- •There were 143 metabolic gens that differentially expressed in the survival group comparing with the dead group.
- •SVM classifier based on 21 feature metabolic genes have a good performance in samples classifying.
- •RS prognostic prediction model constructed by 10 metabolic genes have great potential in prognostic prediction.
- •GSEA showed complement and coagulation cascades, PPAR signaling pathway and hematopoietic cell lineage were activated in high risk group.
- •High risk group had obviously lower counts of b cell naive and t cell CD4+ memory resting, while higher counts of b cell plasma and macrophage M2.
Our study aimed to reveal the metabolic-related gene signatures for survival prediction and immune cell subtypes associated with IHCC prognosis.
Differentially expressed metabolic genes were identified between survival group and dead group which were divided according to survival at discharge. Recursive feature elimination (RFE) and randomForest (RF) algorithms were applied to optimize the combination of feature metabolic genes, which were used to generate SVM classifier. Performance of SVM classifier was evaluated by receiver operating characteristic (ROC) curves. Gene set enrichment analysis (GSEA) was conducted to uncover the activated pathways in high risk group, and differences in immune cell distributions were revealed.
There were 143 differentially expressed metabolic gens. RFE and RF identified 21 overlapping differentially expressed metabolic genes, and the constructed SVM classifier had excellent accuracy in training and validation dataset. RS survival prediction model was consisted of 10 metabolic genes. RS model had reliable predictive capability in the training and validation dataset. GSEA revealed 15 significant KEGG pathways that were relatively activated in the high risk group. High risk group had obviously lower counts of B cell naive and T cell CD4+ memory resting, while higher counts of B cell plasma and macrophage M2.
Prognostic prediction model of 10 metabolic genes could accurately predict the prognosis of IHCC patients.
To read this article in full you will need to make a payment
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:Subscribe to Cancer Genetics
Already a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
- Intrahepatic cholangiocarcinoma.Surg Oncol Clin N Am. 2019; 28: 587-599
- Recurrent intrahepatic cholangiocarcinoma - review.Front Oncol. 2021; 11
- Cholangiocarcinoma 2020: the next horizon in mechanisms and management.Nat Rev Gastroenterol Hepatol. 2020; 17: 557-588
- Management of Intrahepatic Cholangiocarcinoma.J Clin Med. 2021; 10: 2368
- Outcomes for patients with recurrent intrahepatic cholangiocarcinoma after surgery.Ann Surg Oncol. 2016; 23: 4392-4400
- Recurrent intrahepatic cholangiocarcinoma - review.Front Oncol. 2021; 11 (776863-776863)
- Intrahepatic cholangiocarcinoma: continuing challenges and translational advances.Hepatology. 2019; 69: 1803-1815
- Microvessel density and angiogenesis in primary hepatic malignancies: differential expression of CD31 and VEGFR-2 in hepatocellular carcinoma and intrahepatic cholangiocarcinoma.Pathol Res Pract. 2018; 214: 1136-1141
- The tumour microenvironment and immune milieu of cholangiocarcinoma.Liver Int. 2019; 1: 63-78
- Immunobiology of cholangiocarcinoma.JHEP Rep. 2019; 1: 297-311
- Cancer-associated fibroblasts as abettors of tumor progression at the crossroads of EMT and therapy resistance.Mol Cancer. 2019; 18: 019-0994
- Activated fibroblast program orchestrates tumor initiation and progression; molecular mechanisms and the associated therapeutic strategies.Int J Mol Sci. 2019; 20
- Metabolic disorders and the risk of cholangiocarcinoma.Expert Rev Gastroenterol Hepatol. 2021; 15: 999-1007
- Obesity is a risk factor for intrahepatic cholangiocarcinoma progression associated with alterations of metabolic activity and immune status.Sci Rep. 2021; 11: 5845
- Prognostic impact of tumor microvessels in intrahepatic cholangiocarcinoma: association with tumor-infiltrating lymphocytes.Mod Pathol. 2021; 34: 798-807
- Prognostic impact of CD8+ T cell distribution and its association with the HLA class I expression in intrahepatic cholangiocarcinoma.Surg Today. 2020; 50: 931-940
- Functional genomics reveal that the serine synthesis pathway is essential in PHGDH-amplified breast cancer.Nature. 2011; 476: 346-350
- Developing metabolic gene signatures to predict intrahepatic cholangiocarcinoma prognosis and mining a miRNA regulatory network.J Clin Lab Anal. 2022; 36: e24107
- NCBI GEO: archive for functional genomics data sets–update.Nucleic Acids Res. 2013; 41: 27
- The sva package for removing batch effects and other unwanted variation in high-throughput experiments.Bioinformatics. 2012; 28: 882-883
- limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res. 2015; 43 (e47-e47)
- RNA-seq analyses of multiple meristems of soybean: novel and alternative transcripts, evolutionary and functional implications.BMC Plant Biol. 2014; 14: 1-19
- Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.Nat Protoc. 2009; 4: 44-57
- The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.Nucleic Acids Res. 2017; 45: D362-D368
- Machine learning algorithms for outcome prediction in (chemo) radiotherapy: an empirical comparison of classifiers.Med Phys. 2018; 45: 3449-3459
- Classification with correlated features: unreliability of feature ranking and solutions.Bioinformatics. 2011; 27: 1986-1994
- Screening of feature genes in distinguishing different types of breast cancer using support vector machine.Onco Targets Ther. 2015; 8: 2311
- pROC: an open-source package for R and S+ to analyze and compare ROC curves.BMC Bioinformatics. 2011; 12: 1-8
- Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.Stat Med. 1996; 15: 361-387
- L1 penalized estimation in the Cox proportional hazards model.Biometrical J. 2010; 52: 70-84
- clusterProfiler: an R package for comparing biological themes among gene clusters.Omics. 2012; 16: 284-287
- Analysis of prognostic genes in the tumor microenvironment of lung adenocarcinoma.PeerJ. 2020; 23
- Characterization of gut microbiota, bile acid metabolism, and cytokines in intrahepatic cholangiocarcinoma.Hepatology. 2020; 71: 893-906
- Applications of Support Vector Machine (SVM) learning in cancer genomics.Cancer Genomics Proteomics. 2018; 15: 41-51
- Diagnosis of chronic kidney disease using effective classification algorithms and recursive feature elimination techniques.J Healthc Eng. 2021; 2021 (1004767-1004767)
- A pneumonia diagnosis scheme based on hybrid features extracted from chest radiographs using an ensemble learning algorithm.J Healthc Eng. 2021; 2021 (8862089-8862089)
- Single-cell transcriptomic analysis suggests two molecularly subtypes of intrahepatic cholangiocarcinoma.Nat Commun. 2022; 13: 022-29164
- Biogenic amines serotonin and dopamine regulate cholangiocyte hyperplastic and neoplastic growth.World J Gastrointest Pathophysiol. 2010; 1: 63-68
- Increased local dopamine secretion has growth-promoting effects in cholangiocarcinoma.Int J Cancer. 2010; 126: 2112-2122
- Quantitative changes in tumor-associated M2 macrophages characterize cholangiocarcinoma and their association with metastasis.Asian Pac J Cancer Prev. 2015; 16: 3043-3050
- Intrahepatic cholangiocarcinoma induced M2-polarized tumor-associated macrophages facilitate tumor growth and invasiveness.Cancer Cell Int. 2020; 20 (586-586)
Published online: April 12, 2023
Accepted: April 11, 2023
Received in revised form: March 26, 2023
Received: February 15, 2023
© 2023 Elsevier Inc. All rights reserved.