Dataset Information

Prognostic implications of metabolism-associated gene signatures in colorectal cancer.

ABSTRACT: Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS). Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate. Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.

SUBMITTER: Miao Y

PROVIDER: S-EPMC7474523 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prognostic implications of metabolism-associated gene signatures in colorectal cancer.

Miao Yandong Y Li Qiutian Q Wang Jiangtao J Quan Wuxia W Li Chen C Yang Yuan Y Mi Denghai D

PeerJ 20200902

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identifi ...[more]

PMID: 32953273

Similar Datasets

Project description:Background: The potential biological processes and laws of the biological components in malignant tumors can be understood more systematically and comprehensively through multi-omics analysis. This study elaborately explored the role of lipid metabolism in the prognosis of colorectal cancer (CRC) from the metabonomics and transcriptomics. Methods: We performed K-means unsupervised clustering algorithm and t test to identify the differential lipid metabolites determined by liquid chromatography tandem mass spectrometry (LC-MS/MS) in the serum of 236 CRC patients of the First Hospital of Jilin University (JLUFH). Cox regression analysis was used to identify prognosis-associated lipid metabolites and to construct multi-lipid-metabolite prognostic signature. The composite nomogram composed of independent prognostic factors was utilized to individually predict the outcome of CRC patients. Glycerophospholipid metabolism was the most significant enrichment pathway for lipid metabolites in CRC, whose related hub genes (GMRHGs) were distinguished by gene set variation analysis (GSVA) and weighted gene co-expression network analysis (WGCNA). Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis were utilized to develop the prognostic signature. Results: Six-lipid-metabolite and five-GMRHG prognostic signatures were developed, indicating favorable survival stratification effects on CRC patients. Using the independent prognostic factors as variables, we established a composite nomogram to individually evaluate the prognosis of CRC patients. The AUCs of one-, three-, and five-year ROC curves were 0.815, 0.815, and 0.805, respectively, showing auspicious prognostic accuracy. Furthermore, we explored the potential relationship between tumor microenvironment (TME) and immune infiltration. Moreover, the mutational frequency of TP53 in the high-risk group was significantly higher than that in the low-risk group (p < 0.001), while in the coordinate mutational status of TP53, the overall survival of CRC patients in the high-risk group was significantly lower than that in low-risk group with statistical differences. Conclusion: We identified the significance of lipid metabolism for the prognosis of CRC from the aspects of metabonomics and transcriptomics, which can provide a novel perspective for promoting individualized treatment and revealing the potential molecular biological characteristics of CRC. The composite nomogram including a six-lipid-metabolite prognostic signature is a promising predictor of the prognosis of CRC patients.

Project description:BackgroundColorectal cancer (CRC) is a prevalent cause of death from malignant tumors. This study aimed to develop a nicotinamide adenine dinucleotide (NAD+) metabolism and immune-related prognostic signature, providing a theoretical foundation for prognosis and therapy in CRC patients.MethodsNAD + metabolism-related and immune-related subtypes of CRC patients were identified by consistent clustering. Differentially expressed genes (DEGs) between the two subtypes of CRC were identified by overlapping. A risk signature was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Independent prognostic predictors were authenticated by Cox analysis. Gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) were applied to investigate the connection between the prognostic signature and the immune microenvironment. Chemotherapy drug sensitivity and immunotherapy responsiveness were projected using the 'pRRophetic' package and Tumor Immune Dysfunction and Exclusion (TIDE) website. The Human Protein Atlas (HPA) database was used to assess the protein expression of prognostic genes in CRC and normal tissues.ResultsUsing bioinformatics methods, three prognostic genes related to immune-related NAD + metabolism were identified, and the results were used to establish and verify a prognostic signature related to immune-related NAD + metabolism in CRC patients. Cox regression analysis confirmed that the risk score was a reliable independent prognostic predictor. GSVA and ssGSEA indicated that the prognostic signature was associated with the immune microenvironment. TIDE analysis suggested that the signature might act as an immunotherapy predictor. Chemotherapy sensitivity analysis revealed that COMP was correlated with chemotherapy sensitivity in CRC patients and might be a potential therapeutic target.ConclusionThis study identified NAD + metabolism-immune-related prognostic genes (MOGAT2, COMP, and DNASE1L3) and developed a prognostic signature for CRC prognosis, which is significant for clinical prognosis prediction and treatment strategy decisions for CRC patients.

Project description:Hepatocellular carcinoma (HCC) is a global health challenge with an increasing incidence worldwide. Cancer-associated fibroblasts (CAFs) function critically in HCC initiation and development. However, the prognostic significance of CAF-related gene signatures in HCC remains unknown. Therefore, the specific functions of CAF-related genes in HCC were investigated to help develop potential therapeutic strategies. In this study, CAF-related genes were screened from three CAF-related gene sets. HCC data from the Gene Expression Omnibus (GEO) database was applied to verify the screened CAF-related genes. Cluster analysis was used to identify clusters based on the expression pattern of CAF-related genes and two identified clusters were found to have a significant difference in overall survival (OS) and progression free intervals (PFI). The prognosis of HCC patients was predicted using the prognostic risk score model developed based on HCC data from The Cancer Genome Atlas (TCGA) databases. High-risk group patients had a worse OS than those in low-risk group in TCGA. These results were validated in International Cancer Genome Consortium (ICGC) database. Moreover, combining the clinicopathological characteristics related to prognosis with the model, a nomogram was built for a more accurate prediction of OS of HCC patients. In addition, analyses of immune infiltration characteristics of tumor microenvironment (TME), chemosensitivity, and immunotherapy response were conducted to further evaluate the prognostic value of CAF-related genes. Patients with low-risk scores were found to have higher chemosensitivity to cisplatin, doxorubicin, and sorafenib. Individuals with high-risk scores were found with a higher expression of most immune checkpoints which indicated patients with high-risk scores may benefit more from treatment with immune checkpoint inhibitors. Furthermore, a correlation between immune infiltration characteristics of TME and patients with different risk levels was found. These findings provide a possibility for the further development of personalized treatments in HCC.

Dataset Information

Prognostic implications of metabolism-associated gene signatures in colorectal cancer.

Publications

Prognostic implications of metabolism-associated gene signatures in colorectal cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets