A Novel Metabolism-Related Signature as a Candidate Prognostic Biomarker for Hepatocellular Carcinoma

Wang, Wenwen; Embaye, Kidane Siele; Yang, Qing; Qin, Lingzhi; Zhang, Chao; Liu, Liwei; Zhan, Xiaoqian; Zhang, Fengdi; Wang, Xi; Qin, Shenghui

doi:10.2147/jhc.s294108

Cited by 17 publications

(14 citation statements)

References 42 publications

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“…Fortunately, the screening of metabolic biomarkers can specifically detect abnormal changes in organisms to prevent malignant diseases with pathophysiological characteristics ( 13 ). The metabolic markers have been well displayed in a variety of tumors, such as hepatocellular carcinoma ( 14 ), colorectal cancer ( 15 ), endometrial cancer ( 16 ), and clear cell renal cell carcinoma ( 17 ), but research in PCa is still relatively scarce. Therefore, it is of great clinical significance to find a new metabolic marker to predict the prognosis of PCa.…”

Section: Introductionmentioning

confidence: 99%

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

Liang

Yin

et al. 2022

Front. Oncol.

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Prostate cancer is still the main male health problem in the world. The role of metabolism in the occurrence and development of prostate cancer is becoming more and more obvious, but it is not clear. Here we firstly identified a metabolism-related gene-based subgroup in prostate cancer. We used metabolism-related genes to divide prostate cancer patients from The Cancer Genome Atlas into different clinical benefit populations, which was verified in the International Cancer Genome Consortium. After that, we analyzed the metabolic and immunological mechanisms of clinical beneficiaries from the aspects of functional analysis of differentially expressed genes, gene set variation analysis, tumor purity, tumor microenvironment, copy number variations, single-nucleotide polymorphism, and tumor-specific neoantigens. We identified 56 significant genes for non-negative matrix factorization after survival-related univariate regression analysis and identified three subgroups. Patients in subgroup 2 had better overall survival, disease-free interval, progression-free interval, and disease-specific survival. Functional analysis indicated that differentially expressed genes in subgroup 2 were enriched in the xenobiotic metabolic process and regulation of cell development. Moreover, the metabolism and tumor purity of subgroup 2 were higher than those of subgroup 1 and subgroup 3, whereas the composition of immune cells of subgroup 2 was lower than that of subgroup 1 and subgroup 3. The expression of major immune genes, such as CCL2, CD274, CD276, CD4, CTLA4, CXCR4, IL1A, IL6, LAG3, TGFB1, TNFRSF4, TNFRSF9, and PDCD1LG2, in subgroup 2 was almost significantly lower than that in subgroup 1 and subgroup 3, which is consistent with the results of tumor purity analysis. Finally, we identified that subgroup 2 had lower copy number variations, single-nucleotide polymorphism, and neoantigen mutation. Our systematic study established a metabolism-related gene-based subgroup to predict outcomes of prostate cancer patients, which may contribute to individual prevention and treatment.

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Section: Introductionmentioning

confidence: 99%

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

Liang

Yin

et al. 2022

Front. Oncol.

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“…Then, a novel epigenetic-related prognostic signature was developed by Lasso and multivariate Cox regression analyses [ 47 , 48 ]. The risk scores of LUAD patients were calculated according to the formula: the signature risk score = Ʃ (βi × Expi), where βi, the coefficients, represented the weight of the respective signature and Expi represented the prognostic factors expression value as previously described [ 49 ]. According to the signature with identified prognostic factors, a nomogram for predicting the probability of OS was established.…”

Section: Methodsmentioning

confidence: 99%

“…To predict potential drugs for LUAD patients, we utilized the Broad Institute’s Connectivity Map (CMap) to screen candidate molecule drugs as previously described [ 51 , 49 ].…”

Section: Methodsmentioning

confidence: 99%

Development and validation of a novel epigenetic-related prognostic signature and candidate drugs for patients with lung adenocarcinoma

Wang

Embaye

Yang

et al. 2021

Aging

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Background: Epigenetic dysregulation has been increasingly proposed as a hallmark of cancer. Here, the aim of this study is to establish an epigenetic-related signature for predicting the prognosis of lung adenocarcinoma (LUAD) patients. Results: Five epigenetic-related genes (ERGs) (ARRB1, PARP1, PKM, TFDP1, and YWHAZ) were identified as prognostic hub genes and used to establish a prognostic signature. According our risk score system, LUAD patients were stratified into high and low risk groups, and patients in the high risk group had a worse prognosis. ROC analysis indicated that the signature was precise in predicting the prognosis. A new nomogram was constructed based on the five hub genes, which can predict the OS of every LUAD patients. The calibration curves showed that the nomogram had better accuracy in prediction. Finally, candidate drugs that aimed at hub ERGs were identified, which included 47 compounds. Conclusions: Our epigenetic-related signature nomogram can effectively and reliably predict OS of LUAD patients, also we provide precise targeted chemotherapeutic drugs. Methods: The genomic data and clinical data of LUAD cohort were downloaded from the TCGA database and ERGs were obtained from the EpiFactors database. GSE31210 and GSE50081 microarray datasets were included as independent external datasets. Univariate Cox, LASSO regression, and multivariate Cox analyses were applied to construct the epigenetic-related signature.

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“…The GSE135337 dataset incorporated scRNA-seq data of 7 BLCA samples. And 2751 MRGswere obtained from literature reports [20].…”

Section: Data Collectionmentioning

confidence: 99%

Construction and validation of a prognostic model of metabolism-related genes driven by somatic mutation in bladder cancer

Liang

Han

et al. 2022

Preprint

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Background Bladder cancer (BLCA) is a highly malignant disease in the urinary system. Somatic mutation is a key feature in cancer occurrence, development, and treatment. Moreover, altered metabolism contributes to patient prognosis. However, the role of metabolism-related genes (MRGs) driven by somatic mutations in BLCA remains unclear. Methods The data were gathered from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The differentially expressed metabolism-related genes (DEMRGs) between normal and BLCA patients were first identified and the functions were discussed. Then The risk model was construct by the DEMRGs with mutation frequency. The accuracy of the risk model was verified by risk curves, Kaplan-Meier (K-M) curves, and Receiver Operating Characteristic (ROC) curves. Subsequently, the correlation of risk score and clinical traits was also researched. Gene Set Enrichment Analysis (GSEA), immune checkpoints, immune microenvironment, and chemotherapeutic drug sensitivity were performed in high- and low-risk groups. And the scRNA-seq revealed that the expression pattern of prognostic biomarkers and cellular heterogeneity. Eventually, the mRNA expression levels of biomarkers were validated by quantitative real-time PCR (qRT-PCR). Results A total of 201 DEMRGs were retrieved, and the DEMRGs were significantly enriched in alcohol metabolic process, cellular modified amino acid metabolic process, and purine metabolism. Then the 24 DEMRGs of the mutation frequency greater than 3% were further analyzed, and a risk model was constructed by 5 biomarkers (FASN, ABCC4, ATP2B4, ATP8B2, and MTHFD1L). Moreover, the AUCs were all greater than 0.6, indicating the risk model had good efficacy. Meanwhile, the riskScore, T-pathologic, age, and N-pathologic were regarded as independent prognostic indicators. The DEMRGs were enriched in OXIDATIVE_PHOSPHORYLATION. Three immune checkpoints, four types of immune cells, and 146 drugs were substantially different in the two risk groups. And the scRNA-seq further disclosed relationships between genes regulation and tracked the development trajectories of distinct cell lineages. Finally, qRT-PCR results showed the expression levels of FASN and MTHFD1L were significantly higher in carcinoma tissue. Conclusion In brief, this study constructed a novel biomarkers, which could improve the prediction of independent prognosis indicators and guide individualized treatment of BLCA patients.

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A Novel Metabolism-Related Signature as a Candidate Prognostic Biomarker for Hepatocellular Carcinoma

Cited by 17 publications

References 42 publications

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

Development and validation of a novel epigenetic-related prognostic signature and candidate drugs for patients with lung adenocarcinoma

Construction and validation of a prognostic model of metabolism-related genes driven by somatic mutation in bladder cancer

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