkers in CM.

RESULTS

We identified 2 FAM molecular subtypes (cluster 1 and cluster 2). K-M analysis showed that cluster 2 had better OS and PFS than cluster 1 did. GSEA showed that, compared with cluster 1, cluster 2 had significantly upregulated immune response pathways. The TME analysis indicated that immune cell subpopulations and immune functions were highly enriched in cluster 2 as compared with cluster 1. WGCNA identified 6 hub genes (ACSL5, ALOX5AP, CD1D, CD74, IL4I1, and TBXAS1) as FAM biomarkers. CM patients with high expression levels of the six biomarkers had better OS, PFS, and DSS than those with low expression levels of the biomarkers. The Cox regression analyses verified that the 6 FAM biomarkers can be independent prognostic factors for CM patients. The single-gene GSEA showed that the high expression levels of the 6 genes were mainly enriched in T-cell antigen presentation, the PD-1 signaling pathway, and tumor escape. The TME analysis confirmed that the FAM subtype biomarkers were not only related to immune infiltration but also highly correlated with immune checkpoints such as PD-1, PD-L1, and CTLA-4. TIDE scores confirmed that patients with high expression levels of the 6 biomarkers had worse immunotherapy responses. The 6 genes conveyed significant sensitivity to some chemotherapy drugs. qRT-PCR and IHC analyses verified the expression levels of the 6 biomarkers in CM cells.

CONCLUSION

Our FAM subtypes verify that different FAM reprogramming affects the function and phenotype of infiltrating immune cells in the CM TME. The FAM molecular subtype biomarkers can be independent predictors of prognosis and immunotherapy response in CM patients.

Integrated bioinformatics analysis and experimental validation reveals fatty acid metabolism-related prognostic signature and immune responses for uterine corpus endometrial carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is the third most common gynecologic malignancy. Fatty acid metabolism (FAM) is an essential metabolic process in the immune microenvironment that occurs reprogramming in the presence of tumor signaling and nutrient competition. This study aimed to identify the fatty acid metabolism-related genes (FAMGs) to develop a risk signature for predicting UCEC.

Methods

The differentially expressed FAMGs between UCEC samples and controls from TCGA database were discovered. A prognostic signature was then constructed by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses. Based on the median risk score, UCEC samples were categorized into high- and low-FAMGs groups. Kaplan-Meier (K-M) curve was applied to determine patients’ overall survival (OS). The independent prognostic value was assessed by uni- and multivariate analyses. The associations between the risk score and immune status, immune score, and drug resistance were evaluated. Quantitative Real-time PCR (qRT-PCR) was utilized to confirm FAMGs expression levels in UCEC cells.

Results

We built a 10-FAMGs prognostic signature and examined the gene mutation and copy number variations (CNV). Patients with a high-FAMGs had a worse prognosis compared to low-FAMGs patients in TCGA train and test sets. We demonstrated that FAMGs-based risk signature was a significant independent prognostic predictor of UCEC. A nomogram was also created incorporating this risk model and clinicopathological features, with high prognostic performance for UCEC. The immune status of each group was varied, and immune score was higher in a low-FAMGs group. HLA-related genes such as DRB1, DMA, DMB, and DQB2 had higher expression levels in the low-FAMGs group. Meanwhile, high-FAMGs patients were likely to response more strongly to the targeted drugs Bortezomib, Foretinib and Gefitinib. The qRT-PCR evidence further verified the significant expression of FAMGs in this signature.

Conclusions

A FAMGs-based risk signature might be considered as an independent prognostic indicator to predict UCEC prognosis, evaluate immune status and provide a new direction for therapeutic strategies.

Seven Fatty Acid Metabolism-Related Genes as Potential Biomarkers for Predicting the Prognosis and Immunotherapy Responses in Patients with Esophageal Cancer

Background: Esophageal cancer (ESCA) is a major cause of cancer-related mortality worldwide. Altered fatty acid metabolism is a hallmark of cancer. However, studies on the roles of fatty acid metabolism-related genes (FRGs) in ESCA remain limited. Method: We identified differentially expressed FRGs (DE-FRGs). Then, the DE-FRGs prognostic model was constructed and validated using a comprehensive analysis. Moreover, the correlation between the risk model and clinical characteristics was investigated. A nomogram for predicting survival was established and evaluated. Subsequently, the difference in tumor microenvironment (TME) was compared between two risk groups. The sensitivity of key DE-FRGs to chemotherapeutic interventions and their correlation with immune cells were investigated. Finally, DEGs between two risk groups were measured and the prognostic value of key DE-FRGs in ESCA was confirmed in other databases. Results: A prognostic model was constructed based on seven selected DEG-FRGs. TNM staging and CD8+ T cells were significantly correlated with high-risk groups. Low-risk groups exhibited more infiltrated M0 macrophages, an activation of type II interferon (IFN-γ) responses, and were found to be more suitable for immunotherapy. Seven key DE-FRGs with prognostic value were found to be considerably influenced by different chemotherapy drugs. Conclusion: A prognostic model based on seven DE-FRGs may efficiently predict patient prognosis and immunotherapy response, helping to develop individualized treatment strategies in ESCA.

A Potential Fatty Acid Metabolism-Related Gene Signature for Prognosis in Clear Cell Renal Cell Carcinoma

Simple Summary

Clear Cell Renal Cell Carcinoma (ccRCC) is the most common and aggressive subtype of renal cancer. Abnormal fatty acid metabolism (FAM) is reported to be strongly associated with multiple malignancies, yet there is limited research in ccRCC. In this manuscript, we reported the significant role of abnormal FAM in predicting the prognosis of ccRCC. Three independent clinical cohorts (TCGA, EMTAB and our clinical cohorts with prognostic profiles and gene expression data, including RNA-seq, microarray and RT-qPCR) were applied as training and two external validation cohorts. As a result, we successfully constructed and validated a novel FAM-related gene signature (FAMGS) and nomogram for the overall survival of patients with ccRCC. Additionally, our study further elucidated the potential immune relevance and molecular mechanisms of abnormal FAM and the signature. In conclusion, the novel FAMGS constructed in this study offered a promising prognostic tool in clinic and potential therapeutic targets for ccRCC patients.

Abstract

This study aims to explore the role of abnormal fatty acid metabolism (FAM) in ccRCC and construct a novel fatty acid metabolism-related gene signature (FAMGS) for prognosis. Three independent ccRCC cohorts, including The Cancer Genome Atlas, E-MTAB-1980 and our clinical cohort (including RNA-seq, microarray and RT-qPCR data), were applied as training and two independent validation cohorts. Firstly, FAM levels were found to be significantly decreased in ccRCC and correlated with degrees of malignancy, confirming the pivotal role of FAM in ccRCC. Applying the least absolute shrinkage and selection operator cox regression, we established a novel FAMGS for overall survival (OS). The FAMGS divided patients into low or high-risk groups in the training cohort and were successfully validated in both the EMTAB and our clinical validation cohorts. Additionally, the FAMGS serves as an independent risk factor for OS of ccRCC. Results of the immune cell abundance identifier (ImmuCellAI) algorithm and gene set variation analysis (GSVA) revealed that patients in the high-risk group have comprehensively impaired metabolism, including lipids, amino acids and tricarboxylic acid cycle-related pathways and a more immunosuppressive tumor microenvironment. In conclusion, our study constructed and validated a novel FAMGS, which may improve the risk stratification optimization and personalized management of ccRCC.

Identification and validation of a fatty acid metabolism-related lncRNA signature as a predictor for prognosis and immunotherapy in patients with liver cancer

Background

Fatty acid (FA) metabolism is considered the emerging cause of tumor development and metastasis, driving poor prognosis. Long non-coding RNAs (lncRNAs) are closely related to cancer progression and play important roles in FA metabolism. Thus, the discovery of FA metabolism-related lncRNA signatures to predict outcome and immunotherapy response is critical in improving the survival of patients with hepatocellular carcinoma (HCC).

Methods

FA metabolism scores and a FA metabolism-related lncRNA signature were constructed using a single-sample gene set enrichment analysis based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. “ConsensusClusterPlus” was used to screen molecular subtypes. Chi-squared test and Fisher’s exact test were applied to explore the relationship between clinical, genomic mutation characteristics and subtypes. Transcription factor (TF) activity scores, cellular distributions, immune cell infiltration, and immunotherapy response were employed to investigate the functions of FA metabolism-related lncRNA signatures. FA metabolism microarray and western blot were performed to detect the biological function of candidate lncRNAs.

Results

A total of 70 lncRNAs that highly correlated with FA metabolism scores in two cohorts were used to construct two distinct clusters. Patients in cluster 2 had lower FA metabolism scores and worse survival than those in cluster 1. Patients in cluster 2 exhibited a high frequency of DNA damage, gene mutations, oncogenic signaling such as epithelial-to-mesenchymal transition, and a high degree of immune cell infiltration. Moreover, the lncRNA signature could predict the effects of immunotherapy in patients with HCC. Furthermore, three lncRNAs (SNHG1, LINC00261, and SNHG7) were identified that were highly correlated with FA metabolism. Additionally, SNHG1 and SNHG7 were found to regulate various FA metabolism-related genes and ferroptosis-related genes in vitro experiments. GSEA analysis revealed that SNHG1 and SNHG7 promote fatty acid beta-oxidation. SNHG1 and SNHG7 silencing dramatically reduced lipid droplets in HCC cells. Many immune-infiltration genes and TFs were overexpressed in HCC tissues with SNHG1 and SNHG7 high expression.

Conclusions

A novel molecular model of FA metabolism-related lncRNAs was developed, which has significantly prognostic potential in HCC diagnosis and aids in clinical decision making.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10122-4.

Identification of subtypes of clear cell renal cell carcinoma and construction of a prognostic model based on fatty acid metabolism genes

Background: The effects of fatty acid metabolism in many tumors have been widely reported. Due to the diversity of lipid synthesis, uptake, and transformation in clear cell renal cell carcinoma (ccRCC) cells, many studies have shown that ccRCC is associated with fatty acid metabolism. The study aimed was to explore the impact of fatty acid metabolism genes on the prognosis and immunotherapy of ccRCC.

Methods: Two subtypes were distinguished by unsupervised clustering analysis based on the expression of 309 fatty acid metabolism genes. A prognostic model was constructed by lasso algorithm and multivariate COX regression analysis using fatty acid metabolism genes as the signatures. The tumor microenvironment between subtypes and between risk groups was further analyzed. The International Cancer Genome Consortium cohort was used for external validation of the model.

Results: The analysis showed that subtype B had a poorer prognosis and a higher degree of immune infiltration. The high-risk group had a poorer prognosis and higher tumor microenvironment scores. The nomogram could accurately predict patient survival.

Conclusion: Fatty acid metabolism may affect the prognosis and immune infiltration of patients with ccRCC. The analysis was performed to understand the potential role of fatty acid metabolism genes in the immune infiltration and prognosis of patients. These findings have implications for individualized treatment, prognosis, and immunization for patients with ccRCC.

Multi-omics characterization of the unsaturated fatty acid biosynthesis pathway in colon cancer

The biosynthesis of unsaturated fatty acids is involved in the initiation and progression of colon adenocarcinoma (COAD). In this study, we aimed to investigate the multi-omics characteristics of unsaturated fatty acid biosynthesis-related genes and explore their prognostic value in colon cancer by analyzing the data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. An unsaturated fatty acid biosynthesis pathway related-genes enrichment score (BUFAS) was constructed utilizing the single sample gene set enrichment analysis (ssGSEA). We discovered that a high BUFAS was associated with longer overall survival (OS) in both the training and the validation sets. Multivariable analysis including the clinical characteristics further verified the independent prognostic value of the BUFAS in both the TCGA-COAD and the GSE39582 datasets. In addition, GSEA analysis revealed that BUFAS was positively associated with several signaling pathways, including MTORC1, peroxisome, and pathways related to fatty acid metabolism, while was negatively associated with other signaling pathways, such as hedgehog, NOTCH, and Wnt/beta-catenin pathway. Furthermore, in the COAD cell lines of the Genomics of Drug Sensitivity in Cancer (GDSC) database, we found that BUFAS was positively correlated with the drug sensitivities of cisplatin, gemcitabine, camptothecin, lapatinib, and afatinib, while was negatively correlated with that of ponatinib. Moreover, in the COAD single-cell transcriptomic dataset (GSE146771), the BUFAS varied among different cell types and was enriched in mast cells and fibroblasts. Taken together, the BUFAS we constructed could be used as an independent prognostic signature in predicting the OS and drug resistance of colon cancer. Unsaturated fatty acid biosynthesis pathway might serve as potential therapeutic targets for cancer treatment.

Metabolic Rewiring in Glioblastoma Cancer: EGFR, IDH and Beyond

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

Characterization of Fatty Acid Metabolism-Related Genes Landscape for Predicting Prognosis and Aiding Immunotherapy in Glioma Patients

Glioma is a highly malignant brain tumor with a poor survival rate. The involvement of fatty acid metabolism in glioma was examined to find viable treatment options. The information was gathered from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. A prognostic signature containing fatty acid metabolism-dependent genes (FAMDs) was developed to predict glioma outcome by multivariate and most minor absolute shrinkage and selection operator (LASSO) regression analyses. In the TCGA cohort, individuals with a good score had a worse prognosis than those with a poor score, validated in the CGGA cohort. According to further research by “pRRophetic” R package, higher-risk individuals were more susceptible to crizotinib. According to a complete study of the connection between the predictive risk rating model and tumor microenvironment (TME) features, high-risk individuals were eligible for activating the immune cell-associated receptor pathway. We also discovered that anti-PD-1/PD-L1 and anti-CTLA4 immunotherapy are more effective in high-risk individuals. Furthermore, we demonstrated that CCNA2 promotes glioma proliferation, migration, and invasion and regulates macrophage polarization. Therefore, examining the fatty acid metabolism pathway aids our understanding of TME invasion properties, allowing us to develop more effective immunotherapies for glioma.

Identification of an IL-4-Related Gene Risk Signature for Malignancy, Prognosis and Immune Phenotype Prediction in Glioma

Background: Emerging molecular and genetic biomarkers have been introduced to classify gliomas in the past decades. Here, we introduced a risk signature based on the cellular response to the IL-4 gene set through Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Methods: In this study, we provide a bioinformatic profiling of our risk signature for the malignancy, prognosis and immune phenotype of glioma. A cohort of 325 patients with whole genome RNA-seq expression data from the Chinese Glioma Genome Atlas (CGGA) dataset was used as the training set, while another cohort of 667 patients from The Cancer Genome Atlas (TCGA) dataset was used as the validating set. The LASSO model identified a 10-gene signature which was considered as the optimal model. Results: The signature was confirmed to be a good predictor of clinical and molecular features involved in the malignancy of gliomas. We also identified that our risk signature could serve as an independently prognostic biomarker in patients with gliomas (p < 0.0001). Correlation analysis showed that our risk signature was strongly correlated with the Tregs, M0 macrophages and NK cells infiltrated in the microenvironment of glioma, which might be a supplement to the existing incomplete innate immune mechanism of glioma phenotypes. Conclusions: Our IL-4-related gene signature was associated with more aggressive and immunosuppressive phenotypes of gliomas. The risk score could predict prognosis independently in glioma, which might provide a new insight for understanding the IL-4 involved mechanism of gliomas.

Fatty Acid Metabolism-Related lncRNAs Are Potential Biomarkers for Predicting the Overall Survival of Patients With Colorectal Cancer

Abnormal metabolism, including abnormal fatty acid metabolism, is an emerging hallmark of cancer. The current study sought to investigate the potential prognostic value of fatty acid metabolism-related long noncoding RNAs (lncRNAs) in colorectal cancer (CRC). To this end, we obtained the gene expression data and clinical data of patients with CRC from The Cancer Genome Atlas (TCGA) database. Through gene set variation analysis (GSVA), we found that the fatty acid metabolism pathway was related to the clinical stage and prognosis of patients with CRC. After screening differentially expressed RNAs, we constructed a fatty acid metabolism-related competing endogenous RNA (ceRNA) network based on the miRTarBase, miRDB, TargetScan, and StarBase databases. Next, eight fatty acid metabolism-related lncRNAs included in the ceRNA network were identified to build a prognostic signature with Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, and a nomogram was established based on the lncRNA signature and clinical variables. The signature and nomogram were further validated by Kaplan–Meier survival analysis, Cox regression analysis, calibration plots, receiver operating characteristic (ROC) curves, decision curve analysis (DCA). Besides, the TCGA internal and the quantitative real-time polymerase chain reaction (qRT-PCR) external cohorts were applied to successfully validate the robustness of the signature and nomogram. Finally, in vitro assays showed that knockdown of prognostic lncRNA TSPEAR-AS2 decreased the triglyceride (TG) content and the expressions of fatty acid synthase (FASN) and acetyl-CoA carboxylase 1 (ACC1) in CRC cells, which indicated the important role of lncRNA TSPEAR-AS2 in modulating fatty acid metabolism of CRC. The result of Oil Red O staining showed that the lipid content in lncRNA TSPEAR-AS2 high expression group was higher than that in lncRNA TSPEAR-AS2 low expression group. Our study may provide helpful information for fatty acid metabolism targeting therapies in CRC.

A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes

Objective

To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model.

Methods

The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model.

Results

339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage.

Conclusion

The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.

Construction of Prognostic Risk Prediction Model of Oral Squamous Cell Carcinoma Based on Nine Survival-Associated Metabolic Genes

The aim was to investigate the independent prognostic factors and construct a prognostic risk prediction model to facilitate the formulation of oral squamous cell carcinoma (OSCC) clinical treatment plan. We constructed a prognostic model using univariate COX, Lasso, and multivariate COX regression analysis and conducted statistical analysis. In this study, 195 randomly obtained sample sets were defined as training set, while 390 samples constituted validation set for testing. A prognostic model was constructed using regression analysis based on nine survival-associated metabolic genes, among which PIP5K1B, NAGK, and HADHB significantly down-regulated, while MINPP1, PYGL, AGPAT4, ENTPD1, CA12, and CA9 significantly up-regulated. Statistical analysis used to evaluate the prognostic model showed a significant different between the high and low risk groups and a poor prognosis in the high risk group (P < 0.05) based on the training set. To further clarify, validation sets showed a significant difference between the high-risk group with a worse prognosis and the low-risk group (P < 0.05). Independent prognostic analysis based on the training set and validation set indicated that the risk score was superior as an independent prognostic factor compared to other clinical characteristics. We conducted Gene Set Enrichment Analysis (GSEA) among high-risk and low-risk patients to identify metabolism-related biological pathways. Finally, nomogram incorporating some clinical characteristics and risk score was constructed to predict 1-, 2-, and 3-year survival rates (C-index = 0.7). The proposed nine metabolic gene prognostic model may contribute to a more accurate and individualized prediction for the prognosis of newly diagnosed OSCC patients, and provide advice for clinical treatment and follow-up observations.

Characterization of the fatty acid metabolism in colorectal cancer to guide clinical therapy

Colorectal cancer (CRC) is one of the most common malignant tumors, with the second-highest mortality of all 36 cancers worldwide. The roles of fatty acid metabolism in CRC were investigated to explore potential therapeutic strategies. The data files were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to construct a prognostic risk score model with fatty acid metabolism-related genes for predicting prognosis in CRC. Patients with a high-risk score had a poorer prognosis in TCGA cohort than those with a low-risk score and were confirmed in the GEO cohort. Further analysis using the "pRRophetic" R package revealed that low-risk patients were more sensitive to 5-fluorouracil. A comprehensive evaluation of the association between prognostic risk score model and tumor microenvironment (TME) characteristics showed that high-risk patients were suitable for activating a type I/II interferon (IFN) response and inflammation-promoting function. Tumor Immune Dysfunction and Exclusion (TIDE) and SubMap algorithm results also demonstrated that high-risk patients are more suitable for anti-CTLA4 immunotherapy. Therefore, the evaluation of the fatty acid metabolism pattern promotes our comprehension of TME infiltration characteristics, thus guiding effective immunotherapy regimens.

Comprehensive Analysis of Prognostic Markers for Acute Myeloid Leukemia Based on Four Metabolic Genes

Background: Metabolic reprogramming is the core characteristic of tumors during the development of tumors, and cancer cells can rely on metabolic changes to support their rapid growth. Nevertheless, an overall analysis of metabolic markers in acute myeloid leukemia (AML) is absent and urgently needed. Methods: Within this work, genetic expression, mutation data and clinical data of AML were queried from Genotype-Tissue Expression (GTEx) database, The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The tumor samples of TCGA were randomly divided into a training group (64 samples) and an internal validation group (64 samples) at one time, and the tumor samples of GEO served as two external validation groups (99 samples, 374 samples). According to the expression levels of survival-associated metabolic genes, we divided all TCGA tumor samples into high, medium and low metabolism groups, and evaluated the immune cell activity in the tumor microenvironment of the three metabolism groups by single-sample gene set enrichment analysis (ssGSEA) algorithm. Finally, we examined the mutations and prognostic effects of each model gene. Results: Four metabolism-related genes were screened and applied to construct a prognostic model for AML, giving excellent results. As for the area under the curve (AUC) value of receiver operating characteristic (ROC) curve, the training group was up to 0.902 (1-year), 0.81 (3-year), and 0.877 (5-year); and the internal and external validation groups also met the expected standards, showing high potency in predicting patient outcome. Univariate and multivariate prognostic analyses indicated that the riskScore obtained from our prognostic model was an independent prognostic factor. ssGSEA analysis revealed the high metabolism group had higher immune activity. Single and multiple gene survival analysis validated that each model gene had significant effects on the overall survival of AML patients. Conclusions: In our study, a high-efficiency prognostic prediction model was built and validated for AML patients. The results showed that metabolism-related genes could become potential prognostic biomarkers for AML.

Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR = 3.192, 95%CI = 2.182‐4.670; CGGA: HR = 1.922, 95%CI = 1.431‐2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.