Prognosis Prediction for Colorectal Cancer Patients: A Risk Score Based on The Metabolic-Related Genes.
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ABSTRACT:
SUBMITTER: Lu Y
PROVIDER: S-EPMC7797536 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The aims of the present study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment. High-throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Univariate Cox regression, LASSO, and multivariate Cox regression analyses were used to determine the final variables for construction of the prognostic risk score model. GSE17536 from the GEO database was used as an external validation dataset to evaluate the predictive power of the model. A total of 770 candidate DE-IRGs were obtained, and a prognostic risk score model was constructed by variable screening using the following 12 genes: FGFR4, LGR6, TRBV12-3, NUDT6, MET, PDIA2, ORM1, IGKV3D-20, THRB, WNT5A, FGF18, and CCR8. In the external validation set, the survival prediction C-index was 0.685, and the AUC values were 0.583, 0.731, and 0.837 for 1-, 2- and 3-year OS, respectively. Univariate and multivariate Cox regression analyses demonstrated that the risk score model was an independent prognostic factor for patients with stage IV CRC. High- and low-risk patient groups had significant differences in the expression of checkpoint coding genes (ICGs). The prognostic risk score model for stage IV CRC developed in the present study based on immune-related genes has acceptable predictive power, and is closely related to the expression of ICGs.
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Project description:BackgroundColorectal cancer (CRC) is the third-most prevalent cancer globally. The biological significance of telomeres in CRC carcinogenesis and progression is underscored by accumulating data. Nevertheless, not much is known about how telomere-related genes (TRGs) affect CRC prognosis. Therefore, the aim of this study was to investigate the role of TRGs in CRC prognosis.MethodsWe retrospectively obtained the expression profiles and clinical data of CRC patients from public databases. Utilizing least absolute shrinkage and selection operator (LASSO) regression analysis, we created a telomere-related risk model to predict survival outcomes, identifying ten telomere-related differentially expressed genes (TRDEGs). Based on TRDEGs, we stratified patients from The Cancer Genome Atlas (TCGA) into low- and high-risk subsets. Subsequently, we conducted comprehensive analyses, including survival assessment, immune cell infiltration, drug sensitivity, and prediction of molecular interactions using Kaplan-Meier curves, ESTIMATE, CIBERSORT, OncoPredict, and other approaches.ResultsThe model showed exceptional predictive accuracy for survival. Significant differences in survival were observed between the two groups of participants grouped according to the model (P<0.001), and this difference was further confirmed in the external validation set (GSE39582) (P=0.004). Additionally, compared to the low-risk group, the high-risk group exhibited significantly advanced tumor node metastasis (TNM) stages, lower proportions of activated CD4+ T cells, effector memory CD4+ T cells, and memory B cells, but increased ratios of M2 macrophages and regulatory T cells (Tregs), elevated tumor immune dysfunction and exclusion (TIDE) scores, and diminished sensitivity to dabrafenib, lapatinib, camptothecin, docetaxel, and telomerase inhibitor IX, reflecting the signature's capacity to distinguish clinical pathological characteristics, immune environment, and drug efficacy. Finally, we validated the expression of the ten TRDEGs (ACACB, TPX2, SRPX, PPARGC1A, CD36, MMP3, NAT2, MMP10, HIGD1A, and MMP1) through quantitative real-time polymerase chain reaction (qRT-PCR) and found that compared to normal cells, the expression levels of ACACB, HIGD1A, NAT2, PPARGC1A, and TPX2 in CRC cells were elevated, whereas those of CD36, SRPX, MMP1, MMP3, and MMP10 were reduced.ConclusionsOverall, we constructed a telomere-related biomarker capable of predicting prognosis and treatment response in CRC individuals, offering potential guidance for drug therapy selection and prognosis prediction.
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Project description:BackgroundCuproptosis is a newly identified type of programmed cell death, characterized by aggregation of mitochondrial lipoylated proteins and the destabilization of Fe-S cluster proteins triggered by copper. However, its role in hepatocellular carcinoma (HCC) remains unclear.MethodsWe analyzed the expression and prognostic significance of cuproptosis-related genes using the data obtained from TCGA and ICGC datasets. A cuproptosis-related genes (CRG) score was constructed and validated via least absolute shrinkage and selection operator (LASSO) Cox regression, multivariate Cox regression and nomogram model. The metabolic features, immune profile and therapy guidance of CRG-classified HCC patients were processed via R packages. The role of kidney-type glutaminase (GLS) in cuproptosis and sorafenib treatment has been confirmed via GLS knockdown.ResultsThe CRG score and its nomogram model performed well in predicting prognosis of HCC patients based on the TCGA cohort (training set), ICGC cohort and GEO cohort (validation set). The risk score was proved as an independent predictor for overall survival (OS) of HCC. The area under the curves (AUCs) of the model in the training and validation cohorts were all around 0.83 (TCGA, 1- year), 0.73 (TCGA, 3- year), 0.92 (ICGC, 1- year), 0.75 (ICGC, 3- year), 0.77 (GEO, 1- year), 0.76(GEO, 3- year). Expression levels of metabolic genes and subtypes of immune cells, and sorafenib sensitiveness varied significantly between the high-CRG group and low-CRG group. One of the model-included gene, GLS, might be involved in the process of cuproptosis and sorafenib treatment in HCC cell line.ConclusionThe five cuproptosis-related genes model contributed to prognostic prediction and provided a new sight for cuproptosis-related therapy in HCC.
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