ArticleFrontiers in endocrinology2022
Exploration and validation of a combined immune and metabolism gene signature for prognosis prediction of colorectal cancer.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 18 citations in OpenAlex.
- Pharmacogenomic characterization of a uric acid metabolism-related signature associated with prognosis and drug sensitivity in gastric cancer.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Integrative machine learning models reveal immune and metabolic signatures predictive of colorectal cancer prognosis.Discover oncology · 2026Article
- Multi-omics profiling identifies ESM1 as a key mediator of immunoevasion through the SPP1 pathway in bladder cancer.Scientific reports · 2025Article
- Integrating Primary and Metastatic scRNA-Seq and Bulk Data to Develop an Immune-Based Prognosis Signature for Colorectal Cancer.Current issues in molecular biology · 2025Article
- IGF2BP3/ESM1/KLF10/BECN1 positive feedback loop: a novel therapeutic target in ovarian cancer via lipid metabolism reprogramming.Cell death & disease · 2025Article
- Unveiling the prognostic role of FABP4 in early-onset colorectal cancer through big data analysis and preliminary clinical validation.Frontiers in oncology · 2025Article
- STC2 promotes anoikis resistance by modulating TGIF1 mRNA stability in colorectal cancer.Frontiers in cell and developmental biology · 2025Article
- Novel mtDNA methylation-associated prognostic signatures in colorectal cancer.Frontiers in oncology · 2025Article
- New prognostic features and personalized treatment strategies of mitochondrial related genes in colorectal cancer patients.Frontiers in pharmacology · 2025Article
- Article
- Functional analysis of ESM1 by shRNA-mediated knockdown of its expression in papillary thyroid cancer cells.PloS one · 2024Article
- Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma.BMC genomics · 2023Article
- Network pharmacology and bioinformatics were used to construct a prognostic model and immunoassay of core target genes in the combination of quercetin and kaempferol in the treatment of colorectal cancer.Journal of Cancer · 2023Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Colorectal cancer (CRC) is still one of the most frequently diagnosed malignancy around the world. The complex etiology and high heterogeneity of CRC necessitates the identification of new reliable signature to identify different tumor prognosis, which may help more precise understanding of the molecular properties of CRC and identify the appropriate treatment for CRC patients. In this study, we aimed to identify a combined immune and metabolism gene signature for prognosis prediction of CRC from large volume of CRC transcriptional data. Methods: Gene expression profiling and clinical data of HCC samples was retrieved from the from public datasets. IRGs and MRGs were identified from differential expression analysis. Univariate and multivariate Cox regression analysis were applied to establish the prognostic metabolism-immune status-related signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curves were generated for diagnostic efficacy estimation. Real-time polymerase chain reaction (RT-PCR), Western blot and immunohistochemistry (IHC) was conducted to verified the expression of key genes in CRC cells and tissues. Results: A gene signature comprising four genes (including two IRGs and two MRGs) were identified and verified, with superior predictive performance in discriminating the overall survival (OS) of high-risk and low-risk compared to existing signatures. A prognostic nomogram based on the four-gene signature exhibited a best predictive performance, which enabled the prognosis prediction of CRC patients. The hub gene ESM1 related to CRC were selected Conclusions: A novel combined MRGs and IRGs-related prognostic signature that could stratify CRC patients into low-and high- risk groups of unfavorable outcomes for survival, was identified and verified. This might help, to some extent, to individualized treatment and prognosis assessment of CRC patients. Similarly, the mining of key genes provides a new perspective to explore the molecular mechanisms and targeted therapies of CRC.
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