Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer

Lin, Daowei; Fan, Wenhua; Zhang, Rongxin; Zhao, Enen; Li, Pansong; Zhou, Wenhao; Peng, Jianhong; Li, Liren

doi:10.1186/s12967-021-02952-w

Cited by 32 publications

(25 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies showed prognostic models contributed to clinical decision and precision medicine ( Zhang et al, 2020 ; Mohammed et al, 2021 ). The patients were divided into high- and low-risk groups for prediction of overall survival according to the risk stratification in the prognostic models ( Lin et al, 2021 ). It is critical for improvement of the personalized therapies and the quality of life.…”

Section: Discussionmentioning

confidence: 99%

“…Lipid metabolism plays an important role in maintaining of cellular homeostasis ( Rohrig and Schulze, 2016 ; Bian et al, 2021 ). Numerous studies have demonstrated that lipid metabolism was involved in the progression, recurrence and tumor microenvironment (TME) of CRC ( Lin et al, 2021 ). Dysregulation of lipid metabolism occurs in multiple cancers, including CRC ( Dias et al, 2019 ; Haffa et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

et al. 2022

View full text Add to dashboard Cite

Colorectal cancer (CRC) is a common malignant tumor worldwide. Lipid metabolism is a prerequisite for the growth, proliferation and invasion of cancer cells. However, the lipid metabolism-related gene signature and its underlying molecular mechanisms remain unclear. The aim of this study was to establish a lipid metabolism signature risk model for survival prediction in CRC and to investigate the effect of gene signature on the immune microenvironment. Lipid metabolism-mediated genes (LMGs) were obtained from the Molecular Signatures Database. The consensus molecular subtypes were established using “ConsensusClusterPlus” based on LMGs and the cancer genome atlas (TCGA) data. The risk model was established using univariate and multivariate Cox regression with TCGA database and independently validated in the international cancer genome consortium (ICGC) datasets. Immune infiltration in the risk model was developed using CIBERSORT and xCell analyses. A total of 267 differentially expressed genes (DEGs) were identified between subtype 1 and subtype 2 from consensus molecular subtypes, including 153 upregulated DEGs and 114 downregulated DEGs. 21 DEGs associated with overall survival (OS) were selected using univariate Cox regression analysis. Furthermore, a prognostic risk model was constructed using the risk coefficients and gene expression of eleven-gene signature. Patients with a high-risk score had poorer OS compared with patients in the low-risk score group (p = 3.36e-07) in the TCGA cohort and the validationdatasets (p = 4.03e-05). Analysis of immune infiltration identified multiple T cells were associated with better prognosis in the low-risk group, including Th2 cells (p = 0.0208), regulatory T cells (p = 0.0425), and gammadelta T cells (p = 0.0112). A nomogram integrating the risk model and clinical characteristics was further developed to predict the prognosis of patients with CRC. In conclusion, our study revealed that the expression of lipid-metabolism genes were correlated with the immune microenvironment. The eleven-gene signature might be useful for prediction the prognosis of CRC patients.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In short, RDPS represents high predictive accuracy for predicting prognosis in CRC patients. In order to compare our RDPS model with published metabolism-related signatures, we applied the compareC R package to compare the performance among these signatures (28)(29)(30)(31)(32). As illustrated in Figure S1, our RDPS model ranked first in the predictive power in TCGA-CRC, GSE17536, and GSE29621.…”

Section: Rdps Was Well Validated In Four Cohorts and Could Be Used As A Crc Recurrence Risk Assessment Factormentioning

confidence: 99%

Derivation and Clinical Validation of a Redox-Driven Prognostic Signature for Colorectal Cancer

Dang

Liu

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

Colorectal cancer (CRC), a seriously threat that endangers public health, has a striking tendency to relapse and metastasize. Redox-related signaling pathways have recently been extensively studied in cancers. However, the study and potential role of redox in CRC remain unelucidated. We developed and validated a risk model for prognosis and recurrence prediction in CRC patients via identifying gene signatures driven by redox-related signaling pathways. The redox-driven prognostic signature (RDPS) was demonstrated to be an independent risk factor for patient survival (including OS and RFS) in four public cohorts and one clinical in-house cohort. Additionally, there was an intimate association between the risk score and tumor immune infiltration, with higher risk score accompanied with less immune cell infiltration. In this study, we used redox-related factors as an entry point, which may provide a broader perspective for prognosis prediction in CRC and have the potential to provide more promising evidence for immunotherapy.

show abstract

“…In addition, recent studies have focused on the relationship between metabolism and survival: pan-cancer studies have indicated that tumor subtypes with different MAG expression patterns lead to significantly different survival [ 10 , 11 ]. Moreover, several risk models based on MAGs have been proposed for breast cancer [ 6 ], colorectal cancer [ 12 ], gastric cancer [ 13 ] and osteosarcoma [ 14 ]. However, the value of MAGs in DLBCL subtype identification and prognostic prediction remains unclear.…”

Section: Introductionmentioning

confidence: 99%

Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

Chen

Xue

et al. 2022

J Transl Med

View full text Add to dashboard Cite

Background Diffuse large B cell lymphoma (DLBCL) is the most common lymphoma in adults. Metabolic reprogramming in tumors is closely related to the immune microenvironment. This study aimed to explore the interactions between metabolism-associated genes (MAGs) and DLBCL prognosis and their potential associations with the immune microenvironment. Methods Gene expression and clinical data on DLBCL patients were obtained from the GEO database. Metabolism-associated molecular subtypes were identified by consensus clustering. A prognostic risk model containing 14 MAGs was established using Lasso-Cox regression in the GEO training cohort. It was then validated in the GEO internal testing cohort and TCGA external validation cohort. GO, KEGG and GSVA were used to explore the differences in enriched pathways between high- and low-risk groups. ESTIMATE, CIBERSORT, and ssGSEA analyses were used to assess the immune microenvironment. Finally, WGCNA analysis was used to identify two hub genes among the 14 model MAGs, and they were preliminarily verified in our tissue microarray (TMA) using multiple fluorescence immunohistochemistry (mIHC). Results Consensus clustering divided DLBCL patients into two metabolic subtypes with significant differences in prognosis and the immune microenvironment. Poor prognosis was associated with an immunosuppressive microenvironment. A prognostic risk model was constructed based on 14 MAGs and it was used to classify the patients into two risk groups; the high-risk group had poorer prognosis and an immunosuppressive microenvironment characterized by low immune score, low immune status, high abundance of immunosuppressive cells, and high expression of immune checkpoints. Cox regression, ROC curve analysis, and a nomogram indicated that the risk model was an independent prognostic factor and had a better prognostic value than the International Prognostic Index (IPI) score. The risk model underwent multiple validations and the verification of the two hub genes in TMA indicated consistent results with the bioinformatics analyses. Conclusions The molecular subtypes and a risk model based on MAGs proposed in our study are both promising prognostic classifications in DLBCL, which may provide novel insights for developing accurate targeted cancer therapies.

show abstract

Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer

Cited by 32 publications

References 49 publications

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

Derivation and Clinical Validation of a Redox-Driven Prognostic Signature for Colorectal Cancer

Identification of molecular subtypes and a novel prognostic model of diffuse large B-cell lymphoma based on a metabolism-associated gene signature

Contact Info

Product

Resources

About